Performing Under Pressure

Performing Under Pressure: A Biofeedback System for Stress-is-Enhancing Mindset Dovid Kahn dkahn293@stanford.edu

Trijeet Mukhopadhyay trijeetm@stanford.edu

ABSTRACT

Ludwig Schubert schuberl@stanford.edu

BACKGROUND

Psychology research has found that the effect of stress on a subject is determined by the subject’s attitude, or mindset, about stress. Most prior research in the intersection of HCI and stress focuses primarily on mitigation, i.e. calming users down when they are experiencing stress, and the consideration of mindset is absent in these systems. In contrast, we present a biofeedback system that frames a user’s stress levels as enhancing. Key features include a real-time stress monitoring system using a heart rate sensor, and a glanceable display which reframes stress as enhancing. We conducted a preliminary evaluation by measuring user performance in timed mental arithmetic problems, but did not find a statistically significant difference in performance between users who received the biofeedback and those who did not (n = 10, p = .12).

Even so, we observe a promising trend, and a qualitative analysis suggests that users found the system non-distracting and empowering.

Stress Mindset

While many health psychologists, ubicomp health researchers, and the general public consider stress to be bad for health and performance, some evidence suggests a more complicated reality: Psychology research [4] has identified the challenge response, which is activated when a person faces a hurdle they believe they have the capacity to overcome. In this scenario, the body primes itself for action, resulting in immediate enhanced cognitive and physical abilities, as well as long term health benefits. The threat response, on the other hand, is evoked when a person feels they cannot reasonably overcome an obstacle, resulting in cognitive strain and long term health hazards.

Significantly, Crum et al. found that one’s mindset about the nature of stress itself is a significant factor in determining whether one experiences the beneficial challenge response or the deleterious threat response[2]. A stress-is-enhancing mindset implies you believe stress generally enhances your performance, while a stress-is-debilitating mindset implies you believe the opposite. These mindsets are subject to change via intervention and have been shown to influence performance and well-being over time.

ACM Classification Keywords

H.5.m. Information Interfaces and Presentation: Misc.

Author Keywords

Stress; Stress Mindset; Emotion Regulation; Heart Rate Variability; Feedback; Biofeedback

Related Work

INTRODUCTION

In current research of stress biofeedback systems, the stress-isdebilitating mindset is prevailing. All but a few works in the literature [5] assume both that stress is detrimental to health and should generally be avoided, as well as that stress and performance are in opposition. For example, in MoodWings, MacLean et al. considered how a wearable butterfly that communicates stress levels via flapping intensity influences subject performance and stress levels during a demanding driving task [7]. Their intervention actually increased subject stress, and resulted in questionable performance gains. Costa, J. et al. developed EmotionCheck as a critique of the excessively distracting nature of Moodwings and similar work [1]. They design a wearable system that uses vibrations to provide false feedback about the user’s heart rate to induce subjective calm during a social stress test. While effective in calming their subjects, we see the limitations of this study as two-fold: the system relies on deception to be effective and the study does not assess whether a subject’s sense of calm actually improved their performance.

Mindsets are mental frames that define our interpretation of phenomena around us and shape how we interact with the world. With this work, we aim to demonstrate the value of stress mindset research in Ubiquitous Computing. Specifically, our goal with this work is to 1. demonstrate the capacity of this perspective when assessing open questions in ubicomp health research, such as how to create effective continual stress monitoring systems, 2. propose a solution to this problem in the form of a biofeedback system designed to encourage users to adopt a stressis-enhancing mindset, and 3. evaluate that system both quantitatively and qualitatively in a preliminary user study.

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For all other uses, contact the owner/author(s).

Given the partial treatment stress has received in research literature, we seek to lay out a direction for the design of biofeedback systems that leverages the insights of stress mindset research.

© 2016 Copyright held by the owner/author(s).

ACM ISBN 978-1-4503-2138-9.

DOI: 10.1145/1235

1

the H7 also provides the RR-intervals (lengths of intervals between heartbeats), a parameter used to calculate HRV [9].

SYSTEM DESIGN

We have designed a stress-is-enhancing biofeedback system consisting of two components: (1) a real-time stress monitoring system, and (2) a glanceable display which reframes stress positively as alertness.

