Actigraphy-Based Assessment of Sleep Parameters

Daily Associations Between Presenteeism and Health-Related Factors Among Office Workers: An Ecological Momentary Assessment Approach

OBJECTIVE

The study examined daily associations between presenteeism and health-related factors among office workers using ecological momentary assessment (EMA).

METHODS

Diurnal mood and physical symptoms were repeatedly recorded over 2 weeks with EMA. Daily work performance (WP) was also recorded. Recalled WP and baseline health conditions were assessed via questionnaires. Daily sleep was assessed using actigraphy. Reliability between recalled and daily WP was compared. Hierarchical linear modeling (HLM) was used to analyze the effects of sleep, mood, and physical symptoms on daily WP.

RESULTS

Weak yet significant agreement was found between recalled and daily WP, with EMA capturing occasional declines in performance overlooked by recalled assessments. HLM indicated that longer sleep, reduced depressive mood, and decreased shoulder stiffness were significantly associated with increased daily WP.

CONCLUSIONS

These factors are associated with daily fluctuations in presenteeism, suggesting potential targets of intervention.

Perioperative non-pharmaceutical therapies for insomnia in cancer survivors: a systematic review and network meta-analysis protocol

INTRODUCTION

The presence of perioperative insomnia is common but yet often overlooked among cancer survivors. Non-pharmaceutical therapies have shown promise in treating cancer-related insomnia during the perioperative period; however, the existing evidence from various studies remains inconsistent. Therefore, this study aims to systematically review and assess the effectiveness of a wide range of non-pharmaceutical interventions during perioperative period for cancer-related insomnia. Findings from this study will help to make evidence-based treatment decisions.

METHODS AND ANALYSIS

A comprehensive electronic search will be conducted to identify relevant articles from multiple databases, including PubMed, MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials and Chinese literature databases such as CNKI, VIP, Wanfang from inception to 1 December 2023. Language restrictions will not be imposed to ensure inclusivity. The change of the Pittsburgh Sleep Quality Index or the Insomnia Severity Index from baseline will be used as the primary outcome of the study. Studies using these as secondary outcomes are also acceptable. Pairwise meta-analysis and network meta-analysis will be conducted using Stata V.15.0 software. The Cochrane collaboration tool for assessing the Risk of Bias and Risk of Bias in Non-randomised Studies of Interventions will be used for risk and bias assessment. Additionally, the Grading of Recommendations, Assessment, Development and Evaluation scale will be employed to evaluate the quality of the evidence.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study since it involves the analysis of existing studies. The anticipated results will be disseminated through publication in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42023437356.

The effect of cognitive behavioral therapy for insomnia on sleep and glycemic outcomes in people with type 2 diabetes: A randomized controlled trial

STUDY OBJECTIVES

Investigate whether aiding sleep by online cognitive behavioral therapy for insomnia (CBT-I) can improve glycemic and metabolic control, mood, quality of life (QoL) and insomnia symptoms in people with type 2 diabetes and assess the mediating role of lifestyle factors.

METHODS

Adults with type 2 diabetes and insomnia symptoms were randomly assigned to CBT-I or care as usual. At baseline, three and six months we assessed HbA1c as primary outcome and glycemic control, metabolic outcomes, sleep, mood and QoL as secondary outcomes. Mixed models were used to determine within-person and between-persons differences in outcomes and mediation analysis for lifestyle factors.

RESULTS

We randomized 29 participants to CBT-I and 28 to care as usual. Intention-to-treat analysis showed no significant differences in glycemic control, metabolic outcomes, anger, distress or QoL, but showed a significantly larger decrease in insomnia (-1.37(2.65: 0.09)) and depressive symptoms (-0.92(-1.77: 0.06)) and increase in BMI (0.29 kg/m2(0.00:0.57)) in the intervention compared to the control group. Only half of the intervention participants completed the CBT-I. Per protocol analysis showed a not statistically significant decrease in HbA1c (-2.10 mmol/l(-4.83:0.63)) and glucose (-0.39 mmol/l(-1.19:0.42)), metabolic outcomes and increase in QoL. Furthermore, the intervention group showed a significant decrease in insomnia (-2.22(-3.65: 0.78)) and depressive symptoms (-1.18(-2.17: 0.19)) compared to the control group. Lifestyle factors partially mediated the effect of the intervention.

CONCLUSIONS

CBT-I might improve insomnia symptoms and mood, and perhaps improves glycemic control, albeit not significant, in people with type 2 diabetes and insomnia symptoms, compared to care as usual.

In Males with Adequate Dietary Needs Who Present No Sleep Disturbances, Is an Acute Intake of Zinc Magnesium Aspartate, Following Either Two Consecutive Nights of 8 or 4 h of Sleep Deprivation, Beneficial for Sleep and Morning Stroop Interference Performance?

PURPOSE

Purpose: We examined whether supplementation of zinc magnesium aspartate (ZMA) in two groups of males, either partially sleep-restricted (4 h) or with habitual sleep (8 h) for 2 nights, was beneficial for sleep and subsequent morning Stroop performance.

METHODS

Participants were randomly allocated to two independent groups who either had 4 h (33 males) or 8 h (36 males) sleep for two nights. Using a double-blinded, randomised counterbalanced design, they then completed five sessions, (i) two familiarisation sessions including 7 days of sleep and dietary intake, (ii) three conditions with 4 h or 8 h sleep and either NoPill control (NoPill), placebo (PLAC) or ZMA (ZMA). Sleep was assessed by actimetry and sleep questionnaires, and cognitive performance was assessed by the Stroop test. The data were analysed using a general linear model with repeated measures.

RESULTS

A main effect for "sleep" (4 or 8 h) was found, where more opportunity to sleep resulted in better "sleep" metrics (both objective and subjective) as well as better Stroop scores (lower colour-interference and word-interference scores and lower error in words). No main effect for "Pill" was found other than the mood state depression, where subjective ratings for the PLAC group were lower than the other two conditions. Interactions were found in anger, ease to sleep and waking time.

CONCLUSION

Having 8 h opportunity to sleep resulted in better "sleep" metrics as well as better Stroop scores compared to 4 h. Supplementation of ZMA for 4 or 8 h for 2 nights had no effect on subsequent morning cognitive performance but reduced sleep or total sleep time by ~0.46 h compared to the other conditions. An interaction was found where sleep time was reduced by ~0.94 h in the ZMA group in the 8 h condition compared to NoPill or PLAC.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study

BACKGROUND

Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments.

OBJECTIVE

This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults.

METHODS

A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability.

RESULTS

Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18).

CONCLUSIONS

The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information

Purpose

Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model.

Patients and Methods

Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17-70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model.

Results

Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR).

Conclusion

An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.

Activity-Based Prospective Memory in Insomniacs

OBJECTIVE

To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia.

