Sedentary Time and Metabolic Risk in Extremely Active Older Adults
Bibliographic record
Abstract
OBJECTIVE Increasing evidence suggests that time spent sedentary predicts increasing cardiometabolic risk independent of other physical activity. We objectively measured activity levels in active older adults and examined the association between sedentary behavior and the continuous metabolic syndrome risk score (cMSy). RESEARCH DESIGN AND METHODS Older adults (age ≥65 years) were recruited from the Whistler Masters ski team, a group of active older adults who undergo organized group training. Daily activity levels were recorded with accelerometers (SenseWear) worn for 7 days. A compositional approach was used to determine proportion of the time spent sedentary as compared with all other nonsedentary behaviors (isometric log-ratio transformation for time spent sedentary [ILR1]). Waist circumference, triglycerides, HDL, systolic blood pressure, and fasting glucose were measured, and cMSy was calculated using principal component analysis (sum of eigenvalues ≥1.0). RESULTS Fifty-four subjects (30 women and 24 men, mean ± SE age 71.4 ± 0.6 years) were recruited. Subjects demonstrated high levels of physical activity (2.6 ± 0.2 h light activity and 3.9 ± 0.2 h moderate/vigorous activity). In our final parsimonious model, ILR1 showed a significant positive association with increasing cMSy (standardized β = 0.368 ± 0.110, R2 = 0.40, P = 0.002), independent of age and biological sex. CONCLUSIONS Despite high levels of activity, ILR1 demonstrated a strong association with cMSy. This suggests that even in active older adults, sedentary behavior is associated with increasing cardiometabolic risk.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".