To analyze heart rate data, we built a Swift application for macOS which subscribes to the Bluetooth heart rate data using the SIG Health Device Profile (HDP) Data Exchange Specifications [10] from the H7 sensor, and captures the RR-interval stream. One technique to create a PSD from the RR-intervals is by processing a window of RR-intervals through a discrete Fourier transform. Being a frequency-domain analysis method, it requires a window of RR-interval samples. Window lengths are commonly in the ranges of 5 minutes to 24 hours, thus the method is primarily used for post-hoc analysis [12].

So far, the shortest window that has been shown to get a usable estimate of stress has been a 100 second window.[3] To use this measure for real-time stress feedback, we implemented a sliding window version of this transformation, with a window size of 150 RR-interval samples. This approximately equates to the samples captured in a 100 second window. Once every five new RR-intervals we shift the window and re-sample to create a near real-time PSD. The primary caveat of this approach is that the system requires a two to three minute calibration period in which the initial 150 samples required to start measuring are captured. We sum the powers of the LF and HF bands to get the LF/HF ratio. The LF/HF ratio from this period is used as a baseline measure (LFHFbaseline ) for the user’s relaxed state against which we compare the current ratio (LFHFrealtime ), giving us a near real-time measure for stress levels.

Figure 1. The system design with heart rate sensor, glanceable display and mental arithmetic problem.

Real-time stress monitoring system HRV as a measure of stress

Common techniques for detecting stress from physiological signals include electroencephalography, blood volume pulse, heart rate variability (HRV), galvanic skin response (GSR), and electromyography [9]. GSR is generally considered one of the most accurate physiological signals for stress. [3] showed that it is possible to monitor stress through HRV such that the loss in correlation to actual stress compared to GSR is minimal (0.41, as opposed to 0.47 for GSR)1 .

Polar H7 Alertness

HRV is dependent on cumulative (long term, e.g. aging process) and dynamic (activity-induced) load, making it a good measure for stress [3]. Generally, low frequency (LF) and high frequency (HF) bands of HRV are used to analyze stress, as they are believed to reflect the Sympathetic Nervous System (SNS) and Parasympathetic Nervous System (PNS) activity respectively. The LF band has been categorised as the 0.05–0.15 Hz range and the HF band as the 0.15–0.5 Hz range. The very low frequency band (0–0.05 Hz) is ignored as it been found to distort stress detection on short term measurements. The ratio of the total energy in the LF and HF bands (LF/HF) has been shown to correlate to stress levels. A common method of computing the LF/HF ratio is by constructing a Power Spectral Display (PSD) of the HRV [9, 12] then summing the power of the LF and HF frequency bands individually and computing their ratio.

Bluetooth reframe

RR Intervals Stress Measure

FFT calibrate

PSD LF/HF

Glanceable Display

The glanceable display was designed to create a nondistracting way for users to assess their biofeedback with a stress-is-enhancing interpretation. The motivation to create a subtle display is taken from the critique which Moodwings [7] received for being too distracting, and thus increasing stress and decreasing performance[1]. The intention of our system is to allow users to reframe their existing stress in a beneficial manner; we want to avoid adding extra stressors.

For our final design, we decided on displaying the biofeedback using the Touch Bar, a small strip OLED display right beneath the monitor introduced on late 2016 Apple MacBook Pros. Previous designs we piloted included (1) a companion desktop app, which was discarded for being too obtrusive and distracting, and (2) a widget on the system toolbar, which was dismissed for not being salient enough.

Sensor and Signal Processing

The biofeedback display within the Touch Bar itself consists of a discrete five segment indicator whose level is determined by quantizing LFHFrealtime into five buckets with respect to the LFHFbaseline . The ratios (Table 1) were calibrated by running the evaluation on one of the authors. The level of the indicator is displayed by green segments in order associate a positive feeling with rising stress. The indicator was also accompanied by a text label which reinforces the current biofeedback by

We used a Polar H7 Bluetooth Heart Rate Sensor to capture heart rate data in our system [11]. The device is worn using an adjustable, stretchable strap around the chest, and heart rate data is measured with an ECG sensor. In addition to heart rate, 1 We use HRV for our measure because heart rate sensors are available as cheap and reliable off-the-shelf consumer devices and are significantly more portable than GSR measurement systems.