METHODS

A total of 153 patients with insomnia (I, 83 females, mean age + SD = 41.37 + 16.19 years) and 121 healthy controls (HC, 78 females, mean age + SD = 36.99 + 14.91 years) wore an actigraph for one week. Insomnia was classified into sleep onset insomnia (SOI), maintenance insomnia (MaI), mixed insomnia (MixI) and negative misperception insomnia (NMI). To study their activity-based prospective memory performance, all the participants were required to push the actigraph event marker button twice, at bedtime (task 1) and at get-up time (task 2).

RESULTS

Only patients with maintenance and mixed insomnia had a significantly lower accuracy in the activity-based prospective memory task at get-up time compared with the healthy controls.

CONCLUSION

The results show that maintenance and mixed insomnia involve an impaired activity-based prospective memory performance, while sleep onset and negative misperception insomnia do not seem to be affected. This pattern of results suggests that the fragmentation of sleep may play a role in activity-based prospective memory efficiency at wake-up in the morning.

Carpal tunnel syndrome and sleep, a systematic review and meta-analysis

BACKGROUND

The most common symptom and reason patients seek treatment for carpal tunnel syndrome is lack of sleep. Our purpose was to determine how much sleep-related symptoms of carpal tunnel syndrome improve after carpal tunnel release using validated patient-reported outcome measures (PROMs) and objective sleep data as primary measures of interest.

METHODS

A PRISMA-guided literature search was conducted using Ovid MEDLINE, PubMed, Cochrane, and ClinicalTrials.gov. Only interventional clinical trials that examined primary outcome measures of interest were included. Patient-reported outcome measures underwent meta-analysis to determine how much scores improved following carpal tunnel release.

RESULTS

The Pittsburgh Sleep Quality Index improved significantly after carpal tunnel release, by 4.43 points and 6.02 points at 1-3 and 6-12 months postoperatively, respectively, and continued to improve up to 2 years. Improvement on the Insomnia Severity Index after carpal tunnel release was also significant, with improvement up to 1 year postoperatively, by 8.54 points and 9.05 points at 1-3 and 6-12 months, respectively. Insomnia Severity Index scores improved significantly after splinting as well.

CONCLUSIONS

The present meta-analysis determined to what extent patients can expect their sleep to improve after operative and non-operative intervention, as measured by various patient-reported outcome measures that assess sleep. The Pittsburgh Sleep Quality Index and Insomnia Severity Index correlated very well between studies and across hundreds of patients with carpal tunnel syndrome. Data are lacking to define the minimal clinically important difference and assess whether patients achieve a minimal clinically important difference for sleep questionnaires; more information on this topic is needed.

LEVEL OF EVIDENCE

III.

Generalized sleep decoding with basal ganglia signals in multiple movement disorders

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson's disease, Essential Tremor, Dystonia, Essential Tremor, Huntington's disease, and Tourette's syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

Night-to-night variability of objective sleep outcomes in youth Middle Eastern football players

OBJECTIVE

To describe components of night-to-night variation in objective measures of sleep.

METHODS

We conducted a secondary data analysis of consecutive and chronologically ordered actigraphy-based measurements for time in bed (min), time asleep (min), and wake-after-sleep onset (min). This investigation examined 575 individual night-based measures available for a sub-sample of fifty-two, male youth Middle Eastern football players tracked over a 14-day surveillance period (chronological age range: 12.1 to 16 years). Distinct multivariable-adjusted generalized additive models included each objective sleep outcome measure as dependent variable and disaggregated components of variation for night measurement-by-sleep period interaction, week part (weekday or weekend), and study participant random effects from within-subject night-to-night sleep variation.

RESULTS

The within-subject standard deviation (SD) of ±98 min (95% confidence interval [CI], 92 to 104 min) for time in bed, ±87 min (95%CI, 82 to 93 min) for time asleep, and ±23 min (95%CI, 22 to 25 min) for wake-after-sleep-onset overwhelmed other sources of variability and accounted for ∼44% to 53% of the overall night-to-night variation. The night measurement-by-fragmented sleep period interaction SD was ±83 min (95%CI, 44 to 156 min) for time in bed, ±67 min (95%CI, 34 to 131 min) for time asleep, and ±15 min (95%CI, 7 to 32 min) for wake-after-sleep-onset that accounted for ∼22% to 32% of each sleep outcome measure overall variability.

CONCLUSIONS

Substantial random night-to-night within-subject variability poses additional challenges for strategies aiming to mitigate problems of insufficient and inconsistent sleep that are detrimental to school learning and youth athlete development processes.

Construct validity and test-retest reliability of a chrononutrition questionnaire for shift work and non-shift work populations

The irregular eating patterns of both shift workers and evening chronotypes adversely affect cardiometabolic health. A tool that conveniently captures temporal patterns of eating alongside an indicator of circadian rhythm such as chronotype will enable researchers to explore relationships with diverse health outcome measures. We aimed to investigate the test-retest reliability and convergent validity of a Chrononutrition Questionnaire (CNQ) that captures temporal patterns of eating and chronotype in the general population (non-shift workers, university students, retirees, unemployed individuals) and shift work population. Participants attended two face-to-face/virtual sessions and completed the CNQ and food/sleep/work diaries. Outcomes included subjective chronotype, wake/sleep/mid-sleep time, sleep duration, meal/snack regularity, meal/snack/total frequency, times of first/last/largest eating occasions (EO), main meal (MM) 1/2/3, and duration of eating window (DEW). 116 participants enrolled (44.5 ± 16.5 years, BMI: 27.3 ± 5.8 kg/m2, 73% female, 52% general population); 105 completed the study. Reliability was acceptable for chronotype, sleep, and all temporal eating patterns except on night shifts. Convergent validity was good for chronotype and sleep except for certain shift/shift-free days. Generally, meal/snack regularity and frequency, and times of first/last EO showed good validity for the general population but not shift workers. Validity was good for DEW (except work-free days and afternoon shifts) and times of MM 1/2/3 (except afternoon and night shifts), while time of largest EO had poor validity. The CNQ has good test-retest reliability and acceptable convergent validity for the general and shift work population, although it will benefit from further validation, especially regarding regularity, frequency, and times of first and last eating occasions across more days amongst a larger sample size of shift workers. Use of the CNQ by researchers will expand our current understanding of chrononutrition as relationships between timing of food intake and the multitude of health outcomes are examined.

Comparison between self-reported and actigraphy-derived sleep measures in patients receiving home parenteral nutrition: Secondary analysis of observational data

BACKGROUND

Patients receiving home parenteral nutrition (HPN) frequently report disrupted sleep. However, there are often inconsistencies between objectively measured and questionnaire-derived sleep measures. We compared sleep measures estimated from wrist actigraphy and self-report in adults receiving HPN.

