The Sleep Opportunity, Need and Ability (SONA) Theory

To determine whether variation in sleep duration reflects variation in sleep need or self-imposed sleep restriction. After habitual bedrest duration of participants was assessed during a 2-week outpatient protocol, volunteers were scheduled to sleep according to this schedule for 1 week prior to and the first night after admission to a general clinical research center. The inpatient protocol included multiple sleep latency testing on the second day and sleep recordings during a bedrest extension protocol that included 16 hours of sleep opportunity (12 hours at night and 4 hours at midday) for 3 consecutive days. Outpatient monitoring followed by inpatient assessment of sleep. Seventeen healthy volunteers (10 women) aged 18-32 years without clinical sleep disorders. Extension of sleep opportunity. The habitual bedrest duration varied from 6.1 to 10.3 hours. Individuals with shorter habitual bedrest duration fell asleep more quickly and frequently during the multiple sleep latency test than did those with longer habitual bedrest duration. On the first day of extended sleep opportunity, the total sleep time of all individuals was greater than their habitual bedrest duration; the average increase in total sleep time was 4.9 hours (P = 0.001). The increase in total sleep time declined across the 3 day bedrest-extension protocol (P = 0.003 for trend). During the third day of increased sleep opportunity, the total sleep time was negatively associated with habitual bedrest duration (P = 0.005); individuals with shorter habitual bedrest duration continued to sleep more than those with longer habitual bedrest duration. Those individuals with shorter habitual sleep durations carry a higher sleep debt than do those with longer habitual sleep duration. Interindividual variation in sleep duration may primarily reflect variation in self-selected sleep restriction or wake extension.

Introduction Poor sleep hygiene is common in American college students, with the majority reporting insufficient sleep. Previous studies suggest that many students extend sleep during the weekend to recover sleep debt accrued during the week. In the current study we objectively measured sleep to determine if weekend catch-up sleep was practiced. We also provided students with a 9h sleep opportunity in order to observe how an extended sleep period affected sleep architecture. Methods Students (N=36, 20 women, 19.9±1.7 years) participated in the study from September 2019-March 2020. Sleep-wake behavior was assessed for two weeks using wrist actigraphy and a twice-daily diary. During this two-week period, participants wore at-home polysomnography (PSG) on two non-consecutive nights. For these two nights, in counterbalanced order, participants were instructed to follow their typical sleep pattern or to extend their sleep opportunity to 9h. Within-subjects ANOVA were used to compare sleep between week (Sunday-Thursday) and weekend (Friday and Saturday) nights as well as between typical and 9h PSG nights. Results Compared to week nights, participants went to bed 35 minutes later and woke up 58 minutes later on weekend nights (ps<0.01). Measures of sleep duration and sleep continuity were comparable (ps>0.10) between week and weekend nights and indicated that students averaged less than 7h of sleep per night (6.3±1.0h on week nights and 6.7±1.1h on weekend nights). Self-report measures of sleep quality and daytime sleepiness were also comparable between week and weekend nights (ps>0.05). When instructed to extend their sleep opportunity to 9h, participants exhibited high adherence and increased total sleep time by 88 minutes compared to the typical night (p<0.05). This increase in sleep was due to significant increases in time spent in all four stages of sleep (NREM S1-3 and REM sleep, ps<0.05). Conclusion In this study, students did not utilize weekend nights to extend sleep in order to recover accumulated sleep debt from the week. When provided with a 9h sleep opportunity, participants were physically able to extend sleep and exhibited high quality sleep, suggesting that the primary driver of insufficient sleep in this population is a lack of sleep opportunity rather than impaired sleep ability. Support (If Any)

The aim of this study was to examine sleep/wake behaviour and sleep strategies before, during and after ultra-marathon running events exceeding 100 miles (161 km). A total of 119 athletes completed a web-based questionnaire regarding their habitual sleep/wake behaviour before, during, and after ultra-marathon participation. Event-specific data were grouped by race distance categories; 100–149 miles (161–240 km), 150–199 miles (241–321 km), and ≥200 miles (322 km). Athletes commonly reported not sleeping throughout the duration of their races (74%). However, for events that were ≥200 miles, athletes reported more sleep opportunities, longer sleep duration, and more total sleep when compared to events that were 100–149 miles in distance (p ≤ 0.001). This suggests that for races of shorter distances, the benefit of continuous racing outweighs the negative impact of continuous wakefulness/sleep deprivation. However, for longer races (≥200 miles), there is an apparent tradeoff between sleep deprivation and race strategy, whereby athletes cannot sustain a desired level of performance without obtaining sleep. This is consistent with established sleep/wake behaviour models suggesting that sleep need increases as wakefulness increases, or in this case, as race duration increases. For athletes participating in ultra-marathons, sleep management education and/or consultation with a sleep scientist prior to racing may be beneficial. Future research should examine the optimal strategies concerning the frequency and duration of sleep during ultra-marathons and the subsequent impact on performance.

