Development and usability testing of a technology-based intervention for promoting physical activity among post-treatment cancer survivors (WExercise) using the multi-process action control framework
Bibliographic record
Abstract
Background: To promote physical activity in post-treatment cancer survivors, a mobile application WExercise was developed using the Multi-Process Action Control Framework. It contains 10 weekly online lesson to facilitate reflective, regulatory, and reflexive processes to help participants to form and sustain physical activity behavior. Objectives: To test the usability and acceptability of WExercise in post-treatment cancer survivors. Methods: This study involved four phases: (1) preparing application content, (2) expert panel review (comprising oncology healthcare workers, exercise specialists, and behavior change researchers), (3) developing the app, and (4) usability test. The usability test was conducted cross-sectionally using direct observation of application navigation tasks, a quantitative survey, and qualitative interviews among 10 post-treatment cancer survivors. Results: In Phase 2, the expert panel rated the application highly on relevance, accuracy, comprehensiveness, meaningfulness, and easiness to understand (average score = 3.83 out of 4). The application was developed accordingly. In Phase 4, the System Usability Score was 75 %, greater than the cut-off point. Participants gave the items assessing acceptance of the application positive ratings (e.g., satisfaction = 4.30 out of 5). Based on the performance and feedback, the application was modified, including adjusting the font size and improving the visualization of buttons. Conclusion: Overall, experts and potential users considered the application relevant, usable, and acceptable. It has the full potential for further testing in a larger trial for its effectiveness in promoting physical activity in cancer survivors.
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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 | 0.012 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".