Evidence map›Paper›PMID 42214461›Full record

ArticleContemporary clinical trials2026

Practical considerations when designing an online learning algorithm for an app-based mHealth intervention.

Rachel T Gonzalez, Madeline R Abbott, Brahmajee Nallamothu, Scott Hummel, Michael Dorsch, Walter Dempsey

Abstract read
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In one paragraph

Article in Contemporary clinical trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rachel T GonzalezDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Madeline R AbbottDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Brahmajee NallamothuMedical School, University of Michigan, 1301 Catherine Street, Ann Arbor, MI 48019, USA; Frankel Cardiovascular Center, University of Michigan, 1425 East Ann Street, Ann Arbor, MI 48019, USA.
Scott HummelMedical School, University of Michigan, 1301 Catherine Street, Ann Arbor, MI 48019, USA; Frankel Cardiovascular Center, University of Michigan, 1425 East Ann Street, Ann Arbor, MI 48019, USA; VA Ann Arbor Health System, 2215 Fuller Road, Ann Arbor, MI 48105, USA.
Michael DorschFrankel Cardiovascular Center, University of Michigan, 1425 East Ann Street, Ann Arbor, MI 48019, USA; College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI 48109, USA.
Walter DempseyDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA. Electronic address: wdem@umich.edu.

Funding

Diversity Supplement: A Just-In-Time Adaptive Mobile Application Intervention To Reduce Sodium Intake And Blood Pressure In Hypertensive PatientsR33HL155498 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DORSCH, MICHAEL, NALLAMOTHU, BRAHMAJEE K · 2022 to 2025
$3.4M
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
A Just-In-Time Adaptive Mobile Application Intervention To Reduce Sodium Intake And Blood Pressure In Hypertensive PatientsR61HL155498 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DORSCH, MICHAEL, NALLAMOTHU, BRAHMAJEE K · 2021 to 2021
$776k
NHLBI NIH HHS R33 HL155498NHLBI NIH HHS R61 HL155498NIGMS NIH HHS R01 GM152549
6 · The paper itself

Abstract

The ubiquitous nature of mobile health (mHealth) technology has expanded opportunities for the integration of reinforcement learning into traditional clinical trial designs, allowing researchers to learn individualized treatment policies during the study. LOWSALT4LIFE 2 (LS4L2) is a recent trial aimed at reducing sodium intake among hypertensive individuals through an app-based intervention. A reinforcement learning algorithm, which was deployed in one of the trial arms, was designed to send reminder notifications to promote app engagement in contexts where the notification would be effective, i.e., when a participant was likely to open the app in the next 30-minutes and not when prior data suggested reduced effectiveness. Such an algorithm can improve app-based mHealth interventions by reducing participant burden and more effectively promoting behavior change. We encountered various challenges during the implementation of the learning algorithm, which we present as a template to solving challenges in future trials that deploy reinforcement learning algorithms. We provide template solutions based on LS4L2 for solving the key challenges of (i) defining a relevant reward, (ii) determining a meaningful timescale for optimization, (iii) specifying a robust statistical model that allows for automation, (iv) balancing model flexibility with computational cost, and (v) addressing missing values in gradually collected data.

Indexed as

AlgorithmsHypertensionMobile ApplicationsDigital HealthHumansReinforcement Machine LearningReminder SystemsTelemedicineContextual banditsJust-in-time adaptive interventionsMobile healthOnline reinforcement learningThompson sampling

Identifiers

PMID42214461
PMCPMC13374849

What OpenQuestion holds

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Registered trials

None linked

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.