Cultural Adaptation of the Actionable Health App Evaluation in Japan: Protocol for a Web-Based Modified Delphi Expert Consensus Study
Bibliographic record
Abstract
BACKGROUND: With an increase in both the number of mental health disorders people are experiencing and the difficulty in accessing mental health care, the demand for accessible mental health care services has increased. The use of mobile devices has allowed people to receive care in their daily lives without restrictions on time or location. However, the majority of publicly available mobile health apps are not evidence-based, and the top-rated apps are not always safe or user-friendly and may not offer clinically beneficial results. OBJECTIVE: This study aims to create a cultural adaptation of the American Psychiatric Association's comprehensive app evaluation framework in Japan using a web-based modified Delphi expert consensus. METHODS: A web-based modified Delphi study includes developing the Japanese version of the comprehensive app evaluation framework and 3 Delphi rounds. In the first round, our working group sends a questionnaire to the panelists, who then complete it. In the second and third rounds, the working group sends a questionnaire and a summary of the panelists' answers based on each of the previous rounds. The panelists answer the questionnaires based on this summary. The summarization procedure is automated to help reduce the biases that can be generated when panelists' answers are summarized and when the panelists receive them. The working group sends only the result of the summarization with the next round's questionnaire. All interactions between the working group and the panelists will be conducted on Qualtrics (Qualtrics Japan LLC), a questionnaire platform. To culturally validate the comprehensive mental health app evaluation framework, participants from the following three categories will be recruited in Japan: (1) researchers, (2) practitioners, and (3) app developers. RESULTS: This study received funding from a crowdfunding campaign in Japan (April 2023). The Delphi study began in January 2023 and will be completed in December 2023. We had already completed the translation of the 105 original app evaluation item questions by December 2022. CONCLUSIONS: While the need for treatment using mental health apps is increasing, no framework that can be used to develop a centralized database for health apps is available or accessible, and no consensus has been reached among stakeholders in Japan about an appropriate framework. The results of the web-based modified Delphi method presented in this paper may provide direction for the development and use of mental health apps in the future among the relevant stakeholders. Furthermore, this study will enhance recognition of the framework among researchers, clinicians, mental health app developers, and users, in addition to devising new instruments to help users or practitioners efficiently choose the right app for their situations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44469.
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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 | 0.167 | 0.113 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".