MoodMover: Development and usability testing of an mHealth physical activity intervention for depression
Bibliographic record
Abstract
Background: Physical activity (PA) is recognized as a modifiable lifestyle factor for managing depression. An application(app)-based intervention to promote PA among individuals with depression may be a viable alternative or adjunct to conventional treatments offering increased accessibility. Objective: This paper describes the early stages of the development process of MoodMover, a 9-week app-based intervention designed to promote PA for people with depression, including its usability testing. Methods: Development of MoodMover followed the initial stages of the Integrate, Design, Assess, and Share (IDEAS) framework. The development process included (1) identifying intervention needs and planning; (2) intervention development; and (3) usability testing and refinement. Usability testing employed a mixed-methods formative approach via virtual semi-structured interviews involving goal-oriented tasks and administration of the mHealth App Usability Questionnaire (MAUQ). Results: Drawing on formative research, a multidisciplinary research team developed the intervention, guided by the Multi-Process Action Control framework. Nine participants engaged in the usability testing with the MoodMover prototypes receiving an average MAUQ score of 5.79 (SD = 1.04), indicating good to high usability. Necessary modifications were made based on end-users' feedback. Conclusions: The development of MoodMover, the first theoretically informed app-based PA intervention for individuals with depression, may provide another treatment option, which has wide reach. The comprehensive usability testing indicated interest in the app and strong perceptions of usability enabling a user-centered approach to refine the app to better align with end-users' preferences and needs. Testing the feasibility and preliminary efficacy of the refined MoodMover is now recommended.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".