TY - GEN
T1 - Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation
AU - Kha, Rachael T.
AU - Rivera, Daniel E.
AU - Klasnja, Predrag
AU - Hekler, Eric
N1 - Publisher Copyright:
© 2022 American Automatic Control Council.
PY - 2022
Y1 - 2022
N2 - This paper presents the use of discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize dynamical models meaningful for personalized interventions in behavioral medicine, with emphasis on physical activity. DSPSA is used to determine an optimal set of model features and parameter values which would otherwise be chosen either through exhaustive search or be specified a priori. The modeling technique examined in this study is Model-on-Demand (MoD) estimation, which synergistically manages local and global modeling, and represents an appealing alternative to traditional approaches such as ARX estimation. The combination of DSPSA and MoD in behavioral medicine can provide individualized models for participant-specific interventions. MoD estimation, enhanced with a DSPSA search, can be formulated to provide not only better explanatory information about a participant's physical behavior but also predictive power, providing greater insight into environmental and mental states that may be most conducive for participants to benefit from the actions of the intervention. A case study from data collected from a representative participant of the Just Walk intervention is presented in support of these conclusions.
AB - This paper presents the use of discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize dynamical models meaningful for personalized interventions in behavioral medicine, with emphasis on physical activity. DSPSA is used to determine an optimal set of model features and parameter values which would otherwise be chosen either through exhaustive search or be specified a priori. The modeling technique examined in this study is Model-on-Demand (MoD) estimation, which synergistically manages local and global modeling, and represents an appealing alternative to traditional approaches such as ARX estimation. The combination of DSPSA and MoD in behavioral medicine can provide individualized models for participant-specific interventions. MoD estimation, enhanced with a DSPSA search, can be formulated to provide not only better explanatory information about a participant's physical behavior but also predictive power, providing greater insight into environmental and mental states that may be most conducive for participants to benefit from the actions of the intervention. A case study from data collected from a representative participant of the Just Walk intervention is presented in support of these conclusions.
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U2 - 10.23919/ACC53348.2022.9867669
DO - 10.23919/ACC53348.2022.9867669
M3 - Conference contribution
AN - SCOPUS:85134618532
T3 - Proceedings of the American Control Conference
SP - 671
EP - 676
BT - 2022 American Control Conference, ACC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 American Control Conference, ACC 2022
Y2 - 8 June 2022 through 10 June 2022
ER -