Wang Jiayi E, Sindorf Jacob, Chen Pin-Wei, Wu Jessica, Gonzales Adrian, O'Brien Megan K, Sunderrajan Aashna, Knutson Kristen L, Zee Phyllis C, Wolfe Lisa, Arora Vineet M, Jayaraman Arun
School of Medicine, University of Chicago, Chicago, IL, USA.
Max Nader Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA.
Sleep Adv. 2024 Jul 31;5(1):zpae057. doi: 10.1093/sleepadvances/zpae057. eCollection 2024.
Stroke can result in or exacerbate various sleep disorders. The presence of behaviors such as daytime sleepiness poststroke can indicate underlying sleep disorders which can significantly impact functional recovery and thus require prompt detection and monitoring for improved care. Actigraphy, a quantitative measurement technology, has been primarily validated for nighttime sleep in healthy adults; however, its validity for daytime sleep monitoring is currently unknown. Therefore this study aims to identify the best-performing actigraphy sensor and algorithm for detecting daytime sleep in poststroke individuals.
Participants wore Actiwatch Spectrum and ActiGraph wGT3X-BT on their less-affected wrist, while trained observers recorded daytime sleep occurrences and activity levels (active, sedentary, and asleep) during non-therapy times. Algorithms, Actiwatch (Autoscore AMRI) and ActiGraph (Cole-Kripke, Sadeh), were compared with on-site observations and assessed using F2 scores, emphasizing sensitivity to detect daytime sleep.
Twenty-seven participants from an inpatient stroke rehabilitation unit contributed 173.5 hours of data. The ActiGraph Cole-Kripke algorithm (minute sleep time = 15 minutes, bedtime = 10 minutes, and wake time = 10 minutes) achieved the highest F2 score (0.59). Notably, when participants were in bed, the ActiGraph Cole-Kripke algorithm continued to outperform Sadeh and Actiwatch AMRI, with an F2 score of 0.69.
The study demonstrates both Actiwatch and ActiGraph's ability to detect daytime sleep, particularly during bed rest. ActiGraph (Cole-Kripke) algorithm exhibited a more balanced sleep detection profile and higher F2 scores compared to Actiwatch, offering valuable insights for optimizing daytime sleep monitoring with actigraphy in stroke patients.
中风可导致或加重各种睡眠障碍。中风后出现白天嗜睡等行为可能表明存在潜在的睡眠障碍，这会对功能恢复产生重大影响，因此需要及时检测和监测以改善护理。活动记录仪是一种定量测量技术，主要在健康成年人中验证了其对夜间睡眠的有效性；然而，其对白天睡眠监测的有效性目前尚不清楚。因此，本研究旨在确定用于检测中风后个体白天睡眠的最佳活动记录仪传感器和算法。
参与者在受影响较小的手腕上佩戴Actiwatch Spectrum和ActiGraph wGT3X - BT，同时训练有素的观察者记录非治疗期间的白天睡眠情况和活动水平（活跃、久坐和睡眠）。将Actiwatch（自动评分AMRI）和ActiGraph（科尔 - 克里普克、萨德）算法与现场观察结果进行比较，并使用F2分数进行评估，重点是检测白天睡眠的敏感性。
来自住院中风康复单元的27名参与者提供了173.5小时的数据。ActiGraph科尔 - 克里普克算法（分钟睡眠时间 = 15分钟，就寝时间 = 10分钟，唤醒时间 = 10分钟）获得了最高的F2分数（0.59）。值得注意的是，当参与者躺在床上时，ActiGraph科尔 - 克里普克算法继续优于萨德算法和Actiwatch AMRI，F2分数为0.69。
该研究证明了Actiwatch和ActiGraph检测白天睡眠的能力，特别是在卧床休息期间。与Actiwatch相比，ActiGraph（科尔 - 克里普克）算法表现出更平衡的睡眠检测情况和更高的F2分数，为优化中风患者白天睡眠的活动记录仪监测提供了有价值的见解。
