507 Clinical Impression of Excessive Daytime Sleepiness (EDS) at initial Sleep Medicine (SM) consultation and its clinical correlates

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A199-A200
Author(s):  
Leon Rosenthal ◽  
Raúl Aguilar Roblero
Keyword(s):  
Lower Energy ◽  
Energy Levels ◽  
Sleep Onset ◽  
Sleep Time ◽  
Clinical Impression ◽  
Sleep Onset Latency ◽  
Convenience Sample ◽  
Time In Bed ◽  
Number Of Patients ◽  
Cardinal Symptom

Abstract Introduction EDS represents a cardinal symptom in SM. Use of subjective scales are prevalent, which have a modest correlation with the MSLT. While the Clinical Global Impression has been used in research, reports of clinical impression (CI) in medical practice are lacking. We report on the CI of EDS in a convenience sample of patients undergoing initial consultation. Methods Patients reported primary, secondary symptoms and completed the Sleep Wake Activity Inventory (SWAI) prior to Tele-Medicine consultation. A SM physician completed the assessment which included ascertainment of CI of EDS (presence S+ / absence S-). Results There were 39 ♂and 13 ♀. The CI identified 26 patients in each group (S+/S-). Age (52 [14]), BMI (33 [7]), reported time in bed, sleep time, sleep onset latency and # of awakenings did not differ. All identified a primary symptom (S-: 21, S+: 19 reported snoring or a previous Dx of OSA). Sleepiness as a 1ry or 2ry symptom was identified by 0 in the S- and by 13 in the S+ groups. Refreshing quality of sleep differed (χ2 <0.05): un-refreshing sleep was reported by 7 (S-) and by 13 (S+). Naps/week: 0.7 [1.5] and 1.57 [1.5] for the S-, S+ groups respectively (p<0.05). A main effect (p<0.01) was documented on the SWAI. We report on the Sleepiness [SS] and Energy Level [EL] scales (lower scores on the SS reflect higher sleepiness while lower scores on EL denote higher energy). Higher sleepiness (p<0.01) 43 [12] and lower energy levels 24 [6] (p<0.05) were documented on the S+ group (S- 61 [17], and 18 [6] respectively). Available spouse’s Epworth score on 29 patients: S- patients 5.8 [4] and S+ 10.2 [6] (p<0.05). Dx of OSA was identified among all but 1 in the S+ group. Also, Insomnia was diagnosed among 11 (S-) and 19 (S+) patients (p<0.05) despite only 3 and 7 (respectively) identifying it as a presenting symptom. Conclusion While snoring or previous Dx of OSA were prevalent motivations for consultation, sleepiness and insomnia were clinically relevant among a substantial number of patients. Unrefreshing sleep, daytime naps, lower energy, and higher sleepiness were ubiquitous among S+ patients. Support (if any):

scholarly journals Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Isaac Moshe ◽  
Yannik Terhorst ◽  
Kennedy Opoku Asare ◽  
Lasse Bosse Sander ◽  
Denzil Ferreira ◽  
...  
Keyword(s):  
Multilevel Models ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Wearable Devices ◽  
Screening Methods ◽  
Sleep Onset Latency ◽  
Depression And Anxiety ◽  
Symptoms Of Depression ◽  
Time In Bed

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of 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 < 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 Variations in Elite Female Soccer Players' Sleep, and Associations With Perceived Fatigue and Soccer Games

2021 ◽  
Vol 3 ◽  
Author(s):  
Frode Moen ◽  
Maja Olsen ◽  
Gunvor Halmøy ◽  
Maria Hrozanova
Keyword(s):  
Respiration Rate ◽  
Rem Sleep ◽  
Sleep Onset ◽  
Sleep Time ◽  
Self Report ◽  
Ultra Wideband ◽  
Soccer Players ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Time In Bed

