A 90 min Daytime Nap Opportunity Is Better Than 40 min for Cognitive and Physical Performance
Bibliographic record
Abstract
This study examined the effects of different nap durations on attention and physical performance as well as mood states, sleepiness, perceived exertion (RPE), recovery (PRS), and muscle soreness (DOMS) in trained men. Fourteen amateur team sport players (age: 20.3 ± 3.0 years, height: 173.1 ± 6.7 cm, body-mass: 68.1 ± 6.6 kg) performed a maximal voluntary isometric contraction (MVIC) test, 5-m shuttle run, and the digit-cancellation (i.e., attention) test after a no-nap (N0) and 40-min (N40) and 90-min (N90) of nap opportunities. Subjective measurement of mood states, RPE, PRS and DOMS were determined. Compared to N0, both nap durations enhanced attention, MVIC, total distance (TD), and higher distance (HD) (p < 0.001), with a higher gain after N90 compared to N40 for attention (Δ = +3), MVIC (Δ = +30 N) and TD (Δ = +35 m) (p < 0.001). Total mood scores were better after N40 and N90 compared to N0 (p < 0.05), with lower scores after N90 compared to N40 (p < 0.05). DOMS and RPE scores were significantly lower and PRS was significantly higher after N40 and N90 compared to N0 and after N90 compared to N40 (p < 0.05). Although both nap opportunity durations were beneficial, N90 was better than N40 for improving physical performances and attention as well as the perception of recovery, reducing fatigue perception, muscle soreness, and negative mood states.
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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) | 0.003 | 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".