Relationship between physical activity and locomotive syndrome among young and middle-aged Japanese workers
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the relationship between physical activity (PA) and locomotive syndrome (LS) among young and middle-aged Japanese workers. METHODS: This cross-sectional study included 335 participants from a company in Kumamoto, Japan. LS was evaluated using the 25-question Geriatric Locomotive Function Scale (GLFS-25); a GLFS-25 score ≥7 was defined as LS. Weekly PA was measured using the International Physical Activity Questionnaire. Work-related PA (time spent sitting, standing, walking, and strenuous work per day) and sedentary breaks were measured using a Work-related Physical Activity Questionnaire. Screen usage (television [TV], smartphones, tablets, and personal computers) during leisure time was recorded. The association between PA and LS was examined using a multivariate logistic regression analysis adjusted for age, sex, body mass index, history of musculoskeletal disorders, cancer, stroke, occupation, employment type, work time, shift system, employment status, and body pain. RESULTS: A total of 149 participants had LS. Fewer sedentary breaks during work (>70-minute intervals, odds ratio [OR] = 2.96; prolonged sitting, OR = 4.12) and longer TV viewing time (≥180 minutes, OR = 3.02) were significantly associated with LS. In contrast, moderate PA (OR = 0.75) was significantly associated with a lower risk of LS. CONCLUSIONS: Fewer sedentary breaks during work and longer TV viewing time could increase the risk of LS in young and middle-aged Japanese workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".