Bidirectionality Between Sleep Symptoms and Core Depressive Symptoms and Their Long-Term Course in Major Depression

Treating Depression to Improve Survival in Coronary Heart Disease: What Have We Learned?

Major depressive disorder is a well-established risk factor for cardiac events in patients with coronary heart disease, but clinical trials have produced little evidence that treating depression reliably improves cardiac event-free survival in these patients. In this review, we offer evidence that certain symptoms that commonly remain after otherwise successful treatment of depression-insomnia, fatigue, and anhedonia-independently predict cardiac events. This may help to explain the failure of previous depression treatment trials to improve cardiac event-free survival even when other symptoms of depression improve. We thus propose that adverse cardiovascular effects that have long been attributed to syndromal depression may be instead caused by persistent fatigue, insomnia, and anhedonia, regardless of whether other symptoms of depression are present. We also identify interventions for these symptoms and call for more research to evaluate their effectiveness in depressed patients with coronary heart disease.

Relationship of Sleep Disorder with Neurodegenerative and Psychiatric Diseases: An Updated Review

Sleep disorders affect many people worldwide and can accompany neurodegenerative and psychiatric diseases. Sleep may be altered before the clinical manifestations of some of these diseases appear. Moreover, some sleep disorders affect the physiological organization and function of the brain by influencing gene expression, accelerating the accumulation of abnormal proteins, interfering with the clearance of abnormal proteins, or altering the levels of related hormones and neurotransmitters, which can cause or may be associated with the development of neurodegenerative and psychiatric diseases. However, the detailed mechanisms of these effects are unclear. This review mainly focuses on the relationship between and mechanisms of action of sleep in Alzheimer's disease, depression, and anxiety, as well as the relationships between sleep and Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. This summary of current research hotspots may provide researchers with better clues and ideas to develop treatment solutions for neurodegenerative and psychiatric diseases associated with sleep disorders.

Resting-state EEG as a potential indicator to predict sleep quality in depressive patients

OBJECTIVES

To investigate the changes in sleep quality, heart rate variability (HRV) and resting-state electroencephalogram (rsEEG) in patients with major depressive disorder (MDD), and to explore whether HRV and rsEEG may be served as more convenient tools to assess sleep quality in MDD patients.

METHOD

We included a total of 91 subjects (46 healthy controls and 45 MDD patients) and compared their sleep quality, HRV and power spectra of rsEEG. Correlation analyses were conducted to discuss the relationship between HRV and seven factors of PSQI. Multiple linear regression model was used to examine whether absolute band power could predict sleep quality in MDD patients.

RESULTS

We found higher PSQI scores and lower levels of HRV in depressive individuals compared with healthy controls. In MDD patients, sleep latency was negatively correlated with RMSSD and HF. Delta, theta, and alpha band power of rsEEG were higher in MDD patients. Regression analyses showed delta band power of TP8, as well as theta, alpha band power of AF3 predicts PSQI score of MDD patients.

CONCLUSIONS

The findings of our study show that some aspects of sleep problems had negative correlations with parasympathetic activity and the regression model supports that the band power of rsEEG may be used as a potential indicator to evaluate the sleep quality in MDD patients.

SIGNIFICANCE

Cortical hyperarousal may be one of the reasons leading to poor sleep quality of MDD patients. And resting-state EEG can be used as a potential indicator for clinical assessment of MDD patients' sleep quality.

Short sleep duration is associated with a wide variety of medical conditions among United States military service members

OBJECTIVES

This cross-sectional study investigated self-reported sleep duration and its association with a comprehensive range of clinically-diagnosed medical condition categories (CDMCs), as well as the relationship between short sleep duration (≤6 h) and demographic/lifestyle factors, among United States military service members (SMs).

METHODS

A stratified random sample of SMs (n = 20,819) completed an online questionnaire on usual daily hours of sleep and demographic/lifestyle characteristics. CDMCs for a six-month period prior to questionnaire completion were obtained from a comprehensive military electronic medical surveillance system and grouped into 33 CDMCs covering both broad and specific medical conditions. Prevalence of CDMCs was compared among three sleep duration categories (≤4, 5-6 and ≥7 h).

