Effectiveness of Oncologist-Referred Exercise and Healthy Eating Programming as a Part of Supportive Adjuvant Care for Early Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Randomized trials have established efficacy of supervised exercise training during chemotherapy for breast cancer for numerous health outcomes. The purpose of this study was to assess reach, effectiveness, maintenance, and implementation of an evidence-based exercise and healthy eating program offered within an adjuvant care setting. SUBJECTS, MATERIALS, AND METHODS: Women receiving adjuvant chemotherapy for breast cancer were given a prescription by their oncologist to participate in the Nutrition and Exercise during Adjuvant Treatment (NExT) program. The NExT program consisted of supervised, moderate-intensity, aerobic and resistance exercise three times a week during adjuvant therapy, followed by a step-down in supervised sessions per week for 20 additional weeks, plus one group-based healthy eating session. Usual moderate-to-vigorous physical activity (MVPA) and health-related quality of life (HRQoL) were assessed by questionnaire at baseline, program completion, and one year later, along with measures of satisfaction and safety. RESULTS: < .001) were significantly higher than baseline. Exercise adherence was 60% ± 26% to three sessions per week during treatment. No major adverse events occurred and injury prevalence did not change relative to baseline. Participants were highly satisfied. CONCLUSION: This oncologist-referred exercise and healthy eating supportive-care program for breast cancer patients receiving chemotherapy was safe, successful in reaching oncologists and patients, and effective for improving MVPA and maintaining HRQoL. IMPLICATIONS FOR PRACTICE: Despite evidence that exercise is both safe and efficacious at improving physical fitness, quality of life, and treatment side effects for individuals with cancer, lifestyle programming is not offered as standard of cancer care. This study describes an oncologist-referred, evidence-based exercise and healthy eating program offered in collaboration with a university as supportive care to women with breast cancer receiving chemotherapy. The program was well received by oncologists and patients, safe, and relatively inexpensive to operate. Importantly, there was a significant positive impact on physical activity levels and health-related quality of life lasting for 2 years after initiation of therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".