Observational studyJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2025
Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.
Observational study in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Using Artificial Intelligence-Based Technologies for the Early Detection of Behavioral and Psychological Symptoms of Dementia: Scoping Review.JMIR aging · 2025Article
- Day-to-day sleep efficiency and driving behaviors in older adults with and without cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Can Sensor Technologies Accurately Detect and Monitor Behavioural and Psychological Symptoms of Dementia? A Systematic Review.Dementia and geriatric cognitive disorders extraReview
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesSleep disturbances lead to negative health outcomes and caregiver burden, particularly in community settings. This study aimed to investigate a predictive model for sleep efficiency and its associated features in older adults living with dementia in their own homes.
methodsThis was an exploratory, observational study. A total of 69 older adults diagnosed with dementia were included in this study. Data were collected via actigraphy for sleep and physical activity for 14 days, a sweat patch for cytokines for 2-3 days, and a survey of diseases, medications, psychological and behavioral symptoms, functional status, and demographics at baseline. Using 730 days of actigraphy, sweat patches, and baseline data, the best prediction model for sleep efficiency was selected and further investigated to explore its associated top 10 features using machine learning analysis.
resultsThe CatBoost model was selected as the best predictive model for sleep efficiency. In order of importance, the most important features were sleep regularity, number of medications, dementia medication, daytime activity count, instrumental activities of daily living, neuropsychiatric inventory, hypnotics, occupation, tumor necrosis factor-alpha, and waking hour lux.
conclusionsThis study established the best sleep efficiency predictive model among community-dwelling older adults with dementia and its associated features using machine learning and various sources, such as the Internet of Things. This study highlights the importance of individualized sleep interventions for community-dwelling older adults with dementia based on associated features. CITATION: Lee JY, Yang E, Cho AY, Choi Y, Lee S, Lee KH. Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.
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