RESULTS

SMs reported a mean ± standard deviation of 6.3 ± 1.4 h of sleep per day. After adjustment for demographic/lifestyle characteristics, shorter sleep duration was associated with higher odds of a medical condition in 25 of 33 CDMCs, with most (n = 20) demonstrating a dose-response relationship. The five CDMCs with the largest differences between ≤4 vs ≥ 7 h sleep were: diseases of the nervous system (odds ratio [OR] = 2.9, 95% confidence interval [95%CI] = 2.4-3.4), mental/behavioral diseases (OR = 2.7, 95%CI = 2.3-3.2), diseases of the musculoskeletal system (OR = 1.9, 95%CI = 1.6-2.1), diseases of the circulatory system (OR = 1.7, 95%CI = 1.3-2.2), and diseases of the digestive system (OR = 1.6, 95%CI = 1.2-2.0). Six hours of sleep or less was independently associated with older age, less formal education, race, Hispanic ethnicity, higher body mass index, smoking, and military service branch.

CONCLUSIONS

In this young, physically active population, reporting shorter sleep duration was associated with a higher risk of multiple CDMCs.

Monitoring and Model Analysis of Vocal Performance Teaching Environment Using Cluster Analysis from the Perspective of Core Literacy

To cultivate students' artistic quality, enhance their vocal music quality, and prepare them to make great contributions to the innovation and development of my country's vocal music art is the main goal of opening vocal music performance major in colleges and universities. With the advancement of technology and the demands of talent development, the vocal music teaching methodology for the vocal music performance major in colleges and universities must be continuously enhanced. Otherwise, there will be an issue of disconnect between teaching style and talent development, which will harm both the development of high-quality vocal music talents and the innovation and growth of vocal music performance majors in colleges and universities. The vocal music performance major at colleges and universities should actively support the reform and innovation of the vocal music teaching mode in order to extend students' knowledge, develop their all-around ability, and provide a strong foundation for vocal music performance, to develop students' all-encompassing musical abilities. This research suggests a design strategy for the monitoring and model optimization of the teaching environment for vocal performance majors from the standpoint of core literacy. To increase the efficiency and objectivity of course instruction, cluster analysis aids students in categorising and searching for vocal music performance main repertoire as well as using collaborative filtering recommendations to locate their own vocal music performance. The simulation test analysis is completed lastly. The method has a certain accuracy, which is 7.59% higher than the conventional algorithm, according to the simulation findings. In addition to significantly increasing student interest in studying vocal music performance courses, we further reform and innovation of the teaching method for these courses at colleges and universities can also strengthen students' understanding of various repertoire styles and significantly enhance their musical literacy.

Using 24-h Heart Rate Variability to Investigate the Sleep Quality and Depression Symptoms of Medical Students

There have been numerous studies on the relationship between sleep and depression, as well as the relationship between sleep and depression, and heart rate variability (HRV), respectively. Even so, few studies have combined 24-h HRV analysis to study sleep quality and depressive symptoms. The purpose of this cross-sectional study was to investigate the relationship between depressed symptoms, sleep quality, and 24-h HRV in medical students. The particiants were all students at a medical university in Guangdong province, China. A total of 74 college students participated. They were asked to complete a questionnaire that included the Pittsburgh Sleep Quality Index (PSQI), the Beck Depression Inventory-II (BDI-II), the Positive and Negative Affect Scale (PANAS), and 24-h ECG monitoring. The results showed that 41.7% of the medical students had poor sleep quality, with higher levels of depressive symptoms and more negative emotions, and there was no difference in 24-h HRV indices between the low PSQI group and the high one. Correlation analysis showed that there was a significant relationship between sleep quality and depressive symptoms (r = 0.617), but the relationship between 24-h HRV indices and PSQI global scores, BDI scores were not significant. However, the correlation analysis of PSQI components and 24-h HRV showed that sleep disturbance was significantly negatively correlated with SDNN and LF in waking period (r = -0.285, -0.235), and with SDNN in sleeping period (r = -0.317). In general, the sleep disturbance in PSQI components can sensitively reflect the relationship between sleep quality and 24-h HRV of medical students. Individuals with higher sleep disturance may have lower SDNN during awake period and bedtime period, and lower LF in awake period. Twenty-four hour HRV has certain application value in clinical sleep quality monitoring, and its sensitivity and specificity in clinical application and daily life are still worth further investigation.

