Measuring Sleep Efficiency: What Should the Denominator Be?

Background Disturbances in emotion regulation and sleep are shared across anxiety and mood disorders. Poor sleep has been shown to impair cognitive processes which may undermine cognitive regulatory function. However, it remains unknown if sleep quality impacts regulatory mechanisms in clinical anxiety and depression. Methods During fMRI, 78 patients with social anxiety disorder, generalized anxiety disorder, and/or major depressive disorder completed a validated emotion regulation task, which involved reappraisal (i.e., decrease negative affect) as compared to viewing aversive images. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI) and actigraphy, representing subjective and objective measures of sleep, respectively. Regression analysis was conducted with the PSQI and actigraphy sleep efficiency, duration, and wake-after sleep onset variables. Results PSQI and actigraphy measures indicated that the majority of patients experienced problematic sleep, however, subjective and objective sleep measures were uncorrelated. Whole-brain voxel-wise regression analysis, controlling for diagnosis, revealed worse self-reported sleep corresponded with less reappraise-related activation in the dorsal anterior cingulate cortex (DACC). The same analysis performed with actigraphy data showed less sleep efficiency positively corresponded with DACC activation. Post-hoc stepwise regression analysis showed these sleep measures predicted DACC activity whereas anxiety and depression symptoms did not. Conclusions Individual differences in self-perceived and objective sleep quality differentially modulated the DACC, which is implicated in cognitive reappraisal. Findings suggest neural correlates of emotion regulation tracks different aspects of the sleep experience. Results also indicate sleep disturbance may play a role in the emotion dysregulation observed in anxiety and depressive disorders.

BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

Parents experience many stressful situations when their child is ill and needs medical care, irrespective of the child’s age, diagnosis or the severity of the illness. Poor sleep quality and negative mood decrease the parents’ ability to sustain attention and focus, to care for their ill child, and to cope with the challenges they face. The overall aim of this thesis was to evaluate sleep, mood, cortisol response, and sense of coherence (SOC) in parents caring for children in need of medical care, and to identify factors that may influence parents’ sleep. This thesis includes four original studies; two of these are quantitative, prospective, descriptive and comparative studies including parents (n=82) accommodated in six pediatric wards with their ill child, using questionnaires and sleep logs to measure sleep, mood and SOC, and saliva cortisol to measure cortisol response. A follow-up was performed four weeks later at home, after hospital discharge. The other two studies are qualitative, inductive and explorative interview studies, including parents (n=12) staying overnight with their preterm and/or ill infant in three neonatal intensive care units, and parents (n=15) with a child receiving hospital-based home care in two pediatric outpatient clinics. The interviews were analyzed with a phenomenographic method. Being together with one’s family seems beneficial for sleep and may decrease stress. The ability to stay with the child, in the hospital or at home, was highly appreciated by the parents. When caring for a child with illness, parents’ sleep quality was sufficient in the hospital; however, sleep quality improved further (p<0.05) at home after discharge. The parents reported frequent nocturnal awakenings in the hospital caused by the child, medical treatment and hospital staff. Concern and anxiety about the child’s health, and uncertainty about the future were stressors affecting the parents’ sleep and mood negatively. The parents had lower (p=0.01) morning awakening cortisol levels in the pediatric ward compared to at home, and parents accommodated for more than one night had lower (p<0.05) post-awakening cortisol levels compared to parents staying their first night. The findings of this thesis conclude that being together as a family is important for the parents’ sleep. The ability to be accommodated in the hospital and gather the family around the child may have given the parents time for relaxation and recovery, that in turn may lead to a less stressful hospital stay. When it is beneficial for the child, the whole family should be included in the pediatric care. Moreover, pediatric nurses must acknowledge parents’ sleep, in hospital and at home. Medical treatment and care at night should be scheduled and sleep promoted for the parents in order to maintain health and well-being in the family.

The electronic version of the thesis is a corrected version of the printed thesis.

This thesis has also been funded by Barnklinikens 60-årsfond, Filip SchelinsStiftelse, Riksföreningen för barnsjuk...