Physical Activity and Fruit and Vegetable Intake: Correlations between and within Adults in a Longitudinal Multiethnic Cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To determine if changes in physical activity were associated with changes in fruit and vegetable intake. DESIGN: Prospective cohort. SETTING: Hawaii. SUBJECTS: Seven hundred adults (18+ years) sampled from the general population. MEASURES: Computer-assisted telephone interviews conducted at 0, 3, 6, 9, 12, 18, and 24 months; the International Physical Activity Questionnaire; the National Cancer Institute's Fruit and Vegetable Screener. ANALYSIS: Between-individual correlations of each individual's mean physical activity and mean fruit and vegetable intake were estimated with Pearson correlations. Correlations of physical activity and fruit and vegetable intake within individuals over time were calculated from analysis of covariance models to factor out the variation between individuals. RESULTS: Individuals with a higher mean physical activity duration tended to eat more fruits and vegetables (r = .30, p < .0001). Within individuals, no average correlation between physical activity and intake of fruit and vegetables was observed over time (r = .03). The variation was great in that some individuals, these behaviors changed simultaneously, but in others, they did not. CONCLUSION: Although individuals who are more physically active tend to eat more fruits and vegetables (i.e., there is a weak correlation between individuals), on average, individuals do not simultaneously change these behaviors. Implications are that health behaviors may not covary, or that intervention is necessary to bring about covariation in health behaviors. The great variation from individual to individual in the extent to which these two behaviors covaried needs to be studied to determine if the individual tendency for behaviors to covary could be measured and used to individually tailor multiple behavior interventions.
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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) | 0.001 | 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 it