scholarly journals 227 Poor sleep as a predictor of COVID-19 related stress, fear and sadness in young adolescents: a longitudinal study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A90-A91
Author(s):  
Orsolya Kiss ◽  
Elisabet Alzueta ◽  
Dilara Yuksel ◽  
Ingrid Durley ◽  
Laila Volpe ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Sleep Duration ◽  
Perceived Stress ◽  
Life Stage ◽  
Machine Learning Algorithms ◽  
Self Report ◽  
Poor Sleep ◽  
Targeted Prevention ◽  
Sleep Onset Latency ◽  
Young Adolescents

Abstract Introduction Adolescence is a transitional life-stage accompanied by large biopsychosocial changes and greater psychophysiological vulnerability. Global events like the COVID-19 pandemic may increase vulnerability to depression and anxiety in this population. Poor sleep is often associated with depression, and both sleep and mood have been shown to be strongly impacted by the COVID-19 pandemic, with most studies focusing on adults. The current study investigates psychological distress in young adolescents during the pandemic, and specifically, whether poor sleep before the pandemic predicts psychological distress. Methods Self-report data were analyzed from 3099 adolescents (9-10 years at baseline) in the population-based, demographically diverse, Adolescent Brain Cognitive Development (ABCD) study across three pre-pandemic annual visits and 3 monthly time points during the COVID-19 pandemic (ages 11-13 years). At each assessment, children and their guardians completed questionnaires including those about sleep, environment, and psychological wellbeing. Gradient Boosted Tree machine learning algorithms were used to identify the strongest predictors of pandemic-related psychological distress in individuals. We trained models using pre-pandemic sleep measures along with demographics, economic, and social measures during the pandemic. We evaluated the performance of the models using area under curve (AUC) metrics and interpreted the models by using the recently proposed SHapley Additive exPlanations methodology. Results Pandemic-related perceived stress, fear and sadness were accurately detected with our classifiers (AUC = 0.83 for perceived stress, AUC = 0.73 for fear, AUC = 0.79 for sadness). Across all models, shorter sleep duration, prolonged sleep onset latency, and longer time between waking and getting out of bed predicted greater distress. Moreover, female sex, and pandemic-related factors, including greater family conflict, fewer economic resources, and more screen time contributed to prediction performance in all three models. Conclusion Findings highlight the importance of addressing sleep problems and ensuring sufficient sleep duration in children to protect against the psychological impact of major life events, including the COVID-19 pandemic. Considering the long-lasting effects of sleep, it would be crucial to improve sleep health by targeted prevention, intervention and increased awareness among adolescents. Support (if any) NIH U01DA041022

scholarly journals COVID-19 uncertainty and sleep: the roles of perceived stress and intolerance of uncertainty during the early stage of the COVID-19 outbreak

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dan Wu ◽  
Tingzhong Yang ◽  
Daniel L. Hall ◽  
Guihua Jiao ◽  
Lixin Huang ◽  
...  
Keyword(s):  
Path Analysis ◽  
Perceived Stress ◽  
Mediation Analysis ◽  
Early Stage ◽  
Intolerance Of Uncertainty ◽  
Policy Implications ◽  
Self Report ◽  
Poor Sleep ◽  
The Relationship

Abstract Background The COVID-19 pandemic brings unprecedented uncertainty and stress. This study aimed to characterize general sleep status among Chinese residents during the early stage of the outbreak and to explore the network relationship among COVID-19 uncertainty, intolerance of uncertainty, perceived stress, and sleep status. Methods A cross-sectional correlational survey was conducted online. A total of 2534 Chinese residents were surveyed from 30 provinces, municipalities, autonomous regions of China and regions abroad during the period from February 7 to 14, 2020, the third week of lockdown. Final valid data from 2215 participants were analyzed. Self-report measures assessed uncertainty about COVID-19, intolerance of uncertainty, perceived stress, and general sleep status. Serial mediation analysis using the bootstrapping method and path analysis were applied to test the mediation role of intolerance of uncertainty and perceived stress in the relationship between uncertainty about COVID-19 and sleep status. Results The total score of sleep status was 4.82 (SD = 2.72). Age, place of residence, ethnicity, marital status, infection, and quarantine status were all significantly associated with general sleep status. Approximately half of participants (47.1%) reported going to bed after 12:00 am, 23.0% took 30 min or longer to fall asleep, and 30.3% slept a total of 7 h or less. Higher uncertainty about COVID-19 was significantly positively correlated with higher intolerance of uncertainty (r = 0.506, p < 0.001). The mediation analysis found a mediating role of perceived stress in the relationship between COVID-19 uncertainty and general sleep status (β = 0.015, 95%C.I. = 0.009–0.021). However, IU was not a significant mediator of the relationship between COVID-19 uncertainty and sleep (β = 0.009, 95%C.I. = − 0.002–0.020). Moreover, results from the path analysis further showed uncertainty about COVID-19 had a weak direct effect on poor sleep (β = 0.043, p < 0.05); however, there was a robust indirect effect on poor sleep through intolerance of uncertainty and perceived stress. Conclusions These findings suggest that intolerance of uncertainty and perceived stress are critical factors in the relationship between COVID-19 uncertainty and sleep outcomes. Results are discussed in the context of the COVID-19 pandemic, and practical policy implications are also provided.

