Variations in Elite Female Soccer Players' Sleep, and Associations With Perceived Fatigue and Soccer Games

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.

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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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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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Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.625247 ◽

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.

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Greatest changes in objective sleep architecture during COVID-19 lockdown in night-owls with increased REM sleep

SLEEP ◽

10.1093/sleep/zsab075 ◽

2021 ◽

Author(s):

Jean-Louis Pépin ◽

Sébastien Bailly ◽

Ernest Mordret ◽

Jonathan Gaucher ◽

Renaud Tamisier ◽

...

Keyword(s):

Rem Sleep ◽

Sleep Onset ◽

Eating Behaviour ◽

Depression Scale ◽

Severity Index ◽

Smartphone Application ◽

Sleep Time ◽

Sleep Architecture ◽

Physiological Data ◽

Sleep Stages

Abstract Study Objectives The Covid-19 pandemic has had dramatic effects on society and people’s daily habits. In this observational study we recorded objective data on sleep macro- and microarchitecture repeatedly over several nights before and during the Covid-19 government-imposed lockdown. The main objective was to evaluate changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control. Methods Participants were regular users of a sleep-monitoring headband that records, stores, and automatically analyses physiological data in real time, equivalent to polysomnography. We measured: sleep onset duration (SOD), total sleep time (TST), duration of sleep stages (N2, N3 and REM), and sleep continuity. Via the user’s smartphone application participants filled-in questionnaires on how lockdown changed working hours, eating behaviour, and daily-life at home. They also filled-in the Insomnia Severity Index, reduced Morningness-Eveningness Questionnaire and Hospital Anxiety and Depression Scale questionnaires allowing us to create selected sub-groups. Results The 599 participants were mainly men (71%) of median age 47 [IQR: 36;59]. Compared to before lockdown, during lockdown individuals slept more overall (mean +3·83 min; SD: ±1.3), had less deep sleep (N3), more light sleep (N2) and longer REM sleep (mean +3·74 min; SD: ±0.8). They exhibited less week-end specific changes, suggesting less sleep restriction during the week. Changes were most pronounced in individuals reporting eveningness preferences, suggesting relative sleep deprivation in this population and exacerbated sensitivity to societal changes. Conclusions This unique dataset should help us understand the effects of lockdown on sleep architecture and on our health.

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

Innovation in Aging ◽

10.1093/geroni/igab046.2982 ◽

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.

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

Arquivos de Neuro-Psiquiatria ◽

10.1590/s0004-282x2006000600014 ◽

2006 ◽

Vol 64 (4) ◽

pp. 958-962 ◽

Cited By ~ 2

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.

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The First-Night Effect in Elite Sports: An Initial Glance on Polysomnography in Home-Based Settings

Frontiers in Psychology ◽

10.3389/fpsyg.2021.641451 ◽

2021 ◽

Vol 12 ◽

Author(s):

Annika Hof zum Berge ◽

Michael Kellmann ◽

Sarah Jakowski

Keyword(s):

Effect Size ◽

Elite Athletes ◽

Sleep Onset ◽

Sleep Stages ◽

Sleep Onset Latency ◽

Sleep Monitoring ◽

Time In Bed ◽

Home Based ◽

Small Effect Size ◽

First Night Effect

Self-applied portable polysomnography is considered a promising tool to assess sleep architecture in field studies. However, no findings have been published regarding the appearance of a first-night effect within a sport-specific setting. Its absence, however, would allow for a single night sleep monitoring and hence minimize the burden on athletes while still obtaining the most important variables. For this reason, the aim of the study was to assess whether the effect appears in home-based sleep monitoring of elite athletes.The study sample included eight male and 12 female German elite athletes from five different sports. Participants slept with a portable polysomnography for two nights, which they self-applied at night before going to bed. Time in bed and wake-up time in the morning were freely chosen by each individual athlete without any restrictions regarding time or sleeping environment. Participants were asked to keep the same location and time frame during the two days of monitoring and stick to their usual sleeping schedules. Sleep stages were manually scored using 30-s epochs. Sleep parameters and stages were later compared with the help of linear mixed models to investigate the factor time.Significant differences between the two nights were found for percentage of Non-REM sleep [T(19) = −2,10, p < 0.05, d = −0.47, 95%-CI (−7.23, −0.01)] with small effect size, Total Wake Time [T(19) = 2.30, p = 0.03, d = 0.51, 95%-CI (1.66, 35.17)], Sleep Efficiency [T(19) = −2.48, p = 0.02, d = −0.55, 95%-CI (−7.43, −0.63)], and Wake percentage [T(19) = 2.47, p = 0.02, d = 0.55, 95%-CI (0.61, 7.43)] with moderate effect sizes, and N3 Sleep Onset Latency [T(19) = 3.37, p < 0.01, d = 0.75, 95%-CI (7.15, 30.54)] with large effect size. Confidence Intervals for all other indices range from negative to positive values and hence specify, that parameters were not systematically negatively affected in the first night.Findings suggest that some individuals are more affected by the first-night effect than others. Yet, in order to keep the measurement uncertainties to a minimum, a more conservative approach with at least two monitoring nights should be used whenever possible, if no other supporting information on the athletes says otherwise.

