Understanding the main trends in human behavior is fundamental to developing effective adaptive treatments. Inspired by this insight, this paper presents a mathematical quantification of the change of human behavior following external stimuli. In particular, statistical methods are applied to real physical activity data collected intensively using mobile wearable technologies. We explain the setup of the study conducted with multiple participants. Then, a preprocessing of the collected measurements, required to overcome the hurdles associated with behavioral data, is briefly discussed. Furthermore, we identify a dynamical affine model that approximates humans' sedentary behavior. The affine model is simple yet insightful. We show results of fitting time-invariant as well as switched models along with a quantification of the prediction errors. Moreover, the effect of various types of treatments on the sedentary behavior of several subjects is investigated. As expected, the results show that people react differently to external stimuli. However, common tendencies are clearly observed. Our findings emphasize the necessity of the application of personalized adaptive intervention. Future research directions are discussed accordingly.
['Mahmoud Ashour', 'Korkut Bekiroglu', 'Chih-Hsiang Yang', 'Constantino M. Lagoa', 'David E. Conroy', 'Joshua M. Smyth', 'Stephanie T. Lanza']
On the mathematical modeling of the effect of treatment on human physical activity