Associations between sleep duration and sleep debt with insulin sensitivity and insulin secretion in the EGIR-RISC Study

Unhealthy sleep behaviors and sleep disturbances are associated with higher risk of multiple diseases and mortality. The current profiles of sleep habits and disturbances, particularly the differences between workdays and free days, are unknown in the contemporary US. To comprehensively evaluate sleep habits on workdays and free days and the prevalence of sleep disturbances among US adults. This study is a cross-sectional analysis of US nationally representative data from the National Health and Nutrition Examination Survey (2017-2020) among adults aged 20 years or older. Data analysis was performed from February to May 2022. The main outcomes were means and/or distributions of sleep habits, including sleep duration and sleep-wake timing on workdays and free days, sleep debt (ie, the difference between sleep duration on free days and mean weekly sleep duration), and social jet lag (ie, the difference between the midpoint between sleep and wake time on workdays and free days). Prevalence of trouble sleeping (ie, participants told a doctor or other health professional that they have trouble sleeping) and daytime sleepiness (ie, self-reported feeling of being overly sleepy during the day ≥5 times per month) were also determined. A total of 9004 individuals (mean [SE] age, 48.3 [0.53] years; 4635 women [51.9%]; 3158 non-Hispanic White [62.8%]) were included in the current study. The mean sleep duration was 7.59 hours (95% CI, 7.54 to 7.64 hours) on workdays and 8.24 hours (95% CI, 8.17 to 8.31 hours) on free days (difference, 0.65 hour). The mean sleep and wake times were at 11:02 pm (95% CI, 10:57 pm to 11:17 pm) and 6:41 am (95% CI, 6:36 am to 6:45 am), respectively, on workdays and 11:25 pm (95% CI, 11:21 pm to 11:35 pm) and 7:41 am (95% CI, 7:37 am to 7:46 am), respectively, on free days (differences, 0.23 hour for sleep time and 1.00 hour for wake time). On workdays, 23.1% (95% CI, 21.3% to 24.9%) of adults slept less than 7 hours and 25.4% (95% CI, 24.1% to 26.6%) went to sleep at midnight or later; the corresponding percentages changed to 12.9% (95% CI, 11.6% to 14.1%) and 40.9% (95% CI, 38.4% to 43.5%), respectively, on free days. Furthermore, the mean sleep debt was 0.73 hours (95% CI, 0.68 to 0.77 hours), and mean social jet lag was 1.10 hours (95% CI, 1.05 to 1.15 hours); 30.5% (95% CI, 26.8% to 33.3%) of adults experienced 1 hour or more of sleep debt, and 46.5% (95% CI, 42.6% to 50.3%) experienced 1 hour or more of social jet lag. The prevalence of trouble sleeping was 29.8% (95% CI, 28.2% to 31.5%), and that of daytime sleepiness was 27.2% (95% CI, 25.0% to 29.5%). In 2017 to 2020, US adults showed variability in sleep habits between workdays and free days, with longer sleep duration and later sleep-wake phases on free days, and high percentages of US adults experienced long-term sleep deprivation, chronic social jet lag, and frequent sleep disturbances. These findings provide evidence to further investigate potential approaches to optimize overall US sleep health.