METHODS

In this secondary analysis, we pooled data from two sleep-related studies enrolling adults receiving habitual HPN. We compared measures from 7-day averages of wrist actigraphy against comparable responses to a sleep questionnaire. Sleep measures included bedtime, wake time, time in bed, total sleep time, and sleep onset latency (SOL). Spearman correlation coefficients, Bland-Altman plots, and linear regression models for each set of sleep measures provided estimates of agreement.

RESULTS

Participants (N = 35) had a mean age of 52 years, body mass index of 21.6 kg/m2 , and 77% identified as female. Correlation coefficients ranged from 0.35 to 0.90, were highest for wake time (r = 0.90) and bedtime (r = 0.74), and lowest for total sleep time (r = 0.35). Actigraphy overestimated self-reported bedtime, wake time, and total sleep time and underestimated self-reported time in bed and SOL. Regression coefficients indicated the highest calibration for bedtime and wake time and lower calibration for time in bed, total sleep time, and SOL.

CONCLUSION

We observed strong-to-moderate agreement between sleep measures derived from wrist actigraphy and self-report in adults receiving HPN. Weaker correlations for total sleep time and SOL may indicate low wrist actigraphy sensitivity. Low-quality sleep resulting from sleep disruptions may have also contributed to an underreporting of perceived sleep quantity and lower concordance.

Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Hierarchical organization of human physical activity

Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.

Associations between actigraphy-measured sleep duration, continuity, and timing with mortality in the UK Biobank

STUDY OBJECTIVES

To examine the associations between sleep duration, continuity, timing, and mortality using actigraphy among adults.

METHODS

Data were from a cohort of 88 282 adults (40-69 years) in UK Biobank that wore a wrist-worn triaxial accelerometer for 7 days. Actigraphy data were processed to generate estimates of sleep duration and other sleep characteristics including wake after sleep onset (WASO), number of 5-minute awakenings, and midpoint for sleep onset/wake-up and the least active 5 hours (L5). Data were linked to mortality outcomes with follow-up to October 31, 2021. We implemented Cox models (hazard ratio, confidence intervals [HR, 95% CI]) to quantify sleep associations with mortality. Models were adjusted for demographics, lifestyle factors, and medical conditions.

RESULTS

Over an average of 6.8 years 2973 deaths occurred (1700 cancer, 586 CVD deaths). Overall sleep duration was significantly associated with risk for all-cause (p < 0.01), cancer (p < 0.01), and CVD (p = 0.03) mortality. For example, when compared to sleep durations of 7.0 hrs/d, durations of 5 hrs/d were associated with a 29% higher risk for all-cause mortality (HR: 1.29 [1.09, 1.52]). WASO and number of awakenings were not associated with mortality. Individuals with L5 early or late midpoints (<2:30 or ≥ 3:30) had a ~20% higher risk for all-cause mortality, compared to those with intermediate L5 midpoints (3:00-3:29; p ≤ 0.01; e.g. HR ≥ 3:30: 1.19 [1.07, 1.32]).

CONCLUSIONS

Shorter sleep duration and both early and late sleep timing were associated with a higher mortality risk. These findings reinforce the importance of public health efforts to promote healthy sleep patterns in adults.

Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Does Wrist-Worn Accelerometer Wear Compliance Wane over a Free-Living Assessment Period? An NHANES Analysis

PURPOSE

Accelerometers are used to objectively measure physical behaviors in free-living environments, typically for seven consecutive days or more. We examined whether participants experience "wear fatigue," a decline in wear time day over day, during typical assessment period acquired in a nationally representative sample of 6- to 80-yr-olds in the United States.

METHODS

Participants were instructed to wear an ActiGraph GT3X+ on their nondominant wrist continuously for seven consecutive days. Participants with seven complete days of recorded data, regardless of wear status, were included in the analyses ( N = 13,649). Wear was scored with the sleep, wake, and nonwear algorithm.

RESULTS

Participants averaged 1248 ± 3.6 min·d -1 (mean ± SE) of wear over the assessment, but wear time linearly decreased from day 1 (1295 ± 3.2 min) to day 7 (1170 ± 5.3 min), resulting in a wear fatigue of -18.1 ± 0.7 min·d -1 ( β ± SE). Wear fatigue did not differ by sex but varied by age-group-highest in adolescents (-26.8 ± 2.4 min·d -1 ) and lowest in older adults (-9.3 ± 0.9 min·d -1 ). Wear was lower in evening (1800-2359 h) and early morning (0000-0559 h) compared with the middle of the day and on weekend days compared with weekdays. We verified similar wear fatigue (-23.5 ± 0.7 min·d -1 ) in a separate sample ( N = 14,631) with hip-worn devices and different wear scoring. Applying minimum wear criteria of ≥10 h·d -1 for ≥4 d reduced wear fatigue to -5.3 and -18.7 min·d -1 for the wrist and hip, respectively.

CONCLUSIONS

Patterns of wear suggest noncompliance may disproportionately affect estimates of sleep and sedentary behavior, particularly for adolescents. Further study is needed to determine the effect of wear fatigue on longer assessments.

Effect of a Single Light-intensity Walking Session on Sleep Quality of Patients with Bladder Cancer: A Randomized Cross-Over Study

OBJECTIVES

Exercise has been recommended to enhance sleep. However, there is a paucity of studies investigating the relationships between exercise and sleep problems in patients with bladder cancer. The authors explored the effects of a single bout of light-intensity walking on the sleep quality of patients with bladder cancer who have sleep disorders.

DATA SOURCES

A total of 14 patients with bladder cancer with sleep disorders were recruited for this trial. The participants were randomly assigned to the walking or control condition in a cross-over design to explore the effects of a single light-intensity walking session on objectively measured sleep quality. A two-way repeated measures analysis of variance and a nonparametric permutation test were used to examine intervention effects. Twelve participants (85.7%) completed the trial. A significant group × time interaction for sleep latency (P = .023) was identified. The pairwise comparison showed significant results (P = .012) for the difference between the post-test sleep latency and the pre-test. No significant group × time interactions were observed for the remaining seven sleep parameters. Additionally, only the main effects of time on length of awakening and time in bed were significant (P < .001).

CONCLUSION

A single bout of light-intensity walking has a positive effect on shortening the sleep latency of patients with bladder cancer who have sleep disorders.

IMPLICATIONS FOR NURSING PRACTICE

Oncology nurses can encourage patients with bladder cancer to exercise, even light-intensity walking, which may improve sleep quality.

