Personalized Policy Learning using Longitudinal Mobile Health Data

TL;DR

This paper develops a novel personalized policy learning method using longitudinal mobile health data, estimating individual-specific policies to improve user engagement, demonstrated by a significant increase in prompt response rates.

Contribution

It introduces a new estimation approach for personalized policies with many random effects, addressing high-dimensional integrals and establishing theoretical guarantees.

Findings

Personalized policies varied significantly among users.

Estimated policies increased prompt response rate from 11% to 23%.

Method outperforms existing approaches in simulations.

Abstract

We address the personalized policy learning problem using longitudinal mobile health application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimension integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous app usage. We apply our method to develop personalized push ("prompt") schedules in 294 app users, with a goal to maximize the prompt response rate given past app usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function "glmer" in a simulation study.