Ghazi Sarah Nauman, Behrens Anders, Berner Jessica, Sanmartin Berglund Johan, Anderberg Peter
Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
School of Health Sciences, University of Skövde, Västra Götalands län, Sweden.
J Sleep Res. 2024 Dec 9:e14436. doi: 10.1111/jsr.14436.
Inadequate sleep in older adults is linked to health issues such as frailty, cognitive impairment and cardiovascular disorders. Maintaining regular sleep patterns is important for healthy aging, making effective sleep monitoring essential. While polysomnography is the gold-standard for diagnosing sleep disorders, its regular use in home settings is limited. Alternative objective monitoring methods in the home can offer insights into natural sleep patterns and factors affecting them without the limitations of polysomnography. This scoping review aims to examine current technologies, sensors and sleep parameters used for home-based sleep monitoring in older adults. It also aims to explore various predictors and outcomes associated with sleep to understand the factors of sleep monitoring at home. We identified 54 relevant articles using PubMed, Scopus, Web of Science and an AI tool (Research Rabbit), with 48 studies using wearable technologies and eight studies using non-wearable technologies. Further, six types of sensors were utilized. The most common technology employed was actigraphy wearables, while ballistocardiography and electroencephalography were less common. The most frequent objective parameters of sleep measured were total sleep time, wakeup after sleep onset and sleep efficiency, with only six studies evaluating sleep architecture in terms of sleep stages. Additionally, six categories of predictors and outcomes associated with sleep were analysed, including Health-related, Environmental, Interventional, Behavioural, Time and Place, and Social associations. These associations correlate with total sleep time, wakeup after sleep onset and sleep efficiency, and include in-bed behaviours, exterior housing conditions, aerobic exercise, living place, relationship status, and seasonal thermal environments.
老年人睡眠不足与身体虚弱、认知障碍和心血管疾病等健康问题有关。保持规律的睡眠模式对健康老龄化很重要，因此有效的睡眠监测至关重要。虽然多导睡眠图是诊断睡眠障碍的金标准，但其在家庭环境中的常规使用受到限制。家庭中的替代客观监测方法可以提供对自然睡眠模式及其影响因素的深入了解，而不受多导睡眠图的限制。本范围综述旨在研究用于老年人家庭睡眠监测的当前技术、传感器和睡眠参数。它还旨在探索与睡眠相关的各种预测因素和结果，以了解家庭睡眠监测的因素。我们使用PubMed、Scopus、Web of Science和一个人工智能工具（Research Rabbit）识别了54篇相关文章，其中48项研究使用了可穿戴技术，8项研究使用了非可穿戴技术。此外，还使用了六种类型的传感器。使用最普遍的技术是活动记录仪可穿戴设备，而心冲击图和脑电图则不太常见。测量最频繁的睡眠客观参数是总睡眠时间、睡眠开始后醒来时间和睡眠效率，只有六项研究从睡眠阶段的角度评估了睡眠结构。此外，还分析了与睡眠相关的六类预测因素和结果，包括与健康相关的、环境的、干预的、行为的、时间和地点的以及社会关联。这些关联与总睡眠时间、睡眠开始后醒来时间和睡眠效率相关，包括床上行为、外部居住条件、有氧运动、居住地点、关系状况和季节性热环境。
