Sleep and memory: mechanisms and implications for psychiatry

Insomnia Severity Predicts Psychiatric Symptoms: A Cross-Sectional Study Investigating the Partial Mediations of Worry and Rumination

OBJECTIVE

Insomnia as a disorder on its own or as a symptom of other mental disorders can lead to significant distress and lower quality of life. By exacerbating negative affect and emotion dysregulation, poor sleep and insomnia can contribute to the initiation and maintenance of mental disorders. The aim of this cross-sectional study was to investigate the relationship between insomnia severity and overall psychiatric symptoms (anxiety, depression, obsessive-compulsive symptoms, somatization, phobic anxiety, hostility, interpersonal sensitivity, paranoid ideation, and psychoticism), and the mediational roles of worry and rumination in this relationship.

METHOD

The data was collected from a community sample of 1444 participants (females 69.39%, Mage = 27.95, SD = 9.37) who completed self-report measures of insomnia severity, worry, rumination, and psychiatric symptoms. The mediational roles of worry and rumination were tested with mediation analysis using the PROCESS Macro.

RESULTS

It was found that insomnia severity (β = 0.20, p < .001) significantly predicted psychiatric symptoms directly and via worry and rumination (β = 0.33, p < .001), meaning that worry and rumination partially mediated the relationship between insomnia severity and psychiatric symptoms. The findings were similar after controlling for smoking status, daily screen time, coffee consumption in the evening, weekly exercise frequency, and pre-sleep screen time.

CONCLUSIONS

Interventions targeting the reduction of insomnia severity and maladaptive emotion regulation strategies (e.g., worry and rumination), as well as the enhancement of adaptive emotion regulation strategies (e.g., positive refocusing and mindfulness), may alleviate the adverse effects of insomnia on psychiatric symptoms.

Sleep patterns and awareness of the consequences of sleep deprivation in Saudi Arabia

PURPOSE

Sleep deprivation is a growing challenge in modern society and places a burden on people's physical and mental well-being. However, only a few studies have investigated sleep patterns and awareness of the associated risks of sleep deprivation in Middle Eastern countries. This study aimed to explore these issues in adults in Saudi Arabia.

METHODS

This was a quantitative cross-sectional study. We collected information on the participants' demographic characteristics, sleep hours, and sense of feeling sleep-deprived over the previous 3 months regardless of sleep duration. Current knowledge of the health consequences associated with chronic sleep deprivation and attitudes toward seeking in-person health care for sleep deprivation symptoms were additionally assessed.

RESULTS

Among 1449 adults, only 41% of the participants slept for the recommended duration for adults of 7 h or more. However, 70% reported feeling sleep-deprived at least some of the time regardless of their sleep duration. Smokers, students, and those on sleep medications were more likely to report feeling sleep-deprived. Most participants (strongly) agreed that chronic sleep deprivation had a negative effect on their health (92%) and social (88%) and professional lives (91%), yet only 10% of our sample had visited a doctor for their sleep deprivation symptoms.

CONCLUSIONS

We found a high level of sleep deprivation among residents of Saudi Arabia and a strong awareness of the association between chronic sleep deprivation and negative consequences, yet a lower awareness of the association with specific medical comorbidities. Our findings also suggested a lack of health care engagement among sleep-deprived participants.

Screen use, sleep duration, daytime somnolence, and academic failure in school-aged adolescents

In this study, we examined the relationship between screen time use, sleep characteristics, daytime somnolence, and academic performance in school-aged adolescents. We surveyed 1,257 12- to 18-year-old adolescents attending 52 schools in urban or suburban areas of Argentina. We recorded the daily exposure to various screen-based activities, including video- and online-gaming, social media, TV or streaming. Screen time and device type in the hour before bedtime, sleep patterns during weekdays and weekends, somnolence (Pediatric Daytime Sleepiness Scale score), and grades in language and mathematics were also assessed. Structural Equation Modelling was used to identify a path connecting the latent variables. Results are expressed as standardized regression weights (srw). Missing data were present in 393 subjects, and thus the final sample consisted of 864 complete responses. Daytime somnolence (i.e., PDSS score ≥ 15) was observed in 614 participants (71%), and academic failure (i.e., grades < 7/10) in 352 of them (41%). Time spent using video gaming consoles was negatively associated with sleep duration (srw = -0.22, p<0.01) and positively connected with daytime somnolence (srw = 0.11, p<0.01). Use of mobile devices was associated with lower academic performance (srw = -0.11, p<0.01). Sleep duration was inversely related to daytime somnolence (srw = -0.27, p<0.01), which was in turn negatively associated with academic performance (srw = -0.18, p<0.05). Bedtime computer use did not influence any outcome. In summary, among adolescents, screen use adversely affected nighttime sleep, daytime somnolence, and academic performance. These findings call for the implementation of educational public campaigns aimed at promoting healthy sleep and reducing screen exposure among adolescents.

Sleep, insomnia and mental health

While sleep serves important regulatory functions for mental health, sleep disturbances, in particular insomnia, may favour a state of allostatic overload impairing brain neuroplasticity and stress immune pathways, hence contributing to mental disorders. In this framework, the aim of this work was to link current understanding about insomnia mechanisms with current knowledge about mental health dysregulatory mechanisms. The focus of the present work was on mood, anxiety, and psychotic disorders, which represent important challenges in clinical practice. Literature searches were conducted on clinical, neurobiological, and therapeutic implications for insomnia comorbid with these mental disorders. Given the complexity and heterogeneity of the existing literature, we ended up with a narrative review. Insomnia may play an important role as a risk factor, a comorbid condition and transdiagnostic symptom for many mental disorders including mood/anxiety disorders and schizophrenia. Insomnia may also play a role as a marker of disrupted neuroplasticity contributing to dysregulation of different neurobiological mechanisms involved in these different mental conditions. In this framework, insomnia treatment may not only foster normal sleep processes but also the stress system, neuroinflammation and brain plasticity. Insomnia treatment may play an important preventive and neuroprotective role with cognitive behavioural therapy for insomnia being the treatment with important new evidence of efficacy for insomnia, psychopathology, and indices of disrupted neuroplasticity. On the other hand, pharmacological pathways for insomnia treatment in these mental conditions are still not well defined. Therapeutic options acting on melatonergic systems and new therapeutic options acting on orexinergic systems may represents interesting pathways of interventions that may open new windows on insomnia treatment in mental disorders.

Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach

A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient’s treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity = .80, specificity = .77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response.