Physical Activity and Sedentary Behaviour are Associated with Cognitive Function in Healthy Older Adults But Not Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
PURPOSE: Given the staggering impact of dementia worldwide—and lack of effective treatment options—investigating non-pharmaceutical solutions to reduce dementia incidence are greatly needed. Increased moderate-to-vigorous physical activity (MVPA) and limited sedentary behavior (SB) are both pillars of healthy cognitive aging. However, it is unclear if changes in cognitive status—such as developing Mild Cognitive Impairment (MCI) which is a precursor to dementia—can affect the associations between MVPA, SB and cognition. Thus, we investigated how MCI can affect the association between MVPA, SB and cognitive function. METHODS: We observed MVPA and SB of adults aged 55+ (N=122) for 14 days using the MotionWatch 8©. Following observation, participants were screened for MCI using the Montreal Cognitive Assessment (MoCA) with a score of <26 indicating probable MCI. We measured cognitive function using Digit Span (DS; Forward - Backward), Trail Making Test (TMT; Trail B - Trail A), and Animal Fluency (AF). We conducted bivariate analyses—stratified by MCI status—for associations between MVPA, SB and cognitive function. Regression models were generated for each cognitive task stratified by MCI status while controlling for age, sex, and education. RESULTS: Participants (71.64 ± 7.25 years) were predominantly female (65.10%) and retired (84.90%). Participants spent an average 59.45% (SD=12.38%) and 10.26% (SD=6.74%) of the day in SB and MVPA, respectively. Bivariate analyses for adults without MCI indicated higher SB was associated with poorer performance on DS (r=0.47, p<0.01) and TMT (r=0.31, p=0.03); higher MVPA was associated with improved performance on DS (r=-0.45, p<0.01) and AF (r=0.29, p=0.04). However, no associations between SB or MVPA and cognitive function were found for individuals with MCI. For those without MCI, the regression model indicated both increased MVPA (β= -0.31, p=0.02) and reduced SB (β= 0.34, p<0.01) were associated with better DS performance, with both behaviours being the strongest contributors to DS performance. The models for MCI participants were not significant. CONCLUSIONS: The influence of MVPA and SB on cognitive function may have the greatest effect on older adults prior to development of MCI. Encouraging regular MVPA and reducing SB may promote healthy cognitive aging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".