Incidental movement, lifestyle-embedded activity and sleep: new frontiers in physical activity assessmentThis article is part of a supplement entitled <i>Advancing physical activity measurement and guidelines in Canada: a scientific review and evidence-based foundation for the future of Canadian physical activity guidelines</i> co-published by <i>Applied Physiology, Nutrition, and Metabolism</i> and the <i>Canadian Journal of Public Health</i>. It may be cited as Appl. Physiol. Nutr. Metab. 32(Suppl. 2E) or as Can. J. Public Health 98(Suppl. 2).
Bibliographic record
Abstract
Canadian public health messages relating to physical activity have historically focused on the prescription of purposeful exercise, most often assessing leisure-time physical activity (LTPA). Although LTPA contributes to total energy expenditure (TEE), a large part of the day remains neglected unless one also considers the energy expended outside of purposeful exercise. This paper reviews the potential impact of incidental (non-exercise or non-purposeful) physical activity and lifestyle-embedded activities (chores and incidental walking) upon TEE and indicators of health. Given that incidental movement occurs sporadically throughout the day, this form of energy expenditure is perhaps most vulnerable to increasingly ubiquitous mechanization and automation. The paper also explores the relationship of physical inactivity, including sleep, to physical activity, TEE, and health outcomes. Suggestions are provided for a more comprehensive physical activity recommendation that includes all components of TEE. Objective physical activity monitors with time stamps are considered as a better means to capture and examine human movements over the entire day.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".