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Record W4402438775 · doi:10.1038/s41598-024-72159-8

Depressive symptoms mediate the longitudinal relationships between sleep quality and cognitive functions among older adults with mild cognitive impairment: A cross-lagged modeling analysis

2024· article· en· W4402438775 on OpenAlexaboutno aff
Jiayu Wang, Shulin Chen, Jiang Xue

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceHumanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of ChinaMinistry of Education of the People's Republic of China
KeywordsCognitionDepressive symptomsSleep qualityCognitive impairmentSleep (system call)Clinical psychologyDepression (economics)PsychologyMedicineGerontologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Although it is generally recognized that sleep quality, depressive symptoms, and cognitive functions are related respectively, the main ambiguity comes from difficulties in determining their cause-effect relationships. The present study aimed to explore the longitudinal causation relationships among sleep quality, depressive symptoms, and cognitive functions in older people with mild cognitive impairment (MCI). A total of 134 patients from 24 communities in Ningbo City, Zhejiang Province, China with MCI were interviewed at baseline, while 124 of them were re-interviewed 2 months later, and 122 were re-interviewed 4 months later. The Patient Health Questionnaire-9, the Pittsburgh Sleep Quality Index and the Montreal Cognitive Assessment Scale were assessed in the interview. Cross-lagged models were tested to disentangle the relationships among sleep quality, depressive symptoms, and cognitive functions using structural equation modeling with latent variables on the four-mouth longitudinal data. The correlation coefficients between sleep quality and depressive symptoms were significant showing the stability across time points of assessment, while the correlation coefficient of cognitive function was not significant (r = 0.159, p > 0.05). The results of index of model fit indicated that the cross-lagged model was acceptable (CFI = 0.934, TLI = 0.899, RMSEA = 0.075, χ 2 /df = 1.684). The results of cross-lagged model analysis supported the complete mediating role of depressive symptoms in the association between sleep quality and cognitive functions, where worse sleep quality may lead to more severe depressive symptoms, which in turn leads to more severe cognitive decline. In Conclusion, sleep quality is significantly correlated with cognitive functions in patients with mild cognitive impairment, which association is fully mediated by depressive symptoms. Approaches addressing sleep quality and depressive symptoms are recommended and hold promise for the management of mild cognitive impairment.

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)0.005
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.327
Teacher spread0.294 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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