Assessing the Sleep-wake Pattern in Cancer Patients for Predicting a Short Sleep Onset Latency

The lifestyles change of children and adolescents during the COVID-19 pandemic due to antipandemic measures can affect their sleep health. Existing studies have used convenient samples and focused on the initial months of the pandemic, leaving a knowledge gap on changes in young people's sleep patterns under the "new normal" under COVID-19. As part of a territory-wide epidemiological study in Hong Kong, this cross-sectional study recruited primary and secondary school students by stratified random sampling. Sleep parameters were collected using the structured diagnostic interview for sleep patterns and disorders. We investigated the pandemic's effects on sleep parameters by comparing data of participants recruited pre-COVID and those recruited during COVID using multivariate regression, adjusting for age, sex, household income, seasonality, and presence of mental disorders, and the moderators and mediators of the effects. Between September 1, 2019 and June 2, 2021, 791 primary and 442 secondary school students were recruited and analyzed. Primary school and secondary school participants assessed before COVID had a longer sleep latency on school days (95% confidence interval [CI] = 1.0-5.2 minutes, adjusted P-value = .010; and 95% CI= 3.9-13.0 minutes, adjusted P-value = .004, respectively) and nonschool days (95% CI = 1.7-7.2 minutes, adjusted P-value = .005; 95% CI = 3.4-13.7 minutes, adjusted P-value = .014, respectively). Low household income was a moderator for later bedtime (adjusted P-value = .032) and later sleep onset (adjusted P-value = .043) during nonschool days among secondary school students. Changes associated with COVID have a widespread and enduring effect on the sleep health of school-aged students in Hong Kong. Household income plays a role in adolescent sleep health resilience, and the impact of antiepidemic measures on the health gaps of the youth should be considered. Chau SWH, Hussain S, Chan SSM, etal. A comparison of sleep-wake patterns among school-age children and adolescents in Hong Kong before and during the COVID-19 pandemic. J Clin Sleep Med. 2023;19(4):749-757.

Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL). Polysomnography (PSG) and electrocardiography (ECG) were simultaneously recorded in 186 healthy subjects during a single night. Autonomic activity was assessed based on frequency domain analysis of RR intervals and results show that the analysis of RR intervals differs significantly between the short SOL and the long SOL groups. We found that the spectral power in the low frequency band (LF) was significantly higher in the long SOL group compared to the short SOL group in the first 10 minutes in bed intended to sleep. There was no significant difference for LF and the spectral power in the high frequency band (HF) 10 minutes before and after sleep onset between the two groups. Only in the short SOL group there was a significant increase in HF from the first 10 minutes in bed intended to sleep to 10 minutes before SO, while LF decreased significantly in both groups. The effect of time (5.5-min bin) on the heart rate variability (HRV) features around sleep onset showed that both LF and HF differed significantly during the period surrounding sleep onset only in the short SOL group.

Patients with cancer can often experience insomnia or sleep disturbances. This study aimed to explore whether the discrepancy between a patient's desired time in bed and desired total sleep time (DBST index) can be used as a measurement tool for insomnia severity or sleep onset latency [SOL] in patients with cancer. This retrospective medical records review study gathered clinical information and scores from scales and indices such as the Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep (C-DBS) scale, Patient Health Questionnaire-9 items (PHQ-9), State subcategory of State and Trait Anxiety Inventory, and the short form of the Fear of Progression Questionnaire. Sleep indices of time variables (bedtime, sleep onset time, and wake-up time), duration variables [SOL, time in bed (TIB), time in bed over 24 hours (TIB/d), and duration from wake-up time to bedtime (WTB)], and DBST index were calculated. ISI scores were predicted by the PHQ-9 (β = 0.34, P < 0.001), C-DBS scale (β = 0.17, P = 0.034), and DBST indices (β = 0.22, P = 0.004). Long SOL value was predicted by early bedtimes (β = -0.18, P = 0.045), short WTB durations (β = -0.26, P = 0.004), and high DBST index values (β = 0.19, P = 0.013). The DBST index was significantly correlated with both insomnia severity and SOL in patients with cancer.

