TIFFS, TOSSES, AND TURNS: EFFECTS OF AFFECTIVE REACTIVITY TO INTERPERSONAL STRESSORS DURING THE DAY ON NIGHTLY SLEEP
Bibliographic record
Abstract
Abstract Background Sleep has been recognized as an antecedent as well as a consequence of daytime stress. However, less research has compared the role of different stressor types on same-night sleep in the context of daily life. Interpersonal stressors may be particularly important, given that social stressors to elicit greater stress responses than other forms of stressors. This study tested the hypothesis that following days when a person exhibits greater negative affect (NA) reactivity to stressors (versus on days with less NA reactivity), sleep quality will be lower. This link between affective reactivity to stressors and subsequent sleep was expected to be more pronounced for interpersonal stressors versus non-interpersonal stressors. Methods In this pre-registered study, 252 adults in British Columbia, Canada (ages 25 to 87y; 68% women; 64% White) completed mobile surveys 5x/day for 14 days to assess daily stressors and NA. Self-reported sleep quality was assessed in morning surveys. Multilevel-models tested daily stressors (interpersonal, non-interpersonal, or no stressors), daily NA (averaged across the day), and their interaction as predictors of subsequent sleep quality, controlling for prior-night sleep quality and sociodemographics. Results Daily NA and stressor occurrence independently predicted poorer subsequent sleep quality. Stressor type moderated the relationship between NA and sleep quality, such that this association was stronger for interpersonal compared to non-interpersonal stressors. Discussion The findings suggest that encountering interpersonal stressors may be particularly impactful to one’s subsequent sleep. Future studies should consider investigating potential mechanisms that may underlie this association, such as pre-sleep cognitive, emotional, and physiological arousal.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".