Evidence map›Paper›PMID 37294612›Full record

Trial reportJMIR mHealth and uHealth2023

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.

Lauren Bell, Claire Garnett, Yihan Bao, Zhaoxi Cheng, Tianchen Qian, Olga Perski, Henry W W Potts, Elizabeth Williamson

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
4.3field-weighted citation impact, top 5% of its field
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  7. Design, Implement, Sustain: Advancing Program-Led Interventions for Binge Eating, a Narrative Review.European eating disorders review : the journal of the Eating Disorders Association · 2026
    Review
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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

8 authors at 6 institutions in 2 countries.

Lauren BellDepartment of Medical Statistics, The London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-5408-8525
Claire GarnettResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID 0000-0002-6589-299X
Yihan BaoDepartment of Statistics and Data Science, Yale University, New Haven, CT, United States.ORCID 0000-0001-6344-0052
Zhaoxi ChengDepartment of Biostatistics, Harvard University, Cambridge, MA, United States.ORCID 0009-0008-7800-0883
Tianchen QianDepartment of Statistics, University of California Irvine, Irvine, CA, United States.ORCID 0000-0003-4282-7826
Olga PerskiResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID 0000-0003-3285-3174
Henry W W PottsInstitute of Health Informatics, University College London, London, United Kingdom.ORCID 0000-0002-6200-8804
Elizabeth WilliamsonDepartment of Medical Statistics, The London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0001-6905-876X
University College London · GBHarvard University · USLondon School of Hygiene & Tropical Medicine · GBUniversity of California, Irvine · USUniversity of Cambridge · GBYale University · US

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
TRAINING U54EB020404 · NIBIB · UNIVERSITY OF MEMPHIS · PI KUMAR, SANTOSH · 2014 to 2018
$11.3M
Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs SupplementU01CA229437 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NAHUM-SHANI, INBAL BILLIE, WETTER, DAVID W · 2018 to 2022
$2.8M
Data-Based Methods for Just-In-Time Adaptive Interventions in Alcohol UseR01AA023187 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MURPHY, SUSAN A · 2015 to 2019
$2.6M
Cancer Research UKMedical Research Council MR/S01442X/1NCI NIH HHS U01 CA229437NIAAA NIH HHS R01 AA023187NIBIB NIH HHS U54 EB020404NIDA NIH HHS P50 DA039838Wellcome Trust
6 · The paper itself

Abstract

backgroundDrink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement.

objectiveOur objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy.

methodsWe conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement.

resultsReceiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different.

conclusionsWe found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18690.

Indexed as

AlcoholismMobile ApplicationsAdolescentAdultAlcohol DrinkingHumansSelf ReportUnited Kingdomadaptive interventionalcoholbehavior changedigital behavior changedigital healthdrinkerdrinkingengagementexcessive alcohol consumptionjust-in-time adaptive interventionmHealthmicro-randomized trialmobile healthmobile phonenotificationpush notificationrandomizationrandomized trialsmartphone app

Identifiers

PMID37294612
PMCPMC10337295
OpenAlexW4361984316

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.