Racial, Ethnic, and Nativity Disparities in Physical Activity and Sedentary Time among Cancer Prevention Study-3 Participants
Bibliographic record
Abstract
PURPOSE: Understanding racial/ethnic and nativity disparities in physical activity (PA) is important, as certain subgroups bear a disproportionate burden of physical inactivity-related diseases. This descriptive study compared mean leisure-time moderate-to-vigorous intensity physical activity (LTMVPA) by race/ethnicity and nativity. METHODS: The Cancer Prevention Study-3 (78.1% women; age, 47.9 ± 9.7 yr) includes 4722 (1.9%) Asian/Pacific Islander; 1232 (0.5%) Black/Indigenous (non-White) Latino; 16,041 (6.5%) White Latino; 9295 (3.8%) non-Latino Black; 2623 (1.1%) Indigenous American; and 210,504 (85.7%) non-Latino White participants across the United States and Puerto Rico. Participants completed validated LTMVPA and 24-h time use surveys at enrollment (2006-2013). Differences in LTMVPA across race/ethnicity and nativity were examined by ANCOVA with paired Tukey tests adjusting for age and sex. The proportion of time spent sitting, sleeping, and on PA by race/ethnicity was also compared. RESULTS: There were significant differences in LTMVPA by race/ethnicity (race main effect, P < 0.001; nativity, P = 0.072; interaction, P < 0.001). Pairwise comparisons showed that White participants born abroad were the most active (23.8 MET-h·wk-1) and non-White Latino participants born abroad were the least active (17.9 MET-h·wk-1). Among Latinos, participants born in Puerto Rico were 6.6-7.3 MET-h·wk-1 less active than participants born in Mexico, the United States/Canada, or other countries. There were variations in time use by race/ethnicity, with the largest difference in time spent sitting while watching TV. Black participants spent 14.8% of the day (~3.5 h) sitting watching TV, which was 78 min longer than Asian/Pacific Islander participants. CONCLUSIONS: This study suggests that there are differences in LTMVPA accumulation by race, ethnicity, and nativity. Results can be used to identify demographic groups that may benefit from culturally tailored PA interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".