Bioanalytics Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, WV, USA.
Department of Epidemiology, School of Public Health, West Virginia University, Morgantown, WV, USA.
Ann Work Expo Health. 2020 Apr 30;64(4):350-367. doi: 10.1093/annweh/wxaa007.
Actigraphy, a method for inferring sleep/wake patterns based on movement data gathered using actigraphs, is increasingly used in population-based epidemiologic studies because of its ability to monitor activity in natural settings. Using special software, actigraphic data are analyzed to estimate a range of sleep parameters. To date, despite extensive application of actigraphs in sleep research, published literature specifically detailing the methodology for derivation of sleep parameters is lacking; such information is critical for the appropriate analysis and interpretation of actigraphy data. Reporting of sleep parameters has also been inconsistent across studies, likely reflecting the lack of consensus regarding the definition of sleep onset and offset. In addition, actigraphy data are generally underutilized, with only a fraction of the sleep parameters generated through actigraphy routinely used in current sleep research. The objectives of this paper are to review existing algorithms used to estimate sleep/wake cycles from movement data, demonstrate the rules/methods used for estimating sleep parameters, provide clear technical definitions of the parameters, and suggest potential new measures that reflect intraindividual variability. Utilizing original data collected using Motionlogger Sleep Watch (Ambulatory Monitoring Inc., Ardsley, NY), we detail the methodology and derivation of 29 nocturnal sleep parameters, including those both widely and rarely utilized in research. By improving understanding of the actigraphy process, the information provided in this paper may help: ensure appropriate use and interpretation of sleep parameters in future studies; enable the recalibration of sleep parameters to address specific goals; inform the development of new measures; and increase the breadth of sleep parameters used.
活动记录仪是一种基于活动记录仪收集的运动数据推断睡眠/觉醒模式的方法,由于其能够在自然环境中监测活动,因此在基于人群的流行病学研究中越来越多地被使用。使用特殊软件对活动记录仪数据进行分析,以估计一系列睡眠参数。迄今为止,尽管活动记录仪在睡眠研究中得到了广泛应用,但缺乏专门详细说明推导睡眠参数方法的文献;这些信息对于正确分析和解释活动记录仪数据至关重要。睡眠参数的报告在不同的研究中也不一致,这可能反映出缺乏关于睡眠起始和结束定义的共识。此外,活动记录仪数据通常未得到充分利用,只有当前睡眠研究中常规使用的一小部分通过活动记录仪生成的睡眠参数。本文的目的是回顾用于从运动数据估计睡眠/觉醒周期的现有算法,演示估计睡眠参数的规则/方法,提供参数的明确技术定义,并提出可能反映个体内变异性的新措施。我们利用使用 Motionlogger Sleep Watch(Ardsley,NY 的 Ambulatory Monitoring Inc.)收集的原始数据,详细介绍了 29 个夜间睡眠参数的方法学和推导过程,包括在研究中广泛使用和很少使用的参数。通过提高对活动记录仪过程的理解,本文提供的信息可能有助于:确保在未来的研究中正确使用和解释睡眠参数;能够重新校准睡眠参数以解决特定目标;为新措施的制定提供信息;并增加使用的睡眠参数的范围。