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THE BEECHWOOD JOURNAL of STUDENT

INTRO

The inaugural volume of The Beechwood Journal of Student Research represents a vision brought to life: giving high school students the opportunity to engage in undergraduate-level research while pursuing questions that connect their interests, EDGE minor experiences, and future aspirations. This vision is rooted in Beechwood’s EDGE program (Educational Design Geared toward Experience), which combines rigorous academics with meaningful, experiential learning. From kindergarten through senior year, the EDGE program develops students’ ability to sustain inquiry, think critically and creatively, research and analyze data, communicate and collaborate, and reflect on their growth. Through personalized courses of study and experiences connected to potential careers, students learn to apply these skills with increasing independence and purpose. In-depth research offers a powerful opportunity to bring that learning together as a capstone experience at Beechwood. During the 2025–2026 school year, nine Beechwood seniors became our inaugural research cohort. Each student selected an area of interest, identified a question deserving further investigation, and undertook an extended research process. The opportunity was designed to build on students’ studies within the EDGE program, allowing them to explore topics relevant to their future educational and professional plans while building a high-level experience that would set them apart from other students at the university level. These students earned college credit through AP Research and pursued experiences beyond the course requirements. Through Beechwood’s partnership with Northern Kentucky University, they secured Institutional Review Board (IRB) approval for their research and worked with expert mentors in their respective fields. In addition to completing a research paper and formal presentation, each student created a university-level research poster to communicate their work that now hangs in Beechwood’s alumni atrium. Together, these experiences challenged students to approach research with care, independence, and attention to ethical practice, while presenting their findings to others. The work collected in this journal reflects the curiosity, resilience, and dedication of these nine student researchers, supported by their teachers, mentors, and university partners. As an annual publication, The Beechwood Journal of Student Research will provide a lasting forum for student scholarship and an invitation for future cohorts to ask meaningful questions, investigate thoughtfully, and contribute to a growing body of knowledge. We are proud to share this first volume and celebrate the students whose work establishes its foundation.

CONTENTS

01 02 03 04 05 06 07 08 09

BENNETT AHLERS | 04 How News Framing Shapes Public Support for Foreign Aid: Evidence from the Sudanese Civil War

SAWYER CARLISLE | 11 Effects of Creatine Supplementation and Hydration on Simple Reaction Time to Visual Cues in High School Athletes

HUDSON EDWARDS | 20 Spin Rate and Exit Velocity: How a Pitcher’s Spin Rate Affects the Exit Velocity Which a Batted Ball Can Be Hit With

MAYA GEHLHOFF | 26 Rural-Urban Disparities in Vaccine Hesitancy Among Kentucky High School Students

HENRY HUDEPOHL | 36 Low-Cost Surface EMG Hand Gesture Classification Using Genetic Algorithm Feature Selection and Random Forest Classification ENOCH PAULSON | 46 Soil Health and Median Household Income In Suburban Areas

CONO PRESTI | 55 How Observation Completeness Shapes the Empirical Value of Information in Capacitated Production Scheduling: A Simulation Study Using Hearts of Iron IV NEEL REDDY | 67 Background Music and Verbal Memory Retention in Adolescents: A Between-Subjects Experimental Investigation JACOB SIPPLE | 77 Consumer-Grade Cardiac Chest Wearable

How News Framing Shapes Public Support for Foreign Aid: Evidence from the Sudanese Civil War

BENNETT AHLERS

ABSTRACT This mixed-methods study examined whether news framing influences support for humanitarian aid during the Sudanese Civil War. Coverage from Fox News, CNN, and NPR was analyzed for framing, tone, featured actors, and calls for intervention. Eighteen participants were then assigned to read humanitarian, anti-aid, or neutral accounts of the conflict. Mean support for increased aid was highest under the humanitarian frame (4.4 out of 7), compared with the anti-aid (3.8) and control (3.6) conditions. Although empathy remained high across groups, anti-aid framing substantially reduced trust in aid organizations. These exploratory findings suggest that personal narratives can translate empathy into policy support, while skeptical framing primarily weakens institutional trust.

RESEARCH QUESTION:

To what extent does the framing and tone of news coverage about the Sudan civil war influence American public support for foreign aid intervention?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

How News Framing Shapes Public Support for Foreign Aid Evidence from the Sudanese Civil War AP Research | 2025-2026 | Beechwood High School

Introduction

Methodology

Results: Primary Outcome

Results: Key Findings

Content Analysis Findings

The role of media in shaping public opinion has long been debated within political communication and sociology. Research by McCombs and Shaw (1972) established that news coverage influences not only what issues audiences consider important but also how those issues are understood. Entman (1993) extended this by showing that frames promote particular problem definitions and treatment recommendations through selective salience. Existing research has largely focused on high-profile or domestic conflicts, leaving the relationship between framing of lesser-known international crises and public attitudes toward foreign aid underexplored (Baum and Potter, 2008). With political polarization at record highs, echo chambers may further amplify framing effects (Stroud, 2011; Cookson et al., 2023).

Two-part mixed-methods design combining a content analysis with an experimental survey.

Mean support for increased U.S. and Western humanitarian aid to Sudan (1 to 7 scale):

Empathy Across Conditions (1 to 7)

Coverage patterns from Fox News, CNN, and NPR (April 2023 to April 2024):

Research Question

To what extent does the framing and tone of news coverage about the Sudan civil war influence American public support for foreign aid intervention?

Part 1: Content Analysis

CNN | Center-Left Frame: Humanitarian

Examined coverage from Fox News (right), CNN (center-left), and NPR (center) during the first year of the conflict (April 2023 to April 2024).

Tone:

NPR | Center

Dominant frame: humanitarian, geopolitical, or economic

Frame: Humanitarian / Geopolitical

Tone toward foreign intervention (strongly opposed to strongly supportive) Actor prominence: military leaders, civilian victims, international organizations, Western governments Presence or absence of calls for U.S. action Inter-rater reliability check applied to approximately 20% of articles.

Part 2: Experimental Survey Why Sudan? Short Duration Ongoing since April 15, 2023. Limited time for participants to have formed independent views compared to longer conflicts like the DRC civil war.

Participants from Beechwood High School were randomly assigned to one of three conditions. Each read a short news article (~300 words) framing the Sudan conflict differently. Survey A | Humanitarian Frame Centered on the personal account of Amira, a seven-months-pregnant civilian who fled RSF-controlled territory. Emphasized individual suffering and displacement. Drawn from a real BBC account.

4.4

3.8

3.6

Humanitarian

Anti-Aid

Control

The humanitarian frame produced the highest support for foreign aid, consistent with the hypothesis. The small gap between the anti-aid and control conditions (3.8 vs. 3.6) suggests that neutral factual information produces comparable levels of passivity as skeptical framing in the absence of a personal narrative. This finding is consistent with research showing that individual stories generate stronger behavioral intention.

Trust in AId Organizations (1 to 7)

Empathy scores were high across all three conditions, which means the humanitarian frame did not uniquely generate empathy. Instead, it was more effective at converting empathy into concrete policy support. Concern for the conflict was widespread, but the frame determined whether that concern translated into support for intervention.

Tone:

Participants described what stood out most about their assigned article:

Tone: Skeptical to Neutral

Neutral to Supportive

Actors: Mixed: civilians and diplomats

Fox News | Right-Leaning Frame: Economic / Geopolitical

Actors: Military leaders, policy costs

Survey A (Humanitarian) | Democrat

"As a mother myself, the fact that she is pregnant while fearing for the world around her stood out to me." Aid Support: 6 / 7 Survey A (Humanitarian) | Independent

"People are being targeted because of their ethnicities. I can easily empathize with a woman who is seven months pregnant.” Aid Support: 6 / 7 Survey B (Anti-Aid) | Democrat (Prior support: 6/7)

"This exposes me to the drawbacks of sending unoversighted aid to bad governments abroad."

Low U.S. Involvement Minimal American intervention means participants have not formed views tied to party doctrine or domestic political stakes.

Supportive

Actors: Civilian victims, aid organizations

Each article was coded for:

Survey B | Anti-Aid Frame

Aid Support: 2 / 7

Focused on structural obstacles to aid delivery: convoys looted, supplies confiscated by armed factions, and the near-impossibility of operating in active conflict zones.

Survey B (Anti-Aid) | Independent (Sudan familiarity: 6/7)

"It is nearly impossible to deliver aid in areas controlled by both the SAF and RSF." Aid Support: 1 / 7

All three outlets devoted limited coverage to Sudan relative to conflicts with direct U.S. involvement. These patterns informed the three experimental conditions used in the survey.

Discussion The results provide preliminary evidence that media framing influences foreign aid attitudes even on politically unfamiliar topics. The anti-aid frame suppressed institutional trust specifically (avg. 2.6 vs. 5.0 and 5.8), decoupling empathy from policy support rather than reducing concern for the conflict. The Democratic participant who entered with prior aid support of 6/7 and left at 2/7 illustrates how skeptical framing can override strong prior predispositions (Entman, 2007). Empathy being uniformly high suggests concern is easy to generate; what the frame determines is whether that concern converts into support for foreign aid. intervention. Limitations

Survey C (Control) | Republican

Politically Uncoded Unlike Russia-Ukraine or Israel-Palestine, Sudan has not been claimed by either political party, allowing framing effects to be studied without partisan interference.

Survey C | Control Condition

The anti-aid frame produced a substantially lower trust score (2.6) compared to both the humanitarian (5.0) and control (5.8) conditions. This suggests skeptical framing suppresses institutional trust specifically, rather than just reducing general support for aid as a policy.

Neutral factual background on the conflict: origins, death tolls, displacement statistics, and failed mediation efforts. No emotional framing or implied call to action.

Urgency Scores (1 to 7)

"What stood out most is how massive the humanitarian crisis has become, with over fourteen million people displaced. Aid support: 4 / 7 Survey C (Control) | Republican

"I do not believe that either side is competent or possesses integrity; therefore they cannot be trusted to manage aid."

Aid Support: 1 / 7 Section 1 (pre-reading): political affiliation, prior aid support, donation history, familiarity with Sudan and foreign aid policy. Section 2 (post-reading): seven-point Likert scale measuring Sources: Entman (1993, 2007) | McCombs & Shaw (1972) | Slovic (2007) | Green & Brock (2000) | Stroud (2011) | Baum & Potter (2008) | Cookson et al. (2023) | Plett Usher, BBC News (2025) | Kohnert, GIGA (2023)

empathy, urgency, effectiveness, trust, moral responsibility,

Small sample (n=18 total) means no results are statistically significant. Findings are exploratory only. Beechwood High School sample limits demographic diversity, geographic range, and generalizability. Single article exposure likely underestimates cumulative real-world framing effects over time. Minor inconsistencies in question sets across surveys complicate direct item-by-item comparison.

Conclusion

INTRODUCTION ---------------The role of media (which hereafter refers to news organizations i.e. the BBC, CBS, Fox News) in shaping public opinion has long been debated within political communication and sociology. Existing research (McCombs and Shaw) suggests that news coverage influences not only what issues audiences consider important but also how those issues are understood (Entman 52). In particular, the selection and framing of information by major news organizations can affect public perceptions of political and social topics, though such influence may be unintentional. However, existing research has largely focused on high-profile conflicts or domestic issues, leaving the relationship between media framing of lesser-known international crises and public attitudes toward foreign aid underexplored (Baum and Potter). With political polarization at its highest level in American history, according to Gallup, it is more important than ever to understand its root causes. It is well documented in studies like (Stroud) that one root cause of this issue is media bias. Due to the well-documented tendency of voters to form online echo chambers (consuming news and opinions primarily from like-minded sources) media framing effects may be amplified rather than challenged. To further understand the media's role in thinking, this study examines how different framings of the Sudan crisis and their tone affect American attitudes toward foreign aid intervention. If people react differently to stories about Sudan (a topic largely absent from partisan discourse), it would suggest that BENNETT AHLERS

the media frames opinion at a more fundamental level than familiar political lines. Understanding the way that this works has deep implications for journalistic standards, particularly as different crises compete for news coverage in a fractured political environment. The findings could determine whether certain frames, such as emphasizing human suffering versus strategic interests, are more effective at garnering public support regardless of political affiliation. This study therefore asks: To what extent does the framing and tone of news coverage about the Sudan civil war influence American public support for foreign aid intervention?

LITERATURE REVIEW ---------------For the purpose of this study, the Sudanese Civil War was selected as a case study for little known international crises. In reality, the situation in Sudan could serve as a stand in for any number of conflicts or disasters going on in the world, such as the Congolese Civil War or the earthquakes in Myanmar. However, the Sudanese Civil War was selected for analysis due to a variety of attributes it possesses. Firstly, it has been ongoing for a relatively short period of time (since April 15, 2023). This means that there has been a short period of time for participants in the second part of this study to have come across information about the war independently, as opposed to something like the Democratic Republic of Congo civil war, which has been ongoing in various forms for nearly 30 years, stemming from the 1994 Rwandan genocide. While the death count has been high in Sudan, exposure was less likely due to 05

LITERATURE REVIEW, CONT. ---------------the duration of the conflict. Another reason is how little major countries like the US have intervened. With so little global attention, potential subjects haven’t formed firm views tied to party doctrine. When the US deploys overseas or sends large amounts of weapons, public scrutiny is applied, yet because of this lack of geopolitical interest, Sudan rarely appears in discussions about America's role abroad. In short, it remains largely peripheral to American foreign policy discourse. This isolation from direct US interests makes it an ideal case for studying framing effects without the confounding variable of perceived national stakes. Finally, unlike conflicts that have become politically coded (Russia-Ukraine, Israel-Palestine), Sudan hasn't been ‘claimed’ by either political party due to both sides’ similar authoritarian leadership. There has been significant research on how media framing affects public understanding of conflict. For example, Entman’s framing research shows that news coverage can shape which aspects of an issue audiences notice, how they judge responsibility, and what responses they favor (Entman). In the context of humanitarian crises, Tuman and Strand argue that media attention can help mobilize international pressure, while Baum et al. find that news coverage can influence emergency humanitarian aid decisions (Tuman and Strand; Baum et al.). Research on war coverage has also found that selective framing and visual emphasis can shape empathy and public perceptions of distant conflicts, making some crises more salient than others (American University; Harris School of Public Policy). Before examining media portrayal of the Sudanese conflict, it is important to first understand what is actually going on through an independent lens. The conflict has its immediate roots in a power struggle between General Abdel Fattah al-Burhan, head of the Sudanese Armed Forces (SAF), and General Mohamed Hamdan Dagalo "Hemedti", commander of the Rapid Support Forces (RSF). The two men jointly participated in the coup that overthrew the previous president of Sudan, Omar al-Bashir, in 2019. Since then, the two men shared power under essentially parallel armies, with alBurhan in command of the regular army and Hemedti in command of the RSF, a paramilitary force. Hemedti and the RSF participated, as mercenaries, in several conflicts. Notably, in Yemen as part of the Saudi intervention, in Libya backing General Haftar, and in other regions of the Sahel. This combat experience, according to Rodriguez, made the RSF “a more effective force than the regular army.” In 2024, negotiations between the two sides collapsed. The RSF demanded that the army honor promises to arrest former National Congress Party officials, remove Islamist leaders from its ranks, and guarantee a political role for Hemedti. The SAF, for their part, insisted that Hemedti fully integrate the RSF into the armed forces and assume a purely political role, and that the RSF evacuate Khartoum before any agreement. Both sides refused and conflict erupted in the capital city. Since then, there has been large-scale fighting and alleged atrocities committed by both sides, with the RSF making rapid gains at first but the army recently digging in its heels.

BENNETT AHLERS

This combat experience, according to Rodriguez, made the RSF “a more effective force than the regular army.” In 2024, negotiations between the two sides collapsed. The RSF demanded that the army honor promises to arrest former National Congress Party officials, remove Islamist leaders from its ranks, and guarantee a political role for Hemedti. The SAF, for their part, insisted that Hemedti fully integrate the RSF into the armed forces and assume a purely political role, and that the RSF evacuate Khartoum before any agreement. Both sides refused and conflict erupted in the capital city. Since then, there has been large-scale fighting and alleged atrocities committed by both sides, with the RSF making rapid gains at first but the army recently digging in its heels.

METHODOLOGY ---------------This study employed a two-part mixed methods design which combined a content analysis of Western news coverage of the Sudanese civil war with a survey looking at the effect of media framing and bias on public attitude towards foreign aid. Both components were designed to complement each other. The content analysis showed how the conflict has been portrayed, while the survey tested whether the frames really influence public opinion.

PART ONE: CONTENT ANALYSIS The content analysis component examines how major American news sites framed the civil war during its first year (April 2023-April 2024). This was selected because it captures the period when these news organizations made their decisions about how to portray the conflict to their readers or listeners before there was a collective tone. Articles were collected from three outlets representing different positions on the political spectrum: Fox News (right), CNN (center-left), and NPR (center). Articles were identified through keyword searches for “Sudan” and “civil war” within each outlet’s online archives, and a consistent selection process was applied across all three to minimize selection bias. Each article was coded based on several elements. First, the overarching frame was identified as one of three types: humanitarian (defined as emphasizing civilian suffering, displacement, and refugee crises), geopolitical (focusing on regional instability, the involvement of external powers, and implications for international security), or economic (highlighting the financial costs of intervention and competing domestic priorities). Second, the overall tone towards foreign intervention was rated on a scale from strongly opposed to strongly supportive. Third, the prominence given to different kinds of actors was recorded, namely military leaders, civilian victims, international organizations, and Western governments. Finally, the presence or absence of explicit or implied calls for US action or inaction was noted for each article. To ensure coding consistency and to minimize researcher bias, around 20% of articles were independently coded by a second reviewer and inter-rater reliability was calculated. The findings of the content analysis as a whole served a dual purpose: 06

first, to identify whether systematic differences exist in how outlets with different political orientations frame the Sudan crisis; and second, to inform the construction of materials for the experimental portion of the study to ensure that the conditions in the survey reflected patterns in real world media coverage.

responsibility to intervene, and support for increased US and Western humanitarian aid to Sudan. An open-ended question also asked participants to describe in one or two sentences what stood out most about the article, which gave qualitative insight into how participants processed and responded to their assigned frame.

PART TWO: EXPERIMENTAL SURVEY

The primary dependent variable for quantitative analysis is participant support for increased US and Western humanitarian aid to Sudan, measured on the seven-point scale. Responses across the three conditions were compared to determine whether framing produced statistically meaningful differences in support levels. Baseline measures of prior aid support and political affiliation will be used to control for pre-existing differences between participants across conditions.

The experimental part tested the effect of media framing on attitudes toward foreign aid by comparing groups of subjects. Participants were recruited from Beechwood High School and randomly assigned to one of three experimental conditions. Random assignment was used to ensure that any differences between groups in post-reading attitudes could be attributed to the framing manipulation rather than preexisting differences between participants (party affiliation). Each condition consisted of a short news article of around 300 words presenting factual information about the Sudan conflict through a distinct frame. Survey A (referred to as the humanitarian frame) centered on the personal account of a seven months pregnant civilian named Amira, who fled her city after it was seized by the RSF. The article emphasized individual suffering and the cost of displacement, drawing on a real account which was published by the BBC. The anti-aid frame (Survey B) discussed structural obstacles to aid delivery, including the confiscation of humanitarian supplies by armed factions and the near impossibility of operating in active conflict zones, showing skepticism about whether foreign aid could be effective given the realities on the ground. The control condition (Survey C) presented factual background information about the conflict without emotional framing or implied calls to action, serving as a neutral baseline against which the other two conditions could be compared. It covered the historical origins of the war, the scale of displacement and casualties, the backgrounds of the two warring factions, and the repeated failure of international mediation efforts, giving participants context while deliberately avoiding the kind of narrative or evaluative framing present in the other two conditions. As stated, this was to provide a frame of reference for comparison. Surveys were administered digitally in two sections. Section 1, completed before participants read their assigned article, collected demographic and attitudinal data. This included political party affiliation, prior level of support for foreign aid abroad, history of donating to international humanitarian organizations, and self-reported familiarity with both foreign aid policy generally and the Sudan conflict specifically. Collecting this data prior to the reading ensured it reflected genuine prior attitudes rather than attitudes influenced by the experimental stimulus. Section 2, completed after reading the assigned article, measured attitudinal outcomes across several dimensions using a seven-point Likert scale ranging from strongly disagree to strongly agree. Items measured empathy for those affected by the conflict, perceived urgency of humanitarian aid, belief in the effectiveness of foreign aid, trust in aid organizations operating in Sudan, perception of moral BENNETT AHLERS

RESULTS ---------------PRIMARY DEPENDENT VARIABLE: SUPPORT FOR INCREASED AID The primary dependent variable (participant support for increased US and Western humanitarian aid to Sudan, measured on a seven point Likert scale) showed differences across the three conditions. Participants in the humanitarian survey (Survey A) produced a mean score of 4.4 out of 7, compared to 3.8 for the anti aid survey (Survey B) and 3.6 for the control survey (Survey C). These results are consistent with the study's central hypothesis: that a humanitarian frame emphasizing personal suffering and large scale displacement would generate higher support for foreign aid in general than either a skeptical frame or a neutral factual presentation. These findings are broadly consistent with Entman's 1993 framing theory, which says that media frames promote particular problem definitions and treatment recommendations by selectively making certain aspects of reality more ‘salient.’ In this vein, the humanitarian survey made personal suffering the salient element of the Sudan conflict, which appears to have translated into greater willingness to support intervention. The gap between the anti-aid condition and the control condition was notably small - 3.8 versus 3.6 - which suggests that presenting factual information alone produced roughly similar levels of apathy as the explicitly anti-aid frame. This is a counterintuitive finding and may imply that without an explicit call to action, readers were more likely to feel empathy without thinking about how to act on it. Research on the relative persuasive power of statistical versus narrative information suggests that personal stories tend to generate stronger emotional engagement and behavioral intention than abstract data, which may help explain why the humanitarian narrative in Survey A outperformed both other conditions despite all three articles presenting factually accurate information about the same conflict (Slovic; Green and Brock).

SECONDARY MEASURES Across all secondary attitudinal measures, the humanitarian frame condition generally produced more pro-aid responses, though the pattern was not entirely consistent. Empathy scores were high across all three conditions (Survey A averaged 5.75, Survey B averaged 6.2, and Survey C averaged 5.8) indicating that exposure to any information about the Sudan conflict tended to generate empathetic responses regardless of frame. This is a notable finding in itself because it suggests that the humanitarian frame did not uniquely generate empathy, but was more effective at converting pre-existing empathetic responses into policy support. Research on media framing and public support for intervention has shown that the way articles that emphasize human outcomes can shape both policy attitudes and opinion and the present results are consistent with this. Empathy alone did not drive support, but the frame that tied empathy to a specific person and a specific crisis did. The most pronounced divergence between conditions appeared in trust in aid organizations. Survey A participants rated their trust in organizations operating in Sudan at an average of 5.0 out of 7, while Survey C participants rated it at 5.8. Survey B participants, however, averaged just 2.6 on this measure which was substantially lower than either other condition. This suggests that the anti-aid frame undermined trust rather than simply reducing overall support for aid as a policy. The CNN Effect hypothesis suggests that continuous coverage of distant suffering stimulates public opinion to act, while alternative models suggest that media content shapes aid-related attitudes through more indirect pathways, including assessments of organizational credibility and effectiveness. The present findings support the idea that skeptical framing operates like this. Participants who read about aid convoys being looted and supplies being confiscated did not merely become less supportive of aid in the abstract; they became specifically less trusting of the organizations tasked with delivering it. Perceived urgency of aid also varied considerably by condition. Survey A produced a mean urgency score of 5.25, Survey C produced 5.2, and Survey B produced just 4.6. The relatively low urgency score in the anti-aid condition, despite high empathy scores in that same condition (6.2), reinforces the interpretation that the anti-aid frame separated empathy from policy support. Participants felt for those affected by the conflict while also doubting whether aid could reach them. This effect has important implications for how skeptical framing operates: it does not reduce concern for suffering so much as redirect that concern away from institutional solutions. The qualitative responses gave important context for the quantitative patterns above. In the humanitarian condition, responses reflected personal identification with Amira, the pregnant civilian whose story anchored the article. One Democratic participant wrote that “as a mother myself, the fact that she is pregnant while fearing for the world around her stood out to me," and rated support for increased aid at 6 out of 7. An Independent participant noted that “people are being targeted because of their ethnicities. I can easily empathize with a woman who is seven months pregnant," also rating support BENNETT AHLERS

at 6. These responses illustrate the mechanism by which the humanitarian frame operates: by centering a specific person, it created an emotional connection that motivated policy support. Research on framing suggests that content that emphasizes personal stories drive higher engagement and can influence action beyond a wareness alone. In the anti-aid condition, the most statistically significant response came from a Democratic participant who had indicated a prior aid support level of 6 out of 7 (among the highest in the entire sample) yet rated support for more aid at just 2 out of 7 after reading the anti-aid article. Their open ended response read: "This exposes me to the drawbacks of sending unoversighted aid to bad governments abroad." This case shows the power of skeptical framing to override previous ideological predispositions. The participant entered the study with a strong disposition toward foreign aid, but the framing of the article was sufficient to reverse that disposition substantially. In the control survey, responses tended to focus on the scale and complexity of the crisis rather than individual actors or emotional narratives. One Republican participant noted that “what stood out most is how massive the humanitarian crisis has become, with over fourteen million people displaced,” and rated support for increased aid at 4 out of 7, which is a moderate response that is consistent with a factual frame. Another Republican participant, however, wrote that "I do not believe that either side is competent or possesses integrity; therefore they cannot be trusted to manage aid," and rated support at just 1 out of 7. This response suggests that even neutral factual information about a conflict characterized by authoritarian actors on both sides can generate skepticism about aid independently of any actual anti-aid framing.

CONTENT ANALYSIS FINDINGS ---------------The content analysis of coverage from Fox News, CNN, and NPR during the first year of the conflict (April 2023-April 2024) gave results mostly consistent with existing research on partisan news coverage. CNN’s coverage skewed toward humanitarian framing, adopting a tone generally supportive of intervention. NPR occupied a middle position that combined humanitarian and geopolitical frames and maintained a measured tone toward intervention. Fox News coverage was much more likely to be about strategic and economic considerations, with a skeptical/neutral tone toward foreign aid and a greater focus on military actors and policy costs. These findings are consistent with broader trends in the literature which suggests that media across the political spectrum report conflicts differently. Notably, all three outlets devoted relatively limited coverage to Sudan compared to other conflicts with more direct US involvement, which is why Sudan was chosen in the first place. These content analysis findings informed the construction of the three experimental conditions the surveys used and ensured that the frames reflected patterns observable in real media coverage rather than more artificially constructed scenarios.

DISCUSSION ----------------

LIMITATIONS ----------------

The findings of this study show preliminary evidence that media framing influences public attitudes toward foreign aid intervention, even on politically unfamiliar topics. Across all three conditions, participants given the humanitarian survey produced the highest mean support for Increased US aid to Sudan compared to both the anti aid frame and the control condition. While these differences are small in magnitude, they are significant given that all three groups read articles presenting the same factual information about the same war. The difference in outcomes suggests that it was the framing of this information, not the information itself, that drove these differences, which is consistent with Entman’s foundational argument that framing involves selecting aspects of perceived reality and making them salient to promote problem definitions and treatment recommendations.

There are several limitations that must be acknowledged. Firstly, sample size. With only 18 total participants distributed across three conditions, no findings can be considered statistically significant. The results shown here are descriptive and exploratory and should not be generalized beyond the sample. At least 30 participants per condition would be needed to detect effects with reasonable confidence, which means that the current study is best understood as a pilot that establishes proof of concept.

Perhaps the most significant finding is not the difference in overall aid support but the mechanism by which the anti aid frame operated. Instead of just reducing support for aid as a policy, the skeptical frame appeared to suppress trust in aid organizations specifically, producing a mean trust score of just 2.6 compared to 5.0 in the humanitarian condition and 5.8 in the control. This suggests that skeptical framing works not by making people care less about a conflict but by convincing them that institutional responses to that conflict are ineffective or corrupt. The Democratic participant who entered the study with a prior aid support score of 6 out of 7 and left with a post-reading support score of 2 demonstrates this most effectively. This is consistent with theoretical models suggesting that media content shapes ideas through including assessments of organizational credibility and effectiveness, rather than through direct changes in values or political identity. Additionally, the finding that empathy was high across all conditions also carries implications for how humanitarian organizations think about their work. Generating concern for those affected by a conflict is less difficult than generating a desire to act. The humanitarian frame ‘succeeded’ not because it uniquely generated empathy but because it tied empathy to an individual who was easy to identify with. This has relevance for journalistic and advocacy networks because coverage that centers statistical information about the scale of a crisis, as in the control article, may be as effective without using behavioral outcomes as coverage that centers on a specific human story, provided the statistics are alarming enough.

BENNETT AHLERS

The composition of the sample also limits generalizability. All participants were recruited from Beechwood High School, which obviously comes with several disadvantages. The sample is geographically concentrated, age restricted, and underrepresentative of the broader American public in terms of education, political and ethnic diversity, and media consumption habits. The relatively high proportion of Democratic or Independent participants means that partisan differences in framing susceptibility could not be meaningfully analyzed. Finally, this study measured attitudes at a single point in time immediately after reading one article. Real world media effects are acquired by repeated exposure across multiple outlets and over extended periods. A single article read in an experimental setting may underestimate the durability and magnitude of framing effects as they operate in natural media environments. Longitudinal designs that track attitude change over multiple exposures would offer a more valid picture of how framing shapes public opinion about international crises.

CONCLUSION ---------------Despite these limitations, this study makes a meaningful contribution to the understanding of media framing effects. By selecting the Sudan civil war as a case study because of its political neutrality, the research was able to isolate framing effects from the confounding influence of partisan predispositions that complicate studies of more politically coded conflicts. The finding that framing produced measurable differences on thoughts about a topic about which participants had little prior knowledge or strong prior opinions suggests that media frames operate at a more fundamental level than partisan propaganda. This has significant implications for journalistic practice, especially as global crises compete for limited news coverage in an increasingly fragmented media environment. Editorial decisions about which story to tell, whose perspective to center, and what aspects of a conflict to emphasize are not neutral choices. Understanding the consequences of this more precisely, through larger and more rigorous studies, remains an important task for political communication research.

REFERENCES ---------------Al Nahed, Sumaya, and Philip Hammond. "Framing War and Conflict: Introduction to the Special Issue." Media, War & Conflict, vol. 11, no. 4, 2018, pp. 367-370. SAGE Journals, https:// doi.org/10.1177/1750635218798992. Atadoga, Adukwu Samuel. "Western media coverage of international news: A content analysis of Associated Press coverage of Africa." (2020). Baum, Matthew A., and Philip B. K. Potter. "The Relationships Between Mass Media, Public Opinion, and Foreign Policy." Annual Review of Political Science, 2008. Booty, Natasha, et al. "A Simple Guide to What Is Happening in Sudan." BBC News, 13 Nov. 2025, www.bbc.com/news/worldafrica-60058473. Cookson, J. Anthony, Joseph E. Engelberg, and William Mullins. "Echo chambers." The Review of Financial Studies 36.2 (2023): 450-500. Entman, Robert M. "Framing bias: Media in the distribution of power." Journal of Communication 57.1 (2007): 163-173. Entman, Robert M. "Framing: Toward Clarification of a Fractured Paradigm." Journal of Communication, 1993. Green, Melanie C., and Timothy C. Brock. "The Role of Transportation in the Persuasiveness of Public Narratives." Journal of Personality and Social Psychology, 2000. Iyengar, Shanto, and Donald R. Kinder. News that Matters: Television & American Opinion. University of Chicago Press, 2010. Kohnert, Dirk. "On the Impact of the 2023 Sudanese War on Africa and Beyond." Zenodo, GIGA Institute for African Affairs, 10 June 2023, doi.org/10.5281/zenodo.8021443. Lee, Michelle J. "Media Influence on Humanitarian Interventions: Analysis of the Rohingya Refugee Crisis and International Media Coverage." Journal of International Humanitarian Action, vol. 6, no. 1, 2021, article 20. PubMed Central, https://pmc.ncbi.nlm. nih.gov/articles/PMC8519737/. McCombs, Maxwell E., and Donald L. Shaw. "The Agenda-Setting Function of Mass Media." Public Opinion Quarterly, 1972. Odine, Maurice. "Media coverage of conflict in Africa." Global Media Journal-African Edition 7.2 (2013): 201-225. Plett Usher, Barbara. "A Pregnant Woman's Diary of Escape from War Zone: 'I Prayed the Baby Wouldn't Come.'" BBC News, 27 Aug. 2025, www.bbc.com/news/articles/c9870xjezk4o. Slovic, Paul. "If I Look at the Mass I Will Never Act: Psychic Numbing and Genocide." 2007. Stroud, Natalie Jomini. Niche News: The Politics of News Choice. Oxford University Press, 2011. BENNETT AHLERS

Effects of Creatine Supplementation and Hydration on Simple Reaction Time to Visual Cues in High School Athletes

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ABSTRACT This 21-day observational study examined relationships among creatine supplementation, hydration-related behaviors, and simple reaction time in 15 high school athletes. Seven creatine users and eight non-users completed weekly computerized reaction-time tests and maintained journals tracking supplementation, fluid intake, sleep, and related behaviors. Both groups improved with repeated testing, but creatine users improved by an average of 41.8 milliseconds, compared with 16.6 milliseconds among non-users. Creatine users also reported slightly greater thirst, while better sleep and fluid intake were loosely associated with improvement across the sample. Because the study was observational and relied partly on self-reported data, the findings do not establish causation but suggest that creatine use may accompany faster reaction-time improvement.

RESEARCH QUESTION:

How do hydration status and creatine supplementation practices relate to simple reaction time in cognitively healthy high school athletes?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

Effects of Creatine Supplementation and Hydration on Simple Reaction Time to Visual Cues in High School Athletes Sawyer Carlisle | Beechwood High School Advisor: Coach Colt Place INTRODUCTION ●

Background: Split-second decision making dictates athletic outcomes and

Group Mean Simple Reaction Time (ms) Trajectory 360 ms

●

Efficacy: Accelerated improvement in creatine users aligns with

cognitive processing speed.

the theory that added phosphocreatine supports high-demand

Missing Connection: Creatine increases available brain energy, and

●

neural processing. Context: Better sleep loosely aligned with SRT gain. Creatine

timed academic success. Simple reaction time (SRT) measures this critical

●

DISCUSSION

hydration impacts alertness. However, almost no research examines these

users reported 24% higher average thirst (2.6/5 vs 2.1/5),

together in active adolescents.

345 ms

330 ms

suggesting osmotic demands are linked to cognitive performance. Research Question: How do hydration status and creatine supplementation practices relate to simple reaction time in cognitively

315 ms

●

Peak Improvement: ~103 ms non-Creatine Peak: ~64 ms

Week 1

descriptive trends, not causal proofs.

healthy high school athletes?

Max Individual Change in SRT (ms)

Limitations: Small sample (n=15); reliant on self-report journals;

●

Overall: All athletes improved over time due to task adaptation.

●

Creatine Group avg improvement: 41.8 ms (from ~362ms to ~320ms).

Baseline

Non-Creatine Group avg improvement: 16.6 ms (from ~333ms to

Design: 21-day observational correlational design capturing daily habits without experimental manipulation.

●

Participants (n=15): High school athletes (Grades 9-12). 7 in Creatine Group (>1 mo. use); 8 in Non-Creatine Group.

●

Procedure: Participants kept unsupervised Google Form journals tracking daily sleep (hrs), fluid intake (oz), and doses. Standardized weekly SRT testing occurred on identical Lenovo laptops in a quiet environment.

~316ms).

●

Week 3

Non-Creatine

METHODS

RESULTS SYNOPSIS

●

Week 2 Creatine

●

Variance: Creatine users improved by a margin >2.5x larger than non-

Task: SRT test involved pressing spacebar when visual color changed (red to green) after random 3–8 second delay.

users, closing the performance gap by Week 3.

CONCLUSION Creatine supplementation, paired with adequate sleep and fluid intake, may align with quicker simple reaction time improvements in adolescent athletes.