Sleep Disturbances as a Consequence of Long COVID-19: Insights from Actigraphy and Clinimetric Examinations-An Uncontrolled Prospective Observational Pilot Study

The COVID-19 pandemic represents a global health and social challenge. However, the impact of a SARS-CoV-2 infection itself on mental health and sleep quality remains poorly understood. The purpose of the present uncontrolled prospective observational pilot study was to evaluate the impact of past COVID-19 disease on the incidence of quantitative and qualitative sleep disturbances. A group of 61 subjects (37 female, 24 male) reported sleep disturbances that had lasted for at least one month and had started after recovery from COVID-19. The study used self-reported instruments: the Epworth Sleepiness Scale (ESS) and Insomnia Severity Index (ISI), as well as an objective method-actigraphy-for quantitative analysis of sleep architecture. It was shown that sleep disturbances most commonly started after recovery (68.3%, n = 41) and lasted for more than one month (83.6%, n = 51). ESS scores of 7.8 ± 5.0 points indicate moderate daytime sleepiness, and ISI scores of 16.3 ± 5.8 points denote moderate insomnia. ESS scores were negatively correlated with total time in bed (r = -0.3780, p = 0.003), total sleep time (r = -0.2969, p = 0.020), and wakefulness after sleep onset (r = -0.2654, p = 0.039). In addition, ESS scores were correlated negatively with the respondents' age (B = -0.17, p = 0.000) and time from COVID-19 onset. A positive correlation was found between wakefulness after sleep onset and ESS scores (B = -0.05, p = 0.039). ISI scores were positively correlated with time in bed (r = 0.3275, p = 0.010). Female gender was found to be a significant predictor of insomnia's severity (B = 2.159, t = 3.04, p = 0.004). In conclusion, patients with a history of COVID-19 report long-lasting sleep disturbances that do not subside spontaneously. In the patient group studied, moderate levels of daytime sleepiness and insomnia were found. The most frequently reported problems included irregular sleep, frequent awakenings, and difficulty maintaining sleep, while normal sleep duration was preserved. These findings underscore the need for continued attention to the long-term consequences of COVID-19 on sleep health and emphasize the importance of targeted interventions to address these enduring sleep disturbances in affected individuals.

General spectral characteristics of human activity and its inherent scale-free fluctuations

The scale-free nature of daily human activity has been observed in different aspects; however, the description of its spectral characteristics is incomplete. General findings are complicated by the fact that-although actigraphy is commonly used in many research areas-the activity calculation methods are not standardized; therefore, activity signals can be different. The presence of 1/f noise in activity or acceleration signals was mostly analysed for short time windows, and the complete spectral characteristic has only been examined in the case of certain types of them. To explore the general spectral nature of human activity in greater detail, we have performed Power Spectral Density (PSD) based examination and Detrended Fluctuation Analysis (DFA) on several-day-long, triaxial actigraphic acceleration signals of 42 healthy, free-living individuals. We generated different types of activity signals from these, using different acceleration preprocessing techniques and activity metrics. We revealed that the spectra of different types of activity signals generally follow a universal characteristic including 1/f noise over frequencies above the circadian rhythmicity. Moreover, we discovered that the PSD of the raw acceleration signal has the same characteristic. Our findings prove that the spectral scale-free nature is generally inherent to the motor activity of healthy, free-living humans, and is not limited to any particular activity calculation method.

Effects of Supplementing Zinc Magnesium Aspartate on Sleep Quality and Submaximal Weightlifting Performance, following Two Consecutive Nights of Partial Sleep Deprivation

Purpose: We examined whether supplementation of zinc magnesium aspartate (ZMA), while partially sleep deprived, was beneficial to sleep quality and subsequent morning (07:00 h) submaximal weightlifting. Methods: Using a double-blinded, randomized counterbalanced design, sixteen trained males were recruited and completed six sessions: (i) one repetition max (1 RM) for bench press and back squat; (ii) two familiarisation sessions; (iii) three conditions with 4 h sleep and either: ZMA, placebo (PLA), or NoPill control (NoPill). Submaximal exercise session consisted of three repetitions at 40, 60 and 80% of 1 RM for bench press and back squat. Average power (AP), average velocity (AV), peak velocity (PV), displacement (D) and time-to-peak velocity (tPV) were recorded using MuscleLab linear encoders. Data were analysed using a general linear model with repeated measures and linear correlation. Results: No significant main effect for condition was found for performance values or subjective ratings of fatigue. Main effect for "load" on the bar was found, where AP and tPV values increased with load (p < 0.05). No significant relationship between dose of zinc or magnesium ingested and change in performance for 80% 1 RM power-outputs was found. Conclusion: Supplementation of ZMA for two nights of partial sleep deprivation had no effect on sleep or subsequent morning performance.

Imputing missing sleep data from wearables with neural networks in real-world settings

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.

Sleep in the Athlete

Sleep is important for not only general health but also for lowering injury risk and maintaining athletic performance. Sleep disorders are prevalent in athletes, and taking a sleep history, evaluating sleep quality, and addressing other related factors including mental health are essential in diagnosing and understanding sleep disorders. Other methods such as polysomnography, actigraphy, and sheet sensors can also be used. Treatment options for sleep disorders include sleep hygiene, cognitive behavioral therapy, medication, and addressing contributing factors. For athletes, sleep can also be affected by factors such as travel fatigue and jet lag, which should be taken into consideration.

Microbiota Composition and Probiotics Supplementations on Sleep Quality-A Systematic Review and Meta-Analysis

The gut microbiota (GM) plays a crucial role in human health. The bidirectional interaction between GM and the central nervous system may occur via the microbiota-gut-brain axis, possibly regulating the sleep/wake cycle. Recent reports highlight associations between intestinal dysbiosis and sleep disorders, suggesting that probiotics could ameliorate this condition. However, data are poor and inconsistent. The aim of this quantitative metanalytic study is to assess the GM composition in sleep disturbances and evaluate probiotics' effectiveness for managing sleep disorders. A systematic review was carried out until July 2022 in online databases, limiting the literature research to human studies and English language articles. No significant GM diversity between patients with sleep disturbances versus healthy controls was found, revealed by α-diversity, while β-diversity is missing due to lack of proper reporting. However, probiotics supplementation significantly reduced the self-assessed parameter of sleep quality and disturbances Pittsburgh Sleep Quality Index (PSQI) score compared with the placebo. No difference in the Epworth Sleepiness Scale (ESS) score was found. While available data suggest that GM diversity is not related to sleep disturbances, probiotics administration strongly improves sleep quality as a subjective perception. However, heterogeneity of data reporting in the scientific literature should be considered as a limitation.

Effect of Baduanjin Qigong on Sleep Quality and Hyperarousal State in Adults With Chronic Insomnia: Protocol for a Randomized Controlled Trial

BACKGROUND

Chronic insomnia (CI) is a mind-body disease that is commonly defined as a state of having disturbed daytime activities due to poor nighttime sleep quality. Baduanjin qigong (BDJQG) is widely used for CI in China. However, there is little scientific evidence to evaluate its effects on the hyperarousal state, which is closely associated with improved sleep quality.