A-124 Sleep Quality and Quantity in Individuals Aging with Traumatic Brain Injury: Associations with Psychosocial Outcomes and Health Conditions

2021 ◽  
Vol 36 (6) ◽  
pp. 1174-1174
Author(s):  
Rachael M Riccitello ◽  
Amanda R Rabinowitz ◽  
Umesh M Venkatesan ◽  
Kristine C Dell ◽  
Samantha M Vervoordts ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Brain Injury ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Chronic Health Conditions ◽  
Health Conditions ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Chronic Health

Abstract Objective To examine the association of sleep quality/duration with chronic health conditions, psychological distress, and quality of life (QOL) in older adults with chronic traumatic brain injury (TBI). Methods 120 older adults (x-age = 64.2 ± 8.3) 1 or more years (med = 9.8, range = 1.1–45.6) post moderate–severe TBI reported on history of chronic health conditions and current sleep duration and quality. Participants were categorized by sleep duration (&lt; 6, 6–8, &gt;8 hours) and whether or not they felt well-rested. Outcome measures were QOL (Quality of Life after Brain Injury questionnaire) and psychological distress (Brief Symptom Inventory-18). Results 65% of individuals reported receiving 6–8 hours of sleep; 78% reported feeling well-rested. 17.5% reported no health conditions, 47.5% one condition, and 35% reported two or more. High blood pressure, high cholesterol, and diabetes were the most common. Number of health conditions was not related to sleep quality χ2(2,N = 120) =0.83, p = 0.66, or quantity, χ2(4,N = 120) =7.4, p = 0.12. MANCOVA controlling for age, chronicity, and injury severity revealed a significant association between poor sleep quality and decreased QOL across multiple life domains, V = 0.30, F(6,105) = 4.6, p &lt; 0.001, ηp2 = 0.21. Sleep duration was also associated with QOL, Λ = 0.80, F(12,208) = 2.1, p &lt; 0.05, ηp2 = 0.108. In ANCOVAs, poor sleep quality was related to increased psychological distress, F(1,110) = 18.3, p &lt; 0.001, ηp2 = 0.142, but sleep duration was not, F(2,109) = 2.2, p = 0.12, ηp2 = 0.038. Conclusion Although most participants received the recommended amount of sleep, poor sleep quality/quantity were associated with poorer QOL and sleep quality was additionally associated with psychological distress. Chronic health conditions were prevalent in the sample, but not related to self-reported sleep quality/duration.

scholarly journals An automatic estimation of the rest-interval for MotionWatch8© using uniaxial movement and lux data

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel Backhouse ◽  
Ryan Stanley Falck ◽  
Teresa Liu-Ambrose
Keyword(s):  
Older Adults ◽  
Sleep Duration ◽  
Light Exposure ◽  
Objective Measure ◽  
Self Report ◽  
Time Interval ◽  
Poor Sleep ◽  
Rest Interval ◽  
Scoring Method ◽  
Field Methods

Abstract Background Poor sleep is linked with chronic conditions common in older adults, including diabetes, heart disease, and dementia. Valid and reliable field methods to objectively measure sleep are thus greatly needed to examine how poor sleep impacts older adults. Wrist-worn actigraphy (WWA) is a common objective measure of sleep that uses motion and illuminance data to estimate sleep. The rest-interval marks the time interval between when an individual attempts to sleep and the time they get out of bed to start their day. Traditionally, the rest-interval is scored manually by trained technicians, however algorithms currently exist which automatically score WWA data, saving time and providing consistency from user-to-user. However, these algorithms ignore illuminance data and only considered motion in their estimation of the rest-interval. This study therefore examines a novel algorithm that uses illuminance data to supplement the approximation of the rest-interval from motion data. Methods We examined a total of 1086 days of data of 129 participants who wore the MotionWatch8© WWA for ≥14 nights of observation. Resultant sleep measures from three different parameter settings were compared to sleep measures derived following a standard scoring protocol and self-report times. Results The algorithm showed the strongest correlation to the standard protocol (r = 0.92 for sleep duration). There were no significant differences in sleep duration, sleep efficiency and fragmentation index estimates compared to the standard scoring protocol. Conclusion These results suggest that an automated rest-interval scoring method using both light exposure and acceleration data provides comparable accuracy to the standard scoring method.