Critical Selected References

Avgerinos, K. I., et al. (2018). Effects of creatine on cognitive function. *Experimental Gerontology*, 108, 166-173. Benjamin, C. L., et al. (2022). Fluid restriction negatively influences visuomotor ability. *IJERPH*, 19(1), 370. Masento, N. A., et al. (2014). Effects of hydration status on cognitive performance. *British Journal of Nutrition*, 111(10). Xu C., et al. (2024). Creatine supplementation and cognitive function: a systematic review. *Front. Nutr.* 11.

INTRODUCTION ---------------Background For many athletes, split-second decision making can determine the outcome of a play or game. On timed tasks or assessments, cognitive processing of visual cues is also vital for academic success in the classroom. Simple reaction time (SRT) measures the interval between the presentation of a stimulus and the expected response and has been linked to athletic and cognitive performance. However, challenges to adolescent athletes’ reaction times are unique in some ways and yet to be studied in others. One aspect that is crucial for athletes to consider when attempting to improve reaction time is hydration status, as fluid restriction has been linked with negative effects on alertness and visuomotor ability (Benjamin et al.) Creatine, a widely-known dietary supplement is typically used to enhance muscle endurance in strenuous anaerobic exercise. However, recent studies have shown that creatine may also have beneficial cognitive effects, which could potentially be useful for athletes when quick decision making is vital. These studies have been mainly focused on cognitively impaired or sleep deprived populations as “Creatine supplementation has been reported to improve certain aspects of cognitive and psychomotor function in older individuals and in young subjects following 24 and 36 h of sleep deprivation”, and “the effects of creatine supplementation on cognitive processing and psychomotor SAWYER CARLISLE

performance in non-sleep deprived young adults have not been assessed with a comprehensive battery of neurocognitive tests” (Rawson et al.). Therefore, study into the cognitive and psychomotor effects of creatine supplementation in nonimpaired and non-sleep deprived individuals on reaction time/ cognitive processing speed is necessary. This study attempts to draw a connection between subjective and objective hydration measures, creatine supplementation regiment, and simple reaction time to a visual cue in a non-impaired high school athlete population. Hydration and Cognitive Performance Primarily, the effects of dehydration on mood and cognition are "particularly relevant for those with poor fluid regulation, such as the elderly and children" (Masento et al.). Therefore, an adolescent population may display differing reaction time results than previously studied adult populations due to fluid regulation, and it is important that this gap in knowledge be understood. Although a difference in results is likely, it is unclear how far reaching the effects of hydration status may be on simple reaction time and cognitive processing speed. There have been few studies showing that “neurocognitive functioning may be impacted by dehydration. There are issues with study design, inconsistency in hydration status measurement and different measures used for outcome assessment” (Edmonds et al.). So, while hydration has been 12

shown to affect mortality rates and disease prevalence, there is a lack of data on the possible cognitive effects of hydration or lack thereof, and this study aims to partially fill this gap. In particular, SRT will be the metric used to gauge effects as to ensure narrow and descriptive data. However, the effects of hydration on SRT will not be studied independently, so causal relationships between hydration and SRT cannot be made. The interaction between hydration and creatine supplementation will be studied in relation to SRT in high school athletes. Creatine and Cognitive Performance Creatine is one of the most widely consumed supplements by athletes that functions through increasing phosphocreatine availability in tissues with high energy demands, including brain tissue. “Creatine is a non-proteinogenic amino acid available from various animal-based foods or synthesized endogenously in the human body. A number of recent population-based studies demonstrate a lower-than-expected dietary creatine intake across age- and gender-specific cohorts, with low creatine consumption accompanied by various health risks” (Ostojic 1). Due to these properties, creatine has been implemented to better performance in anaerobic exercise. Recently, there has been increased prevalence in creatine supplementation backed by studies supporting its safety and efficacy, especially pertaining to muscle endurance and strength. The most studied and widely used type of creatine is creatine monohydrate, while, “It should be noted that although some of the alternative forms of creatine are classified as having some evidence to support their bioavailability, efficacy, and safety, none have been to be superior to CM in terms of increasing muscle creatine content despite the commonly used marketing claims/ marketing hyperbole” (Guillermo et al.). Although benefits related to muscle endurance, growth, and strength are most known, neurons also carry out energy-intensive processes and “Ιt has been shown that exogenously administrated creatine increases energy supply to neurons in healthy adults (Persky and Brazeau; Rawson and Volek; Klivenyi et al.)” (Avgerinos et al.). This increase in neuron energy supply may suggest possible cognitive benefits, especially in relation to energy intensive functions, such as quick decision making or reacting. However, the possible cognitive benefits associated with creatine remain understudied, especially in adolescent populations. Cognitive performance literature surrounding creatine presents conflicting findings that may be dependent on population and testing conditions. Early research found that "there were no differences between creatine and placebo supplemented groups at any time" in healthy, non-sleepdeprived young adults aged 19-23 (Rawson et al.). However, a decade later, a review reached contrasting findings that "oral creatine supplementation improves memory of healthy adults" and "suggest potential benefit for aging and stressed individuals" (Avgerinos et al.). This study aims to inch closer to a consensus about possible cognitive benefits of creatine, particularly in relation to reaction time. Connecting Hydration and Creatine The relationship between hydration and creatine supplementation is nuanced, but has been made clear with more literature available. Total body water (TBW) has been SAWYER CARLISLE

shown to increase significantly with creatine supplementation as, “The increases in muscle Cr were associated with increases in both TBW and body mass” (Powers et al.). There has been 1 report of a shift in fluid distribution between muscles and the rest of the body (Harris, Hultman, and Nordesjö). This specific study supports the narrative that creatine supplementation may lead to a trapping of water in muscles, restriction from other parts of the body, and possible heat illness or cramping. However, this claim is speculative since that study did not actually measure muscle creatine content, only predicted it. In more recent literature, it has been shown that fluid distribution remains normal—intracellular water increases proportionally with total body water rather than at the expense of extracellular water. “At this time, however, the only side effect directly associated with Cr supplementation is weight gain” (Powers et al.). Due to the interdependence of muscle creatine concentration and TBW, both hydration markers and creatine supplementation of participants should be examined in the simple reaction time testing. Therefore, the research question formed is: To what extent are creatine supplementation, hydration, and other behavioral factors impactful in simple reaction time among high school athletes?

LITERATURE REVIEW ---------------Measuring Simple Reaction Time Simple reaction time is one of the most representative metrics of cognitive processing speed, but there are often multiple confounding factors impacting the baseline data. For an adolescent population specifically, age-related effects are some of the most important. Research has shown that SRT increases roughly 0.55 ms per year through adulthood, with a mean of around 231 ms in young adults (Woods et al.). Since the participant age range of this study is roughly 15-18, developmental trends may suggest the bettering of reaction time independent of hydration levels or creatine supplementation. Other than age, the type of stimulus is important in reaction time as, “The mean reaction time was different according to the methods of visual stimuli, such as color and size(p<0.05). The larger and more intence visual stimuli were, the faster sim-ple reaction time was” (Kim et al.). These findings all impact study design, especially encouraging a standardized test and environment. Computerized testing also creates concern for standard measurement as web-based platforms typically produce timing offsets–approximately 37 seconds when compared to laboratory equipment. Despite this inconsistency, “taskspecific effects were reproduced” across the different testing environments (Semmelmann and Weigelt). This finding, in fact, supports computerized reaction time testing for observational studies as long as the examined population is all tested uniformly. This computerized platform is therefore useful for measuring changes in reaction time over a number of weeks in response to applied variables instead of measuring their absolute values compared to other populations.

Hydration and Cognitive Performance Research has demonstrated that dehydration effects are “particularly relevant for those with poor fluid regulation, such as the elderly and children” (Masento et al.), which suggests that an adolescent population, which this study examines, may be particularly sensitive to fluid balance alterations. However, only some studies report neurocognitive decrements and study design inconsistency is a large issue. In an observational study such as this, monitoring all factors on hydration status and reaction time is near impossible, warranting the need for a daily journal for participants to include important behaviours or substances consumed. For example, as caffeine is very related to alertness and cognitive function, “Confounding factors, such as caffeine intake… need to be considered in the design and conduct of such studies”(Lieberman). Edmonds et al. also reported that “subjective thirst also had large impacts” on reaction time tests, so a measure of subjective thirst must also be included. While not all confounding variables may be covered, the goal is to gain a holistic view of hydration trends. Studies examining acute dehydration have most clearly evidenced hydration’s effects on cognition and processing speed. Benjamin et al. studied two groups with different fluid intake protocols: one consuming 500 mL of fluid the night before testing showed, “significantly improved peripheral visuomotor reaction time” compared to the other 12-hour fluid restricted group. This proves that hydration or lack thereof prior to testing is an important marker of prospective reaction time and a good time to measure hydration status would be the night before a reaction time test. Creatine Supplementation & Cognitive Performance The cognitive effects of creatine seem to be dependent on the energy expenditure of the body/brain, while the most benefits appear in high physical and neurological demand. One of the largest current limitations in current studies is the cloudiness in optimal dosage for cognitive benefits. Since creatine has been most heavily researched for its effects on skeletal muscle it is generally accepted that, “a creatine maintenance phase (i.e., 3–5 g/day) appears sufficient to produce skeletal muscle benefits” (Candow et al.). However, while a general range to increase brain creatine levels of 4g/ day for multiple months is supported, “the optimal dosage and/or duration of ingestion to enhance brain function is unclear” (Candow et al.). This gap is most important in a reallife situation, when athletes are very variable in their dosing protocols and may not align with standardized regimens. This gap will be explored in this observational study, especially in adolescent’s supplementation practices, as they tend to be less standardized. The efficacy and safety of creatine in adolescent populations participating in high intensity exercise is less established than in adult populations, but studies “generally report improvements in several ergogenic outcomes with limited evidence of ergolytic properties and consistent reports indicating no adverse events associated with supplementation” (Jagim and Kerksick). Despite the established safety of creatine in adolescent athlete

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populations, the question as to whether cognitive benefits extend to adolescents as far as to adults is still to be answered. Creatine & Hydration Interaction No research has examined both of these variables combined effects on cognitive outcomes, particularly simple reaction time in adolescent athletic populations where both factors naturally vary. This gap requires investigation of how naturally-occurring variation in both hydration practices and creatine supplementation influences reaction time performance, warranting observation over a 30 day period.

METHODOLOGY ---------------Research Design This study utilized an observational correlational design with repeated trials over a 21-day period to examine the potential relationship between hydration status, creatine supplementation practice, and simple reaction time to visual cues among high school student athletes. This design was selected as it allowed for examination of variation in every-day behaviors in relation to a testable cognitive variable without experimental manipulation. Since participants weren’t randomly assigned to groups, this design does not allow for causal inference. This study instead targeted a correlational understanding between these variables in an understudied population of cognitively healthy adolescent athletes. Participants This study recruited student athletes in grades 9 to 12 from Beechwood High School in Northern Kentucky with a maximum sample size of 20 participants. The target composition was 10 students who continuously supplemented creatine for a minimum of one month before participation and 10 students who had not been supplementing creatine for at least one month before participation. The one month requirement made sure that participants would have a reasonably steady creatine concentration before testing took place. Student athletes who had any history of negative effects from creatine or had injuries that may impact their reaction time were excluded from participation. Recruitment lasted two weeks, prioritizing all eligible creatine-using athletes from the available population before filling remaining slots with non-using athletes, in order to get as close to a half and half split as possible. Firstly, an online posting directed interested students to a Qualtrics eligibility survey that collected demographic information, creatine use history, and other information that may have impacted their results. The researcher reviewed responses and contacted eligible students via email with informed consent forms for parents and assent forms for students. Participants were officially enrolled only after both signed documents were received via email. Operational Definitions Dependent Variable: Simple reaction time was operationally defined as the time interval in milliseconds between when a computer screen changed from red to green and when the 14

participant pressed the spacebar. Each participant completed 10 trials per weekly testing session, with a 3–8 second delay to prevent anticipatory responses. Trials from each week were averaged to produce one mean SRT value per participant per week.

4. Opened the reaction time website with configured settings 5. Completed 10 trials: screen displayed red, changed to green after random 3–8 second delay, participant pressed spacebar as quickly as possible when green appeared 6. Participant continued to next class

Independent Variables: Creatine supplementation status was categorical, supplementing >1 month. Within the supplementing group, daily dosage and frequency were documented via self-report journals, and no standard was imposed. Hydration status was also assessed through daily self reported estimates of total water and other beverage intake documented in journals. No objective hydration measures (urine specific gravity, body mass changes) were utilized due to practical constraints. Potential Confounding Variables: Daily journals capturing general activities including sleep, physical exertion, and stress. Age was partially controlled by restricting enrollment to grades 9–12.

Standardized Variables: All participants used identical equipment, software settings, location, time of day (2nd-3rd period transition), and environment conditions across all four sessions.

Instruments Simple reaction time was measured using a web-based platform (https://cpstest.org/reaction-time-test/) accessed through a Lenovo Yoga laptop. The testing platform was configured with the following settings: Simple Reaction Test, 10 Trials per Session, 3-8 Second Delay Range, and Spacebar Input. The visual stimulus was a red screen that flashed to green when the participant was supposed to react. All testing used the same laptop to minimize variability as literature has demonstrated that task specific effects are reproduced when participants use identical technology. All testing occurred in the same classroom, sitting at a desk, with little to no distracting noise. Participants completed a Google Form “Daily Journal”, taking approximately 3-5 minutes and covered the following information: daily general activities, whether creatine was consumed and the amount, estimated fluid intake between water and other beverages, and sleep details. This provided daily data on behaviors and allowed for a better interpretation of reaction time changes. For example, if the creatine group got slower reaction times during a testing session, the journal data could help determine whether this reflected supplementation effects or alternative effects like dehydration or poor sleep. Procedure Beginning on Day 1 and continuing through Day 21, participants independently completed the daily journal from home each evening. Its completion was unsupervised and depended on participant honesty. Testing occurred once a week during the transition between second and third period. One group of 10 student athletes tested on one day (ex. Thursdays) and the other group tested on another day (ex. Fridays) in order to spread the testing time across days to prevent tardiness. Each group was roughly half creatine users and half non-creatine users. Testing sessions followed this protocol: 1. Participants arrive at the testing classroom 2. Sat at the designated desk with the Lenovo Yoga computer 3. Face is positioned 2–2.5 feet from screen with thumb resting on spacebar SAWYER CARLISLE

RESULTS ---------------Group Level Simple Reaction Time Changes In total, 15 students completed all three weekly testing sessions, as 7 individuals were classified as creatine users and 8 individuals were classified as non-creatine users. Across the 3-week period, the average simple reaction time decreased for both groups. In week one, the creatine group had a mean SRT of roughly 362 ms, while the non-creatine group began with a simple reaction time of roughly 333 ms. By week three, the creatine group had decreased to roughly 320 ms, while the non-creatine group decreased to about 316 ms. This showed faster responses over time which must be analyzed as a result. However, this also corresponded to a larger average improvement of 41.8 ms over the period for creatine users over the roughly 16.6 ms improvement from the non-creatine group.

Group Level Simple Reaction Time Changes When examining individual trajectories over the three week period, each participant showed an improvement in the simple reaction time test at some point. However, the magnitude greatly varied. The biggest positive change in the creatine group was in one participant whose SRT decreased by approximately 103 ms over the three weeks while another participant in the creatine group improved by only 7 ms, which is unlikely to be significant. In the non-creatine group, the largest improvement was by 64 ms while there were several other individuals with minimal change in simple reaction time. 15

while some non-creatine individuals had more consistent daily training schedules. Scatterplots also related behavior changes with changes in SRT over the three week period, but no inferential statistics were calculated. Those with higher mean sleep quality were loosely correlated with greater improvements in reaction time and those with lower mean sleep quality through the period showed smaller changes in reaction time. Hydration and activity level were of course paired as more activity calls for more hydration. The two generally had a positive correlation with reaction time improvement but this relationship was not uniform across the sample.

Past the absolute changes in reaction time from the beginning of the period to the end, there were differences in the pace of improvement between groups. Most SRT change in the creatine group occurred between Week 1 and Week 2, with some small decreases in the last week. In contrast, the non-creatine group had a more gradual decrease in SRT throughout the three weeks. There were some performance plateaus for individuals in both groups displaying differing performance paths. Behavioral Data The daily journal used by participants gave further context to the performance changes observed in this study. Among creatine users, dosing was highly consistent as all 7 reported at least one 5-gram supplementation on most days (averaged 1.1 doses/day). One participant in the creatine group reported much higher dosage amounts (15 grams/day) occasionally. In the non-supplementing group, zero creatine intake was reported as expected. Across the entire sample, athletes roughly drank between 90-115 ounces of fluid per day and slept between 7 and 8 hours per night, on average. By group, non-creatine participants reported slightly longer sleep duration and higher sleep quality scores than those averaged statistics of the creatine group. On average, participants in the creatine group also averaged higher caffeine intake but this can be mainly attributed to those who consumed energy drinks on most school days. Caffeine intake did not show a clear linear relationship with SRT change across the entire sample as caffeine may have interacted with those participants differently. Thirst levels varied slightly between groups. The average thirst level across the whole study was 2.3/5, as creatine users reporting a higher thirst level of 2.6/5 than non-creatine users average rating of 2.1/5. This may possibly reflect creatine’s osmotic effects, but no formal hydration measures were recorded. For the creatine group, 85% of doses were reported occurring between 6-9 AM.With regards to sleep schedules, bedtimes averaged 10:45 PM and wake times hovered around 6:45 AM. Creatine users on average went to bed 15 minutes later than non-creatine users. Average daily activity levels were high in both groups and ranged from moderate to vigorous exercise on most days SAWYER CARLISLE

Since the study was observational and not suited for inferential statistics, no significance tests were performed. The results are instead presented to characterize the trends and correlative relationships between simple reaction time improvements and creatine use, hydration, sleep, and caffeine use.

DISCUSSION ---------------Interpretation of Group Differences The most evident finding of this study was that creatine supplementers demonstrated a larger average improvement in simple reaction time testing across the three week period. The creatine group improved on average 41.8 ms from 362 ms at Week 1 to 320 ms at Week 3. The non-creatine group improved by 16.6 ms from 333 ms to 316 ms throughout the three weeks of testing. The creatine group improved more than twice as much as the non-creatine group, despite beginning the period with slower average reaction times. This may posit that creatine users in the study adapted to the reaction time task and caught up at an accelerated pace compared to non-creatine users. The respective group variabilities were mainly stable through all three weeks–standard deviations ranged from 25 to 28 ms, which suggests that even though the groups improved rather dramatically as a whole, some participants remained consistent in their testing performances. The two slowest performers to begin the study were both creatine users who recorded simple reaction times of 399 ms and 379 ms, while the two fastest performers were both non-creatine users who recorded simple reaction times of 313 ms and 315 ms. However, over the testing period, this dynamic changed and by the third week, the fastest reaction time belonged to a creatine user at 276 ms while the slowest time remained a creatine user with a 392 ms reaction time. The individual movement within the larger group displays a meaningful intricacy that cannot be captured by group means. Comparison to Existing Literature The pattern observed in the creatine group is moderately consistent with recent meta-analysis evidence on creatine supplementation and cognitive processing speed. Xu et al. (2024) conducted a systematic review of 16 randomized controlled trials of 492 adults and found that creatine supplementation produced a statistically significant improvement in information processing speed (SMD = -.58, 95% Confidence Interval: -1.01 to -.01), which displays a reduction in time-on-task. In that study, participants stayed aligned with a maintenance phase daily dose of 5g. That is consistent with the bulk of participant practice in this study, which may allow some inference of similar creatine efficacy. On one hand, the present study is simply observational and causal claims cannot be made. On the other, the general agreement between these findings and broad literature does support the hypothesis that creatine supplementation could play a role in accelerated improvement between creatine supplementers. Additionally, the no-creatine group’s improvement by 16.6 ms over the testing period was mostly expected and could be attributed to practice effects, in which repeated exposure to the same simple reaction time test through many trials produces better results due to response preparation or increased recognition of the stimulus. Therefore, the most meaningful finding contributions that this study could convey are found in the difference between the two groups’ improvements. There were notably slower SRT’s of SAWYER CARLISLE

participants in this study compared to typical young adult SRT benchmarks. This is likely due to the unorthodox testing method utilized and systematic timing delays. However, those factors only affected absolute reaction times which were not integral to the study and changes in participant SRT could still be confidently monitored. Sleep as an Impactful Variable Among the behavioral variables tracked by the daily journal, sleep quality displayed some weak association with SRT changes throughout the sample. Particularly, some of the participants who reported better sleep quality showed larger SRT improvements while those who reported lower sleep quality showed less SRT improvement. This correlation is not near linear and the sleep quality reported was measured on a 1-5 Likert scale, which includes subjectivity, so this data is not entirely reliable.

Despite this more subjectively reported data, the sleep quality and reaction time relationship is well-supported by current research. Ren et al. (2025) found that after a 24 hour sleep deprivation protocol, the acutely sleep deprived participants experienced statistically significant increases in reaction time of 83.69 ms (F = 11.179, P–also extended by 18.76 ms. These findings confirm that even a single disrupted night of sleep can seriously slow reaction time. The slowing of reaction time was found to be 83.69 ms due to sleep deprivation, which is even larger than the group level improvement observed in the creatine group across the three-week testing period. This places the effects of poor sleep or other behaviors at the forefront of reaction time impact, meaning that any gains associated with the creatine or non-creatine group may be affected or masked by the group’s sleep behavior or other behaviors. Taher and Arabameri (2012) also demonstrated how sleep deprivation impacted choice reaction time in college student athletes, whose population is directly comparable to this study’s population. Hydration and Thirst Patterns Hydration data was also captured from daily journals and provided relevant context to the study due to creatine’s osmotic properties. As previously stated, creatine promotes intracellular water retention in muscle tissues, which may increase bodily fluid demand. Consistently, creatine users 17

reported slightly higher average thirst levels (2.6/5) compared to non-users (2.1/5) despite the two group’s comparable water intake. Alternatively, higher water intake was not necessarily associated with larger SRT improvements

Limitations and Implications for Future Research Multiple limitations decrease the strength of conclusions in this study. Primarily, the observational correlational design denies causal claims, but is necessary regarding the population sampled. There is also natural behavioral variability in this sample that cannot be controlled for such as training intensity, dietary habits, sleep habits, and genetic traits. The sample size of 15 participants also fell short of the targeted sample size and underpowered for inferential statistics. Daily Journal compliance was also below target at 11.5 entries per participant against a 14 entry target. The selfreport nature of these journal entries brings biases including recall bias, estimation error, and even social desirability bias. No biological tests for any reported metrics were recorded, which certainly decreases precision. Past these limitations, this study gives a descriptive look into how creatine supplementation and behavioral data may be associated with accelerated SRT improvement in high school athletes. With many related variables to reaction time, a randomized, double blind, placebo controlled experiment should be performed with a larger and more diverse sample to isolate variables and examine exact effects. The nature of these effects was important to observe in a healthy high school population, as very little research has been previously conducted around this population. This study did, however, display that favorable outcomes regarding SRT occurred when a mix of sufficient sleep, hydration, dietary, and activity variables aligned.

CONCLUSION ---------------This study examined how creatine supplementation and common hydration, mixed with some other behaviors may be connected with simple reaction time changes in an understudied high school athlete population. Through the sample, both creatine users and non-users improved their reaction times, but creatine users improved more than twice as much than non-creatine users on average (-41.8 ms SAWYER CARLISLE

vs. -16.6). This pattern could suggest possible real creatine supplementation effects on reaction time, but due to the observational and unreliable nature of behavioral data reported, no causal or definite interpretation can be made based on creatine’s effects alone. This behavioral data showed that creatine was simply one component of a larger context affecting performance. Participants with higher sleep quality, higher fluid intake levels, and consistent activities often recorded larger reaction time improvements, though this is not an extremely strong relationship. Several limitations, including observational study design, small sample size, participant reliability, and testing inconsistencies decreases the strength of all conclusions. Resultingly, this study is best understood as exploratory literature showcasing that creatine supplementation paired with adequate sleep and fluid intake may align with quicker improvements in simple reaction times for adolescent athletes, although creatine cannot be picked out as an explanatory variable. Even with these constraints, this study covers an intersection of sports, health supplements, and neural performance that is covered too little. It proves that collecting this type of behavioral data among adolescents along with objective reaction time testing data can be collected in a high school setting. Future research should continue to look into these potential findings using tighter experimental control with randomization, placebo treatments, and larger samples while using objective biological tests to cement creatine’s specific effects on reaction time in high school athletes.

REFERENCES ---------------Avgerinos, K. I., Spyrou, N., Bougioukas, K. I., & Avgerinos, D. I. (2018). Effects of creatine supplementation on cognitive function of healthy individuals: A systematic review of randomized controlled trials. Experimental Gerontology, 108, 166–173. https://doi.org/10.1016/j.exger.2018.04.013 Benjamin, C. L., et al. (2022). Fluid restriction negatively influences perceived morning alertness and visuomotor ability. International Journal of Environmental Research and Public Health, 19(1), 370. https://doi.org/10.3390/ ijerph19010370 Candow, D. G., et al. (2024). Does one dose of creatine supplementation fit all? Advanced Exercise and Health Science, 1(2), 99–107. https://doi.org/10.1016/j. aehs.2024.05.002 Edmonds, C. J., Crombie, R., & Gardner, M. R. (2013). Subjective thirst moderates changes in speed of responding associated with water consumption. Frontiers in Human Neuroscience, 7, 363. https://doi.org/10.3389/ fnhum.2013.00363 Edmonds, C. J., et al. (2021). Dehydration in older people: A systematic review of the effects of dehydration on health outcomes, healthcare costs and cognitive performance. Archives of Gerontology and Geriatrics, 95, 104380. https://doi. org/10.1016/j.archger.2021.104380 18

Escalante, G., et al. (2022). Analysis of the efficacy, safety, and cost of alternative forms of creatine available for purchase on Amazon.com: Are label claims supported by science? Heliyon, 8(12), e12113. https://doi.org/10.1016/j.heliyon.2022.e12113 Harris, R. C., et al. (1974). Glycogen, glycolytic intermediates and high-energy phosphates determined in biopsy samples of musculus quadriceps femoris of man at rest. Methods and variance of values. Scandinavian Journal of Clinical and Laboratory Investigation, 33(2), 109–120.

Woods, D. L., et al. (2015). Factors influencing the latency of simple reaction time. Frontiers in Human Neuroscience, 9, 131. https://doi.org/10.3389/fnhum.2015.00131 Xu C, Bi S, Zhang W and Luo L (2024) The effects of creatine supplementation on cognitive function in adults: a systematic review and meta-analysis. Front. Nutr. 11:1424972. https://doi. org/10.3389/fnut.2024.1424972

Jagim, A. R., & Kerksick, C. M. (2021). Creatine supplementation in children and adolescents. Nutrients, 13(2), 664. https://doi.org/10.3390/nu13020664 Kim, J. S., et al. (1996). Effects of number and method of visual stimuli on simple reaction time. Korean Journal of Occupational and Environmental Medicine, 8(3), 477. https:// doi.org/10.35371/kjoem.1996.8.3.477 Lieberman, H. R. (2007). Hydration and cognition: A critical review and recommendations for future research. Journal of the American College of Nutrition, 26(sup5), 555S–561S. https://doi.org/10.1080/07315724.2007.10719658 Masento, N. A., et al. (2014). Effects of hydration status on cognitive performance and mood. British Journal of Nutrition, 111(10), 1841–1852. https://doi.org/10.1017/ S0007114513004455 Ostojic, S. M. (2021). Creatine as a food supplement for the general population. Journal of Functional Foods, 83, 104568. https://doi.org/10.1016/j.jff.2021.104568 Powers, M. E., et al. (2003). Creatine supplementation increases total body water without altering fluid distribution. Journal of Athletic Training, 38(1), 44–50. Rawson, E. S., et al. (2008). Creatine supplementation does not improve cognitive function in young adults. Physiology & Behavior, 95(1–2), 130–134. https://doi.org/10.1016/j. physbeh.2008.05.009 Ren Z, Mao X, Zhang Z and Wang W (2025) The impact of sleep deprivation on cognitive function in healthy adults: insights from auditory P300 and reaction time analysis. Front. Neurosci. 19:1559969. https://doi.org/10.3389/ fnins.2025.1559969 Semmelmann, K., & Weigelt, S. (2017). Online psychophysics: Reaction time effects in cognitive experiments. Behavior Research Methods, 49, 1241–1260. https://doi.org/10.3758/ s13428-016-0783-4 Taheri, M., & Arabameri, E. (2012). The effect of sleep deprivation on choice reaction time and anaerobic power of college student athletes. Asian journal of sports medicine, 3(1), 15–20. https://doi.org/10.5812/asjsm.34719

SAWYER CARLISLE

Spin Rate and Exit Velocity: How a Pitcher’s Spin Rate Affects the Exit Velocity Which a Batted Ball Can Be Hit With

HUDSON EDWARDS

ABSTRACT This study investigated whether a four-seam fastball’s spin rate affects the exit velocity of a batted ball. Three hitters faced pitches delivered by a pitching machine at 70 and 80 miles per hour and at spin rates of 1,900, 2,200, and 2,500 revolutions per minute. Each hitter recorded 20 barreled balls under every velocity-and-spin condition, producing 360 measured pitches. Across the tested range, average exit velocity decreased by approximately 0.20% and maximum exit velocity by approximately 0.13% as spin increased. These differences were too small to meaningfully affect game outcomes. The results suggest that spin rate has little direct effect on exit velocity once solid contact occurs, although it may influence contact type or launch angle.

RESEARCH QUESTION:

How does a baseball pitch’s spin rate affect the exit velocity that pitch has once it turns into a batted ball?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

Effects of Creatine Supplementation and Hydration on Simple Reaction Time to Visual Cues in High School Athletes Sawyer Carlisle | Beechwood High School Advisor: Coach Colt Place INTRODUCTION ●

Background: Split-second decision making dictates athletic outcomes and

Group Mean Simple Reaction Time (ms) Trajectory 360 ms

●

Efficacy: Accelerated improvement in creatine users aligns with

cognitive processing speed.

the theory that added phosphocreatine supports high-demand

Missing Connection: Creatine increases available brain energy, and

●

neural processing. Context: Better sleep loosely aligned with SRT gain. Creatine

timed academic success. Simple reaction time (SRT) measures this critical

●

DISCUSSION

hydration impacts alertness. However, almost no research examines these

users reported 24% higher average thirst (2.6/5 vs 2.1/5),

together in active adolescents.

345 ms

330 ms

suggesting osmotic demands are linked to cognitive performance. Research Question: How do hydration status and creatine supplementation practices relate to simple reaction time in cognitively

315 ms

●

Peak Improvement: ~103 ms non-Creatine Peak: ~64 ms

Week 1

descriptive trends, not causal proofs.

healthy high school athletes?

Max Individual Change in SRT (ms)

Limitations: Small sample (n=15); reliant on self-report journals;

●

Overall: All athletes improved over time due to task adaptation.

●

Creatine Group avg improvement: 41.8 ms (from ~362ms to ~320ms).

Baseline

Non-Creatine Group avg improvement: 16.6 ms (from ~333ms to

Design: 21-day observational correlational design capturing daily habits without experimental manipulation.

●

Participants (n=15): High school athletes (Grades 9-12). 7 in Creatine Group (>1 mo. use); 8 in Non-Creatine Group.

●

~316ms).

●

Week 3

Non-Creatine

METHODS

RESULTS SYNOPSIS

●

Week 2 Creatine

●

Variance: Creatine users improved by a margin >2.5x larger than non-

Task: SRT test involved pressing spacebar when visual color changed (red to green) after random 3–8 second delay.

users, closing the performance gap by Week 3.

CONCLUSION Creatine supplementation, paired with adequate sleep and fluid intake, may align with quicker simple reaction time improvements in adolescent athletes.

Critical Selected References

INTRODUCTION ---------------Over the past two decades, the game of baseball has gone through a drastic transformation, mainly due to the evolution of analytics in the sport. The analytics have gone through a very rapid progression which has changed the game. Baseball has always had statistics, some historians even argue the first stats were kept as early as the mid 19th century. The original stats used throughout most of the game's history are simple and result oriented. These include batting average, on-base percentage, slugging percentage, earned-run average, WHIP (walks plus hits per inning pitched), and fielding percentage. These stats did not track anything except the outcome of a play (Bean)(Catapult). For example, if a hitter had one hit through four at bats, the hitter would have a batting average of 25%, or as it is kept in baseball, .250. The first true analytic stat to be tested and recorded was a pitcher’s throwing velocity. Attempts to track throwing velocity first started in the early 20th century with crude and inaccurate experiments. These include having a pitcher throw a ball with a motorcycle with a speedometer driving next to the ball. In the mid to late 20th century the original radar guns were first used in baseball. These tracked a pitcher’s throwing velocity more accurately than ever. As technology to track throwing velocity became more advanced, other technology got developed. Major League Baseball implemented its first analytical technologies in 2006 with their PITCHf/x system. This system HUDSON EDWARDS

used two cameras inside the stadium to track the velocity, spin rate, and movement of a pitcher’s pitch. It also tracked trajectory, velocity, and distance of the ball once it was hit. This technology becoming implemented changed the game forever as it was the first time any of this data could be recorded in a game all in one system. MLB continued to improve this system, and in 2015 they installed a new system, called Statcast. This new system used a combination of radar and cameras to track all of the same data as before much more accurately. It also tracked other data such as player speed and distance ran. This system was updated once again in 2020 with the new Hawkeye system. Again, this system tracked everything it did before, but much more accurately using a set-up which includes 12 highspeed cameras to get a 360 degree view of anything happening on the field (MLB) (StatsPerform). Although all of these metrics and new data points are important to the game. Some of the most important and most talked about metrics are spin rate, which is measured in revolutions per minute (RPMs), exit velocity, which is measured in miles per hour (MPH), and throwing velocity which is also measured in miles per hour. Spin rate describes how fast the ball rotates as it makes its way to home plate. Spin rate has become an important tool used to evaluate pitchers, as more or less spin, depending on what pitch is being evaluated, can show and predict the effectiveness of the pitch. Pitches such as fastballs, 21

sliders, and curveballs tend to be more effective pitches when they are thrown with a higher spin rate. Pitches such as change-ups, splitters, and knuckleballs tend to be more effective when thrown with lower spin rates. This is because spin rate can affect the pitch's movement after it is thrown. Exit velocity is a metric which describes numerically how fast or how hard a ball is hit by the batter. This has a very strong correlation with offensive success as it can lead to more hits, extra base hits, and even more homeruns (O’Connell et al.) (Vogt) (Driveline) (Rapsodo) (PerfectGameUSA). Although both of these metrics (spin rate and exit velocity) have gotten a tremendous amount of attention from the baseball world due to how much they show about a player's abilities, there is no true understanding of how they are affected by each other. There are many studies that show how a higher or lower spin rate can affect the batter’s ability to make contact with the ball, but there is almost no research on how spin rate affects the ball once contact is made. Essentially, it is not understood whether or not a pitch’s spin rate affects the exit velocity of a batted ball. This gap in research is very significant because understanding the relationship between spin rate and exit velocity could influence both pitching strategies and offensive approaches. If higher spin rates lead to lower exit velocities, pitchers may benefit from maximizing spin. However, if spin rate has little effect, its importance may lie primarily in deception and movement rather than contact quality. Therefore, this study seeks to answer the following research question: How does a baseball pitch’s spin rate affect the exit velocity of a batted ball? By focusing specifically on four-seam fastballs—pitches known for their relatively straight trajectory and consistent spin characteristics—this study aims to isolate the variable of spin rate and determine whether a measurable relationship exists.

LITERATURE REVIEW ---------------The integration of analytics into baseball has been one of the most significant developments in the sport’s history. First popularized by the “Moneyball” era in the early 2000s, teams and even fans began to prioritize data-driven decisions over traditional scouting methods. Over time, technological advancements have allowed for increasingly detailed measurements of player performance in a way that the traditional, result driven statistics cannot show. For example, using the previous example of a batter going 1-4 in a game with a single, a batting average of .250 does not show us how hard the batter hit the balls they put in play, which can predict future batting performance. Whereas the modern data of exit velocity shows us average and max exit velocity. So if the hitter that went 1-4 had a 103 exit velocity on his hit, but exit velocities of 70-90 miles per hour on his other batted balls, we can see the direct correlation between a higher exit velocity and the batter’s ability to get a hit.

