Participatory Intervention Development of a Peer-Guided Self-Help App for Anxiety Disorders: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders affect approximately 27% of the global population, posing a major mental health challenge. Limited access to treatment due to resource constraints highlights the need for scalable solutions. Web-based self-help programs provide low-threshold access to evidence-based strategies. When guided by peers, these programs enhance engagement and acceptability by merging autonomy with support. Peer-guided self-help apps offer a cost-effective alternative to traditional care, reaching those who might otherwise remain untreated. OBJECTIVE: This study aims to describe the development of a peer-guided self-help app for anxiety, incorporating input from individuals with lived experience. It assesses user feedback on usability and helpfulness during the development process. METHODS: The intervention was developed in 3 iterative stages using the integrate, design, assess, and share framework. In stage 1, a prototype was cocreated by employees of a German self-help organization with lived experience, software engineers, and psychologists. In stage 2, qualitative feedback was collected from a focus group (n=5) and interviews (n=4), with participants recruited through group leaders of the organization. The research team directly contacted the participants. Qualitative data were analyzed with inductive and deductive content analysis (interrater reliability Cohen κ=0.88), which informed the minimum viable product (MVP) development. In stage 3, the MVP was pilot-tested with a larger online sample (N=126) recruited via the organization's website, accessible to all. Anxiety (Generalized Anxiety Disorder-7) and well-being (the World Health Organization-Five Well-Being Index) were assessed at baseline, 4, 8, and 12 weeks. Use metrics (eg, log-ins, time spent, and feature use) were recorded automatically. Quantitative data were analyzed descriptively. RESULTS: Stage 1 produced no data. In stage 2, feedback revealed unclear functionality, confusion in peer interaction, and safety concerns, leading to MVP revisions. In stage 3 (N=126), engagement was low-average log-ins were 3.15 (SD 14.37), with only 20 (SD 15.9) participants completing follow-ups. While many joined exposure (79/126, 62.7%) or activity scheduling groups (104/126, 82.5%), 123 (98.4%) did not send messages, undermining peer support goals. Baseline scores showed moderate anxiety (Generalized Anxiety Disorder-7: mean 10.52, SD 5.15), low well-being (World Health Organization-Five Well-Being Index: mean 15.80, SD 6.17), and low social support (Oslo Social Support Scale-3: mean 7.25, SD 2.68), consistent with the target group. Low engagement and high attrition indicated usability problems and limited perceived value. CONCLUSIONS: Despite rapid sign-ups, user engagement was low and dropout rates high, indicating poor acceptance. Key barriers included user confusion, underused peer features, and technical issues. Future development should include structured onboarding for better clarity. Peer engagement be improved with prompts and enhanced safety perception. The participatory approach was challenging and fell short of expectations. Smaller testing phases with regular user feedback will ensure user-centered refinement. Insights from successful peer communities can inform a more intuitive, engaging design.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".