Theta activity during encoding interacts with NREM sleep oscillations to predict memory generalization

Relatively little is known regarding the interaction between encoding-related neural activity and sleep-based memory consolidation. One suggestion is that a function of encoding-related theta power may be to 'tag' memories for subsequent processing during sleep. This study aimed to extend previous work on the relationships between sleep spindles, slow oscillation-spindle coupling and task-related theta activity with a combined Deese-Roediger-McDermott (DRM) and nap paradigm. This allowed us to examine the influence of task- and sleep-related oscillatory activity on the recognition of both encoded list words and associative theme words. Thirty-three participants (29 females, mean age = 23.2 years) learned and recognised DRM lists separated by either a 2hr wake or sleep period. Mixed-effects modelling revealed the sleep condition endorsed more associative theme words and fewer list words in comparison to the wake group. Encoding related theta power was also found to influence sleep spindle density, and this interaction was predictive of memory outcomes. The influence of encoding-related theta was specific to sleep spindle density, and did not appear to influence the strength of slow oscillation-spindle coupling as it relates to memory outcomes. The finding of interactions between wakeful and sleep oscillatory-related activity in promoting memory and learning has important implications for theoretical models of sleep-based memory consolidation.

Job burnout and its impact on work ability in biosafety laboratory staff during the COVID-19 epidemic in Xinjiang

Background The coronavirus disease 2019 (COVID-19) has increased the physical and psychological stress of medical workers. This study was designed to investigate the prevalence and risk factors of job burnout and its impact on work ability among Biosafety Laboratory (BSL) staffs during the COVID-19 epidemic in Xinjiang. Methods A total of 7911 qualified BSL staffs in Xinjiang were investigated by electronic questionnaires. The Maslach Burnout Inventory-General Survey (MBI-GS) was used for job burnout survey. Work Ability Index (WAI) was used for work ability survey. The prevalence and risk factors of job burnout in BSL staffs were analyzed through chi square test, t-test and one-way ANOVA. And then, the influence of demographic and job-related variables, i.e., confounding factors, were eliminated to the greatest extent by the propensity score analysis (PSA) method, to investigate the impact of job burnout on work ability in BSL staffs. Results A total of 67.6% BSL staffs experienced job burnout. There were significant differences in the detection rate of job burnout among demographic and job-related variables, including gender, age, ethnicity, education, working years, professional title, marital status, number of night shift per month and overall sleep condition (all P < 0.05). The detection rate of job burnout in female was higher than that in male. The detection rates of job burnout in 45–50 years old, Han ethnicity, education of postgraduate or above, 11–20 years of working, intermediate professional title, married, staff with many night shifts per month and poor overall sleep condition were higher than that of other groups. The average burnout scores of the Emotional Exhaustion (EE), Cynicism (CY), Reduced Personal Accomplishment (PA) scale were 10.00 ± 5.99, 4.64 ± 4.59 and 15.25 ± 8.16, respectively. Multiple logistic regression analysis showed that the three dimensions of job burnout, i.e., EE, CY, PE, were negatively correlated with work ability and significantly affected the work ability of BSL staffs (all P < 0.001). Conclusions Our results suggest that the prevalence of job burnout is extremely common among BSL staffs. In addition, the work ability decreases with the increase of job burnout and the improvement of job burnout can enhance work ability among BSL staffs.

The Musculoskeletal Disorders, Pain Characteristics, and Activity Levels of Caregivers During the Rehabilitation Process and the Consequences on Sleep Condition, Health Status, and Quality of Life

