Biobehavioral Factors Mediate Exercise Effects on Fatigue in Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE: This study aimed to examine mediators of fatigue response to an exercise intervention for breast cancer survivors in a pilot randomized controlled trial. METHODS: Postmenopausal breast cancer survivors (n = 46; ≤stage 2), off primary treatment, and reporting fatigue and/or sleep dysfunction were randomized to a 3-month exercise intervention (160 min·wk of moderate-intensity aerobic walking, twice weekly resistance training with resistance bands) or control group. Six discussion group sessions provided behavioral support to improve adherence. Fatigue, serum cytokines, accelerometer physical activity, cardiorespiratory fitness, sleep dysfunction, and psychosocial factors were assessed at baseline and 3 months. RESULTS: The exercise intervention effect sizes for fatigue were as follows: fatigue intensity d = 0.30 (P = 0.34), interference d = -0.38 (P = 0.22), and general fatigue d = -0.49 (P = 0.13). Using the Freedman-Schatzkin difference-in-coefficients tests, increase in fatigue intensity was significantly mediated by interleukin 6 (IL-6) (82%), IL-10 (94%), IL-6/IL-10 (49%), and tumor necrosis factor-α (TNF-α):IL-10 (78%) with reduced sleep dysfunction increasing the relationship between intervention and fatigue intensity rather than mediating intervention effects (-88%). Decrease in fatigue interference was mediated by sleep dysfunction (35%), whereas IL-10 and pro-anti-inflammatory cytokine ratios increased the relationship between intervention and interference (-25% to -40%). The reduction in general fatigue was significantly mediated by minutes of physical activity (76%), sleep dysfunction (45%), and physical activity enjoyment (40%), with IL-10 (-40%) and IL-6/IL-10 (-11%) increasing the intervention-fatigue relationship. In the intervention group, higher baseline fatigue, anxiety, depression, and perceived exercise barrier interference predicted a greater decline in fatigue interference and/or general fatigue during the intervention. CONCLUSIONS: Biobehavioral factors mediated and enhanced intervention effects on fatigue, whereas psychosocial factors predicted fatigue response. Further study is warranted to confirm our results and to improve understanding of relationships that mediate and strengthen the intervention-fatigue association.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".