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Understanding the relationship between sleep quality and cognitive function in older adults

2025· article· W7154835843 on OpenAlexaboutno aff
A. C. Nielsen, Sofie Andersen, Mads Christensen, Emma Larsen

Bibliographic record

VenueInternational Journal of Psychology Sciences · 2025
Typearticle
Language
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersRigshospitaletDanmarks Frie Forskningsfond
KeywordsPittsburgh Sleep Quality IndexCognitionSleep (system call)Montreal Cognitive AssessmentEffects of sleep deprivation on cognitive performanceActigraphySleep medicineSleep debtCognitive decline

Abstract

fetched live from OpenAlex

How does the quality of nightly sleep influence cognitive abilities as individuals advance through the later decades of life, and what sleep parameters demonstrate the strongest relationships with specific cognitive domains? This cross-sectional research examined the relationship between sleep quality parameters and cognitive function among community-dwelling older adults aged 65 years and above residing in the Copenhagen metropolitan area of Denmark. Participants were recruited from primary care practices and senior community centers between February 2019 and November 2020, with 523 individuals completing comprehensive sleep and cognitive assessments at the Danish Center for Sleep Medicine at Rigshospitalet. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index measuring subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction over the preceding month. Cognitive function was assessed using the Montreal Cognitive Assessment examining attention, executive function, memory, language, visuospatial abilities, and orientation domains representing the full spectrum of cognitive abilities. Results revealed that 47.2% of participants demonstrated poor sleep quality defined by Pittsburgh Sleep Quality Index scores exceeding five points, with poor sleepers showing significantly lower cognitive scores compared to good sleepers across all measured domains. Multiple regression analysis controlling for age, education, depression, and medical comorbidities demonstrated that sleep quality independently predicted cognitive performance, with each one-point increase in Pittsburgh Sleep Quality Index associated with 0.34-point decrease in Montreal Cognitive Assessment scores. Sleep efficiency emerged as the strongest individual predictor of cognitive function among the seven sleep parameters examined, followed by sleep duration and daytime dysfunction severity. Domain-specific analyses revealed particularly strong associations between poor sleep and executive function deficits, with weaker but still significant relationships observed for memory and processing speed outcomes. Participants meeting screening criteria for mild cognitive impairment demonstrated substantially worse sleep quality profiles compared to cognitively normal individuals, suggesting bidirectional relationships between sleep disturbance and cognitive decline in aging populations requiring further longitudinal investigation. These findings underscore the importance of sleep quality assessment and intervention in geriatric clinical practice as a potentially modifiable risk factor for cognitive decline among older adults. Healthcare providers should routinely screen older patients for sleep disturbances and consider sleep optimization as a component of comprehensive cognitive health promotion strategies targeting the aging Danish population.

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)0.003
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.167
GPT teacher head0.454
Teacher spread0.287 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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