ArticleJournal of the American Statistical Association2021
Personalized Policy Learning using Longitudinal Mobile Health Data.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Digital Behavior Change Interventions for Digital Overuse: Scoping Review of Current Approaches and Emerging AI-Enabled Systems.Journal of medical Internet research · 2026Article
- A multiagent reinforcement learning algorithm for personalized recommendations in bipolar disorder.PNAS nexus · 2025Article
- Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025Article
- Tip optofluidic immunoassay: Evaluating COVID-19 antibody protection with 1 μL fingertip blood.hLife · 2025Article
- Emotionally adaptive support: a narrative review of affective computing for mental health.Frontiers in digital health · 2025Review
- Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials.Statistical methods in medical research · 2024Article
- A Robust Mixed-Effects Bandit Algorithm for Assessing Mobile Health Interventions.Advances in neural information processing systems · 2024Article
- Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data.Harvard data science review · 2022Article
- Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial.JMIR research protocols · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Personalized policy represents a paradigm shift from 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.