Occupational physical activity as a target for obesity prevention: a lack of effect or a lack of evidence?
Bibliographic record
Abstract
Overweight and obesity are increasingly prevalent in high-income countries, with societal shifts towards sedentary living and poorer quality diets regarded as major contributors to an energy imbalance of increased energy intake and reduced energy expenditure.1 By focusing on the ‘energy expenditure’ side of the energy balance equation, studies suggest that increasing physical activity (PA) levels can be an effective strategy to prevent or minimise weight gain in adults.2 Occupational PA can be an important intervention target as most adults will participate in the labour market at some point of their lives and can spend a third of their day or more at work. There is mixed evidence for the association between occupational PA and changes in weight gain. It also is unclear whether reverse causation is a possibility, that having a higher weight at baseline is related to a risk of later occupational physical inactivity. The study by Sagelv et al 3 helps fill this research gap. The study of 11 308 participants assessed over three or more consecutive follow-up periods over four decades and found no association between changes in occupational PA and future body mass index (BMI) and weight changes after adjusting for previous PA levels. No effect modification was also found in the association between occupational PA and BMI …
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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 | 0.049 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".