2

Figure 2. A selection of considered mockups of the glanceable display. The bottom one was chosen for evaluation. Note that it is centered, as the left aligned versions are obstructed when users type on the main keyboard. The row of buttons on the right side, together with the escape button on the left, are part of macOS system controls and while visible to the user are not part of the design.

rephrasing it in terms of alertness. We chose alertness as it, according to Russell’s circumplex model of emotion [8], ranks the highest on the arousal scale while being neutral on the valence scale, which ranges from sad to happy.

Level 1 2 3 4 5

LFHFrealtime LFHFbaseline

Display Label

2.0 – ∞ 1.5 – 2.0 0.7 – 1.5 0.5 – 0.7 0.0 – 0.5

Very Low Alertness Low Alertness Normal Alertness High Alertness Very High Alertness

Experimental Conditions

In the control group condition, participants were outfitted with the heart rate sensor but did not view the biofeedback system.

In the treatment group condition, participants were outfitted with the heart rate sensor and viewed the biofeedback system.

Participants

11 subjects participated in the study. All participants were students at a large US university. All subjects were colleagues of at least one of the researchers. When possible, the task was administered by a researcher with no prior personal history with the participant. In general, participants were not compensated for their time, however, the top three highest scoring participants were awarded with a $5 gift card to a local cafe.

Table 1. Relationship between stress measurement & display parameters

Additional reasons for choosing this design include: (1) a discrete indicator, as opposed to a continuous scale, is easier for users to internalize, (2) its large disjoint colored segments are easy to see peripherally, and (3) the use of green, which is associated with positivity, instead of a spectrum from red to green, helps to avoid associating the low end of the indicator as negative. Finally, a significant change informed by our pilot was the shift of the biofeedback display to the center of the Touch Bar. We found that the natural position of the hands on the keyboard occluded the users view of the display.

Procedure

Participants were instructed to put on the heart rate sensor strap which has to be tightly worn around their torso with the sensing unit resting at the base of their sternum. Once the strap was adjusted, we began the two minute calibration phase. During this period, participants filled out our pre-task survey and consented to being in the study. The task was then thoroughly explained to each participant. Additionally, they were told that the three participants with the highest scores would be rewarded with a gift card (amount was left ambiguous) and were encouraged to give the task their earnest attention and effort.

METHOD

To test the effectiveness of the biofeedback system, we conducted a preliminary study with a between-groups design.

Participants in the treatment group were introduced to the biofeedback system. The study administrator explained that data collected from the heart rate sensor was being processed by algorithms that determined the participant’s alertness level on a five point scale. The grey segments of the alertness indicator were visible on the MacBook’s Touch Bar during the calibration stage, and it was explained that their alertness level would be indicated by the appropriate number of grey segments turning green from left to right. Participants in the control group received no instruction about the biofeedback system.

Task Design

We evaluated participant performance under pressure by administering a timed mental arithmetic challenge. The challenge consisted of four consecutive two minute rounds of questions with enforced 20 second rest breaks in between each round. Questions ranged from intermediate (e.g. 4 × 68) to difficult (e.g. 516 ÷ 6). Each participant received the exact same test. Participants were presented with a question and were prompted to enter their response into a text field. If their answer was correct, the screen would flash pale green and progress to the next question. If their answer was incorrect, the screen would flash pale red and they would be given another attempt to solve the problem; this process would repeat until they entered the correct answer or until the round ended.

The task was built as a module of the biofeedback system’s macOS application. It led participants through the study using textual prompts without intervention by the authors.

Once participants confirmed that they had no further questions they were left alone in the room to complete the task. After the nine minutes of the task were up, the study administrator re-entered the room and instructed the participant to complete a post-task survey. As participants removed the heart rate indicator, they were asked to share any remaining general 3

80 70 60 50 40 30

Number of correct task completions

Confidence in ability to solve mental arithmetic problems

We verified that there was no significant difference in participants self-reported confidence in their ability to solve mental arithmetic problems by evaluating their responses to the question “How confident are you in your ability to solve mental math problems under time pressure?”. While both groups scores decreased slightly by 0.6 points, there where no significant differences between groups either before (Control: µ = 5.3, σ = 1.3, Treatment: µ = 5.2, σ = 1.3) or after the evaluation. (Control: µ = 4.6, σ = 1.9, Treatment: µ = 4.6, σ = 1.75) Control

Treatment

System Distraction

Figure 3. Boxplot showing the difference in number of completed mental arithmetic problems per session between treatment and control group.