Sleep problems in adolescence are prospectively linked to later depressive symptoms via the cortisol awakening response

Sleep disturbance is a symptom of and a well-known risk factor for depression. Further, atypical functioning of the HPA axis has been linked to the pathogenesis of depression. The purpose of this study was to examine the role of adolescent HPA axis functioning in the link between adolescent sleep problems and later depressive symptoms. Methods: A sample of 157 17-18 year old adolescents (61.8% female) completed the Pittsburgh Sleep Quality Inventory (PSQI) and provided salivary cortisol samples throughout the day for three consecutive days. Two years later, adolescents reported their depressive symptoms via the Center for Epidemiological Studies Depression Scale (CES-D). Results: Individuals (age 17-18) with greater sleep disturbance reported greater depressive symptoms two years later (age 19-20). This association occurred through the indirect effect of sleep disturbance on the cortisol awakening response (CAR) (indirect effect = 0.14, 95%CI [.02 -.39]). Conclusions: One pathway through which sleep problems may lead to depressive symptoms is by up-regulating components of the body's physiological stress response system that can be measured through the cortisol awakening response. Behavioral interventions that target sleep disturbance in adolescents may mitigate this neurobiological pathway to depression during this high-risk developmental phase.

Relationship between insomnia and depression in a community sample depends on habitual sleep duration

Sleep disturbances, such as short sleep duration and insomnia, are core features of depression. However, it is unclear if sleep duration and insomnia have an interactive effect on depression severity or individual symptoms. Data were drawn from a community sample (N = 1007) containing responses on the Insomnia Severity Index, Patient Health Questionnaire-9 (PHQ-9), and average sleep duration. Regression analyses determined the prevalence risks (PR) of symptoms of depression based on insomnia severity and sleep duration. Depression severity was related to insomnia severity (PR 1.09, p < 0.001) and short sleep duration (PR 1.52, p < 0.001), but the interaction between the two was negative (PR 0.97, p < 0.001). Insomnia severity increased the prevalence risk of all individual depression symptoms between 8 and 15%, while sleep duration increased the prevalence risk of appetite dysregulation (PR 1.86, p < 0.001), fatigue (PR 1.51, p < 0.001), difficulty concentrating (PR 1.61, p = 0.003), feelings of failure (PR 1.58, p = 0.002), and suicidal behavior (PR 2.54, p = 0.01). The interaction of sleep duration and insomnia was negative and ranged between 3 and 6%. In clinically significant depression (PHQ >=10), only insomnia severity increased the prevalence risk of depression severity (PR 1.02, p = 0.001). Insomnia and short sleep predict prevalent depression, but their interactive effect was negative. Thus, while insomnia had a greater association with depression severity and symptoms, this association was dependent on habitual sleep duration.

Unravelling the Prospective Associations Between Mixed Anxiety-Depression and Insomnia During the Course of Cognitive Behavioral Therapy

OBJECTIVE

Previous studies have suggested that there is a reciprocal relationship between anxiety/depression and insomnia. However, little is known about the prospective relationships between these constructs across the course of cognitive behavioral therapy (CBT). The aim of the study was to examine these relationships in clients who received short-term CBT in a primary care setting.

METHODS

A total of 653 clients (mean [SD] age = 37.8 [12.9], 26.4% men) with mild to moderate levels of anxiety and depression and a treatment duration of at least 7 weeks were included for analyses. The clients completed questionnaires measuring mixed anxiety-depression (MAD - Patient Health Questionnaire Anxiety and Depression Scale) and insomnia (3 items derived from the Karolinska Sleep Questionnaire representing core DSM-V criteria) on a session-to-session basis. The data were analyzed using latent growth curve models and random intercept cross-lagged panel models.

