scholarly journals Finding a Composite Measure for Data From Wrist Actigraphy in Bipolar Disorder

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 338-339
Author(s):  
Ellen Lee ◽  
David Wing ◽  
Sonia Ancoli-Israel ◽  
Colin Depp ◽  
Ho-Kyoung Yoon ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Composite Measure ◽  
Subjective Sleep ◽  
Wrist Actigraphy ◽  
Z Scores ◽  
Participant Characteristics ◽  
Composite Scores

Abstract Actigraphy can objectively measure sleep in studies on Bipolar Disorder (BD) where subjective sleep ratings might be influenced by affect. Actigraphy data are complex necessitating data reduction approaches. We created a composite score of actigraphy sleep metrics (total sleep time [TST], wake after sleep onset [WASO], and percent sleep [PS]) in BD. We computed z-scores of sleep measures for n=51 BD vs. n=80 healthy subjects and averaged scores. We examined associations with participant characteristics and used LASSO to identify metrics best explaining composite variability. Higher composite scores (better sleep) were seen in employed vs. unemployed (t=2.40, df=34, p=0.02), and correlated with higher medication load (r=0.41, p=0.004), lower mania symptomatology (r=-0.33, p=0.04) and lower interleukin (IL)-6 levels (r=-0.32, p=0.02). TST best explained variability in medication load and PS best explained employment, mania symptoms and IL-6. Given observed specificity of associations, selecting theory-driven sleep metrics may be more appropriate than a composite.

scholarly journals P086 The organization of sleep-wake patterns around daily schedules in college students

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A49-A49
Author(s):  
S Lu ◽  
E Klerman ◽  
J Stone ◽  
A McHill ◽  
L Barger ◽  
...  
Keyword(s):  
College Students ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Total Duration ◽  
Onset Time ◽  
Sleep Time ◽  
Previous Night ◽  
Daily Schedule ◽  
Wrist Actigraphy ◽  
Wake Patterns

Abstract A potential contributor to insufficient sleep among college students is their daily schedule, with sleep sacrificed for other waking activities. We investigated how daily schedules predict day-to-day sleep-wake timing in college students. 223 undergraduate college students (M±SD = 19.2±1.4 years, 37% females) attending a Massachusetts university in the US between 2013–2016 were monitored for approximately 30 days during semester. Sleep-wake timing was measured using daily online sleep diaries and wrist-actigraphy. Daily schedules were measured using daily online diaries that included self-reported timing and duration of academic, exercise-based, and extracurricular activities, and duration of self-study. Linear mixed models were used to examine the association between sleep-wake patterns and daily schedules at both the between-person and within-person levels. An earlier start time of the first-reported activity predicted earlier sleep onset (between and within: p<.001) and shorter total sleep time (within: p<.001) for the previous night, as well as earlier wake onset on the corresponding day (between and within: p<.001). A later end time of the last-reported activity predicted later sleep onset (within: p=.002) and shorter total sleep time (within: p=.02) on that night. A more intense daily schedule (i.e., greater total duration of reported activities) predicted an earlier wake onset time (between: p=.003, within: p<.001), a later sleep onset time (within: p<.001), a shortened total night-time sleep duration (between: p=.03, within: p<.001), and greater sleep efficiency (within: p<.001). These results indicate that college students may organize their sleep and wake times based on their daily schedule.

scholarly journals Delayed Lactogenesis II is Associated With Lower Sleep Efficiency and Greater Variation in Nightly Sleep Duration in the Third Trimester

2019 ◽  
Vol 35 (4) ◽  
pp. 713-724
Author(s):  
Theresa Casey ◽  
Hui Sun ◽  
Helen J. Burgess ◽  
Jennifer Crodian ◽  
Shelley Dowden ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Blood Glucose ◽  
Sleep Duration ◽  
Body Mass ◽  
Sleep Disturbances ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Efficiency ◽  
Delayed Onset ◽  
Wrist Actigraphy

