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Record W4414246709 · doi:10.2196/77036

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

2025· article· en· W4414246709 on OpenAlexvenueno aff
Stephanie Ming Yin Wong, Melody Ho Ching Ip, Jessica Kang Qi Lee, Terry Yat Sang Lum, Ralf Schwarzer

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityPsychological interventionIntervention (counseling)Mental healthCognitive behavioral therapyDigital healthCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.153
GPT teacher head0.513
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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