The association between physical activity and mammography screening utilization: a longitudinal analysis, health retirement study (2004–2016)
Bibliographic record
Abstract
PURPOSE: Physical inactivity is a well-known factor associated with an increased risk of breast cancer. However, there is a disparity in physical activity levels among women in the United States. These disparities are associated with differences in women’s mammography screening behaviors, which may contribute to disparities in breast cancer incidence and outcomes. This study aims to evaluate the association between physical activity and the utilization of mammography screening. It also assesses whether this association is modified by women’s race/ethnicity and age. METHODS: This is a longitudinal study that used the Health and Retirement Study data from 2004 to 2016. A total of 18,157 women aged 40 years and older were included. The 2004 wave was used as the baseline, with follow-up conducted in 2008, 2012, and 2016 (wave 9, 11, and 13 respectively). Mixed-effects logistic regression models were used, and odds ratios were reported. RESULTS: The study found a significant positive association between physical activity and mammography utilization. After adjusting for confounding variables, women who were physically active had 1.31 times the odds of undergoing mammography screening compared to those who were inactive (95% CI: 1.13–1.51, p < 0.001). The association between physical activity and mammography screening utilization was weaker among Hispanic women. CONCLUSION: Interventions encouraging physical activity targeting racial/ethnic minorities may contribute to increasing mammography screening utilization and reducing breast cancer disparity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".