Bangerter Abigail, Chatterjee Meenakshi, Manyakov Nikolay V, Ness Seth, Lewin David, Skalkin Andrew, Boice Matthew, Goodwin Matthew S, Dawson Geraldine, Hendren Robert, Leventhal Bennett, Shic Frederick, Esbensen Anna, Pandina Gahan
Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, NJ, United States.
Computational Biology, Discovery Sciences, Janssen Research & Development, Spring House, PA, United States.
Front Neurosci. 2020 Mar 24;14:211. doi: 10.3389/fnins.2020.00211. eCollection 2020.
The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD.
Caregivers of ASD ( = 144) and typically developing (TD) ( = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety.
Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly ( < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly ( < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety.
Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.
研究自闭症谱系障碍(ASD)儿童和成人的睡眠(由照料者报告及通过活动记录仪测量)与其他照料者报告的行为之间的关系,包括使用机器学习来识别对预测ASD焦虑症至关重要的睡眠变量。
ASD参与者(n = 144)和发育正常(TD)参与者(n = 41)的照料者报告了睡眠及其他行为情况。在一项为期8或10周的非干预性研究中,ASD参与者夜间佩戴活动记录仪。计算了中点和终点前一周ASD参与者活动记录仪测量值的均值和变异性,并使用混合效应模型将其与照料者报告及临床医生报告的症状进行比较。开发了一个弹性网络模型来研究哪些睡眠指标可能推动对焦虑症的预测。
ASD中照料者报告的睡眠困难患病率约为70%,且与通过活动记录仪测量的睡眠效率显著相关(P < 0.05)。睡眠效率和觉醒次数等活动记录仪测量指标的均值和变异性与ASD症状严重程度、多动及焦虑显著相关(P < 0.05)。在弹性网络模型中,照料者报告的睡眠、睡眠效率和觉醒次数的变异性是焦虑症的重要预测指标。
大多数ASD儿童和成人的照料者报告存在睡眠问题。报告的睡眠问题及活动记录仪测量的睡眠情况,尤其是变异性,与家长报告的行为有关。测量睡眠变异性可能有助于理解ASD患者睡眠问题与行为之间的关系。这些发现可能对ASD的干预和监测结果具有启示意义。