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Record W4254656390 · doi:10.32920/ryerson.14645433.v1

An investigation of the relationship between sleep and fatigue in those with and without insomnia

2021· preprint· en· W4254656390 on OpenAlexaff
Andrea L. Harris

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsActigraphyInsomniaSleep (system call)PsychologyMediationRuminationPrimary InsomniaClinical psychologySleep onsetSleep disorderPhysical therapyCognitionAudiologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

There is currently mixed evidence for the relationship between poor sleep and daytime fatigue. It is well documented that retrospective measures of insomnia and fatigue are highly correlated with one another. However, other studies fail to demonstrate a link between objectively less sleep and fatigue; that is, individuals with shorter sleep times do not necessarily report increased fatigue. As such, the relationship between these two constructs remains unclear. The current investigation will help to elucidate the complex relationship between sleep and fatigue among those with and without insomnia by advancing the existing literature in two important ways. First, this study proposed to examine the temporal relationship between sleep and fatigue across two weeks, thereby investigating whether sleep and fatigue occur in accordance with one anotherover time. Second, this study utilized a multi-method approach by collecting subjective (i.e.,sleep diary) and objective (i.e., actigraphy) measures of sleep, as well as retrospective (i.e.,visual analogue scales: VAS) and prospective (i.e., momentary ratings) measures of fatigue. Two separate hierarchical linear models were used to test whether sleep (measured by sleep quality and total sleep time) predicted daytime fatigue on the VAS and actigraph, respectively. The secondary objective asked whether cognitive-behavioural variables (i.e., maladaptive sleep beliefs, fear and avoidance of fatigue, and fatigue-based rumination) may help account for the relationship between sleep and fatigue using mediation. The results of the primary analyses suggested that sleep quality significantly predicted VAS fatigue ratings, whereas total sleep time was a significant predictor of fatigue within- but not between-persons. No significant relationships were found between objective measures of sleep and momentary fatigue ratings. Finally, each of the cognitive-behavioural variables, with the exception of avoidance of fatigue, were significant mediators of the relationship between sleep and fatigue. The results demonstrated that compared to sleep quantity, our perception of sleep may play a more important role in predicting reports of daytime fatigue. These findings could help decrease the burden that individuals with insomnia place on their total sleep times, and instead, treatment could focus on challenging maladaptive sleep-related cognitions, which ultimately could lessen the overall sleep-related anxiety.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.334
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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