scholarly journals The Many Faces of Sleep Disorders in Post-Traumatic Stress Disorder: An Update on Clinical Features and Treatment

2021 ◽  
pp. 1-13
Author(s):  
Franziska C. Weber ◽  
Thomas C. Wetter

Sleep disorders and nightmares are core symptoms of post-traumatic stress disorder (PTSD). The relationship seems to be bidirectional, and persistent disturbed sleep may influence the course of the disorder. With regard to sleep quality, insomnia and nocturnal anxiety symptoms, as well as nightmares and stressful dreams, are the most prominent sleep symptoms. Polysomnographic measurements reveal alterations of the sleep architecture and fragmentation of rapid eye movement sleep. In addition, sleep disorders, such as sleep-related breathing disorders and parasomnias are frequent comorbid conditions. The complex etiology and symptomatology of trauma-related sleep disorders with frequent psychiatric comorbidity require the application of multimodal treatment concepts, including psychological and pharmacological interventions. However, there is little empirical evidence on the effectiveness of long-term drug treatment for insomnia and nightmares. For nondrug interventions, challenges arise from the current lack of PTSD-treatment concepts integrating sleep- and trauma-focused therapies. Effective therapy for sleep disturbances may consequently also improve well-being during the day and probably even the course of PTSD. Whether early sleep interventions exert a preventive effect on the development of PTSD remains to be clarified in future studies.

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Zainab Ifthikar ◽  
Saima Sajjad Fakih ◽  
Saumy Johnson ◽  
Johnson Alex

Abstract Background In recent times, COVID-19 has been recognized as a public health emergency and thus far, most papers published on it are focused only on the clinical characteristics of infected patients. This pandemic has also made phenomenal emotional impact among the young and the old. We aimed to find out the impact of the COVID-19 pandemic on the psychological well-being of medical students in a University at Riyadh. Results There were 309 participants in the study. Out of them 44% did not have PTSD, 29% had score more than 37 which might contribute to immune suppression, in 18.4% PTSD was a clinical concern and 8.6% had probable PTSD. Female participants were the majority in the group and they also had higher chance of having consequences than the male counterparts (P < 0.001). Avoidance score between male and female gender was significantly different. Conclusion COVID-19 pandemic has not just affected the physiological functioning of the affected individuals but also has had a probable post-traumatic stress disorder among young college students. Screening for psychological well-being and the treatment for PTSD is imperative in college, school and general population.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Nisha Kader ◽  
Bushra Elhusein ◽  
Nirvana Swamy Kudlur Chandrappa ◽  
Abdulqadir J. Nashwan ◽  
Prem Chandra ◽  
...  

Abstract Background Intensive care unit (ICU) staff have faced unprecedented challenges during the coronavirus disease 2019 (COVID-19) pandemic, which could significantly affect their mental health and well-being. The present study aimed to investigate perceived stress and post-traumatic stress disorder (PTSD) symptoms reported by ICU staff working directly with COVID-19 patients. Methods The Perceived Stress Scale was used to assess perceived stress, the PTSD Diagnostic Scale for the Diagnostic and Statistical Manual of Mental Disorders (5th edition) was used to determine PTSD symptoms, and a sociodemographic questionnaire was used to record different sociodemographic variables. Results Altogether, 124 participants (57.2% of whom were men) were included in the analysis. The majority of participants perceived working in the ICU with COVID-19 patients as moderately to severely stressful. Moreover, 71.4% of doctors and 74.4% of nurses experienced moderate-to-severe perceived stress. The staff with previous ICU experience were less likely to have a probable diagnosis of PTSD than those without previous ICU experience. Conclusions Assessing perceived stress levels and PTSD among ICU staff may enhance our understanding of COVID-19-induced mental health challenges. Specific strategies to enhance ICU staff’s mental well-being during the COVID-19 pandemic should be employed and monitored regularly. Interventions aimed at alleviating sources of anxiety in a high-stress environment may reduce the likelihood of developing PTSD.


SLEEP ◽  
2019 ◽  
Vol 43 (4) ◽  
Author(s):  
M de Boer ◽  
M J Nijdam ◽  
R A Jongedijk ◽  
K A Bangel ◽  
M Olff ◽  
...  

Abstract Study Objectives Sleep problems are a core feature of post-traumatic stress disorder (PTSD). The aim of this study was to find a robust objective measure for the sleep disturbance in patients having PTSD. Methods The current study assessed EEG power across a wide frequency range and multiple scalp locations, in matched trauma-exposed individuals with and without PTSD, during rapid eye movement (REM) and non-REM (NREM) sleep. In addition, a full polysomnographical evaluation was performed, including sleep staging and assessment of respiratory function, limb movements, and heart rate. The occurrence of sleep disorders was also assessed. Results In patients having PTSD, NREM sleep shows a substantial loss of slow oscillation power and increased higher frequency activity compared with controls. The change is most pronounced over right-frontal sensors and correlates with insomnia. PTSD REM sleep shows a large power shift in the opposite direction, with increased slow oscillation power over occipital areas, which is strongly related to nightmare activity and to a lesser extent with insomnia. These pronounced spectral changes occur in the context of severe subjective sleep problems, increased occurrence of various sleep disorders and modest changes in sleep macrostructure. Conclusions This is the first study to show pronounced changes in EEG spectral topologies during both NREM and REM sleep in PTSD. Importantly, the observed power changes reflect the hallmarks of PTSD sleep problems: insomnia and nightmares and may thus be specific for PTSD. A spectral index derived from these data distinguishes patients from controls with high effect size, bearing promise as a candidate biomarker.


2020 ◽  
Vol 20 (S4) ◽  
Author(s):  
Nur Hafieza Ismail ◽  
Ninghao Liu ◽  
Mengnan Du ◽  
Zhe He ◽  
Xia Hu

Abstract Background Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma and fear of cancer recurrence. The widespread use of Twitter for socializing has been the alternative medium for data collection compared to traditional studies of mental health, which primarily depend on information taken from medical staff with their consent. These social media data, to a certain extent, reflect the users’ psychological state. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming. Methods We stream the data using cancer as a keyword to filter the tweets with cancer-free and use post-traumatic stress disorder (PTSD) related keywords to reduce the time spent on the annotation task. Convolutional Neural Network (CNN) learns the representations of the input to identify cancer survivors with PTSD. Results The results present that the proposed CNN can effectively identify cancer survivors with PTSD. The experiments on real-world datasets show that our model outperforms the baselines and correctly classifies the new tweets. Conclusions PTSD is one of the severe anxiety disorders that could affect individuals who are exposed to traumatic events, including cancer. Cancer survivors are at risk of short-term or long-term effects on physical and psycho-social well-being. Therefore, the evaluation and treatment of PTSD are essential parts of cancer survivorship care. It will act as an alarming system by detecting the PTSD presence based on users’ postings on Twitter.


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