Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. RESEARCH QUESTION: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. STUDY DESIGN AND METHODS: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. RESULTS: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. INTERPRETATION: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Interaction of sleep and emotion across the menstrual cycle.

Menstruating individuals experience an increased risk for sleep and affective disorders, attributed in part to monthly oscillations in sex hormones. Emotional functioning and sleep continuity worsens during the perimenstrual phase of the menstrual cycle. This study examined the interactive effects of sleep, menstrual phase, and emotion in healthy women. Participants (N = 51, 43% Caucasian) aged 18-35 (m = 24 years) completed actigraphy and daily sleep/emotion diaries over two menstrual cycles (m days = 51.29). Diary and actigraphic total wake time at night (TWT) and daily ratings of positive and negative affect were compared across four phases of the menstrual cycle: perimenstrual, mid-follicular, periovulatory, and mid-luteal. Relationships between phase, sleep, and emotion were estimated using multistep hierarchical linear modelling. Mean menstrual cycle length was 28.61 ± 2.69 days. Perimenstrual phase positively predicted anger (p < 0.001) but no other emotions. Additionally, the perimenstrual phase predicted higher rates of TWT, such that diary TWT was 8-16 min longer during the perimenstrual (m = 67.54, SE = 3.37) compared to other phases (p < 0.001). Actigraphic TWT was also increased by 4-7 min (m = 61.54, SE = 3.37) in the perimenstrual phase (p < 0.001). Positive emotions were 0.05-0.10 points lower (p = 0.006-0.02) when TWT was greater in the perimenstrual phase. Greater rates of anger and sleep disruption were seen during the perimenstrual phase compared with other phases. When poor sleep occurred during the perimenstrual phase individuals reported reduced positive emotions. Reducing perimenstrual sleep disruptions may be an important intervention target for those at risk for affective disorders.

Sleep duration, sleep quality, and weapon carrying in a sample of adolescents from Texas.

Objectives: Studies are beginning to document an association between sleep duration and a range of adolescent delinquent behaviors, including weapon carrying. However, little is known about whether and to what extent sleep quality - another dimension of sleep for healthy adolescent functioning - is associated with weapon carrying. We address this gap in knowledge by evaluating the role of restless sleep and sleep duration in adolescent weapon carrying. Methods: We analyze data from a diverse sample of 994 adolescents from Texas, USA collected in 2010. Multivariate logistic regression models estimate the association of sleep duration and restless sleep on weapon carrying after controlling for theoretical covariates and demographic characteristics. Results: Adolescents sleeping 4 h or less on school nights were more than twice as likely to report carrying a weapon (OR = 2.38, 95% CI = 1.16-4.89, p =.018). Sleeping 5-6 h was associated with higher odds of carrying a weapon, while 6-7 h and 8 or more hours were associated with lower odds; however, all associations were non-significant. Restless sleep was associated with weapon carrying at the bivariate level (Pearson χ2 (3) = 10.56, p =.014), but not at the multivariate (OR = 1.10, 95% CI = 0.91-1.32, p =.299). Conclusions: Our findings align with previous research demonstrating that sleeping 4 or less hours increases the likelihood of adolescent weapon carrying. Restless sleep appears to play less of a role. Future research should elucidate the longitudinal pathways between sleep duration, sleep quality, and forms of adolescent weapon carrying.

The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.

Objectives: Managing multimorbidity as aging stroke patients is complex; standard self-management programs necessitate adaptations. We used visual analytics to examine complex relationships among aging stroke survivors' comorbidities. These findings informed pre-adaptation of a component of the Chronic Disease Self-Management Program. Methods: Secondary analysis of 2013-2014 Medicare claims with stroke as an index condition, hospital readmission within 90 days (n = 42,938), and 72 comorbidities. Visual analytics identified patient subgroups and co-occurring comorbidities. Guided by the framework for reporting adaptations and modifications to evidence-based interventions, an interdisciplinary team developed vignettes that highlighted multimorbidity to customize the self-management program. Results: There were five significant subgroups (z = 6.19, p < .001) of comorbidities such as obesity and cancer. We constructed 6 vignettes based on the 5 subgroups. Discussion: Aging stroke patients often face substantial disease-management hurdles. We used visual analytics to inform pre-adaptation of a self-management program to fit the needs of older adult stroke survivors.

Mortality Patterns Associated with Central Sleep Apnea among Veterans: A Large, Retrospective, Longitudinal Report.

