Abstract: Actigraphy, a method for inferring sleep/wake patterns based on movement data gathered using actigraphs, is increasingly used in population-based epidemiologic studies because of its ability to monitor activity in natural settings. Using special software, actigraphic data are analyzed to estimate a range of sleep parameters. To date, despite extensive application of actigraphs in sleep research, published literature specifically detailing the methodology for derivation of sleep parameters is lacking; such information is critical for the appropriate analysis and interpretation of actigraphy data. Reporting of sleep parameters has also been inconsistent across studies, likely reflecting the lack of consensus regarding the definition of sleep onset and offset. In addition, actigraphy data are generally underutilized, with only a fraction of the sleep parameters generated through actigraphy routinely used in current sleep research. The objectives of this paper are to review existing algorithms used to estimate sleep/wake cycles from movement data, demonstrate the rules/methods used for estimating sleep parameters, provide clear technical definitions of the parameters, and suggest potential new measures that reflect intraindividual variability. Utilizing original data collected using Motionlogger Sleep Watch (Ambulatory Monitoring Inc., Ardsley, NY), we detail the methodology and derivation of 29 nocturnal sleep parameters, including those both widely and rarely utilized in research. By improving understanding of the actigraphy process, the information provided in this paper may help: ensure appropriate use and interpretation of sleep parameters in future studies; enable the recalibration of sleep parameters to address specific goals; inform the development of new measures; and increase the breadth of sleep parameters used.

Abstract: Sleep disturbance and cancer-related fatigue (CRF) are among the most commonly reported symptoms associated with breast cancer and its treatment. This study identified symptom cluster groups of breast cancer patients based on multidimensional assessment of sleep disturbance and CRF prior to and during chemotherapy. Participants were 152 women with stage I–IIIA breast cancer. Data were collected before chemotherapy (T1) and during the final week of the fourth chemotherapy cycle (T2). Latent profile analysis was used to derive groups of patients at each timepoint who scored similarly on percent of the day/night asleep per actigraphy, the Pittsburgh Sleep Quality Index global score, and the five subscales of the Multidimensional Fatigue Symptom Inventory-Short Form. Bivariate logistic regression evaluated if sociodemographic/medical characteristics at T1 were associated with group membership at each timepoint. Three groups (Fatigued with sleep complaints, Average, Minimal symptoms) were identified at T1, and five groups (Severely fatigued with poor sleep, Emotionally fatigued with average sleep, Physically fatigued with average sleep, Average, Minimal symptoms) at T2. The majority of individuals in a group characterized by more severe symptoms at T1 were also in a more severe symptom group at T2. Sociodemographic/medical variables at T1 were significantly associated with group membership at T1 and T2. This study identified groups of breast cancer patients with differentially severe sleep disturbance and CRF symptom profiles prior to and during chemotherapy. Identifying groups with different symptom management needs and distinguishing groups by baseline sociodemographic/medical variables can identify patients at risk for greater symptom burden.

Abstract: Individuals vary in how their circadian system synchronizes with the cyclic environment (zeitgeber). Assessing these differences in “phase of entrainment”—often referred to as chronotype—is an impo...

Abstract: Background: Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. Objective: This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. Methods: We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. Results: We found significant correlations between the ring’s and actigraphy’s TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. Conclusions: In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.

