Environmental Correlates of Physical Activity Among Individuals With Diabetes in the Rural Midwest
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between physical and social environment attributes and levels of physical activity in a population-based sample of diabetic individuals living in rural areas. RESEARCH DESIGN AND METHODS: Cross-sectional telephone survey data from rural communities of southeastern Missouri, Tennessee, and Arkansas were used. Logistic regression was used to calculate crude and adjusted prevalence odds ratios (PORs) and 95% CIs. RESULTS: A total of 278 (11%) individuals with diabetes were identified. Almost 37% of this group reported no leisure-time physical activity. Individuals with diabetes who reported regular physical activity were more likely to report better general health status, normal BMI, and no physical impairment. After adjustment, regular activity was positively associated with use of three or more facilities (POR 14.3, 95% CI 3.0-67.3) in the past 30 days, the availability of many nearby places to walk (2.3, 1.1-4.8), the availability of shoulders on streets (2.4, 1.3-4.5), often walking to nearby places (4.1, 2.0-8.3), and rating the community for physical activity as generally pleasant (2.3, 1.1-4.8). Additionally, the regular activity group was more likely to report their physician had helped make a plan to increase physical activity (2.8, 1.3-5.8) and followed up on their plan (2.2, 1.1-4.4). Social environment variables were not associated with physical activity after adjustment. CONCLUSIONS: Physical inactivity is a significant problem in rural diabetic populations. We have identified aspects of the social and physical environment that are positively associated with physical activity. Understanding the role of the environment may result in increased physical activity for individuals with diabetes.
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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 | 0.001 |
| 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".