OBJECTIVE

The objective of the trial is to assess the therapeutic effects of BDJQG on sleep quality in patients with CI.

METHODS

A randomized controlled trial will be conducted on 86 patients, who will be divided into a BDJQG group and a cognitive behavioral therapy for insomnia group at a ratio of 1:1. Interventions in both groups will be given to the participants 7 times a week for 8 weeks, and the participants will be followed up for 4 weeks. The primary outcome is the change in the Pittsburgh Sleep Quality Index from baseline to week 8. The secondary outcomes are the changes in the Hyperarousal Scale, Insomnia Severity Index, Fatigue Scale-14, wrist actigraphy, salivary cortisol level, and functional magnetic resonance imaging from baseline to week 8. All main analyses will be carried out on the basis of the intention-to-treat principle.

RESULTS

This study was funded from January 2023. As of the submission of the manuscript, there were 86 participants. Data collection began in April 2023 and will end in January 2024. Data analysis is expected to begin in January 2024, with the publication of results expected in February 2024.

CONCLUSIONS

This study will present data concerning the clinical effects of BDJQG on CI. The results will help to demonstrate whether BDJQG is an effective therapy for improving sleep quality in association with a decreased hyperarousal level as a possible underlying mechanism. This study will provide much-needed knowledge for complementary and alternative therapy for patients with CI.

TRIAL REGISTRATION

China Clinical Registration Agency ChiCTR2300069241; https://chictr.org.cn/bin/project/ChiCTR2300069241.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/53501.

An Overview of the Methods Used to Measure the Impact of Mindfulness-Based Interventions in Sleep-Related Outcomes

Introduction  Systematic reviews and metanalyses have shown that mindfulness-based interventions can have positive effects on health, such as reducing anxiety, depression, and chronic pain. However, their effect on sleep-related outcomes is not yet well established. Sleep can be assessed subjectively (questionnaires, sleep logs, self-reporting) and/or objectively (actigraphy, polysomnography, biological markers), and outcomes may differ depending on which type of assessment is used. Objective  In this study, we present a literature overview on mindfulness and sleep, innovatively presenting and discussing studies that address sleep subjectively and objectively. Methods  The search was undertaken using four databases (Pubmed Medline, Scopus, Web of Science, Psychinfo) in September 2019, and repeated in May 2021. Studies were analyzed through a two-step process: (1) reading titles and abstracts, and (2) full text analysis that met the review's eligibility criteria, with the final sample comprising 193 articles. We observed a growth in the number of studies published, particularly since 2005. However, this was mostly due to an increase in studies based on subjective research. There is a moderate to nonexistent agreement between objective and subjective sleep measures, with results of subjective measures having higher variability and uncertainty.We identified 151 articles (78%) using an exclusively subjective sleep evaluation, which can cause a misperception about mindfulness effects on sleep. Conclusion  Future studies should place greater emphasis on objective measurements to accurately investigate the effects of mindfulness practices on sleep, although subjective measures also have a role to play in respect of some aspects of this relationship.

Near-Death Experiences and Sleep Disturbance: An Exploratory Study Using Wrist Actigraphy

ABSTRACT

Near-death experiences (NDEs) are nonordinary states of consciousness typically occurring on the brink of death. Sleep changes after NDEs have been described, including shorter sleep duration, longer sleep latency, and more sleep disturbances; however, objective verification is lacking. In this exploratory research, 57 participants took part in a 14-day actigraphy study and were assigned to three groups: those who have had an NDE ( n = 26); those who experienced a near-death event but without NDE ( n = 12); and those who had never come close to death ( n = 19). No significant differences were found between groups for actigraphy data. Paired samples t tests indicated significant differences between subjective and objective measures of sleep onset latency, sleep duration, and sleep efficiency, notably among the NDE group. Findings are indicative of the phenomenon known as sleep state misperception (SSM), which may have clinical implications for the study of NDEs and SSM.

Overview of methods and available tools used in complex brain disorders

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.

Actigraphic characterization of sleep and circadian phenotypes of PER3 gene VNTR genotypes

The molecular circadian timing system involves genes known as "clock genes," such as the PER3 gene. Studies have demonstrated associations among a repeat polymorphism (VNTR) of the PER3 gene with chronotypes, with the occurrence of circadian rhythm disorders and with sleep homeostasis phenotypes. The aim of this study was to investigate, by actigraphy, sleep and circadian rhythm profiles of people with different genotypes for the VNTR polymorphism of the PER3 gene. We genotyped 467 individuals (46,39% male) for the PER3 VNTR polymorphism. The mean age of the participants was 21.84 ± 2.64, ranging from 18 to 30 y old. Actigraphy data were collected from a subsample of 81 subjects with PER3 4-repeats homozygous (PER34/4) or 5-repeats homozygous (PER35/5) genotypes from April to June of 2021. From this sample, 48 PER34/4 and 33 PER35/5 subjects wore a wrist actigraph between 12 and 19 d. The sleep onset (weekdays, p = 0.015; weekend, p = 0.022) and offset (weekdays, p = 0.004; weekend, p = 0.041) of the PER35/5 group occurred later than the PER34/4 group. Similar results were observed for the mid-sleep phase of weekdays (MSW) (p = 0.008) and free days (MSF) (p = 0.019), and for the mid-sleep phase corrected for sleep debt accumulated over the week (MSFsc) (p = 0.024). Despite the phase differences found between the PER34/4 and PER35/5 groups, no differences were found in sleep duration and social jet lag. However, the PER34/4 group presented, on average, a longer sleep rebound on the days off when compared to the PER35/5 (p = 0.002). The PER35/5 group showed lower interdaily stability (IS) (p = 0.032) and higher daily activity rhythm variability (IV) (p = 0.035). The findings of the present study revealed associations between the PER3 gene, sleep, and circadian rhythms. In general, we found that the gene is associated with the expression of sleep timing and duration and to the phase of the activity rhythm. The experiments carried out here occurred in the COVID-19 pandemic scenario, which should be considered as an environmental element with potential effects on the results obtained.

Associations between sleep characteristics and glycemic variability in youth with type 1 diabetes

OBJECTIVE

This study aimed to determine sleep characteristics and their associations with glycemic variability in youth with type 1 diabetes (T1D).

MATERIAL AND METHODS

This cross-sectional study conducted at two pediatric diabetes centers in Istanbul, Turkey, included 84 children with T1D (mean age 10.5 years). Sleep characteristics and glycemic variability were determined by actigraphy, DSM-5 Level 2-Sleep Disturbance Scale Short Form and continuous glucose monitoring. Circadian preference was evaluated by the Children's Chronotype Questionnaire. Sleep disturbances were assessed by the. The sleep quality was determined by actigraphy-derived sleep measures.

