Graduate Center, City University of New York, USA.
Infant Behav Dev. 2022 Nov;69:101775. doi: 10.1016/j.infbeh.2022.101775. Epub 2022 Sep 18.
Research studying the role of sleep in development abounds but focuses on global aspects of sleep like quality or timing. Far fewer studies include the ultradian cycle, or patterns of REM and non-REM (NREM), because doing so is costly in time and resources. In a complete, lab-based sleep study, individuals are monitored by a technician overnight while wearing a host of sensors to capture brain activity, eye and limb movement, and cardiorespiratory rates. There is a need for creative, minimally disruptive solutions to study sleep that do not compromise the richness and accuracy of the measurements. The current pilot study parsed down the physiological measures to only movement and cardiorespiratory rates, creating a protocol simple enough for caregivers to incorporate into bedtime. Ten 12-month-old infants (+/- 3 weeks) wore an actigraph and wireless cardiorespiratory sensor for five nights of data collection. Of this, 92% were useable data. Actigraphy was analyzed with the Sadeh algorithm to delineate sleep from wake. Heart rate and respiration were then used to state score visually or via an algorithm; greater variability demarcated REM from NREM. Time spent in each state was compared between scoring methods as well as to published results from age matched infants who underwent polysomnography (PSG). Visually scored data, using a 1-hour viewing window, was in line with peer's PSG values. To automatically state score, epoch-by-epoch cardiorespiratory and actigraphy files were produced for each minute of data collection. Heart and respiratory rates were transformed into z-scores and iterations of scoring, using increasingly greater z score thresholds, were compared to determine which identified state proportions most similar to data collected with PSG. Based on these results, our novel method appears to be a feasible choice for studying the ultradian cycle. The combination of actigraphy and cardiorespiratory monitoring is uniquely advantageous because it is less resource intensive and more naturalistic, being put on by caregivers while still resulting in high rates of good data. Taken together, it is a quality option for infant researchers interested in incorporating sleep into their paradigms.
研究睡眠在发育中的作用的文献很多，但这些研究主要关注睡眠的整体方面，如睡眠质量或时间。很少有研究包括超日周期，即 REM 和非 REM（NREM）的模式，因为这样做在时间和资源上代价高昂。在一项完整的、基于实验室的睡眠研究中，研究人员在一夜之间通过技术人员监测个体，同时佩戴许多传感器来捕捉大脑活动、眼动和肢体运动以及心肺呼吸频率。需要有创造性的、最小干扰的睡眠研究解决方案，这些方案不会影响测量的丰富性和准确性。目前的初步研究将生理测量简化为仅运动和心肺呼吸频率，创建了一个足够简单的方案，以便护理人员将其纳入睡前。十名 12 个月大的婴儿（+/-3 周）佩戴活动记录仪和无线心肺呼吸传感器进行了五晚的数据收集。其中，92%的数据是可用数据。使用 Sadeh 算法对活动记录仪进行分析，以区分睡眠和清醒状态。然后使用心率和呼吸来直观地或通过算法对状态进行评分；更大的可变性将 REM 与 NREM 区分开来。还比较了两种评分方法之间以及与接受多导睡眠图（PSG）的年龄匹配婴儿的发表结果之间的每个状态的时间。使用 1 小时观察窗口进行的视觉评分数据与同行的 PSG 值一致。为了自动状态评分，为每分钟的数据采集生成了逐epoch 的心肺呼吸和活动记录仪文件。将心率和呼吸率转换为 z 分数，并比较使用越来越大的 z 分数阈值进行的评分迭代，以确定哪种方法确定的状态比例与使用 PSG 收集的数据最相似。基于这些结果，我们的新方法似乎是研究超日周期的可行选择。活动记录仪和心肺呼吸监测的结合具有独特的优势，因为它资源密集度较低，更具自然性，由护理人员佩戴，同时仍能获得高比例的良好数据。总的来说，对于有兴趣将睡眠纳入其研究范式的婴儿研究人员来说，这是一个高质量的选择。
