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 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 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.

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

2019 ◽  
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, <i>P</i>=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, <i>P</i>=.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.

Actigraphy prior to MSLT: nighttime total sleep time predicts sleep onset latency

2021 ◽  
Author(s):  
Monica R. Kelly ◽  
Michelle R. Zeidler ◽  
Sharon DeCruz ◽  
Caitlin L. Oldenkamp ◽  
Karen R. Josephson ◽  
...  
Keyword(s):  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Onset Latency

scholarly journals Home-Based Sleep Sensor Measurements in an Older Australian Population: Before and during a Pandemic

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5993
Author(s):  
Mahnoosh Kholghi ◽  
Claire M. Ellender ◽  
Qing Zhang ◽  
Yang Gao ◽  
Liesel Higgins ◽  
...  
Keyword(s):  
Older Adults ◽  
Time Course ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Community Dwelling ◽  
Sleep Efficiency ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Short Sleep Duration

Older adults are susceptible to poor night-time sleep, characterized by short sleep duration and high sleep disruptions (i.e., more frequent and longer awakenings). This study aimed to longitudinally and objectively assess the changes in sleep patterns of older Australians during the 2020 pandemic lockdown. A non-invasive mattress-based device, known as the EMFIT QS, was used to continuously monitor sleep in 31 older adults with an average age of 84 years old before (November 2019–February 2020) and during (March–May 2020) the COVID-19, a disease caused by a form of coronavirus, lockdown. Total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, time to bed, and time out of bed were measured across these two periods. Overall, there was no significant change in total sleep time; however, women had a significant increase in total sleep time (36 min), with a more than 30-min earlier bedtime. There was also no increase in wake after sleep onset and sleep onset latency. Sleep efficiency remained stable across the pandemic time course between 84–85%. While this sample size is small, these data provide reassurance that objective sleep measurement did not deteriorate through the pandemic in older community-dwelling Australians.

scholarly journals Sleep Quality in Family Caregivers and Matched Non-Caregiving Controls: The REGARDS Study

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 818-818
Author(s):  
Marcela Blinka ◽  
Adam Spira ◽  
Orla Sheehan ◽  
Tansu Cidav ◽  
J David Rhodes ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Family Caregivers ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Care Recipient ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Time In Bed ◽  
Regards Study

Abstract The high levels of stress experienced by family caregivers may affect their physical and psychological health, including their sleep quality. However, there are few population-based studies comparing sleep between family caregivers and carefully-matched controls. We evaluated differences in sleep and identified predictors of poorer sleep among the caregivers, in a comparison of 251 incident caregivers and carefully matched non-caregiving controls, recruited from the national REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Incident caregivers and controls were matched on up to seven demographic and health factors (age, sex, race, education level, marital status, self-rated health, and self-reported serious cardiovascular disease history). Sleep characteristics were self-reported and included total sleep time, sleep onset latency, wake after sleep onset, time in bed, and sleep efficiency. Family caregivers reported significantly longer sleep onset latency, before and after adjusting for potential confounders, compared to non-caregiving controls (ps &lt; 0.05). Depressive symptoms in caregivers predicted longer sleep onset latency, greater wake after sleep onset, and lower sleep efficiency. Longer total sleep time in caregivers was predicted by employment status, living with the care recipient, and number of caregiver hours. Employed caregivers and caregivers who did not live with the care recipient had shorter total sleep time and spent less time in bed than non-employed caregivers. Additional research is needed to evaluate whether sleep disturbances contributes to health problems among caregivers.

scholarly journals Comparative analysis of multiple sleep latency tests (MSLT) parameters and occurrence of dreaming in patients with daytime sleepiness of narcoleptic and non-narcoleptic origin

2006 ◽  
Vol 64 (4) ◽  
pp. 958-962 ◽  
Author(s):  
Eduardo Siqueira Waihrich ◽  
Raimundo Nonato Delgado Rodrigues ◽  
Henrique Aragão Silveira ◽  
Fernando da Fonseca Melo Fróes ◽  
Guilherme Henrique da Silva Rocha
Keyword(s):  
Comparative Analysis ◽  
Rem Sleep ◽  
Daytime Sleepiness ◽  
Sleep Latency ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Multiple Sleep Latency