RESULTS

Eighty-eight percent of participants had insufficient age-appropriate total sleep time (TST) (<9 h for 6-13-year-olds and <8 h for 14-17-year-olds). Chronotype was classified as intermediate in 50%, evening in 45.2%, and morning in 4.8%. A higher chronotype score indicating a stronger eveningness preference was associated with more time spent in hypoglycemia (β = 0.433, p = 0.002). On nights when participants had lower sleep efficiency and longer sleep onset latency, they had significantly higher overnight glycemic variability (β = -0.343, p = 0.016, β = 0.129, p = 0.017, respectively). Prolonged nocturnal wake duration was significantly associated with more time spent in daytime hypoglycemia (β = 0.037, p = 0.046) and higher overnight glycemic variability (J index, β = 0.300, p = 0.015). The associations between TST and glycemic variability indices were not significant.

CONCLUSIONS

Sleep quality rather than TST was significantly associated with glycemic variability in children with T1D. Eveningness preference might contribute to an increased risk of hypoglycemia. Addressing sleep patterns and chronotypes can be crucial in management plans for youth with T1D.

Neighborhood environment and adolescent sleep: The role of family socioeconomic status

OBJECTIVES

Socioeconomic status (SES) and neighborhood context are influential predictors of adolescent sleep, yet little is known about how they may interact to influence sleep. We examined multiple dimensions of family SES as moderators of associations between neighborhood risk and multiple sleep parameters.

METHODS

Participants were 323 adolescents (Mage = 17.4 years, SD = 0.86; 48% male; 60% White/European American, 40% Black/African American). Sleep was assessed using 7 nights of actigraphy from which sleep duration (minutes from onset to wake time), efficiency, long wake episodes, and variability in minutes over the week were derived. Youth reported on their sleep/wake problems and sleepiness, as well as their perceptions of safety and violence in their neighborhoods. Parents reported on SES indices, including income-to-needs ratio and perceived financial stability.

RESULTS

Lower SES (income-to-needs, perceived financial stability) was associated with lower sleep efficiency and more frequent long wake episodes. Lower neighborhood safety and greater community violence concerns were related to greater subjective sleep problems. Moderation effects illustrated two general patterns. For actigraphy-derived sleep variables, lower neighborhood safety was associated with poor sleep only among youth from lower-income families. For subjective sleep/wake problems and daytime sleepiness, associations between neighborhood risk and sleep difficulties were pronounced for higher SES youth, while lower SES youth had greater sleep problems regardless of neighborhood factors.

CONCLUSIONS

Findings suggest that several dimensions of SES and neighborhood risk may be consequential for adolescents' sleep. Moderation effects highlight the significance of considering multiple contextual influences towards a better understanding of adolescents' sleep.

Estimating sleep duration: performance of open-source processing of actigraphy compared to in-laboratory polysomnography in the community

Comparisons of actigraphy findings between studies are challenging given differences between brand-specific algorithms. This issue may be minimized by using open-source algorithms. However, the accuracy of actigraphy-derived sleep parameters processed in open-source software needs to be assessed against polysomnography (PSG). Middle-aged adults from the Raine Study (n = 835; F 58%; Age 56.7 ± 5.6 years) completed one night of in-laboratory PSG and concurrent actigraphy (GT3X+ ActiGraph). Actigraphic measures of total sleep time (TST) were analyzed and processed using the open-source R-package GENEActiv and GENEA data in R (GGIR) with and without a sleep diary and additionally processed using proprietary software, ActiLife, for comparison. Bias and agreement (intraclass correlation coefficient) between actigraphy and PSG were examined. Common PSG and sleep health variables associated with the discrepancy between actigraphy, and PSG TST were examined using linear regression. Actigraphy, assessed in GGIR, with and without a sleep diary overestimated PSG TST by (mean ± SD) 31.0 ± 50.0 and 26.4 ± 69.0 minutes, respectively. This overestimation was greater (46.8 ± 50.4 minutes) when actigraphy was analyzed in ActiLife. Agreement between actigraphy and PSG TST was poor (ICC = 0.27-0.44) across all three methods of actigraphy analysis. Longer sleep onset latency and longer wakefulness after sleep onset were associated with overestimation of PSG TST. Open-source processing of actigraphy in a middle-aged community population, agreed poorly with PSG and, on average, overestimated TST. TST overestimation increased with increasing wakefulness overnight. Processing of actigraphy without a diary in GGIR was comparable to when a sleep diary was used and comparable to actigraphy processed with proprietary algorithms in ActiLife.

Characteristics of macroscopic sleep structure in patients with mild cognitive impairment: a systematic review

Objectives

Conducting a systematic analysis of objective measurement tools to assess the characteristics of macroscopic sleep architecture in patients with mild cognitive impairment (MCI), amnestic MCI (aMCI), and non-amnestic MCI (naMCI) in order to provide sleep disorder guidance for MCI patients.

Methods

PubMed, EMbase, Web of Science, Cochrane Library, CNKI, SinoMed, Wanfang Data, and VIP Data were examined to find literature relating to sleep in patients with MCI, aMCI, and naMCI, with a search time frame of build to April 2023. Following independent literature screening, data extraction, and quality evaluation by two researchers, statistical analysis was performed using RevMan 5.4 software.

Results

Twenty-five papers with 1,165 study subjects were included. Patients with MCI and aMCI were found to have altered total sleep time (TST), reduced sleep efficiency (SE), more wake-time after sleep onset (WASO), longer sleep latency (SL), a higher proportion of N1 stage and a lower proportion of N2 and N3 stage. naMCI was only found to have statistically significant differences in WASO.

Conclusions

The results of this study provide evidence for macroscopic sleep architecture abnormalities among MCI patients with sleep disorders. Maintaining a normal sleep time, improving SE, and reducing sleep fragmentation may have an association with a slowed development of cognitive impairment. Further exploration is required of the effects each component of macroscopic sleep structure after the intervention has on altered sleep disturbance and cognition in MCI, aMCI, and naMCI.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023401937, identifier: CRD42023401937.

The contribution of sleep and co-occurring neurodevelopmental conditions to quality of life in children with epilepsy

BACKGROUND

Health-related quality of life (HRQOL) in children with epilepsy (CWE) is multifactorial and can be affected not only by epilepsy-specific variables but also co-occurring conditions such as sleep disturbances, autism, and attention deficit hyperactivity disorder (ADHD). While highly prevalent in CWE, these conditions are underdiagnosed despite having a significant impact on HRQOL. Sleep problems have a complex relationship with epilepsy and neurodevelopmental characteristics. However, little is known about how these issues interact and contribute to HRQOL.

OBJECTIVES

The current study aims to explore the relationship between sleep and neurodevelopmental characteristics on HRQOL in CWE.

