Understanding Behavioral Influences on Eating Disorders and App Engagement to Inform Eating Disorder App Development: Qualitative Online Focus Groups With Adults With Lived Experience
Bibliographic record
Abstract
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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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".