Early identification of sleep phenotypes in infants by videosomnography: a cross-sectional study.

Abstract

Study objectives: We aimed to identify different sleep phenotypes in infancy, relying on auto-videosomnography metrics.

Methods: 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.

Results: 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.

Conclusions: 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.

Citation: Breda M, Lucchini M, Barnett N, Bruni O. Early identification of sleep phenotypes in infants by videosomnography: a cross-sectional study. J Clin Sleep Med. 2025;21(5):867-874.