ArticleThe International journal of eating disorders2020
Past, current, and future willingness to engage with treatment targets: Applying user-centered design to inform the design of a mobile behavioral intervention.
Article in The International journal of eating disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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.
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Who cites it
9 citing papers in PubMed.
- Understanding self-monitoring to inform a mobile intervention for binge eating and weight management: A proof-of-concept randomized trial.The International journal of eating disorders · 2022Trial
- A qualitative exploration of 40 Hz sound and music for older adults with mild cognitive impairment.Geriatric nursing (New York, N.Y.)Trial
- Using night shift worker and employee health stakeholder perspectives to inform the development of ArcashiftBMC digital health · 2025Article
- Realizing the untapped promise of single-session interventions for eating disorders.The International journal of eating disorders · 2023Article
- Designing Digital Interventions for Eating Disorders.Current psychiatry reports · 2023Review
- Centering Lived Experience in Developing Digital Interventions for Suicide and Self-injurious Behaviors: User-Centered Design Approach.JMIR mental health · 2021Article
- Perceived Facilitators and Barriers to Engaging with a Digital Intervention among Those with Food Insecurity, Binge Eating, and Obesity.Nutrients · 2021Article
- Integrating User-Centered Design and Behavioral Science to Design a Mobile Intervention for Obesity and Binge Eating: Mixed Methods Analysis.JMIR formative research · 2021Article
- Applying User-Centered Design Methods to Understand Users' Day-to-Day Experiences Can Inform a Mobile Intervention for Binge Eating and Weight Management.Frontiers in digital health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveUser-centered design can improve engagement with and the potential efficacy of behavioral interventions, but is underutilized in health care. This work demonstrates how design methodologies can inform the design of a mobile behavioral intervention for binge eating and obesity.
methodA needs assessment was conducted with end-users (N = 22 adults with obesity and recurrent binge eating [≥12 episodes in 3 months] who were interested in losing weight and addressing binge eating), which included assessing participants' past/current and future willingness to engage with 20 treatment targets for managing binge eating and weight. Targets focused on improving dietary intake, increasing physical activity, and reducing overvaluation of weight and/or shape, unhealthy weight control practices, and negative affect.
resultsParticipants' past and current use of targets varied. For all targets except those addressing unhealthy weight control practices, on average, participants had increasing levels of willingness to try targets. Among participants not currently using a target, at least some were willing to use every target again. DISCUSSION: Findings inform ways to personalize how users begin treatment. Furthermore, this study exemplifies how user-centered design can inform ways to ensure that digital interventions are designed to meet end-users' needs to improve engagement and clinical impact.
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Registered trials
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.