The effects of napping on night‐time sleep in healthy young adults
Bibliographic record
Abstract
Summary The discrepancies in the effects of napping on sleep quality may be due to differences in methodologies, napping behaviours, and daytime activity levels across studies. We determined whether napping behaviours and daytime activity levels are associated with night‐time sleep fragmentation and sleep quality in young adults. A total of 62 healthy adults (mean [SD] age 23.5 [4.2] years) completed screening questionnaires for sleep habits, physical activity, medical and psychological history. Actigraphy was used to record sleep including naps. The fragmentation algorithm (KRA) was applied to the actigraphic data to measure night‐time sleep fragmentation. We classified participants’ nap frequency as “non‐nappers” (0 naps/8 days), “moderate nappers” (1–2 naps/8 days) or “frequent nappers” (≥3 naps/8 days) naps. Nap duration was defined as “short” (≤60 min) or “long” (>60 min). Naps’ proximity to the night sleep episode was defined as “early” (≥7 h) and “late” (<7 h) naps. Outcome variables were night‐time KRA and actigraphic sleep variables. Frequent nappers had a significantly higher KRA than moderate nappers (p < 0.01) and non‐nappers (p < 0.02). Late naps were associated with poorer measures of night sleep quality versus early naps (all p ≤ 0.02). Nap duration and daytime activity were not associated with significant differences in the outcome variables (all p > 0.05). KRA correlated with sleep duration, sleep efficiency, and awakenings (r = −0.32, −0.32, and 0.53, respectively; all p < 0.05). Frequent napping and late naps may be associated with increased sleep fragmentation and poorer sleep quality, reflected in longer sleep onsets and increased awakenings. These findings have implications for public health sleep hygiene recommendations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".