Key result
A Model Predictive Control approach was developed to design personalized, adaptive text messaging interventions that maximize the probability of achieving physical activity goals while minimizing treatment burden.
A probabilistic model predictive control framework can optimize the timing and frequency of physical activity messaging interventions to maximize effectiveness while minimizing user burden.
May inform adaptive physical activity interventions in CV care; leaves open randomized validation before clinical adoption.
In this work, we have developed a framework for synthesizing data driven controllers for a class of uncertain switched systems arising in an application to physical activity interventions. In particular, we present an application of probabilistic model predictive control to design an efficient, tractable, and adaptive intervention using behavioral data sets i.e. physical activity behavior. The models of physical activity for each individual are provided for the design of controllers that maximize the probability of achieving a desired physical activity goal subject to intervention specifications. We have tailored the mixed-integer programming-based approach for evaluating the Model Predictive Control decision at each time step.
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Bardakci et al. (2021) studied Physical inactivity. Model Predictive Control (MPC) adaptive messaging intervention was evaluated on Probability of achieving a desired physical activity goal (step counts). A Model Predictive Control approach was developed to design personalized, adaptive text messaging interventions that maximize the probability of achieving physical activity goals while minimizing treatment burden.
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