Resistance Training in Women Diagnosed with Breast Cancer: A Pilot Single Arm Pre–Post Intervention
Bibliographic record
Abstract
Background: Resistance training (RT) yields physical and psychological benefits for women living with and beyond breast cancer (WBC). This study examined the feasibility of a virtually delivered 8-week socially supportive RT intervention among WBC and assessed changes in physical activity and body image. Methods: A pilot single-arm 8-week pre–post intervention study design was implemented. Forty-one WBC were matched as exercise partners and asked to complete two RT sessions per week—one with a qualified exercise professional (QEP) and one with their peer. Data were collected at baseline (T1), post-intervention (T2), and 20 weeks post-baseline (T3). Results: The enrolment rate was 42%, the attendance rate for the QEP sessions was 63.8% and 40.0% for the peer sessions, and the retention rate was 87%. No adverse events were reported. Total weekly minutes of RT significantly (p < 0.05) increased by 42 minutes/week during the intervention and significantly decreased by 25 min/week at follow-up. Upper and lower body muscle strength increased (p < 0.01) during the intervention. Increased RT was associated with favorable activity self-perceptions. Conclusions: This pilot intervention study was feasible, safe, and demonstrated preliminary evidence for increasing RT time and strength among WBC. Virtually delivered socially supportive RT interventions can improve access for WBC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".