Exploring Therapists’ Approaches to Treating Eating Disorders to Inform User-Centric App Design: Web-Based Interview Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».