scholarly journals The Impact of Sleep Timing, Sleep Duration, and Sleep Quality on Depressive Symptoms and Suicidal Ideation amongst Japanese Freshmen: The EQUSITE Study

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Atin Supartini ◽  
Takanori Honda ◽  
Nadzirah A. Basri ◽  
Yuka Haeuchi ◽  
Sanmei Chen ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Suicidal Ideation ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Poor Sleep Quality ◽  
The Impact

Aim. The aim of this study was to identify the impact of bedtime, wake time, sleep duration, sleep-onset latency, and sleep quality on depressive symptoms and suicidal ideation amongst Japanese freshmen.Methods. This cross-sectional data was derived from the baseline survey of the Enhancement of Q-University Students Intelligence (EQUSITE) study conducted from May to June, 2010. A total of 2,631 participants were recruited and completed the following self-reported questionnaires: the Pittsburgh Sleep Quality Index (PSQI), the Center for Epidemiologic Studies Depression Scale (CES-D), and the original Health Support Questionnaires developed by the EQUSITE study research team.Results. Of 1,992 participants eligible for analysis, 25.5% (n=507) reported depressive symptoms (CES-D total score ≥ 16), and 5.8% (n=115) reported suicidal ideation. The present study showed that late bedtime (later than 01:30), sleep-onset latency (≥30 minutes), and poor sleep quality showed a marginally significant association with depressive symptoms. Poor sleep quality was seen to predict suicidal ideation even after adjusting for depressive symptoms.Conclusion. The current study has important implications for the role of bedtime in the prevention of depressive symptoms. Improving sleep quality may prevent the development of depressive symptoms and reduce the likelihood of suicidal ideation.

scholarly journals Mental health screening questionnaire: A Study on reliability and correlation with perceived stress score

2019 ◽  
Vol 8 (2) ◽  
pp. 4-8
Author(s):  
P. Sharma ◽  
G. Devkota
Keyword(s):  
Mental Health ◽  
Psychological Distress ◽  
Perceived Stress ◽  
Health Screening ◽  
Mental Health Screening ◽  
Self Report ◽  
Perceived Stress Scale ◽  
Screening Questionnaire ◽  
Stress Scale ◽  
Distress Scale

 Introduction: Screening of mental disorders and psychological distress is important in clinical as well as research setting. The objective of this study is to test the reliability of mental health screening questionnaire developed by authors and see its correlation with perceived stress scale scores. Material and Method: A self-report screening instrument was designed by the authors in consultation with experts and was tested for reliability among 162 participants from general population gathered for stress management program. The correlation of the designed scale was tested with the Perceived Stress Scale score. Results: Scale reliability (Cronbach’s alpha) for the designed psychological distress scale was found to be 0.7558 which is regarded as having acceptable internal consistency. The questions of the designed scale had weak to moderate positive correlation with the score on Perceived Stress Scale. Conclusion: Despite many shortcomings of the designed scale we may be able to use it for basic screening of psychological distress and mental health problems. We recommend the validity of scale be tested in larger sample size.

scholarly journals An Automatic Estimation of the Rest-Interval for MotionWatch8© Using Uniaxial Movement and Lux Data

2020 ◽  
Author(s):  
Daniel Backhouse ◽  
Ryan Stanley Falck ◽  
Teresa Liu-Ambrose
Keyword(s):  
Older Adults ◽  
Sleep Duration ◽  
Light Exposure ◽  
Objective Measure ◽  
Self Report ◽  
Time Interval ◽  
Poor Sleep ◽  
Rest Interval ◽  
Scoring Method ◽  
Field Methods

Abstract Poor sleep is linked with chronic conditions common in older adults, including diabetes, heart disease, and dementia. Valid and reliable field methods to objectively measure sleep are thus greatly needed to examine how poor sleep impacts older adults. Wrist-worn actigraphy (WWA) is a common objective measure of sleep that uses motion and illuminance data to estimate sleep. The rest-interval marks the time interval between when an individual attempts to sleep and the time they get out of bed to start their day. Traditionally, the rest-interval is scored manually by trained technicians, however algorithms currently exist which automatically score WWA data, saving time and providing consistency from user-to-user. However, these algorithms ignore illuminance data and only considered motion in their estimation of the rest-interval. This study therefore examines a novel algorithm that uses illuminance data to supplement the approximation of the rest-interval from motion data. We examined a total of 1086 days of data of 129 participants who wore the MotionWatch8© WWA for ≥14 nights of observation. Resultant sleep measures from three different parameter settings were compared to sleep measures derived following a standard scoring protocol and self-report times. The algorithm showed the strongest correlation to the standard protocol (r= 0.92 for sleep duration). There were no significant differences in sleep duration, sleep efficiency and fragmentation index estimates compared to the standard scoring protocol. These results suggest that an automated rest-interval scoring method using both light exposure and acceleration data provides comparable accuracy to the standard scoring method.

