Sedentary time in older adults: absolute versus relative measures and their respective association with health conditions and multimorbidity
Bibliographic record
Abstract
Older adults (OA) accumulate a greater amount of sedentary time (ST) compared to other subgroups of the population, which is associated with deleterious effects on multiple health outcomes and mortality. This study compared absolute sedentary behavior time (ASBT), which is generally used in studies, to relative SB time (RSBT), defined as the percentage of daily wake time, for their respective association with health conditions and multimorbidity. Two-thousand-four-hundred-sixty-one older adults (65–79 years) participated in the Canadian Health Measures Survey (2007–2017) and wore an accelerometer for ≥4 days, including a weekend day. Information regarding six health condition categories was extracted: cancer, cardiovascular, metabolic, musculoskeletal, psychological, and pulmonary. We combined these health conditions to create a multimorbidity variable. Participants were divided into ASBT quartiles and RSBT quartiles. Comparing the most sedentary (Q4) to the least sedentary (Q1) groups, we found no significant associations with any health conditions for ASBT. However, the same comparison for RSBT showed that RSBT-Q4 (the most sedentary; n = 660), compared to RSBT-Q1 ( n = 660), was associated with a significant ( p < 0.01) greater prevalence of cancer (160 > 110), cardiovascular (422 > 326), metabolic (276 > 194), and musculoskeletal (407 > 345), as well as multimorbidity (462 > 350). After adjusting for confounding factors (moderate to vigorous physical activity, housing, income, education level, relationship status, accelerometer wear season, and status of drinking and smoking), these associations persisted with the exception of musculoskeletal conditions. These results demonstrate that RSBT could be more appropriate to capture the association between a sedentary lifestyle and health profiles in older adults.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".