Abstract: To examine the effectiveness and safety of yoga of women with sleep problems by performing a systematic review and meta-analysis. Medline/PubMed, ClinicalKey, ScienceDirect, Embase, PsycINFO, and the Cochrane Library were searched throughout the month of June, 2019. Randomized controlled trials comparing yoga groups with control groups in women with sleep problems were included. Two reviewers independently evaluated risk of bias by using the risk of bias tool suggested by the Cochrane Collaboration for programming and conducting systematic reviews and meta-analyses. The main outcome measure was sleep quality or the severity of insomnia, which was measured using subjective instruments, such as the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), or objective instruments such as polysomnography, actigraphy, and safety of the intervention. For each outcome, a standardized mean difference (SMD) and confidence intervals (CIs) of 95% were determined. Nineteen studies in this systematic review included 1832 participants. The meta-analysis of the combined data conducted according to Comprehensive Meta-Analysis showed a significant improvement in sleep (SMD = − 0.327, 95% CI = − 0.506 to − 0.148, P < 0.001). Meta-analyses revealed positive effects of yoga using PSQI scores in 16 randomized control trials (RCTs), compared with the control group in improving sleep quality among women using PSQI (SMD = − 0.54; 95% CI = − 0.89 to − 0.19; P = 0.003). However, three RCTs revealed no effects of yoga compared to the control group in reducing insomnia among women using ISI (SMD = − 0.13; 95% CI = − 0.74 to 0.48; P = 0.69). Seven RCTs revealed no evidence for effects of yoga compared with the control group in improving sleep quality for women with breast cancer using PSQI (SMD = − 0.15; 95% CI = − 0.31 to 0.01; P = 0.5). Four RCTs revealed no evidence for the effects of yoga compared with the control group in improving the sleep quality for peri/postmenopausal women using PSQI (SMD = − 0.31; 95% CI = − 0.95 to 0.33; P = 0.34). Yoga was not associated with any serious adverse events. This systematic review and meta-analysis demonstrated that yoga intervention in women can be beneficial when compared to non-active control conditions in term of managing sleep problems. The moderator analyses suggest that participants in the non-breast cancer subgroup and participants in the non-peri/postmenopausal subgroup were associated with greater benefits, with a direct correlation of total class time with quality of sleep among other related benefits.

Abstract: Summary Background Circadian disturbances are commonly seen in people with Alzheimer's disease and have been reported in individuals without symptoms of dementia but with Alzheimer's pathology. We aimed to assess the temporal relationship between circadian disturbances and Alzheimer's progression. Methods We did a prospective cohort study of 1401 healthy older adults (aged >59 years) enrolled in the Rush Memory and Aging Project (Rush University Medical Center, Chicago, IL, USA) who had been followed up for up to 15 years. Participants underwent annual assessments of cognition (with a battery of 21 cognitive performance tests) and motor activities (with actigraphy). Four measures were extracted from actigraphy to quantify daily and circadian rhythmicity, which were amplitude of 24-h activity rhythm, acrophase (representing peak activity time), interdaily stability of 24-h activity rhythm, and intradaily variability for hourly fragmentation of activity rhythm. We used Cox proportional hazards models and logistic regressions to assess whether circadian disturbances predict an increased risk of incident Alzheimer's dementia and conversion of mild cognitive impairment to Alzheimer's dementia. We used linear mixed-effects models to investigate how circadian rhythms changed longitudinally and how the change integrated to Alzheimer's progression. Findings Participants had a median age of 81·8 (IQR 76·3–85·7) years. Risk of developing Alzheimer's dementia was increased with lower amplitude (1 SD decrease, hazard ratio [HR] 1·39, 95% CI 1·19–1·62) and higher intradaily variability (1 SD increase, 1·22, 1·04–1·43). In participants with mild cognitive impairment, increased risk of Alzheimer's dementia was predicted by lower amplitude (1 SD decrease, HR 1·46, 95% CI 1·24–1·72), higher intradaily variability (1 SD increase, 1·36, 1·15–1·60), and lower interdaily stability (1 SD decrease, 1·21, 1·02–1·44). A faster transition to Alzheimer's dementia in participants with mild cognitive impairment was predicted by lower amplitude (1 SD decrease, odds ratio [OR] 2·08, 95% CI 1·53–2·93), increased intradaily variability (1 SD increase, 1·97, 1·43–2·79), and decreased interdaily stability (1 SD decrease, 1·35, 1·01–1·84). Circadian amplitude, acrophase, and interdaily stability progressively decreased over time, and intradaily variability progressively increased over time. Alzheimer's progression accelerated these aging effects by doubling or more than doubling the annual changes in these measures after the diagnosis of mild cognitive impairment, and further doubled them after the diagnosis of Alzheimer's dementia. The longitudinal change of global cognition positively correlated with the longitudinal changes in amplitude and interdaily stability and negatively correlated with the longitudinal change in intradaily variability. Interpretation Our results indicate a link between circadian dysregulation and Alzheimer's progression, implying either a bidirectional relation or shared common underlying pathophysiological mechanisms. Funding National Institutes of Health, and the BrightFocus Foundation.

