Fruit and Vegetable Intake, Physical Activity, and Mortality in Older Community‐Dwelling Women
Bibliographic record
Abstract
OBJECTIVES: To examine the relationship between fruit and vegetable intake, physical activity, and all-cause mortality in older women. DESIGN: Six Cox proportional hazards models examined independent and additive relationships between physical activity, carotenoids, and all-cause mortality. Additional models tested whether physical activity and carotenoids were conjointly related to mortality. Models were adjusted for age, education, and race and ethnicity. SETTING: Baltimore, Maryland. PARTICIPANTS: Seven hundred thirteen women aged 70 to 79 participating in the Women's Health and Aging Studies. MEASUREMENTS: Total serum carotenoids, a marker of fruit and vegetable intake, and physical activity were measured at baseline. Physical activity was measured according to kilocalorie expenditure. RESULTS: During 5 years of follow-up, 82 (11.5%) participants died. Measured continuously, physical activity improved survival (HR = 0.52, 95% CI = 0.41-0.66, P < .001). The most active women were more likely to survive than the least physically active women (HR = 0.28, 95% CI = 0.13-0.59, P < .001). Continuous measures of carotenoids improved survival (HR = 0.67, 95% CI = 0.51-0.89, P = .01). Women in the highest tertile of total carotenoids were more likely to survive those in the lowest (HR = 0.50, 95% CI = 0.27-0.91, P = .03). When examined in the same model, continuous measures of physical activity (HR = 0.54, 95% CI = 0.42-0.68, P < .001) and carotenoids (HR = 0.76, 95% CI = 0.59-0.98, P = .04) predicted survival during follow-up. CONCLUSION: The combination of low total serum carotenoids and low physical activity, both modifiable risk factors, strongly predicted earlier mortality. These findings provide preliminary support that higher fruit and vegetable intake and exercise improve survival.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".