A comparison of actigraphy and polysomnography in older adults treated for chronic primary insomnia

Sleep trajectories across three cognitive-aging pathways in community older adults

INTRODUCTION

Comparing sleep and rest-activity rhythms across different cognitive aging pathways can identify novel risk factors and potential mechanisms. However, our current understanding is restricted by differences in sleep measurement, limited longitudinal data, and heterogeneous cognitive aging processes.

METHODS

We applied cubic splines to longitudinal self-reported sleep and actigraphy data from 1449 participants in the Rush Memory and Aging Project and quantified differences in the levels and trajectories of sleep amount, regularity, and timing within and between three cognitive aging pathways: normal, stable mild cognitive impairment, dementia.

RESULTS

Sleep amount was lowest in the dementia pathway prior to cognitive impairment but increased with age, most rapidly after dementia. Regularity declined across all pathways, most rapidly after cognitive diagnoses. Timing advanced across all pathways.

DISCUSSION

Shorter sleep amount in cognitively healthy older adults may be a risk factor or prodromal indicator of dementia, while longer sleep amounts and decreasing regularity may reflect neurodegeneration.

HIGHLIGHTS

We quantified longitudinal changes in sleep across three cognitive-aging pathways. We incorporated both subjective and objective measures of sleep health. Self-report duration increased noticeably from before to after cognitive diagnosis. Sleep irregularity increased most prominently after cognitive diagnosis. Advances in sleep timing occurred in both normal and pathological aging.

Moderately strong intraclass correlations between actigraphic and polysomnographic total sleep time and sleep efficiency in older adults with sleep disturbance

OBJECTIVE

To evaluate the reliability of the GeneActiv actigraphy device in measuring sleep parameters and compare its performance with polysomnography (PSG) in older adults with self-reported sleep disturbances.

METHODS

This sub-study was part of a pilot double-blinded randomized controlled crossover trial (CleverLights Study, ANZCTR ID 12619000138189). Participants (n = 12, mean age 67.7 years) underwent two nights of sleep studies with simultaneous GeneActiv actigraphy and PSG, separated by a 2-week interval. Sleep parameters including time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings were assessed. Intraclass Correlation Coefficients (ICCs) and Bland-Altman plots were used to determine reliability and agreement between methods.

RESULTS

GeneActiv actigraphy demonstrated strong correlations with PSG for TST (ICC = 0.79, p = 0.001) and SE (ICC = 0.85, p < 0.001), but tended to overestimate these parameters. Actigraphy also significantly underestimated the number of awakenings (ICC = 0.45, p = 0.021). Correlations with observed TIB (ICC = 0.30, p = 0.433), WASO (ICC = 0.33, p = 0.386), and SOL (ICC = 0.32, p = 0.056) were non-significant. Bland-Altman plots revealed proportional bias, especially in SOL and the number of awakenings.

CONCLUSION

Compared to PSG, the GeneActiv actigraphy device provides reliable measurements for total sleep time and sleep efficiency, but agreement was weaker for wake after sleep onset, sleep onset latency, and the number of awakenings. The device showed consistent performance across multiple nights, suggesting good reproducibility. However, it systematically overestimated total sleep time and underestimates wake-related parameters, hence it may not fully replace PSG for detailed sleep assessments.

Sleep Spindle Alterations in Children With Migraine

BACKGROUND

The modulation of thalamocortical activity is the most important site of several levels of interference between sleep spindles and migraine. Thalamocortical circuits are responsible for the electrophysiological phenomenon of sleep spindles. Spindle alterations may be used as a beneficial marker in the diagnosis and follow-up of children with migraine. We aimed to formulate the hypothesis that there is a shared mechanism that underlies migraine and sleep spindle activity.

METHODS

We analyzed the amplitude, frequency, duration, density, and activity of sleep spindles in non-rapid eye movement stage 2 sleep in patients with migraine without aura when compared with healthy control subjects.

RESULTS

The amplitudes of average, slow, and fast sleep spindles were higher in children with migraine without aura (P = 0.020, 0.013, and 0.033, respectively). The frequency of fast spindles was lower in children with migraines without aura when compared with the control group (P = 0.03). Although not statistically significant, the fast sleep spindle duration in the migraine group was shorter (P = 0.055). Multivariate analysis revealed an increased risk of migraine associated with increased mean spindle amplitude and decreased fast spindle frequency and duration.

CONCLUSIONS

Our data suggest that spindle alterations may correlate with the vulnerability to develop migraine and may be used as a model for future research about the association between the thalamocortical networks and migraine.

Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording

STUDY OBJECTIVE

To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries).

METHODS

Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups.

RESULTS

GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used.

CONCLUSIONS

We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices

AIMS

Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker).

FOCUS TECHNOLOGY

Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3.

REFERENCE TECHNOLOGY

In-lab polysomnography with 3-reader, consensus sleep scoring.

SAMPLE

Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years).

DESIGN

Overnight in a sleep laboratory from habitual sleep time to wake time.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography).

CORE OUTCOMES

EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33).

IMPORTANT ADDITIONAL OUTCOMES

Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset).