Abstract: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea, OSA) but relies on labor-intensive EEG-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and has low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to automatically identify the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. PPG signals from the diagnostic polysomnographies of patients suspected of OSA (n=894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for 3-stage (wake/NREM/REM), 4-stage (wake/N1+N2/N3/REM), and 5-stage (wake/N1/N2/N3/REM) classification of sleep. The 3-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's kappa (κ) of 0.65. The 4-stage and 5-stage models achieved 68.5% (κ=0.54), and 64.1% (κ=0.51) accuracies, respectively. With the 5-stage model, the total sleep time was underestimated with mean (standard deviation) error of 7.5 (55.2) min. The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.

Abstract: This study investigates the same-day associations between discrimination and sleep among 350 adolescents ages 13?15 (M = 14.29, SD = 0.65; Asian = 41%, Black = 22%, Latinx = 37%). Assessing sleep duration, sleep onset latency, and wake minutes after sleep onset using wrist actigraphy, Black adolescents slept 35 min less than Asian and 36 min less than Latinx youth. Black adolescents suffered the most wake minutes after sleep onset, followed by Latinx and Asian youth. Latinx youth reported the highest levels of sleep disturbance, whereas Asian youth reported the highest levels of daytime dysfunction. Daily discrimination was associated with lower levels of same-night sleep onset latency, more sleep disturbance, more next-day daytime dysfunction, and higher next-day daytime sleepiness.

Abstract: STUDY OBJECTIVES Stress is associated with poor and short sleep, but the temporal order of these variables remains unclear. This study examined the temporal and bi-directional associations between stress and sleep and explored the moderating role of baseline sleep complaints, using daily, intensive longitudinal designs. METHODS Participants were 326 young adults (Mage = 23.24 ± 5.46), providing >2,500 nights of sleep altogether. Prospective total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were measured using actigraphy and sleep diaries. Perceived stress was reported three times daily between: 11:00-15:00, 15:30-19:30, and 20:00-02:00. Sleep complaints were measured at baseline using the PROMIS sleep disturbance scale. Within- and between-person sleep and stress variables were tested using cross-lagged multilevel models. RESULTS Controlling for covariates and lagged outcomes, within-person effects showed that higher evening stress predicted shorter actigraphic and self-reported TST (both p < .01). Conversely, shorter actigraphic and self-reported TST predicted higher next-day stress (both p < .001). Longer self-reported SOL and WASO (both p < .001), as well as lower actigraphic (p < .01) and self-reported SE (p < .001), predicted higher next-day stress. Between-person effects emerged only for self-reported TST predicting stress (p < .01). No significant results were found for the moderating role of baseline sleep complaints. CONCLUSIONS Results demonstrated bi-directional relations between stress and sleep quantity, and a consistent direction of worse sleep quantity and continuity predicting higher next-day stress. Results highlighted within-individual daily variation as being more important than between-individual differences when examining sleep and daytime functioning associations.

Abstract: STUDY OBJECTIVES Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric-the Composite Phase Deviation (CPD)-to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. METHODS Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy-Alert, Sad-Happy, Sluggish-Energetic, Sick-Healthy, and Stressed-Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. RESULTS CPD for sleep was a significant predictor of average well-being (e.g. Stressed-Calm: b = -6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed-Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. CONCLUSIONS Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. CLINICAL TRIAL REGISTRATION NCT02846077.

Abstract: Suboptimal sleep causes cognitive decline and probably accelerates Alzheimer's Disease (AD) progression. Several sleep interventions have been tested in established AD dementia cases. However early intervention is needed in the course of AD at Mild Cognitive Impairment (MCI) or mild dementia stages to help prevent decline and maintain good quality of life. This systematic review aims to summarize evidence on sleep interventions in MCI and mild AD dementia. Seven databases were systematically searched for interventional studies where ≥ 75% of participants met diagnostic criteria for MCI/mild AD dementia, with a control group and validated sleep outcome measures. Studies with a majority of participants diagnosed with Moderate to Severe AD were excluded. After removal of duplicates, 22,133 references were returned in two separate searches (August 2019 and September 2020). 325 full papers were reviewed with 18 retained. Included papers reported 16 separate studies, total sample (n = 1,056), mean age 73.5 years. 13 interventions were represented: Cognitive Behavioural Therapy - Insomnia (CBT-I), A Multi-Component Group Based Therapy, A Structured Limbs Exercise Programme, Aromatherapy, Phase Locked Loop Acoustic Stimulation, Transcranial Stimulation, Suvorexant, Melatonin, Donepezil, Galantamine, Rivastigmine, Tetrahydroaminoacridine and Continuous Positive Airway Pressure (CPAP). Psychotherapeutic approaches utilising adapted CBT-I and a Structured Limbs Exercise Programme each achieved statistically significant improvements in the Pittsburgh Sleep Quality Index with one study reporting co-existent improved actigraphy variables. Suvorexant significantly increased Total Sleep Time and Sleep Efficiency whilst reducing Wake After Sleep Onset time. Transcranial Stimulation enhanced cortical slow oscillations and spindle power during daytime naps. Melatonin significantly reduced sleep latency in two small studies and sleep to wakefulness transitions in a small sample. CPAP demonstrated efficacy in participants with Obstructive Sleep Apnoea. Evidence to support other interventions was limited. Whilst new evidence is emerging, there remains a paucity of evidence for sleep interventions in MCI and mild AD highlighting a pressing need for high quality experimental studies exploring alternative sleep interventions.

