Walking, Cycling, and Obesity Rates in Europe, North America, and Australia
Bibliographic record
Abstract
PURPOSE: This study was designed to examine the relationship between active transportation (defined as the percentage of trips taken by walking, bicycling, and public transit) and obesity rates (BMI > or = 30 kg . m-2) in different countries. METHODS: National surveys of travel behavior and health indicators in Europe, North America, and Australia were used in this study; the surveys were conducted in 1994 to 2006. In some cases raw data were obtained from national or federal agencies and then analyzed, and in other cases summary data were obtained from published reports. RESULTS: Countries with the highest levels of active transportation generally had the lowest obesity rates. Europeans walked more than United States residents (382 versus 140 km per person per year) and bicycled more (188 versus 40 km per person per year) in 2000. DISCUSSION: Walking and bicycling are far more common in European countries than in the United States, Australia, and Canada. Active transportation is inversely related to obesity in these countries. Although the results do not prove causality, they suggest that active transportation could be one of the factors that explain international differences in obesity rates.
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 | 0.000 | 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".