This study assessed whether scheduling (start time and day type) and workload variables influenced sleep markers (activity monitor) in professional academy footballers (n=11; 17.3±0.7 yrs) over a 10-week in-season period. Separate linear mixed regressions were used to describe the effect of start time on the previous nights sleep, and the effect of day type (match day, match day+1) and workload on subsequent sleep. Workload variables were modelled by day (day), 7-day (acute), and 28-day (chronic) periods. Sleep duration following match day+1 (400 mins; 95%CI:368-432) was significantly reduced compared to all other day types (p<0.001). Sleep onset time following match day (00:35; CI:00:04-01:12) and wake time on match day+1 (09:00; CI:08:37-09:23) were also significantly later compared to all other day types (p<0.001). Sleep duration (19.1 mins; CI:9.4-28.79), wake time (18 mins; CI:9.3-26.6), and time in bed (16.8 mins; CI:2.0-31.5) were significantly increased per hour delay in start time. When no activity was scheduled, sleep duration (37 mins; CI:18.1-55.9), sleep onset (42.1 mins; CI:28.8-56.2), and wake times (86 mins; CI:72-100) were significantly extended, relative to a 09:00 start time. Day, acute, and chronic workloads were associated with sleep onset and wake times only. Scheduled start times were associated with changes in sleep duration. Therefore, delaying start times may increase sleep in this population.

Validation of the Korean version of the Time Monitoring Behavior-10 (TMB-10) and its relationship with sleep effort and adaptive sleep-related cognition.

Background: While sleep duration’s association with stroke is established, the combined influence of sleep onset time and duration on stroke subtypes remains inadequately explored. Since circadian biology links sleep onset timing to vascular risk via mechanisms operating independently of sleep duration, we quantified their joint contributions to the risk of stroke. Methods: In this population-based cross-sectional study, from 31 December 2021 to 31 March 2022, we recruited 8168 ischemic stroke cases, 3172 intracerebral hemorrhage cases, and 13,458 control participants across 152 survey centers in 28 counties in Hunan Province, China. Standardized computer-assisted interviews collected sleep parameters. Conjoint analysis identified protective sleep profiles. Results: Short sleep duration (<6 h) was consistently associated with elevated ischemic risk across all sleep onset times (p < 0.05 in all cases, i.e., sleep before 10 p.m. [odds ratio (95%CI): 1.886(1.606, 2.214)], 10–11 p.m. [1.740(1.336, 2.265)], 11 p.m.–12 a.m. [2.335(1.190, 4.581)], and after 12 a.m. [2.834(1.193, 6.728)]). A sleep duration of 6–8 h with a sleep onset time between 10 p.m. and 12 a.m. was associated with the lowest ischemic risk (p < 0.001 in all cases). Conversely, prolonged sleep (>8 h) with an early sleep onset time (<10 p.m.) increased ischemic risk (OR 1.194, 95% CI 1.090–1.308, p < 0.001), whereas a late sleep onset time (11 p.m.–12 a.m.) in long sleepers was protective (OR 0.580, 95% CI 0.352–0.956, p < 0.001). Similar trends were observed for ICH, though the effect sizes were attenuated. Conclusion: Sleep duration and onset time interact to influence stroke risk. Optimal cerebrovascular protection requires ≥6 h of sleep, ideally initiated between 10 p.m. and 11 p.m. These findings highlight sleep optimization as a potential modifiable target for high-risk populations.

Understanding the interplay between trajectories of sleep duration, sleep onset timing, and glycemic dynamics is crucial for improving preventive strategies against diabetes and related metabolic diseases. To examine the associations of sleep duration and onset timing trajectories with continuous glucose monitoring (CGM)-derived glycemic metrics in adults. This cohort study analyzed data collected from January 2014 to December 2023 in the Guangzhou Nutrition and Health Study, a prospective cohort in Guangdong province, China, among participants aged 46 to 83. Participants who had repeated sleep assessments at several study visits and were equipped with CGM devices at the last visit were included. Data analyses were conducted between January and June 2024. The trajectories of sleep duration and onset timing were constructed using self-report sleep duration and sleep onset timing, recorded at multiple study visit points. Measurements of glycemic variability and glycemic control were collected using a masked CGM device worn by patients for 14 consecutive days. Huber robust regression models were used to assess the associations between sleep trajectories and CGM-derived metrics. In this study of 1156 participants (mean [SD] age, 63.0 [5.1] years, 816 [70.6%] women), we identified 4 distinct sleep duration trajectory groups: severe inadequate, moderate inadequate, mild inadequate, and adequate. Severe sleep inadequacy was associated with an increment of glycemic variability indicators: 2.87% (95% CI, 1.23%-4.50%) for coefficient of variation and 0.06 (95% CI, 0.02-0.09) mmol/L for mean of daily differences. We found 2 trajectories of sleep onset timing: persistent early and persistent late groups. Late sleep onset was associated with larger coefficient of variation (β = 1.18%; 95% CI, 0.36%-2.01%) and mean of daily differences (β = 0.02 mmol/L; 95% CI, 0.01-0.04 mmol/L). Inappropriate sleep duration and timing trajectories in combination were associated with greater glycemic variability. In this cohort study of middle-aged and older participants, persistent inadequate sleep duration and late sleep onset, whether alone or in combination, were associated with greater glycemic variability. These findings emphasize the importance of considering both sleep duration and timing for optimizing glycemic control in the general population.

