Mobile App–Delivered Motivational Interviewing for Women on Eating Disorder Treatment Waitlists (MI-Coach: ED): Protocol for an App Development and Pilot Evaluation
Bibliographic record
Abstract
BACKGROUND: A significant increase in eating disorder (ED) service waitlists has been observed in the past several years, exacerbating existing barriers to care (eg, long waitlists, scarcity of treatment centers, and positive beliefs surrounding pathology). Given that treatment delays have important clinical correlates (eg, entrenchment of ED pathology), exploring new methods of mental health service delivery for this population is of critical concern. App-based motivational interviewing (MI) delivered prior to the start of treatment has the potential to improve accessibility by simultaneously addressing structural (eg, travel costs) and individual (eg, low motivation) barriers to care. Despite the potential benefits, there remains a lack of empirically validated, ED-specific MI-based mobile apps. Evaluating the feasibility and acceptability of such interventions is a crucial first step before progressing to full-scale efficacy trials. OBJECTIVE: This multiphasic mixed methods study aims to develop and assess the feasibility and acceptability of MI-Coach: ED, a novel app designed to increase motivation among women waitlisted for ED treatment. Specifically, this study seeks to determine participant engagement levels, user satisfaction, and perceived usability of the app, as well as to explore preliminary trends in motivation and ED-related symptoms following app use. METHODS: Phase I adapted the content and interface of an existing app based on evidence-based principles (MI-Coach) for an ED population. Phase II pilot tested the app through a pre-post evaluation. Participants (n=30) aged 18 years and older were recruited from ED treatment waitlists in British Columbia, Canada. After completing baseline assessments evaluating demographic and clinical variables (eg, motivation, eating pathology, depression, and anxiety symptoms), participants were provided access to MI-Coach: ED for 1 month. Participants completed postintervention assessments and provided both quantitative and qualitative feedback on the app. Feasibility will be evaluated through the total number of participants recruited, study dropout rates, and engagement indicators (eg, modules completed) within the app. Acceptability will be assessed through self-report measures and semistructured exit interviews, which will explore user experiences, perceived benefits, and barriers to app engagement. Additionally, exploratory analyses will examine changes in motivation and ED symptoms before and after the intervention. RESULTS: The MI-Coach: ED app has been developed, and recruitment was initiated in November 2022 and terminated in May 2024. Results are being analyzed and will be submitted for publication in May 2025. CONCLUSIONS: This study has the potential to transform ED service delivery and mitigate the impacts of existing treatment barriers for this population. By leveraging a digital MI-based intervention, MI-Coach: ED could serve as a scalable and accessible pretreatment tool, helping to bridge the gap between initial help-seeking and formal ED treatment. Findings from this study will inform the refinement of the intervention and recruitment strategies for future large-scale efficacy trials. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66298.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".