Breda Maria, Lucchini Maristella, Barnett Natalie, Bruni Oliviero
Department of Social and Developmental Psychology, Sapienza University of Rome, Rome, Italy.
Nanit Lab, New York, New York.
J Clin Sleep Med. 2025 May 1;21(5):867-874. doi: 10.5664/jcsm.11576.
We aimed to identify different sleep phenotypes in infancy, relying on auto-videosomnography metrics.
In this cross-sectional study, objective infant sleep metrics of 623 infants aged 9-13 months, recruited among users of Nanit baby monitors in the United States, were obtained from Nanit auto-videosomnography (1 week of data averaged) in the children's natural sleep environment. A cluster analysis was conducted to group infants based on sleep metrics.
Three reproducible and stable sleep phenotypes were identified: Long Sleepers (n = 338), Interrupted Sleepers (n = 130), and Short Sleepers (n = 155). All sleep metrics were statistically significantly different in the 3 groups. Long Sleepers had longer nighttime sleep duration than Interrupted and Short Sleepers. Interrupted Sleepers presented more awakenings than Short and Long Sleepers and more parental interventions. Short Sleepers presented later bedtimes and earlier wake times compared with Long and Interrupted Sleepers. Nighttime sleep efficiency was better in Long Sleepers than in Interrupted and Short Sleepers, but Short Sleepers presented better sleep efficiency than Interrupted Sleepers.
Cluster analysis based on objective sleep metrics offers a novel multidimensional approach for the early identification of infants' sleep patterns. Phenotyping sleep patterns is extremely important in identifying the risk for developing neurobehavioral disorders, because night wakings and reduced sleep duration in infancy might be predictive of the development of emotional and behavioral problems later in childhood.
Breda M, Lucchini M, Barnett N, Bruni O. Early identification of sleep phenotypes in infants by videosomnography: a cross-sectional study. 2025;21(5):867-874.
我们旨在依靠自动视频多导睡眠图指标来识别婴儿期不同的睡眠表型。
在这项横断面研究中，从美国Nanit婴儿监视器用户中招募的623名9至13个月大婴儿的客观睡眠指标，是在儿童自然睡眠环境中通过Nanit自动视频多导睡眠图（平均1周的数据）获得的。进行聚类分析以根据睡眠指标对婴儿进行分组。
识别出三种可重复且稳定的睡眠表型：长睡眠者（n = 338）、睡眠中断者（n = 130）和短睡眠者（n = 155）。三组中的所有睡眠指标在统计学上均有显著差异。长睡眠者的夜间睡眠时间比睡眠中断者和短睡眠者更长。睡眠中断者比短睡眠者和长睡眠者有更多的觉醒次数以及更多的父母干预。与长睡眠者和睡眠中断者相比，短睡眠者的就寝时间更晚且起床时间更早。长睡眠者的夜间睡眠效率比睡眠中断者和短睡眠者更好，但短睡眠者的睡眠效率比睡眠中断者更好。
基于客观睡眠指标的聚类分析为早期识别婴儿的睡眠模式提供了一种新颖的多维度方法。对睡眠模式进行表型分析在识别神经行为障碍的发展风险方面极其重要，因为婴儿期的夜间醒来和睡眠时间缩短可能预示着儿童后期情绪和行为问题的发展。
Breda M, Lucchini M, Barnett N, Bruni O. Early identification of sleep phenotypes in infants by videosomnography: a cross-sectional study. 2025;21(5):867 - 874.