The current study investigated the associations between female perceived fatigue of elite soccer players and their sleep, and the associations between the sleep of players and soccer games. The sample included 29 female elite soccer players from the Norwegian national soccer team with a mean age of ~26 years. Perceived fatigue and sleep were monitored over a period of 124 consecutive days. In this period, 12.8 ± 3.9 soccer games per player took place. Sleep was monitored with an unobtrusive impulse radio ultra-wideband Doppler radar (Somnofy). Perceived fatigue was based on a self-report mobile phone application that detected daily experienced fatigue. Multilevel analyses of day-to-day associations showed that, first, increased perceived fatigue was associated with increased time in bed (3.6 ± 1.8 min, p = 0.037) and deep sleep (1.2 ± 0.6 min, p = 0.007). Increased rapid eye movement (REM) sleep was associated with subsequently decreased perceived fatigue (−0.21 ± 0.08 arbitrary units [AU], p = 0.008), and increased respiration rate in non-REM sleep was associated with subsequently increased fatigue (0.27 ± 0.09 AU, p = 0.002). Second, game night was associated with reduced time in bed (−1.0 h ± 8.4 min, p = <0.001), total sleep time (−55.2 ± 6.6 min, p = <0.001), time in sleep stages (light: −27.0 ± 5.4 min, p = <0.001; deep: −3.6 ± 1.2 min, p = 0.001; REM: −21.0 ± 3.0 min, p = <0.001), longer sleep-onset latency (3.0 ± 1.2 min, p = 0.013), and increased respiration rate in non-REM sleep (0.32 ± 0.08 respirations per min, p = <0.001), compared to the night before the game. The present findings show that deep and REM sleep and respiration rate in non-REM sleep are the key indicators of perceived fatigue in female elite soccer players. Moreover, sleep is disrupted during game night, likely due to the high physical and mental loads experienced during soccer games. Sleep normalizes during the first and second night after soccer games, likely preventing further negative performance-related consequences.

scholarly journals 0004 TS-142: A Novel and Potent Dual Orexin Receptor Antagonist with Sleep-Promoting Effects in Rats

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A2-A2
Author(s):  
D Kambe ◽  
H Hikichi ◽  
Y Tokumaru ◽  
M Ohmichi ◽  
Y Konno ◽  
...  
Keyword(s):  
Receptor Antagonist ◽  
Half Life ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Orexin A ◽  
Pharmacokinetic Profiles ◽  
Orexin Receptors ◽  
Elimination Half ◽  
Emg Electrodes

Abstract Introduction The orexin system plays a pivotal role in regulating sleep and wakefulness, thus, orexin receptors (OX1 and OX2 receptors) have gained much attention as promising therapeutic targets for the treatment of insomnia. We synthesized a novel and potent dual orexin receptor antagonist (DORA), ORN0829 (investigation code name as TS-142), which was designed to have short-acting effects. Here we report pharmacological and pharmacokinetic profiles of ORN0829 in rats. Methods The antagonistic activities of ORN0829 were assessed using calcium mobilization assays. Ala-orexin A-induced [Ca2+]i response was measured with CHO-K1 cells stably expressing human/rat orexin receptor. Rats implanted the EEG/EMG electrodes were orally administrated ORN0829 at doses of 1, 3 or 10 mg/kg at the dark onset and sleep-wake stages were inspected visually. In addition, pharmacokinetic profiles of ORN0829 were investigated in rats. Results ORN0829 inhibited Ala-orexin A-increased [Ca2+]i response with a Kb of 0.67/0.44 nmol/L (for human/rat OX1 receptor), and with a Kb of 0.84/0.80 nmol/L (for human/rat OX2 receptor), respectively, indicating that ORN0829 is a potent DORA with no species differences. ORN0829 dose-dependently increased total sleep time and reduced sleep onset latency at doses of 1, 3 and 10 mg/kg. Importantly, the ORN0829 levels in plasma and cerebrospinal fluid rapidly reached a maximum concentration, and decreased with an elimination half-life of less than 1 h. Conclusion The present study indicates that ORN0829 is a novel and potent DORA with sleep-promoting effects, and that it exhibits ideal pharmacokinetic profiles (rapid absorption and short half-life) in rats. A phase 2a study of TS-142 using patients with insomnia has been completed, which is presented in a separate poster. Support Taisho Pharmaceutical. Co., Ltd.

