User-centered development of a smartphone application (Fit2Thrive) to promote physical activity in breast cancer survivors
Why this work is in the frame
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Bibliographic record
Abstract
Increased moderate and vigorous physical activity (MVPA) is associated with better health outcomes in breast cancer survivors; yet, most are insufficiently active. Smartphone applications (apps) to promote MVPA have high scalability potential, but few evidence-based apps exist. The purpose is to describe the testing and usability of Fit2Thrive, a MVPA promotion app for breast cancer survivors. A user-centered, iterative design process was utilized on three independent groups of participants. Two groups of breast cancer survivors (group 1 n = 8; group 2: n = 14) performed app usability field testing by interacting with the app for ≥3 days in a free-living environment. App refinements occurred following each field test. The Post-Study System Usability Questionnaire (PSSUQ) and the User Version Mobile Application Rating Scale (uMARS) assessed app usability and quality on a 7- and 5-point scale, respectively, and women provided qualitative written feedback. A third group (n = 15) rated potential app notification content. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using a directed content analysis. The PSSUQ app usability score (M1= 3.8; SD = 1.4 vs. M2= 3.2; SD = 1.1; lower scores are better) and uMARS app quality score (M1 = 3.4; SD = 1.3 vs. M2= 3.4; SD = 0.6; higher scores are better) appeared to improve in Field Test 2. Group 1 participants identified app "clunkiness," whereas group 2 participants identified issues with error messaging/functionality. Group 3 "liked" 53% of the self-monitoring, 71% of the entry reminder, 60% of the motivational, and 70% of the goal accomplishment notifications. Breast cancer survivors indicated that the Fit2Thrive app was acceptable and participants were able to use the app. Future work will test the efficacy of this app to increase MVPA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it