To investigate sleep duration and sleep loss during antenatal period and assess associative factors, including maternal characteristic and mood symptoms. A cohort of 3038 women was enrolled. Self-reported sleep duration and sleep loss, the latter being calculated from preferred sleep need and actual sleep duration, were measured in early, mid- and late pregnancy, and at delivery. The associations with age, BMI, parity, education, smoking, napping, and depressive and anxiety symptoms were evaluated. Sleep duration was longest in early pregnancy and shortest at delivery (7.93h-7.76h, p < 0.001). The proportion of short sleepers (< 6h) increased from 1.4% to 5.9% throughout the studied period (p < 0.001). Mean sleep loss remained stable in early- and mid-pregnancy, lowering in late pregnancy (p < 0.001) and increasing again until delivery (p = 0.003). The number of women with notable sleep loss (> 2h) was similar during the first three measurement points (9.4%, 8.9% and 9.5%), but increased until delivery (14.1%, p < 0.001). Older, multiparous, and more-depressive women slept less (p < 0.001, p < 0.001, p = 0.017). Women with higher BMI were more likely to sleep < 6h in late pregnancy (p = 0.012). Multiparous, more-depressive, and higher-BMI women reported more sleep loss (p < 0.001, p < 0.001, p = 0.049). We confirmed earlier reported decrease in sleep duration at the end of pregnancy. As a novel finding, we showed a notable increase in sleep loss during the last month of pregnancy. Various factors were associated with both short sleep and sleep loss, especially multiparity, napping and depressive symptoms.

Introduction College students in general tend to have poor sleep quality and face stressors that impact their sleep and mental health. Further, women may have a greater sleep need compared to their male counterparts. The purpose of this study was to investigate sleep, mental health, and gender factors in college students via objective and subjective data collection. Methods Participants included 32 (male=16) full-time undergraduate students enrolled in a Psychology of Sleep course. Participants wore a Philips Respironics Actiwatch Spectrum device during one week of typical sleep. After one week, participants completed an online survey that consisted of a series of questionnaires that examined demographics, depression, anxiety, and stress symptoms (Depression Anxiety Stress Scale, DASS-21), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Multidimensional Assessment of Fatigue, MAF), and sleep quality and duration (Pittsburgh Sleep Quality Index, PSQI). Results Preliminary data comparison tests were conducted. There were gender differences in sleep duration (Mfemale=7.31h, Mmale=6.47h; p=.011), but no differences in sleep efficiency (Mfemale=84.57% vs Mmale=82.66%), wake after sleep onset (Mfemale=55.88 min vs Mmale=49.84 min), or number of awakenings (Mfemale=26.35 vs Mmale=25.89). There were no significant differences in sleep quality (PSQI), although the scores did represent poor quality (Mfemale=7.50 vs Mmale=6.13). Results showed that women experienced significantly greater sleepiness (Mfemale=9.88 vs Mmale=5.94; p=.033), and approaching significance in terms of greater fatigue (Mfemale=20.22 vs Mmale=13.89; p=.056) compared to men. There were no significant gender differences in depressive symptoms (Mfemale=6.31 vs Mmale=5.31), but women experienced greater anxiety (Mfemale=8.81 vs Mmale=3.69; p=.017) and stress (Mfemale=13.44 vs Mmale=6.38; p=.018) symptoms. Conclusion Preliminary results showed that the female college students taking the sleep course were obtaining significantly more sleep than men, with similar quality and efficiency. However, their increased sleepiness, fatigue, anxiety, and stress symptoms indicated that they were experiencing a more negative impact compared to men. It is likely that women generally have a greater sleep need compared to men, however, simply increasing sleep duration is likely not the answer. This study was limited by observational data collection, and further study is warranted to determine causal relationships and future intervention plans to improve sleep in women. Support (if any) None

Introduction Sleep is a crucial measure of health and wellbeing. Individuals with good sleep health tend to have better physical, emotional, cognitive, and social functioning than individuals with poor sleep health. The National Sleep Foundation developed the Sleep Health Index® (SHI) in 2016 as a means to monitor the sleep health of the general population. Since its inception, the National Sleep Foundation has administered the SHI annually to a representative sample of the US population. This study reports on the 2022 SHI and examines US population trends in sleep health from 2019 through 2022. Methods The SHI is a valid, reliable measure consisting of 16 questions related to the three discrete constructs of sleep duration, sleep quality, and disordered sleep. In 2022, the SHI was administered to a nationally representative, random sample of 1,042 Americans via the probability-based Ipsos KnowledgePanel®, in which panelists complete surveys online in English or Spanish. Results have a margin of sampling error of 3.2 points. We examined sleep health trends from 2019 through 2022 and examined whether sleep health in 2022 differed from previous years. Group differences in sleep health were also investigated. Results Americans’ average SHI score in 2022 was 75 (on a 0–100 scale), which is consistent with SHI scores observed in previous years. The SHI total score was statistically highest among college graduates, Hispanic adults, and people with household incomes of $100,000+ a year and statistically lowest among those with household incomes less than $50,000. Despite stable SHI total scores, there were statistically significant declines in both average weekday sleep scores (76 in 2021 to 71 in 2022) and in sleep deficit scores (86 in 2021 to 82 in 2022), which are based on the difference between weekday sleep and the amount of sleep respondents say they need to be well-rested and feel their best. Conclusion While overall sleep health remained stable, weekday sleep duration and sleep deficit worsened considerably as pandemic-related restrictions came to an end. The sociocultural and political contexts in which sleep health occurs, as well as associated features, like mental and physical health, deserve further consideration. Support (if any)

Impact of sleep debt on metabolic and endocrine function

Épidémiologie des accidents liés aux troubles du sommeil.

