SHORT-TERM INTRAINDIVIDUAL DYNAMICS OF HEART RATE AND COGNITIVE FUNCTIONING IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Short-term intraindividual variability in cognition can provide insights into the underlying processes that cannot be captured by only examining mean levels (MacDonald et al., 2003). This variability is often dependent on a number of individual or contextual characteristics. Thus, modeling the heterogeneity of the within-person variance and including meaningful covariates to account for why individuals are more variable on some occasions than others is an alternate way to understand process. The present study utilized a 14-day intensive measurement design to examine the effects of fluctuations in daily heart rate on variability in cognitive performance. Fifty-five older adults (Mage = 70.1 years) completed daily cognitive tasks and measures of well-being, while also wearing an accelerometer to capture physical activity, sleep, and continuous heart rate. Data were examined with a mixed effects location scale model (Hedeker et al., 2008; Rast et al., 2012), which incorporates both an individual’s measured level (location) and their variability around that level (scale). Both the location and the scale are permitted to be random such that on any given occasion an individual may deviate from their typical level and they may also deviate from their typical amount of variability. In this way, the location scale model allows for the heterogeneity of the variance in cognition to be modeled and accounted for with other covariates. Results revealed that there were significant amounts of variability in daily heart rate and cognition across 14 days. The impact of heart rate variability on cognitive performance will be discussed.
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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 | 0.005 |
| 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 | 0.001 | 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".