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Record W2987698575 · doi:10.1093/eurpub/ckz185.298

Neighborhood walkability related to knee and low back pain for older people: A multilevel analysis

2019· article· en· W2987698575 on OpenAlexaboutno aff
Daichi Okabe, Taishi Tsuji, Masamichi Hanazato, Nao Asada, Katsunori Kondo

Bibliographic record

VenueEuropean Journal of Public Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsWalkabilityMedicineStairsKnee painConfoundingQuarter (Canadian coin)PopulationPhysical therapyCross-sectional studyGerontologyPhysical activityDemographyEnvironmental healthGeographyOsteoarthritisInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.316
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
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