Personalized physical activity prescription: Physical activity monitor development and applications.

摘要

For almost 20 years, public health organizations have published numerous reports espousing the benefits of being physically active in addition to providing evidence-based recommendations to achieve these health gains. These guidelines, however, have been ineffective, and the percentage of inactive American adults has remained alarmingly high. While advances in technology have contributed to the decline of physical activity, these same improvements provide new opportunities to promote physical activity. Through the development of affordable and wearable technologies to accurately monitor physical activity and physiological data, new approaches to personalize physical activity prescriptions and assess risk for chronic disease are possible. The present work describes 1) the development of an activity monitor (AM) to quantify physical activity and heart rate response, 2) the application of this device to explore the relationships between physical activity exposure, health outcomes, and lower-extremity musculoskeletal injuries, and 3) the integration of AM data into a dynamic web-based application to personalize physical activity prescription. An inexpensive, multi-sensor AM was developed to quantify physical activity frequency, duration, and intensity. The AM provides advantages over existing commercial devices namely the ability to simultaneously and accurately record heart rate and multidirectional hip accelerations during walking and running. The use of open-source hardware and software in the AM design permits researchers to access raw sensor data, information critical to standardizing AM outcomes. Multiple human subject studies were conducted to expand the applications of the multi-sensor AM for future physical activity measurement and coronary heart disease risk assessment studies. Generalized regression models were developed to predict energy expenditure and vertical, propulsive, and braking ground reaction forces using raw AM sensor data and subject characteristics during walking and running. A 12-minute run/walk exercise field test completed while wearing the AM was also demonstrated as an effective approach to assess cardiorespiratory fitness and post-exercise heart rate recovery, important prognostic markers for coronary heart disease, in an asymptomatic population. Additionally, a regression model that included distance achieved during the exercise field test and sex was developed and shown to accurately predict cardiorespiratory fitness for men and women across a range of fitness levels. A personalized physical activity prescription (PPAP) application that combines multi-sensor AM data with dynamic web-based guidance was constructed and tested during a 12-week pilot study. The PPAP application created daily, personalized physical activity prescriptions that adapted to participant's compliance with the recommendations and physiologic response to aerobic training recorded by the AM during physical activity and resting heart rate sessions. Interactive features provided by the website enabled participants to track their progress and set training goals using calendars and training tables and system administrators to remotely monitor subject compliance, data quality, and AM functionality.