Objective Taking care of a patient can significantly impact both physical and psychological statuses of caregivers. This study aimed to examine musculoskeletal problems, health status, and quality of life of caregivers. This study is novel in determining musculoskeletal disorders, pain characteristics, activity levels, sleep condition, general and psychological health statuses, and quality of life of caregivers. Design A cross-sectional study Patients and Methods A total of 240 participants were enrolled in this prospective and cross-sectional study conducted at a tertiary rehabilitation center (patients, n = 120; caregivers, n = 120). The demographic and clinical characteristics of the participants were recorded during the evaluation process. The Functional Ambulation Classification Scale (FAS) and Barthel Scale scores of the patients were determined. The pain level of the caregivers was evaluated according to the Visual Analog Scale (VAS). The International Physical Activity Questionnaire (IPAQ)–short form was used to evaluate caregivers’ activity levels. The quality of life of caregivers was evaluated with the World Health Organization Quality of Life Assessment Scales score (WHOQOL-BREF). The anxiety and depression status of the caregivers were interpreted using the Hospital Anxiety and Depression Scale, The health level of the caregivers was evaluated using the Health Assessment Questionnaire. Results A statistically significant positive correlation was found between the duration of caregiving (hours per week) and the pain duration of the caregiver (month) (P = 0.000, r = 0.766). the caregivers who provided longer-term care for their patients (hours per week) had higher VAS scores (P = 0.000, r = 0.944). A significant reverse correlation was found between the duration of caregiving (hours per week) and IPAQ-Walking MET (metabolic equivalent) scores (minutes/week) (P = 0.000, r = –0.811). On the contrary, a positive significant association was detected between the duration of caregiving (hours per week) and IPAQ-Vigorous MET scores (minutes/week) due to the caregiving activities of the patients such as lifting, positioning, and so forth. Also, a significant positive correlation was observed between the duration of caregiving (hours per week) and Hospital Depression Scale scores (P = 0.000, r = 0.394), Hospital Anxiety Scale scores (P = 0.000, r = 0.548), and Health Assessment Questionnaire scores (P = 0.000, r = 0.415). Conclusion Providing protective exercise programs, including walking activity, to caregivers and organizing education programs that include caregiving techniques can positively affect the quality of life of caregivers.

Can a sleep disorder intervention-embedded self-management programme contribute to improve management of diabetes? A pilot single-arm pretest and post-test study

ObjectiveTo investigate the efficacy and feasibility of a self-management programme incorporating a sleep intervention for improving diabetes outcomes.DesignA single-arm pre-test and post-test study was conducted within a community setting in Hiroshima, Japan.ParticipantsParticipants were aged 52–74 years and diagnosed with type 2 diabetic nephropathy stages 1–3.InterventionsParticipants received self-management education from nurses for 6 months. First, the nurses assessed their sleep conditions using insomnia scales and a sleep metre. Then, the participants learnt self-management to increase their physical activity and improve their sleep condition. They also implemented diet therapy and medication adherence.Outcome measuresPhysiological indicators, subjective and objective indicators of sleep quality, self-management indicators, quality of life (QOL) and feasibility were evaluated. To confirm the efficacy of intervention, Freidman tests, analysis of variance, Wilcoxon signed-rank test and t-test were performed. Pearson’s correlations were analysed between activities and sleep condition.ResultsOf the 26 enrolled participants, 24 completed the programme and were analysed. Among them, 15 participants (62.5%) had sleep disorders caused by multiple factors, such as an inappropriate lifestyle and physical factors that interfere with good sleep. Although insomnia scales did not change for the sleep disorders, their subjective health status improved. Regarding indicators related to diabetes management, lifestyles improved significantly. Haemoglobin A1c, body mass index, systolic blood pressure, non-high-density lipoprotein-cholesterol and QOL also improved. All participants except one were satisfied with the programme. However, use of the sleep metre and nurses’ consultation about sleep disturbance were not well evaluated.ConclusionsThis programme was effective in improving diabetes status, lifestyle and behaviour changes. However, its effect on sleep condition was limited because of its complexity. A simple and novel approach is needed to strengthen the motivation for sleep behaviour change and to increase programme efficacy and feasibility.Trial registration numberUMIN000025906.