CORE CONCLUSION

EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

No phenotypic or genotypic evidence for a link between sleep duration and brain atrophy

Short sleep is held to cause poorer brain health, but is short sleep associated with higher rates of brain structural decline? Analysing 8,153 longitudinal MRIs from 3,893 healthy adults, we found no evidence for an association between sleep duration and brain atrophy. In contrast, cross-sectional analyses (51,295 observations) showed inverse U-shaped relationships, where a duration of 6.5 (95% confidence interval, (5.7, 7.3)) hours was associated with the thickest cortex and largest volumes relative to intracranial volume. This fits converging evidence from research on mortality, health and cognition that points to roughly seven hours being associated with good health. Genome-wide association analyses suggested that genes associated with longer sleep for below-average sleepers were linked to shorter sleep for above-average sleepers. Mendelian randomization did not yield evidence for causal impacts of sleep on brain structure. The combined results challenge the notion that habitual short sleep causes brain atrophy, suggesting that normal brains promote adequate sleep duration-which is shorter than current recommendations.

Efficacy of Blue LED Phototherapy on Sleep Quality and Behavioral and Psychological Symptoms of Dementia: A Double-Blind Randomized Controlled Trial

INTRODUCTION

People with dementia often experience behavioral and psychological symptoms of dementia (BPSD), which are a major cause of caregiver burden and institutionalization. Therefore, we conducted a double-blind, parallel-group randomized controlled trial to examine the efficacy of blue-enriched light therapy for BPSD in institutionalized older adults with dementia.

METHODS

Participants were enrolled and randomly allocated into blue-enriched light therapy (N = 30) or the conventional light group (N = 30) for 60 min in 10 weeks with five sessions per week. The primary outcome was sleep quality measured by actigraphy and Pittsburgh Sleep Quality Index (PSQI). The secondary outcome was overall BPSD severity (Cohen-Mansfield Agitation Inventory [CMAI] and Neuropsychiatric Inventory [NPI-NH]). The outcome indicators were assessed at baseline, mid-test, immediate posttest, 1-month, 3-month, and 6-month follow-up. The effects of the blue-enriched light therapy were examined by the generalized estimating equation model.

RESULTS

Blue-enriched light therapy revealed significant differences in the objective sleep parameters (sleep efficiency: β = 5.81, Waldχ2 = 32.60, CI: 3.82; 7.80; sleep latency: β = -19.82, Waldχ2 = 38.38, CI:-26.09; -13.55), subjective sleep quality (PSQI: β = -2.07, Waldχ2 = 45.94, CI: -2.66; -1.47), and overall BPSD severity (CMAI: β = -0.90, Waldχ2 = 14.38, CI: -1.37; -0.44) (NPI-NH: β = -1.67, Waldχ2 = 30.61, CI: -2.26; -1.08) compared to conventional phototherapy immediate posttest, 1-month, 3-month, and 6-month follow-up. Furthermore, the effects for sleep efficiency and sleep latency lasted for up to 6 months. In the subscale analysis, the differences of the behavioral symptoms changed significantly between the groups in physical/nonaggressive (CI: -1.01; -0.26) and verbal/nonaggressive (CI: -0.97; -0.29).

CONCLUSIONS

Blue-enriched light therapy is a feasible low-cost intervention that could be integrated as a comprehensive therapy program for BPSD among older adults with dementia.

The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review

Low levels of physical activity (PA) and sleep disruption are commonly seen in older adult inpatients and are associated with poor health outcomes. Wearable sensors allow for objective continuous monitoring; however, there is no consensus as to how wearable sensors should be implemented. This review aimed to provide an overview of the use of wearable sensors in older adult inpatient populations, including models used, body placement and outcome measures. Five databases were searched; 89 articles met inclusion criteria. We found that studies used heterogenous methods, including a variety of sensor models, placement and outcome measures. Most studies reported the use of only one sensor, with either the wrist or thigh being the preferred location in PA studies and the wrist for sleep outcomes. The reported PA measures can be mostly characterised as the frequency and duration of PA (Volume) with fewer measures relating to intensity (rate of magnitude) and pattern of activity (distribution per day/week). Sleep and circadian rhythm measures were reported less frequently with a limited number of studies providing both physical activity and sleep/circadian rhythm outcomes concurrently. This review provides recommendations for future research in older adult inpatient populations. With protocols of best practice, wearable sensors could facilitate the monitoring of inpatient recovery and provide measures to inform participant stratification and establish common objective endpoints across clinical trials.

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs' performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.

Sleep disturbances and the association with attenuated psychotic symptoms in individuals at ultra high-risk of psychosis

Sleep disturbances are common in individuals at ultra high-risk (UHR) of psychosis and have proven to play a causal role in the occurrence of psychotic symptoms in healthy individuals. Only a few studies have systematically investigated sleep disturbances in UHR individuals. The help-seeking UHR individuals were 18-40 years old, and we included 72 UHR individuals according to the Comprehensive Assessment of At-Risk Mental State criteria (CAARMS) and 36 healthy controls. Sleep was measured with a modified version of the Karolinska Sleep Questionnaire and actigraphy for one night, and melatonin was measured at awakening and bedtime. We compared subjective rated sleep and actigraphy between healthy and UHR individuals (t-test and chi-square test) and examined the association between a CAARMS-composite score (linear regression). UHR individuals subjectively experienced poor sleep, categorised as disturbed sleep- and awakening index compared with healthy controls. We found no differences in actigraphy variables or morning/evening melatonin between UHR and healthy controls (t-test and chi-square). A high CAARMS-composite score was associated with high morning melatonin (B = 0.15, CI 0.02 to 0.27, p = 0.024) and high awakening index (B = 1.86, CI 0.58 to 3.14, p = 0.004) in UHR individuals. The results suggest that UHR individuals with high CAARMS scores have a delayed sleep phase; they have difficulties waking up and feel exhausted at awakening. It might be necessary to evaluate how UHR individuals sleep, and it would be of great interest to follow these patients over time according to the development of psychosis.

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy

Introduction

Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy.

Methods

For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models.

Results

With HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86.

