Energy expenditure per minute in different activities and body positions and its association with the classification as physically active or inactive in daily life in individuals with COPD
Bibliographic record
Abstract
Objective: To describe and compare energy expenditure (EE)/minute walking and in different body postures in individuals with COPD; and to investigate if EE/minute walking is a predictor of their classification as physically active or inactive. Methods: Physical activity (PA) in daily life was objectively assessed using two PA monitors for 7 days and data were analyzed on a minute-by-minute basis. Predominant minutes were separated into walking, standing, sitting, and reclined, and EE/minute (a reflection of PA intensity) was then calculated in each of these four activities and postures. Participants were classified as active and inactive according to the criteria proposed by the American College of Sports Medicine (ACSM). Results: 43 individuals were evaluated (65±8 years; FEV 1 50±14% predicted). A binary logistic regression model yielded that, regardless of the time spent walking/day, EE/minute walking was a significant predictor of the classification as physically active (OR=18.2 [2 – 165]; p=0.01), together with BMI (OR=0.68 [0.5 - 0.9]; p=0.008) (model: Chi-square = 22.431, p< 0.05; R 2 [Nagelkerke] = 0.556). In the active group, significantly higher EE/minute was observed for walking and standing in comparison both to sitting and reclined. However, in the inactive group, there were significant differences in EE/minute only when comparing walking versus reclined and standing versus reclined. Conclusion: In individuals, with COPD, EE/minute walking is a significant predictor of being classified as physically active, independently of the time spent walking/day. Each additional kilocalorie/minute spent walking increases in 18 times the chances to be classified as physically active in daily life.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".