User Experiences of and Preferences for Self-Guided Digital Interventions for the Treatment of Mild to Moderate Eating Disorders: Systematic Review and Metasynthesis
Bibliographic record
Abstract
BACKGROUND: Digital interventions typically involve using smartphones or PCs to access online or downloadable self-help and may offer a more accessible and convenient option than face-to-face interventions for some people with mild to moderate eating disorders. They have been shown to substantially reduce eating disorder symptoms, but treatment dropout rates are higher than for face-to-face interventions. We need to understand user experiences and preferences for digital interventions to support the design and development of user-centered digital interventions that are engaging and meet users' needs. OBJECTIVE: This study aims to understand user experiences and user preferences for digital interventions that aim to reduce mild to moderate eating disorder symptoms in adults. METHODS: We conducted a metasynthesis of qualitative studies. We searched 6 databases for published and unpublished literature from 2013 to 2024. We searched for studies conducted in naturalistic or outpatient settings, using primarily unguided digital self-help interventions designed to reduce eating disorder symptoms in adults with mild to moderate eating disorders. We conducted a thematic synthesis using line-by-line coding of the results and findings from each study to generate themes. RESULTS: A total of 8 studies were included after screening 3695 search results. Overall, 7 metathemes were identified. The identified metathemes included the appeal of digital interventions, role of digital interventions in treatment, value of support in treatment, communication at the right level, importance of engagement, shaping knowledge to improve eating disorder behaviors, and design of the digital intervention. Users had positive experiences with digital interventions and perceived them as helpful for self-reflection and mindfulness. Users found digital interventions to be convenient and flexible and that they fit with their lifestyle. Overall, users noticed reduced eating disorder thoughts and behaviors. However, digital interventions were not generally perceived as a sufficient treatment that could replace traditional face-to-face treatment. Users have individual needs, so an ideal intervention would offer personalized content and functions. CONCLUSIONS: Users found digital interventions for eating disorders practical and effective but stressed the need for interventions to address the full range of symptoms, severity, and individual needs. Future digital interventions should be cocreated with users and offer more personalization. Further research is needed to determine the appropriate balance of professional and peer support and whether these interventions should serve as the first step in the stepped care model. TRIAL REGISTRATION: PROSPERO CRD42023426932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=426932.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".