Physical activity, sedentary behavior, and sleep across lifespan in adults across European countries: Background and design
Bibliographic record
Abstract
Introduction Regular physical activity (PA) has benefits for health throughout the lifespan. PA benefits musculoskeletal, cardio metabolic health, and overall well-being (Kapoor et al., 2022). However, data from 2017 shows that about 55-83% of women and 47-74% of men from European countries (EU) do not meet these guidelines (Lübs et al,. 2018). Moreover, adults at the age of 40-54 (aOR: 0.65, 95% CI: 0.52-0.81) and 55-64 (aOR: 0.61, 95% CI: 0.49-0.77) are less likely to have moderate or high levels of PA in comparison with those 18-24 years of age. (Nikitara et al., 2021). Sedentary behavior (SB) including activities such as watching television, playing computer games, browsing the internet has increased (Wang et al., 2019). Canada has an established track record in the development of 24-hour movement guidelines on PA, SB, and sleep. They have previously release guidelines for all age groups. Following Canada’s lead, several jurisdictions including Australia, New Zealand, South Africa have incorporated the 24-hour movement concepts (Tremblay, 2020). There is a lack of data for 24-hour behavior guideline in the European context. There are no specific recommendations on SB and sleep. Therefore, this project will present the background, methods and design of a 24-hour movement summary of behaviors (PA, SB and sleep) in Europe. Methods We will utilize existing PA, SB and sleep data from the World Health Organization on European adults 18+. Analysis using mean, median, and 95% confidence Intervals, will be complemented by frequency distributions and histograms. These will be stratified by age and sex subgroups for a more comprehensive overview. Results The findings from this research have the potential to inform surveillance efforts, shape policies and public health strategies, improve overall well-being, and contribute to the development of evidence-based guidelines. Discussion/Conclusion Implications of this research may inform researchers on further questions to pursue, policy makers in resource allocation, and practitioners on where to focus intervention efforts. References Kapoor, G., Chauhan, P., Singh, G., Malhotra, N., & Chahal, A. (2022). Physical activity for health and fitness: Past, present and future. Journal of Lifestyle Medicine, 12(1), 9-14. https://doi.org/10.15280/jlm.2022.12.1.9 Lübs, L., Peplies, J., Drell, C., & Bammann, K. (2018). Cross-sectional and longitudinal factors influencing physical activity of 65 to 75-year-olds: A pan European cohort study based on the survey of health, ageing and retirement in Europe (SHARE). BMC Geriatrics, 18, Article 94. https://doi.org/10.1186/s12877-018-0781-8 Nikitara, K., Odani, S., Demenagas, N., Rachiotis, G., Symvoulakis, E. K., & Vardavas, C. (2021). Prevalence and correlates of physical inactivity in adults across 28 European countries. European Journal of Public Health, 31(4), 840-845. https://doi.org/10.1093/eurpub/ckab067 Tremblay, M. S. (2020). Introducing 24-hour movement guidelines for the early years: A new paradigm gaining momentum. Journal of Physical Activity and Health, 17(1), 92-95. https://doi.org/10.1123/jpah.2019-0401 Wang, N. X., Chen, J., Wagner, N., Rebello, S. A., Petrunoff, N., Owen, N., & Müller‐Riemenschneider, F. (2019). Understanding and influencing occupational sedentary behavior: A mixed-methods approach in a multiethnic Asian population. Health Education & Behavior, 47(3), 419-429. https://doi.org/10.1177/1090198119885431
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".