Discussion

Our model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.

Systematic review and meta‐analysis of sleep quality in inactive inflammatory bowel disease

Poor sleep in people with inflammatory bowel disease (IBD) has been demonstrated to be prevalent and has been associated with disease activity. This meta‐analysis aimed to assess the prevalence of poor sleep in inactive IBD and in controls by considering cohort and cross‐sectional studies. Electronic databases were searched for publications from inception to 1 November 2021. Poor sleep and IBD activity were defined according to self‐reported subjective sleep measures. A random effects model was used to determine the standardized mean difference between poor sleep in inactive IBD and healthy controls. Publication bias was assessed by funnel plot and Egger's test. Five hundred and nineteen studies were screened with 9 studies included in the meta‐analysis incorporating a total of 729 people with IBD and 508 controls. A random effects model showed a standardized mean difference with poor sleep being more frequent in those with inactive IBD than controls with moderate effect size (Hedge's g 0.41, CI [0.22–0.59]) and no significant heterogeneity. There was no publication bias evident. Poor sleep is more common in individuals with inactive IBD than healthy controls. Further studies should consider potential mechanisms to explain this result, including the role of subclinical inflammation and psychosocial factors that may influence sleep quality in people with IBD.

Sleep patterns of patients receiving home parenteral nutrition: A home-based observational study

BACKGROUND

Patients supported with home parenteral nutrition (HPN) often report poor sleep; however, limited research has been conducted to objectively measure sleep patterns of HPN-dependent patients.

METHODS

We aimed to characterize the sleep patterns of patients receiving HPN through 7-day actigraphy in a home-based observational study. Sleep measures of clinical importance were derived from actigraphy, including sleep duration, sleep efficiency, sleep onset latency, and wake after sleep onset. Participants also completed validated sleep surveys.

RESULTS

Twenty participants completed all study procedures (mean [SD]: age = 51.6 [13.9] years, body mass index = 21.4  [4.6], and 80% female). The population median (IQR) for sleep duration, sleep efficiency, sleep onset latency, and wake after sleep onset was 6.9 (1.1) h, 83.3% (7.8%), 11.8 (7.1) min, and 57.2 (39.9) min, respectively, and 55%, 60%, 35%, and 100% of participants did not meet the recommendations for these measures from the National Sleep Foundation. Sixty-five percent of participants reported napping at least once during the 7-day period. Based on the Insomnia Severity Index, 70% of participants were classified as having subthreshold or more severe insomnia. Based on the Pittsburgh Sleep Quality Index, 85% were classified as having significant sleep disturbance.

CONCLUSION

Most HPN-dependent patients likely have disrupted sleep largely driven by difficulty maintaining sleep. The extent to which HPN contributed to poor sleep cannot be elucidated from this observational study. Addressing known factors that contribute to sleep disruption and considering sleep interventions may improve the overall quality of life of patients receiving HPN.

Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people's productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80-86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.

Actigraphic Physical Activity, Pain Intensity, and Polysomnographic Sleep in Fibromyalgia

INTRODUCTION

Fibromyalgia involves chronic pain and disrupted physical activity and sleep. Research examining the relationship between pre-bedtime physical activity, pain, and objective sleep is limited. This study examined whether objectively measured physical activity levels (via actigraphy), pain intensity, or their interaction are associated with polysomnographic sleep outcomes.

METHODS

Adults with fibromyalgia and insomnia complaints (n = 134, mean age = 52 yrs, SD = 12 yrs, 94% female) completed 14 days of biaxial, wrist worn actigraphy, pain ratings, and a single night of polysomnography (PSG). Average activity for intervals 9:00-12:00, 12:00-15:00, 15:00-18:00, 18:00-21:00 was computed. Multiple regressions examined whether average activity, average evening pain, or their interaction were associated with PSG outcomes: sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency, %stage1, %stage2, %stage3, and %rapid eye movement. Analyses controlled for age, body mass index, average bedtime, time in bed, and sleep/pain medication use.

RESULTS

Greater morning actigraphic physical activity from 9:00 to 12:00 was independently associated with greater %stage 1 sleep (B = 0.01, SE = 0.00, p < .01). Greater afternoon activity from 12:00 to 15:00 independently predicted a higher WASO (p < .001). Associations between afternoon physical activity from 12:00 to 15:00 and greater %stage 1 (p < .001) were significant for at higher (~71/100), average (~52/100), but not lowest (~32/100) pain.

CONCLUSION

Greater morning and afternoon activity is associated with greater PSG sleep fragmentation and greater %stage 1 sleep in individuals with fibromyalgia and insomnia complaints, and the relationship between higher physical activity and greater %stage 1 is stronger for individuals with higher pain. Further studies examining causal pathways between physical activity, activity pacing, and sleep are warranted in fibromyalgia.

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

STUDY OBJECTIVES

The objective assessment of insomnia has remained difficult. Multisensory devices collecting heart rate (HR) and motion are regarded as the future of ambulatory sleep monitoring. Unfortunately, reports on altered average HR or heart rate variability (HRV) during sleep in insomnia are equivocal. Here, we evaluated whether the objective quantification of insomnia improves by assessing state-related changes in cardiac measures.

METHODS

We recorded electrocardiography, posture, and actigraphy in 33 people without sleep complaints and 158 patients with mild to severe insomnia over 4 d in their home environment. At the microscale, we investigated whether HR changed with proximity to gross (body) and small (wrist) movements at nighttime. At the macroscale, we calculated day-night differences in HR and HRV measures. For both timescales, we tested whether outcome measures were related to insomnia diagnosis and severity.

