Benefits of Sleep Extension on Sustained Attention and Sleep Pressure Before and During Total Sleep Deprivation and Recovery
Bibliographic record
Abstract
OBJECTIVES: To investigate the effects of 6 nights of sleep extension on sustained attention and sleep pressure before and during total sleep deprivation and after a subsequent recovery sleep. DESIGN: Subjects participated in two experimental conditions (randomized cross-over design): extended sleep (EXT, 9.8 ± 0.1 h (mean ± SE) time in bed) and habitual sleep (HAB, 8.2 ± 0.1 h time in bed). In each condition, subjects performed two consecutive phases: (1) 6 nights of either EXT or HAB (2) three days in-laboratory: baseline, total sleep deprivation and after 10 h of recovery sleep. SETTING: Residential sleep extension and sleep performance laboratory (continuous polysomnographic recording). PARTICIPANTS: 14 healthy men (age range: 26-37 years). INTERVENTIONS: EXT vs. HAB sleep durations prior to total sleep deprivation. MEASUREMENTS AND RESULTS: Total sleep time and duration of all sleep stages during the 6 nights were significantly higher in EXT than HAB. EXT improved psychomotor vigilance task performance (PVT, both fewer lapses and faster speed) and reduced sleep pressure as evidenced by longer multiple sleep latencies (MSLT) at baseline compared to HAB. EXT limited PVT lapses and the number of involuntary microsleeps during total sleep deprivation. Differences in PVT lapses and speed and MSLT at baseline were maintained after one night of recovery sleep. CONCLUSION: Six nights of extended sleep improve sustained attention and reduce sleep pressure. Sleep extension also protects against psychomotor vigilance task lapses and microsleep degradation during total sleep deprivation. These beneficial effects persist after one night of recovery sleep.
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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) | 0.002 | 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".