RESULTS

The results of the latent growth curve models showed that there was a significant decrease in both MAD (cubic slope; B = .002, p < .001, quadratic slope; B = .036, p < .001, linear slope; B = -.205, p < .001) and insomnia (linear slope; B = -.080, p < .001) across treatment. A strong correlation (r = .838, p < .001) between the linear slopes indicated co-occurring change processes. The cross-lagged panel model showed that insomnia significantly predicted MAD at the subsequent measurements (B = .190, p < .001), but not vice versa (B = .252, p = .343).

CONCLUSIONS

Changes in MAD and insomnia are co-occurring processes during the course of CBT. Changes in insomnia predicted prospectively changes in MAD, but not vice versa. Targeting insomnia in the context of brief CBT in clients with mild to moderate anxiety and depression may therefore further reduce not only symptoms of insomnia but also symptoms of anxiety and depression.

How to Deal With Temporal Relationships Between Biopsychosocial Variables: A Practical Guide to Time Series Analysis

OBJECTIVE

Longitudinal data allow for conclusions about the temporal order of events and interactional dynamics between several processes. The aim of this article is to provide a concise and pragmatic description of time series analyses (TSAs) of patient samples with numerous (or daily) repeated biological, behavioral, or psychological measurements. In addition, the article demonstrates how to implement the described analyses with the software R.

METHODS

To illustrate the concrete application of the time series method, we use two case series of patients with anorexia nervosa. Upon awakening, the patients collected salivary cortisol on a daily basis and answered several questions on a handheld computer (electronic diary) regarding psychosocial variables at the time of salivary collection.

RESULTS

Basic concepts of time series analysis such as stationarity, auto- and cross-correlation, Granger causality, impulse response function, and variance decomposition are presented. In addition, we demonstrate vector autoregressive analyses with three variables. For Patient 1, we demonstrate how TSA is used to detect cortisol and anxiety decreases during inpatient treatment and also how TSA can be used to show that an increase in cortisol is followed by a next-day increase in anxiety. For Patient 2, TSA was used to show higher salivary cortisol upon awakening on the days the patient was weighed compared with other days. In addition, we show how interdependencies of depressive feelings, positive anticipations, and cortisol values can be quantified using TSA.

CONCLUSIONS

Time series designs enable modeling of temporal relationships and bidirectional associations between biopsychosocial variables within individuals. These individual patterns cannot be derived from traditional group-based statistical analyses. This article provides accessible research tools for conducting TSA relevant to psychosomatic and biobehavioral research.

Insomnia among Cancer Caregivers: A Proposal for Tailored Cognitive Behavioral Therapy

Caregivers are relatives, friends, or partners who have a significant relationship with and provide assistance (i.e., physical, emotional) to a patient with often life-threatening, serious illnesses. Between 40 and 76 percent of caregivers for people with cancer experience sleep disturbance. This is thought to be due, in part, to the unique responsibilities, stressors, and compensatory behaviors endemic to caregiving that serve as precipitating and perpetuating factors of insomnia. Sleep disturbances are associated with significant alterations in one's mental and physical health. Once chronic, insomnia does not remit naturally. Cognitive-behavioral therapy for insomnia (CBT-I) is well-suited to address the multifaceted contributing factors unique to caregivers' sleep disturbance, yet only one intervention has tested a CBT-I informed intervention among cancer caregivers. Toward the goal of developing effective, tailored treatments for insomnia in caregivers, we address the distinct presentation of insomnia among cancer caregivers and describe key modifications to standard CBT-I that address these specific needs and enhance sensitivity and feasibility, modeled in a demonstrative case vignette. Future research must seek to provide a wide range of effective treatment options for this population, including internet-based, dyadic, and alternative integrative medicine treatments. Applicability of key modifications for caregivers of patients with other chronic illnesses is discussed. Establishing empirically-supported interventions for insomnia among cancer caregivers has the potential to enhance their quality of life and care provided, lead to improved bereavement outcomes, and attenuate the notable mental and physical health disparities present in this vulnerable population.