Caffeine improves physical performance during 24 h of active wakefulness.
Bibliographic record
Abstract
BACKGROUND: Reductions in both cognitive and physical performance occur during periods of sleep loss with sustained operations. It was the purpose of this study to examine the effects of caffeine on activities chosen to simulate the physical challenges that might occur during a military scenario involving a period of sleep loss. METHODS: There were 16 subjects (26.7 +/- 7.8 yr, 83.8 +/- 11.0 kg) who completed a double-blind caffeine and placebo trial involving a control day and sleep period followed by 28 h of sleep deprivation. A 400-mg dose of caffeine was administered at 21:30 followed by subsequent 100-mg doses at 03:00 and 05:00. At 22:00, subjects began a 2-h forced march followed by a sandbag piling task. A treadmill run to exhaustion at 85% of maximal aerobic power was performed at 07:00 of the second day of sleep deprivation. RESULTS: Caffeine had no effect on the heart rate or oxygen consumption, but rating of perceived exertion (RPE) was reduced with caffeine during the forced march. Time to complete the sandbag piling task during set 1 was significantly reduced with caffeine (12.9 +/- 1.0 min) compared with placebo (13.8 +/- 1.0 min) but there was no difference during set 2 and RPE was increased. Time to exhaustion was significantly increased 25% during the run with caffeine (17.0 +/- 4.4 min) compared with placebo (13.5 +/- 3.3 min), and caffeine maintained performance at control levels (16.9 +/- 4.6 min). CONCLUSIONS: It was concluded that caffeine is an effective strategy to maintain physical performance during an overnight period of sleep loss at levels comparable to the rested state.
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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) | 0.001 | 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) | 0.002 | 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".