HUDSON EDWARDS

The implementation of PITCHf/x marked the first major step in this evolution, providing data on pitch velocity, location, and movement. Statcast expanded these capabilities by introducing advanced tracking of batted balls, including exit velocity and launch angle. More recently, Hawkeye systems have utilized a network of high-speed cameras to provide even more precise measurements. These technologies have enabled researchers to study the physics of baseball with never before available accuracy, leading to a deeper understanding of how various factors (such as spin rate and exit velocity) influence performance. Spin rate is a critical component of pitch movement or “behavior”. For a four-seam fastball, higher spin rates generally result in greater resistance to gravity, causing the pitch to drop less than expected. This creates the illusion of a “rising” fastball, which can be difficult for hitters to track and time correctly. At the professional level, the average spin rate for a four-seam fastball is approximately 2,300 RPM. Collegiate pitchers typically fall within a similar but slightly lower range, closer to 2,200 RPM. While high school pitchers tend to have slightly lower spin rates, averaging around 2,000 RPM. These differences are influenced by factors such as strength, mechanics, and training. However, there is a correlation between throwing harder and having a higher spin rate. Spin rate is often associated with pitching success because it can enhance pitch movement and deception. Higher spin rates can make pitches appear faster than they actually are, increasing the likelihood of swings and misses or weak contact, thus inducing lower average and max exit velocities. The primary physical principle underlying the effects of spin rate is the Magnus Effect. The Magnus Effect occurs when a spinning object moves through a fluid, creating a pressure differential that alters its trajectory. In baseball, this means that a spinning pitch experiences a force perpendicular to its direction of motion, influencing how it moves through the air. For a four-seam fastball with backspin, the Magnus Effect generates an upward force that counteracts gravity. As a result, the pitch drops less than expected, giving it a “rising” appearance. This can disrupt a hitter’s timing and lead to less optimal and less consistent contact with the ball. The Magnus Effect plays a crucial role in how pitchers use spin rate to their advantage. By increasing spin, pitchers can create pitches that are more difficult to hit squarely. However, while this may affect contact quality, it does not necessarily determine how hard the ball is hit once ideal contact is made (Greismer) (Lasky). Previous research has largely focused on the relationship between spin rate and a batter’s ability to make contact. Studies have shown that higher spin rates are associated with increased swing-and-miss rates and reduced batting averages. Additionally, research has explored how spin rate affects batted ball outcomes such as ground balls, fly balls, and line drives. However, there is limited research specifically examining the relationship between spin rate and exit velocity. Most studies treat contact as a binary outcome, either the batter makes contact or does not, without considering the quality of that contact in terms of exit velocity. This gap is crucial because exit velocity is one of the most reliable 22

predictors of offensive success. Understanding whether spin rate influences exit velocity could provide valuable insights into both pitching and hitting strategies for teams and coaches at all levels of the game.

RESULTS & DISCUSSION ----------------

METHODOLOGY ---------------This study used experimental design to attempt to isolate the effect of spin rate on exit velocity as much as possible. Other variables were strictly controlled to allow the results to be as accurate as possible. This allowed for clear-cut and accurate data. The independent variable was the RPMs the pitched balls had. Three different spin rates were tested throughout the experiment: 1,900 RPM, 2,200 RPM, and 2,500 RPM. These three spin rates give the wide range of values that can be seen in a baseball game when facing different pitchers. The dependent variable was the exit velocity of batted balls. Variables such as pitch velocity, pitch type, batters, bats used, and the balls used were all kept consistent in order to make the data as accurate as possible. A pitching machine was used throughout all testing. Although not perfect, this is the most accurate and consistent way to deliver pitches to batters. It keeps velocity and location very consistent. Two sets of testing were done, one set at 70 MPH and one set at 80 MPH. All pitches were fed into the machine as four-seam fastballs, which keeps velocity and movement the most consistent as this pitch is considered flat and straight. Using this pitch allows spin rate to be isolated as the variable that may or may not be affecting exit velocity. The sample size was three hitters, who each completed sets of 20 “barreled-up” balls at each combination of spin rate and pitch velocity. In this test, only what was defined as “barreled-up” balls counted towards the 20 hit total for each data set. A barreled-up ball for this experiment is defined as a batted ball hit at 90% or more of a player's max exit velocity. Max exit velocity was found for this experiment by having each hitter hit 20 balls off of a tee. After each hitter took 20 swings off the tee, their max was recorded and increased by 7.5 MPH. This 7.5 MPH increase was due to the commonly accepted idea that for every 10 MPH of pitched ball speed, exit velocity increases by one mile per hour. Since testing was being conducted at 70 and 80 MPH, using an increase of 7.5 MPH allowed for the increase in exit velocity due to pitched ball speed to be taken into account. This ensures that only hard hit balls counted towards the exit velocity data that was tracked. Without this, a mishit ball, such as a weak ground ball or popup could easily skew data. These weakly hit balls can often be hit at 40-70 MPH, which is a drastic change from the 90+ MPH exit velocities that are seen in the experiment. This mitigates bias that could occur from many factors, including differences in pitch velocity and location, batter skill, and overall human error. Because testing occurred at different spin rates and pitch velocities, this experiment will truly show the effect of spin rate on a batted ball’s exit velocity.

HUDSON EDWARDS

This chart shows the average and max exit velocities for different spin rate and pitch velocity combinations for each hitter. The chart also shows averages for overall average and max exit velocities for the different values for spin rate and pitch velocity.

The results of the study indicated that spin rate has a very minimal impact on exit velocity. When comparing the different spin rate conditions, only slight variations in exit velocity were observed. From 1,900 RPM to 2,200 RPM, there was a small increase in exit velocity. However, from 2,200 RPM to 2,500 RPM, exit velocity decreased slightly. Overall, the data showed a very weak negative relationship between spin rate and exit velocity. Across the full range of spin rates tested, the average exit velocity decreased by approximately 0.20%, while the maximum exit velocity decreased by approximately 0.13%. These differences are extremely small and fall within a range that is unlikely to have any real significance in a game. The results suggest that spin rate does not truly have a meaningful 23

influence on how hard a ball is hit once contact is made, due to the spin rate of the pitch. Overall, the data showed the lower spin rate had a slight increase, less than a quarter of a percent, compared to higher spin rates. The most likely possible explanation for this trend lies in the physics of contact. Higher spin rate fastballs tend to have a “rising” effect, which can cause hitters to make contact underneath the ball. This type of contact often results in fly balls, which often have slightly lower exit velocities but higher launch angles, due to a slightly less direct contact of the ball with the barrel of the bat. In contrast, lower spin rate pitches may lead to more direct contact, resulting in line drives or ground balls which typically yield slightly higher exit velocities, due to more direct contact with the bat. This suggests that spin rate may influence the type of contact rather than the speed of the ball off the bat, however, because launch angle was not tracked in this study, this correlation cannot be confirmed. Another important consideration is the relationship between exit velocity and launch angle. A ball hit at a slightly lower exit velocity but with an optimal launch angle can travel farther than a harder hit ball with a poor launch angle. This highlights the true complexity of batted ball outcomes, and suggests that exit velocity alone may not fully capture the effects of spin rate. This goes back to the previous literature on the subject, of how a pitcher’s spin rate and pitch movement is given credit for being able to disrupt a batter’s ability to make consistent contact.

CONCLUSION, LIMITATIONS, IMPLICATIONS, AND FUTURE RESEARCH ---------------The experiment found that spin rate has a negligible impact on how hard a ball is hit. The differences observed across different spin rates were less than 0.25%, indicating that spin rate does not meaningfully influence exit velocity. This less than a quarter of a percent difference is negligible because it does not affect whether a batted ball is a hit or out for almost every play on a baseball field. A baseball hit at 100 MPH with a 10 degree launch angle travels on average between 260-290 feet. A ball hit at 100.25 MPH with the same 10 degree launch angle travels on average about 265-295 feet, which is only a five foot difference. A ball hit at 100 MPH with a launch of 15 degrees travels around 300-330 feet. A ball hit at 100.25 MPH with the same 15 degree launch angle travels around 305-335 feet. Again, only around a five foot difference. On average, the quarter of a percent exit velocity difference leads to a difference of one to five feet of total distance depending on the launch angle the ball is hit with. For almost every play on a baseball field, this distance difference does not lead to a change in whether the ball is a hit or out, which again shows that the quarter of a percent difference in exit velocities between the different spin rates tested is negligible and does not truly affect any real outcomes in a baseball game. Several limitations should be considered when interpreting these results. The sample size was relatively small, consisting of only three hitters and 360 pitches. Over a full major league season, around 700,000-750,000 pitches are thrown, with around 100,000 of those being four seam fastballs. Using a HUDSON EDWARDS

much larger sample size such as this, more accurate and more reliable results could be found. Additionally, while efforts were made to control pitch location and speed, minor variations may still have occurred, which although these were minimized by only measuring what was defined as a “barreled up” ball, these variations still need to be taken into account. Also, differences in hitter skill level may have influenced the results, despite efforts to control for this through the use of “barreled up” balls. The findings of this study could have very important implications for baseball analytics. They suggest that pitchers with higher spin rates do not necessarily give up harderhit balls due to spin alone. However, it may suggest that pitchers with higher spin rates tend to give up more fly balls than ground balls due to the rising effect of pitches thrown with that higher spin, However, because launch angle was not tracked in this study, this idea cannot be confirmed. Future research could expand on this study by examining the relationship between spin rate and batted ball distance by incorporating launch angle data. Additionally, studies with larger and more diverse samples (such as using data from a full MLB or NCAA season) could help confirm these findings and provide a more comprehensive understanding of the relationship between spin rate and contact quality.

REFERENCES ---------------Bean, Randy. “Moneyball 20 Years Later: A Progress Report on Data and Analytics in Professional Sports.” Forbes, 18 Sept. 2022, www.forbes.com/sites/randybean/2022/09/18/ moneyball-20-years-later-a-progress-report-on-data-andanalytics-in-professional-sports/\ Vogt, Greg. “Pitch Profiles and Cues.” PRP Baseball, www. prpbaseball.com/blog/pitch-profiles-and-cues Griesmer, Andrew. “The Physics Behind Baseball Pitches.” COMSOL Blog, 17 July 2014, www.comsol.com/blogs/physicsbehind-baseball-pitches/ Lasky, Jack. “Magnus Effect.” EBSCO Research Starters: Science, EBSCO Information Services, www.ebsco.com/ research-starters/science/magnus-effect O’Connell, Michael, et al. “Spin Rate: What We Know Now.” Driveline Baseball, 17 Nov. 2016, www.drivelinebaseball. com/2016/11/spin-rate-what-we-know-now/ “Baseball Analytics: What It Is & How to Win More Games with Data.” Catapult, 25 Oct. 2024, www.catapult.com/blog/ baseball-data-analytics “Statcast.” MLB.com, Major League Baseball, www.mlb.com/ glossary/statcast

“State of Analytics: How the Movement Has Forever Changed Baseball – For Better or Worse.” Stats Perform, www. statsperform.com/resource/state-of-analytics-how-themovement-has-forever-changed-baseball-for-better-or-worse/ “The Impact of Hitting Spin Rate and Direction on Batted Ball Outcomes.” Rapsodo, 29 Oct. 2024, www.rapsodo.com/blogs/ baseball/the-impact-of-spin-rate-and-direction-on-battedball-outcomes “Article 10457.” Perfect Game USA, www.perfectgame.org/ Articles/View.aspx?article=10457

HUDSON EDWARDS

Rural-Urban Disparities in Vaccine Hesitancy Among Kentucky High School Students

MAYA GEHLHOFF

ABSTRACT This study examined vaccine hesitancy among Kentucky high school students and sought to compare attitudes in rural and urban counties. An anonymous survey collected responses from 62 students, including 60 urban and two rural participants; a planned public-records comparison could not be completed because the requested state data were not received. Overall hesitancy was low and centered primarily on the COVID-19 vaccine rather than required childhood vaccines. Hesitant respondents were 7.5 times more likely to cite religious, philosophical, or political beliefs and 2.5 times more likely to cite distrust of government or public-health officials. Although the sample could not support a ruralurban comparison, the results identify parental influence, online information, political beliefs, and institutional trust as important factors in adolescent hesitancy.

RESEARCH QUESTION:

How do the attitudes of Kentucky high school students towards vaccines differ across rural and urban counties, and why?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

Rural-Urban Disparities in Vaccine Hesitancy Among KY High School Students Expert Advisor: Rachel Gaither, PhD

Principal Investigator: Maya Gehlhoff

Introduction Vaccine Hesitancy: “a delay in acceptance or outright refusal of safe vaccines despite their availability” - WHO Top ten global health threat (WHO, 2019) Documented differences between rural and urban vaccination rates Goes beyond attitude Structural access barriers Appalachian Kentucky specifically sees significantly lower vaccination rates, but has significant access barriers Extremely high chronic disease burden → more pressing medical concerns than vaccination for most people Geographic isolation Important distinction: Access vs. Attitude Research Question: How do the attitudes of Kentucky high school students towards vaccines differ across rural and urban counties, and why?

Gap in the Literature Three overlapping gaps justify this study: existing research’s reliance on adult self-report surveys, the absence of adolescents as independent respondents, and the absence of any Kentucky specific study that combines survey data with exemption records across the rural-urban spectrum. This study addresses all three by design.

Survey Sample Percent of Respondents from this County 4.80%

County Name Boone

Percent Pop. Urban 88.53%

Percent Pop. Rural 11.47%

Classification

Boone Fayette

3.20%

Fayette

97.07%

1.63%

Urban Rural

County Name

Urban

Grant

1.60%

Grant

0.00%

100.00%

Hardin

1.60%

Hardin

48.50%

51.50%

Rural

Jefferson

27.40%

Jefferson

98.37%

1.63%

Urban

Kenton

58.10%

Kenton

93.28%

6.72%

Urban

Oldham

1.60%

Oldham

74.84%

25.16%

Urban

Warren

1.60%

Warren

72.69%

27.31%

Urban

Results Public records data was never returned. Kentucky Open Records Act only requires the department acknowledge your request within 5 days, but there is no deadline on when they send back the data Sample not adequate for rural-urban data analysis

Limitations Sample was convenience sample (n = 62) Not enough data to draw accurate conclusions about disparities between rural-urban respondents Did not gather information about parental political affiliation or demographic barriers Public records request not fulfilled → data was exclusively selfreport

Research Design Mixed Methods Survey among KY high school students What high school students believe about vaccines Public records request from the KY Department of Public Health By county data from the 11th grade checkpoint for number of religious of vaccine exemptions, number of enrolled 11th graders, number of students not up to date with all vaccines, number of students with no records or shots Documented behavior Helps combat self-report bias

Hesitant respondents 1.5 times more likely to gather information from parents Hesitant respondents twice as likely to gather information from social media or online news websites Non-hesitant respondents 1.9 times more likely to gather information from public health officials

Future Directions Hesitant respondents 7.5 times more likely to cite religious or philosophical Hesitant respondents 2.5 more likely to cite distrust

Best way to reach adolescents: social media, online news websites, parental advice Media literacy emphasized More research similar to these with less time and financial barriers should be conducted

INTRODUCTION ---------------Vaccines are one of the most significant public health tools in modern medicine. They control and sometimes even eliminate diseases that have caused suffering on a significant scale in society. Vaccines are credited with drastically reducing mortality from diseases such as polio, measles, and smallpox globally (Andre et al., 2008). However, the effectiveness of any vaccine depends on community uptake. The World Health Organization (WHO) defines vaccine hesitancy as “the delay in acceptance or outright refusal of safe vaccines despite their availability.” In fact, in 2019 the WHO designated vaccine hesitancy as one of the ten greatest threats to global health, warning that the persistent vaccine hesitancy would undermine the herd immunity threshold necessary to protect populations from disease outbreaks (WHO, 2019). In the United States, vaccine hesitancy does not exist uniformly across geographical, demographical, or community lines. Studies have shown that rural communities consistently have lower vaccination rates than their urban counterparts. The rural-urban vaccination gap is one of public health research’s most consistent findings, from COVID-19 to influenza, HPV, and routine childhood vaccines. To understand why rural communities are less likely to vaccinate, one must differentiate hesitance as an attitude from vaccination as a behavior. A 2023 study in the American Journal of MAYA GEHLHOFF

Public Health found that among unvaccinated individuals, hesitance attitudes were similar between rural and urban. This indicates that structural access barriers, rather than just vaccine attitudes, go a long way in explaining the vaccination gap (Soorapanth et al., 2023). Rural areas make up 65.6% of federally designated primary care health professional shortage areas and report significantly more difficulties accessing medical care compared to their suburban or urban counterparts (Health Resources & Services Administration [HRSA], 2023). Early polling around the COVID-19 vaccine reflected this complexity. According to the Kaiser Family Foundation (2020), only 31% of rural adults said they would take the COVID-19 vaccine compared to 43% of urban adults. This suggests that access and hesitancy are real and separate contributors to lower vaccination rates in rural areas. This investigation is Kentucky specific. Kentucky includes a significant part of Appalachian America, which faces geographic isolation, chronic health care shortages, and socioeconomic disadvantage. Vaccination rates in Appalachian counties have consistently fallen behind state and national benchmarks. However, much of the literature on rural vaccine hesitancy is based on aggregated national data, and Kentucky’s specific rural-urban dynamics remain under-researched, which constrains the development of public health interventions.

Two methodological gaps compound this problem. First, the field of vaccine hesitancy research has largely been based on self-reported survey data; however, self-reported hesitancy estimates are unreliable, as they underrepresent the unvaccinated populations by up to 32 percentage points due to social desirability and nonresponse bias (Nguyen et al., 2022). Further, it has been shown that survey data can be augmented with administrative records for greater validity (Salathe & Khandelwal, 2024). Second, adolescents are primarily missing from the hesitancy literature as independent respondents, with most studies relying on parental proxy responses that do not adequately capture adolescent views (Gowda & Dempsey, 2022). The few studies that do focus on teen hesitancy suggest a very different hesitancy profile than for adults (Rozbroj et al., 2024). This study attempts to fill these gaps by surveying Kentucky high school students and requesting county-level school immunization data from the Kentucky Department of Public Health via public records. Attitudinal survey data combined with administrative records allows more valid measures of hesitancy than those obtained from either data source alone. The central research question is: How do the attitudes of Kentucky high school students towards vaccines differ across rural and urban counties, and why?

LITERATURE REVIEW ---------------Defining Vaccine Hesitancy Vaccine hesitancy is conceptualized along a spectrum from complete acceptance to refusal of vaccines. It includes delay, selective acceptance, conditional acceptance, and total rejection (WHO, 2015). The WHO’s Strategic Advisory Group of Experts on Immunization (SAGE) developed the 3 C’s model to organize drivers of vaccine hesitancy. According to SAGE’s model, complacency (low perceived disease risk), convenience (structural and logistical barriers to access), and confidence (trust in vaccine safety, efficacy, and health systems) are the “primary axes of hesitancy.” Importantly, this does not conceptualize hesitancy as a binary, irrational refusal of vaccination; rather it is a complicated response to real and perceived risks, failures of access, and believed credibility of institutions. Later, outside the WHO, a fourth component, collective responsibility, was conceptualized as to what degree individuals feel vaccination is a responsibility toward their communities. This is another important driver of vaccine attitudes (Betsch et. al., 2018). Since hesitancy manifests differently across populations, political contexts, and geographic settings, national-level models will require locallevel data to be meaningful for public health policymaking. The Rural-Urban Vaccination Disparity The rural-urban vaccination gap is well defined across vaccine types and time periods. For example, in 2024, rural communities had mean vaccination rates that were about 40% lower than urban communities (Taber et al., 2024). According to the Centers for Disease Control and Prevention (CDC), during the first year the COVID-19 vaccine was available, 74% of residents of urban counties received MAYA GEHLHOFF

their first dose, but only 59% of residents of rural counties received theirs. Between April 2021 and early 2022, this gap more than doubled, and did so faster than anticipated (Murthy et al., 2022). This rural-urban disparity is evident even among adolescents. CDC National Immunization Survey data shows that Human Papillomavirus (HPV) vaccination coverage among adolescents in urban areas is about 10.5 percentage points higher than in rural areas. This gap has remained unchanged for nearly a decade, despite public health campaigns targeting adolescent vaccination (CDC, 2025). This gap occurs for several reasons, including fewer provider recommendations in rural settings, lower parental health literacy, and less clinic availability (Regan et al., 2019). Messaging alone does not surmount structural factors like these. The distinction between access and attitude has immediate implications for the design of intervention programs. This disparity exists across vaccine types, with more recent increases since COVID-19 pandemic. Trust, Misinformation, and Political Identity Trust in scientific institutions is the most reliable crossnational predictor of vaccine willingness; it was found to be more significant than political trust and socioeconomic variables (Robertson et al., 2023). Where institutional trust is low, misinformation fills the vacuum. During the COVID-19 pandemic, six categories of vaccine misinformation were outlined: scientific (false claims about ingredients or mechanism), political (framing vaccination as government overreach), media-based (conspiracy stories amplified via social media), religious (linking vaccination to theological objections), technological (microchipping claims, etc.), and personal (anecdotal horror stories) (Roozenbeek et al., 2022). Indeed, the same study noted that scientific countermessaging alone could not overpower hesitancy arising from political or religious frames. Attitude change only occurred when messages came from community members like physicians, faith leaders, and local officials, who shared the same social and ideological structures with the hesitant communities (Roozenbeek et al., 2022). Political identity further compounds hesitancy; high misinformation exposure among conservative respondents yielded much higher hesitancy, although this effect was reduced among people with established vaccination habits (Quinn et al., 2023). These dynamics matter particularly in rural Kentucky, where conservative political identity, religious community ties, and distrust of institutions coalesce in ways that national communications strategies fail to recognize. Appalachian Kentucky: A Convergence of Hesitancy Risk Factors The above-mentioned structural, attitudinal, and contextual risk factors for vaccine hesitancy converge in Appalachian Kentucky. For example, a high chronic disease burden can indicate inadequate preventative care and limited health care infrastructure, and prevent people from focusing on vaccination. The Appalachian Regional Commission (ARC) documents that heart disease mortality in the region is 45% above the national rate, cancer mortality is 35% above the national rate, and Chronic Obstructive Pulmonary Disease (COPD) is 88% above the national rate (ARC, 2017). In this context, vaccination must compete with more immediately 28

pressing health concerns and a strained credibility of the health care system due to failures to counter chronic disease. The University of Kentucky’s K-VAC project frames Eastern Kentucky hesitancy as a complex issue being shaped by geographic isolation, low socioeconomic status, provider shortages, and institutional mistrust (University of Kentucky Office of Research, 2023). These conditions had significant ramifications during the COVID-19 pandemic. In 2021, fewer than 40% of Appalachian county residents were vaccinated. In 31 of Kentucky’s 54 Appalachian counties, transmission was higher than the overall statewide rate, and six Appalachian counties had incidence rates over double the overall statewide rate (WKYT News, 2021). According to local health officials, the barriers these communities have identified are misinformation and distrust, not supply or access failures. Adolescent Hesitancy Adolescents are conspicuously missing from the literature on hesitancy as independent respondents. This is surprising, because adolescents are a population with a distinct vaccine attitude profile and growing autonomy. Most studies on vaccination in the 10-19 age group use parental proxies (Gowda & Dempsey, 2022). That methodological choice masks young people’s distinct attitudes and rationales. Adolescent attitudes are not simply an echo of parental beliefs; they are shaped by peer networks, school environments, and exposure to social media in ways that existing adult-focused research cannot trace. Dubé et al. (2025) identified peer norms, trusted school-based sources of information, and family physician recommendations as key determinants of adolescent attitudes, and argued that such determinants differ between rural and urban settings. With adolescents maturing and gaining autonomy in health care decision-making, the hesitancy profile they carry into adulthood will determine community vaccination coverage. This population is crucial but understudied regarding research and intervention focus. Kentucky’s Exemption Framework Kentucky’s school immunization law provides context for this study’s public records component. According to Kentucky Revised Statutes § 214.036 (2024), school vaccination is mandatory and exemptions are only permitted on medical or religious grounds. Unlike other states, Kentucky does not permit exemptions on philosophical grounds. However, there is no effective method to fairly evaluate whether or not people have legitimate objections to vaccines based on religious grounds, so it is possible that residents could falsely claim a religious objection. To obtain a religious exemption, a notarized form is required. This is an administrative hurdle that can act as a filter for casual non-vaccinators. The statewide non-medical exemption rate among kindergartners is 2% below the national median (Johns Hopkins Bloomberg School of Public Health [JHU IVAC], 2025). However, this study only examined the kindergarten checkpoint, which is the first of three, and the data could conceal county level variation that might correlate with rural-urban geographic differences. Gap in the Literature Three overlapping gaps justify this study: existing research’s reliance on adult self-report surveys, the absence of adolescents as independent respondents, and the absence MAYA GEHLHOFF

of any Kentucky specific study that combines survey data with exemption records across the rural-urban spectrum. This study addresses all three by design.

METHODOLOGY ---------------Research Design This study employs a mixed methods design combining survey data with vaccine exemption data to examine rural-urban disparities in vaccine hesitancy among Kentucky high school students. The idea behind combining two data sources is methodological. Combining self-reported hesitancy attitudes with documented exemption decisions allows the investigation of both what adolescents believe about vaccines and whether those beliefs align with their behavior. The survey data captures nuance that the administrative records cannot, while the administrative records provide a bias-resistant data point that examines documented behavior. The data analysis of survey results was guided by Brown University School of Public Health Research Scientist and expert advisor of this study, Rachel Gaither, PhD. County Classification To classify whether a county was rural or urban, county-level data from the 2020 U.S. Census was used. The percent of population in rural areas was the determining factor. If a county had 50% or higher percentage of population living in rural areas, it was classified as rural. If a county had 50% or higher percentage of population living in urban areas, it was classified as urban. This metric was chosen over metrics relying on land area or housing, because these metrics do not identify what portion of the population is experiencing rural conditions or urban conditions. Survey Instrument A researcher-designed survey was administered to Kentucky high school students across Kentucky counties. The survey questionnaire (see Appendix A) referred to awareness of vaccine exemption requests, self-reported attitudes towards specific vaccines, considerations influencing vaccine attitudes, sources for vaccine information, and hypothetical vaccination behavior for future children. The survey addressed both the required school vaccines in Kentucky as well the recommended vaccines (COVID-19, Influenza, and HPV). All participants provided electronic informed consent. They were not able to move on in the electronic survey without explicitly providing informed consent. To promote honesty in survey responses, the survey was kept completely anonymous. No identifying information was collected, and participants were informed of this before being asked to provide consent. The survey was promoted through paper flyers (see Appendix B) hung around the principal investigator’s high school. The flyer and link to the online survey was posted on the social media accounts of the principal investigator and the principal investigator’s friends. The flyer and link to the online survey was posted to AP Research forums on FaceBook. Finally, the survey link and electronic flyer was emailed to the principal of every high school that taught AP Research in the state of Kentucky. 29

Survey Sample The survey was a convenience sample of 62 respondents across eight Kentucky counties: Kenton County (n = 36), Jefferson County (n = 17), Boone County (n = 3), Fayette County (n = 2), and one respondent from Grant, Hardin, Oldham, and Warren counties (Table 1). Kenton, Jefferson, Boone, Fayette, Warren, and Oldham county are all classified as urban for the purposes of this study; Grant and Hardin are rural (see Table 2). This distribution provides 60 urban respondents and 2 rural respondents. This geographic imbalance is a primary limitation of this study, addressed further in the Discussion section.

Figure 1 Public Records Request Screenshot

The request was never fulfilled. The Kentucky Open Records Act requires that the Kentucky Cabinet for Health and Family Services respond within five business days from the receipt of the request, acknowledging that they have received the request. However, there is no mandate regarding the timeline for when the request is fulfilled.

RESULTS ---------------Vaccine Exemption Survey Data Respondents were prompted asking if they were aware of any vaccine exemptions their parent/guardian requested for them. 6.5% of respondents reported that they had received/ requested a vaccine exemption, 14.5% reported being unsure, and 79% reported that they had not received an exemption. Of the respondents who replied yes, they had requested/received a vaccine exemption (n = 4), 50% reported receiving a medical exemption, 25% reported receiving a religious exemption, and 25% reported “I don’t know.” When asked what vaccines their exemptions were for, the respondent who reported receiving a religious exemption reported DTaP (diphtheria, tetanus, and pertussis), Polio (IPV), MMR (measles, mumps, rubella), Varicella (chickenpox), Tdap (tetanus, diptheria, and pertussis), Pneumococcal (PCV), and Meningococcal (MenACWY). Of the two respondents who reported receiving a medical exemption, one was unsure of what vaccines their exemption was for, and the other reported Polio (IPV), MMR (measles, mumps, rubella), Varicella (chickenpox), and Meningococcal (MenACWY). Finally, the respondent who reported they had received a vaccine exemption but was unsure if it was medical or religious reported that their exemption was for the Meningococcal (MenACWY) vaccine.

Public Records Request Administrative exemption data was attempted to be obtained through a formal public records request to the Kentucky Department of Public Health under Kentucky’s Open Records Act. The request targeted the eleventh grade vaccine checkpoint for the 2023-2024 school year and 2024-2025 school year. The data requested was the by-county number of students enrolled in the eleventh grade, number of religious exemptions, number of students not up to date with all vaccines, and the number of students with no shots or no records of immunizations ( Figure 1).

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Attitude Survey Data When asked if they wanted their potential children to receive all of their required vaccinations, 91.9% of respondents reported wanting their children to receive all required vaccinations, while 6.5% reported they wanted their potential children to receive some of their required vaccinations, and 1.6% reported that they did not want their children to receive any of their required vaccinations (Table 3). Urban only respondents did not significantly differ from the overall population. 100% of rural respondents reported that they wanted their children to receive all required vaccinations.

Next, respondents were asked whether or not there were any vaccines they disagreed with receiving. 78.3% of participants agreed with receiving all vaccines, and 21.67% reported disagreeing with some vaccines (Table 5). There is no significant difference between the overall population and urban population. 100% of the rural population reported agreeing with all vaccines. These responses allow for the sample to be broken down into hesitant (those who responded “Yes”) and non-hesitant (those who responded “No”) for later data analysis.

When asked if they would give their potential child the recommended by not required vaccines (COVID-19, Influenza, and Human Papillomavirus (HPV)), 77.4% of respondents said yes, 11.3% of respondents said no, and 11.3% of respondents reported being unsure (Table 4) (Figure 2). The urban only population did not significantly differ from the overall population, but 100% of the rural population reported that yes, they would give their child the recommended vaccinations (Table 4).

Attitude Multi-Select Survey Data For these questions, respondents were allowed to select more than one option leading to totals higher than 100%. Additionally, the difference between overall, rural, and urban was statistically insignificant. Instead of breaking down the responses into overall, urban, and rural, the responses are broken down into overall, hesitant only, and non-hesitant for relevant questions. As shown in Table 6 and Figure 3, when asked what specific vaccines they opposed, 75.8% of participants reported that they did not oppose any vaccines. The most opposed vaccine was the COVID-19 vaccine, with 12.9% of respondents disagreeing with their hypothetical child receiving the vaccine and 11.3% of respondents disagreeing with receiving the vaccine for themselves. 6.7% of people reported some variation of “I don’t know” or “unsure.” 4.8% of respondents disagreed with receiving the HPV vaccine, and 3.2% of respondents disagreed with their children receiving the HPV vaccine. 3.2% of respondents disagreed with receiving the Flu vaccine, and 3.2% of respondents disagreed with their child receiving the Flu vaccine. Finally, 1.6% of respondents disagreed with receiving the Polio vaccines, and 1.6% of respondents disagreed with their child receiving the Polio vaccine.

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However, when respondents are broken down into the categories of hesitant and non-hesitant, interesting disparities are revealed (Figure 5). Hesitant respondents were 7.5 times more likely to cite religious or philosophical beliefs and political beliefs than non-hesitant respondents. Hesitant respondents were also 2.5 times more likely to cite distrust of government or public health officials than non-hesitant respondents. Notably, non-hesitant respondents were twice as likely to cite distrust of pharmaceutical companies than hesitant respondents.

The final question respondents were asked was, “Where do you gather most of your information regarding vaccines?” The most reported source was public health officials, with 70% of participants citing this, immediately followed by parents, with 65% of participants citing them as a key source of information. After these two sources, the next most commonly cited sources were online articles (38.3%) and academic research papers (33.3%). See Figure 6 for complete response breakdown. Next, respondents were asked, “What considerations, if any, influence your decisions regarding vaccines?” 50% of respondents reported having no hesitations regarding vaccines. The most common hesitations after that were concerns about safety or side effects (45.2%), distrust of government or public health officials (16.1%), and distrust of pharmaceutical companies (12.9%). Figure 4 shows the complete breakdown of responses.

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Once again, these responses were broken down by hesitant and non-hesitant respondents, and revealed significant differences (Figure 7). Hesitant respondents were approximately twice as likely to report online news websites, and over twice as likely to report gathering information from social media posts made by news organizations, governmental organizations, or other sources. Additionally, 85.7% of non-hesitant reported gathering information from public health officials, while just 46.2% of hesitant respondents reported this. 76.9% of hesitant respondents also reported parents as a primary source of information compared to only 49.2% of non-hesitant respondents.

have a significant impact on hesitancy, as was previously concluded in Quinn et al. (2023). They were also 2.5 times more likely to cite distrust of government or public health officials. These patterns replicate the findings that were discussed in Robertson et al. (2023). That study found that the strongest predictor of vaccine willingness was trust. Hesitant respondents were significantly more likely to be distrustful of government or public health officials, aligning with previous findings. Interestingly, non-hesitant respondents were twice as likely to be distrustful of pharmaceutical companies than hesitant respondents. This is seemingly counterintuitive, and suggests that pharmaceutical distrust and vaccine hesitancy are not correlated within this sample. The findings about sources of vaccine information carry large implications for intervention strategies. Among non-hesitant respondents, 85.7% said they gathered information from public health officials compared to only 46.2% of hesitant respondents. Additionally, hesitant respondents were much more likely to rely on parents for vaccine information, with 76.9% of hesitant respondents citing parents as a source compared to 46.2% of non-hesitant respondents. Hesitant respondents were also twice as likely to rely on social media and online news websites. This is consistent with the finding from Dubé et al (2025), where parental influence and peer information were large factors in teen hesitancy. These findings suggest that school-based messaging is unlikely to be effective with the hesitant population.

DISCUSSION ---------------This study demonstrated that COVID-19 vaccines drive hesitancy in high school students. The most opposed vaccine by far was COVID-19: 11.3% disagreed with receiving it themselves and 12.9% disagreed with their children receiving it. The majority of required vaccines received no oppositions, and the next most opposed vaccine after COVID only 3.2% of participants disagreed with. This is likely due to the charged social and cultural landscape surrounding the COVID-19 vaccine. Because the vaccine was pushed out more rapidly and in a time of crisis, fearmongering and misinformation were much more prevalent regarding the vaccine. COVID-19 hesitancy is much more politically and ideologically charged than hesitancy towards more established vaccines, and the misinformation surrounding the COVID-19 was uniquely intense (Quinn et al., 2023). This study is consistent with these claims, and these justifications for COVID-19 vaccine hesitancy are in line with the findings of this study. The concentration of opposition to COVID-19 vaccines in this sample suggests that hesitancy is due to attitude.

This study had several substantial limitations. First, the survey sample was a convenience sample with 62 respondents, and there was not enough data to accurately conclude anything about rural-urban disparities. The two rural respondents reported minimal hesitancy and 100% agreement with all vaccines. This aligns with previous findings that among already vaccinated individuals, hesitance attitudes are similar across rural and urban areas (Soorapanth et al., 2023). However, there is not enough data to successfully draw any conclusions regarding differences between the rural and urban samples. Second, the survey did not gather information about parental political affiliation and other demographic barriers, so we do not have data on these potential drivers of vaccine hesitancy among adolescents. Third, the public records request was never fulfilled, and we were not able to compare self-report data to state-wide administrative data collected by the Kentucky Department of Public Health.

