Associations among physical activity, body mass index, and health‐related quality of life by race/ethnicity in a diverse sample of breast cancer survivors
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQOL), body mass index (BMI), and physical activity (PA) levels have all been associated with prognosis following breast cancer and may explain partially the higher mortality for breast cancer in certain racial/ethnic subgroups. In this study, associations between PA, BMI, and HRQOL by race were examined in a sample of breast cancer survivors. METHODS: Measures of PA, BMI, and HRQOL as well as demographic and medical characteristics of women (N = 3013, 13% nonwhite) who participated in the Women's Healthy Eating and Living Study were assessed at baseline. Analysis of covariance was used to examine the relationship between PA and obesity with HRQOL outcomes. Statistical tests were 2-sided. RESULTS: African American women were less likely to meet guidelines for PA and more likely to be obese than women from other ethnic groups (P < .05). In adjusted models, women who met guidelines for PA reported significantly higher physical health composite (point differences ranged from 10.5 to 21.2 points, all P < .05) and vitality (point differences ranged from 9.9 to 16.5 points, all P < .05) scores than those who did not, regardless of race/ethnicity. Associations between obesity and HRQOL were mixed with fewer associations for Asian American and African American women and stronger associations for whites. CONCLUSIONS: Breast cancer survivors from racially and ethnically diverse populations have lower levels of PA and higher rates of obesity that are generally associated with poorer HRQOL. Culturally sensitive PA and weight loss interventions may improve these lifestyle characteristics and result in improved HRQOL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".