Evidence map›Paper›PMID 41027004›Full record

Observational studyJMIR mHealth and uHealth2025

How Engagement Changes Over Time in a Digital Eating Disorder App: Observational Study.

Rachael E Flatt, Laura M Thornton, Jenna Tregarthen, Stuart Argue, Cynthia M Bulik

Abstract readObservational Study
In one paragraph

Observational study in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
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

5 authors.

Rachael E FlattDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0002-2661-7987
Laura M ThorntonDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-9384-7988
Jenna TregarthenRecovery Record (United States), Palo Alto, CA, United States.ORCID 0000-0002-4787-7059
Stuart ArgueRecovery Record (United States), Palo Alto, CA, United States.ORCID 0000-0001-6823-8574
Cynthia M BulikDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.ORCID 0000-0001-7772-3264

Funding

Predicting Binge and Purge Episodes from Passive and Active Apple Watch Data Using a Dynamical Systems ApproachR01MH119084 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BULIK, CYNTHIA M, BUTNER, JONATHAN · 2019 to 2022
$2.8M
1/7 Psychiatric Genomics Consortium: Finding actionable variationU01MH109528 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SULLIVAN, PATRICK F · 2016 to 2020
$2.5M
National Science Foundation DGE-1650116NIMH NIH HHS R01 MH119084NIMH NIH HHS U01 MH109528
6 · The paper itself

Abstract

Background: Engagement with digital mental health interventions is often measured as a summary-level variable and remains underresearched despite its importance for meaningful symptom change. This study deepens understanding of engagement in a digital eating disorder intervention, recovery record, by measuring engagement with unique components of the app, on 2 different devices (phone and watch), and at a summary level. Objective: This study described and modeled how individuals engaged with the app across a variety of measures of engagement and identified baseline predictors of engagement. Methods: Participants with current binge-eating behavior were recruited as part of the Binge Eating Genetics Initiative study to use a digital eating disorder intervention for 4 weeks. Demographic and severity of illness variables were captured in the baseline survey at enrollment, and engagement data were captured through both an iPhone and Apple Watch version of the intervention. Engagement was characterized by log type (urge, behavior, mood, or meal), device type (logs on phone or watch), and overall usage (total logs) and averaged each week for 4 weeks. Descriptives were tabulated for demographic and engagement variables, and multilevel growth models were conducted for each measure of engagement with baseline characteristics and time as predictors. Results: Participants (N=893) self-reported as primarily White (743/871, 85%), non-Hispanic (801/893, 90%), females (772/893, 87%) with a mean age of 29.6 (SD 7.4) years and mean current BMI of 32.5 (SD 9.8) kg/m2 and used the app for a mean of 24 days. Most logs were captured on phones (217,143/225,927; 96%), and mood logs were the most used app component (174,818/282,136; 62% of logs). All measures of engagement declined over time, as illustrated by the visualizations, but each measure of engagement illustrated unique participant trajectories over time. Time was a significant negative predictor in every multilevel model. Sex and ethnicity were also significant predictors across several measures of engagement, with female and Hispanic participants demonstrating greater engagement than male and non-Hispanic counterparts. Other baseline characteristics (age, current BMI, and binge episodes in the past 28 days) were significant predictors of 1 measure of engagement each. Conclusions: This study highlighted that engagement is far more complex and nuanced than is typically described in research, and that specific components and mode of delivery may have unique engagement profiles and predictors. Future work would benefit from developing early engagement models informed by baseline characteristics to predict intervention outcomes, thereby tailoring digital eating disorder interventions at the individual level.

Indexed as

Feeding and Eating DisordersMobile ApplicationsPatient ParticipationAdultFemaleHumansMaleMiddle AgedSurveys and Questionnairesbaselineeating disordersengagementmobile applicationwearable technology

Identifiers

PMID41027004
PMCPMC12483339

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.