The breakdown of vaccine considerations by hesitant respondents vs. non-hesitant respondents brought forward some truly unique findings. Hesitant respondents were 7.5 times more likely to cite religious or philosophical beliefs and political beliefs as considerations influencing their vaccine decisions. This indicates that political identity can MAYA GEHLHOFF

CONCLUSION ----------------

REFERENCES ----------------

Initially, this study was conducted to investigate rural-urban disparities among Kentucky high school students. It intended to combine a survey with an administrative public records request. The rural-urban comparison was not possible due to an imbalance of survey results and the non-fullfillment of the public records request. However, the study still produced significant data about vaccine hesitancy in Kentucky high school students.

Andre, F. E., Booy, R., Bock, H. L., Clemens, J., Datta, S. K., John, T. J., Lee, B. W., Lolekha, S., Peltola, H., Ruff, T. A., Santosham, M., & Schmitt, H. J. (2008). Vaccination greatly reduces disease, disability, death and inequity worldwide. Bulletin of the World Health Organization, 86(2), 140–146. https://doi.org/10.2471/BLT.07.040089

Overall hesitancy was low among urban respondents. It was almost entirely driven by opposition to the COVID-19 vaccine and other recommended vaccines, not required ones. Hesitant respondents were more strongly affected by political and religious drivers, governmental distrust, and heavy reliance on parental and online information sources. Non-hesitant respondents relied much more on public health officials as an information source and had much more trust in government and public health officials. These findings have practical implications for public health intervention in Kentucky. This study indicates that the best way to reach hesitant adolescents is through social media, online websites, and parental advice. It is important to pursue messaging through these routes, rather than the traditional methods in order to actually reach the desired audience. Additionally, media literacy should be more heavily emphasized in high school education curriculums to help students identify sources of misinformation and reliable sources. Based on the limitations discussed above, there are several avenues for future research that are important to consider. Subsequent studies should be done over a longer time period with a more targeted distribution of the survey, potential financial incentives for participation, and ample time to fulfill public records requests. There is valuable information to be gathered from a public records request, including demonstrated behavior rather than self-reported hesitancy. Additional surveys should also question parental political affiliation and other demographic barriers to further solidify drivers of vaccine hesitancy among adolescents. Overall, it is important for future research focusing on adolescent hesitancy to occur. Adolescents are the future of public health, and their views will have large scale ramifications for global health and future generations.

Appalachian Regional Commission. (2017). Health disparities in Appalachia. https://www.arc.gov/report/health-disparitiesin-appalachia/ Betsch, C., Schmid, P., Heinemeier, D., Korn, L., Holtmann, C., & Böhm, R. (2018). Beyond confidence: Development of a measure assessing the 5C antecedents of vaccination. PLOS ONE, 13(12), e0208601. https://doi.org/10.1371/journal. pone.0208601 Centers for Disease Control and Prevention. (2025). Vaccination coverage among adolescents aged 13–17 years — National Immunization Survey–Teen, United States, 2024. Morbidity and Mortality Weekly Report, 74(30). https://doi. org/10.15585/mmwr.mm7430a1 Dubé, E., Gagnon, D., & MacDonald, N. E. (2025). Factors influencing adolescents' decision-making about COVID-19 vaccination: A systematic review. Frontiers in Public Health, 13, 1563677. https://doi.org/10.3389/fpubh.2025.1563677 Gowda, C., & Dempsey, A. F. (2022). The rise (and fall?) of parental vaccine hesitancy. Human Vaccines & Immunotherapeutics, 9(8), 1755–1762. https://doi. org/10.4161/hv.25085 Health Resources & Services Administration. (2023). Health professional shortage areas (HPSAs). https://bhw.hrsa.gov/ workforce-shortage-areas/shortage-designation Johns Hopkins Bloomberg School of Public Health, Institute for Vaccine Safety. (2025). Status of childhood immunization in Kentucky. https://publichealth.jhu.edu/institute-for-vaccinesafety/resources/state-vaccine-policy Kaiser Family Foundation. (2020, December). KFF COVID-19 vaccine monitor: Vaccine hesitancy in rural America. https:// www.kff.org/coronavirus-covid-19/poll-finding/kff-covid-19vaccine-monitor-vaccine-hesitancy-in-rural-america/ Kentucky Revised Statutes § 214.036. (2024). Immunization of students — Exemptions. https://apps.legislature.ky.gov/law/ statutes/statute.aspx?id=33878 Murthy, B. P., Zell, E., Fast, H. E., Murthy, N., Reynolds, L., Gibbs-Scharf, L., & Bhatt, A. (2022). COVID-19 vaccination coverage and trends among adults aged ≥65 years — United States, December 14, 2020–April 19, 2021. Morbidity and Mortality Weekly Report, 71(9), 336–342. https://doi. org/10.15585/mmwr.mm7109a2

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Nguyen, K. H., Anneser, E., Toppo, A., Allen, J. D., Chung, S. E., & Martínez, M. E. (2022). Disparities in national and state estimates of COVID-19 vaccination receipt and intent to vaccinate by race/ethnicity, income, and age group among adults, United States, April 2021. Vaccine, 40(22), 3014–3022. https://doi.org/10.1016/j.vaccine.2022.04.019 Quinn, S. C., Jamison, A. M., & Freimuth, V. (2023). Determinants of influenza vaccination in hard-to-reach populations: Focus on the urban poor. Human Vaccines & Immunotherapeutics, 19(1), 2190716. https://doi.org/10.1080/ 21645515.2023.2190716 Regan, A. K., Bloomfield, L., Peters, I., & Effler, P. V. (2019). Randomized controlled trial of text message reminders for influenza vaccination in a high-risk adult population. American Journal of Infection Control, 45(8), 905–907. https:// pmc.ncbi.nlm.nih.gov/articles/PMC6669111/ Robertson, E., Reeve, K. S., Niedzwiedz, C. L., Moore, J., Blake, M., Green, M., Katikireddi, S. V., & Benzeval, M. (2023). Predictors of COVID-19 vaccine hesitancy in the UK household longitudinal study. Brain, Behavior, and Immunity, 94, 264–278. https://doi.org/10.1016/j.bbi.2021.03.008

University of Kentucky Office of Research. (2023, January). UK’s K-VAC project to shed light on vaccine hesitancy in Eastern Kentucky. https://uknow.uky.edu/research/uks-k-vacproject-shed-light-vaccine-hesitancy-eky World Health Organization. (2015). Vaccine hesitancy: A growing challenge for immunization programmes. https:// www.who.int/news/item/18-08-2015-vaccine-hesitancy-agrowing-challenge-for-immunization-programmes World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/tenthreats-to-global-health-in-2019 WKYT News Staff. (2021, August 26). Great health divide: Rural vaccine disparities increasingly evident as cases rise in Appalachia. WKYT. https://www.wkyt.com/2021/08/26/greathealth-divide-rural-vaccine-disparities-increasingly-evidentcases-rise-appalachia/

Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., van der Bles, A. M., & van der Linden, S. (2022). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10), 201199. https://doi.org/10.1098/rsos.201199 Rozbroj, T., Lyons, A., & Lucke, J. (2024). Factors affecting COVID-19 vaccine decision-making among U.S. high school students: A national survey. Vaccines, 12(5), 532. https://pmc. ncbi.nlm.nih.gov/articles/PMC11102307/ Salathe, M., & Khandelwal, S. (2024). Understanding the determinants of vaccine hesitancy: A comparison of social surveys and social media. PLOS ONE, 19(4), e0301488. https:// doi.org/10.1371/journal.pone.0301488 Soorapanth, S., Cheung, R., Graitcer, S. B., & Schaffner, W. (2023). Rural–urban differences in vaccination coverage and hesitancy rates and trust in COVID-19 vaccine information sources among adults — United States. American Journal of Public Health, 113(4), 407–416. https://doi.org/10.2105/ AJPH.2022.307181 Taber, P., Arem, H., Castro Sweet, C., Krist, A. H., & Schroeder, M. O. (2024). A qualitative analysis of rural community vaccination barriers and facilitators during COVID-19. Vaccines, 12(12), 1398. https://pmc.ncbi.nlm.nih.gov/articles/ PMC11680148/ U.S. Census Bureau. (2020). Urban and rural. https://www. census.gov/programs-surveys/geography/guidance/geoareas/urban-rural.html

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Low-Cost Surface EMG Hand Gesture Classification Using Genetic Algorithm Feature Selection and Random Forest Classification

HENRY HUDEPOHL

ABSTRACT Surface electromyography (sEMG) is a non-invasive method for measuring muscle activity with applications in prosthetic control, rehabilitation, human-computer interaction, and gesture-based gaming control. This study examined whether a low-cost, computationally optimized sEMG system could classify hand gestures using a genetic algorithm for feature selection and a Random Forest for classification. A genetic algorithm reduced the initial feature set from eight features to four, and a Random Forest classifier was evaluated using a 70/30 train-test split. The final model attained an overall accuracy of 84.57%, with classwise accuracies of 92.38% for Rest, 87.07% for Finger Motion, and 78.37% for Gross Motion.

RESEARCH QUESTION:

To what extent can a low-cost sEMG gesture classification pipeline using genetic algorithm feature selection and Random Forest classification distinguish grouped hand gestures?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

Low-Cost Surface EMG Hand Gesture Classification Using Genetic Algorithm Feature Selection and Random Forest Classification ,ĞŶƌǇ ,ƵĚĞƉŽŚů ĞĞĐŚǁŽŽĚ ,ŝŐŚ ^ĐŚŽŽů Abstract

Methodology

^ƵƌĨĂĐĞ ĞůĞĐƚƌŽŵǇŽŐƌĂƉŚǇ ;Ɛ D'Ϳ ŝƐ Ă ŶŽŶͲŝŶǀĂƐŝǀĞ ŵĞƚŚŽĚ ĨŽƌ

^ŝŐŶĂů ĐƋƵŝƐŝƚŝŽŶ͗

ŵĞĂƐƵƌŝŶŐ ŵƵƐĐůĞ ĂĐƚŝǀŝƚǇ ǁŝƚŚ ĂƉƉůŝĐĂƚŝŽŶƐ ŝŶ ƉƌŽƐƚŚĞƚŝĐ ĐŽŶƚƌŽů͕

^ƵƌĨĂĐĞ D' ƐŝŐŶĂůƐ ǁĞƌĞ ĂĐƋƵŝƌĞĚ ƵƐŝŶŐ ƚŚĞ DǇŽtĂƌĞ Ϯ͘Ϭ ŵŽĚƵůĂƌ D' ƉůĂƚĨŽƌŵ͕ ŝŶĐůƵĚŝŶŐ

ƌĞŚĂďŝůŝƚĂƚŝŽŶ͕ ŚƵŵĂŶͲĐŽŵƉƵƚĞƌ ŝŶƚĞƌĂĐƚŝŽŶ͕ ĂŶĚ ŐĞƐƚƵƌĞͲďĂƐĞĚ

ŐĂŵŝŶŐ ĐŽŶƚƌŽů͘ dŚŝƐ ƐƚƵĚǇ ĞǆĂŵŝŶĞĚ ǁŚĞƚŚĞƌ Ă ůŽǁͲĐŽƐƚ͕ ĐŽŵƉƵƚĂƚŝŽŶĂůůǇ

Results

ƚŚĞ DǇŽtĂƌĞ Ϯ͘Ϭ DƵƐĐůĞ ^ĞŶƐŽƌ͕ ^ƉĂƌŬ&ƵŶ ZĞĚ ŽĂƌĚ WůƵƐ ŵŝĐƌŽĐŽŶƚƌŽůůĞƌ͕ DǇŽtĂƌĞ ƐŚŝĞůĚƐ͕

^ůůŝĚŝŶŐͲWindow Segmentation: ĂĐŚ D' ƐŝŐŶĂů ǁĂƐ ĚŝǀŝĚĞĚ ŝŶƚŽ ŽǀĞƌůĂƉƉŝŶŐ ǁŝŶĚŽǁƐ ŽĨ ϮϬϬ ƐĂŵƉůĞƐ ǁŝƚŚ Ă ƐƚĞƉ ƐŝǌĞ ŽĨ ϭϬϬ samples, resulting in 50% overlap between consecutive windows.

ĚŝƐƉŽƐĂďůĞ ďŝŽŵĞĚŝĐĂů ƐƵƌĨĂĐĞ ĞůĞĐƚƌŽĚĞƐ͕ ĂŶĚ ĞůĞĐƚƌŽĚĞ ůĞĂĚ ĐĂďůĞƐ͘

KǀĞƌĂůů ĂĐĐƵƌĂĐǇ͗ ϴϰ͘ϱϳй Feature reduction: 8 → 4 &ŝŶĂů ĚĂƚĂƐĞƚ͗ ϴ͕ϭϵϬ ǁŝŶĚŽǁƐ

dƌĂŝŶͬƚĞƐƚ ƐƉůŝƚ͗ ϳϬͬϯϬ

ŽƉƚŝŵŝǌĞĚ Ɛ D' ƐǇƐƚĞŵ ĐŽƵůĚ ĐůĂƐƐŝĨǇ ŚĂŶĚ ŐĞƐƚƵƌĞƐ ƵƐŝŶŐ Ă

dŚĞ ŐƌĞĂƚĞƐƚ ĐŽŶĨƵƐŝŽŶ ŽĐĐƵƌƌĞĚ ďĞƚǁĞĞŶ 'ƌŽƐƐ DŽƚŝŽŶ

ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ĨŽƌ ĨĞĂƚƵƌĞ ƐĞůĞĐƚŝŽŶ ĂŶĚ Ă ZĂŶĚŽŵ &ŽƌĞƐƚ ĨŽƌ

ĂŶĚ &ŝŶŐĞƌ DŽƚŝŽŶ

ĐůĂƐƐŝĨŝĐĂƚŝŽŶ͘ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ƌĞĚƵĐĞĚ ƚŚĞ ŝŶŝƚŝĂů ĨĞĂƚƵƌĞ ƐĞƚ ĨƌŽŵ ĞŝŐŚƚ

ĨĞĂƚƵƌĞƐ ƚŽ ĨŽƵƌ͕ ĂŶĚ Ă ZĂŶĚŽŵ &ŽƌĞƐƚ ĐůĂƐƐŝĨŝĞƌ ǁĂƐ ĞǀĂůƵĂƚĞĚ ƵƐŝŶŐ Ă ϳϬͬϯϬ ƚƌĂŝŶͲƚĞƐƚ ƐƉůŝƚ͘

dŚĞ ĨŝŶĂů ŵŽĚĞů ĂƚƚĂŝŶĞĚ ĂŶ ŽǀĞƌĂůů ĂĐĐƵƌĂĐǇ ŽĨ ϴϰ͘ϱϳй͕ ǁŝƚŚ ĐůĂƐƐͲ ǁŝƐĞ ĂĐĐƵƌĂĐŝĞƐ ŽĨ ϵϮ͘ϯϴй ĨŽƌ ZĞƐƚ͕ ϴϳ͘Ϭϳй ĨŽƌ &ŝŶŐĞƌ DŽƚŝŽŶ͕ ĂŶĚ

ϳϴ͘ϯϳй ĨŽƌ 'ƌŽƐƐ DŽƚŝŽŶ͘

Introduction ,ƵŵĂŶͲĐŽŵƉƵƚĞƌ ŝŶƚĞƌĂĐƚŝŽŶ ŚĂƐ ďĞĐŽŵĞ ŝŶĐƌĞĂƐŝŶŐůǇ

ŵĂŝŶƐƚƌĞĂŵ͕ ĂƐ ƐĞĞŶ ŝŶ ƚĞĐŚŶŽůŽŐŝĞƐ ƐƵĐŚ ĂƐ EĞƵƌĂůŝŶŬΖƐ ƌĂŝŶͲ

Gesture Grouping͗ dŚĞ ĚĂƚĂƐĞƚ ŽƌŝŐŝŶĂůůǇ ĐŽŶƐŝƐƚĞĚ ŽĨ ŐĞƐƚƵƌĞͲƐƉĞĐŝĨŝĐ ƌĞĐŽƌĚŝŶŐƐ͕ ŝŶĐůƵĚŝŶŐ ZĞůĂǆĞĚ,ĂŶĚ͕ dŚƵŵďƐhƉ, Victory, fist, index, middle, ring, and pinky. dŽ ƌĞĚƵĐĞ ĐůĂƐƐ ŽǀĞƌůĂƉ ĂŶĚ ŽĨĨĞƌ Ă ŵŽƌĞ ƌĞĂůŝƐƚŝĐ ĨŝƌƐƚ ĂƉƉƌŽĂĐŚ͕ ƚŚĞ ŐĞƐƚƵƌĞƐ ǁĞƌĞ grouped into three broader categories: Rest, Gross Motion, and Finger Motion. ZĞƐƚ с ZĞůĂǆĞĚ,ĂŶĚ ͮ 'ƌŽƐƐ DŽƚŝŽŶ с dŚƵŵďƐhƉ͕ sŝĐƚŽƌǇ͕ ĨŝƐƚ ͮ &ŝŶŐĞƌ DŽƚŝŽŶ с ŝŶĚĞǆ͕ middle, ring, pinky

Feature Extraction: dŚĞ ŝŶŝƚŝĂů ĨĞĂƚƵƌĞ ƐĞƚ ĐŽŶƚĂŝŶĞĚ ĞŝŐŚƚ ĨĞĂƚƵƌĞƐ ĚĞƌŝǀĞĚ ĨƌŽŵ ďŽƚŚ ƚŝŵĞͲ ĂŶĚ ĨƌĞƋƵĞŶĐǇͲ domain analyses. Put this as small commaͲseparated text: MAV, RMS, WL, ZC, VAR, IAV, spectral centroid, spectral energy

Discussion dŚĞ ŚŝŐŚĞƐƚ ĐůĂƐƐͲǁŝƐĞ ĂĐĐƵƌĂĐǇ ǁĂƐ ĂĐŚŝĞǀĞĚ ďǇ ƚŚĞ ZĞƐƚ ĐůĂƐƐ͕ Ăƚ ϵϮ͘ϯϴй͘ dŚĞ &ŝŶŐĞƌ DŽƚŝŽŶ ĐůĂƐƐ ĂůƐŽ ƉĞƌĨŽƌŵĞĚ ƐƚƌŽŶŐůǇ͕ǁŝƚŚϴϳ͘Ϭϳй ĂĐĐƵƌĂĐǇ͘ dŚĞ 'ƌŽƐƐ DŽƚŝŽŶ ĐůĂƐƐ ŚĂĚ ƚŚĞ ůŽǁĞƐƚ ĐůĂƐƐͲǁŝƐĞ ĂĐĐƵƌĂĐǇ͕ Ăƚ ϳϴ͘ϯϳй͘ KǀĞƌĂůů͕ ƚŚĞ ƌĞƐƵůƚƐ ƐŚŽǁ ƚŚĂƚ Ă ůŽǁͲĐŽƐƚ Ɛ D' ƐǇƐƚĞŵ ĐĂŶ ĂĐŚŝĞǀĞ ŵŽĚĞƌĂƚĞůǇ ŚŝŐŚ ĐůĂƐƐŝĨŝĐĂƚŝŽŶ ƉĞƌĨŽƌŵĂŶĐĞ͘

ŽŵƉƵƚĞƌ /ŶƚĞƌĨĂĐĞ͘ ,ŽǁĞǀĞƌ͕ ŵƵĐŚ ƌĞĐĞŶƚ ŝŶŶŽǀĂƚŝŽŶ ŝŶ ƚŚŝƐ

ƐƉĂĐĞ ŝƐ ŶŽƚ ĂĐĐĞƐƐŝďůĞ ďĞĐĂƵƐĞ ŽĨ ŚŝŐŚ ĐŽƐƚ ĂŶĚ ŝŶǀĂƐŝǀĞ ŶĂƚƵƌĞ͘ /Ŷ ĐŽŶƚƌĂƐƚ͕ ƐƵƌĨĂĐĞ ĞůĞĐƚƌŽŵǇŽŐƌĂƉŚǇ ;Ɛ D'Ϳ ŝƐ Ă ŶŽŶͲ

ŝŶǀĂƐŝǀĞ ƚĞĐŚŶŽůŽŐǇ ƚŚĂƚ ƌĞĐŽƌĚƐ ƚŚĞ ĚŝƌĞĐƚ ĞůĞĐƚƌŝĐĂů ĂĐƚŝǀŝƚǇ ŽĨ ƐŬĞůĞƚĂů ŵƵƐĐůĞƐ ĚƵƌŝŶŐ ĐŽŶƚƌĂĐƚŝŽŶ͕ ĂůůŽǁŝŶŐ ŚĂŶĚ ŵŽǀĞŵĞŶƚƐ

ƚŽ ďĞ ƐƚƵĚŝĞĚ ƚŚƌŽƵŐŚ ƚŚĞŝƌ ĐŽƌƌĞƐƉŽŶĚŝŶŐ ďŝŽůŽŐŝĐĂů ƐŝŐŶĂůƐ ƌĂƚŚĞƌ ƚŚĂŶ ŝŵĂŐĞƐ͘

dŚŝƐ ƐƚƵĚǇ ƐĞĞŬƐ ƚŽ ĂŶƐǁĞƌ ƚŚĞ ƋƵĞƐƚŝŽŶ͗ dŽ ǁŚĂƚ ĞǆƚĞŶƚ ĐĂŶ Ă ůŽǁͲĐŽƐƚ Ɛ D' ŐĞƐƚƵƌĞ ĐůĂƐƐŝĨŝĐĂƚŝŽŶ ƉŝƉĞůŝŶĞ ƵƐŝŶŐ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ĨĞĂƚƵƌĞ ƐĞůĞĐƚŝŽŶ ĂŶĚ ZĂŶĚŽŵ &ŽƌĞƐƚ ĐůĂƐƐŝĨŝĐĂƚŝŽŶ ĚŝƐƚŝŶŐƵŝƐŚ ŐƌŽƵƉĞĚ ŚĂŶĚ ŐĞƐƚƵƌĞƐ͍

WƌĞƉƌŽĐĞƐƐŝŶŐ͗ ĂĐŚ ƐŝŐŶĂů ǁĂƐ ĨŝƌƐƚ ŵĞĂŶͲĐĞŶƚĞƌĞĚ ďǇ ƐƵďƚƌĂĐƚŝŶŐ ƚŚĞ ƌĞĐŽƌĚŝŶŐΖƐ average value. dŚĞ ƐŝŐŶĂů ǁĂƐ ƚŚĞŶ ƌĞĐƚŝĨŝĞĚ ďǇ ƚĂŬŝŶŐ ƚŚĞ ĂďƐŽůƵƚĞ value of each sample.

'ĞŶĞƚŝĐ ůŐŽƌŝƚŚŵ &ĞĂƚƵƌĞ ^ĞůĞĐƚŝŽŶ͗ dŚĞ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ƌĞƉƌĞƐĞŶƚĞĚ ĞĂĐŚ ĐĂŶĚŝĚĂƚĞ ĨĞĂƚƵƌĞ ƐƵďƐĞƚ ĂƐ Ă ďŝŶĂƌǇ ŵĂƐŬ͘ dŚĞ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ƌĞĚƵĐĞĚ ƚŚĞ ŽƌŝŐŝŶĂů ĨĞĂƚƵƌĞ ƐĞƚ ĨƌŽŵ ϴ ƚŽ ϰ͘ dŚĞ ƐĞůĞĐƚĞĚ ĨĞĂƚƵƌĞƐ ŝŶĐůƵĚĞĚ ZŽŽƚ DĞĂŶ ^ƋƵĂƌĞ͕ tĂǀĞĨŽƌŵ >ĞŶŐƚŚ͕ sĂƌŝĂŶĐĞ͕ ĂŶĚ ƐƉĞĐƚƌĂů ĐĞŶƚƌŽŝĚ͘ ϳ͘ ZĂŶĚŽŵ &ŽƌĞƐƚ ůĂƐƐŝĨŝĐĂƚŝŽŶ dŚĞ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵΖƐ ŽƵƚƉƵƚ ĨĞĂƚƵƌĞƐ ǁĞƌĞ ƚŚĞŶ ƵƐĞĚ ƚŽ ƚƌĂŝŶ Ă ZĂŶĚŽŵ &ŽƌĞƐƚ ĐůĂƐƐŝĨŝĞƌ͘ dŚĞ ĚĂƚĂƐĞƚ ǁĂƐ ƐƉůŝƚ ŝŶƚŽ ϳϬͬϯϬ ƚƌĂŝŶͬƚĞƐƚ͕ ǁŝƚŚ ϳϬй ŽĨ ƚŚĞ ĚĂƚĂ ƵƐĞĚ ĨŽƌ ƚƌĂŝŶŝŶŐ ĂŶĚ ϯϬй ĨŽƌ ƚĞƐƚŝŶŐ͘

Conclusion dŚŝƐ ƐƚƵĚǇ ĚĞǀĞůŽƉĞĚ Ă ůŽǁͲĐŽƐƚ Ɛ D' ŐĞƐƚƵƌĞͲĐůĂƐƐŝĨŝĐĂƚŝŽŶ ƉŝƉĞůŝŶĞ ƵƐŝŶŐ ŐĞŶĞƚŝĐ ĂůŐŽƌŝƚŚŵ ĨĞĂƚƵƌĞ ƐĞůĞĐƚŝŽŶ ĂŶĚ ZĂŶĚŽŵ &ŽƌĞƐƚ ĐůĂƐƐŝĨŝĐĂƚŝŽŶ͘ dŚĞ ĨŝŶĂů ŵŽĚĞů ĂĐŚŝĞǀĞĚ ϴϰ͘ϱϳй ĂĐĐƵƌĂĐǇ ǁŚŝůĞ ƌĞĚƵĐŝŶŐ ƚŚĞ ĨĞĂƚƵƌĞ ƐĞƚ ĨƌŽŵ ϴ ƚŽ ϰ͕ ĚĞŵŽŶƐƚƌĂƚŝŶŐ ĂŶ ĂĐĐĞƐƐŝďůĞ ĂŶĚ ĐŽŵƉƵƚĂƚŝŽŶĂůůǇ ŝŶĞǆƉĞŶƐŝǀĞ ĂƉƉƌŽĂĐŚ ƚŽ ĂƚͲ ŚŽŵĞ ŚƵŵĂŶͲĐŽŵƉƵƚĞƌ ŝŶƚĞƌĂĐƚŝŽŶ ĞǆƉĞƌŝŵĞŶƚĂƚŝŽŶ

References

Acknowledgements dŚĂŶŬ ǇŽƵ Dƌ͘ zĂŶŬĞ Θ DƐ͘ DĂůŽƚƚ ĨŽƌ ĂůůŽǁŝŶŐ ŵĞ ƚŽ ĐŽŵƉůĞƚĞ ƚŚŝƐ ƉƌŽũĞĐƚ ŝŶĚĞƉĞŶĚĞŶƚůǇ

ϭ͘ ůŝ ͕ DƵƐŚƚĂƋ ͕ DĞŵŽŶ ͘ ,ĂŶĚ ŐĞƐƚƵƌĞ ŝŶƚĞƌƉƌĞƚĂƚŝŽŶ ƵƐŝŶŐ ƐĞŶƐŝŶŐ ŐůŽǀĞ ŝŶƚĞŐƌĂƚĞĚ ǁŝƚŚ ŵĂĐŚŝŶĞ ůĞĂƌŶŝŶŐ ĂůŐŽƌŝƚŚŵƐ͘ /Ŷƚ : DĞĐŚ DĞĐŚĂƚƌŽŶ DĂŶƵĨ ŶŐ͘ ϮϬϭϲ͖ϭϬ;ϭϭͿ͗ϭϴϱϳͲϭϴϲϭ͘ Ϯ͘ zĂƐĞŶ D͕ :ƵƐŽŚ ^͘ ƐǇƐƚĞŵĂƚŝĐ ƌĞǀŝĞǁ ŽŶ ŚĂŶĚ ŐĞƐƚƵƌĞ ƌĞĐŽŐŶŝƚŝŽŶ ƚĞĐŚŶŝƋƵĞƐ͕ ĐŚĂůůĞŶŐĞƐ ĂŶĚ ĂƉƉůŝĐĂƚŝŽŶƐ͘ WĞĞƌ: ŽŵƉƵƚ ^Đŝ͘ ϮϬϭϵ͖ϱ͗ĞϮϭϴ͘ ĚŽŝ͗ϭϬ͘ϳϳϭϳͬƉĞĞƌũͲĐƐ͘Ϯϭϴ͘ ϯ͘ DŝĐŚĞůů t͘ hŶĚĞƌƐƚĂŶĚŝŶŐ D'͘ KǆĨŽƌĚ͗ KǆĨŽƌĚ hŶŝǀĞƌƐŝƚǇ WƌĞƐƐ͖ ϮϬϭϯ͘ ϰ͘ <ĂŵĂǀƵĂŬŽ E͕ ^ĐŚĞŵĞ :͕ ŶŐůĞŚĂƌƚ < ͘ ŽŵďŝŶĞĚ ƐƵƌĨĂĐĞ ĂŶĚ ŝŶƚƌĂŵƵƐĐƵůĂƌ D' ĨŽƌ ŝŵƉƌŽǀĞĚ ƌĞĂůͲ ƚŝŵĞ ŵǇŽĞůĞĐƚƌŝĐ ĐŽŶƚƌŽů ƉĞƌĨŽƌŵĂŶĐĞ͘ ŝŽŵĞĚ ^ŝŐŶĂů WƌŽĐĞƐƐ ŽŶƚƌŽů͘ ϮϬϭϰ͖ϭϬ;ϭͿ͗ϭϬϮͲϭϬϳ͘ ĚŽŝ͗ϭϬ͘ϭϬϭϲͬũ͘ďƐƉĐ͘ϮϬϭϰ͘Ϭϭ͘ϬϬϳ͘ ϱ͘ ŚŵĂĚ /͕ ŶƐĂƌŝ &͕ ĞǇ h<͘ ƌĞǀŝĞǁ ŽĨ D' ƌĞĐŽƌĚŝŶŐ ƚĞĐŚŶŝƋƵĞ͘ /Ŷƚ : ŶŐ ^Đŝ dĞĐŚŶŽů͘ ϮϬϭϮ͖ϰ;ϮͿ͗ϱϯϬͲ ϱϯϵ͘ ϲ͘ ĞŚƌ W͘ /ŶǀĞŶƚŝŶŐ /ƌŽŶ DĂŶ͗ dŚĞ WŽƐƐŝďŝůŝƚǇ ŽĨ Ă ,ƵŵĂŶ DĂĐŚŝŶĞ͘ ĂůƚŝŵŽƌĞ͗ :ŽŚŶƐ ,ŽƉŬŝŶƐ hŶŝǀĞƌƐŝƚǇ WƌĞƐƐ͖ ϮϬϭϭ͘ ϳ͘ WŚŝŶǇŽŵĂƌŬ ͕ >ŝŵƐĂŬƵů ͕ WŚƵŬƉĂƚƚĂƌĂŶŽŶƚ W͘ ŶŽǀĞů ĨĞĂƚƵƌĞ ĞǆƚƌĂĐƚŝŽŶ ĨŽƌ ƌŽďƵƐƚ D' ƉĂƚƚĞƌŶ ƌĞĐŽŐŶŝƚŝŽŶ͘ Ăƌyŝǀ͘ ϮϬϬϵ͖Ăƌyŝǀ͗ϬϵϭϮ͘ϯϵϳϯ͘ ϴ͘ <ĂĚĂǀĂƚŚ DZ<͕ EĂƐŽƌ D͕ /ŵƌĂŶ ͘ ŶŚĂŶĐĞĚ ŚĂŶĚ ŐĞƐƚƵƌĞ ƌĞĐŽŐŶŝƚŝŽŶ ǁŝƚŚ ƐƵƌĨĂĐĞ ĞůĞĐƚƌŽŵǇŽŐƌĂŵ ĂŶĚ ŵĂĐŚŝŶĞ ůĞĂƌŶŝŶŐ͘ ^ĞŶƐŽƌƐ͘ ϮϬϮϰ͖Ϯϰ;ϭϲͿ͗ϱϮϯϭ͘ ĚŽŝ͗ϭϬ͘ϯϯϵϬͬƐϮϰϭϲϱϮϯϭ͘

INTRODUCTION ----------------

dĞŵƉůĂƚĞ / ͗ ĚĞůŝďĞƌĂƚŝŶŐǁĂƚĞƌŵĞůŽŶ ^ŝǌĞ͗ ϯϲǆϮϰ In recent years, human-computer interaction has become

increasingly mainstream, as seen in technologies such as Neuralink's Brain-Computer Interface. However, much recent innovation in this space is not accessible because of high costs and its invasive nature. Moreover, traditional input systems, such as keyboards and cameras, do not directly measure the bodily indicators underlying movement. One method of obtaining data for gesture classification is vision-based systems, which recognize visible motion but depend on external lighting and line of sight to make classifications, rather than on a direct biological indicator [1,2]. In contrast, surface electromyography (sEMG) records the direct electrical activity of skeletal muscles during contraction, allowing hand movements to be studied through their corresponding biological signals rather than images [3,4]. Despite their potential, sEMG-based gesture classification systems remain technically challenging. Muscle signals are inherently noisy, show considerable overlap, and vary widely with placement, contraction strength, electrode contact, skin surface materials, and individual physiology [3,4,6-9]. Different gestures can also activate overlapping forearm muscle groups, making raw signals difficult to separate. Therefore, most sEMG classification systems require a structured processing pipeline that converts raw voltage data into usable features, such as the shape and size of the electrical signal. HENRY HUDEPOHL

Previous research has applied several machine learning models to EMG-based gesture recognition, including support vector machines, k-nearest neighbors, artificial neural networks, hidden Markov models, and long short-term memory networks [5,14-17]. These models demonstrate that machine learning is a suitable approach for EMG-based gesture classification, but practical challenges remain. Most notably, the current literature focuses largely on larger datasets, more expensive acquisition setups, greater computing power, or more complex models that are not practical for real-time or low-cost applications [14,15,19,20]. Therefore, an important area to investigate is whether a lightweight, inexpensive sEMG pipeline can achieve classification performance. One way to improve classification performance in sEMG models is feature selection. Electromyography recordings can generate multiple time-domain and frequency-domain features. However, not all features are as important in the final classification task. For example, uninformative or nondifferentiating features can greatly increase computational cost, computation time, and even reduce the overall performance of the model [12,13].

Genetic algorithms provide a straightforward approach to this problem by searching for possible combinations of feature subsets to identify which features are most important for classification, without requiring the best combination to be known in advance [12,18]. When paired with a classifier such as Random Forest, genetic algorithm-based feature selection may help preserve classification accuracy while reducing the number of input features. This study seeks to answer the question: To what extent can a low-cost sEMG gesture classification pipeline using genetic algorithm feature selection and Random Forest classification distinguish grouped hand gestures? To answer this question, sEMG signals were collected using a MyoWare 2.0 Muscle Development Kit, which included a MyoWare 2.0 Muscle Sensor and a SparkFun RedBoard Plus microcontroller. MATLAB was used to segment the signal into multiple overlapping windows and extract relevant features. MATLAB also provides access to machine learning toolkits for both genetic algorithms and Random Forest classifiers.

