Physical activity and inactivity profiling: the next generationThis 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
The accurate measurement of habitual physical activity is fundamental to the study of the relationship between physical activity and health. However, many physical activity measurement techniques produce variables accurate to only the day level, such as total energy expenditure via self-report questionnaire, pedometer step counts, or accelerometer measurements of minutes of moderate to vigorous physical activity. Monitoring technologies providing more detailed information on physical activity and inactivity behaviour can now be used to explore the relationships between health and movement frequency, intensity, and duration more comprehensively. This paper explores the activity–inactivity profile that can be acquired through objective monitoring, with a focus on accelerometry. Using previously collected objective data, a detailed physical activity profile is presented and case study examples of data utilization and interpretation are provided. The rich detail captured through comprehensive profiling creates new surveillance and study possibilities and could possibly inform new physical activity guidelines. Data are presented in various formats to demonstrate the dangers of misinterpretation when monitoring population adherence to Canada’s physical activity guidelines. Recommendations for physical activity–inactivity profiling are provided and future research needs identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".