Participatory Intervention Development of a Peer-Guided Self-Help App for Anxiety Disorders: Mixed Methods Study
Notice bibliographique
Résumé
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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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 | 0,022 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».