Neighborhood walkability related to knee and low back pain for older people: A multilevel analysis
Bibliographic record
Abstract
Abstract Background Previous research has linked built environments to physical activity. However, the association between built environments and musculoskeletal pain is unknown. This study aimed to investigate the effects of neighborhood walkability as a built environment on the knee and low back pain for older people. Methods Data were from the Japan Gerontological Evaluation Study (JAGES) 2013, a population-based study of independent people from aged 65 and older. A cross-sectional multilevel analysis was performed on 22,892 subjects in 792 neighborhoods. We used neighborhood walkability as regional level residents’ perception of access to parks and sidewalks and fresh food stores and difficulty in walking due to slopes and stairs and population density. Knee and low back pain which restrict daily life within the past year were our objective variables. Results The prevalence of knee pain was 26.2 %, and that of low back pain was 29.3 %. Neighborhood walkability was positively associated with knee and low back pain after adjusting for individual confounders and mediators such as physical activity, a quarter increased perception of access to parks and sidewalks decreased the prevalence of knee pain (PR: 0.85, 95% CI: 0.77-0.94), a quarter increased perception of access to fresh food store decreased the prevalence of knee and low back pain (PR: 0.90, 95% CI: 0.84-0.96, PR: 0.92, 95% CI: 0.86-0.98), a quarter increased population density decreased the prevalence of knee and low back pain (PR: 0.95; 95% CI: 0.93-0.98, PR: 0.96; 95% CI: 0.94-0.98). This trend remains after adjusting the population density, and higher difficulty in walking due to slopes and stairs is newly significant to knee pain (PR: 1.09; 95% CI: 1.01-1.18). Conclusions Some of the neighborhood walkability has a protective relation to the knee and low back pain for older people. Longitudinal and intervention studies of the built environment for musculoskeletal pain are required. Key messages To our knowledge, this is the first study to discover that neighborhood walkability has a protective relation to knee and low back pain considering various adjustments in a large-scale survey. Improvement of built environments could potentially reduce musculoskeletal pain. In the future, not only individual factors but also environmental determinants of pain should be studied.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".