East Carolina University : Research and Creative Achievement Week 2012
Assessing Relationships between Health Literacy, Diabetes Status, and Income, Sarah Williamson, Kenda Lewis, Holly Mahoney, Joy King, Emily DiNatale, Lesley Lutes, PhD, East Carolina University, Greenville, NC 27858 In spite of improvements in global health over the years, the prevalence of type-two diabetes is increasing worldwide. If uncontrolled, diabetes can lead to several other chronic diseases, which increases the risk of mortality. Two possible contributors to the rise in diabetes diagnoses within the United States involve low heath literacy scores of diabetes patients along with their socioeconomic status. Low health literacy is a risk factor for several adverse outcomes, such as the misuse of preventative services, poor self-management, increased hospitalizations, higher medical costs and worsening health outcomes. The purpose of this pilot study was to assess whether the mediator variable, income, contributes to health literacy scores and diabetes diagnoses in a clinical sample of a community population within Eastern North Carolina. A brief health literacy tool was administered to gather demographic information and assess participant's knowledge of health issues related to obesity, diabetes, and weight management. We gathered a convenience sample of 60 middle-aged participants who were primarily female, 35% of whom were diagnosed with type-two diabetes. We predict that income serves as a mediator variable that explains the relationship between a patient’s health literacy and their diabetes diagnosis. Results will be presented.
GP78
Knee Moment and Force Predictions Using Ultrasound-Based VS. Scaled Musculoskeletal Models, John Pope, Paul DeVita, Anthony S. Kulas, Patrick Rider, East Carolina University, Greenville, NC 27858 A musculoskeletal model's ability to accurately predict joint moments is subject to error based on the generic and scaled parameters used. Muscle moment estimations are typically based on musculotendon parameters that are either scaled or scaled and then optimized. Using a musculoskeletal model with ultrasound-derived muscle force generating parameters may lead to more accurate estimations of joint moments compared to using a model with scaled muscle force generating parameters. Our objective was to determine if subject-specific ultrasound derived parameters can more accurately predict inverse-dynamics calculated knee moments compared to a model with scaled muscle force generating parameters. Ultrasound images were taken from four subject's right leg. Cross sectional and longitudinal images were taken from the quadriceps, hamstrings, and gastrocnemius. Each subject performed single-leg squats during which surface EMG, force plate and motion data were collected. Maximal voluntary isometric contractions on a dynamometer were used to normalize the EMG. Measurements of the subject s ultrasound-based muscle parameters (maximal isometric force, optimal fiber length, and tendon slack length) were applied to a Hill muscle model and static optimization was used to produce knee muscle moments within SIMM. This process was repeated using a second model which utilized scaled muscle parameters. We compared knee moments produced from the scaled and ultrasound derived models to inverse dynamics calculated knee moments. Both the scaled and ultrasound models moment predictions showed strong positive correlations (scaled r=.996, ultrasound r=.998). On average, the ultrasound based models produced knee moments with 65% less root mean squared errors compared to the scaled model. Both models (scaled & ultrasound) showed a strong correlation with the inverse dynamics predicted model. But the ultrasound model's smaller errors verses the scaled model's favors the use of ultrasoundderived models to estimate knee moments in a professional setting in a single le squat. The effect that these errors had on muscle force estimations will also be reported for each model. 151
GP79