713 Sleep of infants and toddlers during 12 months of the COVID-19 pandemic

Abstract Introduction SARS-CoV-2 changed the lives of children and their parents in 2020. To our knowledge, no studies have examined infant and toddler sleep during this pandemic. We sought to compare parent-reported sleep characteristics of infants and toddlers over successive quarters of the past year. Methods Parents of children aged 0–36 months were surveyed primarily in the Midwestern USA between 01/17/2020 and 12/07/2020. Each parent responded only once. Age was categorized as: <6 months, 6–12 months, 12–24 months, and 24–36 months. Income was categorized as: <$50,000, $50-100,000, $100-150,000, and >$150,000. The year was divided into quarters. Multivariable linear regression included Total Sleep Time (TST), Sleep Onset Latency (SOL) and parental frustration with sleep (any frustration, scale of 1–5) as dependent variables and year quarter, child’s age, prematurity, child’s comorbidities, maternal age (during their child’s birth), parenting experience, household income, and room sharing as independent variables. Logistic regression included nap consistency (napping at the same time daily) as the dependent variable, and year quarter, child’s age, prematurity, comorbidity, maternal age, parenting experience, household income, and room sharing as independent variables. Results Of 594 children, mean age was 18.5±9.7 months and 52% were female. Prematurity and comorbidities were reported for 8% and 15%, respectively. Mean maternal age was 31.8±4.5 years. Neither TST (β=-0.488; p= 0.16) nor SOL (β= 0.029; p=0.23) were associated with year quarter. SOL was 3 minutes less for each increase in income category (β =-0.051; p= 0.003). TST (β = -0.994; p<0.001) and SOL (β =0.092; p<0.0001) were most associated with child’s age. Parental frustration was associated with child’s age (β=0.12; p= 0.04), comorbidity (β=0.30; p=0.05) and room sharing (β= -0.38; p=0.006), but not year quarter. Nap consistency was associated with increased child age category (OR 1.47; 95% CI 1.13, 1.94) and lack of room sharing (OR=2.09; 1.10, 3.97), but not year quarter. Conclusion Parent-estimated TST, nap consistency and sleep-related frustration did not differ significantly over the first 12 months of the pandemic. Yet, our results underscore that special attention should be given to the sleep of infants and toddlers with comorbidities, who share a room, and who have a lower household income. Support (if any) 2T32HL110952-06

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

SLEEP ◽

10.1093/sleep/zsaa056.003 ◽

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.

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

SLEEP ◽

10.1093/sleep/zsaa056.1247 ◽

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

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The Cycle of Daily Stress and Sleep: Sleep Measurement Matters

Annals of Behavioral Medicine ◽

10.1093/abm/kaaa053 ◽

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.

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

SLEEP ◽

10.1093/sleep/zsab072.506 ◽

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):

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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

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.042 ◽

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.

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

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.108 ◽

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.

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O020 What helped you and what prevented you from getting good sleep? Contribution of daily facilitators and barriers to adolescent sleep

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.019 ◽

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.

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

SLEEP ◽

10.1093/sleep/zsab072.253 ◽

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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Sleep Patterns and Affect Dynamics Among College Students during COVID-19 Pandemic (Preprint)

10.2196/preprints.33964 ◽

2021 ◽

Author(s):

Zahra Mousavi ◽

Jocelyn Lai ◽

Katharine Simon ◽

Alexander P. Rivera ◽

Asal Yunusova ◽

...

Keyword(s):

Mental Health ◽

Young Adults ◽

Longitudinal Design ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Onset Latency ◽

Sleep Patterns ◽

Daily Lives ◽

Affect Dynamics

BACKGROUND Sleep disturbance is a transdiagnostic risk factor so prevalent among young adults it is considered a public health epidemic, exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant. OBJECTIVE In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic. METHODS College student participants (N=20, 65% female) wore an Oura ring continuously for 3-months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency (SE), and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment (EMA) using a mobile health app to evaluate positive (PA) and negative affect (NA), and COVID-worry once per day. RESULTS Participants with higher SOL and TST on the prior day had lower PA the next day. Further, higher average TST across the 3-month period predicted lower average PA. TST variability predicted higher affect variability across all affect domains. CONCLUSIONS Fluctuating sleep patterns are associated with affect dynamics at daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.

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

Journal of Physical Activity and Health ◽

10.1123/jpah.2016-0513 ◽

2017 ◽

Vol 14 (6) ◽

pp. 465-473 ◽

Cited By ~ 6

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.

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