Matos Joana, Ramos Beatriz, Fernandes Joana, Hansen Clint, Maetzler Walter, Vila-Chã Nuno, Maia Luís F
Department of Neurology, Centro Hospitalar Universitário de Santo António, 4099-001 Porto, Portugal.
Department of Neurology, Kiel University, 24105 Kiel, Germany.
Biosensors (Basel). 2025 Mar 25;15(4):212. doi: 10.3390/bios15040212.
Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neural pathways, leading to a broad spectrum of motor and non-motor symptoms. Sleep disorders, such as insomnia and excessive daytime sleepiness, are prevalent among PD patients and significantly impact symptomatology and patients' quality of life. Wearable technology presents an opportunity to study these interactions in patients' daily life environments without the limitations of in-clinic sleep studies. Thus, this review aims to explore how wearable technology has been employed or developed for the sleep monitoring of PD patients in free-living environments. A comprehensive search was conducted across PubMed, Scopus, and IEEE Xplore to identify original research articles focusing on wearable sleep technology for the ambulatory monitoring of PD patients. Twenty-six studies fulfilled the inclusion criteria and underwent structured data extraction and quality assessment. Key aspects analysed included subject demographics, extracted sleep parameters, identified sleep disorders, and the application of machine-learning algorithms. Wearable devices could offer a practical solution for long-term sleep monitoring in PD, though further validation is needed. The absence of standardised protocols and the lack of device validation within PD populations remain significant challenges. The evidence gathered in this study remains insufficient to define a standardised protocol for sleep assessment of PD patients in free-living environments.
帕金森病(PD)是一种神经退行性疾病,会影响多个神经通路,导致广泛的运动和非运动症状。睡眠障碍,如失眠和日间过度嗜睡,在帕金森病患者中很普遍,并且会显著影响症状表现和患者的生活质量。可穿戴技术为在患者日常生活环境中研究这些相互作用提供了机会,而不受临床睡眠研究的限制。因此,本综述旨在探讨可穿戴技术是如何被应用或开发用于在自由生活环境中对帕金森病患者进行睡眠监测的。我们在PubMed、Scopus和IEEE Xplore上进行了全面搜索,以识别专注于可穿戴睡眠技术用于帕金森病患者动态监测的原创研究文章。26项研究符合纳入标准,并进行了结构化数据提取和质量评估。分析的关键方面包括受试者人口统计学、提取的睡眠参数、识别出的睡眠障碍以及机器学习算法的应用。可穿戴设备可为帕金森病的长期睡眠监测提供一种实用的解决方案,不过还需要进一步验证。缺乏标准化方案以及在帕金森病患者群体中缺乏设备验证仍然是重大挑战。本研究收集的证据仍不足以确定在自由生活环境中对帕金森病患者进行睡眠评估的标准化方案。