Don’t Walk So Close to Me: Physical Distancing and Adult Physical Activity in Canada
Bibliographic record
Abstract
Background: In response to the COVID-19 pandemic, physical distancing measures have been implemented globally. Canadians have been instructed to stay at home which has likely resulted in significant changes in their physical activity. Using data from a national physical activity tracking app (PAC app), we aimed to determine device-measured physical activity levels immediately prior to and following the implementation of physical distancing measures in Canada to provide evidence for the development of physical activity recommendations for future pandemics or second-wave infections. Methods: Demographic and physical activity data were extracted from the PAC app, using a 10-week (10 February to 19 April 2020) quasi-experimental design to determine changes in physical activity 4 weeks pre- and 6 weeks post-pandemic declaration. Weekly physical activity levels were monitored through wearable fitness trackers and health apps linked to the PAC app, to record moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), and steps. Repeated measure ANOVA were used to determine changes over time (mean SE). Findings: A total of 2,338 Canadians who were mostly 35-44 years old (26.6%), and female (90.2%) were included in the analysis. MVPA, LPA, and steps significantly declined immediately following the declaration of the pandemic (MVPA: pre-pandemic: 194.25.2 min, post-pandemic: 176.75.0min, p<0.001; LPA: pre-pandemic: 1000.517.0min, post-pandemic: 874.115.6min, p<0.001; steps: pre-pandemic: 48,625745 steps, post-pandemic: 43395705 steps, p<0.001). However, 6 weeks following pandemic declaration, MVPA (week 6: 204.45.4 min, p=0.498) had returned to pre-pandemic levels. LPA (week 6: 732.014.3 min, p=<0.001) and steps (week 6: 41,946763, p<0.001) remained significantly lower than pre-pandemic levels at week 6. Interpretation: Although MVPA returned to pre-pandemic levels, significant and sustained declines in incidental LPA and steps were observed. Attenuating the loss of incidental physical activity should be a public health priority in response to future pandemics or a second wave of a COVID-19 infection, as it may have significant long-term implications for the physical and mental health of Canadians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".