Chronic sleep restriction (CSR) has been associated with adverse effects including cognitive impairment and increased risk of diabetes and cardiovascular disease. Yet, sleep restriction therapy is an essential component of most behavioral treatments for insomnia. Moreover, little is known about the impact of CSR on sleep continuity and structure in healthy people whose need for sleep is satiated. We investigated the impact of CSR on sleep continuity and structure in nine healthy participants. They had 4 nights of sleep extension, 2 nights of post-extension sleep, 21 nights of CSR (5/5.6-hour time-in-bed), and 9 nights of recovery sleep. Compared to postextension sleep, during CSR sleep duration was reduced by 95.4 ± 21.2 min per night, Slow-Wave Activity was significantly increased, and sleep was more consolidated. During recovery, sleep duration was increased by 103.3 ± 23.8 min compared to CSR, and the CSR-induced increase in Slow-Wave Activity persisted, particularly after the 5-hour exposure. Yet, we found that sustained vigilant attention was not fully recovered even after nine nights of recovery sleep. Our results suggest that CSR improves traditional metrics of sleep quality and may have a persistent impact on sleep depth, which is consistent with the reported benefits on sleep continuity and structure of sleep restriction therapy. However, these improvements in traditional metrics of sleep quality were associated with deterioration rather than improvement in neurobehavioral performance, demonstrating that sleep duration should be included in assessments of sleep quality. These results have implications for the long-term use of sleep restriction in the behavioral treatment of insomnia. Clinical Trial Registration: Impact of Chronic Circadian Disruption vs. Chronic Sleep Restriction on Metabolism (https://clinicaltrials.gov/ct2/show/; #NCT02171273).

Introduction Insufficient sleep duration has negative consequences for health and performance and is alarmingly common in adolescents and young adults. The primary aim of the meta-analysis and systematic review was to assess whether at-home sleep extension is a feasible means to improve sleep duration and daytime sleepiness without negative consequences for sleep quality or efficiency in adolescents and young adults. An additional aim of the review was to provide a qualitative summary of the health and performance outcomes associated with at-home sleep extension. Methods Peer-reviewed journal articles and doctoral dissertations available in English were searched and screened. Eligible studies had at least five consecutive days of at-home sleep extension, measurement of sleep duration during baseline/habitual sleep and extension of sleep opportunity, and participants 13–30 years of age. Information on primary sleep outcome (i.e., sleep duration), available secondary sleep outcomes (i.e., sleep opportunity, sleep efficiency, sleep quality, daytime sleepiness), and health and performance outcomes were extracted for quantitative synthesis and qualitative review. Results Of the 2254 articles assessed for eligibility, 17 studies (seven in adolescents and ten in young adults) met the eligibility criteria for this review. The average number of days of sleep manipulation was 14.29 (range: 5 to 49 nights). At-home extension of sleep opportunity reliably increased objective (ES = 0.97) and subjective sleep duration (ES = 2.19) and sleep quality (ES = 0.24), and decreased daytime sleepiness (ES = -0.39), when compared to unmanipulated sleep opportunity. Sleep extension was also found to have additional health (e.g., lower psychological stress) and performance benefits (e.g., better athletic performance) across ages and populations. A potential upward publication bias was found based on the distribution of within-subject effect sizes of actigraphic sleep duration. Conclusion The review indicates that at-home sleep extension is feasible in adolescents and young adults to improve sleep duration and daytime sleepiness, and maintain or improve sleep quality. However, the degree of improvement in sleep duration, sleep quality, and daytime sleepiness varied by study population and sleep extension method. Future research should investigate how variations in population and methods of sleep extension impact health and performance outcomes. Support (if any):

