Exploring Therapists’ Approaches to Treating Eating Disorders to Inform User-Centric App Design: Web-Based Interview Study
Bibliographic record
Abstract
BACKGROUND: The potential for digital interventions in self-management and treatment of mild to moderate eating disorders (EDs) has already been established. However, apps are infrequently recommended by ED therapists to their clients. Those that are recommended often have poor engagement and user satisfaction, leading to unsatisfactory outcomes. Barriers to recommendation include patient safety, data privacy, and a perception that they may not be effective. Many existing interventions have limited functionality or do not differ much from manual cognitive behavioral therapy (CBT) or self-help books, which may not adequately support the therapeutic process or sustain user engagement. OBJECTIVE: This study aims to explore the perspectives of therapists who support people with mild to moderate EDs in the community, exploring their existing treatment approach and how an ED app might fit in the treatment pathway alongside treatment. METHODS: Semistructured web-based interviews were completed with ED therapists in the United Kingdom. Participants were recruited from First Steps ED, a specialist community-based ED service, and Thrive Mental Wellbeing, a workplace mental health provider. Five main themes were covered: (1) therapists' treatment approach, (2) how therapy was implemented in practice, (3) strategies for engaging and motivating clients, (4) perspectives on a potential ED app, and (5) suggestions for app content and design. A structured thematic analysis was validated by 2 researchers. RESULTS: Overall, 12 ED and mental health therapists (mean age 28.7, SD 7.3 y; female therapists: n=7, 58%; male therapists: n=5, 42%) participated. Therapists dealing with complex ED issues went beyond traditional CBT using additional therapeutic techniques and a flexible, person-centered approach to treatment. This included engagement and motivational strategies to support the client, elements of which could be mirrored in an app. Therapists identified the therapeutic relationship as key to success, which might have been hard to replicate in an app. They saw the potential for evidence-based apps across all stages of the treatment pathway. The need to address safeguarding, data privacy, and the potential for triggering content within the app was vital. CONCLUSIONS: This study advanced our understanding of how to design and develop clinically safe, evidence-based ED apps that can complement therapy by extending the continuity of care and the self-management and psychoeducation of clients. It emphasized integrative, adaptive CBT that incorporated other therapeutic approaches based on individuals' needs, which could be replicated in an app, as could the strategies to support engagement and motivation. It gave a cautious yet optimistic perspective on the potential integration of apps into ED treatment across all stages of the treatment pathway, from pretreatment maintenance to posttreatment maintenance. It highlighted various concerns that could be addressed and potential limitations, such as the therapeutic relationship, while recognizing the growing potential of apps with rapid technology and artificial intelligence advancements.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".