Because biofeedback systems are often too distracting to be useful, we evaluated the treatment group’s responses to the question “Did you feel that the heart rate indicator distracted you?” after the task. Responses were collected on a scale ranging from 1 (“Not at all”) through 7 (“Very much”). Treatment group participants on average reported that the system did not distract them from focusing on their task (µ = 2.6, σ = 1.5).

The outlier participant’s data point was removed from this analysis because a bug in our system altered his indicator design.

thoughts or impressions they had about the system which was recorded and later transcribed. Participants were then thanked for their time and dismissed.

Hypothesis

We predict that participants in the treatment group would on average complete more math problems than participants in the control group. Given the preliminary nature of the evaluation, however, we did not expect to disprove the null hypothesis.

We also run the evaluation to gain qualitative insights from participant commentary and questionnaire responses.

Qualitative Feedback Perception of System

Responses ranged regarding the amount of attention participants paid to the biofeedback system during the task. Some participants paid only peripheral attention (P6), others kept track of the indicator status throughout the task (P1, P2, P3, P5), and others even actively attempted to increase the indicator reading (P4). Notably, P3 felt empowered by our system.

The participant claimed that “when it was 5/5 I felt in the zone.

Empowered.”

RESULTS Effect of the System on Task Performance

A two-sample t-test was performed to compare the number of completed mental arithmetic problems between the two groups (treatment and control). Figure 3 shows a box plot that compares both groups. One participant of the treatment group was an outlier (Σ = 117) and was removed from the analysis.

The two-sample t-test did not reveal a statistically significant difference (t = 1.8141, p = .1151). At ≈ 1σ effect size was much larger than expected.

Worry About Low Alertness

Some users’ alertness level was lower than they expected or desired. This either slightly confused them or motivated them to try to increase the meter’s reading. For example, P4 noted that they felt pressure because they were “trying to do well on the test and also maintain a high level of alertness according to the indicator.” P5 on the other hand noted: “I was actually a little confused because I thought I was reasonably stressed or excited... so I thought “Oh I am calmer than I actually am”.

That was kind of nice.”

Effect on Self Reported Measurements Stress Mindset

In order to evaluate if the design of the system affected participants perception of their own stress, we evaluated their responses to the question “How would you characterize the effects of stress on your performance under pressure?” both before and after the task. Responses were collected on a scale ranging from 1 (“Stress significantly hinders my performance”) through 7 (“Stress significantly boosts my performance”). In the control condition, participants self-reported stress mindset score slightly decreased by 0.7 points on the above scale. (µpre = 4.5, σpre = 1.8; µpost = 3.8, σpost = 1.9) In the treatment condition on the other hand, the score did slightly increase by 0.9 points. (µpre = 4.3, σpre = 2.0; µpost = 5.2, σpost = 1.3)

Ecological Validity Concerns

Users were split about whether they would find the system useful in real-life scenarios. Half P2, P3, P4 where highly enthusiastic, while half P1, P5, P6 were indifferent.

DISCUSSION Evaluation Results

Our main hypothesis was that participants in the treatment group would, on average, complete more mental arithmetic problems than participants in the control group. While not statistically significant, the differences in task performance between the control and the treatment group are suggestive. At the measured effect size a power analysis suggest that a study with 30 participants could prove this effect with statistical

We asked participants to answer the question “How stressed did you feel during the math test?”. The treatment group showed a 1.5 point higher score than the control group. (Control: µ = 3.3, σ = 1.4, Treatment: µ = 4.7, σ = 1.7) 4

significance. Since we do not expect to see such a large effect size, we would suggest an even larger n, though.

Erik Santoro for their encouraging comments on the system and study design.

The widened gap between the self-reported stress mindset scores in the two groups is promising as it suggests that our system design may have succeeded at subconsciously encouraging a stress-is-enhancing mindset.

