TY - GEN
T1 - A control engineering approach for optimizing physical activity behavioral interventions
AU - Martin, Cesar A.
AU - Rivera, Daniel
AU - Hekler, Eric B.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/21
Y1 - 2016/11/21
N2 - This paper presents the use of control engineering principles to optimize mobile and wireless health (mHealth) adaptive behavioral interventions for physical activity based on Social Cognitive Theory (SCT). SCT is a conceptual framework that describes human behavior and has been used in many health behavior interventions. An intervention for physical activity is formulated as a control systems problem relying on a dynamical model of SCT that is developed utilizing fluid analogies. To obtain values for model parameters, system identification experiments are designed including two phases: An initial informative stage followed by an optimized stage that incorporates 'patient-friendly' conditions. With the estimated model, a closed-loop intervention is formulated relying on Hybrid Model Predictive Control (HMPC). The HMPC algorithm includes a representation of categorical and discrete constraints that are inherent to behavioral interventions, and the recognition of behavioral initiation and maintenance phases. A simulation study is performed illustrating representative scenarios of the system (in both open and closed-loop).
AB - This paper presents the use of control engineering principles to optimize mobile and wireless health (mHealth) adaptive behavioral interventions for physical activity based on Social Cognitive Theory (SCT). SCT is a conceptual framework that describes human behavior and has been used in many health behavior interventions. An intervention for physical activity is formulated as a control systems problem relying on a dynamical model of SCT that is developed utilizing fluid analogies. To obtain values for model parameters, system identification experiments are designed including two phases: An initial informative stage followed by an optimized stage that incorporates 'patient-friendly' conditions. With the estimated model, a closed-loop intervention is formulated relying on Hybrid Model Predictive Control (HMPC). The HMPC algorithm includes a representation of categorical and discrete constraints that are inherent to behavioral interventions, and the recognition of behavioral initiation and maintenance phases. A simulation study is performed illustrating representative scenarios of the system (in both open and closed-loop).
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U2 - 10.1109/ETCM.2016.7750851
DO - 10.1109/ETCM.2016.7750851
M3 - Conference contribution
AN - SCOPUS:85007021327
T3 - 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
BT - 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
Y2 - 12 October 2016 through 14 October 2016
ER -