OBJECTIVE: To compare MSLT parameters in two groups of patients with daytime sleepiness, correlated to the occurrence and onset of dreams. METHOD: Patients were submitted to the MSLT between January/1999 and June/2002. Sleep onset latency, REM sleep latency and total sleep time were determined. The occurrence of dreams was inquired following each MSLT series. Patients were classified as narcoleptic (N) or non-narcoleptic (NN). RESULTS: Thirty patients were studied, 12 were classified as narcoleptics (N group; 40%), while the remaining 18 as non-narcoleptic (NN group; 60%). Thirty MSLT were performed, resulting in 146 series. Sleep was detected in 126 series (86%) and dreams in 56 series (44.44%). Mean sleep time in the N group was 16.0±6.3 min, while 10.5±7.5 min in the NN group (p<0.0001). Mean sleep latency was 2.0±2.2 min and 7.2±6.0 min in the N and NN group, respectively (p<0.001). Mean REM sleep latency in the N group was 3.2±3.1min and 6.9±3.7 min in the NN group (p=0.021). Dreams occurred in 56.9% of the N group series and 28.4% in that of the NN group (p=0.0009). Dream frequency was detected in 29.8% and 75% of the NREM series of the N and NN groups, respectively (p=0.0001). CONCLUSION: Patients from the N group, compared to the NN group, slept longer and earlier, demonstrated a shorter REM sleep onset and greater dream frequency. NN patients had a greater dream frequency in NREM series. Thus, the occurrence of dreams during NREM in the MSLT may contribute to differentially diagnose narcolepsy and daytime sleepiness.

767 Sleep architecture and sleep problems in adolescents and young adults with and without ADHD: A systematic review and meta-analysis

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A298-A299
Author(s):  
Finja Marten ◽  
Lena Keuppens ◽  
Dieter Baeyens ◽  
Bianca Boyer ◽  
Marina Danckaerts ◽  
...  
Keyword(s):  
Sleep Problems ◽  
Total Sleep Time ◽  
Meta Analysis ◽  
Sleep Onset ◽  
Sleep Hygiene ◽  
Sleep Time ◽  
Sleep Architecture ◽  
Adhd Group ◽  
Sleep Onset Latency ◽  
Onset Latency

Abstract Introduction During the past years, an increasing number of articles has focused on comparing sleep in youths with and without ADHD. However, so far no meta-analysis has been conducted summarizing the findings. Therefore, the current study assesses sleep architecture (i.e. the basic sleep structure), sleep problems, and sleep hygiene. Sleep was assessed both subjectively and objectively and the two groups were compared on multiple variables. Methods Two researchers independently performed a literature search (1980–2020). Studies using a case-control design comparing sleep in youths (12–25 years) with and without ADHD were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Standardized mean differences were calculated for each outcome domain being reported by at least two studies. Results 10379 publications were screened, resulting in 11 studies and 52 effect sizes (nADHD=2377, ncontrol=21687). These effect sizes were summarized into 7 objective and 11 subjective variables measuring sleep. Two objective sleep variables were significantly worse in the ADHD group; total sleep time (z=2.16, p=.03) and sleep onset latency (z=2.39, p=.02). The two groups did not differ on sleep efficiency, sleep onset/offset time, and time in bed. Comparing the groups on subjective variables resulted in the same pattern, with total sleep time (z=21.27, p&lt;.001) being significantly shorter in the ADHD group, and sleep onset latency (z=15.39, p&lt;.001) and wake after sleep onset (z=13.50, p&lt;.001) being significantly longer. Additionally, the ADHD group reported a significantly lower sleep efficiency (z=20.15, p&lt;.001) and subjective sleep satisfaction (z=3.50, p&lt;.001). Wake time and number of awakenings during the night were not significant. Youths with ADHD also reported significantly more sleep problems, including insomnia (z=6.38, p&lt;.001), daytime sleepiness (z=26.68, p&lt;.001) and sleep disturbances (z=8.00, p&lt;.001). Due to only two studies measuring it, with a focus on different variables, sleep hygiene could not be included. Conclusion In general, youths with ADHD have a disrupted sleep architecture and experience more sleep problems compared to their typically developing peers. Consequently, sleep assessment should become a routine part during the diagnostic process of ADHD. Additionally, more research is needed focusing on sleep architecture and sleep hygiene, and on the development of a sleep intervention for youths with ADHD. Support (if any):

scholarly journals Melatonin for the management of sleep problems in children with neurodevelopmental disorders: a systematic review and meta-analysis

