Effects of a Physical Activity mHealth Intervention (Fit2Thrive) on WCRF/AICR Cancer Prevention Recommendations among Breast Cancer Survivors: A Secondary Data Analysis
Bibliographic record
Abstract
BACKGROUND: Interventions targeting moderate-to-vigorous physical activity (MVPA) may be a catalyst for improving other lifestyle behaviors in breast cancer survivors (BCS). We examined whether Fit2Thrive, an mHealth MVPA intervention, influenced adherence to cancer prevention recommendations. METHODS: BCSs (N = 269; age, mean = 52.9; SD = 9.9) received a 12-week mHealth MVPA intervention and were randomized to "on" or "off" level of five intervention components. The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) score was calculated (0 = high cancer risk, 6 = low cancer risk) based on cancer prevention recommendations: sugar-sweetened beverages, fast food, fruit/vegetable intake, body mass index, alcohol consumption, and MVPA (baseline, 12 weeks, and 24 weeks). Mixed-effects models examined changes in the WCRF/AICR score and each risk factor and the effects of each intervention component (telephone support calls, Fitbit Buddy, tailored text messages, deluxe app, online gym) level on the WCRF/AICR score. RESULTS: The WCRF/AICR total score significantly improved at 12 and 24 weeks (P values < 0.001). MVPA improved at 12 and 24 weeks (P values < 0.001). Fruit and vegetable consumption improved at 12 weeks (P = 0.01). No changes in other risk factors were observed. CONCLUSIONS: Participation in a mHealth MVPA intervention may influence cancer risk in BCS and have effects on certain untargeted behaviors (fruit and vegetable consumption) but not on other risk factors (sugar-sweetened beverages, fast food, body mass index, alcohol consumption). Future work should explore how to maximize these effects and determine if resource-efficient dietary intervention components improve cancer outcomes. IMPACT: Understanding the impact of an mHealth MVPA intervention on untargeted dietary behaviors may guide the development of scalable interventions targeting lifestyle behaviors.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".