REFERENCES

1. Jean Costa, Alexander T Adams, Malte F Jung, François Guimbetiere, and Tanzeem Choudhury. 2016.

EmotionCheck: leveraging bodily signals and false feedback to regulate our emotions. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 758–769.

Our findings also suggest that the system achieved its goal of providing biofeedback in a non distracting manner. Furthermore, the system was perceived as empowering and useful for a number of users. Our findings also highlighted the need to redesign our text prompt to avoid misinterpreting the low end of the alertness indicator as negative. For example, we could redesign the indicator such that the leftmost position will indicate neutral alertness, thus not causing users to worry.

2. Alia J Crum, Peter Salovey, and Shawn Achor. 2013.

Rethinking stress: The role of mindsets in determining the stress response. Journal of Personality and Social Psychology 104, 4 (2013), 716.

3. J. A. Healey and R. W. Picard. 2005. Detecting Stress During Real-world Driving Tasks Using Physiological Sensors. Trans. Intell. Transport. Sys. 6, 2 (June 2005), 156–166.

Limitations and Future Work

To compute the LF/HF energy ratio, we currently resample and compute a Fourier transformation. We could avoid artifacts and noise from this process by switching to a wavelet entropy based method that can compute spectral density in the two frequency ranges LF and HF directly. We did not use this method primarily due to implementation effort.

4. Richard S Lazarus and Susan Folkman. 1984. Stress, appraisal, and coping. Springer.

5. Chun-Tung Li, Jiannong Cao, and Tim M. H. Li. 2016.

Eustress or Distress: An Empirical Study of Perceived Stress in Everyday College Life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16).

ACM, New York, NY, USA, 1209–1217.

A different limitation of this evaluation is that most participants were young male college students. Even ignoring the small sample size, our participants may not be representative of the population at large.

Going forward we suggest to primarily focus on replicating the possible effect on a larger sample size study. A within-subject study on a longer task design would lend additional credibility to the assumed effect by showing a more direct connection between the use of the system and its effect on performance.

A replication of the effect on a GSR system, potentially with an evaluation of the current HRV based system’s accuracy in stress detection similar to [6] would help to decouple the effect from its current implementation.

6. David Liu and Mark Ulrich. Autumn 2013-2014. Listen to Your Heart: Stress Prediction Using Consumer Heart Rate Sensors. Stanford CS 229 (Machine Learning) (Autumn 2013-2014).

CONCLUSION

8. J. A Russell. 1980. A circumplex model of affect. Journal of personality and social psychology 6 (1980), 1161–1178.

7. Diana MacLean, Asta Roseway, and Mary Czerwinski.

2013. MoodWings: a wearable biofeedback device for real-time stress intervention. In Proceedings of the 6th international conference on PErvasive Technologies Related to Assistive Environments. ACM, 66.

In this paper, we presented a biofeedback system for the stressis-enhancing mindset, and show that it might plausibly improve performance on mental tasks—even when they require focus, such as in our evaluation of mental arithmetic. We also show a viable process for using frequency domain heart rate variability analysis for near real-time biofeedback.

9. Nandita Sharma and Tom Gedeon. 2012. Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer Methods and Programs in Biomedicine 108, 3 (2012), 1287 – 1301.

Our work motivates both further research into the effects of mindset on performance as well as a re-framing of the role of stress in HCI research. These initial findings do not invalidate stress reduction and calmness research. They do, however, suggest that in applications in which users care about their performance in addition to their well-being, applying an unreflected stress-is-debilitating mindset will lead to non-optimal results. They remind us not to ignore the rich potential of designing systems that aid users in reframing their stress responses constructively.

10. The Bluetooth SIG. 2016. The Bluetooth SIG Health Device Profile (HDP) Data Exchange Specifications.

(2016). https://www.bluetooth.com/specifications/ assigned-numbers/health-device-profile

11. Polar USA. 2016. H7 Heart Rate Sensor. (2016). https://www.polar.com/

12. Yalçın ˙I¸sler and Mehmet Kuntalp. 2007. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine 37, 10 (2007), 1502 – 1510.

ACKNOWLEDGMENTS

We thank the CS 376 staff for their guidance and valuable input during this project. We are grateful to Dr. Alia Crum and 5