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Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous

Rachael T Kha1, Daniel E Rivera1, Predrag Klasnja2

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Proceedings of the ... American Control Conference. American Control Conference
|November 7, 2022
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This study introduces discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize dynamical models for personalized physical activity interventions in behavioral medicine, enhancing participant-specific insights.

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Area of Science:

  • Behavioral Medicine
  • Dynamical Systems Modeling
  • Optimization Algorithms

Background:

  • Personalized interventions in behavioral medicine require accurate dynamical models.
  • Traditional modeling approaches may be suboptimal or require extensive prior specification.
  • Optimizing model features and parameters is crucial for effective interventions.

Purpose of the Study:

  • To apply discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) for optimizing dynamical models.
  • To enhance Model-on-Demand (MoD) estimation for personalized behavioral interventions.
  • To improve the explanatory and predictive power of models for physical activity.

Main Methods:

  • Utilized discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize model parameters.
  • Employed Model-on-Demand (MoD) estimation for synergistic local and global modeling.
  • Applied the combined DSPSA and MoD approach to a case study from the 'Just Walk' intervention.

Main Results:

  • DSPSA effectively determined optimal model features and parameter values, avoiding exhaustive search.
  • The DSPSA-enhanced MoD approach provided better explanatory information on physical behavior.
  • The method demonstrated predictive power regarding environmental and mental states conducive to intervention success.

Conclusions:

  • DSPSA and MoD offer a powerful combination for creating individualized models in behavioral medicine.
  • This approach enhances understanding and prediction of physical activity behavior.
  • The findings support the development of more effective, participant-specific interventions.