Abstract: Importance Sleep is a resource that has been associated with health and well-being; however, sleep insufficiency is common among adolescents. Objective To examine how delaying school start time is associated with objectively assessed sleep duration, timing, and quality in a cohort of adolescents. Design, Setting, and Participants This observational cohort study took advantage of district-initiated modifications in the starting times of 5 public high schools in the metropolitan area of Minneapolis and St Paul, Minnesota. A total of 455 students were followed up from grade 9 (May 3 to June 3, 2016) through grade 11 (March 15 to May 21, 2018). Data were analyzed from February 1 to July 24, 2019. Exposures All 5 participating schools started early (7:30amor 7:45am) at baseline (2016). At follow-up 1 (2017) and continuing through follow-up 2 (2018), 2 schools delayed their start times by 50 and 65 minutes, whereas 3 comparison schools started at 7:30amthroughout the observation period. Main Outcomes and Measures Wrist actigraphy was used to derive indices of sleep duration, timing, and quality. With a difference-in-difference design, linear mixed-effects models were used to estimate differences in changes in sleep time between delayed-start and comparison schools. Results A total of 455 students were included in the analysis (among those identifying sex, 225 girls [49.5%] and 219 boys [48.1%]; mean [SD] age at baseline, 15.2 [0.3] years). Relative to the change observed in the comparison schools, students who attended delayed-start schools had an additional mean 41 (95% CI, 25-57) objectively measured minutes of night sleep at follow-up 1 and 43 (95% CI, 25-61) at follow-up 2. Delayed start times were not associated with falling asleep later on school nights at follow-ups, and students attending these schools had a mean difference-in-differences change in weekend night sleep of −24 (95% CI, −51 to 2) minutes from baseline to follow-up 1 and −34 (95% CI, −65 to −3) minutes from baseline to follow-up 2, relative to comparison school participants. Differences in differences for school night sleep onset, weekend sleep onset latency, sleep midpoints, sleep efficiency, and the sleep fragmentation index between the 2 conditions were minimal. Conclusions and Relevance This study found that delaying high school start times could extend adolescent school night sleep duration and lessen their need for catch-up sleep on weekends. These findings suggest that later start times could be a durable strategy for addressing population-wide adolescent sleep deficits.

Abstract: The aim of the study was to compare the WHOOP strap – a wearable device that estimates sleep based on measures of movement and heart rate derived from actigraphy and photoplethysmography, respectiv...

Abstract: OBJECTIVE Poor sleep has been identified as a risk factor for poor glycemic control in individuals with type 2 diabetes (T2D). As optimal sleep can be characterized in several ways, we evaluated which sleep characteristics are most strongly associated with glycated hemoglobin A1c (HbA1c). RESEARCH DESIGN AND METHODS A total of 172 patients with T2D completed 7-day wrist-actigraphy and sleep questionnaires. Linear regression was used to evaluate associations between sleep measures (total sleep duration, variability in sleep duration, midsleep time, variability in midsleep time, sleep efficiency, subjective sleep quality, and subjective insomnia symptoms) and HbA1c, individually and in concert. RESULTS Variability in sleep duration was individually most strongly associated with HbA1c (β = 0.239; P = 0.002; R2 = 4.9%), followed by total sleep duration (U-shaped: β = 1.161/β2 = 1.044; P = 0.017/0.032; R2 = 4.3%), subjective sleep quality (β = 0.191; P = 0.012; R2 = 3.6%), variability in midsleep time (β = 0.184; P = 0.016; R2 = 3.4%), and sleep efficiency (β = −0.150; R2 = 2.3%). Midsleep time and subjective insomnia symptoms were not associated with HbA1c. In combination, variability in sleep duration, total sleep duration, and subjective sleep quality were significantly associated with HbA1c, together explaining 10.3% of the variance in HbA1c. Analyses adjusted for covariates provided similar results, although the strength of associations was generally decreased and showing total sleep duration and subjective sleep quality to be most strongly associated with HbA1c, together explaining 6.0% of the variance in HbA1c. CONCLUSIONS Sleep in general may be a modifiable factor of importance for patients with T2D. The prevention of sleep curtailment may serve as a primary focus in the sleep-centered management of T2D.