RESULTS

At the microscale, an increase in HR was often detectable already 60 s prior to as well as following a nocturnal chest, but not wrist, movement. This increase was slightly steeper in insomnia and was associated with insomnia severity, but future EEG recordings are necessary to elucidate whether these changes occur prior to or simultaneously with PSG-indicators of wakefulness. At the macroscale, we found an attenuated cardiac response to sleep in insomnia: patients consistently showed smaller day-night differences in HR and HRV.

CONCLUSIONS

Incorporating state-related changes in cardiac features in the ambulatory monitoring of sleep might provide a more sensitive biomarker of insomnia than the use of cardiac activity averages or actigraphy alone.

Objective sleep assessments for healthy people in environmental research: A literature review

To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring-oxygen saturation monitoring (RM-OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM-OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non-contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

Insomnia with objective short sleep duration in women with temporomandibular joint disorder: quantitative sensory testing, inflammation and clinical pain profiles

OBJECTIVES/BACKGROUND

Temporomandibular joint disorder (TMD) is a disabling facial pain syndrome with a high prevalence of insomnia that primarily affects women. Insomnia with objective short sleep duration (ISSD) is an emerging phenotype linked to cardiometabolic morbidity and increased mortality. The present report examines the association of ISSD on clinical and laboratory pain and systemic inflammation in TMD.

METHODS

We collected baseline data from 128 women with TMD and insomnia as part of a clinical trial evaluating psychological interventions for sleep and pain. Participants completed self-report questionnaires, one-night polysomnography, a two-week actigraphy assessment, quantitative sensory testing (QST) to assess cold pain tolerance, pain sensitivity and central sensitization and circulating Interleukin-6 levels were measured to assess systemic inflammation.

RESULTS

24.2% (n = 31) of the sample met criteria for ISSD [polysomnography (sleep duration <6 h)]. Compared to those with insomnia and normal sleep duration, ISSD were older (40.4 vs. 34.9,p < 0.05) and a greater proportion self-identified as Black (48.4% vs 11.3%,p < 0.001). Multivariate regressions revealed that ISSD endorsed higher self-report pain severity and functional limitation of the jaw. ISSD also demonstrated increased generalized pain sensitivity, enhanced central sensitization, cold pressor tolerance and higher resting interleukin-6 levels.

CONCLUSIONS

This is the first study to characterize the ISSD phenotype in a chronic pain sample and expand the scope of its negative health outcomes to chronic pain. ISSD may be an important chronic pain phenotype associated with a more severe clinical and laboratory pain profile, and future studies should focus on implications for treatment response and disease trajectory.

CLINICAL TRIAL

ClinicalTrials.gov Identifier: NCT01794624.

Concordance between self-reported sleep and actigraphy-assessed sleep in adult survivors of childhood cancer: the impact of psychological and neurocognitive late effects

PURPOSE

To examine self-reported (30-day) sleep versus nightly actigraphy-assessed sleep concordance in long-term survivors of childhood cancer.

METHODS

Four hundred seventy-seven participants enrolled in the St. Jude Lifetime Cohort (53.5% female, median (range) age 34.3 (19.3-61.6) years, 25.4 (10.9-49.3) years from diagnosis) completed the Pittsburgh Sleep Quality Index and ≥ 3 nights of actigraphy. Participants had neurocognitive impairment and/or a self-reported prolonged sleep onset latency (SOL). Self-reported 30-day sleep and nightly actigraphic sleep measures for sleep duration, SOL, and sleep efficiency (SE) were converted into ordinal categories for calculation of weighted kappa coefficients. General linear models estimated associations between measurement concordance and late effects.

RESULTS

Agreements between self-reported and actigraphic measures were slight to fair for sleep duration and SOL measures (kw = 0.20 and kw = 0.22, respectively; p < 0.0001) and poor for SE measures (kw = 0.00, p = 0.79). In multivariable models, severe fatigue and poor sleep quality were significantly associated with greater absolute differences between self-reported and actigraphy-assessed sleep durations (B = 26.6 [p < 0.001] and B = 26.8 [p = 0.01], respectively). Survivors with (versus without) memory impairment had a 44-min higher absolute difference in sleep duration (B = 44.4, p < 0.001). Survivors with, versus without, depression and poor sleep quality had higher absolute discrepancies of SOL (B = 24.5 [p = 0.01] and B = 16.4 [p < 0.0001], respectively). Poor sleep quality was associated with a 12% higher absolute difference in SE (B = 12.32, p < 0.0001).

CONCLUSIONS

Self-reported sleep and actigraphic sleep demonstrated discordance in our sample. Several prevalent late effects were statistically significantly associated with increased measurement discrepancy. Future studies should consider the impacts of late effects on sleep assessment in adult survivors of childhood cancer.

Thematic trends and knowledge structure on cognitive behavior therapy for insomnia: A bibliometric and visualization analysis

OBJECTIVE

To find publications trend about cognitive behavior therapy for insomnia (CBTI) using bibliometric and visualization analysis. In this study, the authors sought to identify the publication trends of peer-reviewed articles about CBTI.

MATERIALS AND METHODS

Analyses were focused on the past 18 years from 2004 to 2021. All searches were performed on the Web of Science Core Collection database. The search was repeated to include structural cognitive behavior therapy for insomnia. Quantitative analysis was assessed using the bibliometric tool. Visualization analysis was carried out using VOSviewer.

RESULTS

In the 736 articles reviewed, the number of publications has been increasing every year for the past 18 years. Behavioral sleep medicine and sleep were the most active journals published on CBTI. The United States and Canada had the highest scientific publications in the field. Morin CM and Espie CA were the most active authors. The study type mostly observed were randomized controlled trials, meta-analyses, and epidemiological. Publications on digital-based cognitive behavior therapy and accessibility to primary care settings represent the future trends of research on CBTI.

