Emotional Vulnerability to Short Sleep Predicts Increases in Chronic Health Conditions Across 8 Years
Bibliographic record
Abstract
BACKGROUND: Sleep is a robust determinant of next-day emotions, but people vary in the extent that their emotions fluctuate on days following short sleep duration. These individual differences in day-to-day sleep and emotion dynamics may have long-term health implications. PURPOSE: To evaluate emotional vulnerability to short sleep (within-person associations between sleep duration and next-day emotions) as a risk factor for future chronic conditions. METHODS: Adults aged 33-84 (N = 1,426; 57% female) in the Midlife in the United States Study reported sleep duration and emotions by telephone for eight consecutive days. Chronic conditions were assessed via checklist at baseline and at a median follow-up of eight years (range: 5-10 years). Short sleep was examined in three ways: person-centered continuous variable, ≤6 hr, and <7 hr; long sleep was defined as ≥9 hr. RESULTS: Multilevel structural equation models revealed that people with greater negative emotions following nights of sleep ≤6 hr (vs. their negative emotions after longer sleep) had increased chronic conditions at follow-up, compared to people who were less emotionally vulnerable to short sleep (Est. = 1.04, SE = .51, p < .028). Smaller declines in positive emotions following ≤6 hr of sleep were marginally predictive of lower risk for chronic conditions (Est. = -.77, SE = .44, p = .054). Emotional vulnerability to <7, ≥9, and continuous sleep hours were not associated with subsequent chronic conditions. CONCLUSIONS: Emotional vulnerability to short sleep is a unique risk factor for the development of chronic conditions, independent of mean-level sleep duration and emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".