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Record W4413284828 · doi:10.1093/ijnp/pyaf052.269

421. THE RELATIONSHIP BETWEEN SLEEP QUALITY AND NEUROCOGNITIVE FUNCTION IN THE GERIATRIC DEPRESSION: A CASE-CONTROL STUDY

2025· article· en· W4413284828 on OpenAlexaboutno aff
Y-S Huang, P-Y Lee

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveDepression (economics)Sleep qualityPsychologySleep (system call)Clinical psychologyPsychiatryMedicinePhysical medicine and rehabilitationGerontologyCognitionComputer science

Abstract

fetched live from OpenAlex

Abstract Background As the proportion of elderly increases, age-related psychological and physical health issues have gradually been taken seriously, especially depression and neurodegenerative disorders, which are also closely related to sleep. Depressive individuals often report sleep disturbances, and depression can lead to a decline in executive functions, with inhibitory control being the most severely affected. This decline is partly due to the inability to suppress negative rumination and worry, leading to the development of depressive symptoms. Additionally, executive function is also easily affected by sleep deprivation. However, the actual association between depression, sleep, and executive function requires further discussion. Aims & Objectives Therefore, this study focuses on the elderly population to investigate the interconnections between sleep, depression, and neurocognitive function. Method This study included elderly subjects ages 65 and above, with 35 in the depression group and 44 in the non-depression group. After obtaining informed consent and ensuring that participants met the cutoff score on the Montreal Cognitive Assessment (MoCA) screening, they sequentially filled out basic demographic information, the Geriatric Depression Scale (GDS-15), the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), the Activities of Daily Living (ADLs) scale, and the Instrumental Activities of Daily Living (IADLs) scale. They also completed the Conners' Continuous Performance Test (CPT), and wore an actigraphy for one week. The data will be analyzed using statistical methods such as descriptive statistics, correlation analysis, and bootstrapping. Results 79 participants were included (29 men and 50 women; mean age: 73.02±5.85 years). The elderly depression group showed significantly poorer global cognitive function, specific sleep quality indicators, and executive function compared to the non-depressed group. There were correlations between depression, specific sleep quality indicators, and specific components of executive function. Total in-bed time and total sleep time were found to mediate the relationship between elderly depression and global cognitive function. This study also examined the relationship between the severity of elderly depression and inhibitory control. The findings indicated that sleep quality did not mediate the relationship between depression severity and inhibitory control. However, the severity of elderly depression could directly predict certain inhibitory control indicators. Discussion & Conclusions This study found that elderly individuals with depression exhibit impairments in global cognitive function, specific sleep quality indicators, and executive function. Depression affects global cognitive function through total time in bed and total sleep time, indicating that sleep duration plays a crucial role in the relationship between elderly depression and global cognitive function.

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)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.401
Teacher spread0.354 · 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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