Evidence map›Paper›PMID 40895234›Full record

ArticleJournal of technology in behavioral science2025

Pilot Study of Factors Influencing Engagement with an mHealth Intervention Among Teens with Eating Disorder Symptoms.

Erin Kasson, Melissa M Vázquez, Xiao Li, Christine Doroshenko, Hannah S Szlyk, Amanda Montayne, Ellen E Fitzsimmons-Craft, Denise E Wilfley, C Barr Taylor, Patricia A Cavazos-Rehg

Abstract read
In one paragraph

Article in Journal of technology in behavioral science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

10 authors.

Erin KassonWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Melissa M VázquezWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Xiao LiWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Christine DoroshenkoWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Hannah S SzlykWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Amanda MontayneWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Ellen E Fitzsimmons-CraftWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.
Denise E WilfleyWashington University in St. Louis, Department of Psychology, St. Louis, MO 63105.
C Barr TaylorDepartment of Psychiatry and Behavioral Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
Patricia A Cavazos-RehgWashington University School of Medicine, Department of Psychiatry, St. Louis, MO 63110.

Funding

Washington University Nutrition Obesity Research CenterP30DK056341 · NIDDK · WASHINGTON UNIVERSITY · PI Dominic N Reeds · 1999 to 2026
$30.2M
Washington University Center for Diabetes Translation Research P30DK092950 · NIDDK · WASHINGTON UNIVERSITY · PI Ross C Brownson, Debra Haire-Joshu · 2011 to 2026
$11.7M
Developing an Optimized Conversational Agent or "Chatbot" to Facilitate Mental Health Services Use in Individuals with Eating DisordersK08MH120341 · NIMH · WASHINGTON UNIVERSITY · PI FITZSIMMONS-CRAFT, ELLEN E. · 2019 to 2023
$824k
Leveraging Social Media to Identify and Connect Teens with Eating Disorders to a Mobile Guided Self-Help Mobile InterventionR34MH119170 · NIMH · WASHINGTON UNIVERSITY · PI CAVAZOS-REHG, PATRICIA A, WILFLEY, DENISE ELLA · 2019 to 2021
$709k
Leveraging Social Media for Substance Use Behavioral Insight K02DA043657 · NIDA · WASHINGTON UNIVERSITY · PI CAVAZOS-REHG, PATRICIA A · 2017 to 2021
$591k
NIDA NIH HHS K02 DA043657NIDDK NIH HHS P30 DK056341NIDDK NIH HHS P30 DK092950NIMH NIH HHS K08 MH120341NIMH NIH HHS R34 MH119170
6 · The paper itself

Abstract

Purpose: The current pilot study examines engagement with and preliminary effectiveness of an mHealth intervention designed for teens with eating disorders (EDs) to delineate specific user characteristics associated with intervention engagement and the impact of this engagement on ED symptoms. Methods: Teens 14-17 years old with or at high-risk for an ED were recruited from social media (n=29) and provided access to an mHealth intervention for 2 months. At baseline, participants were surveyed on ED and other mental health symptoms and demographics. Bivariate analyses were used to examine associations between baseline characteristics and time spent in the app (<10 vs. ≥ 10 minutes). Qualitative feedback from participants on intervention content and usability was also collected and reported. Results: Out of the 29 participants, 22 (76%) utilized the app at least once after gaining access. The median number of logins for these users was 6, with an interquartile range spanning from 3 to 15. Over half of teens spent 10 minutes or more engaging with the app during the study period (n=15, 52%). Compared to those who spent less than 10 minutes with the app, those who spent more than 10 minutes engaging with the app were slightly younger, more likely to endorse less chronic ED symptoms, and less likely to report social anxiety disorder ( Conclusion: Teens' distinct user characteristics impact rates of uptake and engagement with an ED-focused mHealth intervention and should be considered in the design and iteration of these tools. mHealth tools have the potential to improve ED recovery outcomes among teens, and future studies should further evaluate the effectiveness of these tools and integration of content to support severe ED symptoms and other comorbid mental health issues.

Identifiers

PMID40895234
PMCPMC12396739

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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.