Sleep Problems and School Functioning in Adolescents with ASD

Poster Presentation
Saturday, May 4, 2019: 11:30 AM-1:30 PM
Room: 710 (Palais des congres de Montreal)
I. Schouwenaars1, M. Magnee1, C. van Bennekom2, H. M. Geurts3 and J. P. Teunisse1, (1)HAN University of Applied Sciences, Nijmegen, Netherlands, (2)R&D, Heliomare, Wijk aan Zee, Netherlands, (3)University of Amsterdam, Amsterdam, Netherlands

Sleep problems are common in adolescents with ASD (Cortesi, Giannotti, Ivanenko, & Johnson, 2010), with reported percentages of up to 88% (Hodge, Carollo, Lewin, Hoffman, & Sweeney, 2014). While a growing number of studies show that sleep problems during adolescence have a major impact on school functioning in typically developing adolescents (e.g. Dewald, Meijer, Oort, Kerkhof, & Bögels, 2010; Curcio et al., 2006; Pagel, Forister, & Kwiatkowki, 2007), the relationship between sleep problems and school functioning in adolescent with ASD is not yet clear. The majority of these studies have used subjective measurements of sleep, which are known to have a poor reliability compared with objective sleep measures (like actigraphy). Subjective sleep measurements are important to establish sleep quality, however, objective measurements can’t be ignored (Goelema, 2018). Therefore, both objective and subjective sleep measurements are included to investigate the relationship between sleep and school functioning in adolescents with ASD.


The objective of the present study is to identify the most relevant features of objective and subjective sleep variables in relation to school functioning in adolescents with ASD.


In a repeated measures design (daily measurements over a period of three weeks) 20 adolescents with ASD and sleep problems were assessed on self-reported measures of sleep and school functioning by means of a webapp. The adolescents completed a sleep diary with questions about sleep onset latency (SOL), number of awakenings, wake time after sleep onset (WASO), total sleep time (TST), sleep quality, napping, last night’s activities, electronic media-use and caffeine use. At the end of each day they completed questions about their school functioning (mood, concentration, stress, fatigue, mental effort and physical exercise). Also one of their parents and a teacher completed questions about the adolescents’ functioning. Additionally, in ten adolescents, sleep was measured with a sleep tracker (Elan, Philips ©) throughout the study period. The Elan obtains reflective, green-spectrum photoplethysmography (PPG) data from the wrist and acceleration data (ACC) using an internal 3D accelerometer. Variables used from the Elan device are: SOL, (TST), WASO, number of awakenings and sleep efficiency (SE).

Data were analyzed with network analysis level to explore the relationships between aspects of sleep and school functioning. Network analysis is a new and promising analysis to establish a network representing the relations between directly measured variables (nodes), which can be performed on individual and group level (Costantini et al., 2015; Epskamp, Waldorp, Mõttus, & Borsboom, 2018). In total, the network contains 17 nodes: TST, SOL, WASO, number of awakenings (measured with the Elan and self-reported), sleep quality, mood, fatigue, stress, concentration, electronic media-use and caffeine use, physical exercise and mental effort (self-reported). Three separate network analyses with the same 17 nodes (at group and individual level) are performed: 1) aspects of sleep and school functioning measured by adolescents, 2) aspects of sleep and school functioning measured by parents and 3) aspects of sleep and school functioning measured by teachers.

Results: Results will be presented at the conference.

Conclusions: Conclusions will be presented at the conference.

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See more of: Education