Background: Metabolic and hormonal disturbances are associated with sleep disturbances and delayed onset of lactogenesis II. Research aims: The aim of this study was to measure sleep using wrist actigraphy during gestation weeks 22 and 32 to determine if sleep characteristics were associated with blood glucose, body mass index, gestational related disease, delayed onset of lactogenesis II, or work schedule. Methods: Demographic data were collected at study intake from primiparous women who wore a wrist actigraph during gestation weeks 22 ( n = 50) and 32 ( n = 44). Start and end sleep time, total nighttime sleep, sleep efficiency, wake after sleep onset, and sleep fragmentation were measured. Night to night variability was assessed with the root mean square of successive difference. Blood glucose levels, body mass index, and gestational disease data were abstracted from medical charts. Timing of lactogenesis II was determined by survey. Results: Between gestation week 22 and 32, sleep efficiency decreased and fragmentation increased ( p < .05). During gestation week 32, blood glucose was negatively correlated with sleep duration, and positively related to fragmentation ( p < .05). Women who experienced delayed lactogenesis II had lower sleep efficiency and greater fragmentation ( p < .05), and greater night-to-night variability in sleep start and end time, efficiency, and duration during gestation week 32 ( p < .05). Conclusion: Women with better sleep efficiency and more stable nightly sleep time are less likely to experience delayed onset of lactogenesis II. Interventions to improve sleep may improve maternal health and breastfeeding adequacy.

scholarly journals P062 Sleep parameter validation of a home-based ballistocardiograph sleep tracker

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A41-A42
Author(s):  
M Kholghi ◽  
I Szollosi ◽  
M Hollamby ◽  
D Bradford ◽  
Q Zhang
Keyword(s):  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Daily Routine ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Sleep Monitoring ◽  
Significant Discrepancy ◽  
Home Based ◽  
Wake Detection

Abstract Introduction Consumer home sleep trackers are gaining popularity for objective sleep monitoring. Amongst them, non-wearable devices have little disruption in daily routine and need little maintenance. However, the validity of their sleep outcomes needs further investigation. In this study, the accuracy of the sleep outcomes of EMFIT Quantified Sleep (QS), an unobtrusive and non-wearable ballistocardiograph sleep tracker, was evaluated by comparing it with polysomnography (PSG). Methods 62 sleep lab patients underwent a single clinical PSG and their sleep measures were simultaneously collected through PSG and EMFIT QS. Total Sleep Time (TST), Wake After Sleep Onset (WASO), Sleep Onset Latency (SOL) and average Heart Rate (HR) were compared using paired t-tests and agreement analysed using Bland-Altman plots. Results EMFIT QS data loss occurred in 47% of participants. In the remaining 33 participants (15 females, with mean age of 53.7±16.5), EMFIT QS overestimated TST by 177.5±119.4 minutes (p&lt;0.001) and underestimated WASO by 44.74±68.81 minutes (p&lt;0.001). It accurately measured average resting HR and was able to distinguish SOL with some accuracy. However, the agreement between EMFIT QS and PSG on sleep-wake detection was very low (kappa=0.13, p&lt;0.001). Discussion A consensus between PSG and EMFIT QS was found in SOL and average HR. There was a significant discrepancy and lack of consensus between the two devices in other sleep outcomes. These findings indicate that while EMFIT QS is not a credible alternative to PSG for sleep monitoring in clinical and research settings, consumers may find some benefit from longitudinal monitoring of SOL and HR.

scholarly journals Study of correlation between perceived sleep disturbances in depressed patients with objective changes in sleep architecture using polysomnography before and after antidepressant therapy

2020 ◽  
Vol 8 (7) ◽  
pp. 2551
Author(s):  
Ganesh Ingole ◽  
Harpreet S. Dhillon ◽  
Bhupendra Yadav
Keyword(s):  
Sleep Disturbances ◽  
Total Sleep Time ◽  
Antidepressant Treatment ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Efficiency ◽  
Sleep Architecture ◽  
Wake Time ◽  
Depressed Patients ◽  
Before And After

