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
Bibliographic record
Abstract
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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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".