Short Term Outcome in Sleep Disturbance Intervention in Children with ASD in a Tertiary Care Hospital in Bangladesh

Aim: The aim of this study was to identify the relationship between sleep patterns and behavioral difficulties in children with ASD using sleep disturbance treatments. Methods: We selected a total 41 children with sleep disorders as study population. The sleep services (behavioral sleep management techniques) were given by the child development center team of Dhaka Shishu Hospital comprising of physician , developmental therapist and psychologist. Children were split into eight groups and two gender divisions based on their age, and they were then studied over a period of 6 months . Sleep disorders were investigated both before and after intervention. Results: The results revealed that the children’s sleep dysfunction improved from before, with the lowest improvement percentage decreasing from 62.9 percent to 51.8 percent and the greatest improvement percentage increasing from 100 percent to 59.2 percent, whereas a sleep problem showed no change at all (sleepwalking). Conclusion: The study has tried and succeeded to an extent to intervene in the sleep dysfunction process of children with ASD in a tertiary care hospital. However, there is still much to learn about the clinical efficacy of these types of behavioral interventions in children with ASD who have sleep disruption.

Management of Gut Microbiota to Improve Sleep Dysfunction for Children with Autism-Spectrum Disorders

Many children with Autism-Spectrum Disorders (ASD) struggle with sleep dysfunction. This can be caused by a lack of important gut microbiota (GM) that have the ability to influence functions of the nervous system through the gut-brain axis. The metabolites of GM function are responsible for influencing the production of pertinent sleep hormones, such as melatonin and serotonin. The research has identified lower abundance levels of the gut microbiota species Faecalibacterium and Agathobacter in children with ASD that struggled with sleep disorders. When exploring their impact on sleep hormone production, a positive correlation was identified between these species of GM and melatonin levels, which regulates circadian sleep cycles. In addition, a negative correlation was observed between these species of GM and serotonin levels, which high levels of can lead to wakefulness and sleep dysfunction. To improve GM levels in children with ASD, a Bimuno® galactooligosaccharide (B-GOS®) prebiotic intervention was tested in children with ASD. While this intervention led to improved GM levels and increased levels of Faecalibacterium growth, a significant difference was not noticed between groups. Furthermore, an analysis of probiotic intervention studies in various individuals revealed the ability to improve sleep metric scores through consumption of probiotics. These findings can be further explored in children with ASD for potential treatments for sleep disorders.

Sleep Dysfunction in COVID-19 Patients: Prevalence, Risk Factors, Mechanisms, and Management

During the COVID-19 pandemic, the need to establish the prevalence of sleep dysfunction and psychological distress, identify predisposing and protective factors, and explore effective management strategies remains an important priority. Evidence to date suggests that a considerable proportion of COVID-19 patients experience significant sleep disturbances (estimated to afflict up to 50–75%) as well as psychological distress such as depression, anxiety, and traumatic stress. Duration of hospitalization, pre-existing mental health concerns, lower absolute lymphocyte count, and increased neutrophil-to-lymphocyte ratio have been all associated with a greater risk of sleep dysfunction in infected and hospitalized patients. Furthermore, in this review, we discuss the link between sleep deprivation, susceptibility to viral infections, and psychosocial wellbeing in relevance to COVID-19 and summarize the existing evidence regarding the presence and role of sleep apnea in infected individuals. Finally, we highlight the importance of suitable interventions in order to prevent and manage sleep dysfunction and avoid long-term physical and psychological implications. Future research should aim to provide high-quality information including in high risk, underserved, or difficult to reach populations and on the long-term consequences and effectiveness of applied interventions.

Upper-lower limb and breathing exercise program for improving sleep quality and psychological status in multiple sclerosis: a pilot randomized controlled trial

Purpose: To examine the feasibility and possible effect of an 8-week exercise program on sleep quality, insomnia and psychological distress in individuals with multiple sclerosis (MS). Methods: Twenty-four individuals with MS were recruited into a controlled pre-post feasibility study and divided into 2 groups: exercise (n = 13; Expanded Disability Status Scale (EDSS): 1.0–7.5) and a related control group with no exercise (n = 11; EDSS: 1.0–7.0). The exercise group performed combined upper limb, lower limb and breathing exercises in a controlled group (2d/week, 60 min/session) for 8 weeks. Participants were administered measures to evaluate sleep quality (Pittsburgh Sleep Quality Index, PSQI), insomnia severity (Insomnia Severity Index, ISI), psychological distress (Clinical Outcomes in Routine Evaluation–Outcome Measure, CORE-OM) and additionally impact of fatigue (Modified Fatigue Impact Scale, MFIS) after 8-weeks. Results: Insomnia severity measured with ISI (F(1;22)=5.95, p = 0.023, η p 2 = 0.213, 90% CI = 0.02–0.42) and psychological distress measured with the CORE-OM (F(1;22)=4.82, p = 0.039, η p 2 = 0.179, 90% CI = 0.01–0.40) showed statistically significant group-by-time interaction. Sleep quality measured with the PSQI showed statistically significant group-by-time interaction only in an aspect of daytime sleep dysfunction (F(1;22)=5.33, p = 0.031, η p 2 = 0.195, 90% CI = 0.01–0.40). The fatigue impact measured with the MFIS showed statistically significant group-by-time interaction in physical (F(1;22)=6.80, p = 0.016, η p 2 = 0.236, 90% CI = 0.02–0.44) and cognitive aspects (F(1;22)=9.12, p = 0.006, η p 2 = 0.293, 90% CI = 0.05–0.49), and total score (F(1;22)=11.29, p = 0.003, η p 2 = 0.339, 90% CI = 0.08–0.52). Conclusions: Our 8-week program reduced insomnia severity, psychological distress and some aspects of fatigue (physical; cognitive; total), and improved sleep quality in an aspect of daytime sleep dysfunction in a small group of individuals with MS. Good feasibility and significant positive changes from baseline warrant further exploratory work.