Background: A prospective cohort study to correlate perceived sleep disturbances in depressed patients with objective changes in sleep architecture using polysomnography (PSG) before and after antidepressant therapy.Methods: Patients were recruited into the study after applying strict inclusion and exclusion criterion to rule out other comorbidities which could influence sleep. A diagnosis of Depressive episode was made based on ICD-10 DCR. Psychometry, in the form of Beck Depressive inventory (BDI) and HAMD (Hamilton depression rating scale) insomnia subscale was applied on Day 1 of admission. Patients were subjected to sleep study on Day 03 of admission with Polysomnography. Patients were started on antidepressant treatment post Polysomnography. An adequate trial of antidepressants for 08 weeks was administered and BDI score ≤09 was taken as remission. Polysomnography was repeated post remission. Statistical analysis was performed using Kruskal Wallis test and Pearson correlation coefficient.Results: The results showed positive (improvement) polysomnographic findings in terms of total sleep time, sleep efficiency, wake after sleep onset, percentage wake time and these findings were statistically significant. HAM-D Insomnia subscale was found to correlate with total sleep time, sleep efficiency, wake after sleep onset, total wake time and N2 Stage percentage.Conclusions: Antidepressant treatment effectively improves sleep architecture in Depressive disorder and HAM-D Insomnia subscale correlates with objective findings of total sleep time, sleep efficiency, wake after sleep onset, total wake time and duration of N2 stage of NREM.

Relations Between Sleep and Temperament in Preschool Children With ADHD

2018 ◽  
Vol 24 (4) ◽  
pp. 535-544 ◽  
Author(s):  
Maria Grazia Melegari ◽  
Stefania Sette ◽  
Elena Vittori ◽  
Luca Mallia ◽  
Alessandra Devoto ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Child Temperament ◽  
Novelty Seeking ◽  
Typically Developing ◽  
Temperament And Character Inventory ◽  
Children With Adhd ◽  
Wrist Actigraphy ◽  
Adhd Children ◽  
Character Inventory

Objective: The objective of this study was to assess the links between temperament and sleep in a group of preschoolers with ADHD. Method: Twenty-five ADHD ( M = 5.37 years, SD = 1.09) and 22 typically developing (TD; M = 5.10, SD = 1.18) preschoolers participated in the study. Sleep was assessed with the Sleep Disturbance Scale and wrist actigraphy. The Preschool Temperament and Character Inventory (PsTCI) was used to evaluate the child temperament. Results: ADHD children showed a temperamental profile characterized by higher novelty seeking, lower persistence, self-directness, and cooperativeness and marginally lower harm avoidance (HA) compared with controls. HA was associated negatively to wakefulness after sleep onset and sleep fragmentation and positively with sleep efficiency and sleep time. Reward dependence was negatively associated with wake episode length. Conclusion: Sleep and temperament are correlated in preschoolers with ADHD and temperament might represent an intermediate endophenotype underlying the relation between ADHD and sleep disorders.

Sleep disturbance and quality of life in Rheumatoid Arthritis: a prospective mobile health study (Preprint)

2021 ◽  
Author(s):  
John McBeth ◽  
William G Dixon ◽  
Susan Mary Moore ◽  
Bruce Hellman ◽  
Ben James ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Rheumatoid Arthritis ◽  
Sleep Disturbance ◽  
Mobile Health ◽  
Sleep Disturbances ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Domain Scores