A Low Complex Algorithm for Detection of Sleep Apnea from ECG Signals using Sliding SSA Combined with PCA, KPCA, and SPPCA

Sleep apnea is a potentially life-threatening sleep condition in which breathing stops and resumes repeatedly. It is caused by breathing pauses during sleep, which leads to frequent awakenings. As we all know, computational time and efficiency are essential in the healthcare industry; to address this issue, we proposed an algorithm that performs more computations in less time without compromising the machine learning model’s performance. This study employs a unique technique called Sliding Singular Spectrum Analysis (SSSA) to decompose and de-noise the ECG signals. To identify the significant apnea and non-apnea components from the pre-processed ECG data and to reduce the dimensionality, we used Principal Component Analysis (PCA), Kernal PCA (KPCA), and Sub-Pattern based PCA (SPPCA). These characteristics were then used to train and evaluate various machine learning models, including KNN, SVM, GaussianNB, SGD, and XGBoost, to distinguish between apnea and non-apnea ECG data. The publicly available Physionet Apnea-ECG database is used for the simulation of the proposed algorithm. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

A Low Complex Algorithm for Detection of Sleep Apnea from ECG Signals using Sliding SSA Combined with PCA, KPCA, and SPPCA

Sleep apnea is a potentially life-threatening sleep condition in which breathing stops and resumes repeatedly. It is caused by breathing pauses during sleep, which leads to frequent awakenings. As we all know, computational time and efficiency are essential in the healthcare industry; to address this issue, we proposed an algorithm that performs more computations in less time without compromising the machine learning model’s performance. This study employs a unique technique called Sliding Singular Spectrum Analysis (SSSA) to decompose and de-noise the ECG signals. To identify the significant apnea and non-apnea components from the pre-processed ECG data and to reduce the dimensionality, we used Principal Component Analysis (PCA), Kernal PCA (KPCA), and Sub-Pattern based PCA (SPPCA). These characteristics were then used to train and evaluate various machine learning models, including KNN, SVM, GaussianNB, SGD, and XGBoost, to distinguish between apnea and non-apnea ECG data. The publicly available Physionet Apnea-ECG database is used for the simulation of the proposed algorithm. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

THE POWER OF HABITS: EVALUATION OF A MOBILE HEALTH SOLUTION FOR THE MANAGEMENT OF NARCOLEPSY

Mobile health, or mHealth, solutions offer great potential in the area of self-monitoring of chronic conditions, where most of the day-to-day management of the condition is done at home by the patient or their caregivers. Narcolepsy is a chronic sleep condition caused by an orexin deficiency in the brain resulting in its inability to regulate sleep cycles, causing poor quality sleep during the day and problems with wakefulness during the day. This paper set out to investigate whether Habitual, an app for tracking symptoms, daily habits and medication adherence for people with narcolepsy could increase their sense of empowerment. Ten participants were asked to test the app during a period of 30 days, after which they were asked to answer a survey to investigate whether their perception of their empowerment towards the management of narcolepsy had changed. Although using the app for only 30 days provides a very limited understanding of the impact of Habitual, this study shows positive indication for future mHealth solutions for the management of narcolepsy. Future studies should test the openness to using an app for the management of narcolepsy with a wider cohort.

Effect of Sleep Quality on Mental Health during COVID- 19 Lockdown

The study was done with the aim of understanding the “Effect of Sleep Quality on Mental Health of Adults during COVID-19 Lockdown”. 136 adults aged between 18 to 32 years were selected through snowball sampling from social media platforms for being a part of the study sample. The Sleep Condition Indicator (Espie, 2014) and General Health Questionnaire (GHQ-12) (Goldberg and Williams, 1988) were used to measure their sleep quality and mental health. It was hypothesized that the sleep quality will have a significant effect on the mental health of adults. It was found out that sleep quality is a significant predictor of mental health. The researcher stated that clinicians and psychologists should investigate about the sleeping patterns and quality of sleep in patients reporting mental health issues, and screen sleeping disorders regularly in population to avoid development of mental health disorders.

