The cost of physical inactivity: moving into the 21st century
Bibliographic record
Abstract
Physical inactivity is increasingly being recognised as a major problem in global health. The WHO estimates that 3.3 million people die around the world each year due to physical inactivity, making it the fourth leading underlying cause of mortality.1 Physical activity has beneficial effects on 23 diseases or health conditions.2 However, in most countries fewer than half of adults are active enough to reap most of these benefits.3 ,4 Given that inactivity increases the risk for many of the most costly medical conditions such as type 2 diabetes, stroke, ischaemic heart disease, falls and hip fractures, and depression, it is not surprising that physical inactivity has a substantial cost burden in addition to a large health burden. Despite impressive health and economic consequences, it is only recently that addressing physical inactivity has become a mainstream part of public health and health policy.5 However, this is clearly occurring. The WHO Global Action Plan for NCDs emphasises physical activity as an important element of primary and secondary prevention, WHO released a global recommendations for physical activity in 2010,6 the September 2011 United Nations General Assembly Summit on NCDs prominently include physical activity4 and national public health policy in influential countries such as Brazil and the USA substantively incorporates physical activity promotion.7 ,8 However, these are initial steps in addressing a global epidemic of NCDs and inactivity. The gap between the size of the problem and the scale of the public health response remains large. In such situations, effective advocacy is called for9 and often times this means economic data which highlight the costs of not taking action. This seems to be an argument for more and better analyses and research publications on the costs of physical inactivity. However, in the following paragraphs …
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".