Quality of Life and Sleep Among Brazilian Speech-Language Pathology Students During the COVID-19 Pandemic

Purpose The aim of this study was to identify the factors that are related to the quality of life and sleep of Brazilian speech-language pathology students during the COVID-19 pandemic. Method This study adopted a cross-sectional, descriptive observational design. A total of 161 undergraduate speech-language pathology students participated in this study. The relationship between quality of life and sleep, socioeconomic characteristics, and health problems was examined using correlational analysis. Participant data were collected using an online form, which included questions that assessed their socioeconomic and health characteristics during the pandemic; the Pittsburgh Sleep Quality Index; and the first two questions of the World Health Organization Quality of Life–Brief Version. Results There were relationships between sleep dysfunction, a reduction in household income during the pandemic, membership to a risk group, living with at-risk individuals, time spent on the Internet (hours), and the purpose underlying Internet use. Satisfaction with health was related to membership to a risk group. Perceptions of the negative impact of the pandemic on mental health were related to sleep dysfunction and quality of life during the pandemic. Conclusion The present findings underscore the need for policies that promote health, prevent diseases, and address mental health problems during times of crisis, such as the COVID-19 pandemic.

798 Diffusion tensor imaging as a potential biomarker of sleep dysfunction in warfighters with chronic, severe, traumatic brain injury

Introduction Traumatic brain injury (TBI) plagues service members in times of war and training. Diagnosis and management of TBI remain challenging, with many suffering from sleep disorders. We hypothesized that TBI-related damage to the hypothalamus, a master regulator of breathing and sleep, could be related to post-TBI obstructive sleep apnea (OSA) and serve as a pathophysiological biomarker for a subpopulation of OSA patients. Methods This was a retrospective study of warfighters with TBI from the National Intrepid Center of Excellence (NICoE). Subjects were identified by severe TBI on neuroimaging and compared against a control group without TBI. All subjects underwent screening polysomnography (PSG). MRI was acquired via 3T scanner. The hypothalamus was automatically segmented using a diffeomorphic algorithm. DTI scalar values were analyzed with scalar t-tests between subjects and controls. Generalized linear modeling with DTI scalar values was used to predict AHI in subjects. Results 6 subjects and 61 controls were identified. There was significant sleep dysfunction amongst TBI subjects (mean apnea-hypopnea index (AHI) 5.1+/-6.6 events/hour; mild OSA incidence 33.3%; Pittsburgh Sleep Quality Index (PSQI) mean 13.3+/-2.6). Radial diffusivity (RD), axial diffusivity (AD) and mean diffusivity (MD) were significantly higher among subjects (control RD 9.64x10^-10+/-7.54x10^-11 m^2/s, subject RD 1.13x10^-9+/-1.20x10^-10m^2/s, p = 0.023; control AD 1.32x10^-9+/-7.64x10^-11m^2/s, subject AD 1.50x10^-9+/-1.43x10^-10m^2/s, p = 0.029; control MD 1.08x10^-9+/-7.43x10^-11m^2/s, subject MD 1.25x10^-9+/-1.34x10^-10m^2/s, p = 0.025). There were no differences in age or body-mass index. Generalized linear modeling with diffusivity measures as predictors of AHI in subjects was not significant. Conclusion Using a diffeomorphic algorithm to define the hypothalamus reveals significantly elevated scalar DTI measures in chronic, severe TBI compared to controls. DTI differences in the hypothalamus are a novel finding and possibly underlie part of the pathophysiology of TBI. Although this may have potential to serve as a biomarker in severe TBI patients with sleep disorders, these initial data do not support a relationship between DTI and AHI, despite high incidence of OSA and subjective sleep dysfunction. Future studies with more subjects may better elucidate the changes in hypothalamic DTI after TBI for clinical outcomes analysis. Support (if any) This work was supported by grant 130132 from USAMRMC.

Sleep Dysfunction is an Independent Predictor of Productivity Losses in Patients with Chronic Rhinosinusitis

Background: Chronic rhinosinusitis (CRS) is known to have a significant impact on economic productivity. Sleep dysfunction is associated with staggering productivity losses and is highly prevalent in patients with CRS. The effect of sleep dysfunction on productivity in CRS has not been elucidated. The objective of this study was to determine the relationship between sleep dysfunction and lost productivity in patients with CRS. Methods: Eighty-two adult patients with CRS were prospectively enrolled into a cross-sectional cohort study. Patients with obstructive sleep apnea were excluded. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Presenteeism (reduced work efficiency), absenteeism (missed work days), and lost work, household, and overall productivity were analyzed. The primary aim was assessing the correlation between PSQI and productivity. Regression analyses were performed to account for disease severity, pain, and depression. Results: Sleep dysfunction is significantly correlated with overall lost productivity (R2 = 0.397, P < .05). Presenteeism is the most strongly affected by sleep dysfunction (R2 = –0.441, P < .001). Higher PSQI scores were significantly associated with productivity losses, whereas lower scores were not. Sleep remained an independent predictor of productivity when regression analysis accounted for disease severity, depression, and pain. Conclusion: Sleep dysfunction has a significant association with lost productivity in patients with CRS, particularly with worsening PSQI scores. More clearly defining those components of CRS that most impact a patient’s daily function will allow clinicians to more optimally manage and counsel patients with CRS.