Physical activity in a multi ethnic Canadian population: The association of physical activity with cardiovascular risk factors and exploration of physical activity patterns
Bibliographic record
Abstract
Obesity has become a global epidemic in children and adults and a decline in physical activity (P A) has contributed to this phenomenon. Low P A has been identified as a risk factor for various diseases, including cardiovascular disease. Past literature has identified variations in P A by sex, ethnicity and socioeconomic status (SES), but the majority of studies have been restricted to American populations. This thesis explored trends in total, work related and leisure time PAin a multiethnic Canadian population. Analyses were conducted using data from the SHARE, SHARE-AP and SHAREAP Action studies. The reliability and validity of physical activity questionnaires (P AQs) were assessed using Pearson and Spearman's rank correlation. Risk factors by PA level were compared using analysis of variance (ANOVA) for continuous variables and chisquare tests for categorical variables. Fisher's exact test was used to determine whether risk factor clustering by P A level was more evident when using P AQs or the RT3 accelerometer (RT3), and Cohen's Kappa coefficient was used to determine the agreement between the PAQs and the RT3 in classifying participants. The Chi-square goodness of fit test was used to determine differences in P A trends and multiple binary logistic regression was performed to determine variable association with low total P A. Reliability coefficients of all PAQs ranged from 0.06 to 0.80 while validity assessments ranged from -0.07 to -0.17 against systolic blood pressure for the modified ARIClBaecke Questionnaire and -0.10 to -0.30 for the PAQs used in SHARE-AP Action. Greater risk factor clustering was seen in low P A groups compared to high PAin SHARE and SHARE-AP data. However, no such association was seen in SHARE-AP Action and there was poor agreement in PA level classification between the PAQs and the RT3. Differences by sex, ethnicity and SES were apparent in different contexts ofPA. As well, low P A was associated with South Asian and Chinese ethnicity, increasing age, low SES and Aboriginals in the low SES category. These findings show that differences in P A exist between different groups. Identifying populations prone to inactivity can assist in the development of health promotion strategies that target individuals susceptible to low PA.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".