Associations of Perceived Community Environmental Attributes with Walking in a Population-Based Sample of Adults with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: No studies have yet examined the associations of physical environmental attributes specifically with walking in adults with type 2 diabetes. PURPOSE: The purpose of this study was to examine associations of perceived community physical environmental attributes with walking for transport and for recreation among adults living with type 2 diabetes. METHODS: Participants were 771 adults with type 2 diabetes who completed a self-administered survey on perceived community physical environmental attributes and walking behaviors. RESULTS: Based on a criterion of a minimum of 120-min/week, some 29% were sufficiently active through walking for transport and 33% through walking for recreation. Significantly higher proportions of those actively walking for transport and for recreation had shops or places to buy things close by (67.8% and 60.9%); lived within a 15-min walk to a transit stop (70.6% and 71.0%); did not have dead-end streets close by (77.7% and 79.8%); reported interesting things to look at (84.8% and 84.4%); and lived close to low-cost recreation facilities (81.3% and 78.8%). In addition, those actively walking for transport reported living in a community with intersections close to each other (75.6%) and with sidewalks on their streets (88.1%). When these variables were entered simultaneously into logistic regression models, living close by to shops was positively related to walking for transport (OR = 1.92, 99% CI = 1.11-3.32). CONCLUSIONS: Consistent with findings from studies of healthy adult populations, positive perceptions of community environmental attributes are associated with walking for transport among adults with type 2 diabetes. The now-strong public health case for environmental innovations to promote more walking for transport is further reinforced by the potential to benefit those living with diabetes.
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 | 0.002 |
| 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 | 0.000 |
| 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".