The present study aimed at further investigating trait aspects of sleep-related cognitive arousal and general cognitive arousal and their association with both objective and subjective sleep parameters in primary insomnia patients. A clinical sample of 182 primary insomnia patients and 54 healthy controls was investigated using 2 nights of polysomnography, subjective sleep variables, and a questionnaire on sleep-related and general cognitive arousal. Compared to healthy controls, primary insomnia patients showed both more sleep-related and general cognitive arousal. Furthermore, sleep-related cognitive arousal was closely associated with measures of sleep-onset and sleep-maintenance problems, while general cognitive arousal was not. Cognitive-behavioral treatment for insomnia might benefit from dedicating more effort to psychological interventions that are able to reduce sleep-related cognitive arousal.

Background and Objective We considered the concept of the DBST, the discrepancy between a patient’s desired time in bed (TIB) and desired total sleep time (TST). The DBST index can be used to easily assess a patient’s thoughts on their desired TST and dysfunctionally long TIB. This study aimed to explore whether the DBST index can predict the severity of insomnia.Methods A total of 374 members of the general population participated in this e-survey study. The participants answered questions regarding their bedtime, sleep onset time, wake-up time, desired TST, and desired TIB, and psychological symptoms were assessed using the Insomnia Severity Index (ISI), Patients Health Questionnaire–9 items (PHQ-9), Dysfunctional Beliefs and Attitudes about Sleep–16 items (DBAS-16), and Glasgow Sleep Effort Scale (GSES).Results The DBST index was significantly correlated with the ISI (r = 0.20, p &lt; 0.01), PHQ-9 (r = 0.15, p &lt; 0.01), GSES (r = 0.14, p &lt; 0.01), DBAS-16 (r = 0.16, p &lt; 0.01), desired TST (r = -0.62, p &lt; 0.01), and desired TIB (r = 0.52, p &lt; 0.01). Linear regression analysis showed that insomnia severity was predicted by persistent preoccupation with sleep (beta = 0.64, p &lt; 0.001), dysfunctional beliefs about sleep (beta = 0.06, p &lt; 0.001), depression (beta = 0.23, p &lt; 0.001), and DBST (beta = 0.32, p = 0.035). The DBST directly influenced insomnia severity, and this association was shown to be mediated by dysfunctional beliefs and attitudes about sleep, preoccupation with sleep, and depression.Conclusions The DBST index could be a possible new sleep index due to its relationship with insomnia severity, depression, dysfunctional beliefs about sleep, and preoccupation with sleep. Further studies are needed to explore the consistency of the clinical sample.

This study aimed to assess the reliability and validity of the Arabic version of the Glasgow Sleep Effort Scale (GSES) as an indicator of sleep effort within the Lebanese general population. A total of 360 members of the general population were included in this e-survey study. The sample completed the following measures: GSES, Insomnia Severity Index (ISI), Dysfunctional Beliefs about Sleep-2 (DBS-2), Patient Health Questionnaire-9 (PHQ-9), and a sleep diary regarding their bedtime, sleep onset time, wake-up time, desired total sleep time, and desired time in bed. The results supported the single-factor structure through confirmatory factor analysis. It also showed internal consistency with Cronbach’s alpha and omega values of 0.857 and 0.858, respectively. Further, Convergent validity was established with significant correlations between GSES, DBS-2, ISI, and PHQ-9. This suggests that GSES is valid and reliable for measuring sleep effort among the Lebanese general population.

