Understanding Behavioral Influences on Eating Disorders and App Engagement to Inform Eating Disorder App Development: Qualitative Online Focus Groups With Adults With Lived Experience
Notice bibliographique
Résumé
BACKGROUND: Eating disorders (EDs) are severe mental health conditions driven by psychological, social, and emotional factors and have the highest mortality rate of any psychiatric disorder. Although evidence-based, theory-driven behavior change interventions are the gold standard, access to treatment remains limited. Digital interventions, such as apps, may offer accessible support for individuals with mild to moderate EDs; however, their development has rarely been guided by systematic behavior change frameworks. Consequently, many interventions inadequately target the mechanisms underlying ED behaviors and commonly lack involvement of people with lived experience. OBJECTIVE: This study aimed to identify priority behavioral change targets for ED apps by capturing lived experience perspectives on the psychological, behavioral, and contextual factors maintaining disordered eating and driving app engagement. Using the capability, opportunity, motivation-behavior (COM-B) and theoretical domains framework (TDF), we mapped these determinants to identify where and how apps can most effectively enhance capability, opportunity, and motivation. METHODS: In total, 6 small focus groups (2-5 participants per group) were conducted with 13 female and 5 male adults, including minority ethnic backgrounds, living in the United Kingdom with lived experience of an ED. Discussions explored (1) the psychological, social, and environmental determinants underpinning participants' disordered eating behaviors and (2) the behavioral and contextual mechanisms influencing engagement with an ED app. A hybrid deductive-inductive thematic analysis was performed using the COM-B model and the TDF. Themes were mapped onto evidence-based behavior change techniques using the Theory and Techniques Tool. RESULTS: This study identified clear behavior change targets for digital ED interventions, identifying requirements in 5 of 6 (83%) COM-B domains and 13 of 14 (93%) associated TDF domains for changing maladaptive ED behaviors and 5 of 6 (83%) COM-B and 12 of 14 (86%) TDF domains for sustaining app engagement. Although social support and emotional regulation were key influences, less commonly targeted domains, such as social or professional role and identity and belief in capabilities, emerged as powerful drivers in this population. Crucially, it demonstrated that effectiveness depended not only on which behavior change techniques were included but also on how they were implemented, as poorly delivered techniques can undermine engagement and exacerbate symptoms. Sex and cultural background moderated almost every domain, highlighting the necessity of personalized, adaptive delivery and the inadequacy of one-size-fits-all approaches. CONCLUSIONS: As the first study to apply the COM-B and TDF frameworks to both disordered eating behaviors and app engagement, it identifies previously overlooked behavioral mechanisms and design pitfalls, including how poorly delivered techniques can undermine recovery. It provides a practical blueprint for developing safer, more personalized, and behaviorally effective ED apps. Significant work is needed to advance apps in line with these recommendations, supported by ongoing collaboration with diverse people with lived experience.
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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 | 0,012 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».