Leveraging No-Code Digital Platforms for Designing an Integrated Smartphone-Based Ecological Momentary Intervention With Cognitive Behavioral Therapy for Mental Health Care: Development and Usability Study
Notice bibliographique
Résumé
Background: The rising burden of disease associated with mental disorders calls for evidence-based psychological interventions that can be swiftly scaled up. Blending smartphone-based mental health apps (MHapps) for delivering ecological momentary interventions (EMIs) with traditional in-person interventions may have the benefits of improving treatment adherence, facilitating the application of learned techniques into everyday life, and, in turn, enhancing clinical response. However, previous work has shown that most existing MHapps were developed for specific research studies or for profit, thereby making them difficult to adapt, particularly in time-limited and resource-constrained settings. Objective: This study aimed to demonstrate how a person-centered and theory-informed MHapp could be developed in a timely and low-cost manner for use as part of blended care, using a phased approach. Given the scarcity of digital mental health interventions for older adults, we adopted a participatory research approach to co-design the blended intervention with 2 groups of older adults. Methods: In Phase 1, we reviewed existing MHapps with consideration of whether they could be adapted by individual researchers or clinicians, their key functions, and whether their efficacy had been tested. "No-code" app builders were additionally reviewed, which may be alternatives if no MHapp can be used. In Phase 2, following the IDEAS (Integrate, Design, Assess, and Share) framework, we built a prototype according to users' needs, with its content informed by theories of cognitive behavioral therapy (CBT) and the Health Action Process Approach. The prototype was then tested and refined over 2 rounds of 3-session co-design workshops with peer supporters (n=8) and service users (n=5) from a stepped-care intervention for older adults with depressive symptoms. Usability testing was conducted with both stakeholder groups in Phase 3. Results: Of the 149 MHapps identified, only 43 (28.9%) can be publicly downloaded. Four (8.3%) of them can be partially adapted, although no new content can be directly added. We therefore developed the MHapp using m-Path (a spin-off from KU Leuven's Faculty of Psychology), which was the only existing no-code app development platform designed for mental health interventions. A prototype incorporating CBT-based homework and behavior change techniques informed by the Health Action Process Approach was built, with its refined version rated as highly easy to use and acceptable by both stakeholder groups. Conclusions: By integrating CBT with EMI, we demonstrated the feasibility and acceptability of a novel blended care model for reference in future work. Preliminary findings suggest high usability and clinical relevance, highlighting the potential of leveraging no-code platforms to facilitate scalable, theory-driven interventions that extend mental health support beyond traditional settings. Grounding the blended intervention in evidence-based psychological and health behavior change theories, coupled with user involvement throughout the design process, may improve clinical efficacy and reduce implementation barriers, which are areas for further investigation in future work.
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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 | 0,009 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».