Sleep measures, such as duration and onset timing, are associated with adiposity outcomes among children. Recent research among adults has considered variability in sleep and wake onset times, with the Sleep Regularity Index (SRI) as a comprehensive metric to measure shifts in sleep and wake onset times between days. However, little research has examined regularity and adiposity outcomes among children. This study examined the associations of three sleep measures (i.e., sleep duration, sleep onset time, and SRI) with three measures of adiposity (i.e., body mass index [BMI], waist circumference, and waist-to-height ratio [WHtR]) in a pediatric sample. Children (ages 4-13 years) who were part of the U.S. Newborn Epigenetic STudy (NEST) participated. Children (N = 144) wore an ActiGraph for 1 week. Sleep measures were estimated from actigraphy data. Weight, height, and waist circumference were measured by trained researchers. BMI and WHtR was calculated with the objectively measured waist and height values. Multiple linear regression models examined associations between child sleep and adiposity outcomes, controlling for race/ethnicity, child sex, age, mothers' BMI and sleep duration. When considering sleep onset timing and duration, along with demographic covariates, sleep onset timing was not significantly associated with any of the three adiposity measures, but a longer duration was significantly associated with a lower BMI Z-score (β = -0.29, p < 0.001), waist circumference (β = -0.31, p < 0.001), and WHtR (β = -0.38, p < 0.001). When considering SRI and duration, duration remained significantly associated with the adiposity measures. The SRI and adiposity associations were in the expected direction, but were non-significant, except the SRI and WHtR association (β = -0.16, p = 0.077) was marginally non-significant. Sleep duration was consistently associated with adiposity measures in children 4-13 years of age. Pediatric sleep interventions should focus first on elongating nighttime sleep duration, and examine if this improves child adiposity outcomes.

Accurate assessment of sleep quality is crucial for understanding sleep problems and their impact on health. This study analyzed the agreement between subjective sleep assessments and objective sleep monitoring in adolescents with mood disorders, aiming to provide a reliable methodological foundation for related research. Adolescents with mood disorders were recruited from psychiatric outpatient clinics of three domestic hospitals. A consensus sleep diary and an actigraph (activity tracker) were used to monitor sleep for 14 days. The differences between subjective and objective measurements were compared using the Wilcoxon signed-rank test, while Bland-Altman plots and intraclass correlation coefficients (ICC), described their agreement. Spearman's rank correlation coefficients were used to assess their correlation, and mixed-effects models analyzed factors influencing the differences between subjective and objective measurements. Significant differences were observed between subjective and objective measures for the number of awakenings after sleep onset (NWAK), waking after sleep onset (WASO), total sleep time (TST), and wake-up time (P<0.001); however, the difference in sleep onset time was not statistically significant (P=0.283). Subjective and objective measurements of sleep onset (ICC=0.821, r=0.838) and wake-up time (ICC=0.821, r=0.836) demonstrated good agreement and correlation; TST showed moderate agreement (ICC=0.640) and correlation (r=0.682); NWAK (ICC=0.210, r=0.276) and WASO (ICC=0.358, r=0.365) exhibited poor and correlation. In the Bland-Altman plots, most data points for sleep onset, wake-up time, and TST were uniformly distributed within the 95% limits of agreement (LoA). The differences between subjective and objective measurements for WASO and NWAK increased with higher average values. However, the 95% LoA of the differences between subjective and objective measurements of all the above indicators exceeded the acceptable ranges of the corresponding indicators, indicating poor agreement. Baseline depression levels were associated with the differences between subjective and objective measurements of NWAK (β=0.034, P<0.05), TST (β=2.617, P<0.01), and sleep onset (β=1.454, P<0.05), while sleep quality scores were associated with the difference in WASO (β=0.051, P<0.01). Considerable discrepancies remain between subjective sleep diaries and sleep monitoring data from actigraphy (activity tracker). Future research should further investigate factors influencing the discrepancies between subjective and objective measurements and refine measurement methods to obtain more reliable sleep data.

