A Systematic Review of Agreement Between Perceived and Objective Neighborhood Environment Measures and Associations With Physical Activity Outcomes
Bibliographic record
Abstract
A number of review studies document associations between the perceived and objectively measured neighborhood environment and physical activity. However, current evidence does not discern whether perceived or objective variables more consistently predict physical activity. A review is needed to examine the comparability of these variables and the consistency of their respective associations with the same physical activity outcome. We systematically searched three databases for studies that examined agreement between perceived and objective measures and/or associations between comparable variables and physical activity. We abstracted 85 relevant peer-reviewed studies published between 1990 and 2015, synthesized agreement coefficients, and compared these variables’ associations with physical activity. Perceived neighborhood environment variables were significantly associated with physical activity ( p < .05) at slightly higher rates than objective neighborhood environment variables (20.1% and 13.7%). Comparably defined variables exhibited low agreement and only 8.2% were associated with the same outcome. The perceived neighborhood environment and objectively measured neighborhood environment are related but distinct constructs that account for unique variance in physical activity.
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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 | 0.016 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".