BACKGROUND AND RELATED WORK ---------------Surface Electromyography and Gesture Classification Surface electromyography is a non-invasive method for measuring the electrical activity generated by skeletal muscles. During voluntary movement of the forearm muscles, motor neurons activate groups of muscle fibers, generating electrical potentials detectable by electrodes attached to the skin surface. sEMG signals are a viable source of physiological input for hand gesture recognition because hand and finger movements are controlled by coordinated activation of forearm muscles [3,4,22]. In this context, gesture recognition refers to the process of collecting movement-based, biologically generated electrical signals and extracting useful patterns from them to assign each pattern to a specific gesture class, such as a fist, a gross motor movement, or an individual finger movement [1,2,5]. sEMG-based gesture classification is particularly relevant to human-computer interaction because it does not require an external source, such as a camera, to directly observe hand movement. Camera-based architectures depend on visibility, lighting conditions, camera position, and an unobstructed view of the hand [1,2]. This makes them useful for visiblemotion recognition but less direct than sEMG for studying muscle activity. Hand and finger movements are produced by coordinated activation of forearm muscles, and different gestures can recruit overlapping muscle groups. Because of this overlap, sEMG signals may not produce distinct raw waveforms for each gesture [3,4,21,22]. Therefore, high-quality signal acquisition, preprocessing, feature extraction, and model selection are all extremely important because sEMG data are inherently noisy. For this study, sEMG was used to classify grouped hand gestures rather than individual hand positions. Fine-grained gesture classification aims to distinguish many similar HENRY HUDEPOHL

gestures, whereas grouped classification investigates whether broader movement categories can be reliably distinguished. In a low-cost system with limited data, this is a reasonable first step because it reduces classification difficulty while still testing whether electromyography is feasible for low-cost gesture classification. Signal Acquisition Challenges in Low-Cost sEMG Pipelines Although sEMG is non-invasive, relatively inexpensive, and sensitive to muscle activity, several factors can reduce signal quality. Electrode placement, skin-electrode impedance, muscle fatigue, contraction strength, motion artifacts, crosstalk from nearby muscles, and power-line interference can all make classification more difficult [6-9]. Small changes in electrode placement can alter which muscle fibers contribute most heavily to the recorded signal. Likewise, changes in skin contact, surface condition, or electrode pressure can change signal amplitude even when the same gesture is performed. These factors make sEMG classification less trivial than simply matching one gesture to a fixed wave pattern. Moreover, lowcost EMG systems do not capture the rich spatial information from multiple muscle groups as traditional laboratory-grade, multi-channel EMG systems do. However, laboratory-grade systems are more expensive, more complex to set up, less accessible for early-stage development, and less aligned with the goal of lowering the barrier of entry into human-computer interaction. As a result, the research question is not whether a low-cost system can outperform advanced laboratory setups, but whether a low-cost classification system can provide meaningful classification performance when paired with an appropriate machine learning pipeline. By using the MyoWare 2.0 Muscle Sensor and SparkFun RedBoard Plus microcontroller, the system prioritized affordability and ease of use. Nonetheless, the inexpensive and simple nature of the hardware setup had to be compensated for through preprocessing, windowing, feature extraction, feature selection, and classification. Preprocessing and Sliding-Window Segmentation Raw sEMG signals are difficult to classify directly due to noise, baseline drift, motion artifacts, and amplitude variations. One technique to simplify classification is preprocessing, which makes signals more consistent across classes before extracting features. A common preprocessing step is to remove baseline offset, rectify the signal, apply filters, and segment it into shorter analysis windows so the model has more data to train on [7,8,10]. Sliding-window segmentation helps address the nonstationary nature of EMG signals by dividing recordings into shorter local regions and increasing the number of training samples available for classification [10,11]. However, the recorded signal still required software-based preprocessing before feature extraction and classification. Feature Extraction from sEMG Signals Feature extraction is the process of converting EMG signal windows into numerical data that can be more easily used by a machine learning model. This step is important because 38

most classifiers do not operate directly on time-series data. Each window is summarized by features that capture important properties of the signal, such as amplitude, energy, variability, complexity, and frequency content [10-13]. Time-domain features are widely used in EMG classification because they are simple to compute and efficient for real-time systems [10-13]. Features such as Mean Absolute Value, Root Mean Square, Waveform Length, Zero Crossings, Variance, and Integrated Absolute Value capture different parts of a signal. For example, amplitude-related features describe the strength of muscle activation, while waveform- and crossing-based features provide information about signal complexity and temporal changes. Frequency-domain features also provide useful information by describing how signal energy is distributed across frequencies. Features such as spectral energy and spectral centroid can help capture patterns not entirely represented by time-domain features. Feature Selection and Genetic Algorithms Extracting more features does not always improve classification. However, including unnecessary features can increase computational cost. Feature selection addresses this problem by identifying a smaller subset of features that preserves useful classification information while removing less valuable inputs [12,13]. Genetic algorithms provide one method for feature selection. A genetic algorithm is an optimization technique inspired by natural selection. In feature selection, each candidate solution can be represented as a binary mask, where each position corresponds to a feature. A value of 1 indicates the feature is included, while a value of 0 indicates the feature is excluded. The algorithm evaluates each candidate subset using a fitness function, selects stronger candidates, and generates new candidates through operations such as crossover and mutation [12,18]. Random Forest and Machine Learning Classification Machine learning classifiers are used in EMG gesture recognition because they can learn relationships between extracted signal features and gesture labels. Prior work has applied support vector machines, k-nearest neighbors, artificial neural networks, convolutional neural networks, hidden Markov models, long short-term memory networks, and Random Forest models to EMG classification tasks [1417]. Random Forest is an ensemble method that combines multiple decision trees, making it useful for tabular featurebased datasets because it can model nonlinear relationships, tolerate noisy inputs, and estimate feature significance. Random Forest was appropriate for this study because the input data were engineered feature vectors and the goal was to evaluate a practical, low-cost classification pipeline.

the model learns patterns from a person’s physiology and gesture execution style. In inter-subject designs, the model must generalize over multiple people, which is substantially more difficult because muscle anatomy, skin impedance, electrode placement, and contraction behavior vary between users [21-23]. The current study uses an intra-subject framework. This design is appropriate for evaluating whether the low-cost pipeline can classify gestures within a controlled individual setting. In real-world prosthetic control or wearable interaction systems, inter-subject validation and user-specific calibration would be necessary future steps [21-23]. Research Gap and Study Rationale The existing literature shows that sEMG can support hand gesture recognition and that signal preprocessing and feature extraction are necessary for reliable EMG analysis. However, practical difficulties remain. Many high-performing systems depend on highly controlled conditions, huge datasets, multi-channel acquisition setups, or computationally complex models. At the same time, low-cost systems must balance classification performance with accessibility, computational effectiveness, and ease of implementation [14-17,19,20,23]. This study addresses that gap by evaluating a low-cost sEMG gesture classification pipeline using genetic algorithm feature selection and Random Forest classification. The pipeline is designed around accessible hardware, structured preprocessing, compact feature extraction, automatic feature reduction, and straightforward machine learning.

METHODS ---------------System Architecture and Overview The setup of the classification pipeline consisted of six main stages: acquiring signals from gestures, preprocessing the signals to prepare them for classification, segmenting the signals to give the model more data to train on, extracting relevant features for improving the model’s accuracy, selecting which of those features were most crucial to model performance, and classifying the samples using Random Forest. Raw electromyographic signals were collected from forearm activity using the MyoWare muscle sensor and RedBoard Plus. The signals were then stored as commaseparated values (.csv) files and imported into MATLAB for processing and analysis. After preprocessing, the signals were divided into overlapping windows, converted into numerical features, evaluated using a genetic algorithm-based feature selection approach, and classified using Random Forest.

Intra-Subject and Inter-Subject Generalization One major distinction in EMG research is between intrasubject and inter-subject classification. In an intra-subject design, the model is trained and tested on data from a single individual. This usually produces better performance because HENRY HUDEPOHL

The study largely focused on intra-subject classification to establish initial validity within a single person and to test the model's effectiveness. Intra-subject classification means the training and test data come from a single individual rather than a large sample. This is a more realistic first step for those who want to replicate similar results at home, but it also means that the results should not be interpreted as proof that the same trained model would generalize to all users. Hardware Setup and Data Acquisition Surface EMG signals were acquired using the MyoWare 2.0 modular EMG platform, including the MyoWare 2.0 Muscle Sensor, SparkFun RedBoard Plus microcontroller, MyoWare shields, disposable biomedical surface electrodes, and electrode lead cables.

Signal Preprocessing MATLAB was a suitable option for preprocessing due to its variety of built-in functions, downloadable packages, and community support. Each signal was first mean-centered by subtracting the recording's average value. This step reduced baseline offset, making the signal more consistent across recordings. The signal was then rectified by taking the absolute value of each sample. Rectification preserves the magnitude of muscle activation while converting negative values into positive ones, making it easier to summarize amplitude-based features. Recordings containing missing values, invalid entries, or insufficient signal length were excluded from further analysis. One recording was excluded in total. This filtering step prevented data pollution that could interfere with the training and testing process. After preprocessing and exclusion of invalid recordings, the final segmented dataset contained 8,190 windows. Each valid recording was converted into multiple overlapping windows, and each window was treated as a single sample for feature extraction and classification.

The MyoWare 2.0 Muscle Sensor was connected to the Link Shield and Cable Shield using the platform’s integrated snap connectors, and the Cable Shield was connected to the A0 analog input of the Arduino Shield using a 3.5 mm TRS cable. The Arduino Shield was mounted directly onto the SparkFun RedBoard Plus development board. Biomedical electrode lead cables were connected to the Link Shield and attached to disposable biomedical surface electrodes placed on the forearm over the target muscle group responsible for hand and finger movement. The completed system was connected to a laptop via a USB-A-to-USB-C cable, which provided power and enabled serial data transmission. Muscle potentials were recorded using custom software developed in Visual Studio Code with Python. The program sampled the analog output from input A0 and transmitted digitized signal values to the computer over a serial connection. The software also indicated the beginning and end of each measurement cycle, allowing each recording to be associated with a specific gesture trial. The recorded samples were streamed and stored as comma-separated values for later preprocessing as well as analysis in MATLAB. Gesture Classes and Dataset Construction The dataset originally consisted of gesture-specific recordings, including RelaxedHand, ThumbsUp, Victory, fist, index, middle, ring, and pinky. To reduce class overlap and offer a more realistic first approach, the gestures were grouped into three broader categories: Rest, Gross Motion, and Finger Motion. Rest consisted of RelaxedHand recordings. Gross Motion included Thumbs Up, Victory, and Fist. Finger Motion included the index, middle, ring, and pinky fingers. This was a practical grouping to use because the goal of the study was to build a low-cost system that could distinguish broad categories of hand movement, a crucial first step in prosthetics development, rather than a perfectly accurate model for every fine motor function. HENRY HUDEPOHL

Sliding-Window Segmentation After preprocessing, each EMG signal was divided into overlapping windows of 200 samples with a step size of 100 samples, resulting in 50% overlap between consecutive windows. Each window was treated as a single segmented sample for classification. Sliding-window segmentation was used because EMG signals exhibit time-varying properties. Instead of treating an entire recording as a single sample, segmentation enabled the model to analyze shorter regions.

The genetic algorithm represented each candidate feature subset as a binary mask:

The final mask was:

This mask reduced the original input feature set by 50%, thereby lowering the feature space dimensionality and, in turn, improving efficiency before classification.

Feature Extraction Feature extraction converted each segmented EMG window into a numerical feature vector. The initial feature set contained eight features derived from both time- and frequency-domain analyses. The time-domain features were Mean Absolute Value, Root Mean Square, Waveform Length, Zero Crossings, Variance, and Integrated Absolute Value. These features were chosen because they describe relevant properties of EMG signals, such as amplitude, power, variability, and complexity. Two frequency-domain features, spectral centroid and spectral energy, were also extracted. These features provide information on the distribution of signal energy across frequencies. Together, all eight features represented each segmented window in a way that the classification model could use. Genetic Algorithm for Feature Selection After feature extraction, a genetic algorithm was employed to reduce the number of input features, thereby decreasing the model's computational cost and complexity. The genetic algorithm represented each candidate feature subset as a binary mask. In the mask, each position corresponded to one of the extracted features. More specifically, a value of 1 in the feature mask indicated that the feature was included, whereas a value of 0 indicated that the feature was excluded. The genetic algorithm had a population size of 25 over 15 generations. In each generation, the algorithm evaluated 25 candidate feature subsets and repeated the process for 15 generations. Each candidate feature subset was evaluated using a fitness function based on classification performance. Over multiple consecutive generations, the algorithm identified a four-feature subset that contained useful classification information while reducing the model's feature count.

HENRY HUDEPOHL

Random Forest Classification The genetic algorithm's output features were then used to train a Random Forest classifier. Random Forest was a suitable model because it handles tabular feature-based classification well and can model nonlinear, noisy relationships between signal features and gesture classes. Unlike traditional single decision trees, Random Forest classifiers combine an ensemble of trees, reducing the instability that can occur when a single tree overfits the training data. The dataset was split into 70/30 train/test, with 70% of the data used for training and 30% for testing. The model was trained on the selected feature subset and then evaluated on unseen test data, enabling the study to assess how well the classifier generalized to samples not used during training. Finally, the classifier predicted one of three gesture categories for each segmented window of samples: Rest, Gross Motion, or Finger Motion. Model performance was measured using overall accuracy, class-wise accuracy, and a confusion matrix. These three classes provided a simple way to interpret the model's initial accuracy, along with a traditional way to display the data. Evaluation Metrics The primary evaluation metric used in the study was classification accuracy. This was calculated as the proportion of correctly predicted gesture samples out of all test samples. Class-wise accuracy provided slightly more information and was calculated across the three classes to increase specificity, largely because overall accuracy can mask weaker performance in classes with signal overlap. A confusion matrix was also used to visualize the distribution of correct and incorrect predictions across the three classes, enabling further identification of the model’s struggles. The diagonal values of the matrix represented correct predictions, while off-diagonal values represented misclassifications between gesture categories.

In these equations, correctj represents the number of correctly classified samples in class j, and totalj represents the total number of test samples belonging to class j.

RESULTS ---------------Dataset Summary After initial preprocessing and segmentation, the final dataset contained 8,190 segmented sEMG samples, with each sample representing a 200-sample window. The samples were grouped into three classification categories: Rest, Gross Motion, and Finger Motion. The Rest class consisted of RelaxedHand recordings, the Gross Motion class included ThumbsUp, Victory, and fist gestures, and the Finger Motion class included index, middle, ring, and pinky gestures. Unfortunately, the dataset was not evenly distributed. More specifically, the dataset included 12.81% Rest, 36.53% Gross Motion, and 50.66% Finger Motion. This imbalance matters because the classifier performed differently across classes depending on the number of training samples available for each category, which is why it was important to report both overall and class-wise accuracy. Genetic Algorithm Feature Selection The genetic algorithm reduced the original feature set from 8 to 4. The selected features were Root Mean Square, Waveform Length, Variance, and spectral centroid. This means the genetic algorithm removed half of the original input features while preserving the most useful ones for classification. The final selected feature mask was:

This mask shows that the second, third, fifth, and eighth features were selected. In the final feature set, these corresponded to RMS, WL, VAR, and spectral centroid. One particularly relevant benefit of reduced features was significantly faster computation time, achieving relatively similar classification performance.

Random Forest Performance After feature selection using the genetic algorithm, a Random Forest was trained on the selected feature subset. The Random Forest model achieved an overall classification accuracy of 84.57% on the test data. This result indicates that the full pipeline, from low-cost signal acquisition to classification, achieved relatively accurate classification performance. The model was evaluated using a 70/30 train-test split, with 70% of the segmented samples used for training and 30% for testing. Class-Wise Performance Class-wise performance showed that the model did not perform equally across all gesture categories. The highest class-wise accuracy was achieved by the Rest class, at 92.38%. The Finger Motion class also performed strongly, with 87.07% accuracy. The Gross Motion class had the lowest class-wise accuracy, at 78.37%. These results suggest that resting muscle states were the easiest for the model to distinguish because they had a more separable signal pattern. Finger Motion was also classified relatively well, while Gross Motion was more difficult for the model to separate from the other classes. Confusion Matrix Overview A confusion matrix was used to further examine how the Random Forest model’s classifications were distributed across the three gesture categories. The confusion matrix showed that the model classified Rest most accurately, with relatively few Rest samples misclassified as active movement classes. This supports the class-wise accuracy result, where Rest achieved the highest performance. The greatest confusion occurred between Gross Motion and Finger Motion. This result is intuitive because both classes involve active hand and forearm movement, meaning that their sEMG signals may share overlapping patterns of muscle

HENRY HUDEPOHL

activation. Gross Motion had the lowest class-wise accuracy, suggesting that these gestures may have produced signals that were less separable from Finger Motion using the selected features.

Results Summary Overall, the results show that a low-cost sEMG system can achieve moderately high classification performance. The genetic algorithm effectively reduced the feature set from 8 to 4, and the Random Forest classifier achieved an overall accuracy of approximately 85% with the reduced feature set. The model performed best on the Rest and Finger Motion samples; however, it had more difficulty distinguishing Gross Motion from other gestures. The results bolster support for the development of low-cost sEMG systems that are both computationally accessible and accurate.

DISCUSSION ---------------Main Findings The main finding of the study is that a low-cost, low-technicality surface electromyography gesture classification pipeline differentiated among three hand gesture categories, achieving an overall accuracy of ~85% after feature selection with a genetic algorithm and classification with Random Forest. The genetic algorithm reduced the original feature set by 50% while maintaining significant classification performance, suggesting that broad categories of gesturing can be identified from lowcost forearm electromyography signals using a computationally inexpensive machine learning pipeline rather than intensive, expensive deep learning techniques. The use of the MyoWare Muscle Development Kit, MATLAB, a genetic algorithm, and Random Forest demonstrated that a relatively simple, computationally inexpensive, and reproducible pipeline for gesture classification is feasible compared with more complex acquisition and modeling systems [14-17,19,20,23]. Therefore, the results support the idea that low-cost sEMG systems can achieve meaningful performance when paired with careful preprocessing, feature extraction, selection, and classification, and they support future work in developing more accessible human-computer interaction. HENRY HUDEPOHL

Mechanism of Performance The pipeline's performance was likely the result of a combination of preprocessing, feature extraction, genetic algorithm-based feature selection, and Random Forest classification. Preprocessing and sliding-window segmentation made the raw sEMG recordings more usable by standardizing the signal and increasing the number of training samples. Feature extraction converted each window into numerical descriptors, allowing the model to classify patterns from compact feature vectors rather than raw voltage signals. The genetic algorithm further improved practicality by reducing the feature set from eight to four, thereby lowering computational requirements while preserving useful classification performance. Random Forest was well-suited to this reduced feature set because it can model nonlinear relationships between engineered features and class labels. Interpretation of Class-Wise Differences The model performed differently across the three gesture groups. Rest had the highest class-wise accuracy at ~92%, likely because resting muscle states produce distinctive, lowamplitude signals compared with active contraction. Finger Motion also performed relatively well at ~87%, suggesting that the selected features captured useful patterns of fine finger movement. Gross Motion had the lowest accuracy at ~78%, most likely because the class included ThumbsUp, Victory, and fist, which involve different combinations of muscle activation. This suggests that grouping gestures reduced the complexity of the classification task, but also introduced variation within each class, especially Gross Motion. Contribution Relative to Prior Work The contribution of this study should be understood as practical development rather than state-of-the-art performance. Many previous EMG gesture-recognition studies focus on higher accuracy through complex models, larger datasets, multi-channel systems, or more controlled data collection [14-17,19,20,23]. This study showed that a lowcost, accessible intra-subject sEMG system could classify grouped hand gestures with reasonable accuracy using a reduced feature set. The genetic algorithm was also a viable choice because it reduced the original eight features to four, supporting a more efficient pipeline for lower-cost devices. Limitations These findings have several limitations. First, the study used an intra-subject design, meaning the model was trained and tested on data from a single individual. EMG signals vary widely due to differences in anatomy, skin impedance, electrode placement, and overall strength[21-23]. Therefore, the results demonstrate single-subject feasibility but not generalizability to a broader population. Additionally, the dataset was heavily imbalanced. Finger Motion dominated the two other classes. This class imbalance causes models to perform better on more common classes because they can recognize patterns in that data, and leads to less overfitting. Although class-wise accuracy was reported to address the issue, future work should balance the number of segments used for classification.

Third, the classification task examined broader gesture groups rather than individual gestures. This made the problem more straightforward and easier to run on a lower-cost device, while still remaining relevant as it aligns with the development of prosthetics. Yet the system did not prove it could reliably distinguish between gestures, such as index and middle. Future work should test individual gestures with the same system architecture to compare accuracy. Fourth, the system’s preprocessing pipeline was not advanced, excluding methods such as band-pass filtering, notch filtering, or wavelet-based denoising, which could potentially improve signal quality and class differences, as noted in other studies [7,8,10,13]. Yet it is also important to mention that the simplified preprocessing pipeline aligns with the study’s goal of computational efficiency and low-cost implementation. Lastly, the study used a 70/30 split rather than a more robust validation design for training and testing. Other suitable options include k-fold cross-validation or repeated trials across multiple sessions. A holdout split provides an estimate of model performance, but true performance may vary depending on how the data are divided. Future work should focus on stronger validation methods. Future Work Future work should focus on expanding the dataset to include additional samples and subjects. This would determine whether the pipeline can be generalized beyond a single user, since EMG signals vary substantially from person to person [21-23]. Additionally, future work should test more gesture classes rather than the grouped gestures present in this study. This grouping was appropriate for evaluating broad classification performance, but as prosthetic development progresses and finer motor control becomes necessary, the model must be evaluated on individual gestures rather than broad classes. Also, the preprocessing pipeline should be improved and re-evaluated using additional preprocessing techniques and recording conditions to determine which filtering methods yield the best results for data separation and improved classification performance. Finally, a more interpretable pipeline, such as a fuzzy inference system, is worth exploring, as it offers a human-understandable method of classification using clear rules rather than black-box decisions [24-26]. These future studies could evaluate how model accuracy changes when the classification rules are human-defined and expert-developed rather than machine-optimized.

CONCLUSION ---------------This study investigated whether a low-cost surface electromyography gesture-classification pipeline could distinguish grouped hand gestures using genetic algorithm feature selection and Random Forest classification. Surface EMG signals were collected using a MyoWare 2.0 acquisition system, processed in MATLAB, grouped into Rest, Gross Motion, and Finger Motion, and evaluated using a 70/30 traintest split. The final model achieved 84.57% overall accuracy and reduced the feature set from 8 to 4. Class-wise performance showed that Rest was classified most accurately, followed by Finger Motion and Gross Motion. These results suggest that broad categories of hand movement can be identified using a computationally inexpensive, low-cost sEMG pipeline in an intra-subject setting. The main contribution of this study is the development of an accessible pipeline for at-home experimentation with sEMGbased human-computer interaction. However, the dataset was small, imbalanced, and limited to one subject. Future work should test more participants, individual gestures, real-time embedded implementation, and alternative classification models.

REFERENCES ---------------1. Yasen M, Jusoh S. A systematic review on hand gesture recognition techniques, challenges and applications. PeerJ Comput Sci. 2019;5:e218. doi:10.7717/peerj-cs.218. 2. Mitra S, Acharya T. Gesture recognition: a survey. IEEE Trans Syst Man Cybern C Appl Rev. 2007;37(3):311-324. doi:10.1109/ TSMCC.2007.893280. 3. Merletti R, Rainoldi A, Farina D. Surface electromyography for noninvasive characterization of muscle. Exerc Sport Sci Rev. 2001;29(1):20-25. doi:10.1097/00003677-20010100000005. 4. McGill KC. Surface electromyogram signal modelling. Med Biol Eng Comput. 2004;42(3):446-454. doi:10.1007/ BF02344715. 5. Ben Haj Amor A, El Ghoul O, Jemni M. Sign language recognition using the electromyographic signal: a systematic literature review. Sensors. 2023;23(19):8343. doi:10.3390/ s23198343. 6. Day S. Important factors in surface EMG measurement. Calgary: Bortec Biomedical Ltd; 2002. p. 1-17. 7. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech. 2010;43(8):1573-1579. doi:10.1016/j.jbiomech.2010.01.027.

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8. Clancy EA, Morin EL, Merletti R. Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol. 2002;12(1):1-16. doi:10.1016/S10506411(01)00033-5.

21. Zhao K, Zhang Z, Wen H, Scano A. Intra-subject and inter-subject movement variability quantified with muscle synergies in upper-limb reaching movements. Biomimetics. 2021;6(4):63. doi:10.3390/biomimetics6040063.

9. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000;10(5):361-374. doi:10.1016/S1050-6411(00)00027-4.

22. Merletti R, Farina D, editors. Surface Electromyography: Physiology, Engineering, and Applications. Hoboken: IEEE Press/Wiley; 2016.

10. Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003;50(7):848-854. doi:10.1109/TBME.2003.813539. 11. Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng. 1993;40(1):82-94. doi:10.1109/10.204774. 12. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl. 2012;39(8):7420-7431. doi:10.1016/j. eswa.2012.01.102. 13. Wu Y, Hu X, Wang Z, Wen J, Kan J, Li W. Exploration of feature extraction methods and dimension for sEMG signal classification. Appl Sci. 2019;9(24):5343. doi:10.3390/ app9245343.

23. Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643-659. doi:10.1682/JRRD.2010.09.0177. 24. Zadeh LA. Fuzzy sets. Inf Control. 1965;8(3):338-353. doi:10.1016/S0019-9958(65)90241-X. 25. Gacto MJ, Alcalá R, Herrera F. Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf Sci. 2011;181(20):4340-4360. doi:10.1016/j. ins.2011.02.021. 26. Perfilieva I. Logical foundations of rule-based systems. Fuzzy Sets Syst. 2006;157(5):615-621. doi:10.1016/j. fss.2005.10.006.

14. Kadavath MRK, Nasor M, Imran A. Enhanced hand gesture recognition with surface electromyogram and machine learning. Sensors. 2024;24(16):5231. doi:10.3390/s24165231. 15. Lee KH, Min JY, Byun S. Electromyogram-based classification of hand and finger gestures using artificial neural networks. Sensors. 2022;22(1):225. doi:10.3390/s22010225. 16. Palkowski A, Redlarski G. Basic hand gestures classification based on surface electromyography. Comput Math Methods Med. 2016;2016:6481282. doi:10.1155/2016/6481282. 17. Fu J, Xiong L, Song X, Yan Z, Xie Y. Identification of finger movements from forearm surface EMG using an augmented probabilistic neural network. In: Proceedings of the IEEE/SICE International Symposium on System Integration; 2017 Dec 1114; Taipei, Taiwan. p. 547-552. doi:10.1109/SII.2017.8279278. 18. Immanuel SD, Chakraborty UK. Genetic algorithm: an approach on optimization. In: Proceedings of the 2019 International Conference on Communication and Electronics Systems; 2019; Coimbatore, India. p. 701-708. doi:10.1109/ ICCES45898.2019.9002372. 19. Thompson NC, Greenewald K, Lee K, Manso GF. The computational limits of deep learning. arXiv. 2020;arXiv:2007.05558. 20. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539. HENRY HUDEPOHL

Soil Health and Median Household Income In Suburban Areas

ENOCH PAULSON

ABSTRACT This study explored whether median household income correlates with physical indicators of soil health in Northern Kentucky suburban neighborhoods. Soil samples were collected from 15 households across seven census block groups with median incomes ranging from $41,000 to $110,800. Bulk density, saturated water content, and soil organic carbon were measured and compared with neighborhood income. Income was positively correlated with bulk density (r = .741, p = .0016) and negatively correlated with soil organic carbon (r = -.526, p = .044), while saturated water content showed no statistically significant relationship. Contrary to the original hypothesis, the findings suggest that higher-income neighborhoods may have poorer soil on the measured indicators. Small sample size and methodological constraints make these results preliminary.

RESEARCH QUESTION:

To what extent can a low-cost sEMG gesture classification pipeline using genetic algorithm feature selection and Random Forest classification distinguish grouped hand gestures?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

AP Research 2025-2026

Correlating Soil Health and Median Household Income In Kentucky Suburban Neighborhoods

Background

Methods

Soil Health

Research Question

Soil Health is evaluated through three broad categories

Results

Analysis

Does Median Household Income Correlate with Soil Health?

Statistically significant negative

Site Selection

●Chemical ●Biological ●Physical And plays a role in plant growth, carbon sequestration, mental health, food production, water filtration, and exercise

One-third

of all soils are infertile due to unsustainable land-use management (FAO 2025) (Doran & Safely 1997; Tahat et al. 2020; Seeland et al. 2009; Rojas et al. 2016)

Suburban Areas & Environmental Injustice “Suburban studies as a field is largely invisible in the academic landscape.”

●

Environmental benefits and burdens are unevenly

(McCoy-Lewis et al. 2023)

correlation was found between soil health

Seven census block groups in Northern Kentucky were selected based on proximity to the school and 15 households participated (a convenience sample). A range of median annual income: $41.0k $110.8k. Testing Three physical indicators of soil health were evaluated across all samples: Bulk Density, Moisture, and Soil Organic Carbon. While this study does not evaluated soil health as a whole, physical properties of soil often physical conditions of the soil affect the chemical and biological processes present in the soil as well, and thus play a large and fairly comprehensive role in soil quality.

and median household income. Bulk density was found to be positively correlated with income (love bulk densities promote root growth) and Soil Organic Carbon was found to be negatively correlated with income (a higher SOC is Figure 1: Soil Bulk Density from Block Groups of Varying Median Household Income

r = 0.741 p = 0.0016

Created using Vernier Graphical Analysis.

●

neighborhoods with poverty levels over 20% (Berube 2019)

poverty and race can lead to environmental neglect.

poor suburban residents lived in ●

poverty in suburbs between 1998 and 2023 (McCoy-Lewis et al. 2023)

was measured and converted to the Volumetric

possibility that similar

beneath suburban yards (Motairek et al. 2023; Bullard, 1983) (Okunade 2024; Ringquist 2005)

Soil Organic Carbon: Soil Organic Matter was measured using the LOI Method & converted to SOC using the Van

respiration rates) Conclusions & Implications

soil health variation in a limited way ● Further research is needed to affirm conclusions as the power of the data is severely limited by the sample size (n =

Moisture: Gravimetric Saturated Water Content Saturated Water Content

This study explores the

disparities exist in the soil There was a 30% increase in

Bulk Density: Core Method

associated with higher crop yields and

● Income level correlates with suburban

distributed. Between 2010 and 2014, 41% of

Conclusion

Figure 2: GSWC from Block Groups of Varying Median Household Income

Figure 3: SOC from Block Groups of Varying Median Household Income

Created using Vernier Graphical Analysis.

r = 0.186 p = 0.051

r = -0.526 p = 0.044

15) and the methods used. (Beutler et al., 2002; Silva et al., 2007; Bennett et al., 2010; Nelson & Sommers, 1996)

Bemmelen Factor (Methods adapted from the National Resource Conservation Service’s Soil Survey Field and Laboratory Methods Manual)

Doran, J.W.; Safley, M. 1997. Defining and assessing soil health and sustainable productivity. p. 1-28. In: Pankhurst, C.E.; Doube, B.M.; Gupta, V.V.S.R., eds. Biological indicators of soil health. CAB International, Wallingford, UK. Food and Agriculture Organization of the United Nations. (2025). The state of food and agriculture 2025: Addressing land degradation across landholding scales. https://doi.org/10.4060/cd7067en M. Tahat, M., M. Alananbeh, K., A. Othman, Y., & I. Leskovar, D. (2020). Soil Health and Sustainable Agriculture. Sustainability, 12(12), 4859. https://doi.org/10.3390/su12124859 Seeland, K., Dübendorfer, S., & Hansmann, R. (2009). Making friends in Zurich’s urban forests and parks: The role of public green space for social inclusion of youths from different cultures. Forest Policy and Economics, 11(1), 10-17. https://doi.org/10.1016/j.forpol.2008.07.005

Rojas, R. V., Achouri, M., Maroulis, J., & Caon, L. (2016). Healthy soils: a prerequisite for sustainable food security. Environmental Earth Sciences, 75(3), 1-10. https://doi.org/10.1007/s12665-015-5099-7 Food and Agriculture Organization of the United Nations. (2025). The state of food and agriculture 2025: Addressing land degradation across landholding scales. https://doi.org/10.4060/cd7067en Beutler, A.N.; Centurion, J.F.; Souza Z.M.; Andrioli, I.; Roque, C.G. 2002. Water retention in two oxisols under different uses. Revista Brasileira de Ciência do Solo 26: 829-834 Bennett, L.T.; Mele, P.M.; Annett, S.; Kasel, S. 2010. Examining links between soil management, soil health, and public benefits in agricultural landscapes: an Australian perspective. Agriculture, Ecosystems and Environment 139: 1-12. Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. In D. L. Sparks (Ed.), Methods of soil analysis. Part 3: Chemical methods (pp. 961–1010). Soil Science Society of America.

INTRODUCTION ---------------Soil is immensely important to civilization as it is the basis of almost all agriculture. For this reason, it is vitally important that the soils of the world be thoroughly understood to secure our food supply. However, because of shortsighted farming and land use, as well as mankind's limited knowledge about the factors that affect soil , global soil health is in decline. A recent report from the Food and Agriculture Organization of the United Nations explained that one-third of all soils are infertile due to unsustainable land-use management practices (FAO, 2011). In order to understand this decline fully, the soil health of many regions must be assessed. Soil health or soil quality, terms that can be used interchangeably (Doran and Safley, 1997), is evaluated using three broad categories: physical, chemical, and biological. It is not only important for maintaining agricultural production in rural areas, but also to sustain essential ecosystem services such as groundwater filtration, nutrient recycling, and greenhouse gas sequestration even in urban and industrial locations (Tahat et al., 2020). This study focuses mainly on the physical aspects of soil health (bulk density, Soil Organic Carbon, porosity), but it should be noted that the physical conditions of the soil affect the chemical and biological processes present in the soil as well, and thus play a large and fairly comprehensive role in soil quality. Physical indicators can represent overall health due to the complexity of the soil biome. Soil health is critical to the maintenance of green spaces, and understanding the ENOCH PAULSON

green spaces around us is integral to their continued function in the face of land degradation. For this study, suburban soil health was primarily focused on, due to its proximity to daily life for many in the United States and largely unresearched status. To better comprehend and address possible inequalities in soil health, this study proposes that there is a positive correlation between soil health and median income of suburban neighborhoods, as designated by Block Group, a small subdivision created by the US Census. On a larger scale, concerns about suburban sprawl have been prevalent for the past century, but lines of inquiry regarding this have been skewed, as the suburb has been historically regarded as uniformly middle class and well-to-do. However, both recently and historically, suburbs have been home to an increasingly diverse diaspora of income levels. In fact, between 2010 and 2014, 41 percent of poor suburban residents lived in neighborhoods with poverty levels over 20 percent (Berube, 2019), suggesting that poverty is not only present in suburbs, but it is also concentrated in certain suburban neighborhoods, providing a basis for contrast. Poverty in suburbs is not only increasing (a 30 percent increase from 1993 to 2023) but the study of suburbia in general has been neglected (Lewis-McCoy et al., 2023). With this increase 47

in poverty, and the growing realization that suburbs do not contain ‘cookie-cutter’ demographics, it is clear that more research into the effects of income level is needed. Additionally, as land mismanagement is the leading cause of soil degradation, it is also imperative that we understand how income-level may correlate with the health of our soil, as many management practices, such as pesticide, fertilizer, and organic amendment application, are largely dependent on income (Locke et al., 2019). Urban sprawl and development is now affecting ecosystems in direct and indirect ways (Pavao-Suckerman & Pouyat, 2017). Differences in income may influence residential land management practices, such as irrigation, chemical inputs, and maintenance intensity, which in turn affect soil physical properties. Accordingly, this study evaluates whether neighborhood income level is associated with variation in suburban soil health across census block groups.

LITERATURE REVIEW ---------------This study mainly deals with soil health in suburban areas. Soil health has been a nebulous concept for all of its use as an agricultural term. It has been broadly defined as “the continued capacity of soil to function as a vital living system… to sustain biological productivity, maintain environmental quality and promote plant, animal and human health.” (Doran and Safely, 1997). Healthy soil serves a pivotal role in providing many ecosystem services: promoting water quality and plant growth, controlling decomposition, cycling nutrients, preventing flooding, and sequestering carbon, and changes in soil affect ecosystem services (Tahat et al., 2020; Pavao-Suckerman & Pouyat, 2017; Kopittke et al., 2024). Because of healthy soil’s tendency to control flooding and promote water quality, it is of value to many in the world today, and should be better understood. The import of soil health is apparent as it also is tied to a multitude of factors directly affecting human health and quality of life; the presence of a local, healthy green space is dependent on soil health. A 2009 study of green spaces in Zurich, Switzerland found that local green areas afforded opportunities for mental relaxation, exercise, and social integration, contributing to the overall health for people in the area (Seeland et al., 2009). So, soil health affects more than the results of weather events, it indirectly provides mental and social benefits. More obviously, soil is also fundamental for food production stability, nutrient quality, and yield in farming, which in suburban areas is more applicable to gardening (Rojas et al., 2016). Contemporary literature agrees that, in order to preserve these critical functions, it is essential that scientific knowledge on soil function be translated into practical methodologies that enrich landowner’s knowledge of, and therefore ability to change, the sustainability of their management practices (Tahat et al. 2020; Stirling et al. 2017; Locke et al. 2019; Xu et al. 2019).

