Worksite Physical Activity Policies and Environments in Relation to Employee Physical Activity
Bibliographic record
Abstract
PURPOSE: Examine associations between worksite physical activity promotion strategies and employees' physical activity and sedentary behaviors. DESIGN: Cross-sectional. SETTING: Seattle-King County, Washington and Baltimore, Maryland-Washington, D.C. regions. SUBJECTS: Adults working outside the home (n = 1313). Mean age was 45 ± 10 years, 75.8% of participants were non-Hispanic white, 56% were male, and 51% had income ≥$70,000/year. MEASURES: Participants reported demographic characteristics and presence/absence of nine physical activity promotion environment and policy strategies in their work environment (e.g., showers, lockers, physical activity programs). A worksite physical activity promotion index was a tally of strategies. Total sedentary and moderate-to-vigorous physical activity (MVPA) min/d were objectively assessed via 7-day accelerometry. Total job-related physical activity minutes and recreational physical activity minutes were self-reported with the International Physical Activity Questionnaire. ANALYSIS: Mixed-effects models and generalized estimating equations evaluated the association of the worksite promotion index with physical activity and sedentary behavior, adjusting for demographics. RESULTS: A higher worksite promotion index was significantly associated with higher total sedentary behavior (β = 3.97), MVPA (β = 1.04), recreational physical activity (β = 1.1 and odds ratio = 1.39; away from work and at work, respectively) and negatively with job-related physical activity (β = .90). CONCLUSIONS: Multiple worksite physical activity promotion strategies based on environmental supports and policies may increase recreational physical activity and should be evaluated in controlled trials. These findings are particularly important given the increasingly sedentary nature of employment.
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 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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".