Sleep duration, sleep deprivation and the sleep–wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring.Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days.A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h.Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep–seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

To determine the association between common genetic variation in the clock gene pathway and objectively measured acti-graphic sleep and activity rhythm traits. Genetic association study in two population-based cohorts of elderly participants: the Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men (MrOS) study. Population-based. SOF participants (n = 1,407, 100% female, mean age 84 years) and MrOS participants (n = 2,527, 100% male, mean age 77 years) with actigraphy and genotype data. N/A. Common genetic variation in 30 candidate genes was captured using 529 single nucleotide polymorphisms (SNPs). Sleep and activity rhythm traits were objectively measured using wrist actigraphy. In a region of high linkage disequilibrium on chromosome 12p13 containing the candidate gene GNB3, the rs1047776 A allele and the rs2238114 C allele were significantly associated with higher wake after sleep onset (meta-analysis: rs1047776 PADD = 2 × 10(-5), rs2238114 PADD = 5 × 10(-5)) and lower LRRC23 gene expression (rs1047776: ρ = -0.22, P = 0.02; rs2238114: ρ = -0.50, P = 5 × 10(-8)). In MrOS participants, SNPs in ARNTL and NPAS2, genes coding for binding partners, were associated with later sleep and wake onset time (sleep onset time: ARNTL rs3816358 P2DF = 1 × 10(-4), NPAS2 rs3768984 P2DF = 5 × 10(-5); wake onset time: rs3816358 P2DF = 3 × 10(-3), rs3768984 P2DF = 2 × 10(-4)) and the SNP interaction was significant (sleep onset time PINT = 0.003, wake onset time PINT = 0.001). A SNP association in the CLOCK gene replicated in the MrOS cohort, and rs3768984 was associated with sleep duration in a previously reported study. Cluster analysis identified four clusters of genetic associations. These findings support a role for common genetic variation in clock genes in the regulation of inter-related sleep traits in the elderly. Evans DS; Parimi N; Nievergelt CM; Blackwell T; Redline S; Ancoli-Israel S; Orwoll ES; Cummings SR; Stone KL; Tranah GJ. Common genetic variants in ARNTL and NPAS2 and at chromosome 12p13 are associated with objectively measured sleep traits in the elderly. SLEEP 2013;36(3):431-446.

Introduction Irregular sleep schedules can affect mood and risk of developing symptoms of depression. Particularly, individuals with depression tend to sleep more than usual, which appears to be an attempt to avoid emotional distress. Depression and sleep problems are closely linked, but there is a lack of studies on how specific changes in sleep patterns occur in depression. Receiver operating characteristic analysis was performed to examine whether which sleep parameters can discriminate between the individuals with depression and those with non-depression. Methods Participants above 50 years old were recruited at Chosun University Hospital. The Korean Version of the Patient Health Questionnaire-9(PHQ-9) was administered. Actigraphy monitorings were conducted at home, and the following sleep parameters were included: bedtime(BT), wake time, time in bed(TIB), total sleep time, sleep onset latency, sleep efficiency, wake time after sleep onset, and fragmentation index. Participants with the PHQ-9 score ≥ 10 were classified into depression group(DG). Thirteen DG(67.46 ±9.3 years) and 6 non-depression group(NDG)(70.0±6.4years) were finally selected. Results The DG had significantly higher scores in the PHQ-9 and MDRS compared to the NDG (p&amp;lt; 0.01). There were significant differences in the BT and TIB of sleep parameters between the NDG and DG, showing the DG had earlier BT and longer TIB compared to the NDG (p&amp;lt; 0.05). The TIB among the sleep parameters were derived as indicators for discriminating depression (p=0.04). The area under the ROC for the TIB was 0.795 (cut-off scores ≥ 9h: 58m, sensitivity= 0.62, specificity=0.83). Conclusion We found the changes of sleep habits in individuals with depressive symptoms, including earlier bedtimes and longer time in bed, compared to those without symptoms.In particular, the TIB for individuals with depressive symptoms was more than 2 hours longer and was identified as a predictor of depression, suggesting that longer TIB is a risk factor for depression. Support (if any) Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020R1F1A1050416). Global Learning &amp; Academic research institution for Master’s· PhD students, and Postdocs (G-LAMP) Program of the National Research Foundation of Korea(NRF) grant funded by the Ministry of Education (No. RS-2023-00285353).