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Record W4416438823 · doi:10.1111/sjop.70047

Sleep More, Quarrel Less: Associations Between Day‐to‐Day Variations in Objective Sleep and Interpersonal Behavior and Perception

2025· article· en· W4416438823 on OpenAlexfundno aff
Teus Mijnster, Maaike van Veen, Gretha J. Boersma, Fiona Ter Heege, Tom F. Wilderjans, Marike Lancel, Marije aan het Rot

Bibliographic record

VenueScandinavian Journal of Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsSleep (system call)MediationAngerAffect (linguistics)PerceptionInterpersonal relationshipInterpersonal communication

Abstract

fetched live from OpenAlex

The relation between sleep and irritable affect has been studied extensively. However, whether this relation is bidirectional remains unclear. Furthermore, less is still known about associations between sleep and interpersonal behaviors and perceptions during social interactions. The current study examined bidirectional within-person relations between actigraphy-based sleep, and irritable affect, quarrelsome behavior, and agreeable perceptions of others assessed using event-contingent recording of social interactions in a sample of n = 50 participants during either 20 or 40 days. We found that after a night of worse sleep than usual, participants reported more anger and frustration the next day (-0.12, p ≤ 0.01). The reverse was not found. There were no direct associations between sleep and quarrelsome behavior or between sleep and agreeable perceptions. However, worse sleep than usual was indirectly related to more quarrelsome behavior (-0.05, p ≤ 0.001) and less agreeable perceptions (0.06, p ≤ 0.001); that is, via increased irritable affect. These mediation effects imply that poor sleep may impair the quality of one's social interactions. Therefore, targeting sleep could be a means to improve personal and professional relationships.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.353
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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