Rationale: Central sleep apnea (CSA) is associated with high mortality. Current knowledge stems from studies with limited sample size (fewer than 100 subjects) and in homogeneous populations such as heart failure (HF). Objectives: To address this knowledge gap, we compared the mortality pattern and time to death between the CSA and obstructive sleep apnea (OSA) patients in the large Veterans Health Administration patient population using the big data analytic approach. Methods: This is a retrospective study using national Veterans Health Administration electronic medical records from October 1, 1999, through September 30, 2020. We grouped the patients with underlying sleep disorders into CSA and OSA, using the International Classification of Diseases, Ninth and Tenth Revision codes. We applied Cox regression analysis to compare the mortality rate and hazard ratio (HR) among the two groups and adjusted HR by gender, race, body mass index (BMI), age, and Charlson Comorbidity Index. In CSA groups, a machine-learning algorithm was used to determine the most important predictor of time to death. Further subgroup analysis was also performed in patients that had comorbid HF. Results: Evaluation of patients resulted in 2,961 grouped as CSA and 1,487,353 grouped as OSA. Patients with CSA were older (61.8 ± 15.6 yr) than those with OSA (56.7 ± 13.9 yr). A higher proportion of patients with CSA (25.1%) died during the study period compared with the OSA cohort (14.9%). The adjusted HR was 1.53 (95% confidence interval [CI], 1.43-4.65). Presence of HF history of cerebrovascular disease, hemiplegia, and having a BMI less than 18.5 were among the highest predictors of mortality in CSA. The subgroup analysis revealed that the presence of HF was associated with increased mortality both in CSA (HR, 7.4; 95% CI, 6.67-8.21) and OSA (HR, 4.3; 95% CI, 4.26-4.34) groups. Conclusions: Clinically diagnosed CSA was associated with a shorter time to death from the index diagnostic date. Almost one-fifth of patients with CSA died within 5 years of diagnosis. The presence of HF, history of cerebrovascular disease and hemiplegia, male sex, and being underweight were among the highest predictors of mortality in CSA. CSA was associated with higher mortality than OSA, independent of associated comorbidity.

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.

BACKGROUND: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. OBJECTIVE: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. METHODS: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. RESULTS: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. CONCLUSIONS: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports.

Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000−30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.

Sleep During Pregnancy.

PURPOSE OF REVIEW: This article will provide a brief review of most recent findings of sleep, insomnia, obstructive sleep apnea, and restless leg syndrome during pregnancy. RECENT FINDINGS: Recent meta-analyses have found that 38.2% of pregnant women experience insomnia, 15% experience obstructive sleep apnea, and 20% experience restless leg syndrome. Poor sleep during pregnancy is associated with increased odds of preterm birth, cesarean section, hypertension, gestational diabetes, and longer deliveries. OSA is also associated with poor outcomes such as maternal hypertension, gestational diabetes, preterm birth, and low infant Apgar score. Incidence of new-onset RLS increases with parity, gestational age, maternal age, and increased maternal adiposity. Typical treatments have been found to be effective for treating sleep disorders in pregnant women. Obtaining sufficient sleep quality and quantity in addition to assessing and treating sleep disorders should be a priority for pregnant women and their providers.

Association between Sleep Quality and Mental Health among Patients at a Post-COVID-19 Recovery Clinic.

A growing body of research documents the persistence of physical and neuropsychiatric symptoms following the resolution of acute COVID-19 infection. To the best of our knowledge, no published study has examined the interaction between insomnia and mental health. Accordingly, we proposed to examine new diagnoses of insomnia, and referrals to pulmonary and sleep medicine clinics for treatment of sleep disorders, in patients presenting to one post-acute COVID-19 recovery clinic. Additionally, we aimed to examine the relationship between poor sleep quality, depression, anxiety, and post-traumatic stress. Patients presented to the clinic on average 2 months following COVID-19 infection; 51.9% (n = 41) were hospitalized, 11.4% (n = 9) were in the intensive care unit, 2.5% (n = 2) were on a mechanical ventilator, and 38.0% (n = 30) were discharged on oxygen. The most commonly reported symptom was fatigue (88%, n = 70), with worse sleep following a COVID-19 infection reported in 50.6% (n = 40). The mean PSQI score was 9.7 (82.3%, n = 65 with poor sleep quality). The mean GAD-7 score was 8.3 (22.8%, n = 14 with severe depression). The mean PHQ-9 was 10.1 (17.8%, n = 18 with severe anxiety). The mean IES-6 was 2.1 (54.4%, n = 43 with post-traumatic stress). Poor sleep quality was significantly associated with increased severity of depression, anxiety, and post-traumatic stress. Future work should follow patients longitudinally to examine if sleep, fatigue, and mental health symptoms improve over time.