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 The Cycle of Daily Stress and Sleep: Sleep Measurement Matters

2020 ◽  
Author(s):  
Danica C Slavish ◽  
Justin Asbee ◽  
Kirti Veeramachaneni ◽  
Brett A Messman ◽  
Bella Scott ◽  
...  
Keyword(s):  
Sleep Disturbances ◽  
Multilevel Models ◽  
Single Channel ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Daily Stress ◽  
Sleep Diaries ◽  
Sleep Measurement

Abstract Background Disturbed sleep can be a cause and a consequence of elevated stress. Yet intensive longitudinal studies have revealed that sleep assessed via diaries and actigraphy is inconsistently associated with daily stress. Purpose We expanded this research by examining daily associations between sleep and stress using a threefold approach to assess sleep: sleep diaries, actigraphy, and ambulatory single-channel electroencephalography (EEG). Methods Participants were 80 adults (mean age = 32.65 years, 63% female) who completed 7 days of stressor and sleep assessments. Multilevel models were used to examine bidirectional associations between occurrence and severity of daily stress with diary-, actigraphy-, and EEG-determined sleep parameters (e.g., total sleep time [TST], sleep efficiency, and sleep onset latency, and wake after sleep onset [WASO]). Results Participants reported at least one stressor 37% of days. Days with a stressor were associated with a 14.4-min reduction in actigraphy-determined TST (β = −0.24, p = 0.030), but not with other actigraphy, diary, or EEG sleep measures. Nights with greater sleep diary-determined WASO were associated with greater next-day stressor severity (β = 0.01, p = 0.026); no other diary, actigraphy, or EEG sleep measures were associated with next-day stressor occurrence or severity. Conclusions Daily stress and sleep disturbances occurred in a bidirectional fashion, though specific results varied by sleep measurement technique and sleep parameter. Together, our results highlight that the type of sleep measurement matters for examining associations with daily stress. We urge future researchers to treat sleep diaries, actigraphy, and EEG as complementary—not redundant—sleep measurement approaches.

scholarly journals O043 “My Fitbit tells me I don’t sleep” – Validation of a consumer-wearable device (Fitbit Charge 3TM) using gold standard in-laboratory polysomnography to assess sleep in adults presenting for medical evaluation in a sleep laboratory

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A19-A19
Author(s):  
M Munsif ◽  
R Jumabhoy ◽  
K Rangamuwa ◽  
D Mansfield ◽  
S Drummond ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Medical Evaluation ◽  
Insomnia Disorder ◽  
Epoch Analysis ◽  
A Prospective Study ◽  
Screening Device ◽  
Wake State

Abstract Background There has been a rapid growth in wearable devices marketed for sleep. Trackers such as the Fitbit collect data through an accelerometer and use heart rate variability to estimate the sleep-wake state. Currently, Fitbit validation studies have only been with “healthy” adults and Insomnia Disorder. Aims The purpose of this study is to evaluate the accuracy of Fitbit Charge3TM compared to in-lab polysomnography (PSG) in patients with sleep disorders. Our hypothesis is that Fitbit Charge 3TM will perform with less sensitivity and specificity relative to PSG in the presence of sleep disorders. Methods A prospective study of patients attending a PSG through Epworth Camberwell Sleep Lab between 2019–2021 will be conducted. Fitbit Charge3TM will be worn on the wrist with concurrent PSG monitoring. Parameters measured with both PSG and Fitbit Charge3TM will include total sleep time, Sleep onset latency, wake after sleep onset and time spent in N1, N2, N3 and REM sleep (min). Standard PSG data will be evaluated to diagnose sleep-disordered breathing. Progress to date:Ethics approval has been obtained, and 110 participants have been recruited. 30-second epoch-by-epoch analysis will now be conducted. Bland-Altman analyses will be performed to assess agreement between the Fitbit and PSG. Intended outcome and impact: Our novel study findings will provide evidence to address queries regarding the accuracy of the Fitbit trackers to evaluate sleep and may support the use of Fitbit Charge3TM as an initial screening device to assess sleep duration and sleep architecture in select patients.

