Musculoskeletal Fitness and Weight Gain in Canada
Bibliographic record
Abstract
BACKGROUND: Obesity is a growing health issue in Canada, and identifying the determinants of weight gain is important for the development of appropriate prevention strategies. PURPOSE: To quantify the association between musculoskeletal fitness (MSF) and subsequent weight gain and development of obesity. METHODS: The sample included 606 participants (20-69 yr; 291 men, 315 women) from the Physical Activity Longitudinal Study (PALS), a follow-up of participants from the 1981 Canadian Fitness Survey. Standardized assessments of height, weight, MSF (push-ups, sit-ups, grip strength, and trunk flexibility), and cardiorespiratory fitness were made at baseline (1981). Follow-up data on self-reported height and weight and body mass index (BMI) were collected by survey in 2002-2004. Logistic regression was used to predict obesity and weight gain of > or = 10 kg between 1981 and 2002-2004. RESULTS: During the 20-yr follow-up, the prevalence of obesity (BMI > or = 30 kg.m(-2)) increased from 3.1 to 15.2%, reflecting a mean weight gain of 7.4 kg (men: 6.7 kg; women: 8.1 kg). Further, independent of age, sex, baseline BMI, physical activity, cardiorespiratory fitness, smoking, alcohol consumption, and income, low MSF was associated with significantly higher odds of having gained at least 10 kg during follow-up (OR: 1.78, 95% CI: 1.14-2.79). CONCLUSIONS: The results indicate that MSF is a significant predictor of weight gain during a 20-yr period. Promoting participation in activities that enhance MSF may be beneficial in attenuating age-related weight gain and in preventing obesity among Canadians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".