Lee Ji Yeon, Yang Eunjin, Cho Ae Young, Choi YeonKyu, Lee SungHee, Lee Kyung Hee
School of Nursing, Inha University, Michuhol-Gu, Incheon, Republic of Korea.
College of Nursing, Research Institute of AI and Nursing Science, Gachon University, Yeonsu-Gu, Incheon, Republic of Korea.
J Clin Sleep Med. 2025 Feb 1;21(2):393-400. doi: 10.5664/jcsm.11436.
Sleep 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.
This 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.
The 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.
This 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.
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. . 2025;21(2):393-400.
睡眠障碍会导致负面的健康后果和照护者负担，在社区环境中尤其如此。本研究旨在探讨居家患有痴呆症的老年人睡眠效率的预测模型及其相关特征。
这是一项探索性观察研究。本研究共纳入69名被诊断患有痴呆症的老年人。通过活动记录仪收集14天的睡眠和身体活动数据、2 - 3天的细胞因子汗液贴片数据，并在基线时进行疾病、药物、心理和行为症状、功能状态及人口统计学调查。利用730天的活动记录仪、汗液贴片和基线数据，选择睡眠效率的最佳预测模型，并使用机器学习分析进一步探究其相关的前10个特征。
CatBoost模型被选为睡眠效率的最佳预测模型。按重要性排序，最重要的特征依次为睡眠规律性、药物数量、痴呆症药物、白天活动计数、日常生活工具性活动、神经精神科问卷、催眠药、职业、肿瘤坏死因子 - α和清醒时光照强度。
本研究利用机器学习和物联网等各种来源，建立了社区居家患有痴呆症老年人的最佳睡眠效率预测模型及其相关特征。本研究强调了基于相关特征对社区居家患有痴呆症老年人进行个性化睡眠干预的重要性。
Lee JY, Yang E, Cho AY, Choi Y, Lee S, Lee KH. 社区居家患有痴呆症者的睡眠效率：机器学习的探索性分析。. 2025;21(2):393 - 400。
