Review Of Exercise Studies In Breast Cancer Survivors: Attention To Principles Of Training
Bibliographic record
Abstract
Current research supports the use of exercise to minimize side effects of breast cancer and its treatment, such as decreased functional capacity and increased body weight. Exercise prescriptions in research protocols should attend to the principles of exercise training in order to deliver an effective intervention. Previous reviews have summarized the exercise protocols used in research studies and focused on reporting outcomes, but have not critically examined the application of training principles (specificity, progression, overload, initial values, reversibility and diminishing returns) to evaluate the appropriateness of the intervention for desired outcomes within a target population. PURPOSE: To evaluate how intervention studies attended to the principles of exercise training in exercise trials in breast cancer survivors. METHODS: Researchers searched MEDLINE, CINAHL, SPORT DISCUS and EMBASE databases from 1990 to May 2010 for randomized controlled trials (RCTs) of exercise in women diagnosed with breast cancer both during and after treatment. Only RCTs with at least one treatment arm involving aerobic and/or resistance exercise and one control arm were included. Data was extracted on the intervention to evaluate the appropriate use of the principles of exercise training. RESULTS: Of 754 studies identified, 29 met the inclusion criteria. The type of interventions reported and the application of training principles in the prescription varied greatly across aerobic and/or resistance training interventions. No intervention properly applied all six training principles. Specificity was the most commonly attended to principle (72%), while only 10% of trials considered reversibility and/or diminishing returns. CONCLUSIONS: The inattention to the basic exercise training principles in research protocols limits our ability to draw conclusions from the literature about the efficacy of exercise to produce specific benefits for breast cancer survivors. Future studies should attend to most, if not all, of the principles of training in their study design and interpretation of study outcomes so that ultimately the most sound and effective exercise prescription for breast cancer survivors during and after treatment can be developed.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".