Why so slangry (sleepy and angry)? Shorter sleep duration and lower sleep efficiency predict worse next‐day mood in adolescents
Bibliographic record
Abstract
PURPOSE: The goal of this study was to evaluate the relationships of actigraphic nighttime sleep duration and quality with next-day mood among urban adolescents using a micro-longitudinal design. METHODS: A subsample (N = 525) of participants from the Fragile Families & Child Wellbeing Study (mean age: 15.4 years; 53% female; 42% Black non-Hispanic, 24% Hispanic/Latino, 19% White non-Hispanic) in the United States between 2014 and 2016 concurrently wore a wrist actigraphic sleep monitor and rated their daily mood in electronic diaries for about 1 week. Multilevel models tested the within-person temporal associations of nightly sleep duration and sleep maintenance efficiency with next-day reports of happiness, anger, and loneliness. The models also tested the between-person associations of sleep variables and mood. Models adjusted for sociodemographic and household characteristics, weekend, and school year. RESULTS: After nights when adolescents obtained longer sleep duration than their usual, they reported lower ratings of anger (B = -.03, p < .01) the next day. After nights when adolescents had higher sleep maintenance efficiency than their usual, they reported higher ratings of happiness (B = .02, p < .01) the next day. Adolescents who had longer average sleep duration reported lower ratings of anger (B = -.08, p < .01) and loneliness (B = -.08, p < .01) compared to others. There was no within-person association of sleep duration or efficiency with loneliness. Sleep duration was not associated with happiness between adolescents, and sleep maintenance efficiency was not associated with any mood measure between adolescents. CONCLUSIONS: Improvements to nightly sleep may help increase happiness and decrease anger the following day in adolescents. Promoting sleep health is recommended to improve mood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".