Longitudinal Analysis Evaluating Self-Reported CPAP Use for OSA during the COVID-19 Pandemic.

Continuous positive airway pressure therapy (CPAP) is a highly effective treatment for obstructive sleep apnea (OSA), but CPAP adherence remains suboptimal. The COVID-19 pandemic significantly altered sleep medicine services and aspects of daily living for sleep medicine patients, which may further compromise CPAP adherence. Sleep medicine patients were distributed an online survey at baseline and six months later (January-May 2021). Participants answered questions regarding CPAP use (any changes in CPAP use, sleep quality with CPAP use, CPAP use as advised, and changes in daily habits). Eighty-one adults completed the baseline survey, and 54 adults completed the follow-up survey. Twenty-seven participants reported a diagnosis of OSA and were prescribed CPAP (mean age 58 ± 18.2 years, 48% female, 67% Caucasian). Longitudinal analysis with chi-square association testing showed significant changes in CPAP use as advised and significant improvements in sleep quality with CPAP use when comparing the baseline to six-month follow-up survey. Additionally, logistic regression was performed to determine if pre-pandemic sleep study results (apnea-hypopnea index and respiratory disturbance index) predicted self-reported CPAP use during the pandemic, though no association was found. Throughout the pandemic, sleep medicine patients improved their CPAP use as advised and reported significant improvements in sleep quality with CPAP use.

Menstrual regularity and bleeding is associated with sleep duration, sleep quality and fatigue in a community sample.

Female menstrual health and its relationship with sleep is an understudied subject. The aim of this investigation was to determine the association between the two in a community sample. Data were obtained from n = 579 menstruating females who participated in the Sleep and Health Activity, Diet, Environment, and Socialization (SHADES) study, a community-based sample of adults aged 22-60 years. Participants were asked, "How regular is your period?", with response choices of "very regular", "mostly regular", "fairly regular" and "not regular". They were also asked, "How much bleeding do you usually experience during your period?" Response choices were: "very heavy", "heavy", "medium", "light" or "very light". These were evaluated as ordinal outcomes. Sleep-related predictors included sleep duration (in hr; ≤ 6 [short], 7-9 [normal] and ≥ 9 [long]), Insomnia Severity Index score, Pittsburgh Sleep Quality Index score, Epworth Sleepiness Scale score and Fatigue Severity Scale score. Covariates included age, education, income, race/ethnicity and body mass index. Short sleep duration was associated with heavier bleeding (odds ratio = 1.46, p = 0.026) and greater cycle irregularity (odds ratio = 1.44, p = 0.031) as compared with normal sleep. Higher Pittsburgh Sleep Quality Index score was associated with greater cycle irregularity (odds ratio = 1.05, p = 0.022). Higher Fatigue Severity Scale score was associated with heavier bleeding (odds ratio = 1.02, p = 0.003) and greater cycle irregularity (odds ratio = 1.02, p = 0.008). Long sleep, Insomnia Severity Index and Epworth Sleepiness Scale were not associated with either outcome. These results demonstrate an association between short sleep duration, poor sleep quality, fatigue, stress and depression with heavier bleeding and menstrual cycle irregularity, highlighting the need for further studies to improve treatment options.

Improvements in sleep quality and fatigue are associated with improvements in functional recovery following hospitalization in older adults.

Study objectives: Poor sleep quality, a frequent problem in older adults, has been shown to be associated with reduced physical function and wellbeing. However, little is known about the relationship between sleep quality and the recovery of physical function following hospitalization. Thus, we conducted this study to examine the association between sleep quality and functional recovery after an acute hospitalization in community dwelling older adults. Methods: Older adult patients (N = 23, mean age = 74 ± 9 years) were recruited during an acute hospitalization (average length of stay 3.9 days) with a cardiovascular (56%), pulmonary (22%), or metabolic (13%) admission diagnosis. Objective physical function was measured using the Short Physical Performance Battery (SPPB) and self-reported function was assessed with Katz Index of Independence in Activities of Daily Living (ADL) and Lawton Instrumental Activities of Daily Living Scale (IADL). Sleep quality was measured using Pittsburgh Sleep Quality Index (PSQI) global score and Iowa Fatigue Score (IFS). Testing was performed prior to discharge (baseline) and 4-weeks post-discharge (follow-up). Results: Regression models showed PSQI Subjective Sleep Quality change scores from baseline to 4-week follow-up predicted a change in ADL (ß = -0.22); PSQI Use of Sleep Medications change scores predicted a change in SPPB Total (ß = 1.62) and SPPB Chair Stand (ß = 0.63); IFS change scores predicted SPPB Total (ß = -0.16) and SPPB Chair Stand performance (ß = -0.07) change scores. Conclusions: For older adults, changes in sleep medication use, daytime dysfunction, and fatigue were associated with improvements in functional recovery (including physical performance and independence) from acute hospitalization to 4-week follow-up. These results suggest that interventions focused on improving sleep quality, daytime consequences, and fatigue might help enhance physical functioning following hospitalization. Clinical trial registration: ClinicalTrials.gov, identifier: NCT02203656.

