Physical activity levels are associated with sleep efficiency and cerebral blood flow in healthy older adults
Bibliographic record
Abstract
Abstract Background Identifying modifiable risk factors that prevent or delay the onset of dementia remains a priority. While there is evidence that physical activity (PA) can improve cognition (Hillman et al., 2008; Kramer & Colcombe 2018) and prevent cognitive decline (Sofi et al., 2011; Hamer & Chida, 2009), our understanding of the mechanisms through which PA exerts these effects remains unclear. Two recently proposed mechanisms which warrant further investigation are cerebral blood flow (CBF) and sleep efficiency (Stillman et al., 2016). Method We examined relationships between PA, sleep efficiency, CBF, and performance on neuropsychological assessments (MoCA, Flanker, CVLT) in a sample of non‐demented, older adults (N=43, 67% female, age=66.67+6.83). An average of 36.97 days of objective PA and sleep efficiency were recorded using the GENEActiv tri‐axis accelerometer prior to participants’ in‐person visits. CBF was quantified using a pseudo‐continuous arterial spin labeling MRI scan. Linear regression analyses adjusting for age, sex, and education were conducted to examine the relationships between PA, sleep efficiency, CBF, and cognition. As an exploratory analysis we examined the moderating effect of APOE4 status. Result Higher levels of light PA were associated with greater levels of global CBF (p=0.03) which was in turn associated with higher MoCA scores (p=0.03). In APOE4 non‐carriers (n=32), higher levels of vigorous PA were associated with greater sleep efficiency (p=0.03). No significant relationships between PA and cognition or between sleep efficiency and cognition were observed. Additionally, CBF was not a significant mediator of the relationship between PA and cognition. Exploratory analyses revealed a significant moderating effect of APOE4, such that elevated right hippocampal CBF was associated with poorer sleep efficiency in APOE4 carriers only (p<0.01). Conclusion These results provide evidence that baseline levels of light PA are associated with greater CBF throughout the brain. However, more intense levels of PA are necessary to influence sleep efficiency and differ based on APOE4 status. The lack of relationship between PA and cognition may be due to the fact that participants did not meet a necessary PA threshold to elicit a relationship. Future intervention studies assessing whether increasing levels of PA impacts sleep efficiency, CBF, and cognition are needed.
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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 | 0.000 | 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) | 0.002 | 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".