Feasibility and acceptability of the Fit2Thrive mHealth physical activity promotion intervention components in breast cancer survivors
Bibliographic record
Abstract
BACKGROUND: Most breast cancer survivors (BCS) are insufficiently active. mHealth moderate-to-vigorous physical activity (MVPA) interventions for BCS are highly scalable, but the feasibility and acceptability of specific intervention components are unknown. PURPOSE: The purpose of this study is to examine the feasibility and acceptability of the Fit2Thrive MVPA promotion intervention components. METHODS: Using Multiphase Optimization Strategy methodology, inactive BCS [n = 269; Mage = 52.5; (SD ± 9.9)] received a core intervention (Fitbit + Fit2Thrive smartphone app) and were randomly assigned to receive zero to five components for 12 weeks: (i) support calls; (ii) deluxe app; (iii) text messages, (iv) online gym; and (v) Buddy. Feasibility was measured through study accrual, retention, and adherence rates. Acceptability was measured via post-program evaluations. RESULTS: Enrollment rates were high; 419 BCS expressed interest in the study, 348 (83%) passed screening, and 269 (77%) were randomized; 98% (n = 264) received the intervention. Retention was 94% at 12 weeks. Fitbits were worn on 93% of study days. Most reported using the app ≥5 days/week (67%), enjoyed using the Fitbit (79%), and were satisfied with their study experience (88%) and the Fit2Thrive app design (79%). Component adherence rates and acceptability varied by intervention component. Component-specific effects on MVPA goal adherence and overall acceptability ratings were significant for telephone support calls. CONCLUSIONS: Findings indicate Fit2Thrive's feasibility and acceptability were high overall but may vary by component. Future work should refine and test components to maximize participant engagement, efficacy, and scalability. CLINICAL TRIAL INFORMATION: The Clinical Trials Registration NCT03131440.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".