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.

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