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Updated: May 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Sleep structure of short-term insomnia disorder with mild cognitive impairment in older adults and their correlation
Jinkun Zeng1, Jia Wei1, Ruobing Qi1
1Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Objectives:
This study seeks to delineate the sleep architecture characteristics in older adults with short-term insomnia and mild cognitive impairment (MCI) and to explore their association with cognitive performance.
Methods:
Ninety elderly individuals with short-term insomnia were enrolled and stratified into two cohorts based on their Montreal Cognitive Assessment (MoCA) scores: the Short-Term Insomnia Group (STID) comprising 35 participants and the Short-Term Insomnia with Cognitive Impairment Group (STID-MCI) with 55 participants. Demographic data, Pittsburgh Sleep Quality Index (PSQI), MoCA, Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), and polysomnography (PSG) parameters were compared between groups. Correlations between MoCA scores and PSG metrics were also analyzed.
Results:
No significant disparities were noted between groups in terms of HAMD-17, HAMA, and PSQI scores (p > 0.05). However, marked differences were identified in MoCA scores and its subdomains (p < 0.05). Significant variations were also observed in the duration and proportion of slow-wave sleep (N3) between groups (p < 0.05). In STID-MCI patients, memory scores correlated positively with N3 duration and percentage (p < 0.05), while verbal functions and attention were positively associated with rapid eye movement (REM) sleep duration.
Conclusion:
This study highlights the potential of PSG in the clinical assessment of cognitive function and underscores the need for targeted interventions to improve sleep quality in this vulnerable population.
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