CONCLUSION

Possible explanations for CBTI publication trends were discussed, including the emergence of the evidence-based therapy, feasibility, and scalability. Potential CBTI publications trends in the future and clinical implications were also discussed.

Subjective Sleep Quality and Sleep Architecture in Patients With Migraine: A Meta-analysis

BACKGROUND AND OBJECTIVES

Sleep disturbance is often associated with migraine. However, there is a paucity of research investigating objective and subjective measures of sleep in patients with migraine. This meta-analysis aims to determine whether there are differences in subjective sleep quality measured using the Pittsburgh Sleep Quality Index (PSQI) and objective sleep architecture measured using polysomnography (PSG) between adult and pediatric patients and healthy controls.

METHODS

This review was preregistered on PROSPERO (CRD42020209325). A systematic search of 5 databases (Embase, MEDLINE, Global Health, APA PsycINFO, and APA PsycArticles, last searched on December 17, 2020) was conducted to find case-control studies that measured PSG or PSQI in patients with migraine. Pregnant participants and those with other headache disorders were excluded. Effect sizes (Hedges g) were entered into a random effects model meta-analysis. Study quality was evaluated with the Newcastle Ottawa Scale and publication bias with the Egger regression test.

RESULTS

Thirty-two studies were eligible, of which 21 measured PSQI or Migraine Disability Assessment Test in adults, 6 measured PSG in adults, and 5 measured PSG in children. The overall mean study quality score was 5/9; this did not moderate any of the results and there was no risk of publication bias. Overall, adults with migraine had higher PSQI scores than healthy controls (g = 0.75, p < 0.001, 95% confidence interval [CI] 0.54-0.96). This effect was larger in those with a chronic rather than episodic condition (g = 1.03, p < 0.001, 95% CI 0.37-1.01; g = 0.63, p < 0.001, 95% CI 0.38-0.88, respectively). For polysomnographic studies, adults and children with migraine displayed a lower percentage of rapid eye movement sleep (g = -0.22, p = 0.017, 95% CI -0.41 to -0.04; g = -0.71, p = 0.025, 95% CI -1.34 to -0.10, respectively) than controls. Pediatric patients displayed less total sleep time (g = -1.37, p = 0.039, 95% CI -2.66 to -0.10), more wake (g = 0.52, p < 0.001, 95% CI 0.08-0.79), and shorter sleep onset latency (g = -0.37, p < 0.001, 95% CI -0.54 to -0.21) than controls.

DISCUSSION

People with migraine have significantly poorer subjective sleep quality and altered sleep architecture compared to healthy individuals. Further longitudinal empirical studies are required to enhance our understanding of this relationship.

A New Sleep Staging System for Type III Sleep Studies Equipped with a Tracheal Sound Sensor

Type III sleep studies record cardio-respiratory channels only. Compared with polysomnography, which also records electrophysiological channels, they present many advantages: they are less expensive, less time-consuming, and more likely to be performed at home. However, their accuracy is limited by missing sleep information. That is why many studies present specific cardio-respiratory parameters to assess the causal effects of sleep stages upon cardiac or respiratory activities. For this paper, we gathered many parameters proposed in literature, leading to 1,111 features. The pulse oximeter, the PneaVoX sensor (recording tracheal sounds), respiratory inductance plethysmography belts, the nasal cannula and the actimeter provided the 112 worthiest ones for automatic sleep scoring. Then, a 3-step model was implemented: classification with a multi-layer perceptron, sleep transition rules corrections (from the AASM guidelines), and sequence corrections using a Viterbi hidden Markov model. The whole process was trained and tested using 300 and 100 independent recordings provided from patients suspected of having sleep breathing disorders. Results indicated that the system achieves substantial agreement with manual scoring for classifications into 2 stages (wake vs. sleep: mean Cohen's Kappa of 0.63 and accuracy rate Acc of 87.8%) and 3 stages (wake vs. R stage vs. NREM stage: mean of 0.60 and Acc of 78.5%). It indicates that the method could provide information to help specialists while diagnosing sleep. The presented model had promising results and may enhance clinical diagnosis.

Objective measurement of sleep by smartphone application: comparison with actigraphy and relation to self-reported sleep

Aim: Smartphone technology is increasingly used by the public to assess sleep. Specific features of some sleep-tracking applications are comparable to actigraphy in objectively monitoring sleep. The clinical utility of smartphone apps should be investigated further to increase access to convenient means of monitoring sleep. Methods: Smartphone and subjective sleep measures were administered to 29 community-dwelling healthy adults [aged 20-67, Mean (M) = 26.8; 18 women, 11 men], and actigraphy to 19 of them. Total sleep time (TST) and sleep efficiency were measured with actigraphy and the Sleep Time app (Azumio Inc.). Sleep diaries captured subjective TST and sleep efficiency, and the Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index provided self-report data. An exit questionnaire was administered to examine app feasibility and likelihood of future use. Results: The app significantly overestimated TST when compared to actigraphy. There was no significant difference in sleep efficiency between methodologies. There was also no significant difference between TST recorded through the app and through sleep diaries. Participants’ self-reported ease of use of the smartphone app positively correlated with likelihood of future use. Conclusions: Based on the current findings, future research is needed to investigate the utility and feasibility of multiple smartphone applications in monitoring sleep in healthy and clinical populations.