2018 ◽  
Vol 103 (12) ◽  
pp. 1155-1162 ◽  
Author(s):  
Ibtihal Siddiq Abdelgadir ◽  
Morris A Gordon ◽  
Anthony K Akobeng
Keyword(s):  
Neurodevelopmental Disorders ◽  
Sleep Problems ◽  
Total Sleep Time ◽  
Meta Analysis ◽  
Sleep Onset ◽  
Sleep Time ◽  
Controlled Trials ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Randomised Controlled

ImportanceChildren with neurodevelopmental disorders have a higher prevalence of sleep disturbances. Currently there is variation in the use of melatonin; hence, an up-to-date systematic review is indicated to summarise the current available evidence.ObjectiveTo determine the efficacy and safety of melatonin as therapy for sleep problems in children with neurodevelopmental disorders.Data sources and study selectionsPubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature and the Cochrane Central Register of Controlled Trials were searched from inception up to January 2018. Two reviewers performed data assessment and extraction. We assessed randomised controlled trials that compared melatonin with placebo or other intervention for the management of sleep disorders in children (<18 years) with neurodevelopmental disorders.Data extraction and synthesisWe identified 3262 citations and included 13 studies in this meta-analysis.Main outcomesMain outcomes included total sleep time, sleep onset latency, frequency of nocturnal awakenings and adverse events.ResultsThirteen randomised controlled trials (n=682) met the inclusion criteria. A meta-analysis of nine studies (n=541) showed that melatonin significantly improved total sleep time compared with placebo (mean difference (MD)=48.26 min, 95% CI 36.78 to 59.73, I2=31%). In 11 studies (n=581), sleep onset latency improved significantly with melatonin use (MD=−28.97, 95% CI −39.78 to −18.17). No difference was noted in the frequency of nocturnal awakenings (MD=−0.49, 95% CI −1.71 to 0.73). No medication-related serious adverse event was reported.ConclusionMelatonin appeared safe and effective in improving sleep in the studied children. The overall quality of the evidence is limited due to heterogeneity and inconsistency. Further research is needed.

scholarly journals P121 Comparison of Actigraphy versus PSG and Perception of Sleep in Patients with Excessive Daytime Sleepiness

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A60-A61
Author(s):  
T Roebuck ◽  
E McDermott ◽  
R Cuesta ◽  
R Nguy ◽  
M Spiteri ◽  
...  
Keyword(s):  
Sleep Latency ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Activity Data ◽  
Significant Difference ◽  
Sleep Parameters ◽  
Perception Of Sleep

Abstract Actigraphy is used as a validated measure of rest and sleep, however, there are reported differences in WASO in healthy individuals (Chinoy, 2021). Methods This study compares the sleep parameters from PSG with simultaneous overnight actigraphy on patients the night prior to MSLT. We also compare the actigraphy data collected on the week prior to the PSG with the patient’s sleep diary. 22 subjects, age 38.7 ± 3.1 years, BMI 23.5 ± 1.4 kg/m2, 40.1% male, 4 participants were treated with CPAP. Results WASO was found to be under estimated by actigraphy versus PSG (y=-0.957x+18.014, R2=0.51), there is an increase in underestimation beyond 18minutes. Our data also show on overestimation of sleep onset latency by actigraphy versus PSG when sleep latency is longer than 12 minutes (y=0.27x-12.04, R2=0.08). Total sleep time was perceived to be longer on the PSG night than the PSG data shows (y=0.68x-4.65, R2=0.21). Data demonstrated participants to overestimate their sleep period in their sleep diary compared to the actigraphy data (y=-0.87x+6.58, R2=0.21). T-tests showed a significant difference between WASO (minutes) detected by PSG and the actigraphy data (67.4 ± 8.9 vs 33.3 ± 3.9 p=0.0007). There were no other significant differences in the datasets. Conclusion Actigraphy uses activity data and light detection to estimate rest and sleep periods in wearers. Our data reflects expected differences reported in the literature of actigraphy data versus PSG due to the limitation of actigraphy being able to differentiate between sleep and motionless wakefulness.

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