Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation.
Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation.
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DOI：
10.23919/acc53348.2022.9867669
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发表时间：
2022-06
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--
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--
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本文介绍了使用离散同时扰动随机逼近（DSPSA）优化动态模型有意义的个性化干预行为医学，强调身体活动。DSPSA是用来确定一组最佳的模型功能和参数值，否则将选择通过穷举搜索或指定的先验。本研究中研究的建模技术是按需模型（MoD）估计，它协同管理本地和全局建模，是ARX估计等传统方法的一种有吸引力的替代方案。行为医学中DSPSA和MOD的结合可以为参与者特定的干预提供个性化的模型。通过DSPSA搜索增强的MOD估计可以制定为不仅提供关于参与者的身体行为的更好的解释信息，而且还提供预测能力，提供对环境和精神状态的更深入了解，这可能最有利于参与者从干预行动中受益。一个案例研究，从一个代表性的参与者收集的数据，只是步行干预支持这些结论。
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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影响因子：
2.1
作者：
Braun, MW;Rivera, DE;Stenman, A
通讯作者：
Stenman, A
影响因子：
4.5
作者：
Phatak, Sayali S.;Freigoun, Mohammad T.;Hekler, Eric B.
通讯作者：
Hekler, Eric B.
影响因子：
5.1
作者：
Aksakalli, Vural;Malekipirbazari, Milad
通讯作者：
Malekipirbazari, Milad
影响因子：
6.4
作者：
Schwartz, Jay D.;Wang, Wenlin;Rivera, Daniel E.
通讯作者：
Rivera, Daniel E.