Supporting Individuals on Eating Disorder Waitlists Through App‐Based Motivational Interviewing: A Qualitative Evaluation of a Program‐Led Pilot Intervention
Bibliographic record
Abstract
OBJECTIVE: Individuals with eating disorders (EDs) often face long wait times before receiving formal treatment, which can exacerbate distress and undermine motivation for recovery. Despite this risk, few structured interventions are available to support individuals during the pretreatment period. This qualitative study examined the feasibility, acceptability, and perceived utility of motivational interviewing (MI)-Coach: ED, a structured and focused program-led mobile app intervention grounded in MI, designed to support individuals with EDs while waitlisted for treatment. METHODS: Semi-structured interviews were conducted with two interest-holder groups: individuals with lived ED experience (n = 14) who completed a 4-week pilot study and clinicians (n = 5) in ED treatment settings. Data were analyzed using framework analysis to integrate themes across groups. RESULTS: Both groups described MI-Coach: ED as accessible, flexible, and relevant to recovery. Preliminary indications of clinical benefit in the lived-experience group were observed, including clarity surrounding reasons for change, more manageable near-term goals, and renewed motivation. Clinicians described similar motivational benefits for clients facing lengthy treatment delays. Both groups emphasized the importance of a supportive digital relationship and suggested accessibility improvements. DISCUSSION: Findings support the feasibility and acceptability of MI-Coach: ED as a program-led digital tool for individuals awaiting ED treatment. The framework approach integrated cross-group themes and informed ongoing refinements to strengthen emotional safety, accessibility, and equitable use and will guide a future randomized controlled trial evaluating clinical impact and implementation. PUBLIC SIGNIFICANCE STATEMENT: People with eating disorders often wait a long time for treatment, leaving them feeling isolated and unmotivated. This study conducted interviews with people with eating disorders and clinicians to explore the value of MI-Coach: ED, a guided mobile app designed to support people during this waiting period. Feedback from both groups showed that the app was helpful, and suggestions are now shaping future improvements and research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".