The built environment correlates of objectively measured physical activity in Norwegian adults: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Built environments that are designed to provide accessible, attractive, and convenient locales promote regular physical activity (PA). Norway has great variability in its geographic, natural, and built environment features. Urban areas have well-developed built environment features, whereas the rural areas are less walkable and this may influence the mode of transport. In general, active transport is more common in urban centers. Further, public transportation is more developed in urban areas, whereas motorized transport may be more widespread in the rural areas. Despite this, in Sogn & Fjordane, a rural county in western Norway, high PA levels are frequently observed. Thus, the aims of this study were to (1) explore perceived built environment features and characterize their associations with objectively measured PA levels in Norwegian adults and (2) explore the differences in these correlates between Sogn & Fjordane and the rest of Norway. METHODS: In this cross-sectional study, participants used questionnaires to rate perceptions of their built environments, and their PA was objectively measured for 7 consecutive days using the ActiGraph GT1M accelerometer. There were 972 Norwegian adults who were included in the study. The average age was 46.9 ± 6.5 years and 43.8% of participants were men. Data were analyzed using multiple linear regression. RESULTS: = 0.03) but not in the rest of Norway. CONCLUSION: Total PA level and MVPA were associated with built environment factors, such as perceptions of community, perceived walkability, and engaging in active transport for commuting. Geographic differences in the PA correlates were observed, and thus, locally customized environmental population approaches aimed at increasing PA levels may be essential complements to individual behavior and lifestyle strategies. Further, objective measures of Norwegian built environments, such as geographic information system data, and validated walk- and bike-scores would advance the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".