Sedentary behaviors of adults in relation to neighborhood walkability and income.
Bibliographic record
Abstract
OBJECTIVE: Sedentary (sitting) time is a newly identified risk factor for obesity and chronic diseases, which is behaviorally and physiologically distinct from lack of physical activity. To inform public health approaches to influencing sedentary behaviors, an understanding of correlates is required. METHODS: Participants were 2,199 adults aged 20-66 years living in King County/Seattle, WA, and Baltimore, MD, regions, recruited from neighborhoods high or low on a "walkability index" (derived from objective built environment indicators) and having high or low median incomes. Cross-sectional associations of walkability and income with total sedentary time (measured by accelerometers and by self-report) and with self-reported time in seven specific sitting-related behaviors were examined. RESULTS: Neighborhood walkability and income were unrelated to measures of total sitting time. Lower neighborhood walkability was significantly associated with more driving time (difference of 18.2 min/day, p < .001) and more self-reported TV viewing (difference of 14.5 min/day, p < .001). Residents of higher income neighborhoods reported more computer/Internet and reading time, and they had more objectively measured sedentary time. CONCLUSIONS: Neighborhood walkability was not related to total sedentary time but was related to two specific sedentary behaviors associated with risk for obesity-driving time and TV viewing time. Future research could examine how these prevalent and often prolonged sedentary behaviors mediate relationships between neighborhood walkability and overweight/obesity. Initiatives to reduce chronic disease risk among residents of both higher-and lower-income low-walkable neighborhoods should include a focus on reducing TV viewing time and other sedentary behaviors and enacting policies that can lead to the development or redevelopment of more-walkable neighborhoods. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
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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 | 0.000 | 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 | 0.000 |
| 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".