BACKGROUND Sleep disturbance and poor health related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of non-restorative sleep, may be a driver of HRQoL. However, understanding if these sleep disturbances reduce HRQoL has, to date, been challenging due to the need to collect complex time-varying data in high resolution. Such data collection has now been made possible by the widespread availability and use of mobile health (mHealth) technologies. OBJECTIVE In a mobile health (mHealth) study we tested whether sleep disturbance (both absolute values and variability) caused poor HRQoL. METHODS The Quality of life, sleep and rheumatoid arthritis (QUASAR) study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, and for 30 days wore a triaxial accelerometer to objectively assess sleep, and provided daily reports via a smartphone app of sleep (Consensus Sleep Diary (CSD)), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multi-level modelling tested the relationship between sleep variables and WHOQoL-BREF domains (physical, psychological, environment and social). RESULTS Of 268 recruited participants, 254 were included in this analysis. Across all WHOQoL-BREF domains, participant’s scores were lower than the population average. CSD sleep parameters predicted WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β = 1.11 (0.07, 2.15)) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, levels of anxiety, sleep quality, or clinical sleep disorders. They were, however, attenuated and no longer significant when pain, fatigue and mood were included in the model. Increased variability in the total time asleep, was associated with poorer physical and psychological domain scores independently of all covariates. There were no patterns of association between actigraphy measured sleep and WHOQoL-BREF. CONCLUSIONS Optimising total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing the variability in total sleep time could improve HRQoL in people with RA.

scholarly journals 1059 Sleep Parameters In People With Type 2 Diabetes With And Without Insomnia Symptoms

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A402-A403
Author(s):  
M Alshehri ◽  
A Alkathiry ◽  
A Alenazi ◽  
S Alothman ◽  
J Rucker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Circular Data ◽  
Objective Measures ◽  
Subjective Measures ◽  
Sleep Parameters ◽  
Insomnia Symptoms

Abstract Introduction There is an increasing awareness of the high prevalence of insomnia symptoms in people with type 2 diabetes (T2D). Past studies have demonstrated the importance of measuring sleep parameters in both averages and variabilities using subjective and objective methods. Thus, we aimed to compare the averages and variability of sleep parameters in people with T2D with and without insomnia symptoms. Methods Actigraph measurements and sleep diaries were used in 59 participants to assess sleep parameters, including sleep efficiency (SE), sleep latency, total sleep time, and wake after sleep onset over seven nights. Validated instruments were used to assess the symptoms of depression, anxiety, and pain. Circular data were used to describe the distribution of bed distribution with SE as a magnitude for both groups. Mann Whitney U test was utilized to compare averages and variability of sleep parameters between the two groups. Multivariable general linear model to control for demographic and clinical variables. For the secondary aim, multiple linear regression tests were utilized to assess the association between averages and variability values for both groups. Results SE was found to be lower in average and higher in variability for participants with T2D and insomnia symptoms, than those with T2D only subjectively and objectively. SE variability was also the only sleep parameter higher in people with T2D and insomnia symptoms, with psychological symptoms potentially playing a role in this difference. We observed that people in T2D+Insomnia tend to go to bed earlier compared to the T2D only group based on objective measures, but no difference was observed between groups in subjective measures. The only significant relationship in both objective and subjective measures was between the averages and variability of SE. Conclusion Our findings suggest a discrepancy between subjective and objective measures in only average of total sleep time, as well as agreement in measures of variability in sleep parameters. Also, the relationship between averages and variabilities suggested the importance of improving SE to minimize its variability. Further research is warranted to investigate the complex relationship between sleep parameters and psychological factors in people with T2D and insomnia symptoms. Support None

scholarly journals Sleep and Circadian Rhythm Disturbance in Remitted Schizophrenia and Bipolar Disorder: A Systematic Review and Meta-analysis

2020 ◽  
Vol 46 (5) ◽  
pp. 1126-1143 ◽  
Author(s):  
Nicholas Meyer ◽  
Sophie M Faulkner ◽  
Robert A McCutcheon ◽  
Toby Pillinger ◽  
Derk-Jan Dijk ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Circadian Rhythm ◽  
Motor Activity ◽  
Sleep Latency ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Mean Difference ◽  
Effect Sizes ◽  
Time In Bed ◽  
Modifiable Factors

