2020/01/01 by Xinyu Hu, Min Qian, Hu, Xinyu +5 · 2 citations
Health Professions · Mathematics · Psychology · Social Sciences · #Artificial intelligence #Computer science #Consistency (knowledge bases) #Covariate #Digital Mental Health Interventions #Dimension (graph theory) #Download #Estimation #Function (biology) #Machine learning #Mathematics #Mobile Health and mHealth Applications #Mobile apps #Personalized medicine #Schedule #Technology Use by Older Adults #World Wide Web #stat.ME
paper · pdf · doi:10.48550/arxiv.2001.03258
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/01/01 · arxiv created 2020/01/09 · arxiv updated 2020/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Personalized policy represents a paradigm shift one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual features are modeled as fixed and random effects, respectively, and synthesized to form the personalized policy. The group lasso type penalty is imposed to avoid overfitting of individual deviations from the population model. We examine the conditions under which the proposed method work in the presence of time-varying endogenous covariates, and provide conditional optimality and marginal consistency results of the expected immediate outcome under the estimated policies. We apply our method to develop personalized push (“prompt”) schedules in 294 app users, with the goal to maximize the prompt response rate given past app usage and other contextual factors. The proposed method compares favorably to existing estimation methods including using the R function “glmer” in a simulation study. Supplementary materials for this article are available online.