Effects of a Physical Activity Behavior Change Intervention on Inflammation and Related Health Outcomes in Breast Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: The goal of this pilot study was to determine the magnitude and direction of intervention effect sizes for inflammatory-related serum markers and relevant health outcomes among breast cancer survivors (BCSs) receiving a physical activity behavior change intervention compared with usual care. METHODS: This randomized controlled trial enrolled 28 stage I, II, or IIIA BCSs who were post-primary treatment and not regular exercisers. Participants were assigned to either a 3-month physical activity behavior change intervention group (ING) or usual care group (UCG). Intervention included supervised aerobic (150 weekly minutes, moderate-intensity) and resistance (2 sessions per week) exercise that gradually shifted to home-based exercise. Outcomes were assessed at baseline and 3 months. RESULTS: Cardiorespiratory fitness significantly improved in the ING versus the UCG (between-group difference = 3.8 mL/kg/min; d = 1.1; P = .015). Self-reported sleep latency was significantly reduced in the ING versus the UCG (between group difference = -0.5; d = -1.2; P = .02) as was serum leptin (between-group difference = -9.0 ng/mL; d = -1.0; P = .031). Small to medium nonsignificant negative effect sizes were noted for interleukin (IL)-10 and tumor necrosis factor (TNF)-α and ratios of IL-6 to IL-10, IL-8 to IL-10, and TNF-α to IL-10, whereas nonsignificant positive effect sizes were noted for IL-6 and high-molecular-weight adiponectin. CONCLUSIONS: Physical activity behavior change interventions in BCSs can achieve large effect size changes for several health outcomes. Although effect sizes for inflammatory markers were often small and not significant, changes were in the hypothesized direction for all except IL-6 and IL-10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".