The Relationship Between Cognitive Decline and Sedentary Time
Bibliographic record
Abstract
Abstract Early identification of functional decline in older adults with mild cognitive impairment (MCI) provides the opportunity to initiate behavioral interventions to slow decline. More frequent breaks in sedentary time has been associated with greater lower extremity function. This longitudinal study examined the effect of 6-month change in cognitive function on monthly sedentary time, controlling for lower extremity function, among community-dwelling older adults with MCI. Twenty adults with Montreal Cognitive Assessment Score (MoCA) between 19-25, who were age ≥ 60 years old, and ambulatory, wore an actigraph for 6 months and participated in monthly in-person assessments. Measures included MoCA change (baseline to month 6), Short Physical Performance Battery (SPPB; baseline, months 3 and 6); sedentary time and physical activity intensity; and falls (monthly). The sample was 70% female, 60% non-Hispanic white, with a mean age of 77 years. Sixteen participants provided complete data for mixed-model analysis. Over 6 months, 11 falls occurred among 7 participants. The mean MoCA score declined from 22.7 to 21.9 while SPPB remained stable. Overall time spent in sedentary behavior was high (71%) and physical activity intensity was low (light and moderate combined= 26.1%). Results of multi-level analysis with sedentary time as a continuous Level-1 variable and MoCA change scores, SPPB scores, and age in Level-2 showed that negative change in MoCA (β=-0.11; p≤0.05) was associated with increased sedentary time. Given sedentary time increases as cognitive function declines, older adults with MCI could benefit from interventions designed to interrupt sedentary time as well as increase physical activity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".