The Effect of Nighttime Snacking on Cognitive Function in Older Adults: Evidence from Observational and Experimental Studies
Bibliographic record
Abstract
Evidence shows that supplementary snacking could provide older adults with nutrients that cannot be obtained through three meals a day. However, whether and how supplementary snacking, especially nighttime snacking, affects older adults’ cognitive function remain unclear. The present study examined the effect of nighttime snacking on cognitive function for older adults. In study 1, we investigated the association between nighttime snacking and cognitive function based on data from 2618 community-dwelling older adults from the China health and nutrition survey (CHNS). In study 2, we conducted an experiment (n = 50) to explore how nighttime acute energy intake influences older adults’ performance on cognitive tasks (immediate recall, short-term delayed recall, and long-term delayed recall). Both the observational and experimental studies suggested that nighttime snacking facilitated older adults’ cognitive abilities, such as memory and mathematical ability, as indicated by subjective measures (study 1) and objective measures (studies 1 and 2). Moreover, this beneficial effect was moderated by cognitive load. These findings bridge the gap in the literature on the relationships between older adults’ nighttime snacking and cognitive function, providing insight into how to improve older adults’ dietary behaviors and cognitive function.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".