County-level socio-environmental factors and obesity prevalence in the United States.

AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

MODELING REALITY: REVISITING CALVERT'S FITNESS SIMULATION.

Wearable sensors have gained mainstream acceptance for health and fitness monitoring despite the absence of clinically validated analytic models for clinical decision support. Individual sensors measuring, say, EKG signal and heart rate can provide insight on cardiovascular response, but a more complete picture of health and fitness requires a more complete portfolio of sensors and data. This paper outlines the research underway to revisit and reconfigure the 1976 Calvert systems model of the effect of training on physical performance. Specifically, we use wearable sensor data from clinical trials to supplement a hybrid model created by nesting Perl's Performance-Potential model within Calvert's transfer function approach to system simulation. Contemporary simulation tools combined with wearables clinical trial data is the foundation for a more agile platform for simulation of fitness and exploration of causality between training and physical performance. This platform offers the opportunity to strategically integrate data from various wearable sensors in a fashion enabling improved support for post-injury and return to sport decision-making.