Associations Of Health-related Fitness With Depression, Sleep Quality, And Fatigue In Newly Diagnosed Breast Cancer Patients
Bibliographic record
Abstract
PURPOSE: Preventing and managing breast cancer-related symptoms soon after diagnosis is essential as they may undermine quality of life, treatment outcomes, and survival. The purpose of this study was to examine the associations of health-related fitness (HRF) with depression, sleep quality, and fatigue in newly diagnosed breast cancer patients. METHODS: Baseline data collected within 90 days of diagnosis as part of the Alberta Moving Beyond Breast Cancer (AMBER) cohort Study were used in this study. Cardiopulmonary fitness (treadmill VO2peak test), muscular strength and endurance (chest and leg press), flexibility (sit-and-reach test), and body composition (DXA scan) were assessed for HRF; and depression severity, sleep quality, and fatigue were assessed for patient-reported symptoms. Adjusted univariate and multivariable logistic regression were performed to examine the associations between HRFs and symptoms. RESULTS: A total of 1,458 participants were included in the analyses of which 10.4% reported moderate to severe depression, 51.5% reported poor sleep quality, and 26.5% reported significant fatigue. In multivariable-adjusted models, significant associations of lower relative VO2peak were identified with moderate to severe depression (p < 0.001), poor sleep quality (p = 0.009), significant fatigue (p = 0.008), any symptom (p < 0.001), and multiple symptoms (p < 0.001). Participants with lower relative upper body endurance were more likely to have significant fatigue in a dose-response manner (p = 0.001) whereas those with higher body weight (2nd and 3rd quartile groups) were more likely to have poor sleep quality in an inverted-U pattern (p = 0.021). CONCLUSIONS: A significant proportion of newly diagnosed breast cancer patients experience poor sleep quality, significant fatigue, and moderate to severe depression. Moreover, relative VO2peak is a critical parameter associated with those patient-reported symptoms. Additionally, improving absolute cardiopulmonary fitness and maintaining a healthy body weight may be effective strategies to manage all three symptoms, and improving upper body muscular endurance may provide additional benefits for fatigue. Future work may consider examining different exercise interventions to address specific symptom management in this population. a Team Grant (#107534), a Project Grant (#155952), and a Foundation Grant (#159927) from the Canadian Institutes of Health Research, the Canada Research Chairs Program, Alberta Innovates Health Senior Scholar Award, y the Alberta Cancer Foundation Weekend to End Women's Cancers BreastCancer Chair
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".