Exercising before a nap benefits memory better than napping or exercising alone
Bibliographic record
Abstract
Sleep leads to the enhancement of memory, and physical exercise also improves memory along with beneficial effects on sleep quality. Potentially, sleep and exercise may operate independently upon memory; alternatively, they may operate synergistically to boost memory above and beyond exercise or sleep alone. We tested this hypothesis in 115 young healthy adults (23 ± 3.9 years) randomly allocated to one of the four conditions in a 2 (exercise vs. no exercise) × 2 (nap vs. no nap) design. The exercise intervention consisted of a 40-minute, moderate intensity cycling, while the no exercise condition was an equivalent period of rest. This was followed by a learning session in which participants memorized a set of 45 neutral pictures for a later test. Subsequently, participants were exposed to either a 60-minute sleep period (nap) or an equivalent time of resting wakefulness, followed by a visual recognition test. We found a significant interaction between the effects of exercise and nap (p = 0.014, η p2 = 0.053), without significant main effects of exercise or nap conditions. Participants who experienced both exercise plus nap were significantly more accurate (83.8 ± 2.9) than those who only napped (81.1 ± 5.4, p = 0.027) and those who only exercised (78.6 ± 10.3, p = 0.012). Within the combined nap plus exercise group, higher recognition accuracies were associated with higher sleep spindle densities (r = 0.46, p = 0.015). Our results demonstrate that short-term exercise and a nap improve recognition memory over a nap or exercise alone. Exercise and sleep are not independent factors operating separately upon memory but work together to enhance long-term memory.
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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 | 0.000 |
| 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) | 0.003 | 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".