Impact Of Covid-19 Stay-at-home Restrictions And Employment Changes On Physical Activity And Sedentary Behavior Changes
Bibliographic record
Abstract
Perceived barriers are associated with decreased moderate-vigorous physical activity (MVPA). COVID-19 pandemic restrictions may strengthen these barriers, causing a decrease in MVPA and increase in sedentary behavior (SB) across United States and Canada. PURPOSE: This study compared self-reported work-, recreation-, and travel-related MVPA and SB prior to and during COVID-19 “stay at home” restrictions across North America. METHODS: The Global Physical Activity Questionnaire was distributed via email and social media from April 21 to May 9, 2020. One- and two-way ANOVAs were used to compare data overall and by group (country, age, sex, race, income and education). RESULTS: After removing incomplete/invalid surveys, 1,336 responses (1,228 from USA, 991 females, 1,187 Caucasian, 634 between 18-29 y) were analyzed. As a result of pandemic restrictions, 51.4% (n = 687) of the sample shifted to work from home and 12.1% (n = 162) lost their job. Overall, during COVID restrictions, 8.3% (n = 110) fewer reported work-related MVPA with a reduction of 178.6 ± 20.9 min/wk (range: -67.6 ± 32.2 min/wk in those with no change in employment to -611.0 ± 91.3 min/wk in those who lost their job). Canada reported less of a decrease in work-related MVPA (-109.4 ± 47.3 min/week; adj. p = 0.08) compared to United States (-184.6 ± 22.4; adj. p < 0.0001). Similarly, 28.0% (n = 374) fewer reported travel-related MVPA, with more of a decrease in females and younger age groups, and no difference across employment change. While 7.3% (n = 98) fewer reported recreational MVPA, there was no significant decrease (-30.4 ± 11.5 min/wk) in reported time overall and in all group comparisons. There was a significant increase in time spent in SB (watching TV and sitting; +94.9 ± 4.1 min/wk) in all groups, except the oldest age group (70+ y). There was no change in PA mode, with locomotive and fitness classes as the predominant mode at both timepoints. Of the 68% (n = 709) participants who reported using PA facilities (gyms, clubs, etc.), 31.3% (n = 418) stated they would not return, with “being unsafe” as the most predominant reason. CONCLUSIONS: As hypothesized, pandemic restrictions had a negative impact on PA and SB in both Canada and United States. More research is needed to examine the impact of these pandemic restrictions long-term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".