A systematic review and meta-analysis of wind turbine noise effects on sleep using validated objective and subjective sleep assessments

Little is known about the potential impacts of wind turbine noise (WTN) on sleep. Previous research is limited to cross-sectional studies reporting anecdotal impacts on sleep using inconsistent sleep metrics. This meta-analysis sought to comprehensively review studies evaluating the impact of WTN using widely accepted and validated objective and subjective sleep assessments. Search terms included: "wind farm noise", "wind turbine noise", "wind turbine sound", "wind turbine noise exposure" AND "sleep". Only original articles published in English published after the year 2000 and reporting sleep outcomes in the presence of WTN using polysomnography, actigraphy or psychometrically validated sleep questionnaires were included. Uniform outcomes of the retrieved studies were meta-analysed to examine WTN effects on objective and subjective sleep outcomes. Nine studies were eligible for review and five studies were meta-analysed. Meta-analyses (Hedges' g; 95% confidence interval [CI]) revealed no significant differences in objective sleep onset latency (0.03, 95%  CI -0.34 to 0.41), total sleep time (-0.05, 95%  CI -0.77 to 0.67), sleep efficiency (-0.25, 95%  CI -0.71 to 0.22) or wake after sleep onset (1.25, 95%  CI -2.00 to 4.50) in the presence versus absence of WTN (all p > .05). Subjective sleep estimates were not meta-analysed because measurement outcomes were not sufficiently uniform for comparisons between studies. This systematic review and meta-analysis suggests that WTN does not significantly impact key indicators of objective sleep. Cautious interpretation remains warranted given variable measurement methodologies, WTN interventions, limited sample sizes, and cross-sectional study designs, where cause-and-effect relationships are uncertain. Well-controlled experimental studies using ecologically valid WTN, objective and psychometrically validated sleep assessments are needed to provide conclusive evidence regarding WTN impacts on sleep.

A systematic review and meta-analysis of individual differences in naturalistic sleep quality and episodic memory performance in young and older adults

Better sleep quality has been associated with better episodic memory performance in young adults. However, the strength of sleep-memory associations in aging has not been well characterized. It is also unknown whether factors such as sleep measurement method (e.g., polysomnography, actigraphy, self-report), sleep parameters (e.g., slow wave sleep, sleep duration), or memory task characteristics (e.g., verbal, pictorial) impact the strength of sleep-memory associations. Here, we assessed if the aforementioned factors modulate sleep-memory relationships. Across age groups, sleep-memory associations were similar for sleep measurement methods, however, associations were stronger for PSG than self-report. Age group moderated sleep-memory associations for certain sleep parameters. Specifically, young adults demonstrated stronger positive sleep-memory associations for slow wave sleep than the old, while older adults demonstrated stronger negative associations between greater wake after sleep onset and poorer memory performance than the young. Collectively, these data show that young and older adults maintain similar strength in sleep-memory relationships, but age impacts the specific sleep correlates that contribute to these relationships.

The effects of auricular acupressure on the sleep of the elderly using polysomnography, actigraphy and blood test: Randomized, single-blind, sham control

AIM

This study was conducted to examine the effects of auricular acupressure on sleep in elderly people with sleep disorders.

METHODS

This was a randomized, double-blind, sham-controlled study. The participants aged over 65 years old were randomly assigned to the experimental group (n = 21) and the sham control group (n = 21). The participants in the experimental group and the sham control group received auricular acupressure on sleep-disorder-related points or to sleep-disorder-unrelated points, respectively. The intervention was implemented for a total of eight weeks. To validate the effects of the treatment, polysomnography with the Alice portable sleep diagnostic system; actigraphy with Fitbit Alta; and melatonin, serotonin, and cortisol blood tests were conducted.

RESULTS

Non-Rapid Eye Movement sleep stage 3 duration change (Z = -2.187, p = .029) and Non-Rapid Eye Movement sleep stage 3 ratio change (Z = -2.423, p = .014), measured by polysomnography, of the experimental group showed a significant increase over time compared to the sham control group.

CONCLUSIONS

Auricular acupressure applied for eight weeks was found to be effective in increasing Non-Rapid Eye Movement sleep stage 3 duration and Non-Rapid Eye Movement sleep stage 3 ratio among sleep stages of the elderly. Consequently, it showed that auricular acupressure can be used as a proven nursing intervention method for sleep disorder in elders to increase deep sleep duration and ratio.

Ambient bright light treatment improved proxy-rated sleep but not sleep measured by actigraphy in nursing home patients with dementia: a placebo-controlled randomised trial

BACKGROUND

Up to 70% of nursing home patients with dementia suffer from sleep problems. Light is the main zeitgeber to the circadian system and thus has a fundamental impact on sleep-wake behaviour. Low indoor light levels in nursing homes have been reported, and in combination with age-related reductions in light sensitivity, insufficient light exposure is likely to contribute to sleep problems in this population. Increasing daytime light exposure using bright light treatment (BLT) may represent a feasible non-pharmacological treatment for sleep problems in nursing home patients with dementia.

METHODS

The present study reports on sleep outcomes, which are the primary outcomes of the DEM.LIGHT trial (Therapy Light Rooms for Nursing Home Patients with Dementia- Designing Diurnal Conditions for Improved Sleep, Mood and Behavioural Problems), a 24-week cluster-randomised placebo-controlled trial including 8 nursing home units and 69 resident patients. The intervention comprised ambient light of 1000 lx and 6000 K from 10:00 to 15:00, with gradually increasing and decreasing light levels prior to and following this interval, using ceiling mounted light-fixtures and light emitting diode technology. The placebo condition had continuous standard light levels (150-300 lx, ~ 3000 K). Sleep was assessed at baseline and follow-up at week 8, 16, and 24, using the proxy-rated Sleep Disorder Inventory (SDI) and actigraphy (Actiwatch II, Philips Respironics). Mixed linear models were used to evaluate intervention effects, adjusting for relevant covariates such as age, gender, number of drugs, severity of dementia, eye disease, and estimated light exposure.