Abstract Background Sleep and circadian rhythm disturbances in schizophrenia are common, but incompletely characterized. We aimed to describe and compare the magnitude and heterogeneity of sleep-circadian alterations in remitted schizophrenia and compare them with those in interepisode bipolar disorder. Methods EMBASE, Medline, and PsycINFO were searched for case–control studies reporting actigraphic parameters in remitted schizophrenia or bipolar disorder. Standardized and absolute mean differences between patients and controls were quantified using Hedges’ g, and patient–control differences in variability were quantified using the mean-scaled coefficient of variation ratio (CVR). A wald-type test compared effect sizes between disorders. Results Thirty studies reporting on 967 patients and 803 controls were included. Compared with controls, both schizophrenia and bipolar groups had significantly longer total sleep time (mean difference [minutes] [95% confidence interval {CI}] = 99.9 [66.8, 133.1] and 31.1 [19.3, 42.9], respectively), time in bed (mean difference = 77.8 [13.7, 142.0] and 50.3 [20.3, 80.3]), but also greater sleep latency (16.5 [6.1, 27.0] and 2.6 [0.5, 4.6]) and reduced motor activity (standardized mean difference [95% CI] = −0.86 [−1.22, −0.51] and −0.75 [−1.20, −0.29]). Effect sizes were significantly greater in schizophrenia compared with the bipolar disorder group for total sleep time, sleep latency, and wake after sleep onset. CVR was significantly elevated in both diagnoses for total sleep time, time in bed, and relative amplitude. Conclusions In both disorders, longer overall sleep duration, but also disturbed initiation, continuity, and reduced motor activity were found. Common, modifiable factors may be associated with these sleep-circadian phenotypes and advocate for further development of transdiagnostic interventions that target them.

A Comparative Study of Sleep and Mood Between Young Elite Athletes and Age-Matched Controls

2017 ◽  
Vol 14 (6) ◽  
pp. 465-473 ◽  
Author(s):  
Anette Harris ◽  
Hilde Gundersen ◽  
Pia Mørk Andreassen ◽  
Eirunn Thun ◽  
Bjørn Bjorvatn ◽  
...  
Keyword(s):  
Physical Activity ◽  
Total Sleep Time ◽  
Elite Athletes ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Social Jetlag ◽  
Weekly Training

Background:Sleep and mood have seldom been compared between elite athletes and nonelite athletes, although potential differences suggest that physical activity may affect these parameters. This study aims to explore whether adolescent elite athletes differ from controls in terms of sleep, positive affect (PA) and negative affect (NA).Methods:Forty-eight elite athletes and 26 controls participating in organized and nonorganized sport completed a questionnaire, and a 7-day sleep diary.Results:On school days, the athletes and the controls who participated in organized and nonorganized sport differed in bedtime (22:46, 23:14, 23:42, P < .01), sleep onset (23:03, 23:27, 00:12, P < .01), and total sleep time (7:52, 8:00, 6:50, P < 01). During weekend, the athletes, the controls who participated in organized and nonorganized sport differed in bedtime (23:30, 00:04, 00:49, P < .01), sleep onset (23.42, 00:18, 01:13, P < .01), rise time (9:15, 9:47, 10:55, P < .01), sleep efficiency (95.0%, 94.2%, 90.0%, P < 05), and sleep onset latency (11.8, 18.0, 28.0 minutes, P < .01). Furthermore, the athletes reported less social jetlag (0:53) and higher score for PA (34.3) compared with the controls who participated in nonorganized sport (jetlag: 1:25, P < .05, PA: 29.8, P < .05).Conclusions:An almost dose-response association was found between weekly training hours, sleep, social jetlag and mood in adolescents.

scholarly journals Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study

10.2196/16880 ◽  
2020 ◽  
Vol 4 (6) ◽  
pp. e16880
Author(s):  
Hirotaka Miyashita ◽  
Mitsuteru Nakamura ◽  
Akiko Kishi Svensson ◽  
Masahiro Nakamura ◽  
Shinichi Tokuno ◽  
...  
Keyword(s):  
Pilot Study ◽  
Regression Coefficient ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Inverse Association ◽  
Onset Latency ◽  
Emotional Status ◽  
Nighttime Sleep

Background Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. Results A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

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