User-centered development of a smartphone application (Fit2Thrive) to promote physical activity in breast cancer survivors
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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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".