METHODS

36 CWE aged 4-16 years old were recruited from two hospitals and asked to wear an actiwatch for a period of 14 days and caregivers completed a series of questionnaires assessing co-occurrences and epilepsy-specific variables.

RESULTS

A high proportion of CWE (78.13%) presented significant sleep problems. Informant-reported sleep problems were significantly predictive of HRQOL above seizure severity and the number of antiseizure medications. Interestingly, informant-reported sleep problems were no longer significantly predictive of HRQOL when neurodevelopmental characteristics were considered, indicating a possible mediating effect. Similarly, actigraphy-defined sleep (variability in sleep onset latency) displayed a similar effect but only for ADHD characteristics, whereas autistic characteristics and variability in sleep onset latency continued to exert an individual effect on HRQOL.

CONCLUSION

These data from our study shed light on the complicated relationship between sleep, neurodevelopmental characteristics and epilepsy. Findings suggest that the impact of sleep on HRQOL in CWE is possibly mediated by neurodevelopmental characteristics. Furthermore, the impact this triangular relationship exerts on HRQOL is dependent on the type of tool used to measure sleep. These findings highlight the importance of a multidisciplinary approach to epilepsy management.

Consistency of chronotype measurements is affected by sleep quality, gender, longitude, and latitude

Chronotype has received increasing research attention. However, there remains some confusion about the influence of gender, age, sleep quality, insomnia severity, longitude, and latitude on the consistency of the chronotype measured by the different tools. Chronotype measurement indicators were collected from 421 participants. The midpoint of sleep for actigraphy (MSF_A), sleep diary (MSF_D), and Munich Chronotype Questionnaire (MCTQ) (MSFsc) on free days and the Morningness-Eveningness Questionnaire (rMEQ) scores were used to measure the chronotype. In addition, demographic information, Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI) were also collected. A significant correlation was identified between the questionnaires (MSF_D, MSFsc, rMEQ) and actigraphy (MSF_A) as measures of chronotype. MSF_A was associated with sleep latency and sleep disturbance in the PSQI subdimensions. The correlation between MS_D and sleep disturbance was significant. Both rMEQ and MSFsc were significantly correlated with PSQI (total scores and daytime dysfunction) and ISI. The consistency of all chronotype measurements for the questionnaires and actigraphy was influenced by gender. Among them, MSF_D is also affected by age, while only the latitude and sleep disturbance effect was found in the MSFsc. The influence of age, gender, sleep quality, and latitude should be emphasized when measuring the chronotype using self-reported methods.

Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor

Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject's sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system's performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.

Sleep disorders in chronic pain and its neurochemical mechanisms: a narrative review

Chronic pain (CP) is a prevalent problem, and more than half of patients with CP have sleep disorders. CP comorbidity with sleep disorders imposes immense suffering and seriously affects the patient's quality of life, which is a challenging issue encountered by clinicians. Although the reciprocal interactions between pain and sleep have been studied to some degree, there is still a lack of awareness and comprehensive description of CP comorbidity with sleep disorders. In this narrative review article, we summarize the current knowledge about the present estimates of the prevalence of comorbid sleep disorders in CP patients, sleep detection methods, sleep characterization in CP, and the effect of sleep disorders on CP and current therapies. We also summarize current knowledge of the neurochemical mechanisms of CP comorbidity with sleep disorders. In conclusion, insufficient attention has been paid to the role of sleep disorders in CP patients, and CP patients should be screened for sleep disorders in the clinic. Special attention should be given to a possible risk of drug-drug interaction when using two types of drugs targeting pain and sleep simultaneously. The current insight into the neurobiological mechanisms underlying CP comorbidity with sleep disorders is still rather limited.

AI-Driven sleep staging from actigraphy and heart rate

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank

BACKGROUND

Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes.

METHODS

Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC).

RESULTS

For MD vs. controls (n_(MD) = 24,229; n_(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n_(atypical) = 958; n_(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes.

LIMITATIONS

Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary.

DISCUSSION

Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.

Non-pharmaceutical interventions in complementary and alternative medicine for insomnia in breast cancer survivors: a protocol for a systematic review and network meta-analysis

INTRODUCTION

Insomnia has a high incidence in patients with breast cancer, which not only affects the quality of life of patients, but also affects the efficiency of later treatment and rehabilitation. Although the sedative and hypnotic drugs commonly used in clinical practice have a rapid onset of action, they are also accompanied by different degrees of sequelae, withdrawal effects and dependence and addiction. Complementary and alternative medicine (CAM) or complementary and integrative medicine, complementary integrative therapies, including natural nutritional supplement therapy, psychotherapy, physical and mental exercise, physiotherapy, have been reported to be used to treat cancer-related insomnia. Its clinical results are increasingly recognised and accepted by patients. However, the effectiveness and safety of these CAM are uneven, and there is no standard clinical application method. Therefore, in order to objectively evaluate the effects of different non-pharmaceutical interventions in CAM on insomnia, we will conduct a network meta-analysis (NMA) to explore the effects of different CAM interventions on improving sleep quality in patients with breast cancer.

METHODS AND ANALYSIS

We will search all Chinese and English databases from the inception of the database to 31 December 2022. The databases include PubMed, Medline, Embase, Web of Science and Cochrane Central Register of Controlled Trials and the Chinese literature databases include CBM, CNKI, VIP, WANFANG. The Insomnia Severity Index and the Pittsburgh Sleep Quality Index will be considered as the primary outcomes in the study. STATA V.15.0 software will be used to conduct pairwise meta-analysis and NMA. Finally, we will use the recommended risk assessment tool RoB2 for risk and bias assessment, and use the Grading of Recommendations Assessment, Development and Evaluation evaluation method to evaluate the quality of evidence.

ETHICS AND DISSEMINATION

Ethical approval will not be required because the study will not include the original information of participants. The results will be published in a peer-reviewed journal or disseminated in relevant conferences.

PROSPERO REGISTRATION NUMBER

CRD42022382602.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Simultaneous Recording of Objective Sleep in Mothers and School-aged Children with Developmental Disabilities: A Pilot Study of Actigraphy and Videosomnography

Mothers of school-aged children ages 3 to 17 years with developmental disabilities (DDs) commonly report sleep problems in their children associated with impaired maternal sleep. However, existing research relies heavily on mothers' self-reported sleep. This study aimed to determine the feasibility of objectively measuring child and mother sleep-wake patterns using actigraphy and videosomnography. This was an observational pilot study. Mothers wore actigraphy watches and video-recorded their child's sleep for 7 nights. Mothers also completed a 7-day sleep diary and questionnaires on sleep quality, depressive symptoms, stress, and child sleep problems. Ten mothers (32-49 years) and ten children with DDs (8-12 years) completed this study. Half of the children were boys with autism spectrum disorders. We successfully recruited 77% of eligible mothers for the study during the pandemic. Eight mothers successfully wore the actigraphy, and nine successfully video-recorded their child's sleep. Mothers rated their participation positively and considered the data collection protocol acceptable. While mothers' sleep patterns from actigraphy were mostly within recommendations, self-reported sleep quality was poor. Child's sleep estimates from videosomnography showed children slept substantially less than recommended sleep hours. Mothers also reported a high frequency of child sleep problems. Consistent with this pattern, mothers also endorsed elevated stress and depression. The use of actigraphy and videosomnography is feasible. Objective sleep measurement for mothers' and children's sleep is needed with self-report to measure multidimensional aspects of sleep and discrepancies between objective and self-report sleep measures. Future studies can use multi-methods sleep measures and work toward interventions that can improve family sleep and reduce mothers' stress and depression.