scholarly journals An Automatic Estimation of the Rest-Interval for MotionWatch8© Using Uniaxial Movement and Lux Data

2020 ◽  
Author(s):  
Daniel Backhouse ◽  
Ryan Stanley Falck ◽  
Teresa Liu-Ambrose
Keyword(s):  
Older Adults ◽  
Sleep Duration ◽  
Light Exposure ◽  
Objective Measure ◽  
Self Report ◽  
Time Interval ◽  
Poor Sleep ◽  
Rest Interval ◽  
Scoring Method ◽  
Field Methods

Abstract Background: Poor sleep is linked with chronic conditions common in older adults, including diabetes, heart disease, and dementia. Valid and reliable field methods to objectively measure sleep are thus greatly needed to examine how poor sleep impacts older adults. Wrist-worn actigraphy (WWA) is a common objective measure of sleep that uses motion and illuminance data to estimate sleep. The rest-interval marks the time interval between when an individual attempts to sleep and the time they get out of bed to start their day. Traditionally, the rest-interval is scored manually by trained technicians, however algorithms currently exist which automatically score WWA data, saving time and providing consistency from user-to-user. However, these algorithms ignore illuminance data and only considered motion in their estimation of the rest-interval. This study therefore examines a novel algorithm that uses illuminance data to supplement the approximation of the rest-interval from motion data. Methods: We examined a total of 1086 days of data of 129 participants who wore the MotionWatch8© WWA for ≥14 nights of observation. Resultant sleep measures from three different parameter settings were compared to sleep measures derived following a standard scoring protocol and self-report times. Results: The algorithm showed the strongest correlation to the standard protocol (r= 0.92 for sleep duration). There were no significant differences in sleep duration, sleep efficiency and fragmentation index estimates compared to the standard scoring protocol. Conclusion: These results suggest that an automated rest-interval scoring method using both light exposure and acceleration data provides comparable accuracy to the standard scoring method.

scholarly journals Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study

SLEEP ◽  
2020 ◽  
Author(s):  
Sigga Svala Jonasdottir ◽  
Kelton Minor ◽  
Sune Lehmann
Keyword(s):  
Sleep Duration ◽  
Life Stage ◽  
Early Adulthood ◽  
Sleep Onset ◽  
Global Scale ◽  
Self Report ◽  
Child Rearing ◽  
Sleep Patterns ◽  
Country Level ◽  
Age Related

Abstract Study Objectives Previous research on sleep patterns across the lifespan have largely been limited to self-report measures and constrained to certain geographic regions. Using a global sleep dataset of in situ observations from wearable activity trackers, we examine how sleep duration, timing, misalignment, and variability develop with age and vary by gender and BMI for nonshift workers. Methods We analyze 11.14 million nights from 69,650 adult nonshift workers aged 19–67 from 47 countries. We use mixed effects models to examine age-related trends in naturalistic sleep patterns and assess gender and BMI differences in these trends while controlling for user and country-level variation. Results Our results confirm that sleep duration decreases, the prevalence of nighttime awakenings increases, while sleep onset and offset advance to become earlier with age. Although men tend to sleep less than women across the lifespan, nighttime awakenings are more prevalent for women, with the greatest disparity found from early to middle adulthood, a life stage associated with child-rearing. Sleep onset and duration variability are nearly fixed across the lifespan with higher values on weekends than weekdays. Sleep offset variability declines relatively rapidly through early adulthood until age 35–39, then plateaus on weekdays, but continues to decrease on weekends. The weekend–weekday contrast in sleep patterns changes as people age with small to negligible differences between genders. Conclusions A massive dataset generated by pervasive consumer wearable devices confirms age-related changes in sleep and affirms that there are both persistent and life-stage dependent differences in sleep patterns between genders.

scholarly journals 0333 Electronic Media Use is Associated with Poor Sleep In 3-6 Year-Old Children

