Estimating leisure-time physical activity energy expenditure in the Canadian population: a comparison of 2 methods
Bibliographic record
Abstract
The purpose of this study was to determine the impact of 2 different calculation methods for total leisure-time physical activity energy expenditure (LTPAEE) and LTPAEE from different intensity activities on the classification of level of physical activity in the population. Nationally representative cross-sectional data from the Canadian Community Health Survey Cycle 4.1 (2007) were used for this study (N = 64, 397). LTPAEE was calculated using a metabolic equivalent (MET) value of 4.0 for all activities in the "other activity" category for method 1 (currently employed by Statistics Canada) and using activity-specific MET values for method 2. The weighted prevalence and 95% confidence intervals of active (> or =3 kcal.kg-1.day-1 (kkd)), moderately active (1.5-2.9 kkd), and inactive (<1.5 kkd) were determined for each method by demographic characteristics. The agreement between the 2 methods was assessed overall, and for light, moderate, and vigorous activities. There was no difference between methods in the proportion classified as active, moderately active, or inactive for any of the subgroups studied and there was no difference in the distribution or mean LTPAEE between methods. However, assessment of the agreement showed a large number of outliers and a tendency to underestimate LTPAEE from light and vigorous activities while overestimating LTPAEE from moderate activities at the individual level. The results of this study should be considered when performing inferential statistics on the relationship between physical activity and health outcomes.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".