Objective sleep characteristics and continuous glucose monitoring profiles of type 2 diabetes patients in real-life settings

AIM

To investigate the association between objective sleep parameters and glycaemic variability determined by continous glucose monitoring (CGM) among patients with type 2 diabetes, given the significant role of sleep in glycaemic control.

METHODS

In this study, CGM was carried out in 28 patients with T2D (aged 62.3 ± 4.8 years, 57% women). Sleep characteristics were assessed by actigraphy within the CGM period. CGM-derived outcomes included glucose level, and percentages of time in range (TIR) and time above range (TAR) during the monitoring period. Associations between intraindividual night-to-night variations in sleep characteristics and overall CGM outcomes were analysed using linear regression. Associations between sleep characteristics during each night and time-matched CGM outcomes were analysed using linear mixed models.

RESULTS

A total of 249 person-days of CGM, coupled with 221 nights of sleep characteristics, were documented. Greater standard deviation (SD) of objective sleep duration (minutes) between measurement nights was associated with higher glucose level (coefficient 0.018 mmol/L [95% confidence interval {CI} 0.004, 0.033], P = 0.017), smaller proportion of TIR (% in observation period; coefficient -0.20% [95% CI -0.36, -0.03], P = 0.023), and greater proportion of TAR (coefficient 0.22% [95% CI 0.06, 0.39], P = 0.011). Later sleep midpoint (minutes from midnight) was associated with greater SD of glucose during the same sleep period (coefficient 0.002 minutes [95% CI 0.0001, 0.003], P = 0.037), longer nocturnal sleep duration was associated with smaller coefficient of variation of glucose level in the upcoming day (-0.015% [95% CI -0.03, -0.001], P = 0.041).

CONCLUSION

Objectively determined sleep duration and sleep midpoint, as well as their daily variability, are associated with CGM-derived glucose profiles in T2D patients.

Investigating mental defeat in individuals with chronic pain: Protocol for a longitudinal experience sampling study

INTRODUCTION

Previous qualitative and cross-sectional research has identified a strong sense of mental defeat in people with chronic pain who also experience the greatest levels of distress and disability. This study will adopt a longitudinal experience sampling design to examine the within-person link between the sense of mental defeat and distress and disability associated with chronic pain.

METHODS AND ANALYSIS

We aim to recruit 198 participants (aged 18-65 years) with chronic pain, to complete two waves of experience sampling over 1 week, 6 months apart (time 1 and time 2). During each wave of experience sampling, the participants are asked to complete three short online surveys per day, to provide in-the-moment ratings of mental defeat, pain, medication usage, physical and social activity, stress, mood, self-compassion, and attention using visual analogue scales. Sleep and physical activity will be measured using a daily diary as well as with wrist actigraphy worn continuously by participants throughout each wave. Linear mixed models and Gaussian graphical models will be fit to the data to: (1) examine the within-person, day-to-day association of mental defeat with outcomes (ie, pain, physical/social activity, medication use and sleep), (2) examine the dynamic temporal and contemporaneous networks of mental defeat with all outcomes and the hypothesised mechanisms of outcomes (ie, perceived stress, mood, attention and self-compassion).

ETHICS AND DISSEMINATION

The current protocol has been approved by the Health Research Authority and West Midlands-Solihull Research Ethics Committee (Reference Number: 17/WM0053). The study is being conducted in adherence with the Declaration of Helsinki, Warwick Standard Operating Procedures and applicable UK legislation.

Contactless Camera-Based Sleep Staging: The HealthBed Study

Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.

Longitudinally tracking personal physiomes for precision management of childhood epilepsy

Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.

White matter microstructure and sleep-wake disturbances in individuals at ultra-high risk of psychosis

Aim

White matter changes in individuals at ultra-high risk for psychosis (UHR) may be involved in the transition to psychosis. Sleep-wake disturbances commonly precede the first psychotic episode and predict development of psychosis. We examined associations between white matter microstructure and sleep-wake disturbances in UHR individuals compared to healthy controls (HC), as well as explored the confounding effect of medication, substance use, and level of psychopathology.

Methods

Sixty-four UHR individuals and 35 HC underwent clinical interviews and diffusion weighted imaging. Group differences on global and callosal mean fractional anisotropy (FA) was tested using general linear modeling. Sleep-wake disturbances were evaluated using the subjective measures disturbed sleep index (DSI) and disturbed awakening index (AWI) from the Karolinska Sleep Questionnaire, supported by objective sleep measures from one-night actigraphy. The primary analyses comprised partial correlation analyses between global FA/callosal FA and sleep-wake measures. Secondary analyses investigated multivariate patterns of covariance between measures of sleep-wake disturbances and FA in 48 white matter regions of interest using partial least square correlations.

Results

Ultra-high risk for psychosis individuals displayed lower global FA (F = 14.56, p < 0.001) and lower callosal FA (F = 11.34, p = 0.001) compared to HC. Subjective sleep-wake disturbances were significantly higher among the UHR individuals (DSI: F = 27.59, p < 0.001, AWI: F = 36.42, p < 0.001). Lower callosal FA was correlated with increased wake after sleep onset (r = -0.34, p = 0.011) and increased sleep fragmentation index (r = -0.31, p = 0.019) in UHR individuals. Multivariate analyses identified a pattern of covariance in regional FA which were associated with DSI and AWI in UHR individuals (p = 0.028), but not in HC. Substance use, sleep medication and antipsychotic medication did not significantly confound these associations. The association with objective sleep-wake measures was sustained when controlling for level of depressive and UHR symptoms, but symptom level confounded the covariation between FA and subjective sleep-wake measures in the multivariate analyses.

Conclusion

Compromised callosal microstructure in UHR individuals was related to objectively observed disruptions in sleep-wake functioning. Lower FA in ventrally located regions was associated with subjectively measured sleep-wake disturbances and was partly explained by psychopathology. These findings call for further investigation of sleep disturbances as a potential treatment target.