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Personalized Policy Learning using Longitudinal Mobile Health Data.

Xinyu Hu1, Min Qian1, Bin Cheng1

  • 1Department of Biostatistics, Columbia University.

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|July 9, 2021
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Summary
This summary is machine-generated.

This study introduces a new personalized policy method for mobile health apps to optimize user engagement. The generalized linear mixed model framework effectively creates individualized decision rules, improving prompt response rates.

Keywords:
contextual banditsendogenous variablesgeneralized linear mixed modelindividualized decision rulepush notifications

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Area of Science:

  • Mobile Health
  • Personalized Medicine
  • Statistical Modeling

Background:

  • Traditional mobile health interventions use a one-size-fits-all approach.
  • Developing personalized policies is challenging due to limited user data and unmeasured contextual factors.

Purpose of the Study:

  • To develop a generalized linear mixed modeling framework for personalized policy creation in mobile health.
  • To optimize immediate rewards by tailoring decision rules to individual users.

Main Methods:

  • Utilized a generalized linear mixed model with fixed and random effects for population and individual features.
  • Applied a group lasso penalty to prevent overfitting of individual deviations.
  • Examined performance with time-varying endogenous covariates and provided optimality and consistency results.

Main Results:

  • The proposed method demonstrated favorable comparisons against existing estimation techniques in simulation studies.
  • Successfully applied to develop personalized push schedules for 294 mobile app users.
  • The personalized policy aimed to maximize prompt response rates based on user behavior and context.

Conclusions:

  • The generalized linear mixed modeling framework offers an effective approach for personalized policy development in mobile health.
  • This method addresses challenges of limited data and unmeasured confounders.
  • The personalized policy framework can enhance user engagement and intervention effectiveness.