A Robust Mixed-Effects Bandit Algorithm for Assessing Mobile Health Interventions.

Easton K Huch, Jieru Shi, Madeline R Abbott, Jessica R Golbus, Alexander Moreno, Walter H Dempsey

Abstract read

In one paragraph

Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Practical considerations when designing an online learning algorithm for an app-based mHealth intervention.Contemporary clinical trials · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Easton K HuchDepartment of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.

Jieru ShiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Madeline R AbbottDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Jessica R GolbusMichigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA.

Alexander MorenoIndependent Researcher†.

Walter H DempseyDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Pilot and Mentoring CoreP50DA054039 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LINDA M COLLINS, SUSAN A MURPHY · 2021 to 2026

$18.2M

Leveraging ML algorithms and data integration techniques to improve efficiency of causal moderation analyses of micro-randomized trial dataR01GM152549 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Walter Dempsey · 2024 to 2026

$918k

NIDA NIH HHS P50 DA054039NIGMS NIH HHS R01 GM152549

6 · The paper itself

Abstract

Mobile health leverages personalized, contextually-tailored interventions optimized through bandit and reinforcement learning algorithms. Despite its promise, challenges like participant heterogeneity, nonstationarity, and nonlinearity in rewards hinder algorithm performance. We propose a robust contextual bandit algorithm, termed "DML-TS-NNR", that simultaneously addresses these challenges via (1) modeling the differential reward with user- and time-specific incidental parameters, (2) network cohesion penalties, and (3) debiased machine learning for flexible estimation of baseline rewards. We establish a high-probability regret bound that depends solely on the dimension of the differential reward model. This feature enables us to achieve robust regret bounds even when the baseline reward is highly complex. We demonstrate the superior performance of the DML-TS-NNR algorithm in a simulation and two off-policy evaluation studies.

Identifiers

PMID40895488

PMCPMC12395203

What OpenQuestion holds

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Registered trials

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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.