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

Abstract

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

Methods: In this cross-sectional study, objective infant sleep metrics of six hundred twenty-three infants aged 9 to 13 months, recruited among users of Nanit baby-monitor in the United States, were obtained from Nanit auto-videosomnography (1 week of data averaged) in the child'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 significant different in the three groups. Long Sleepers had longer nighttime sleep duration than Interrupted and Short Sleepers. Interrupted Sleepers presented more awakenings than Short and Long Sleepers, and similarly more parental interventions. Short Sleepers presented later bedtimes and earlier wake up times when 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 since night wakings and reduced sleep duration in infancy might be predictive of the development of emotional and behavioral problems later in childhood.