ENOCH PAULSON

Moreover, many benefits associated with environmental systems are not distributed evenly. This has been shown throughout history, with the unfair placement of infrastructure, housing, and industry leading to adverse and sometimes poisonous environmental exposures for black and impoverished communities in what is classified as environmental racism or environmental injustice (Motairek et al., 2023). In the famous case of the water crisis in Flint, Michigan, the city with the highest rates of poverty in the state, drinking water was contaminated by lead from old pipes that the city did not have the means to service (Okunade, 2024). In 1983, a study was conducted in Houston, Texas, in which it was found that solid waste sites were disproportionately found in Black communities (Bullard, 1983). Through all related literature, it is clear that those who live in the world’s, and specifically the US’s, most polluted (read: mismanaged) environments are commonly people of color and those living in poverty (Cambell et al., 2025; Midcoh, 2026; Mastaki, 2025; Bharti, 2025; Palmer, 2016; Ringquist, 2005). This demonstrates prior income-based neglect of environmental systems and further purports that maintenance and land use play important roles in soil degradation. The existence of environmental injustice in many forms denotes the possibility of its existence in soil, especially soil in suburban neighborhoods, as injustice in suburbs is often unseen. Because of the aforementioned perceived uniformity of suburban neighborhoods, it is evident that there are many unidentified factors in suburbia that disproportionately affect those in poverty. It can be posited that a difference in soil health is one of these. However, there is a significant lack of prior research into this area and literature on the subject, creating a pressing need for this study. As mentioned, soil health around the globe has been in decline due to erosion from soil mismanagement. So, it is likely that soils in suburban areas are declining in health as well. The full scope of the problem’s effect on suburbia is unknown, but in order to restore degraded soil and prevent further loss, an informed population making evidence-based decisions is crucial (Rojas and Caon, 2016). A 2025 report by the U.N.’s Food and Agriculture Organization strongly stressed the criticality of improving land-use practices, so a survey was created to assess participant’s land management (FAO, 2025). The survey included questions concerning irrigation, pesticide and fertilizer use, and soil amendment use, areas of land management that were highlighted in the report. To evaluate soil health, measures of soil organic carbon, bulk density, and moisture were taken. Severe deficiencies in any of these categories would result in unproductive soil, incapable of air scrubbing, water drainage, or filtration. Thus these traits were deemed adequate to evaluate the soil health in nearby suburbs. All life is based on carbon, so Soil Organic Carbon is an essential component of soil health, shown in an Australian study to be positively correlated with crop yield (Bennett et al., 2010). Soil Organic also affects the nutritional content of the soil, like nitrogen, stability, and water holding capacity 48

(Cardoso et al., 2013), making it an ideal choice for evaluating soil health, as it affects the general viability of the soil. Meanwhile, bulk density is a measure of how tightly packed a soil is. A low bulk density is commonly associated with virgin forests, and is closely, inversely correlated with Soil Organic Carbon (Bini et al., 2013). Bulk density is a factor in root development, and anthropogenic compaction, largely due to traffic, often increases bulk density, adversely affecting plant growth (Bengough et al. 2006) This is a pertinent concern for soils in suburban areas, largely due to the commonality of traffic from domestic vehicles and construction machinery on suburban lawns. Additionally, moisture is critical to a soil’s capacity to function as a living system through both plant growth and microbial activity (Geisseler, 2011). The moisture holding capacity of a soil denotes its usefulness in flood control, and drainage is critical to microbial and root health. Moisture is also a critical factor in microbial health and even Soil Organic Carbon decomposition (Pan et al., 2026). Therefore moisture factors greatly into soil health, and is especially important in populated areas that may experience periodic flooding. Many studies have been conducted to analyze declining soil health in cropland (Jian et al., 2020), which is indisputably beneficial, but, despite its immense importance, soil health in suburban areas has seen a significant gap in research, with only broad census data available concerning land viability1. This study aims to help close that gap, providing data on possible disparities.

METHODOLOGY ---------------There are a multitude of differing ways to measure soil health, but it cannot truly be measured directly (Arias et al., 2005). For this study, three main factors have been identified based on their feasibility and importance to soil health: soil organic carbon, bulk density, and moisture. Seven different block groups were identified for participation in the study. Qualifications were based on proximity to the principal investigator’s high school, where the samples were analyzed, and median annual income, based on data from The Demographic Statistical Atlas of the United States, which employs U.S. Census data to create maps (Esri, n.d.). The seven block groups created an income range from $41,000 to $110,800, which is broad, but undeniably exclusive of lowerincome neighborhoods. The Kentucky block groups selected were 064700-1, 064700-2, 064700-3, 064600-1, 064600-2, and 064000-2, 064600-3. A few mechanisms for possible correlation shown were identified, and a short survey questioning the broader details of lawn-management was administered. The survey gave participants queries regarding irrigation, pesticide/herbicide use, mowing, composting, and land use. Raking practices were also questioned, as some literature posits that leaf litter is more important than land use (Maes et al., 2019). The survey was created for the purpose of qualitatively assessing potential mechanisms that influence soil health, providing contextual variables to be discussed in tandem with the ENOCH PAULSON

measured data on physical soil health. Each sample was collected and immediately split into two sub-samples, the SOC sub-sample, and the moisture subsample. Sampling and analysis methods were based off of and closely follow methods provided by the NRCS (U.S. Department of Agriculture, National Resource Conservation Service, (n.d.). Initial volumes of each sub-sample were recorded prior to pore-space collapse and transportation. The SOC sub-sample was air-dried for two days, sifted through a 2mm sieve, and homogenized via stirring. Each SOC subsample was then oven dried for 24 hours at 105 degrees Celsius, weighed, and cooled in a makeshift dessicator constructed from a sealed glass container and silica gel packets. Soil organic matter was measured using the loss-onignition (LOI) method, a widely used and acceptable proxy technique in soil science when sophisticated equipment is unavailable, and facilities were unavailable for drycombustion procedures (Hoogsteen et al., 2018.) The SOC subsamples were placed in a 350 C kiln for four hours, and left to cool in the dessicators, which had been cleaned and serviced with replacement silica packages. The Soil Organic Matter (SOM) was calculated from the dried and predried masses of the SOC sub-samples using the following equation. SOM(%) = (M2 - M3)/(M2 - M0)*100 The Soil Organic Carbon was then estimated using the Van Bemmelen factor, which assumes the soil organic matter measured is approximately 58% carbon by mass. SOC = SOM/1.724 This assumption is an obvious source of error for a largescale study, but as this is a small study which focuses on contrast between soils while employing the same method of measurement for each sample; the data holds value for comparison purposes. Because the method for determining bulk density and Gravimetric Saturated Water Content were identical until after oven drying, the moisture sub-sample was used to measure both bulk density and moisture. Each moisture sub-sample was oven dried at 105 C and the dried mass was recorded. Bulk density was then determined using the core method by dividing the oven-dry mass of the soil by the initial volume of the sub-sample (Al-Shammary et al., 2018). Lastly, moisture was evaluated by measuring each sample’s Gravimetric Saturated Water Content. Moisture sub-samples, previously oven-dried, were placed on a fine mesh and submerged in water. Once the sub-samples were completely saturated, they were allowed to drain for 30 minutes, and the final mass was recorded. Percent change between the ovendried and final mass was determined, thus calculating the Gravimetric Saturated Water Content. Afterwards, the Volumetric Saturated Water content was calculated for each sample to estimate the maximum waterholding capacity of the soil and to provide an estimate of total porosity by multiplying the Bulk Density by the Gravimetric Saturated Water Content and dividing by the density of water. 49

This results in a porosity with units (g/cm3)(g/g)/(g/cm3), which cancel, leaving a unitless number. Porosity = (BD*GSWC)/W Where BD is bulk density, GSWC is the Gravimetric Saturated Water Content, and W is the density of water. Similarly, the Soil Organic Carbon Stock, a measurement of the mass of carbon in a given area is computed from the following equation described by Zeng et al. (2021) SOC Stock = H × BD × OC × 10 where H is sampling depth (cm), OC is the Soil Organic Carbon (%), and BD is bulk density (g/cm3) Each of these indicators denote something of the total soil health, demonstrating the soil’s ability to support life through carbon content (SOC) available for growth, bulk density for the development of healthy root systems, and moisture holding capacity (Gravimetric Saturated Water Content). Throughout testing, proper safety and laboratory precautions were taken to ensure accuracy of data and purity of samples. Containers were thoroughly cleaned before and after use, and samples were re-sealed immediately after use. However due to the inefficiency of methods used (such as the Loss on Ignition method used to determine Soil Organic Matter), the inherent random variability introduced in collecting soil at different locations, and the unprofessional setting of the experiment (in high school laboratories, conducted by a high school student) it is probable that the data are slightly corrupted. This study was approved by an Institutional Review Board prior to experimentation.

Bulk Density To represent bulk density, the sampled data was used to construct a scatterplot graph (Figure 1.) A Pearson product– moment correlation analysis revealed a statistically significant positive relationship (r(13)=0.741, p=0.0016) indicating that higher median income is associated with higher bulk density values. The coefficient of determination (R2=0.549) suggests that approximately 54.9% of the variation in bulk density can be explained by income. While this relationship is statistically significant and moderately strong at the 0.05 level, the relatively small sample size and the presence of repeated x-values (as more than one sample came from each block group may limit the independence of observations and reduce the generalizability of the findings. Additionally, the assumption of independence may be partially misleading due to multiple observations sharing identical income values, which could influence the stability of the correlation coefficient.

RESULTS ---------------To test for correlation between median household income and soil health, this study collected three sets of data and compared them with the corresponding median household income. The three data sets, as described above, were (1) bulk density, (2) Gravimetric Saturated Water Content, and (3) Soil Organic Carbon. Additionally, (4) other data were collected concerning lawn care practices and land-use. Some conclusions can be drawn from the scatter plots of the data itself (Figures 1-3.) However, due to the need for statistical certainty when describing potential correlational relationships, linear association was measured using the correlation coefficient (r-value). The correlation coefficient is a standard method of measuring the direction and strength of a linear relationship. The sign of the r-value denotes the directionality of the relationship (positive or negative) and the magnitude denotes its strength: R-values closer to 0 denote weaker linear relationships (Starnes et al., 2020). For each data set, a Pearson correlation analysis was conducted to determine the likelihood that a relationship suggested by the correlation coefficient was due to chance. A p-value below 0.05 was considered statistically significant and the null hypothesis was that there was no real correlation between soil health and income. ENOCH PAULSON

Gravimetric Saturated Water Content Conversely, the correlation coefficient of the sampled Gravimetric Saturated Water Content revealed no statistically significant correlation (r = 0.1859) between GSWC and median household income. The r-value denotes an extremely weak positive correlation. From the differences in GSWC between block groups given the insignificant p-value (p = 0.051), it appears that median household income has no statistically significant association with GSWC, and the weak correlation is not distinguishable from random variation (See Figure 2.) Because of this, the total porosity was not calculated, as any trends in the data would be due to the presence of bulk density in the equation, and would not provide any more meaningful data on the evaluation of soil health. These findings do not provide sufficient evidence of a linear association between median household income and GSWC. As with bulk density, the small sample size and repeated x-values limited statistical power and the independence of observations, which should be considered when interpreting the result. 50

Soil Organic Carbon Considering that bulk density and Soil Organic Carbon are understood to be are inversely related by nature (Bini et al., 2013), it was expected from the strong positive linear correlation found between bulk density and median household income (See Figure 1) that Soil Organic Carbon and median household income would be strongly negatively correlated (Bini et al. 2013). However, the correlation between Soil Organic Carbon and median household income was shown to be weaker than expected, with a correlation coefficient of -0.5256. This r-value implies a moderate negative correlation between the two variables, and the results of the Pearson test imply that it is statistically significant (p = 0.044.) The coefficient of determination (R2 =0.277) suggests that about 27.7% of the variation in Soil Organic Carbon can be explained by income. Initially, Soil Organic Carbon Stock was to be calculated using Soil Organic Carbon, bulk density, and the site’s sampling depth. However, due to inconsistent and inaccurately recorded sampling depth on the part of both participants and the principal investigator, the calculation was discounted and Soil Organic Carbon Stock was not calculated.

ENOCH PAULSON

Survey Results The survey was conducted, and all participants were prompted to respond to a quick questionnaire concerning their lawn-care practices. However, results of the survey were widely varied, with around 38% of questions marked with ‘Not sure’ or some equivalent. Questions surrounding fertilizer and pesticide made up the majority of these non-answers, despite that these are the variables most tied to land management. More importantly, however, only four participants responded to the survey, effectively rendering all data collected from the survey useless. Summary Across the three measured soil health indicators, a distinct pattern emerged in their relationships with median household income. Bulk density was found to have a statistically significant and moderately strong positive correlation with income, while Soil Organic Carbon showed a statistically significant moderate negative correlation. In contrast, Gravimetric Saturated Water Content exhibited no statistically significant relationship with income, suggesting that variation in this variable is not meaningfully associated with socioeconomic differences in this sample. The survey data was meant to provide context for lawn-care practices but did not establish statistically testable relationships with soil health metrics due to variability in responses.

DISCUSSION ---------------Based on the significance of correlation between two of the three measured factors of soil health (Bulk density and Soil Organic Carbon) and median household income, it was supported that soil health holds some correlation with median household income. Conversely, the actual hypothesis that soil health and median household income would be positively correlated was not supported. Yet, because of the lack of statistical differences in moisture content, the data is not fully conclusive. However, it was found that bulk density positively correlates with median household income and that Soil Organic Carbon was negatively correlated with median household income, supporting the reverse of the hypothesis: that suburban soil health correlates negatively with median household income. Given that a higher bulk density is commonly a sign of compacted or inactive soil and that a lower Soil Organic Carbon is associated with poor and unproductive soils (Beutler et al., 2002; Silva et al., 2007; Bennett et al., 2010.), the observed correlation in Soil Organic Carbon also indicates that soil health and median household income are negatively correlated. The survey did not provide any valuable explanatory variables, due to the low response rates. Only three participants elected to complete the survey; thus results were discarded, as they would be unable to factually represent the lawn-care management practices of all participants. However, the data must be interpreted with caution, as this study included many ways in which data could be corrupted. First, when collecting samples, inconsistencies in sampling depth and the inclusion of undecomposed detritus may have influenced trends in the data, decreasing accuracy. Additionally, samples were stored before testing for varying amounts of time, an inevitable extraneous variable when conducting a high school research study that relies on the time-table of participants. Furthermore, inaccurate methods are a significant source of error. The LOI method, used to evaluate Soil Organic Matter, is known for general inaccuracy and ability to fail when the samples analyzed are a large part clay. And, as the soils of Northern Kentucky have a high clay content, the LOI method is untrustworthy, which may discount the data collected on Soil Organic Matter, and therefore that on Soil Organic Carbon (Kenton County Planning Commission, 2024). The conversion between Soil Organic Matter and Soil Organic Carbon may also have introduced error, as the Van Belemmen factor was used, making the assumption that organic matter is 58 percent carbon by mass, which is not always true. But, because the results were only compared to each other for this study, this error can be effectively discarded, as comparing Soil Organic Matter to median household income and comparing Soil Organic Carbon to median household income would create the same trends. Moreover, when evaluating bulk density using the core method, it is essential to measure the volume of the sample before the pore spaces collapse. However, due to inexpert handling by both the principal investigator and participants, ENOCH PAULSON

samples were disturbed before their volumes could be measured, generating a source of error. Most importantly, all the measurements taken dealt only with physical properties of the soil, avoiding all chemical and biological indicators of soil health, which play extremely important roles in plant growth and health, and therefore the ecosystem services discussed (Arias et al. 2005) This is an impactful. All these sources of error ultimately contribute to the uncertainty of the study, that further research into the area is needed to draw any meaningful conclusions that could be, in good conscience, accepted at a larger scale. However, for correlation purposes, the data in the study, while incomparable to outside data from other studies, does provide a fine initial measurement of the correlation between soil health and median income level, as most sources of error identified affected all data equally. Therefore, it is likely that there actually is a negative relationship between soil health and median household income in Northern Kentucky neighborhoods, though further research would be needed to declare it conclusively. This conclusion also cannot be definitively applied to all variations of suburbs, as different suburbs vary wildly in age and construction procedures. Additionally, land-use management could be cultural, and different practices affect soil health in different ways (Alvafritz, Luce, & Hertel, 2024). It’s possible that the trend seen in soil health in these Northern Kentucky neighborhoods will not be seen in other areas. This possibility that there is a real negative correlation between suburban soil health and median household income is the first to be suggested in the literature for suburban environments. In the context of environmental justice, trends identified with soil health and income defy historical occurrences. In this case, the correlation between soil health and neighborhood income level appears to be negative, where more affluent neighborhoods correlate with poorer soil. This directly opposes examples given in the Literature Review section, as those were cases where environmental factors negatively affected those in poverty. Ultimately meaning, that unless the results of this study can be falsified by larger, better conducted studies, suburban soil health need not be a concern for environmental justice movements. Considering the possibility and the implications of a trend in the reverse direction, it is recommended that more research into this area be done. Experiments concerning all aspects of soil health, biological, physical, and chemical, should be conducted over a range of neighborhoods throughout the U.S. Such studies should be completed using more sophisticated methods of data collection, most notably in measuring undisturbed volume of the soil before the pore spaces collapse to improve measurements of bulk density and switching to a dry combustion method to directly measure Soil Organic Carbon (Nelson & Sommers, 1996). Higher accuracy should be obtained through the use of larger sample sizes, the exploration of more confounding variables, and better facilities.

Regardless, the degradation of the world's soil remains a serious problem, and it is now understood through the findings of this study that this may affect those in affluent neighborhoods more.

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Locke, D. H., Polsky, C., Grove, J. M., Groffman, P. M., Nelson, K. C., Larson, K. L., Cavender-Bares, J., Heffernan, J. B., Chowdhury, R. R., Hobbie, S. E., Bettez, N. D., Hall, S. J., Neill, C., Ogden, L., & O’Neil-Dunne, J. (2019). Residential household yard care practices along urban-exurban gradients in six climatically-diverse U.S. metropolitan areas. PLoS ONE, 14(11), 1–14. https://doi.org/10.1371/journal.pone.0222630 Maes, S. L., Blondeel, H., Perring, M. P., Depauw, L., Brūmelis, G., Brunet, J., Decocq, G., den Ouden, J., Härdtle, W., Hédl, R., Heinken, T., Heinrichs, S., Jaroszewicz, B., Kirby, K., Kopecký, M., Máliš, F., Wulf, M., & Verheyen, K. (2019). Litter quality, land-use history, and nitrogen deposition effects on topsoil conditions across European temperate deciduous forests. Forest Ecology and Management, 433, 405–418. https://doi. org/10.1016/j.foreco.2018.10.056 Mastaki, G. A. (2025). Environmental justice and planned relocation in Central Africa. Forced Migration Review, 76, 75–78. Midcoh, E. (2026). On behalf of Lake Owakeela. Bulletin of the Atomic Scientists, 82(1), 27–36. https://doi.org/10.1080/00963 402.2026.2604981 Motairek, I., Chen, Z., Makhlouf, M. H. E., Rajagopalan, S., & Al-Kindi, S. (2023). Historical neighbourhood redlining and contemporary environmental racism. Local Environment, 28(4), 518–528. https://doi.org/10.1080/13549839.2022.2155 942 M. Tahat, M., M. Alananbeh, K., A. Othman, Y., & I. Leskovar, D. (2020). Soil Health and Sustainable Agriculture. Sustainability, 12(12), 4859. https://doiorg/10.3390/su12124859 Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. In D. L. Sparks (Ed.), Methods of soil analysis. Part 3: Chemical methods (pp. 961–1010). Soil Science Society of America. Okunade, O. (2024). The Flint Water Crisis and the Perpetuation of Environmental Racism in Flint, Michigan (2014–2018). Journal of African American Studies, 28(3), 233–250. https://doi.org/10.1007/s12111-024-09666-5 Palmer, B. (2016). The history of environmental justice in five minutes. NRDC [Website]. https://www.nrdc.org/stories/ history-environmental-justice-five-minutes Pan, Y., Tan, T., Ren, R., Meng, J., Yu, N., Jin, X., Zhang, Y., Zou, H., Bolan, N., & Siddique, K. H. M. (2026). Soil organic carbon decomposition in response to moisture, microbial communities, and biochar addition in Alfisols. Biochar, 8, Article 5. https://doi.org/10.1007/s42773-025-00513-8

Ringquist, E. J. (2005). Assessing evidence of environmental inequities: A meta-analysis. Journal of Policy Analysis and Management, 24(2), 223–247. https://doi.org/10.1002/ pam.20088 Rojas, R. V., Achouri, M., Maroulis, J., & Caon, L. (2016). Healthy soils: a prerequisite for sustainable food security. Environmental Earth Sciences, 75(3), 1-10. https://doi. org/10.1007/s12665-015-5099-7 Rojas, R. V., & Caon, L. (2016). The international year of soils revisited: promoting sustainable soil management beyond 2015. Environmental Earth Sciences, 75(16), 1–5. https://doiorg.northernkentuckyuniversity.idm.oclc.org/10.1007/s12665016-5891-z Seeland, K., Dübendorfer, S., & Hansmann, R. (2009). Making friends in Zurich’s urban forests and parks: The role of public green space for social inclusion of youths from different cultures. Forest Policy and Economics, 11(1), 10-17. https:// doi.org/10.1016/j.forpol.2008.07.005 Silva, I.R.; Sá Mendonça, E. 2007. Matéria orgânica do solo = Soil organic matter. p. 275-374. In: Novais, R.F.; Alvarez, V.H.; Barros, N.F.; Fontes, R.L.F.; Cantarutti, R.B.; Neves, J.C., eds. Fertilidade do solo = Soil fertility. Sociedade Brasileira de Ciência do Solo, Viçosa, MG, Brazil (in Portuguese). Starnes, D. S., Tabor, J., Yates, D. S., & Moore, D. S. (2020). The practice of statistics for the AP course (6th ed., updated). W. H. Freeman. Stirling, G.; Hayden, H.; Pattison, T.; Stirling, M. Soil health, soil biology, soilborne diseases and sustainable agriculture: A guide. Australas. Plant Pathol. 2017, 46, 387. https://doi. org/10.1007/s13313-017-0493-0 U.S. Department of Agriculture, Natural Resources Conservation Service. (n.d.). Soil quality indicators: Bulk density. https://www.nrcs.usda.gov/sites/default/ files/2023-01/Soil%20Quality-Indicators-Bulk%20Densit y.pdf Xu, G., Song, J., Zhang, Y., & Lv, Y. (2019). Effects of biochar application on soil organic carbon mineralization during drying and rewetting cycles. BioResources, 14(4), 9957–9967. https://doi.org/10.15376/biores.14.4.9957-9967 Zeng, R., Wei, Y., Huang, J., Chen, X., & Cai, C. (2021). Soil organic carbon stock and fractional distribution across central-south China. International Soil and Water Conservation Research, 9(4), 620–630. https://doi. org/10.1016/j.iswcr.2021.04.004

Pavao-Zuckerman, M., & Pouyat, R. V. (2017). The effects of urban expansion on soil health and ecosystem services: An overview. In C. Gardi (Ed.), Urban expansion, land cover and soil ecosystem services (pp. 123–145). Taylor & Francis. https://doi.org/10.4324/9781315715674

ENOCH PAULSON

How Observation Completeness Shapes the Empirical Value of Information in Capacitated Production Scheduling: A Simulation Study using Hearts of Iron IV

CONO PRESTI

ABSTRACT This simulation study measured how observation completeness affects Bayesian adaptive production scheduling under capacity constraints and sequencedependent changeover costs. Fifteen Hearts of Iron IV simulations were analyzed for Germany and the Soviet Union across four observation levels and three scheduling methods, yielding 360 conditions and approximately 45,000 factory-month observations. The Bayesian allocator significantly outperformed the game’s AI baseline, with a medium-to-large effect, and performance increased as more opposing forces became visible. The estimated Empirical Value of Information was 45,567 weighted production units per unit of observation completeness (95% CI [35,398, 55,736]). Although returns diminished at higher visibility, 67% observation captured only about 35% of the available improvement, demonstrating the continuing value of information under binding capacity constraints. RESEARCH QUESTION:

How does the completeness of observation about an opponent's deployed force composition affect the performance of Bayesian adaptive scheduling in a capacitated lot-sizing environment with sequence-dependent changeover costs?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

Does Informa,on Pay?

CONO PRESTI

Expert Advisor: Dr. Lisa Holden

Measuring the Empirical Value of Informa6on in capacitated produc6on scheduling. A simula6on study using Hearts of Iron IV

45,567

0.89

~35%

EMPIRICAL VALUE OF INFORMATION

EVOI REGRESSION FIT

GAP CLOSED AT 67% OBSERVATION

weighted output units / unit observa3on

R² across 360 method-by-condi3on cells

of the AI-baseline-to-MILP-ceiling gap

95% CI [35,398, 55,736] · p < 0.001

linear p < 0.001 · quadra=c = −9,623 (p = 0.053)

the contrarian ﬁnding : Germany scenario

INTRODUCTION

METHODS

RESULTS

DISCUSSION

German produc3on planners between 1936 and 1939 faced a clean version of a recurring problem: ﬁnite factory capacity, decisions commiJed before they knew what their rivals would ﬁeld, and no way to inspect the other side. The same problem appears, stripped of geopoli3cs, anywhere produc3on demand depends on another agent's choices. Howard's 1966 Value of Informa3on framework gives a price for beJer observa3on, but that price has rarely been measured empirically.

Simula'on environment. HOI4 produc,on is modeled as a capacitated lot-sizing problem with sequence-dependent setup costs. Each military factory is a capacity unit; each equipment type is a product.

The Bayesian allocator beats the AI baseline (Cohen's d = 0.78, p < 0.001). EVOI = 45,567 units/unit obs. Concave shape (Howard's predic,on. At 67% visibility, the allocator captures only ~35% of the available gain.

Conclusion. Each unit increase in observa,on completeness adds ~45,567 units of weighted output. The prac,,oner reading, "moderate observa,on is good enough," in not true once capacity is binding.

Daily output, nonlinear efficiency growth, and a five-,er switching reten,on structure are taken from the engine source files and verified against 12 parsed test saves (mean absolute error < 5%).

Mechanism. Diﬀuse posterior leads to MILP hedging, which triggers costly type-change switches. KL divergence falls 0.66 to 0.07 as observa,on rises.

EXPERIMENTAL DESIGN

15 runs × 2 countries × 4 obs. levels × 3 methods = 360 cells

RESEARCH QUESTION How does the completeness of observa3on about an opponent's deployed force composi3on aﬀect the performance of Bayesian adap3ve scheduling in a capacitated lot-sizing environment with sequence-dependent changeover costs? SQ1. Does a Bayesian adap3ve allocator outperform the na3ve AI baseline under par3al observa3on? SQ2. How does Bayesian performance scale with observa3on completeness? SQ3. What is the Empirical Value of Informa3on, and does it show diminishing returns?

Keep buying observa9on as long as marginal cost stays below ~45,567 units / unit completeness.

125-month horizon (Jan 1936 to Jun 1946) · ~45,000 factory-month observa,ons Linear mixed-eﬀects model · 10,000 bootstrap resamples · α = 0.05

MILP

With perfect hindsight SEES

SOLVES

ROLE

En?re future enemy produc?on sequence.

Determinis?c CLSP via 12-month rolling horizon with 3-month commit window.

Prac?cal upper bound. No real agent achieves it.

The Bayesian Adap.ve Loop WHAT THE LOOP IS

Each month, the Bayesian allocator runs the same ﬁve-step cycle. It looks at the enemy divisions it can see, updates its belief about what they are building, picks the right produc?on priori?es, solves a 12-month plan, and commits the next three months. Then it does it again.

STEP 1

STEP 2

STEP 3

STEP 4

STEP 5

Observe

Update

Transfer

Solve

Commit

Sample frac=on f of enemy deployed divisions.

Dirichlet update

Map posterior mean to produc=on priority.

Rolling-horizon MILP over 12 months.

Make decision for next 3 Months

→

Repeat each month. The next observa=on arrives, the posterior sharpens, the schedule re-plans.

Bayesian Adap0ve posterior + rolling-horizon MILP SEES

SOLVES

ROLE

Frac?on f of enemy deployed divisions each month, where f varies.

Updates Dirichlet posterior on enemy composi?on; re-solves rolling MILP with posterior-mean demand.

The system under test. EVOI is measured here.

SEES

SOLVES

ROLE

No informa?on beyond its hardcoded priori?es.

Heuris?c priority system extracted directly from the same save ﬁles.

What HOI4 actually does. The natural control group.

AI Baseline na9ve HOI4 heuris9c

THE UPDATE RULE

αposterior = αprior + nobserved

A uniform Dirichlet prior is updated to a posterior whose concentra=on grows with each observa=on. The posterior mean p̂ = αposterior / Σ αposterior becomes the demand forecast. WHY THE LOOP WORKS The enemy's true produc=on process is hidden. The allocator only sees what the enemy has already ﬁelded. Trea=ng that visible output as a noisy mul=nomial sample lets the Dirichlet posterior do the heavy licing. As more divisions become visible, the posterior concentrates on the truth, the MILP commits to longer correct produc=on runs, and the cumula=ve switching cost falls.

First empirical EVOI esBmate in CLSP-SD under opaque heurisBc demand. Future work: mul-player, noise, RL, pre-registered replica-on.

THE INFORMATION

REFERENCES

Observa=on completeness f is the experimental variable. At f = 0 the posterior never updates. At f = 1 the posterior locks onto the true enemy composi=on within a few months. Everything between is what the EVOI regression measures.

Bayes does not require knowing the enemy's decision logic. Only that their observable output behaves like a piece from some underlying distribu=on.

1. Howard (1966). Informa-on value theory. IEEE Trans. SSC 2, 22-26. 2. Allahverdi et al. (2008). Survey of scheduling problems. EJOR 187, 985-1032. 3. Wagner & Whi-n (1958). Dynamic ELS. Manag. Sci. 5, 89-96. 4. Lariviere & Porteus (1999). Stalking informa-on. Manag. Sci. 45, 346-363. 5. Pochet & Wolsey (2006). Produc-on Planning by MIP. Springer. 6. Vinyals et al. (2019). Grandmaster StarCra\ II. Nature 575, 350-354. 7. Keisler et al. (2014). VOI analysis. Environ. Syst. Decis. 34, 3-23. 8. Bates et al. (2015). lme4. J. Stat. So\w. 67, 1-48.

Full Paper

Code & data

INTRODUCTION ---------------German production planners between 1936 and 1939 faced a clean version of a recurring problem. They had finite factory capacity, they had to commit it to specific equipment families before they knew what the Soviet Union, Britain, or France would field,61,62,65 and they had no real way to inspect their rivals’ decisions. The same shape of problem appears, stripped of the geopolitics, in any production system whose demand depends on another agent’s choices.70 A line that holds one product too long builds up the wrong inventory when demand shifts; switching too frequently to compensate sacrifices throughput to changeover. Capacitated lot-sizing problems with sequencedependent setup costs make the trade-off worse because each switch carries a penalty whose size depends on what is being switched between.2,3 Classical lot-sizing models accommodate uncertainty by drawing demand from a stochastic distribution5, which works as long as the demand process is stationary and independent. That formulation breaks down when demand is set by an agent whose decisions cannot be directly observed, an opaque agent. A firm scheduling against a competitor, or a state scheduling against a rival arms program, sees outputs (deployed equipment, shipped units) and has to infer the underlying priorities behind them66. The forecasting problem at that point is no longer noise estimation. It is partial inference of someone else’s plan from what that plan has so far produced. CONO PRESTI

Howard’s1 Value of Information framework formalizes what those partial exposures are worth. Rationally, a decisionmaker should pay for an observation up to the expected gain in decision quality it produces. The principle rests on Bayesian decision theory20 and has been applied in environmental policy, healthcare evidence synthesis, and engineering design21,24. In a setting with costly switching, every additional unit of information directly purchases efficiency that would otherwise leak away as changeover waste. The empirical literature on VOI in scheduling is thin22: most published work is theoretical, and what empirical results exist come from healthcare evidence synthesis rather than operations research. Strategy games have become a serious decision-making testbed over the past decade27,28,30,34, and Hearts of Iron IV happens to combine three properties that real manufacturing data cannot jointly provide. Its production mechanics are formally documented in the engine’s source files68. Its AI controls opponent nations autonomously, generating opaque nonstationary demand without observing the scheduler. And its save files record every deployed division, which lets me mask observation completeness after the fact without changing the AI’s behavior. The AI itself is heuristic, not strategic, and does not adapt to what the scheduler does, so the results bound the value of information against opaque heuristic demand rather than against a game-theoretic best-responder. 56

The research question is how the completeness of observation about an opponent’s deployed force composition affects the performance of Bayesian adaptive scheduling in a capacitated lot-sizing environment with sequence-dependent changeover costs. Three sub-questions decompose it must be addressed to answer this. Firstly, SQ1 asks whether a Bayesian adaptive allocator outperforms the native AI baseline under partial observation. Next, SQ2 asks how Bayesian performance scales with observation completeness. Lastly, SQ3 asks what the Empirical Value of Information actually is, and whether it shows the diminishing returns Howard’s framework predicts. Bayesian cumulative production value scales nearlinearly with observation, at roughly 45,567 units per unit of completeness, and the curve bends downward at the upper end as predicted. At moderate visibility (67%), though, the Bayesian allocator captures only about a third of the gap to the theoretical ceiling.