Abstract: Using a daily diary design and actigraphy sleep data across 2 weeks among 256 ethnic/racial minority adolescents (Mage = 14.72; 40% Asian, 22% Black, 38% Latinx; 2,607 days), this study investigated how previous-night sleep (duration, quality) moderated the same-day associations between ethnic/racial discrimination and stress responses (rumination, problem solving, family/peer support seeking) to predict daily well-being (mood, somatic symptoms, life satisfaction). On days when adolescents experienced greater discrimination, if they slept longer and better the previous night, adolescents engaged in greater active coping (problem solving, peer support seeking), and subsequently had better well-being. Adolescents also ruminated less when they slept longer the previous night regardless of discrimination. Findings highlight the role of sleep in helping adolescents navigate discrimination by facilitating coping processes.

Abstract: Background: Interest in biological clock pathways in bipolar disorders (BD) continues to grow, but there has yet to be an audit of circadian measurement tools for use in BD research and practice. Procedure: The International Society for Bipolar Disorders Chronobiology Task Force conducted a critical integrative review of circadian methods that have real-world applicability. Consensus discussion led to the selection of three domains to review – melatonin assessment, actigraphy and self-report. Results: Measurement approaches used to quantify circadian function in BD are described in sufficient detail for researchers and clinicians to make pragmatic decisions about their use. A novel integration of the measurement literature is offered in the form of a provisional taxonomy distinguishing between circadian measures (the instruments and methods used to quantify circadian function, such as dim light melatonin onset) and circadian constructs (the biobehavioural processes to be measured, such as circadian phase). Conclusions: Circadian variables are an important target of measurement in clinical practice and biomarker research. To improve reproducibility and clinical application of circadian constructs, an informed systematic approach to measurement is required. We trust that this review will decrease ambiguity in the literature and support theory-based consideration of measurement options.

Abstract: Objective: Poor sleep is associated with higher levels of inflammatory biomarkers. Conventionally, higher average time awake, lower average time asleep, and lower sleep efficiency define poor sleep. Recent research suggests that, in addition to average sleep, sleep inconsistency is an important indicator of sleep dysfunction. The current study sought to extend our knowledge of the relationship between sleep and inflammation through an examination of sleep inconsistency and inflammatory biomarkers. Methods: Secondary analyses of the Survey of Midlife in the United States (MIDUS) sleep study were conducted. Five hundred thirty-three individuals completed nightly sleep diaries, actigraphy, and underwent a blood draw for the inflammatory biomarkers C-reactive protein, interleukin-6, and fibrinogen. Sleep inconsistency was derived from 7 consecutive nights of assessment and was operationalized as nightly fluctuations in the following variables: terminal wakefulness, number of awakenings, time in bed, sleep onset latency, and wake after sleep onset. Structural equation modeling was used to examine the influence of a latent average sleep and a latent sleep inconsistency variable on a latent inflammation variable. Models were subsequently adjusted for age, sex, BMI, health, and medication. Stratified models by sex were also analyzed. Results: The average sleep model would not converge. The sleep inconsistency model fit the data well. A significant positive association between the latent factors sleep inconsistency and inflammation was observed (β = 10.18, SE = 4.40, p = 0.021), suggesting inconsistent sleep is associated with higher levels of inflammatory biomarkers. When stratified by sex, the association between the latent sleep inconsistency factor and inflammation was significant for women (β = 1.93, SE = 0.82, p = 0.018), but not men (β = 0.20, SE = 0.35, p = 0.566). The association between sleep inconsistency and inflammation weakened following multivariate adjustment (β = 6.23, SE = 3.71, p = 0.093). Conclusions: Inconsistent sleep may be an associated feature of inflammatory dysfunction, especially in women. Future studies should build upon this preliminary work and examine these associations longitudinally and through treatment trials.

