Environmental Correlates of Physical Activity, Sedentary Behavior, and Self-Rated Health in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
PURPOSE: Physical activity (PA) predicts important health outcomes in chronic obstructive pulmonary disease (COPD). In the general population, environmental factors have the potential to influence PA; however, data are limited in this clinical population. Therefore, we sought to investigate associations between the environment and PA, sedentary behavior, and self-rated health in COPD. METHODS: Sociodemographic, PA, sedentary behavior, and self-rated health data were collected from a prospective cohort of 418 individuals with COPD (65% female; 58 ± 8 yr), while environmental data were drawn from a national environmental data repository and individually matched to participant postal code. Environmental variables included social and material deprivation, urban form index, surrounding greenness, and air quality (concentrations of air pollution for fine particles, nitrogen dioxide, ozone, and sulphur dioxide). Logistic and multivariate linear regression models were used to investigate the strongest environmental predictors. RESULTS: The models showed a statistically significant negative correlation between PA level and ozone pollution (P = .023; adjusted OR = 0.85: 95% CI, 0.74-0.98). Urban form index was also significantly associated with sedentary behavior (β = 0.113; t value = 1.71; P = .011). Self-rated health was significantly positively correlated with PA level (P = .006; adjusted OR = 2.22: 95% CI, 1.25-3.94), and significantly inversely correlated with sedentary behavior (β = -0.159; t value =-2.42; P = .016). CONCLUSION: These new data may identify barriers to PA and assist clinicians in the prescription of exercise for individuals living with COPD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".