BACKGROUND AND RELATED WORK ---------------Four research streams converge on the question this paper measures: capacitated scheduling with sequence-dependent setup costs, Bayesian demand learning, the Value of Information framework, and game environments as research testbeds. All four are well-developed on their own. None has been combined with the others to ask what observation completeness is worth under demand from an opaque external agent. That intersection is where this paper lives. Capacitated lot-sizing with switching costs The capacitated lot-sizing problem with sequencedependent setup costs traces back to Wagner and Whitin’s4 dynamic economic lot-size formulation. Allahverdi et al.2 surveyed more than 300 papers establishing that sequencedependent changeovers are both practically important and computationally hard. Fleischmann3 formalized the discrete lot-sizing and scheduling problem; Copil et al.5 gave a modern classification. Mixed-integer programming now solves moderate-scale CLSP instances to optimality6; heuristics handle larger problems7,9. The shared assumption across this literature is that demand is either deterministic or drawn from a known stochastic process8. That assumption breaks when demand comes from an external agent whose decision logic is unknown and whose outputs can only be partially observed, which is the setting this paper examines. Bayesian demand learning Bayesian methods give the scheduler a principled way to update production decisions as new observations arrive72,73. Lariviere and Porteus10 showed that an inventory manager can sharpen demand estimates from sales data alone, even when lost sales are not directly observed. Araman and Caldentey11 extended the idea to dynamic pricing under demandparameter uncertainty, and Besbes and Muharremoglu12 characterized adaptive inventory control under unknown demand. The exploration-exploitation tension underneath these results is the multi-armed bandit problem13,14,15,17, with modern Bayesian-sampling treatments from Russo et al.16 CONO PRESTI

and a textbook synthesis from Lattimore and Szepesvári18. Bensoussan et al.19 closed the loop on partially observed demand in a multiperiod newsvendor setting. These results establish that Bayesian learning works when demand is i.i.d. stochastic. None examine demand generated by a complex deterministic agent whose internal logic the scheduler cannot inspect; even formal stochastic-choice models81 assume the response rule is structurally fixed rather than driven by another agent’s evolving plan. Value of Information Howard1 introduced the Value of Information framework, defining the worth of an observation as the expected gain in decision quality it produces. The framework rests on Bayesian decision theory20,67, and Keisler et al.21 survey its applications across environmental, health, and engineering domains. Boncompte Pons74 argues that the standard EVPI calculation can return values no rational decision-maker would accept, which is one reason empirical estimates are worth having alongside analytic bounds. The mismatch between literatures is striking: empirical VOI studies are common in healthcare evidence synthesis24,23 but rare in operations research22, where most VOI work is theoretical. Sharot and Sunstein25 document a behavioral analogue in everyday choice. Further, theory predicts that VOI is concave under standard conditions26. This study tests that prediction empirically. Game environments as research testbeds Buro27 argued that real-time strategy games were a serious AI research challenge. The decade since has produced a steady run of demonstrations: AlphaGo28, its successor AlphaZero29, AlphaStar at Grandmaster level in StarCraft II30, OpenAI Five at superhuman Dota 231, MuZero learning environment dynamics jointly with policy33, Libratus at heads-up no-limit poker32, and Gran Turismo Sophy outracing champion human drivers34. Textbook treatments now exist35,36. Every one of these works targets AI play. None target scheduling. This paper uses Hearts of Iron IV for the property it shares with those environments, formally documented mechanics, but applied to a different question: scheduling against opaque heuristic demand. The gap Each of the four literatures above has matured independently, but their empirical intersection is essentially empty. Stationary demand assumptions still dominate the CLSP work5; the Bayesian inventory and scheduling literature relies on stochastic demand10; analytic treatments dominate the VOI literature, with most empirical results coming from healthcare evidence synthesis rather than operations research21; and the AI-game-testbed literature has converged on AI play rather than the scheduling of resources against AI30. This study takes that empty space as its focus, varying observation completeness in a controlled way against demand from an opaque heuristic agent and producing an empirical EVOI estimate where prior work has produced analytic bounds. Scope limits This paper is not a study of partially observable Markov decision processes39,40, not a multi-armed bandit problem18, 57

and not an information-design problem in the sense of Bayesian persuasion41,42. It is an empirical measurement of Howard’s VOI in a deterministic-mechanics scheduling environment with opaque heuristic demand. The opacity is a property of the AI’s heuristic decision logic37, not adversarial information asymmetry. Observation completeness is varied through post-hoc masking of fully recorded save data, not through the game’s fog-of-war system38, which constrains what an in-game player sees during play and is irrelevant here because analysis runs on fully recorded saves.

Switching between products triggers an efficiency retention penalty whose size depends on how similar the two products are. Table 2 lists the five tiers, drawn from the engine’s source files68 and verified against parsed save data.

METHODS ---------------Simulation environment Hearts of Iron IV satisfies three requirements for empirical VOI measurement that real-world manufacturing data cannot jointly provide. Production mechanics are formally documented and deterministic: factory output, efficiency growth, and switching penalties follow published formulas in the game’s engine source files68 that I independently verified against parsed save data. The AI controls opponent nations autonomously through a heuristic priority system that does not observe the scheduler, generating demand patterns that are opaque, non-stationary, and richer in structure than standard stochastic models37,58. And deployed division compositions of AI-controlled nations are recorded in full in save files, which provides a natural observable that can be masked after the fact to vary observation completeness without altering AI behavior.

I verified equations (1) and (2) and the retention values in Table 2 against 12 test save files (four each from early-game 1936–37, mid-game 1939–41, and late-game 1943–45). Mean absolute error stayed below 5% across factory output, efficiency, and switching retention (Appendix B, Tables B1 and B2). The nonlinear efficiency dynamics and the five-tier retention structure are the two features that distinguish this formulation from standard CLSP models5 and that motivate the piecewise-linear approximation in §3.2.

I model HOI4 production as a capacitated lot-sizing problem with sequence-dependent setup costs. Each military factory is a capacity unit; each equipment type is a product. Daily output per factory follows equation (1), where E is current efficiency, m is the sum of factory output modifiers, and nf is the number of factories assigned to the product:

(1) Efficiency grows nonlinearly toward a 50% cap, with the growth rate given by equation (2). Growth is fastest when efficiency is low and slows as the line approaches its cap, which is what makes a missed switch so costly:

(2)

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Three scheduling methods Three scheduling methods are compared. They differ only in their access to information about future enemy production. The ex-post clairvoyant MILP receives the full enemy production sequence after the fact and solves the resulting deterministic CLSP by mixed-integer linear programming6,60. Its performance defines a practical ceiling. It is practical, not theoretical, because the MILP is solved with a 12-month rolling horizon and a 3-month commitment window45 rather than over the full 125-month horizon at once. The Bayesian adaptive allocator maintains a Dirichlet posterior over enemy equipment composition43,44, updates it from observed deployed divisions at each monthly decision point, and solves the same rolling-horizon MILP using the posterior mean as a demand forecast. The AI baseline is what HOI4’s built-in heuristic AI actually produces, extracted from the same save files used to generate enemy demand. All three share the same horizon, capacity structure, and switching-cost structure; only their information about future enemy production differs.

Bayesian mechanism The Bayesian adaptive allocator follows a five-stage loop. A uniform Dirichlet prior is placed over enemy equipment categories. At each monthly decision point, the allocator observes a random fraction f of enemy deployed divisions, where f corresponds to the experimental observationcompleteness level. Observed division counts update the Dirichlet posterior using the standard multinomial-Dirichlet conjugate update, given by equation (3):

(3) where α is the Dirichlet parameter vector, n is the vector of observed counts at time t, and is the posterior mean estimate of the enemy composition. The multinomialDirichlet conjugate update is a standard tool43,44 and has been extended to nonparametric Dirichlet-process mixtures for retail demand inference75. The posterior mean passes through a demand-transfer function that maps enemy equipment composition to the scheduler’s own production priorities. The MILP solver receives this priority vector and produces a 12-month schedule with a 3-month commit window. The five-tier retention structure described in §3.1 enters the MILP as a piecewise-linear approximation with five breakpoints. Bayesian methods were chosen over reinforcement learning deliberately: a learned policy would conflate optimization quality with observation quality, making EVOI hard to isolate.54,57

Demand transfer function The demand transfer function maps observed enemy deployed force composition to the scheduler’s own production priorities. There is no ground truth for this mapping inside the game itself, so I tested three variants for sensitivity. IC-cost parity weights priorities by the industrial capacity cost of countering each equipment type. Community meta uses category weightings drawn from competitive-play guides. Combat-stat computes priorities from the simulator’s combat-resolution coefficients. To make the mapping concrete: at one mid-1941 decision point, the German Bayesian allocator observed 84 Soviet medium-tank divisions, 21 heavy-tank divisions, and 19 mechanized-infantry divisions. Under IC-cost parity, that observation produces a priority vector of approximately [0.42, 0.31, 0.27] over (medium tanks, heavy tanks, mechanized infantry), heavy on mediums but not so heavy that hedging on heavies stops paying off. Under combat-stat, the same observation yields [0.38, 0.39, 0.23], which shifts the allocator toward heavier production. All three variants enter the same Bayesian-MILP pipeline; only the priority-mapping function differs. Experimental design The experiment uses a factorial design: 15 simulation runs (10 with historical AI focus paths, 5 with ahistorical paths), each analyzed for two countries (Germany and the USSR), at four observation-completeness levels (0%, 33%, 67%, 100%), using three scheduling methods. Germany and the USSR were chosen because they have the most diverse equipment trees and the largest IC pools among the major powers, which makes their CLSP problems nontrivial62,63,64. Ahistorical runs were included since when the AI selects non-standard national focus paths, the resulting division templates differ across runs, which raises variance in demand patterns and prevents EVOI from being measured against a single nearly-deterministic demand sequence. Fifteen runs balance statistical power against computational cost, and a post hoc power analysis46 confirms the design detects medium effect sizes (Cohen’s d ≥ 0.5) at 80% power47. The experiment yielded 360 methodby-condition cells, each a 125-month time series of monthly performance metrics, for roughly 45,000 factory-month observations in total. Data collection used the `-hands_off` launch flag with the player set to Haiti, a minor neutral nation with no scripted focus tree that could influence major-power AI decisions and a small enough starting industrial capacity that the human-controlled slot consumes negligible resources. Uruguay would have served the same purpose. The configuration keeps all major powers under AI control without the AI-distorting effects of the in-game observe mode. Statistical analysis Performance is measured as cumulative weighted production value V across the 125-month horizon, with secondary metrics for stockout frequency and switching waste. The primary analysis is a linear mixed-effects model with a method × observation-completeness interaction and a random intercept per country48,49,54. EVOI itself is estimated by OLS regression with a quadratic term in the observation fraction, with confidence intervals from 10,000 percentile-method bootstrap resamples50,51,52,78,80. A repeated-measures ANOVA was considered and rejected because the design

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is unbalanced across countries and ahistorical splits. EVOI is estimated by equation (4), where f is the observationcompleteness fraction, β1 is the Empirical Value of Information, β2 is the diminishing-returns coefficient, and ucountry is a random intercept by country:

(4) I report bootstrap 95% confidence intervals from 10,000 percentile-method resamples50,51,52,78 and effect sizes per Cohen46 using the reporting conventions of Lakens.53,79 Two design choices deserve a note. I chose the mixed-effects model over a repeated-measures like ANOVA which I noticed in similar use cases because the design is unbalanced across countries and ahistorical splits. I chose bootstrap CIs over parametric ones because n = 15 runs is too small to trust distribution assumptions, and bootstrap convergence on i.i.d. sums is well-characterized for non-Gaussian methods80. As a check on the mechanism, demand-estimation accuracy is reported as the Kullback-Leibler divergence (equation 5) between the Bayesian posterior Q and the true enemy composition P55,77. Reporting KL alongside production value confirms that the Bayesian allocator actually learns from observation, rather than benefiting from some unrelated correlation within the masking procedure.

SQ1: Bayesian outperforms AI baseline Under partial observation, the Bayesian adaptive allocator produced significantly higher weighted production value than the native AI baseline. Figure 4 shows the distribution across the three methods at the 67% observation level. The mixed-effects model estimated a mean difference of 12,803 units between Bayesian and AI baseline (Bayesian = 71,607 [67,383, 75,616]; AI = 58,804 [55,861, 61,432] for Germany; p < 0.001), corresponding to a Cohen’s d of 0.78, a mediumto- large effect. The ex-post clairvoyant MILP achieved the highest mean (94,982 [91,020, 98,483]; Cohen’s d = 2.62 versus AI baseline), confirming that it functions as an appropriate ceiling. The same pattern held for the Soviet Union and was robust across the historical and ahistorical run splits.

(5)

RESULTS ---------------Sample description The 15 simulation runs (10 historical, 5 ahistorical) produced 360 unique scheduling decisions across two countries and a 125-month horizon, January 1936 through roughly June 1946, for about 45,000 factory-month observations in total. Table 1 reports descriptive statistics for the five performance metrics (weighted production value, regret relative to MILP, stockout frequency, switching waste, and demandestimation accuracy), stratified by method and observationcompleteness level. No runs failed; all 360 method-bycondition cells produced complete time series. Weighted production value was approximately normally distributed within each cell, with no extreme outliers. Ahistorical runs showed roughly 2.4× higher variance in enemy force composition than historical runs, which confirms that the mixed design increased demand diversity as intended.

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SQ2: Performance scales with observation completeness Bayesian allocator performance increased monotonically with observation completeness, but the rate of improvement decreased at higher visibility (Figure 5). The linear slope was 45,567 units of weighted production value per unit of observation fraction (95% CI [35,398, 55,736]; p < 0.001). The quadratic coefficient was −9,623 (p = 0.053). The negative sign indicates diminishing returns: the gain from improving visibility from 0% to 33% was larger than the gain from 67% to 100%. Model fit was R2 = 0.89. Country-level random effects accounted for a small but non-trivial share of variance, justifying the mixed-effects specification over pooled OLS for the primary analysis. The EVOI slope was statistically indistinguishable between historical and ahistorical runs (overlapping 95% CIs; Figure 7), so the result is not an artifact of deterministic demand sequences.

SQ3: EVOI estimate and diminishing returns The Empirical Value of Information, defined as the linear regression coefficient of Bayesian allocator performance on observation completeness, is 45,567 (95% CI [35,398, 55,736]). The estimate quantifies the average gain in cumulative weighted production value per unit increase in the fraction of enemy deployed divisions visible to the allocator, after controlling for scenario effects. The quadratic term is negative and borderline-significant (−9,623, p = 0.053), consistent with diminishing returns in this setting. With only four observation levels and two free parameters, the quadratic test has limited power, so the borderline p-value should be read as suggestive rather than decisive. The shape of the EVOI curve has two practically relevant features. Going from blind (0%) to moderate (33%) yields roughly 69% of the eventual improvement reached at full visibility, and the marginal increment from good (67%) to complete (100%) brings total Bayesian performance to about 95.8% of the MILP ceiling. Together, those two features produce a less comforting result than ‘diminishing returns’ alone implies: at 67% observation the Bayesian allocator captures only about 35% of the gap between the AI baseline and the MILP ceiling, and closing the remaining 65% requires pushing visibility all the way to full. The curve shape itself, consistent with Howard’s1 concavity CONO PRESTI

prediction under standard conditions26, is not surprising. The size of the gap that remains at moderate observation has direct operational consequences, which §5 examines. Mechanism: posterior concentration drives regret reduction The Bayesian posterior dynamics show the mechanism directly. KL divergence between the posterior over enemy composition and the true composition fell from 0.66 at 0% observation to 0.07 at 100% observation (Figure 6, left). Cumulative regret71 relative to the MILP ceiling accumulated slowly under high observation (about 12k units over 125 months at 100% visibility) and explosively under sparse observation (about 220k units at 0%) (Figure 6, right). Together the two confirm better observation concentrates the posterior, a sharper posterior produces a sharper schedule, and a sharper schedule avoids costly switches.

Robustness across demand-transfer functions Results were robust across all three demand-transfer-function variants (Figure 7). Point estimates of EVOI ranged from roughly 38,000 to 49,000 across the IC-cost, combat-stat, and community-meta variants, with substantially overlapping confidence intervals. The choice of demand-transfer function did not qualitatively change the findings. The diminishingreturns pattern (negative quadratic coefficient) was present under all three variants (each p < 0.10). This robustness supports the interpretation that the EVOI result reflects the value of observation completeness itself rather than an artifact of any particular demand modeling assumption.

observation reduces the rate at which the allocator selects type changes, and the cumulative-regret figures track that reduction directly. The variance of the Dirichlet posterior scales as:

(6)

DISCUSSION ---------------Principal finding Each unit increase in the fraction of an opponent’s deployed force that the scheduler can see produces, on average, 45,567 additional units of weighted production value across a 125-month horizon (95% CI [35,398, 55,736]; p < 0.001). The shape of that gain matters more than the slope alone. Going from blind to a third visibility recovers about 15,000 units of weighted output. Going from a third to two thirds recovers about the same again. The curve has the diminishing-returns shape Howard’s framework predicts. At 67% visibility, the Bayesian allocator has captured only about a third of the available improvement against the MILP ceiling. Howard was right that the earliest observations carry the most information per unit, which is what produces the diminishingreturns shape on a chart. The practitioner rule drawn from that prediction, that moderate observation should be good enough, does not survive once capacity is binding. Mechanism The mechanism behind these results runs through the Dirichlet posterior. With low visibility, the allocator sees few enemy divisions, the posterior remains diffuse, and the MILP responds by hedging across equipment types. Hedging produces frequent product switches. Each switch incurs an efficiency-retention penalty, and a hedging schedule loses cumulative output to changeover. As visibility rises, the posterior concentrates on the true enemy composition: KL divergence between posterior and truth falls from 0.66 in the blind condition to 0.25 at low visibility and 0.07 at full visibility, and the MILP responds by committing to longer correct runs. The regret consequence is large. By month 125, the Bayesian allocator at 100% observation has accumulated roughly 12,000 units of regret against the MILP ceiling. At 0% observation, that figure rises to about 220,000 units, roughly eighteen times higher. The native AI baseline accumulates around 140,000 units of regret over the same horizon, falling between the two Bayesian extremes. The dominant source of inefficiency in the schedule is the high-penalty type-change category (10% retention), which accumulates whenever the allocator is forced to hedge under uncertainty. Improving

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Because of the inverse-square dependence on the sum of pseudo-counts (equation 6), the earliest observations are disproportionately informative, and the diminishing-returns shape follows from Bayesian updating itself rather than from anything specific to scheduling. The capacity constraint is what changes that picture in the present setting. Lariviere and Porteus’s10 inventory result uses the same Dirichlet structure without a capacity constraint, and their information value drops off faster than the value measured here. Adding hard switching costs and tight capacity to the same Bayesian machinery keeps the EVOI curve from flattening as quickly as uncapacitated theory predicts. Comparison to theory Comparing 45,567 units of weighted output per unit of observation to anything in the published literature is harder than it sounds. The literature does not have an EVPI estimate for a CLSP-SD setup with these dynamics, and the standard analytic bound has itself been criticized74. The honest comparison is qualitative. The estimate here is positive and concave, which is what theory says EVPI should be in a stochastic lot-sizing setting10,11, and the magnitude is larger than the published newsvendor and dynamic-pricing estimates I could find. Three plausible explanations exist for that gap. The capacity constraint is the one most likely to generalize: in an uncapacitated environment, a planner can absorb a wrong forecast by overproducing across products, and the cost of poor observation is bounded by inventory carry; under tight capacity, every factory committed to the wrong product is one not committed to the right one, and a misforecast becomes opportunity cost rather than carry cost. The other two explanations are specific to this environment. Heuristic AI demand is more structured than i.i.d. stochastic demand because the AI follows persistent template-driven patterns37, and HOI4’s switching costs are unusually large relative to production value: the 10% retention for type changes implies about ten months of efficiency recovery per wrong commitment. A future replication of this experiment in a different capacitated setting would help separate these explanations. A magnitude that depends heavily on the specifics of HOI4’s mechanics would point to the structureof-demand and switching-cost factors as the main drivers; a magnitude that holds up across settings would suggest the capacity argument is the more general explanation.

Limitations of the conclusion Five constraints bound the interpretation of the EVOI estimate. They are summarized in Table 4. The most concerning is the heuristic-versus-strategic gap. HOI4’s AI does not adapt to the scheduler. A real opponent does. A strategic adversary might raise EVOI by deliberately exploiting a scheduler’s blind spots, or lower it because equilibrium play makes some observations useless. I have no clean way to bound the direction of that effect from inside this experimental setup, which is why §5.6 puts the multiplayer extension first.

Future work Four directions follow from the limitations. First, multiplayer. Testing the EVOI estimate against a strategic adversary (HOI4 multiplayer32) is the only way I see to bound the strategicgap effect from §5.4 within the current testbed. Second, observation noise. The current experiment varies the fraction of divisions visible but not the noise on what is observed; varying both would map a fuller intelligence-quality surface23. Third, the scheduling method itself. Replacing the Bayesian allocator with a reinforcement-learning agent57,59,76 would test whether end-to-end policy learning compresses EVOI, which would be a clean way to separate observation value from inference machinery. Fourth, replication. The borderline p = 0.053 for the quadratic term deserves a pre-registered replication on a larger sample56 before it is treated as a fact.

CONCLUSION ----------------

Implications For planners facing demand from an opaque external system, the textbook reading is that moderate observation is good enough. In the capacity-constrained regime studied here, that reading is wrong. The curve’s flattening happens above 67% visibility, and a planner with binding capacity who stops at moderate observation gives up roughly two thirds of the available improvement against the MILP ceiling. A more useful investment rule is to keep buying observation as long as its marginal cost stays below roughly 45,567 units per unit of completeness. The methodological implication is just as concrete. This study adds one empirical data point on Howard’s1 concavity prediction in a setting where it had not been tested, and it shows that opaque heuristic demand can serve as a useful CLSP testbed.30,35,34

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Observation completeness about an opponent’s deployed force composition significantly and positively affects the performance of Bayesian adaptive scheduling, with an Empirical Value of Information of 45,567 (95% CI [35,398, 55,736]; p < 0.001) and borderline-significant diminishing returns. The empirical EVOI estimate itself is one contribution, since to my knowledge this is the first measurement of Howard’s value-of-information framework in a capacitated lot-sizing setup with sequence-dependent setup costs and externally generated demand. The methodological move is the second contribution: a formally documented strategy game can host the kind of clean experimental measurement of observation value that operations research has long wanted but rarely produced, because real-world data cannot let an analyst vary observation completeness while holding the demand-generating process constant. At moderate visibility, the kind a competent intelligence operation might plausibly achieve, the Bayesian allocator captures only about a third of the available improvement against the MILP ceiling. The posterior-uncertainty reduction is concentrated in the early observation range. Most of the cumulative-regret reduction against the MILP ceiling happens in the upper range, where the curve appears to flatten on a chart, and that is the range where scheduling gains actually live under capacity constraints. A planner facing demand from an opaque external system in a capacity-constrained setting should keep investing in observation past the point at which the curve appears to flatten. Code and data availability All save files, parser source, MILP formulation, Bayesian allocator implementation, and analysis scripts are archived at https://github.com/ConoPresti/ ResearchHOI.

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Background Music and Verbal Memory Retention in Adolescents: A Between-Subjects Experimental Investigation

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ABSTRACT This study examined how lyrical music, instrumental music, white noise, and silence affect verbal memory in high school students. Forty-four participants were randomly assigned to one audio condition while studying ten meaningful sentences, then completed immediate and 24-hour delayed recall assessments. Silence produced the highest immediate mean score (8.48 out of 10), followed by white noise (6.54), instrumental music (6.50), and lyrical music (5.16). Audio condition had a statistically significant effect at both time points, and the same performance ranking remained after 24 hours. White noise and instrumental music did not differ significantly from each other. The findings indicate that silence best supports adolescent sentence recall, while lyrical music creates the greatest interference with verbal encoding and retention.

RESEARCH QUESTION:

How does audio type - lyrical music, instrumental music, white noise, or silence - affect sentence recall in high school students immediately and 24 hours later?

THE BEECHWOOD JOURNAL OF STUDENT RESEARCH

INTRODUCTION ---------------Music is a very common feature in an adolescent’s study environment. Survey research suggests that up to 90% of high school students report regularly using background audio while completing academic tasks, with lyrical music being the most frequently chosen stimulus (Hallam et al., 2002; North et al., 2004). However, despite the prevalence of this practice, empirical evidence regarding optimal audio environments for studying remains surprisingly limited. Much of the existing evidence is anecdotal, culturally transmitted, or based on elderly populations with neurodegenerative disorders, bearing little resemblance to authentic high school study conditions (Anderson & Fuller, 2010; Schellenberg, 2005). Though the cognitive consequences of background audio during learning are theoretically stable, they are empirically understudied, especially in high school students. Verbal memory encoding engages three overlapping neural networks - the phonological loop component of working memory (Baddeley, 2003), the prefrontal attentional control system (Gathercole et al., 2004), and the hippocampal-dependent consolidation pathway (Diekelmann & Born, 2010). When background audio (particularly lyrical music) is introduced during encoding, these networks are placed under competing demands. Neuroimaging and electrophysiological research NEEL REDDY

suggests that lyrics activate the same left hemisphere languageprocessing regions as the study material, creating direct interference, degrading encoding quality (Koelsch, 2011; Slevc et al., 2013). The theoretical framework most commonly cited in literature is Baddeley’s (2003) multicomponent model of working memory, which proposes a phonological loop responsible for maintaining verbal information through rehearsal. This loop has a finite capacity. When introduced lyrical music competes with verbal stimuli, it effectively occupies rehearsal resources that would typically be allocated to the target learning material. This phonological interference hypothesis is supported by plentiful experimental evidence demonstrating how recall performance is impaired when participants study verbal material in the presence of speech or lyrics (Salamé & Baddeley, 1989; Perham & Curie, 2014). Supporting studies cite the changing-state hypothesis, proposing that sequences of varying auditory stimuli trigger involuntary attentional switches that disrupt memory (Jones et al., 1996; Tremblay et al., 2000). Despite this theoretical foundation, the existing literature displays several limitations. First, the vast majority of studies have been conducted on adult participants - typically university 68

undergraduates - using artificial tasks such as unrelated word lists (Nantais & Schellenberg, 1999; Anderson & Fuller, 2010). These tasks do not adequately capture the semantic integration, syntactic processing, and episodic encoding characteristic of real academic study. Secondly, adolescents represent a developmentally distinct population, whose susceptibility to auditory interference may differ substantially from adults. The prefrontal cortex - the region that governs attentional control and processing - undergoes continued maturation throughout adolescence (Stevens et al., 2009), suggesting that high school students may be less effective at stifling task-irrelevant auditory stimuli than adult participants. Third, most studies measure only immediate recall, failing to test whether interference effects remain through the consolidation window, a critical gap given that academic retention demands persist well beyond a single study session (Wixted, 2004; Roediger & Butler, 2011). This study was designed to address these gaps simultaneously. Using a between-subjects experimental design with high school student participants, this investigation examined how four types of background audio - lyrical music, instrumental music, white noise, and silence - affect sentence memorization at both immediate and 24-hour delayed recall. The central research question was: “How does the type of background audio present during encoding affect the immediate and delayed recall of meaningful sentences in high school students?” Based on the phonological loop theory and existing literature, it was hypothesized that silence would produce the highest recall scores, lyrical music the lowest, and instrumental music and white noise intermediate and roughly equivalent performance.

Neurological Basis of Lyrical Interference Neuroimaging has provided direct evidence to support the cortical overlap hypothesis underlying phonological interference. Koelsch (2011) demonstrated that lyrical music activates left-hemisphere language processing regions, including Broca’s area and Wernicke’s area, that are engaged during verbal encoding. This activation creates competition for the same neural resources, degrading the quality of semantic integration and phonological rehearsal. By contrast, instrumental music is processed primarily in the right auditory cortex, with significantly less overlap with the language network (Koelsch, 2011). This neurological distinction proves why lyrical music produces more interference than instrumental music. Electrophysiological evidence further supports this account. Event-related potential studies have identified specific neural markers that highlight this conflict. The N400 component, a negative deflection occurring ~400 milliseconds poststimulus, indexes semantic processing and integration (Besson et al., 1998). The P600 component, occurring ~600 milliseconds post-stimulus, reflects syntactic processing. Besson et al. (1998) found that background speech and lyrics regulate both components during concurrent verbal learning tasks, indicating measurable disruption of semantic and syntactic processing at the neural level. Slevc et al. (2013) furthered this finding using neuroimaging, demonstrating that task-irrelevant verbal stimuli activate overlapping regions with the primary task, producing interference at both the encoding and consolidation stages. Figure 1 illustrates the multicomponent working memory model and how lyrical interference fits in.

LITERATURE REVIEW ---------------Mechanisms of Auditory Interference in Verbal Memory As described in Baddeley’s (2003) multicomponent model of working memory, the phonological loop maintains verbal information through silent rehearsal with finite capacity. Salamé and Baddeley (1989) proved that spoken words presented as background stimuli during serial recall tasks produced interference even when participants were instructed to ignore them, with the disruption specific to verbal stimuli rather than noise. Further research extended this to lyrical music: songs with intelligible words produce recall deficits comparable to spoken speech, while instrumental versions of the same songs produce significantly less interference, implying that semantic content (not acoustic) is the critical variable (Perham & Currie, 2014; Salamé & Baddeley, 1989). Furthermore, the changing-state hypothesis (Jones et al., 1996), attributed interference to variability in sounds rather than content: unpredictable pitch and rhythm trigger involuntary attentional shifts that disrupt encoding. Finally, it was confirmed that recall performance decreases with increasing sound variability, supporting this proposition (Tremblay et al., 2000).

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Note: Figure 1 depicts Baddeley’s (2003) multicomponent model of working memory. The dashed red arrow indicates the competing lyrical input that disrupts phonological loop rehearsal of study material, resulting in degraded memory encoding and recall. Adolescent Susceptibility and Working Memory Development Adolescence represents a critical period of neurological development with direct implications for auditory interference. The prefrontal cortex, which governs executive functions including attentional control, working memory 69

capacity, and inhibitory processing, undergoes significant maturation during adolescence and doesn’t reach full functional maturity until the mid-twenties (Gathercole et al., 2004; Stevens et al., 2009). As a result, adolescents demonstrate less effective attentional filtering and more limited working memory resources than adults. These characteristics predict that high school students should be more susceptible to interference than the adult populations studied in most prior research. Gathercole et al. (2004) documented the developmental trajectory of working memory throughout childhood, finding that phonological loop capacity and central executive functioning show continued growth throughout mid adolescence. Thompson et al. (2012) examined individual differences in susceptibility to auditory interference. They found that participants with lower working memory spans showed greater performance drop-offs in the presence of background speech than those with higher spans. Given that adolescents have, on average, lower working memory capacity than adults, these findings suggest that the interference effects found in adults might substantially underestimate the same effects in high school students. Task Type and Ecological Validity A significant limitation of existing auditory interference research is its reliance on artificial laboratory tasks that don’t reflect the complexity of real academic learning. The majority of studies have used tasks such as digit span recall (Salamé & Baddeley, 1989), free recall of unrelated word lists (Nantais & Schellenberg, 1999), or serial position recall (Jones et al., 1996). These tasks engage primarily the phonological loop and serial order memory systems, while failing to engage the semantic integration, syntactic processing, and episodic memory systems that are involved in learning from text. Sentence memorization engages a broader and more realistic set of cognitive processes. Conway et al. (2005) demonstrated that sentence-level tasks require simultaneous engagement of the phonological loop, the episodic buffer, and semantic memory networks, making them substantially more demanding and more representative of real classroom learning. According to cognitive load theory (Sweller et al., 2011), this increased cognitive load amplifies the disruptive effect of background audio, predicting larger interference effects for sentence learning than for simpler tasks. Retention Interval and Memory Consolidation The majority of interference studies assess recall immediately following the study phase, providing no information about persistence through the consolidation window. Memory consolidation is time-dependent, occurring over hours to days following encoding, with the hippocampus playing a central role in transferring representations to memory storage (Wixted, 2004). Diekelmann and Born (2010) demonstrated that interference during encoding can compromise hippocampal consolidation, producing forgetting rates that exceed those predicted by normal decay alone. If background audio degrades the quality of initial encoding, it may NEEL REDDY

therefore reduce the durability of the resulting memory trace, a consequence with meaningful implications for academic retention that extends well beyond this study. White Noise as an Alternative Auditory Condition White noise, defined as a broadband auditory stimulus with equal energy across all frequencies, has been proposed as a potentially beneficial or neutral study background. Söderlund et al. (2007) found that low-level white noise improved cognitive performance on attention tasks in children with ADHD. They hypothesize that the noise provided stochastic resonance, a phenomenon in which moderate background noise enhances signal detection in noisy neural systems. Martel et al. (2016) extended this finding to typically developing children, finding performance improvements on working memory tasks under low-intensity white noise conditions. However, the effects of white noise on memory in adolescents without attentional disorders are less clear. Because white noise lacks semantic content and phonological structure, it isn’t expected to produce the same level of interference as lyrical music. Its effect on the changing-state dimension is also minimal, given its steady-state characteristics. These properties suggest that white noise should produce less interference than either form of music, while potentially providing a consistent backdrop that some students find conducive to concentration (Söderlund et al., 2007). Summary & Research Rationale In sum, existing literature presents a theoretically convergent, yet empirically fragmented picture. The phonological loop model (Baddeley, 2003), the changing-state hypothesis (Jones et al., 1996), and cognitive load theory (Sweller et al., 2011) all predict that lyrical music should be the most disruptive audio condition, yet they differ in the mechanism they emphasize. Meanwhile, research on white noise (Söderlund et al., 2007) and working memory development (Gathercole et al., 2004) introduces competing considerations in that non-semantic audio may not be neutral and the adolescent population may respond differently than adults. This gap motivates the present study. By comparing lyrical music, instrumental music, white noise, and silence simultaneously in a high school adolescent sample using sentence-level memorization tasks and assessing recall at both immediate and 24-hour delayed intervals, the study is designed to settle competing theoretical predictions while addressing the population limitations that have constrained prior work. The central research question is positioned at the intersection of these competing perspectives, but still just outside the boundary of what existing literature can answer.

METHODOLOGY ---------------Participants & Recruitment Participants were 44 high school students (grades 9-12) recruited through convenience sampling from multiple local high schools. The final sample consisted of 11 participants in the lyrical music group, 10 in the instrumental, 12 in the white noise, and 11 in the silence. All participants provided voluntary assent to participate, and parental consent was obtained prior to data collection. Participants received no compensation. Exclusion criteria was limited to self-reported hearing impairment. Participants were randomly assigned to conditions. This randomization procedure was employed to eliminate selection bias and ensure that preexisting individual differences in working memory capacity, musical experience, and academic achievement were distributed evenly across conditions (Shadish et al., 2002). Random assignment prevented confounding of condition effects with participant characteristics. Design This study employed a between-subjects experimental design with audio condition as the independent variable and recall accuracy assessed immediately and after 24 hours as the dependent variable. Each participant was exposed to only one audio condition, preventing prior condition exposure from contaminating performance.

All testing was conducted individually. On Day 1, each participant was seated in a quiet area and provided with the printed sentence list. Following a brief set of instructions, participants began listening to their assigned audio condition and were given 90 seconds to study the 10 sentences. Participants were permitted to read the sentences aloud or silently at their discretion but weren’t permitted to write during the study phase. Immediately following the encoding phase, the sentence list was removed and participants were given a blank response sheet and three minutes to write down as many sentences as they could recall in any order. Participants were instructed to reproduce the meaning of each sentence as accurately as possible, with exact recall not required. No audio was played during the recall phase. The researcher recorded the elapsed time and collected response sheets upon completion, no feedback was provided. Approximately 24 hours later, participants completed the delayed recall phase. They were provided with a new blank response sheet and again given three minutes to write down as many sentences as they could recall. No re-study opportunities were provided. The delayed recall phase was also conducted in the absence of audio stimulus. This procedure was designed to isolate the effect of the audio condition on encoding, rather than on retrieval processes. Figure 2 provides an overview of the experimental timeline.

Materials The memory task consisted of a printed list of 10 meaningful sentences of consistent length and complexity. Sentences were constructed to reflect the kind of content encountered in a classroom setting. All sentences were written at a reading level appropriate for high school students were piloted for comprehensibility prior to the study. The same list was used across all conditions. Audio stimuli for the four conditions were as follows. The lyrical music condition used “Don’t Stop Believin’” by Journey, a widely recognized song with clear lyrics and consistent delivery throughout. This song was selected for its familiarity and moderate tempo, representative of the popular music commonly chosen among adolescents. The instrumental music condition used “Spring” from Vivalid’s Four Seasons, a classical composition with no lyrical content and moderate dynamic variation. White consistent broadband static generated at a consistent density. The silence condition used no audio stimulus, though participants wore power off headphones. All audio stimuli were delivered through over-ear headphones at a standardized volume of approximately 65 dB, consistent with prior research on moderate-volume auditory stimuli (Perham & Currie, 2014). This volume level was selected to provide a realistic approximation of the volume at which students typically study with music.

Scoring & Data Analysis Recall responses were scored using a partial-credit rubric based on key ideas within each sentence. Each sentence was decomposed into its core semantic components: subject, verb, direct object, and prepositional phrase, yielding four scorable units per sentence. Each correctly recalled unit was awarded one point, regardless of the exact wording used, provided that the meaning was preserved. Across 10 sentences with four units each, the maximum possible score was 40 points. Scores were then normalized to a scale of 0-10 for ease of interpretation.

IV. Procedure

Two independent raters, both blind to condition assignment, scored all response sheets using the rubric. Inter-rater reliability was assessed by computing percentage agreement on a randomly selected 20% subsample of response sheets.