Abstract: Suboptimal sleep causes cognitive decline and probably accelerates Alzheimer s Disease (AD) progression. Several sleep interventions have been tested in established AD dementia cases. However early intervention is needed in the course of AD at Mild Cognitive Impairment (MCI) or mild dementia stages to help prevent decline and maintain good quality of life. This systematic review aims to summarize evidence on sleep interventions in MCI and mild AD dementia. Seven databases were systematically searched for interventional studies where greater than 75% of participants met diagnostic criteria for MCI/mild AD dementia, with a control group and validated sleep outcome measures. Studies with a majority of participants diagnosed with Moderate to Severe AD were excluded. 20164 references were returned after duplication removal. 284 full papers were reviewed with 12 retained. Included papers reported 11 separate studies, total sample (n=602), mean age 76.3 years. Nine interventions were represented: Cognitive Behavioural Therapy Insomnia (CBT I), A Multi-Component Group Based Therapy, Phase Locked Loop Acoustic Stimulation, Melatonin, Donepezil, Galantamine, Rivastigmine, Tetrahydroaminoacridine and Continuous Positive Airway Pressure (CPAP). Psychotherapeutic approaches utilising adapted CBT-I achieved statistically significant improvements in the Pittsburgh Sleep Quality Index with one study reporting co-existent improved actigraphy variables. Melatonin significantly reduced sleep latency and sleep to wakefulness transitions in a small sample. CPAP demonstrated efficacy in participants with Obstructive Sleep Apnoea. Evidence to support other interventions was limited. There is a paucity of evidence for sleep interventions in MCI and mild AD highlighting a pressing need for high quality experimental studies exploring alternative sleep interventions.

Abstract: We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interp...

Abstract: INTRODUCTION: We investigated and compared associations of objective estimates of sleep and 24-hour activity rhythms using actigraphy with risk of dementia. METHODS: We included 1322 non-demented participants from the prospective, population-based Rotterdam Study cohort with valid actigraphy data (mean age 66 ± 8 years, 53% women), and followed them for up to 11.2 years to determine incident dementia. RESULTS: During follow-up, 60 individuals developed dementia, of which 49 had Alzheimer's disease (AD). Poor sleep as indicated by longer sleep latency, wake after sleep onset, and time in bed and lower sleep efficiency, as well as an earlier "lights out" time, were associated with increased risk of dementia, especially AD. We found no associations of 24-hour activity rhythms with dementia risk. DISCUSSION: Poor sleep, but not 24-hour activity rhythm disturbance, is associated with increased risk of dementia. Actigraphy-estimated nighttime wakefulness may be further targeted in etiologic or risk prediction studies.

Abstract: For patients with cancer, sleep disturbance is commonplace. Using classical test theory and Rasch analyses, the present study compared two commonly used psychometric instruments for insomnia - Athens Insomnia Scale and Insomnia Severity Index - among patients with advanced cancer. Through convenience sampling, patients with cancer at stage III or IV (n = 573; 326 males; mean age = 61.3 years; SD = 10.7) from eight oncology units of university hospitals in Iran participated in the study. All the participants completed the Athens Insomnia Scale, Insomnia Severity Index, Edmonton Symptom Assessment Scale, Hospital Anxiety and Depression Scale, General Health Questionnaire-12, Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index. Additionally, 433 participants wore an Actigraph device for two continuous weekdays. Classical test theory and Rasch analysis both supported the construct validity for Athens Insomnia Scale (factor loadings from confirmatory factor analysis = 0.61-0.87; test-retest reliability = 0.72-0.82; infit mean square = 0.81-1.17; outfit MnSq = 0.79-1.14) and for Insomnia Severity Index (factor loadings from confirmatory factor analysis = 0.61-0.81; test-retest reliability = 0.72-0.82; infit mean square = 0.72-1.14; outfit mean square = 0.76-1.11). Both Athens Insomnia Scale and Insomnia Severity Index had significant associations with Edmonton Symptom Assessment Scale, Hospital Anxiety and Depression Scale, General Health Questionnaire-12, Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index, as well as having good sensitivity and specificity. Significant differences in the actigraphy measure were found between insomniacs and non-insomniacs based on Athens Insomnia Scale or Insomnia Severity Index score. With promising results, healthcare providers can use either Athens Insomnia Scale or Insomnia Severity Index to understand the insomnia of patients with advanced cancer.

