UNDERSTANDING PHYSICAL ACTIVITY BEHAVIOUR IN CANADIANS LIVING WITH CHRONIC DISEASE: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Regular physical activity (PA) can reduce the incidence of many chronic diseases. Rural-dwelling Canadians are at a higher risk of developing chronic diseases than their urban counterparts – potentially due to higher rates of inactivity. There is a scarcity of literature describing PA in these high-risk groups. Smartphones and mHealth apps such as Carrot Rewards (reward-based app downloaded by 1.3+ million Canadians) provide a unique opportunity to measure free-living PA amongst Canadians living with chronic disease. PURPOSE: To determine (1) daily step count averages (data collected by Carrot Rewards) for participants who self-report at least one chronic disease vs. those self-reporting none, and (2) whether these averages vary with living environment. METHODS: In this retrospective cohort study, 12,327 Ontarians (age: M=34.72, SD=13.63, gender: female 62.9%, male 35.3%, other 1.8%), completed a ‘chronic disease’ Carrot Rewards survey adapted from the Canadian Community Health Survey. In this survey, participants could self-report chronic disease diagnoses including: diabetes, cardiovascular disease, chronic obstructive pulmonary disease, cancer and mood/anxiety disorders. Smartphone accelerometers, (HealthKit (iOS), Google Fit (Android) or FitBit) collected step count data which was retrieved by the Carrot Rewards app. Self-reported demographic information indicated participant rural/urban status. RESULTS: 37.7% of survey respondents reported being diagnosed with at least one chronic disease and 33% identified as rural-dwelling. Participants with at least one chronic disease had a significantly lower (p<.001) daily step count average (M=5136.29, SD=3732.83) than those with no diagnosis (M=5724.24, SD=3960.47). Rural-dwelling persons (M=5422.40, SD=3943.49) had lower mean daily step count averages than their urban counterparts (M=5542.61, SD=3858.32), though not statistically significant (p=.123). CONCLUSIONS: This study provides an objective lens into the PA behaviours of understudied Canadian populations. Individuals living with chronic disease had significantly lower daily step counts when compared to their ‘healthy’ counterparts. A fundamental understanding of PA behaviours for at-risk Canadians may help inform the design of targeted PA interventions in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".