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Agreement exceeded 95% for both immediate and delayed recall datasets. Disagreements were resolved through discussion and consensus. Scored data were subjected to a one-way analysis of variance (ANOVA) with audio condition as the between-subjects factor, conducted separately for immediate recall and delayed recall. Following significant ANOVA results, post-hoc pairwise comparisons were conducted using independent-samples t-tests with Welch’s correction for unequal variances (Welch, 1947). The Welch correction was applied because the assumption of homogeneity of variance cannot be guaranteed across conditions with unequal sample sizes. Controls & Limitations Several design features were implemented to control for potential confounds. Random assignment was used to distribute individual differences evenly across conditions. The same sentence materials, delivery format, timing, and instructions were used for all participants. Audio was delivered at a standardized volume through headphones to prevent variation in perceived volume. Acknowledged limitations include the use of convenience sampling from a non-random participant pool, which limits the generalizability of findings beyond motivated high school students. Testing occurred at various times of day in varied real-world environments (home, library, classroom) introducing uncontrolled variability in circadian state and ambient noise. These conditions increase external validity but reduce experimental control relative to a lab setting. Additionally, the 24-hour retention interval, while more relevant than immediate recall alone, falls short of the weeks-long intervals most representative of typical academic retention demands. Future research should employ longer follow-up intervals.

RESULTS ---------------Descriptive Statistics Descriptive statistics for recall performance by condition and time are presented in Table 1. In the immediate recall assessment, the silence condition produced the highest mean score (M= 8.48, SD = 0.59), followed by white noise (M = 6.54, SD = 0.58), instrumental music (M =6.50, SD = 0.73), and lyrical music (M = 5.16, SD = 0.58). The rank ordering of conditions was identical in the delayed recall assessment. All conditions showed a decline in mean recall from day 1 to day 2, consistent with normal forgetting over a 24-hour interval (Wixted, 2004).

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Note: Figure 3. Mean recall scores (out of 10) by audio condition and time point. Dark bars represent immediate recall; light bars represent delayed recall. Error bars indicate ±1 SD. *** p < .001. ANOVA Results One-way ANOVA confirmed a statistically significant effect of audio on recall performance at both time points. For immediate recall, F(3, 47) = 48.41, p < .001, η2 = .76, indicating that audio condition accounted for approximately 76% of variance in immediate recall scores. For delayed recall, F(3, 47) = 43.06, p < .001, η2 = .73. These effect sizes are large by conventional standards (Cohen, 1988), suggesting that audio conditions exert a significant influence on recall performance across both time points. Post-Hoc Comparisons Results of pairwise post-hoc comparisons are presented in Table 2. At both time points, silence outperformed all other conditions with high statistical significance. In the immediate recall assessment, silence was significantly superior to white noise, t(21) = 7.55, p < .001, to instrumental music, t(20) = 6.44, p < .001, and to lyrical music, t(20) = 12.74, p < .001. In the delayed recall assessment, silence again significantly outperformed white noise, t(21) = 7.17, p < .001, instrumental music, t(20) = 5.51, p < .001, and lyrical music, t(20) = 11.22, p < .001.

Both white noise and instrumental music significantly outperformed lyrical music at both time points. However, white noise and instrumental music did not differ significantly from each other at either the immediate recall assessment, t(21) = 0.14, p = .892, or its delayed counterpart, t(20) = −0.22, p = .830. These results indicate that white noise and instrumental music are functionally equivalent in their effects on verbal memory encoding and consolidation, both producing intermediate performance.

DISCUSSION ---------------The research question of this study was whether and how audio type affects sentence recall in high school students at both immediate and 24-hour delayed intervals. The results provide a clear answer. Audio type exerts a large effect at both time points, with a rank ordering of silence > white noise ≈ instrumental > lyrical that held consistently across both assessments. This answer also speaks to the theoretical tensions identified in the literature review. The data support the phonological loop interference account over an attentional account. If interference were driven by distraction, white noise and instrumental music would differ from each other more. Silence as the Optimal Study Environment

Retention Across Time Points All four conditions showed declining mean recall from immediate to delayed assessment, consistent with normal forgetting over a 24-hour interval. The pattern of forgetting was broadly similar, with mean declines of 1.46 (silence), 1.35 (white noise), 1.25 (lyrical), and 1.25 (instrumental). The preservation of rank order across both time points indicates that the interference effects attributable to audio conditions were not improved during the 24-hour consolidation window. Instead, the conditions that produced lower-quality encoding on Day 1 continued to show impaired retention on Day 2, suggesting that audio-related encoding degradation has lasting consequences for long-term memory formation. The forgetting curves for all conditions are displayed in Figure 4.

The superiority of the silence condition provides strong support for the phonological loop interference hypothesis (Baddeley, 2003; Salamé & Baddeley, 1989). In the absence of any competing auditory input, the phonological loop can dedicate its full rehearsal capacity to the target sentences. The prefrontal attentional system can also allocate its resources with the demand of inhibiting task-irrelevant stimuli (Gathercole et al., 2004). The large effect sizes observed (particularly t = 12.74 for silence vs. lyrical music immediately) suggest that the interference produced by moderate-volume background audio is substantial and significant. When no audio is present, the language processing network is engaged solely by the encoding task, allowing for clean hippocampal memory formation (Diekelmann & Born, 2010). Lyrical Music as a Uniquely Disruptive Stimulus The lyrical music condition produced significantly lower recall than all other conditions at both time points, confirming that semantic content is the critical variable driving interference. This replicates Salamé and Baddeley (1989) and subsequent work in various settings (Perham & Currie, 2014; Anderson & Fuller, 2010), and extends those findings to a high school population. The neurological account is compelling. Lyrics activate the same left-hemisphere language regions - Broca’s area and Wernicke’s area - engaged during verbal encoding (Koelsch, 2011). Also, electrophysiological evidence shows this overlap produces measurable disruption of the N400 and P600 components associated with semantic and syntactic integration (Besson et al., 1998; Slevc et al., 2013). Equivalence of Instrumental Music and White Noise The indistinguishable performance of instrumental music and white noise is one of the most theoretically informative findings of this study. This equivalence supports the interpretation that non-semantic audio conditions impose a common level of interference that is different from the interference produced by lyrical music. Both instrumental music and white noise lack the semantic content that directly engages the phonological loop and language processing network, and both produce intermediate recall performance results. The equivalence of these two conditions is surprising from the perspective of the changing-state hypothesis (Jones et al., 2000), which would predict that instrumental music should produce greater

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interference than white noise. The failure to find a significant difference between these conditions in the present study may reflect the relatively moderate volume and stable tempo of the Vivaldi piece used. Alternatively, it may reflect insufficient statistical power given the relatively low sample sizes. Future research with larger samples and a wider range of musical stimuli would be needed to resolve this question. The equivalence of these two conditions is surprising from the perspective of the changing-state hypothesis (Jones et al., 2000), which would predict that instrumental music should produce greater interference than white noise. The failure to find a significant difference between these conditions in the present study may reflect the relatively moderate volume and stable tempo of the Vivaldi piece used. Alternatively, it may reflect insufficient statistical power given the relatively low sample sizes. Future research with larger samples and a wider range of musical stimuli would be needed to resolve this question. Adolescent-Specific Implications The large effect sizes observed in this study are consistent with the prediction that adolescents are more susceptible to auditory interference than adults. As reviewed by Gathercole et al. (2004) and Stevens et al. (2009), the prefrontal attentional control system that filters task-irrelevant stimuli is still maturing during high school, leaving adolescents with a reduced ability to suppress the interference produced by background audio. The magnitude of the difference between silence and lyrical music is substantially larger than differences typically reported in adult studies (Perham & Currie, 2014; Anderson & Fuller, 2010). Educational Implications The findings of this study have direct implications for the study habits of students. The evidence strongly suggests that silence is the optimal environment for verbal learning tasks, and students who consistently study with lyrical music may be imposing a cognitive cost on their learning. Given that study habits established during high school tend to persist into higher education (Hallam et al., 2002), the present findings argue for early guidance regarding the consequences of auditory study environments. For students who dislike silence, the present data suggests that instrumental music or white noise represents a substantially less disruptive alternative to lyrical music. The statistically equivalent performance of these two conditions shows that neither condition is preferable over the other, and the choice may be left to individual preference. However, both remain inferior to silence for verbal memorization tasks.

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CONCLUSION ---------------This study generates a specific contribution to the understanding of interference in adolescent verbal learning. The new understanding produced is as follows: background audio type is not a neutral feature of the study environment, it is a consequential variable that affects both the quality of initial encoding and the durability of memory across a 24hour consolidation interval. Silence is superior for sentence memorization, while lyrical music produces the most severe impairment. Instrumental music and white noise are functionally equivalent to each other. These findings advance on the existing literature by establishing this hierarchy in an adolescent population, a relevant learning task, and with delayed as well as immediate retention measurement - three dimensions in which prior work was insufficient. This study has several limitations. The sample consisted of 44 volunteer students from a limited number of schools,limiting how broadly these results can be generalized. Students who volunteer for research are likely already academically motivated or disciplined, meaning findings may not fully represent the broader high school population. Also, only one song was used per condition. It is difficult to say whether the interference would hold across genres, tempos, or levels of familiarity. Also, the 24-hour retention interval, while an improvement over most prior studies that only test immediately, still doesn’t capture what happens to memory over the course of a week or a month.Finally, because each participant was only assigned to one condition, within-person comparisons weren’t possible, reducing statistical power. Despite these limitations, the findings have meaningful implications for several groups. For students, if the goal is to memorize verbal content, silence is the best environment. If background audio is necessary, instrumental music or white noise appears to be a safer choice than music with lyrics. For teachers and school administrators, these results might provide a basis to try to incorporate proper study habits and methods, including background noise, into their curriculum. They might also reconsider the auditory environments of classrooms. For researchers, this study offers a starting framework, one that uses an adolescent sample, a realistic task, and delayed retention testing, that future work can build on and refine. There are several directions future research could take. The most obvious next step would be to test longer retention intervals to see whether the differences between conditions grow or shrink over time. It would also be valuable to vary the musical stimuli more. Introduce different tempos, different levels of lyrical density or familiarity to understand what it is about the lyrics that is causing the interference. Individual differences are another angle worth exploring, as students with higher working memory capacity, more musical training, or different personality traits may respond to background audio differently than others (Engle, 2002; Schellenberg, 2005; Dobbs et al., 2011). Finally, it would be worth examining whether the silence advantage holds across other academic tasks beyond verbal memorization. Math, reading comprehension, and foreign language learning 74

might be impacted differently than verbal memory. Taken together, these studies would help build a more complete and practical understanding of how auditory environments affect learning in adolescents.

REFERENCES ---------------Anderson, S. A., & Fuller, G. B. (2010). Effect of music on reading comprehension of junior high school students. School Psychology Quarterly, 25(3), 178–187. https://doi.org/10.1037/ a0021213 Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839. https://doi.org/10.1038/nrn1201 Besson, M., Faïta, F., Peretz, I., Bonnel, A.-M., & Requin, J. (1998). Singing in the brain: Independence of lyrics and tunes. Psychological Science, 9(6), 494–498. https://doi. org/10.1111/1467-9280.00091 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum. Conway, A. R. A., Jarrold, C., Kane, M. J., Miyake, A., & Towse, J. N. (Eds.). (2005). Variation in working memory. Oxford University Press. Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews Neuroscience, 11(2), 114–126. https:// doi.org/10.1038/nrn2762 Dobbs, S., Furnham, A., & McClelland, A. (2011). The effect of background music and noise on the cognitive test performance of introverts and extraverts. Applied Cognitive Psychology, 25(2), 307–313. https://doi.org/10.1002/acp.1692 Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11(1), 19–23. https://doi.org/10.1111/1467-8721.00160 Gathercole, S. E., Pickering, S. J., Ambridge, B., & Wearing, H. (2004). The structure of working memory from 4 to 15 years of age. Developmental Psychology, 40(2), 177–190. https://doi. org/10.1037/0012-1649.40.2.177 Hallam, S., Price, J., & Katsarou, G. (2002). The effects of background music on primary school pupils' task performance. Educational Studies, 28(2), 111–122. https://doi. org/10.1080/03055690220124551 Jones, D. M., Madden, C., Miles, C., & Macken, W. J. (1996). Irrelevant changing sounds disrupt order report in immediate serial recall. Memory & Cognition, 24(3), 321–328. https://doi. org/10.3758/BF03213296

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Koelsch, S. (2011). Toward a neural basis of music perception — a review and updated model. Frontiers in Psychology, 2, 110. https://doi.org/10.3389/fpsyg.2011.00110 Martel, M. M., Roberts, B., & Tarle, S. J. (2016). White noise as an attentional support for working memory: An examination with typically developing children. Frontiers in Psychology, 7, 1893. https://doi.org/10.3389/fpsyg.2016.01893 Nantais, K. M., & Schellenberg, E. G. (1999). The Mozart effect: An artifact of preference. Psychological Science, 10(4), 370–373. https://doi. org/10.1111/1467-9280.00170 North, A. C., Hargreaves, D. J., & O'Neill, S. A. (2004). The importance of music to adolescents. British Journal of Educational Psychology, 70(2), 255–272. https://doi.org/10.1348/000709900158083 Perham, N., & Currie, H. (2014). Does listening to preferred music improve reading comprehension performance? Applied Cognitive Psychology, 28(2), 279–284. https://doi.org/10.1002/acp.2994 Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27. https://doi.org/10.1016/j.tics.2010.09.003 Salamé, P., & Baddeley, A. (1989). Effects of background music on phonological short-term memory. The Quarterly Journal of Experimental Psychology Section A, 41(1), 107–122. https://doi. org/10.1080/14640748908402355 Schellenberg, E. G. (2005). Music and cognitive abilities. Current Directions in Psychological Science, 14(6), 317–320. https://doi. org/10.1111/j.0963-7214.2005.00389.x Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin. Slevc, L. R., Rosenberg, J. C., & Patel, A. D. (2013). Making psycholinguistics musical: Self-paced reading time evidence for shared processing of linguistic and musical syntax. Psychonomic Bulletin & Review, 16(2), 374–381. https://doi.org/10.3758/ PBR.16.2.374 Söderlund, G., Sikström, S., & Smart, A. (2007). Listen to the noise: Noise is beneficial for cognitive performance in ADHD. Journal of Child Psychology and Psychiatry, 48(8), 840–847. https://doi. org/10.1111/j.1469-7610.2007.01749.x Stevens, C., Lauinger, B., & Neville, H. (2009). Differences in the neural mechanisms of selective attention in children from different socioeconomic backgrounds. Developmental Science, 12(4), 634–646. https://doi.org/10.1111/j.1467-7687.2009.00807.x Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.

Thompson, W. F., Schellenberg, E. G., & Husain, G. (2001). Arousal, mood, and the Mozart effect. Psychological Science, 12(3), 248–251. https://doi.org/10.1111/1467-9280.00345 Thompson, W. F., Schellenberg, E. G., & Letnic, A. K. (2012). Fast and loud background music disrupts reading comprehension. Psychology of Music, 40(6), 700–708. https://doi.org/10.1177/0305735611400173 Tremblay, S., Nicholls, A. P., Alford, D., & Jones, D. M. (2000). The irrelevant sound effect: Does speech play a special role? Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(6), 1750–1754. https://doi.org/10.1037/02787393.26.6.1750 Welch, B. L. (1947). The generalization of Student's problem when several different population variances are involved. Biometrika, 34(1–2), 28–35. https://doi.org/10.2307/2332510 Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55, 235–269. https:// doi.org/10.1146/annurev.psych.55.090902.141555

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Consumer-Grade Cardiac Chest Wearable

JACOB SIPPLE

ABSTRACT This mixed-methods study investigated whether consumers would adopt a chest-worn device for continuous cardiovascular monitoring. Anonymous responses were collected from 161 participants through LinkedIn and a community pharmacy, with 60.25% reporting a diagnosed cardiovascular condition. Most respondents were comfortable with wearable technology (92.55%), believed existing monitoring tools need improvement (82.61%), and indicated greater willingness to wear a device requiring minimal interaction (84.47%). Comfort was a leading priority for 83.23%, with skin irritation, bulk, heat, and sweating identified as major concerns. Although the study evaluated adoption intent rather than a working prototype or clinical outcomes, the results demonstrate substantial interest in continuous cardiac monitoring and establish comfort, simplicity, and low maintenance as essential design requirements. RESEARCH QUESTION:

Would health-conscious individuals adopt a continuous cardiac chest wearable for cardiovascular monitoring?

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INTRODUCTION ---------------Cardiovascular disease (CVD) is the leading cause of death worldwide.29,39 The CDC reported that heart disease cost the U.S. roughly $168 billion from 2020 to 2021, counting health care services, medications, and lost productivity from death.4 The American Heart Association estimates that over 612 million people live with some form of deficient heart health.2 That is the equivalent of 1 in every 13 people in the world.6 Researchers project a 90% rise in cardiovascular prevalence between 2025 and 2050, with annual deaths expected to climb from 20.5 million to 35.6 million.6 CVD is a broad category covering conditions that attack the heart and vascular system in very different ways. Coronary artery disease narrows the arteries that supply the heart with blood. Heart failure reduces the heart's ability to pump effectively, which often leads to dangerous fluid buildup. Atrial fibrillation causes erratic rhythms that raise stroke risk significantly. Hypertension works quietly for years, stiffening and damaging vessel walls before symptoms show up. And JACOB SIPPLE

pericardial effusion causes fluid to accumulate around the heart in ways that can escalate to tamponade, a life-threatening condition, if not caught in time.4,17 Each of these conditions has its own physiological implications, and every group of people dealing with them has something to gain from continuous, real-time monitoring. An athlete tracking cardiac stress during training, a patient who has had heart failure watching for abnormalities between clinic visits, a 40-year-old with a family history who wants to stay ahead of heart health management. The device explored in this study explores the effects on each of these demographics. Pericardial disease is used as the main clinical reference point because of how clearly it exposes what current monitoring tools are missing. The prevention side of cardiovascular disease is extremely flawed. A cardiologist in the Cincinnati area described it plainly: patients with known cardiovascular disease are screened periodically using chest x-rays and echocardiograms, and the data from a handful of scans is treated as enough to draw 78

conclusions about a condition that can change day to day. That gap on the clinical side is compounded by what is missing on the consumer side. People with money can access exclusive wellness clinics, personal trainers who build individualized physiological baselines, and premium nutrition programs. These help, but they are not monitoring the heart in real time. Everyone else is largely on their own. Until people have continuous access to their own cardiovascular data, early detection will stay fragmented. Consumer wearable companies including Apple, WHOOP, Oura Ring, Fitbit, and Garmin have made real progress in health tracking,23,18 and some of their advanced models now include ECG features and cardiovascular trend data.34,37 But there is a hard ceiling on what any of these devices can actually measure, and it comes down to where they sit on the body. Wrist-based devices lose accuracy capabilities from electrode configuration, signal through tissue, and sit far from the thoracic cavity. That combination creates data noise that makes it impossible to measure disease-specific thresholds accurately. A wrist sensor cannot measure Thoracic Fluid Content (TFC), for example. TFC reflects fluid dynamics around the heart and is one of the biometrics for conditions like pericardial disease. A device worn directly over the thorax can apply bioimpedance to measure TFC continuously and with real clinical accuracy. That reading is simply not available from the wrist. For pericardial effusion specifically, where the rate of fluid buildup determines how quickly a patient needs intervention, that gap in precision matters. The U.S. wearable market is on track to grow at 12.1% annually through 2030,13 and there is still more growth opportunity to improve cardiac health outcomes. In the meantime, a mortality analysis covering 1999 to 2019 found 87,285 pericardial-related deaths in the U.S., averaging around 4,150 per year in adults 25 and older.19 The actions needed for an individual to prevent and treat CVD are straightforward: eat a healthy diet, remain physically active throughout life, don’t use tobacco, and seek health care regularly. The reality is much more complex. Behavior change is difficult, individual choices are influenced by broader social and environmental factors, and many people do not have the resources or access to seek appropriate health care.11 Cardiovascular disease is multifaceted. Pericardial disease is just one layer of cardiovascular issues. In 2023 alone, 919,032 people in the U.S. died from cardiovascular disease, equal to 1 in every 3 deaths that year. 4,28 Left unmanaged, CVD works its way through the body systematically. A continuous monitoring device would not eliminate that risk, but catching changes early could change a lot of outcomes. This study looks at whether there is real consumer demand for a chest wearable designed for continuous cardiovascular monitoring. The device is meant for anyone, though the primary target is health-conscious people who already take their wellness seriously and want actual cardiac data as part of that. Pericardial disease is the main clinical example throughout because of how big the gap is between how serious the condition can get and how little monitoring is available to patients outside of hospital visits. The study answers the question, “Would health-conscious individuals wear a cardiac chest wearable designed for continuous monitoring of cardiovascular health?” JACOB SIPPLE

Pericardial disease is the primary clinical lens for this research, but the device concept is not limited to pericardial patients. Heart failure patients managing fluid retention, people monitoring for arrhythmia, and health-conscious individuals who have never had a cardiac event but want to track one, all stand to benefit from continuous chest-level cardiac data. Pericardial disease is the focus here because it has one of the clearest monitoring gaps in cardiovascular disease prevention. Pericardial effusion is when excess fluid builds up in the pericardium, the membrane sac that surrounds the heart.7,38 Pericarditis is inflammation of that same sac. The most frequent symptom is sharp chest pain that eases when leaning forward.8,30 This occurrence can be very scary for those who experience it. In the U.S., around 6% of adults have some degree of pericardial effusion, putting them at risk for an adverse cardiovascular event.19 That translates to roughly 16 million of the 270 million adults in the United States. Less than 5% of small effusions progress to heart failure, but 20 to 30% of large, untreated effusions do.19,41 The American Heart Association has been direct: “Clinical and public health interventions are needed to effectively manage, stem, and even reverse these adverse trends.”17 Research justifies this claim. Wearable devices work best when they make healthy behavior easier, not when they demand attention from the user.25 Dr. Bob Rako, MD, famously said “The heart attack at 50 began at 20. The aging you want tomorrow begins with the choices you make today.” It is important to take this notion into account and recognize that people would benefit from wearing a continuous monitoring device for cardiovascular disease prevention and accountability if users are at risk of cardiovascular disease or have it currently. One wearing a wearable device for this use case cannot change the damage that has been done to their own body. However, they can work to reverse it by the choices they make moving forward. This conclusion about cardiovascular disease prevention is justified by the Cleveland Clinic, “Because either diseases or injuries can cause it, cardiac tamponade is extremely difficult to predict and prevent.9 Tamponade happens when fluid compresses the heart so rapidly that it cannot pump enough blood to the rest of the body. It is largely preventable. The problem is that most people never know their effusion is progressing until it has already.

LITERATURE REVIEW ---------------The scale of cardiovascular disease globally makes the case for better monitoring tools without much debate. Research projects a 90% increase in CVD prevalence from 2025 to 2050, with annual deaths from ischemic heart disease and hypertensionrelated causes pushing toward 35.6 million by mid-century.6 A comprehensive analysis of CVD mortality from 1990 to 2019 found that ischemic heart disease alone kills over 9 million people per year, and while death rates have improved somewhat in high-income countries, the overall burden keeps growing.29,28 In the U.S., projections show coronary disease, heart failure, stroke, and atrial fibrillation collectively affecting 45 million adults by 2050, driven by rising rates of hypertension, diabetes, and obesity.17,3 These numbers make clear that periodic clinic

visits are not enough. People need a solution to improve their daily lives and stay out of the hospital.1 Current clinical protocols address the acute phase well but leave a lot of space unmonitored. Echocardiography is nearly 100% accurate for sizing effusions and identifying tamponade signs like chamber collapse or abnormal respiratory flow, and it guides pericardiocentesis when things get urgent.38,30 But for stable large effusions, the protocol shifts to watchful waiting, with drainage held back unless infection or malignancy is confirmed. That is a long time to rely on physiological data from a single appointment, especially when fluid dynamics can shift significantly between visits. Some wearable devices are already trying to close part of this gap. The Zio patch, for example, handles ECG monitoring for up to 14 days without leads and lets patients apply it themselves, with data going to a central analysis platform. Still, skin irritation and waterproofing issues make it a poor candidate for permanent wear.1,37 Broader reviews of wearable CVD monitoring confirm that real-time tracking does support risk reduction when devices are built well and validated clinically,21,23 but comfort and usability still keep most people from wearing them consistently. Adhesive-related skin injury is one of the most frequently mentioned reasons people stop using extended wearables, and getting the adhesive formulation right is a design challenge that has not been fully solved.22 On the sensor side, bioimpedance-based technology has shown it can detect fluid volume changes in the body noninvasively and in real time, with results that correlate well with actual fluid loss in healthy adults. That is a meaningful step toward continuous pericardial fluid monitoring.24 These are useful advances. There are not yet enough. The materials side of this problem has seen real progress. Building a wearable that stays on the body comfortably for days or weeks requires technology that is biocompatible, flexible enough to move with the skin, and durable enough to handle thousands of movement cycles without degrading.20,27 Bioinspired adhesives that mimic how octopus suckers or gecko pads grip surfaces have shown promise here, maintaining contact without the trauma that peels skin on removal.33,12 Technical textiles using polyester, silk, and similar materials bring breathability and structural durability to the mix, and prototype wearable ultrasound devices have already demonstrated skin-mounted imaging with cloud data streaming.26 Conformal sensor platforms have proven they can reliably capture both cardiac and respiratory signals during extended wear.10 Miniaturization in nano and microfabrication, along with wireless power transmission, has made it practical to fit multiple sensing modalities into a single chest-mounted device.42 When these sensors feed into connected health platforms, their clinical reach goes well beyond what a standalone device can offer.14 Taken together, the materials and hardware exist to build something small, wearable, and effective. The analytics layer is catching up. Electrochemical and optical biosensor platforms can now detect cardiac markers directly on the body, moving away from the bulky equipment that used to be required.35,15 Adaptive machine learning JACOB SIPPLE

models are learning to read ECG data in the context of individual baselines, which meaningfully improves accuracy over population-average interpretation on mobile devices.16,36 Wearable sensors that combine chemical and electrophysiological readings are enabling simultaneous monitoring of sweat biomarkers and cardiac signals in real time.31 That kind of integrated, multi-signal data stream is what makes predictive alerts for conditions like effusion actually viable.5 The direction of this research was also shaped by direct conversations with clinicians and engineers working in this space. Cardiologists at St. Elizabeth Hospital and Cincinnati Children's Hospital both described the same problem independently: stable patients with cardiovascular disease fall into a monitoring gap between appointments, and by the time imaging confirms progression, the clinical situation has already changed. Engineers at UC Health and health technology founders in the Cincinnati area confirmed that the technical architecture for continuous cardiac sensing already exists and that adapting it for people seeking continuous monitoring is a design problem, not a fundamental technology problem. Theta Neurotech and Genetesis, two companies working in health monitoring and non-invasive physiological analysis, offered additional perspective on where the field is heading and what product-market challenges have slowed adoption. These conversations shaped the research question and pointed toward the design and validation priorities that matter most. Across all of this, one gap keeps coming up.32 The technology is advancing. The clinical understanding is there. What has not been studied is whether patients and health-conscious individuals would actually wear a comfort-optimized chest device every day for ongoing cardiovascular monitoring. That is what this research set out to find out.

METHODOLOGY ---------------Research Design This study used a mixed-methods design, combining quantitative survey data with qualitative open-ended responses to evaluate consumer interest in a chest wearable built for continuous cardiovascular monitoring. The quantitative side captured adoption intent, comfort preferences, and perceived value. Qualitative responses filled in the reasoning behind those numbers. Data Collection Procedures Data were collected through an online survey built in Microsoft Forms. Questions were designed to assess participant interest in a cardiac chest wearable, current attitudes toward cardiovascular monitoring, and willingness to adopt a continuous monitoring device. The survey was shared publicly on LinkedIn and distributed through a printed QR code poster placed in the pharmacy waiting area inside Kroger Pharmacy in Fort Mitchell, Kentucky. That second location was specifically chosen to reach people who were already engaging with health services, 80

not just those who follow health content online. The survey stayed open until 161 responses were collected. No identifying information was collected. No names, emails, or IP addresses. Responses were fully anonymous throughout. Participants 161 people completed the survey, reaching it either through LinkedIn or the Kroger Pharmacy poster. The sample came from two different settings: a professional online network and a community health environment. Age, background, and health status varied widely across participants. Data Processing and Analysis Responses were recorded automatically in Microsoft Forms and then exported into Microsoft Excel for analysis. Quantitative data was summarized using descriptive statistics, including response counts and percentage distributions for each question. Qualitative responses were reviewed for recurring patterns in concerns, motivations, and perceptions about cardiac monitoring technology. Ethical Considerations Participation was voluntary. No personal data was collected. The study did not involve medical interventions, clinical data, or anything that would put participants at risk. All data was used solely for this research. Reliability and Credibility Every participant received the same questions in the same format, which kept the data consistent. Collecting 161 responses anonymously across two channels reduced the likelihood of socially desirable answers and allowed for clear comparison across the sample. Rationale for Research Method A survey was the right method here because the core question is behavioral: would people actually wear this device? Surveys let you collect that kind of perception and intent data from a large group efficiently. The combination of structured questions and open-ended responses gave both statistical patterns and the explanations behind them.

RESULTS ---------------Interest in Continuous Cardiovascular Monitoring The data came back clearly in favor of the concept. Among the 161 respondents, 92.55% reported being comfortable using wearable technology, which means the population this device would target is already familiar with wearing health tech. More telling, 84.47% said they would be more likely to wear a cardiac chest wearable if it required minimal user interaction. That is not a marginal finding. It is a strong signal about what will make or break adoption. Right now, most consumer wearables track heart rate, sleep, and general activity.18 The responses here pointed toward a more specific need. People desire a deeper insight into their actual cardiac health to understand their cardiac physiology and catch early warning signs of heart-related issues before they occur..23 JACOB SIPPLE

Participant Demographics The age range in this sample was 16 to 87 years old. This disparity captures both the younger population that is already comfortable with wearables and the older population that carries more cardiovascular risk. Activity levels split roughly as follows: 28.57% sedentary, 36.65% light activity, 25.47% moderate, and 9.31% high activity. Two-thirds of respondents fell into sedentary or light activity categories, which is consistent with the general U.S. population and with the populations most at risk for cardiovascular events. Relevance for Pericardial Disease Monitoring One of the more striking data points: 60.25% of respondents reported having a diagnosed cardiovascular condition. Only 39.75% reported no known diagnosis. That proportion reflects a sample that is personally invested in this problem, not just theoretically curious about it. These are people who already have a reason to want better monitoring. Pericardial conditions specifically get very little between-visit surveillance. Echocardiograms and imaging scans catch what is happening at a single point in time. For a condition that can progress gradually and silently, that is a real problem. Continuous monitoring on the chest would change what clinicians and patients can see and respond to. Importance of Comfort and Design Comfort ranked as the single most important design factor. 44.72% answered extremely important and 38.51% answered very important, putting 83.23% of respondents in the top two tiers for comfort priority. That is not a preference. That is a baseline requirement. The top design concerns were skin irritation (61.49%), device size or bulkiness (56.52%), and heat or sweating (47.20%). These align with what the clinical literature already shows: adhesiverelated skin irritation is among the most common reasons people stop using extended wearables.22 These are solvable engineering problems, but they have to be treated as primary design constraints, not afterthoughts. Each of these concerns needs to be resolved before a cardiac chest wearable reaches the people who need it most. Ease of Use and Maintenance 42.86% said the device would need to be very easy to use, and 38.51% said somewhat easy, putting 81.37% expecting minimal effort to operate it. On the maintenance side, 24.84% were extremely willing and 41.61% very willing to charge or maintain the device, meaning 66.45% showed strong maintenance willingness. Devices that are complicated to operate are not intriguing to most people. People desire a wearable device that can be seamlessly integrated into daily life.25 Implications for Cardiac Wearable Development The demand for improvement is not subtle. 82.61% of respondents said current cardiovascular monitoring technologies need improvement, while only 1.86% said they do not. There is 81

a clear market need, and the existing tools are not meeting it.21,35 A device that gives real cardiac insight without getting in the way of everyday life would address something that is not currently available.39,40 The demand is there. The clinical rationale is there. The technology is getting there. What has been missing is the evidence that people will actually wear it, which is what this study was designed to provide.34,12

for a medical-grade cardiac device is a capital and timeintensive process that requires clinical trial data, engineering documentation, and sustained regulatory engagement. The 510(k) premarket notification pathway alone typically takes many months to over a year. These constraints made it impossible to conduct a device-based clinical study within the timeline of this project, and the research was therefore limited to a behavioral survey examining consumer perceptions and adoption intent. HIPAA added a second layer of constraint. To avoid collecting Protected Health Information (PHI), the survey was designed to capture only de-identified, self-reported responses. No clinical records, no physiological device data, nothing personally identifiable. That kept the study compliant, but it also meant the findings reflect perceptions rather than clinical measurements. Survey distribution included a QR code poster placed in the pharmacy waiting area at Kroger Pharmacy in Fort Mitchell, Kentucky, specifically to reach people who were actively managing health needs. The result was a sample where 60.25% of respondents reported a diagnosed cardiovascular condition, which is notably high and likely reflects both the LinkedIn audience and the health-focused context of a pharmacy setting. The sample is probably more representative of people with cardiovascular awareness than of the general population.

CONCLUSION ----------------

Limitations The biggest constraints on this study were regulatory. The cardiac chest wearable described in this research has not yet received FDA clearance, which means no working prototype could be tested or deployed. Getting FDA clearance JACOB SIPPLE

The findings of this study point to a clear inflection point in cardiovascular health management, where clinical need, consumer demand, and advancing technology are beginning to align. The findings of this study demonstrate that consumer demand, materials science innovation, and clinical necessity have aligned to make a dedicated cardiac chest wearable not only feasible but imperative. With more than half a billion people living with deficient heart health and global CVD deaths projected to increase drastically by 2050, the time for building something that actually changes cardiac health outcomes is now.2,6 The 161 participants in this study showed that 92.55% are already comfortable with wearable technology, 83.23% put comfort at the top of their requirements, and 82.61% think current monitoring tools need improvement. Those are not soft preferences. They are design requirements backed by a population that has needed this intervention. The research literature confirms that cardiac monitoring can be improved.38,30 The tools to build this device already exist in various forms. Flexible biocompatible substrates and bioinspired adhesives have already been used in research settings.20,33 Multiplexed biosensor platforms and wearable electrophysiological systems can track cardiac biomarkers in real time, continuously.35,15 Adaptive machine learning and mobile ECG analysis have established the computational side of on-body cardiac monitoring.16,27 What this study adds is the consumer evidence layer: the concerns that need to be solved (skin irritation, bulk, heat), the design baselines that matter (comfort-first, minimal interaction), and the adhesive 82

and substrate literature that shows those problems are addressable.22,20 When wearable devices are built to be discrete and low-maintenance, people wear them over time.14,32 The next step is iteration with the populations that carry the most cardiovascular risk, paired with validation against traditional healthcare protocols.34,37 The behavioral findings from this survey will directly guide product market fit efforts and FDA clinical trials for a 510(k) consumer-grade cardiac chest wearable. If the design and validation of a cardiac chest wearable intersect, an innovative solution to cardiac monitoring could help millions of people improve cardiovascular health outcomes.36 These findings also show that people are intrigued about the concept of a futuristic wearable that could transform cardiovascular monitoring, unlocking a way to track heart health without making daily life more complicated. A consumer-grade cardiac chest wearable could give users a more direct physiological scan of the heart, helping detect changes earlier and making prevention more active instead of reactive. That matters because many cardiovascular problems develop slowly and are only caught after symptoms become obvious. A chest-worn device could help close that gap by giving people earlier awareness and a better sense of when something is changing. This is especially important in the context of consumer-driven prevention. People are already using wearables to monitor sleep, movement, and heart rate, but the results of this study suggest there is still room for a more meaningful device that focuses on cardiovascular status itself. Participants made it clear that comfort, simplicity, and low effort are essential. That means the product has to fit into daily routines without feeling intrusive or burdensome. If it does, it could support both patient accountability and earlier action. Taken together, the data support the need for a consumergrade cardiac chest wearable as a real step forward in cardiovascular prevention. The technology is moving in the right direction, the clinical gap is obvious, and the survey responses show that people are willing to wear this kind of device if it is designed well. That combination of factors makes this an important opportunity for better heart health monitoring and more proactive prevention.

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Justin Kaiser, Superintendent Sarah Schobel, Assistant Superintendent Alan Yanke, High School Principal Jana Bromley, High School Assistant Principal Codi Malott, AP Research Teacher Kim Demko, Long-Term Support Teacher for AP Research