Acute evening high-intensity interval training may attenuate the detrimental effects of sleep restriction on long-term declarative memory
Bibliographic record
Abstract
Recent evidence shows that a nap and acute exercise synergistically enhanced memory. Additionally, human-based cross-sectional studies and animal experiments suggest that physical exercise may mitigate the cognitive impairments of poor sleep quality and sleep restriction, respectively. We evaluated whether acute exercise may offset sleep restriction's impairment of long-term declarative memory compared to average sleep alone. A total of 92 (82% females) healthy young adults (24.6 ± 4.2 years) were randomly allocated to one of four evening groups: sleep restriction only (S5, 5-6 h/night), average sleep only (S8, 8-9 h/night), high-intensity interval training (HIIT) before restricted sleep (HIITS5), or HIIT before average sleep (HIITS8). Groups either followed a 15-min remote HIIT video or rest period in the evening (7:00 p.m.) prior to encoding 80 face-name pairs. Participants completed an immediate retrieval task in the evening. The next morning a delayed retrieval task was given after their subjectively documented sleep opportunities. Long-term declarative memory performance was assessed with the discriminability index (d') during the recall tasks. While our results showed that the d' of S8 (0.58 ± 1.37) was not significantly different from those of HIITS5 (-0.03 ± 1.64, p = 0.176) and HIITS8 (-0.20 ± 1.28, p = 0.092), there was a difference in d' compared to S5 (-0.35 ± 1.64, p = 0.038) at the delayed retrieval. These results suggest that the acute evening HIIT partially reduced the detrimental effects of sleep restriction on long-term declarative 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) | 0.001 | 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".