1185The association between neighbourhood built environment and health-related fitness: A systematic review
Bibliographic record
Abstract
Abstract Background Few studies have investigated potential links between the built environment and health-related fitness, even though there is some evidence linking neighbourhood built environments to physical activity behaviors and chronic health conditions. Methods Following PRISMA guidelines eight databases were searched from inception to August 2020 using a combination of built environment and health-related fitness terms. Inclusion criteria was limited to quantitative studies that sampled of adults aged 18 years or older with no physical disabilities or health issues that may impact health-related fitness. Results Of the 26,219 citations identified within our comprehensive search, 25 studies met eligibility and underwent data extraction and quality assessment. Objectively measured built environment characteristics (e.g., improved sidewalks, higher street connectivity, older neighbourhoods, higher residential density, and higher land use mix) were associated with increased flexibility, cardiorespiratory fitness, grip strength, and body composition. Moreover, perceptions of neighbourhood features such as higher neighbourhood walkability, greater park access and quality, and lower neighbourhood crime, were associated with increased perceived cardiorespiratory fitness, muscular strength, flexibility, and overall fitness. However, many of these findings were from cross-sectional studies where adjustment for key confounders varied. Results also varied by sex in the small number of studies that provided sex-specific stratifications. Conclusions This project may help elucidate the pathway between the built environment and health-related fitness. Key messages Neighbourhood built environment features are associated with aspects of health-related fitness.
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 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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".