Device-Measured Physical Activity Before and Throughout the COVID-19 Pandemic in Canada: Results from the INTERACT and COVFIT Studies
Bibliographic record
Abstract
The COVID-19 pandemic disrupted daily routines and prompted widespread changes in health behaviours, including physical activity. This dissertation explores how adult physical activity changed throughout the pandemic in Canada and evaluates the potential of consumer wearable devices to study behaviour change over time and in response to disruptions. I addressed three independent but related research objectives using data from two cohort studies. Objective 1 was to estimate the effect of the pandemic and pandemic-related restriction stringency on daily minutes of moderate-to-vigorous physical activity (MVPA) in fall 2020. I conducted a secondary analysis of INTERACT, an open cohort of adults in Vancouver, Montreal, and Saskatoon, which collected accelerometer data before (2018–2019) and during (2020–2021) the pandemic. Daily MVPA was 21% lower during the pandemic (Oct 2020–Feb 2021) than before (May 2018–Feb 2019), adjusting for sociodemographic factors, weather, and wear time. Restriction stringency was not associated with MVPA during this period. I addressed my second and third objectives using retrospective Apple Watch data from COVFIT, a cohort study of Canadian Apple Watch users that I led and designed. Objective 2 was to estimate the effect of software and hardware upgrades on Apple Watch measurements of daily active calories and exercise minutes. Comparing the 7 days before and after a major upgrade, I found no effects of hardware version but observed changes in both outcomes after some software upgrades. These findings suggest that software version may confound longitudinal analyses of Apple Watch data. Objective 3 was to identify and describe trajectories of daily exercise minutes from March 2020 to December 2022. I identified three groups (high, moderate, and low activity), all of which increased activity over time, with the greatest relative gains in the low group. Cluster membership was associated with education, income, exercise motivation, exercise enjoyment, and baseline health, but not age or employment status. These results describe both the short-term impact of the COVID-19 pandemic on physical activity and common longer-term patterns of recovery. They also highlight the potential benefits and limitations of using consumer wearable data to study longer-term behavioural trajectories during the pandemic and beyond.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".