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.

scholarly journals 1253 Multiple sleep onset REM episodes in middle age woman with excessive daytime sleepiness – Is this automatically assumed narcolepsy?

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A477-A477
Author(s):  
Kamal Patel ◽  
Bianca J Lang
Keyword(s):  
Daytime Sleepiness ◽  
Sleep Latency ◽  
Sleep Onset ◽  
Sleep Time ◽  
Multiple Sleep Latency Test ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Obstructive Sleep ◽  
Falling Asleep ◽  
Multiple Sleep Latency

Abstract Introduction Presence of sleep onset REM episodes often raises concerns of narcolepsy. However other conditions have shown to have presence of sleep on REM episodes which include but not limited to obstructive sleep apnea, sleep wake schedule disturbance, alcoholism, neurodegenerative disorders, depression and anxiety Report of Case Here we present a case of 30 year old female with history of asthma, patent foraman ovale, migraine headache, and anxiety who presented with daytime sleepiness, falling asleep while at work, occasional scheduled naps, non-restorative sleep, sleep paralysis, and hypnopompic hallucination. Pertinent physical exam included; mallampati score of 4/4, retrognathia, high arched hard palate, crowded posterior oropharynx. She had a score of 16 on Epworth sleepiness scale. Patient previously had multiple sleep latency test at outside facility which revealed 4/5 SOREM, with mean sleep onset latency of 11.5 minutes. She however was diagnosed with narcolepsy and tried on modafinil which she failed to tolerate. She was tried on sertraline as well which was discontinued due to lack of benefit. She had repeat multiple sleep latency test work up which revealed 2/5 SOREM, with mean sleep onset latency was 13.1 minutes. Her overnight polysomnogram prior to repeat MSLT showed SOREM with sleep onset latency of 10 minutes. Actigraphy showed consistent sleep pattern overall with sufficient sleep time but was taking hydroxyzine and herbal medication. Patient did not meet criteria for hypersomnolence disorder and sleep disordered breathing. Conclusion There is possibility her medication may have played pivotal role with her daytime symptoms. We also emphasize SOREMs can be present in other disorders such as anxiety in this case and not solely in narcolepsy

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.

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.

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 P003 The impact of forced wake from overnight polysomnography on multiple sleep latency test results

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A22-A23
Author(s):  
A Amaranayake ◽  
S Frenkel ◽  
P Lyell ◽  
A Southcott
Keyword(s):  
Diagnostic Accuracy ◽  
Sleep Latency ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Multiple Sleep Latency Test ◽  
Test Results ◽  
The Impact ◽  
Multiple Sleep Latency ◽  
Overnight Polysomnography

Abstract Introduction The multiple sleep latency test (MSLT) is used to diagnose disorders of hypersomnolence. Although internationally-recognised protocols do not stipulate whether patients should be woken from the preceding overnight polysomnography (PSG), many labs wake their patients for logistic reasons. This study analyses the impact on PSG and MSLT parameters of forced wake (FW) from the overnight PSG compared with unrestricted sleep (US). Methods 400 consecutive patients (FW=200; US=200) undergoing PSG/MSLT were included and the following parameters were compared: Epworth Sleepiness Scale (ESS), Morningness-Eveningness Questionnaire score (MEQ), PSG total sleep time (TST), wake-up time from the PSG, overall MSLT sleep latency (MSL), individual nap latencies (SLNap 1–4), number of MSLT naps with sleep-onset REM periods (#SOREMP), and percentage of MSLTs with overall MSL&lt;8 minutes (%MSLT&lt;8). Results The 2 groups were well-matched for ESS and MEQ. The FW group had more males (49% vs 39%). When compared to FW, patients with US had longer TST (+38 minutes; p=&lt;0.0001), later wake-up time (+52 minutes; p&lt;0.0001), longer MSL (+1.9 minutes; p=0.0049), 50% fewer #SOREMP (p=0.0224), and 16% fewer %MSLT&lt;8 (p=0.0018). SLNap1 increased by 1.5 minutes (p=0.0623), SLNap2 increased by 2.0 minutes (p=0.0067), SLNap3 increased by 0.75minutes (p=0.0533) and SLNap4 increased by 2.5 minutes (p=0.0059). Discussion Allowing patients to have unrestricted sleep on the night prior to the MSLT resulted in significantly longer TST, longer sleep latencies during the MSLT, fewer SOREMP and fewer tests with MSL&lt;8 minutes. International protocols should stipulate unrestricted sleep on the PSG prior to the MSLT to improve diagnostic accuracy.

scholarly journals Correlations between subjective and objective features of nocturnal sleep and excessive diurnal sleepiness in patients with narcolepsy

2009 ◽  
Vol 67 (4) ◽  
pp. 995-1000 ◽  
Author(s):  
Ulises Jiménez-Correa ◽  
Reyes Haro ◽  
Rosa Obdulia González ◽  
Javier Velázquez-Moctezuma
Keyword(s):  
Clinical Symptoms ◽  
Sleep Onset ◽  
Sleep Time ◽  
Control Group ◽  
Sleep Onset Latency ◽  
Sleep Paralysis ◽  
Onset Latency ◽  
Nocturnal Sleep ◽  
Subjective Sleep Quality ◽  
Multiple Sleep Latency

OBJECTIVE: To determine the correlations between excessive daytime sleepiness (EDS), assessed by the Epworth sleepiness scale (ESS), and the multiple sleep latency test (MSLT) and nocturnal sleep architecture features, clinical symptoms of narcolepsy (CSN) and subjective sleep quality (SSQ) in patients with narcolepsy. METHOD: Twenty three untreated patients were studied and compared with a matched control group. Diagnosis of narcolepsy was carried out employing a clinical interview, a polysomnographic (PSG) record, and an MSLT. RESULTS: Subjective number of awakenings was the SSQ indicator that best correlated with EDS (ESS and MSLT). Regarding clinical features, diurnal tiredness and sleep paralysis correlated with ESS values. Increase in ESS was related with decrease in total sleep time, SWS, and sleep onset latency. On the other hand, increase in MSLT was related with decrease in SWS. CONCLUSION: These data suggest that EDS in patients with narcolepsy could be impaired by disturbed nocturnal sleep.

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.

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