Non-pharmacological sleep interventions for pediatric cancer patients and survivors: a systematic review protocol.

BACKGROUND: Sleep disturbances constitute a common complication in pediatric cancer patients and survivors and are frequently severe enough to warrant treatment. Suboptimal sleep has been associated with decreased emotional well-being and cognitive functioning and increased behavioral problems. Standardized guidelines for non-pharmacological sleep interventions for adults with cancer exist, but no standard of care intervention or standard guidelines are available to guide such intervention in pediatric cancer patients and survivors. Therefore, effective behavioral interventions for improving sleep quality need to be identified. The objective of the review is to evaluate the effect of non-pharmacological sleep interventions on sleep quality in pediatric cancer patients and survivors. METHODS: The review will consider studies that include children and adolescents between 0 and 18 years diagnosed with cancer or who have a history of cancer who have non-respiratory sleep disturbance. We will include experimental and quasi-experimental studies evaluating non-pharmacological interventions such as psychological interventions, technical/device interventions, interventions targeting physical activity, and complementary and alternative medicine interventions (e.g., yoga, massage, music). Interventions involving medications, ingestible supplements, products purported to work through absorption, and medical devices will be excluded. Primary outcome will be sleep quality as measured by methods including retrospective ratings, daily sleep diary, and validated questionnaires. Secondary outcomes will include total sleep time, sleep onset latency, wake after sleep onset, daytime sleepiness, and daytime sleep duration (naps) as measured by retrospective ratings, daily sleep diary, validated questionnaires, and/or actigraphy. Databases will include MEDLINE (Ovid), EMBASE (Ovid), Cochrane Library, CINAHL (Ebsco), and PsycINFO (Ovid) and will be queried from database inception to present. Two reviewers will independently screen all citations, full-text articles, and extract data. The study methodological quality will be assessed using Joanna Briggs Institute (JBI) critical appraisal tools. Data will be extracted and findings pooled and synthesized using a meta-aggregation approach via the JBI System for the Unified Management, Assessment, and Review of Information (SUMARI). If feasible, we will conduct random effects meta-analysis. Additional analyses will be conducted to explore the potential sources of heterogeneity (e.g., methodological quality, study design, outcome measures). DISCUSSION: This systematic review will synthesize and consolidate evidence on existing non-pharmacological interventions to improve sleep in pediatric cancer patients and survivors. Findings may help inform practitioners working with pediatric cancer patients and survivors experiencing sleep disturbances and is intended to identify gaps and opportunities to improve methodical quality of further non-pharmacological sleep intervention research in this population toward developing an eventual standard of care. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020200397 .

Cognitive-behavior therapy for sleep disorders at midlife.

ABSTRACT: Insomnia is a clinical disorder characterized as difficulty falling asleep, staying asleep, or waking too early. To meet diagnostic criteria for an insomnia disorder, these difficulties must be present for a minimum of 3 months and cause significant daytime impairment. Insomnia is common in women transitioning through menopause and frequently continues in the years after menopause. Cognitive-behavior therapy for insomnia (CBT-I) is a brief behavior treatment, with decades of evidence supporting its effectiveness across the adult lifespan, including midlife. This Practice Pearl highlights the rationale for CBT-I and describes treatment components, with specific considerations for its use in midlife women.

Cognitive Behavioral Therapy for Mood and Insomnia in Persons With Dementia: A Systematic Review.

BACKGROUND: Cognitive behavioral therapy (CBT) is a goal-oriented intervention that aims to improve detrimental emotional or behavioral distress by modifying individuals' thought processes. This review evaluates the efficacy and specific adaptations of CBT in persons with mild cognitive impairment and dementia. METHODS: A literature search of PubMed, Embase, and PsycINFO was conducted up to March 2020. Study quality was assessed using the Cochrane risk of bias criteria. RESULTS: Twelve publications were identified. Seven of the studies demonstrated CBT efficacy to improve depression, anxiety, and/or quality of life. One study's positive postintervention outcome became insignificant with longer term follow up. Two of the studies improved sleep outcomes. Four studies integrated caregivers into intervention delivery. Three studies utilized content, memory, and adherence adaptations aimed to improve intervention efficacy. Two studies included adaptations to address caregiver burden and depression. CONCLUSION: There is strong evidence to suggest that CBT is associated with improvements in anxiety, depression, and quality of life in persons with mild cognitive impairment and dementia. CBT showed a reduction in insomnia and improvements in sleep quality. However, there is insufficient evidence to draw conclusions on the effects of CBT on insomnia. These results suggest that further investigation into insomnia outcomes is needed.