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A126-A127
Author(s):  
S Wong ◽  
L E Hartstein ◽  
M K LeBourgeois
Keyword(s):  
Young Children ◽  
Sleep Duration ◽  
Electronic Device ◽  
Sleep Problems ◽  
Media Use ◽  
Sleep Onset ◽  
Electronic Media ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Electronic Media Use

Abstract Introduction Recent surveys estimate that electronic media use among young children is increasing and that behavioral sleep problems are prevalent. In this study, we employed assessments of sleep and media use and tested the hypothesis that poor sleeping children would be more likely to engage with media than good sleeping children. Methods Participants were 44 children from two different cohorts: (1) Healthy, good sleepers (n=26, 13 males, 4.3±0.4 years) who reportedly obtained ≥10.5 hours per night and had no behavioral sleep problems and (2) Poor sleepers (n=18, 9 males, 5.5±0.7 years) who reportedly obtained chronic insufficient sleep ≤9 hours per night and/or had behavioral sleep problems for ≥6 months. Sleep duration and sleep onset latency (SOL) were quantified through 7 nights of actigraphy and verified with sleep diaries. Media use, defined as any electronic device involving screen time that engages children, was assessed across 2 weekdays and 2 weekend days through a parental media diary. Independent t-tests compared the duration of media use and actigraphy variables between groups. Results Poor sleeping children on average had longer SOL (28.6±17.9 vs. 17.3±8.66 minutes, t=-2.5, p&lt;0.05) and shorter sleep duration (589.6±37.5 vs. 627.4±27.4 minutes, t=3.7, p&lt;0.01) compared to good sleeping children. Additionally, average daily media use (125.1±88.5 vs. 66.5±48.3 minutes, t=-2.6, p&lt;0.05), evening media use (22.0±21.3 vs. 4.2±10.4 minutes, t=-3.3, p&lt;0.01), and weekend media use (154.4±105.9 vs. 79.8±55.6 minutes, t=-2.7, p&lt;0.05) duration was higher in poor than good sleepers. Conclusion Our findings indicate that media use duration and timing likely play an important role in early childhood sleep health. Young children who use more evening media are more likely to take longer to fall asleep and have shorter sleep duration overall. Time displacement (time spent using media instead of sleeping), psychological stimulation, and the effects of screen light on circadian timing are potential mechanisms underlying these associations. Support NIH R01-MH086566 and R21-MH110765 to MKL

scholarly journals Changes in Prefrontal Gamma-Aminobutyric Acid and Perfusion After the Computerized Relaxation Training in Women With Psychological Distress: A Preliminary Report

2021 ◽  
Vol 12 ◽  
Author(s):  
Eun Namgung ◽  
Jungyoon Kim ◽  
Hyeonseok Jeong ◽  
Jiyoung Ma ◽  
Gahae Hong ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Sleep Quality ◽  
Training Program ◽  
Perceived Stress ◽  
Relaxation Training ◽  
Self Report ◽  
Aminobutyric Acid ◽  
Resonance Spectroscopy ◽  
Gamma Aminobutyric Acid ◽  
Composite Scores

Computerized relaxation training has been suggested as an effective and easily accessible intervention for individuals with psychological distress. To better elucidate the neural mechanism that underpins the effects of relaxation training, we investigated whether a 10-session computerized relaxation training program changed prefrontal gamma-aminobutyric acid (GABA) levels and cerebral blood flow (CBF) in women with psychological distress. We specifically focused on women since they were reported to be more vulnerable to develop stress-related disorders than men. Nineteen women with psychological distress but without a diagnosis of psychiatric disorders received the 10-day computerized relaxation training program that consisted of 30-min cognitive-relaxation training and 10-min breathing-relaxation training per day. At baseline and post-intervention, perceived stress levels, anxiety, fatigue, and sleep quality were assessed by self-report questionnaires. Brain magnetic resonance spectroscopy and arterial spin labeling scans were also performed before and after the intervention to evaluate GABA levels and relative CBF in the prefrontal region. Levels of perceived stress (t = 4.02, P &lt; 0.001), anxiety (z = 2.33, P = 0.02), fatigue (t = 3.35, P = 0.004), and sleep quality (t = 4.14, P &lt; 0.001) improved following 10 sessions of computerized relaxation training, resulting in a significant relief in composite scores of stress-related symptoms (t = −5.25, P &lt; 0.001). The prefrontal GABA levels decreased (t = 2.53, P = 0.02), while relative CBF increased (t = −3.32, P = 0.004) after the intervention. In addition, a greater increase in relative prefrontal CBF was associated with better composite scores of stress-related symptoms following the intervention (t = 2.22, P = 0.04). The current findings suggest that computerized relaxation training may improve stress-related symptoms through modulating the prefrontal GABA levels and CBF in women with psychological distress.

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