Identify small molecule biomarkers of insufficient sleep using untargeted plasma metabolomics in humans undergoing experimental insufficient sleep. We conducted a crossover laboratory study where 16 normal-weight participants (eight men; age 22 ± 5 years; body mass index < 25 kg/m2) completed three baseline days (9 hours sleep opportunity per night) followed by 5-day insufficient (5 hours sleep opportunity per night) and adequate (9 hours sleep opportunity per night) sleep conditions. Energy balanced diets were provided during baseline, with ad libitum energy intake provided during the insufficient and adequate sleep conditions. Untargeted plasma metabolomics analyses were performed using blood samples collected every 4 hours across the final 24 hours of each condition. Biomarker models were developed using logistic regression and linear support vector machine (SVM) algorithms. The top-performing biomarker model was developed by linear SVM modeling, consisted of 65 compounds, and discriminated insufficient versus adequate sleep with 74% overall accuracy and a Matthew's Correlation Coefficient of 0.39. The compounds in the top-performing biomarker model were associated with ATP Binding Cassette Transporters in Lipid Homeostasis, Phospholipid Metabolic Process, Plasma Lipoprotein Remodeling, and sphingolipid metabolism. We identified potential metabolomics-based biomarkers of insufficient sleep in humans. Although our current biomarkers require further development and validation using independent cohorts, they have potential to advance our understanding of the negative consequences of insufficient sleep, improve diagnosis of poor sleep health, and could eventually help identify targets for countermeasures designed to mitigate the negative health consequences of insufficient sleep.

To investigate sleep EEG changes in adolescents across 7 nights of sleep restriction to 5 h time in bed [TIB]) and 3 recovery nights of 9 h TIB. A parallel-group design, quasi-laboratory study was conducted in a boarding school. Fifty-five healthy adolescents (25 males, age = 15-19 y) who reported habitual TIBs of approximately 6 h on week nights (group average) but extended their sleep on weekends were randomly assigned to Sleep Restriction (SR) or Control groups. Participants underwent a 2-week protocol comprising 3 baseline nights (TIB = 9 h), 7 nights of sleep opportunity manipulation (TIB = 5 h for the SR and 9 h for the Control group), and 3 nights of recovery sleep (TIB = 9 h). Polysomnography was obtained on two baseline, three manipulation, and two recovery nights. Across the sleep restriction nights, total SWS duration was preserved relative to the 9 h baseline sleep opportunity, while other sleep stages were reduced. Considering only the first 5 h of sleep opportunity, SR participants had reduced N1 duration and wake after sleep onset (WASO), and increased total sleep time (TST), rapid eye movement (REM) sleep, and slow wave sleep (SWS) relative to baseline. Total REM sleep, N2, and TST duration remained above baseline levels by the third recovery sleep episode. In spite of preservation of SWS duration over multiple nights of sleep restriction, adolescents accustomed to curtailing nocturnal sleep on school day nights evidence residual effects on sleep macro-structure, even after three nights of recovery sleep. Older teenagers may not be as resilient to successive nights of sleep restriction as is commonly believed.

Short sleep duration is a risk factor of chronic kidney disease, along with cardiovascular diseases and all-cause mortality. Several studies reported that many people sleep longer on weekends than on weekdays, suggesting that they should be compensated for their sleep debt on weekdays on the weekends. Few studies have reported the clinical impact of sleep debt on the kidney. This cross-sectional study included 5799 employees of Osaka University who visited its Health Care Center for their annual health examinations and answered ≤ 6h of sleep duration on weekdays. The independent variable was the sleep debt index defined as a gap in self-reported sleep duration (≤ 5, 5-6, 6-7, 7-8, 8-9, and ≥ 9h) between weekdays and weekends, which was categorized into ≤ 0, + 1, + 2, + 3 and ≥+4. An association between the sleep debt index and a prevalence of proteinuria defined as dipstick proteinuria of ≥ 1 + was assessed using logistic regression models adjusting for clinically relevant factors. More than four-fifths of the subjects had a positive sleep debt index (≤ 0, + 1, + 2, + 3, and ≥+4 recorded for 19%, 36%, 28%, 11%, and 6%, respectively). The multivariable-adjusted logistic regression models showed the sleep debt index ≥ 3 + was significantly associated with the prevalence of proteinuria (sleep debt index ≤ 0, adjusted odds ratio 1.13 [0.77, 1.65]; + 1, 1.00 [reference]; + 2, 1.29 [0.93, 1.79]; + 3, 1.54 [1.02, 2.33]; ≥ + 4, 1.87 [1.15, 3.05]). Sleep debt was associated with the prevalence of proteinuria in a dose-dependent manner.