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<0.001) and underestimated WASO by 44.74±68.81 minutes (p<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<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 O020 What helped you and what prevented you from getting good sleep? Contribution of daily facilitators and barriers to adolescent sleep

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A9-A10
Author(s):  
S Maskevich ◽  
L Shen ◽  
J Wiley ◽  
S Drummond ◽  
B Bei
Keyword(s):  
Everyday Life ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Previous Night ◽  
Quality Sleep ◽  
Sleep Environment ◽  
Facilitators And Barriers ◽  
Good Sleep

Abstract Introduction This intense longitudinal study examined factors that facilitate and hinder sufficient and good quality sleep in adolescents’ everyday life. Methods 205 (54.2% female, 64.4% non-white) Year 10–12 adolescents (Mage = 16.9 ± 0.9) completed daily morning surveys and wore actigraphy over 2 school-weeks and 2 subsequent vacation-weeks. Morning surveys assessed self-reported sleep and the usage of 8 facilitators and 6 barriers of sleep from the previous night. Linear mixed-effects models examined contribution of facilitators/barriers to actigraphy and self-reported total sleep time (TST) and sleep onset latency (SOL), controlled for age, sex, race, place of birth, and study day. Schooldays/non-schooldays was included as a moderator. Results Seven facilitators and two barriers were endorsed by high proportions (>30%) of adolescents as frequently (≥50% days) helping/preventing them from achieving good sleep. Facilitators predicting longer TST and shorter SOL, were: “follow body cues”, “manage thoughts and emotions”, “create good sleep environment”, “avoid activities interfering with sleep” and “plan bedtime and go to bed as planned” (only TST on schooldays). Barriers predicting shorter TST and longer SOL, were: “pre-bedtime thoughts and emotions”, “unconducive sleep environment”, “activities interfering with sleep”, “inconsistent routines” and “other household members’ activities”. Overall, facilitators or barriers explained an additional 1–5% (p-values < .001) of variance beyond the covariates. Discussion Adolescents perceive a range of factors as facilitating and as preventing sufficient and good quality sleep in everyday life. These factors are predictive of their sleep duration and onset latency, and need further research to understand their functions and clinical implications.

254 Validation of a Non-Wearable Sleep Tracking Device in Healthy Adults Under Normal and Restricted Sleep Conditions

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A102-A102
Author(s):  
David Hsiou ◽  
Chenlu Gao ◽  
Natalya Pruett ◽  
Michael Scullin
Keyword(s):  
Conflicts Of Interest ◽  
Sleep Onset ◽  
Sleep Time ◽  
Healthy Adults ◽  
Wearable Device ◽  
Self Report ◽  
Sleep Onset Latency ◽  
Clinical Grade ◽  
Tracking Device ◽  
Future Work

Abstract Introduction Polysomnography (PSG) is the gold standard for measuring sleep, but this method is cumbersome, costly, and sometimes does not reflect naturalistic sleep patterns. Leading technology companies have developed non-wearable sleep tracking devices that have attracted public interest. However, the accuracy of these devices has either been shown to be poor or the validation tests have not been conducted by independent laboratories without potential conflicts of interest. Relative to PSG and actigraphy, and under conditions of both normal and restricted sleep, we assessed the accuracy of early and newer versions of a non-wearable sleep tracking device (Beddit, Apple Inc.). Methods Participants were 35 healthy young adults (Mage=18.97, SD=0.95 years; 77.14% female; 42.86% Caucasian). We randomly assigned them to go to bed at 10:30pm (normal sleep) or 1:30am (restricted sleep) in a controlled sleep laboratory environment. Lights-on was 7:00am for all participants. Sleep was measured by the early version (3.0) or newer version (3.5) of a non-wearable device that uses a sensor strip to measure movement, heart rate, and breathing. We also measured PSG, wristband actigraphy, and self-report. For each device, we tested accuracy against PSG for total sleep time (TST), sleep efficiency (SE%), sleep onset latency (SOL), and wake after sleep onset (WASO). Results While the early version displayed poor reliability (ICCs<0.30), the newer version of the non-wearable device yielded excellent reliability with PSG under both normal and restricted sleep conditions. Not only was agreement excellent for TST (ICC=0.96) and SE% (ICC=0.98), but agreement was also excellent for the notoriously difficult metrics of SOL (ICC=0.92) and WASO (ICC=0.92). This newer version significantly outperformed clinical grade actigraphy (ICCs often in the 0.40 to 0.75 range), and self-reported sleep (ICCs often below 0.40). Conclusion Surprisingly, a non-wearable device demonstrated greater agreement with PSG than clinical grade actigraphy. Though the field has generally been skeptical of commercial non-wearable devices, this independent validation provides optimism that some such devices would be efficacious for research in healthy adults. Future work is needed to test the validity of this device in older adults and clinical populations. Support (if any) National Science Foundation (1920730 and 1943323)

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