Association of preoperative sleep pattern with posthysterectomy pain: a pilot study.

STUDY OBJECTIVES: To evaluate the association of preoperative sleep pattern with posthysterectomy pain perception and satisfaction with surgery. METHODS: This pilot study included women undergoing minimally invasive hysterectomy for benign conditions. Sleep quality, insomnia severity, and insomnia risk were assessed pre- and postoperatively via standard questionnaires. Total sleep time, wake after sleep onset, and sleep efficiency were measured before and after hysterectomy using daily sleep diaries and wrist-worn actigraphy. Pain perception and satisfaction with hysterectomy were assessed postoperatively. Repeated-measures analysis of variance, Pearson's correlation, and linear regression were used for analysis. RESULTS: Twenty women participated; of them 16 had complete data and were analyzed. Total sleep time increased from 384 ± 102 minutes before to 468 ± 96 minutes after surgery (P = .023). Wake after sleep onset, a measure of sleep fragmentation, increased from 26 ± 15.1 minutes before to 52 ± 22.9 minutes after surgery (P = .014). Pearson's correlation showed preoperative total sleep time was inversely correlated with postoperative pain intensity (r = -.92, P = .01). Preoperative wake after sleep onset was positively correlated with postoperative pain intensity (r = .86, P = .008). Preoperative insomnia severity and insomnia risk were positively associated with postoperative pain and pain behaviors (ß = 0.41, P < .05; ß = 0.55, P < .01, respectively). Finally, preoperative sleep efficiency was positively associated with overall satisfaction with hysterectomy (ß = 0.39, P < .05). CONCLUSIONS: Sleep duration and fragmentation increase following hysterectomy. Shorter, more fragmented preoperative sleep is associated with greater postoperative pain intensity. Better preoperative sleep was associated with more satisfaction after hysterectomy. Further studies are needed to determine if preoperative sleep interventions such as cognitive behavioral therapy improve pain perception and satisfaction after hysterectomy.

The Society of Behavioral Sleep Medicine (SBSM) COVID-19 Task Force: Objectives and Summary Recommendations for Managing Sleep during a Pandemic.

OBJECTIVE/BACKGROUND: As a response to clinical observations that the pervasive stress and social/environmental disruptions from the 2020 COVID-19 pandemic have also impacted sleep, the Society of Behavioral Sleep Medicine (SBSM) convened the COVID-19 Task Force with goals to identify and disseminate information that could be useful in addressing sleep concerns during this crisis. Participants Members of the SBSM COVID-19 Task Force. Results/Conclusions Herein is a summary of the resources developed by the SBSM COVID-19 Task force, which includes links to online materials developed for use by providers and patients, as well as brief descriptions of key recommendations by the Task Force for specific sleep conditions (e.g., acute insomnia, nightmares) and vulnerable populations (e.g., parents, essential/healthcare workers, older adults).

Sleep, premenstrual mood disorder, and women's health.

Women experience increased vulnerability for both mood and sleep disorders, and the female menstrual cycle represents one fundamental mechanism related to risk. This review evaluates recent literature integrating female reproduction, sleep, and mood. For as many as one third of women, sleep is disrupted premenstrually, and sleep disturbances are particularly prevalent in those with premenstrual mood disorders. Variation in sleep patterns, circadian rhythm alterations, and decreased melatonin secretions due to hormonal fluctuations during the premenstrual phase of the menstrual cycle could explain sleep complaints and have been linked to menstrual irregularity. Menstrual irregularity is also independently associated with increased risk of mood complaints and poor sleep. Therefore, there is growing evidence for the interactional relationships between poor sleep, circadian rhythm disruption, and mood in reproductive-age women, although further research relating to specific mechanisms of risk are needed.

Cognitive Behavioral Therapy for Insomnia and Women's Health: Sex as a Biological Variable.

Differences in sleep for men and women begin at a very early age, with women reporting poorer sleep and having a higher risk for insomnia compared with men. Women are particularly vulnerable to developing insomnia during times of reproductive hormonal change. Sleep across the woman's lifespan and special treatment considerations for using cognitive behavioral therapy for insomnia (CBT-I) in women will be addressed in this review.