Abstract: STUDY OBJECTIVES Sleep disturbances are a feature in people living with dementia, including getting up during the night, difficulty falling asleep, and excessive daytime sleepiness and may precipitate a person with dementia moving into residential care. There are varying estimates of the frequency of sleep disturbances, and it is unknown whether they are a problem for the individual. We conducted the first systematic review and meta-analysis on the prevalence and associated factors of sleep disturbances in the care home population with dementia. METHODS We searched Embase, MEDLINE, and PsycINFO (29/04/2019) for studies of the prevalence or associated factors of sleep disturbances in people with dementia living in care homes. We computed meta-analytical estimates of the prevalence of sleep disturbances and used meta-regression to investigate the effects of measurement methods, demographics, and study characteristics. RESULTS We included 55 studies of 22,780 participants. The pooled prevalence on validated questionnaires of clinically significant sleep disturbances was 20% (95% confidence interval, CI 16% to 24%) and of any symptom of sleep disturbance was 38% (95% CI 33% to 44%). On actigraphy using a cutoff sleep efficiency of <85% prevalence was 70% (95% CI 55% to 85%). Staff distress, resident agitation, and prescription of psychotropic medications were associated with sleep disturbances. Studies with a higher percentage of males had a higher prevalence of sleep disturbance. CONCLUSIONS Clinically significant sleep disturbances are less common than those measured on actigraphy and are associated with residents and staff distress and the increased prescription of psychotropics. Actigraphy appears to offer no benefit over proxy reports in this population.

Abstract: Background. Sleep is important for consolidation of motor learning, but brain injury may affect sleep continuity and therefore rehabilitation outcomes. Objective. This study aims to assess the relationship between sleep quality and motor recovery in brain injury patients receiving inpatient rehabilitation. Methods. Fifty-nine patients with brain injury were recruited from 2 specialist inpatient rehabilitation units. Sleep quality was assessed (up to 3 times) objectively using actigraphy (7 nights) and subjectively using the Sleep Condition Indicator. Motor outcome assessments included Action Research Arm test (upper limb function), Fugl-Meyer Assessment (motor impairment), and the Rivermead Mobility Index. The Functional Independence Measure (FIM) was assessed at admission and discharge by the clinical team. Fifty-five age- and gender-matched healthy controls completed one assessment. Results. Inpatients demonstrated lower self-reported sleep quality (P < .001) and more fragmented sleep (P < .001) than controls. For inpatients, sleep fragmentation explained significant additional variance in motor outcomes, over and above that explained by admission FIM score (P < .017), such that more disrupted sleep was associated with poorer motor outcomes. Using stepwise linear regression, sleep fragmentation was the only variable found to explain variance in rate of change in FIM (R2adj = 0.12, P = .027), whereby more disrupted sleep was associated with slower recovery. Conclusions. Inpatients with brain injury demonstrate impaired sleep quality, and this is associated with poorer motor outcomes and slower functional recovery. Further investigation is needed to determine how sleep quality can be improved and whether this affects outcome.

Abstract: Author(s): Bangerter, Abigail; Chatterjee, Meenakshi; Manyakov, Nikolay V; Ness, Seth; Lewin, David; Skalkin, Andrew; Boice, Matthew; Goodwin, Matthew S; Dawson, Geraldine; Hendren, Robert; Leventhal, Bennett; Shic, Frederick; Esbensen, Anna; Pandina, Gahan | Abstract: Objective:The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Methods:Caregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Results:Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p l 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p l 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Conclusion:Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.