Diagnostic Validity of the Sleep Condition Indicator to Screen for Diagnostic and Statistical Manual-5 Insomnia Disorder in Patients with Parkinson’s Disease

<b><i>Background:</i></b> Insomnia is a highly common sleep disorder in patients with Parkinson’s disease (PD). Yet, no screening questionnaires following the Diagnostic and Statistical Manual-5 (DSM-5) criteria have been validated in PD patients. <b><i>Objectives:</i></b> We assessed the validity and reliability of the French version of the sleep condition indicator (SCI), in patients with PD. <b><i>Methods:</i></b> In a sample of 65 patients (46% women, mean age 63.8 ± 7.9 years) with PD, but without dementia, insomnia was assessed with a clinical interview and the SCI. Statistical analyses were performed to determine the reliability, construct validity, and divergent validity of the SCI. In addition, an explanatory factor analysis was performed to assess the underlying structure of the SCI. <b><i>Results:</i></b> Of the 65 patients (mean duration PD 9.7 ± 6.9 years), 51% met the criteria for insomnia disorder when measured with a clinical interview. The mean SCI score was 18.05 ± 8.3. The internal consistency (α = 0.89) of the SCI was high. Using the previously defined cutoff value of ≤16, the area under the receiver operating characteristic curve was 0.86 with a sensitivity of 86% and a specificity of 87%. Exploratory factor analysis showed a 2-factor structure with a focus on sleep and daytime effects. Additionally, good construct and divergent validity were demonstrated. <b><i>Conclusion:</i></b> The SCI can be used as a valid and reliable screener for DSM-5 insomnia disorder in PD patients. Due to its short length, it is useful in both clinical practice and scientific research.

Abstract P048: A Model Of Chronic Insufficient Sleep Increases Markers Of Insulin Resistance In Postmenopausal But Not Premenopausal Women

Introduction: Insufficient sleep is widely prevalent among US adults and is a risk factor for type 2 diabetes (T2D). Experimental studies show adverse effects of acute, severe short sleep on insulin sensitivity, but it is unclear whether these reflect risks associated with milder short sleep routinely observed in the general population. To date, no study has evaluated the impact of prolonged mild sleep curtailment on markers of insulin resistance in women or whether these effects differ by menopausal status, known to impact insulin sensitivity. Hypothesis: Glucose and insulin levels, as well as a measure of insulin resistance (HOMA-IR), will increase during 6 wk of sleep restriction (SR) relative to adequate sleep (AS). Adverse effects of prolonged short sleep will be exacerbated in postmenopausal women. Methods: Thirty-four women (age: 38±14 y; BMI: 25.6±3.6 kg/m2; n=10 postmenopausal) with adequate habitual total sleep time (TST) (453±33 min) took part in a randomized crossover study with two 6-wk phases: AS and SR. In AS, participants were asked to maintain stable nightly bed and wake times determined from 2 wk of screening with wrist actigraphy and sleep logs. In SR, bedtime was delayed to reduce TST by approximately 1.5 h/night. Sleep was measured continuously using actigraphy and verified weekly for compliance. At wk 0, 3, 4, and 6 fasting blood samples were collected. Outcomes included glucose and insulin levels as well as HOMA-IR scores, calculated from those values. Linear-mixed models tested interactions of sleep condition with week on outcome measures in the full sample and by menopausal status. Results: Sleep condition impacted the change in TST from baseline (P<0.0001), which was reduced in SR and unchanged in AS (-79±6 vs -4±6min). In the full sample, there was no sleep condition by week interactions for glucose (P=0.67), insulin (P=0.14), or HOMA-IR (P=0.16). Similar results were observed in premenopausal women (all P>0.50). However, in postmenopausal women, there was a significant effect of sleep condition on change in insulin (P=0.046) and HOMA-IR (P=0.039) over the 6 wk. In SR, insulin (slope: 0.26±0.28 μU/mL) and HOMA-IR (slope: 0.07±0.08) increased, while AS resulted in reductions in these outcomes (insulin slope: -0.56±0.29 μU/mL; HOMA-IR slope: -0.16±0.08). Conclusions: We provide the first evidence that chronic short sleep, even if mild, adversely affects insulin sensitivity in postmenopausal women. In contrast, maintenance of AS may improve glycemic regulation. Interestingly, prolonged short sleep did not impact markers of insulin resistance in premenopausal women; further investigation into these life-stage related differences, including underlying mechanisms, is warranted. Results suggest that, in postmenopausal women, a group at heightened risk of poor sleep and T2D, achieving adequate sleep may be an effective strategy to improve cardiometabolic health.