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0443 Intra Week Sleep Patterns Analyzed Using Consumer Sleep Tracker Data

SLEEP ◽

10.1093/sleep/zsaa056.440 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A170-A170

Author(s):

L Gahan ◽

M Ruder ◽

A Raj ◽

B O’Mullane ◽

R J Raymann

Keyword(s):

Big Data ◽

Large Population ◽

Sleep Onset ◽

Real Life ◽

Sleep Time ◽

Weekend Effect ◽

Self Report ◽

Sleep Behavior ◽

Time In Bed ◽

The Usa

Abstract Introduction Big data collected using consumer sleep technology can provide objectively measured insights on sleep behavior in the real-life environment. It has the advantage over self-report data of being less prone to bias. Here we used a non-contact bio-motion sensor to remotely capture objective sleep data. We analyzed 168432 nights of sleep data to test if differences between weekday versus weekend sleep behavior, known from self-report, would still hold using objective data in a large population. Methods Sleep data was acquired using the SleepScore Max remote sleep sensor and included 168432 nights (2730 users, mean age: 46.6 +/- 11.8 years, 33% female, all resident in the USA). Analysis was restricted to those of working age; adults between 20-65. Any sleep which ended from Monday to Friday was considered weekday sleep, and any ending on Saturday or Sunday as weekend sleep. Data records were inspected and cleaned before analyzing. Descriptive statistics and independent t-tests were used to analyze the data. Results Total Sleep Time, Time In Bed and Sleep Onset Latencies were longer during weekend (TST: + 20.6 mins, TIB: +22.9 mins, SOL: +1.1 min, all p <0.001), resulting in a slightly poorer Sleep Efficiency (-.016%, p<0.01) for weekend nights. Time to bed and final awakening were both delayed in weekends as compared to weekdays (Time to bed +30.0 mins, and final awakening +53.4 mins, both p<0.001). Conclusion This big data analysis confirms the earlier observed difference in sleep and sleep behavior between weekdays and weekends. This should be considered for optimizing (automated) sleep interventions, that may not normally take the weekend effect into consideration. Support

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Glucocorticoid Replacement Is Permissive for Rapid Eye Movement Sleep and Sleep Consolidation in Patients with Adrenal Insufficiency1

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jcem.85.11.6965 ◽

2000 ◽

Vol 85 (11) ◽

pp. 4201-4206

Author(s):

Diego GarcÍa-Borreguero ◽

Thomas A. Wehr ◽

Oscar Larrosa ◽

Juan J. Granizo ◽

Donna Hardwick ◽

...

Keyword(s):

Rem Sleep ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Stages ◽

Sleep Regulation ◽

Double Blind ◽

Rem Latency ◽

Sleep Parameters ◽

Sleep Consolidation ◽

Permissive Role

There is a well described temporal relation between hormonal secretion and sleep phase, with hormones of the hypothalamic-pituitary-adrenal (HPA) axis possibly playing a role in determining entry into and duration of different sleep stages. In this study sleep features were studied in primary Addison’s patients with undetectable levels of cortisol treated in a double blind, randomized, cross-over fashion with either hydrocortisone or placebo supplementation. We found that REM latency was significantly decreased in Addison’s patients when receiving hydrocortisone at bedtime, whereas REM sleep time was increased. There was a trend toward an increase in the percentage of time in REM sleep and the number of REM sleep episodes. Waking time after sleep onset was increased, whereas no differences were observed between the two conditions when total sleep time or specific non-REM sleep parameters were evaluated. Our results suggest that in Addison’s patients, cortisol plays a positive, permissive role in REM sleep regulation and may help to consolidate sleep. These effects may be mediated either directly by the central effects of glucocorticoids and/or indirectly through CRH and/or ACTH.

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The Association between Worry and Rumination with Sleep in Non-Clinical Populations: A Systematic Review and Meta-Analysis

10.31234/osf.io/w952r ◽

2019 ◽

Author(s):

Faye Clancy ◽

Andrew Prestwich ◽

Daryl Brian O'Connor

Keyword(s):

Protective Factor ◽

Meta Analysis ◽

Sleep Onset ◽

Sleep Time ◽

Self Report ◽

Sleep Onset Latency ◽

Cross Sectional ◽

Perseverative Cognition ◽

Diary Studies ◽

Clinical Populations

Associations have been found between perseverative cognition (PC: worry and rumination) and somatic markers of ill-health. Further studies have reported associations between sleep and both PC and poorer health. As such, sleep disturbance may represent a pathway between PC and ill-health. Therefore, studies assessing the relationship between PC and sleep in non-clinical populations were synthesized. Meta-analyses (k = 55) revealed small- to medium-sized associations between higher PC and poorer sleep quality (SQ, r = -0.28), shorter total sleep time (TST, r = -0.15) and longer sleep onset latency (SOL, r = -0.16). Variations included associations between SQ and rumination (r = -.33) and worry (r = -.23). Associations were stronger in studies measuring SQ via self-report rather than actigraphy, and where SOL and TST outcomes were cross-sectional. Associations with SOL were stronger when outcomes were from non-diary studies and when trait, rather than state PC, was measured, but weaker where studies incorporated more measures of PC. Effect sizes were generally larger where studies were higher quality and being female may act as a protective factor between PC and longer SOL. Therefore, there is a consistent association between PC and sleep which may partially explain the link between PC and ill-health.

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