Abstract: Inadequate sleep (i.e., duration and/or quality) is becoming increasingly recognized as a global public health issue. Interaction via the gut-brain axis suggests that modification of the gut microbial environment via supplementation with live microorganisms (probiotics) or nonviable microorganisms/microbial cell fractions (paraprobiotics) may improve sleep health. This systematic review and meta-analysis aimed to clarify the effect of consuming probiotics/paraprobiotics on subjective and objective sleep metrics. Online databases were searched from 1980 to October 2019 for studies involving adults who consumed probiotics or paraprobiotics in controlled trials, during which, changes in subjective and/or objective sleep parameters were examined. A total of 14 studies (20 trials) were included in meta-analysis. Random effects meta-analyses indicated that probiotics/paraprobiotics supplementation significantly reduced Pittsburgh Sleep Quality Index (PSQI) score (i.e., improved sleep quality) relative to baseline (−0.78-points, 95% confidence interval: 0.395–1.166; p < 0.001). No significant effect was found for changes on other subjective sleep scales, nor objective parameters of sleep (efficiency/latency) measured using polysomnography or actigraphy. Subgroup analysis for PSQI data suggested that the magnitude of the effect was greater (although not statistically) in healthy participants than those with a medical condition, when treatment contained a single (rather than multiple) strain of probiotic bacteria, and when the duration of treatment was ≥8 weeks. Probiotics/paraprobiotics supplementation may have some efficacy in improving perceived sleep health, measured using the PSQI. While current evidence does not support a benefit of consuming probiotics/paraprobiotics when measured by other subjective sleep scales, nor objective measures of sleep; more studies using well-controlled, within-subject experimental designs are needed.

Abstract: Clinical actigraphy devices provide adequate estimates of some sleep measures across large groups. In practice, providers are asked to apply clinical or consumer wearable data to individual patient assessments. Inter-individual variability in device performance will impact such patient-specific interpretation. We assessed two devices, clinical and consumer, to determine the magnitude and predictors of this individual-level variability. One hundred and two patients (55 [53.9%] female; 56.4 [±16.3] years old) undergoing polysomnography wore Jawbone UP3 and/or Actiwatch2. Device total sleep time, sleep efficiency, wake after sleep onset and sleep latency were compared with polysomnography. Demographics, sleep architecture and clinical measures were compared to device performance. Actiwatch overestimated total sleep time by 27.2 min (95% confidence limits [CL], 138.3 min over to 84.0 under), overestimated sleep efficiency by 6.8% (95% CL, 34.1% over to 20.5% under), overestimated sleep onset latency by 2.6 min (95% CL, 63.3 over to 58.2 under) and underestimated wake after sleep onset by 50.7 min (95% CL, 162.5 under to 61.2 over). Jawbone overestimated total sleep time by 59.1 min (95% CL, 208.6 min over to 90.5 under) and overestimated sleep efficiency by 14.9% (95% CL, 52.6% over to 22.7% under). In multivariate models, age, sleep onset latency, wake after sleep onset, % N1 and apnea-hypopnea index explained only some of the variance in device performance. Gender also affected performance. Actiwatch and Jawbone mis-estimate sleep measures with very wide confidence limits and accuracy varies with multiple patient-level characteristics. Given these large individual inaccuracies, data from these devices must be applied only with extreme caution in clinical practice.

Abstract: Although insomnia is common among cancer patients, its prevalence remains variable, and its risk factors and correlation with other cancer-related symptoms are not fully explored in the literature. This study aims to determine the prevalence and severity of insomnia as well as risk factors and sleep-related symptom clusters in a sample of cancer patients. A cross-sectional survey was conducted collecting data from 213 cancer patients undergoing chemotherapy (age = 53.1 ± 11.3 years, 60% female). Insomnia was measured using the Insomnia Severity Index, a sleep log, and Actigraph, while symptoms were assessed using the Memorial Symptom Assessment Scale and the Hospital Anxiety and Depression Scale. Quality of life was measured with the Functional Assessment of Cancer Therapy—General. Of the participants, 42.8% reported insomnia, with 31.9% of those with insomnia reporting severe insomnia. Insomnia occurrence and severity were not correlated with the participants’ characteristics, cancer-related or treatment-related factors, only with the participants’ anxiety/depression scores. Principal component analysis showed that insomnia, depression, and anxiety formed a symptom cluster (p < 0.001). There was no difference between sleep parameters measured by Actigraphy in insomnia and non-insomnia participants. This study demonstrated that the prevalence of insomnia was high and indicated a symptom cluster of insomnia, depression, and anxiety. Therefore, interventions to reduce this symptom cluster may benefit cancer patients who are trying to manage these symptoms.