Associations between Physcial Activity, Body Mass Index, and Healthcare Utilization in Canadians with Diabetes and Cardiovascular Disease
Bibliographic record
Abstract
Background: Healthcare utilization (HCU) is elevated in individuals with a high body mass index (BMI), but the extent to which these relationships are influenced by physical activity (PA) in those with obesity-related diseases requires further study. PURPOSE: To quantify the relationship between self-reported PA, BMI, and HCU by number of overnight hospital stays and physician consultations in the previous year. METHODS: Data from Cycle 3.1 of the Canadian Community Health Survey (N=13 356; 20 to 64 y) was limited to participants with self-reported cardiovascular disease or diabetes. BMI was classified as normal weight (NW), overweight (OV), and obese (OB), and PA status as 'inactive' (<1.5 KKD) or 'active' (≥1.5 KKD). Hospital stays ('low':<10 nights; 'high' ≥10 nights) and physician consultations ('low': <7; 'high': ≥7) were dichotomized to compare across PA/BMI strata using logistic regression. Analyses were weighted to be representative of the Canadian population. RESULTS: In the past year, 7% of the sample had ≥10 hospital stays, and 27% had ≥7 physician consultations. Across BMI categories there were few differences in mean HCU, whereas within each BMI category, 'inactive' individuals had more frequent use. After adjusting for age and sex, compared to normal weight/active, the odds of ≥10 hospital stays were elevated only in those who were 'inactive' (Fig. 1a; ptrend <0.05); to a lesser extent this relationship persisted for 'high' physician consultations (Fig. 1b; ptrend <0.05). Implications: Similar to previously described relationships of a 'fit but fat' paradox for morbidity and mortality, these preliminary analyses suggest a corresponding association with physical inactivity and higher odds of HCU, independent of BMI.Figure
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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 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".