RESULTS

Sleep as measured by the SDI was significantly improved in the intervention group compared to the control group from baseline to week 16 (B = - 0.06, 95% CI -0.11 - -0.01, p < .05) and from baseline to week 24 (B = - 0.05, 95% CI -0.10 - -0.01, p < .05). There was no effect according to the SDI at week 8 and no significant effects in terms of actigraphically measured sleep.

CONCLUSIONS

Proxy-rated sleep improved among nursing home patients with dementia following 16 and 24 weeks of BLT. These improvements were not corroborated by actigraphy recordings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03357328 . Registered 29 November 2017 - Retrospectively registered.

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients

Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth

The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population. This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. An AASM-based sleep stage categorizing method was proposed to standardize the sleep stages scored by different types of wearable trackers. Sleep features and sleep stage architecture were extracted from the PSG and the wearable device's hypnograms. Therefrom, a localized quantifier index was developed to characterize the local mismatch of sleep scoring. We evaluated different commonly used wearable sleep tracking devices with the data collected from 22 different subjects over 30 nights of 8-h sleeping. The proposed localization quantifiers can characterize the chronologically localized mismatches over the sleeping time. The outperformance of the proposed method over existing evaluation methods was reported. The proposed evaluation method can be utilized for the improvement of the sensor design and scoring algorithm.

Physiologic vasomotor symptoms are associated with verbal memory dysfunction in breast cancer survivors

OBJECTIVE

Vasomotor symptoms (VMS), sleep disturbance, and cognitive complaints are common among women with a history of breast cancer and contribute to decreased quality of life. Studies in healthy women showed an association between verbal memory performance and physiologic VMS measured with ambulatory skin conductance monitors but not with VMS by self-report. We hypothesized that we would find a similar association in women with breast cancer.

METHODS

Participants included 30 female breast cancer survivors (mean age 52.7 y; 26.7% African-American) with moderate-to-severe VMS enrolled in a larger clinical trial of a nonhormonal intervention for VMS. At baseline, participants completed assessments of physiologic VMS, actigraphy-based assessments of sleep, questionnaires about mood, and two tests of verbal memory - Logical Memory (LM) and the California Verbal Learning Test (CVLT). Using baseline data, we conducted multivariate regression analyses to examine the association between VMS and memory, controlling for sleep and other factors.

RESULTS

On average, women reported 46% of total physiologic VMS. A higher frequency of physiologic VMS - but not reported VMS - was significantly associated with lower scores on the California Verbal Learning Test short-delay free recall (r[28] = -0.41, P = 0.03), long-delay free recall (r[28] = -0.42, P = 0.03), and total clustering, (r[28] = -0.39, P = 0.04). These associations were independent of sleep, mood, and other factors.

CONCLUSIONS

Independent of their effect on sleep, VMS may be a modifiable contributor to memory difficulties in women with breast cancer. These findings underscore the importance of objective measurement of VMS in cognitive studies. : Video Summary:http://links.lww.com/MENO/A623.

Sleep and its Association With Pain and Depression in Nursing Home Patients With Advanced Dementia - a Cross-Sectional Study

Objective: Previous research suggests a positive association between pain, depression and sleep. In this study, we investigate how sleep correlates with varying levels of pain and depression in nursing home (NH) patients with dementia.

Materials and methods: Cross-sectional study (n = 141) with sleep-related data, derived from two multicenter studies conducted in Norway. We included NH patients with dementia according to the Mini-Mental State Examination (MMSE ≤ 20) from the COSMOS trial (n = 46) and the DEP.PAIN.DEM trial (n = 95) whose sleep was objectively measured with actigraphy. In the COSMOS trial, NH patients were included if they were ≥65 years of age and with life expectancy >6 months. In the DEP.PAIN.DEM trial, patients were included if they were ≥60 years and if they had depression according to the Cornell Scale for Depression in Dementia (CSDD ≥ 8). In both studies, pain was assessed with the Mobilization-Observation-Behavior-Intensity-Dementia-2 Pain Scale (MOBID-2), and depression with CSDD. Sleep parameters were total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), early morning awakening (EMA), daytime total sleep time (DTS) and time in bed (TiB). We registered use of sedatives, analgesics, opioids and antidepressants from patient health records and adjusted for these medications in the analyses.

Results: Mean age was 86.2 years and 76.3% were female. Hierarchical regressions showed that pain was associated with higher TST and SE (p < 0.05), less WASO (p < 0.01) and more DTS (p < 0.01). More severe dementia was associated with more WASO (p < 0.05) and TiB (p < 0.01). More severe depression was associated with less TST (p < 0.05), less DTS (p < 0.01) and less TiB (p < 0.01). Use of sedative medications was associated with less TiB (p < 0.05).

Conclusion: When sleep was measured with actigraphy, NH patients with dementia and pain slept more than patients without pain, in terms of higher total sleep time. Furthermore, their sleep efficiency was higher, indicating that the patients had more sleep within the time they spent in bed. Patients with more severe dementia spent more time awake during the time spent in bed. Furthermore, people with more severe depression slept less at daytime and had less total sleep time Controlling for concomitant medication use did not affect the obtained results.

Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography

STUDY OBJECTIVES

Multisensor wearable consumer devices allowing the collection of multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home environment, are increasingly ubiquitous. However, the validity of such devices for sleep assessment has not been directly compared to alternatives such as wrist actigraphy or polysomnography (PSG).

METHODS

Eight participants each completed four nights in a sleep laboratory, equipped with PSG and several wearable devices. Registered polysomnographic technologist-scored PSG served as ground truth for sleep-wake state. Wearable devices providing sleep-wake classification data were compared to PSG at both an epoch-by-epoch and night level. Data from multisensor wearables (Apple Watch and Oura Ring) were compared to data available from electrocardiography and a triaxial wrist actigraph to evaluate the quality and utility of heart rate and motion data. Machine learning methods were used to train and test sleep-wake classifiers, using data from consumer wearables. The quality of classifications derived from devices was compared.

RESULTS

For epoch-by-epoch sleep-wake performance, research devices ranged in d' between 1.771 and 1.874, with sensitivity between 0.912 and 0.982, and specificity between 0.366 and 0.647. Data from multisensor wearables were strongly correlated at an epoch-by-epoch level with reference data sources. Classifiers developed from the multisensor wearable data ranged in d' between 1.827 and 2.347, with sensitivity between 0.883 and 0.977, and specificity between 0.407 and 0.821.

CONCLUSIONS

Data from multisensor consumer wearables are strongly correlated with reference devices at the epoch level and can be used to develop epoch-by-epoch models of sleep-wake rivaling existing research devices.

An unbiased, efficient sleep-wake detection algorithm for a population with sleep disorders: change point decoder

STUDY OBJECTIVES

The usage of wrist-worn wearables to detect sleep-wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep-wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep.

METHODS

Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD's performance was compared with the performance of the OA in relation to PSG.

RESULTS

On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of -22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep-wake transitions with an error of 64.4. The OA method's performance was 28.6 min, -0.03%, and -17.2, respectively.

CONCLUSIONS

The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep-wake transitions. The CPD could be used as an alternate framework to investigate sleep-wake dynamics within the conventional time frame of 30-s epochs.

Efficient embedded sleep wake classification for open-source actigraphy

This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.

The Development and Accuracy of the THIM Wearable Device for Estimating Sleep and Wakefulness

INTRODUCTION

THIM is a new wearable device worn on the finger that can passively monitor sleep and wakefulness overnight using actigraphy. This article showcases the development of the THIM sleep tracking algorithm (Study 1) and the testing of its accuracy against polysomnography (PSG) with an independent sample of good and poor sleepers (Study 2). The accuracy of THIM was compared to two popular wearables, Fitbit and Actiwatch devices.

METHODS

Twenty-five (Study 1) and twenty (Study 2) healthy individuals with good or poor sleep (defined by scores on the Insomnia Severity Index) slept overnight in the sleep laboratory on one night. Participants slept from their typical bedtime to their typical wake up time with simultaneous recording from PSG, THIM, Fitbit and Actiwatch devices.

RESULTS

In both studies, THIM had lower sensitivity (M = 0.89-0.91) compared to the Actiwatch (M = 0.95) and Fitbit devices (M = 0.96-0.98), yet higher specificity (M = 0.59 vs M = 0.32-0.59) in detecting sleep. There were no significant differences between PSG and THIM in either study for sleep onset latency, total sleep time, wake after sleep onset, or sleep efficiency, p > 0.06. Yet, there was high variability in the accuracy of all three actigraphy devices between individuals (evident on Bland-Altman plots) that was unexplained by sleep quality.

DISCUSSION

Together, these studies suggest that THIM is capable of monitoring sleep and wake overnight in good and poor sleepers to a similar degree of accuracy as two of the most popular actigraphy devices available. Future research will examine the accuracy of THIM for monitoring sleep in people with insomnia.

Emfit Bed Sensor Activity Shows Strong Agreement with Wrist Actigraphy for the Assessment of Sleep in the Home Setting

PURPOSE

Wrist-worn actigraphy via research-grade devices, a well-established approach to the assessment of rest-activity, is limited by poor compliance, battery life, and lack of direct evidence for time spent physically in the bed. A non-invasive bed sensor (Emfit) may provide advantages over actigraphy for long-term sleep assessment in the home. This study compared sleep-wake measurements between this sensor and a validated actigraph.

PATIENTS AND METHODS

Thirty healthy subjects (6 to 54 years) underwent simultaneous monitoring with both devices for 14 days and filled out a daily sleep diary. Parameters included bed entry time, sleep start, sleep end, bed exit time, rest interval duration, and wake after sleep onset (WASO). The agreement between the two devices was measured using Bland-Altman plots and inter-class correlation coefficients (ICC). In addition, sensitivity, specificity, and accuracy were obtained from epoch-by-epoch comparisons of Emfit and actigraphy.

RESULTS

Fifteen percent of the subjects reported that wearing the actigraph was a burden. None reported that using the bed sensor was a burden. The minimal detectable change between Emfit and actigraphy was 11 minutes for bed entry time, 14 minutes for sleep start, 14 minutes for sleep end, 10 minutes for bed exit time, 20 minutes for rest interval duration, and 110 minutes for WASO. Inter-class correlation coefficients revealed an excellent agreement for all sleep parameters (ICC=0.99, 95% CI 98-99) except for WASO (ICC=0.46, 95% CI 0.33-0.56). Sensitivity, specificity, and accuracy were 0.62, 0.93, and 0.88, respectively. Kappa correlation analysis revealed a moderate correlation between the two devices (κ=0.55, p<0.0001).

CONCLUSION

Emfit is an acceptable alternative to actigraphy for the estimation of bed entry time, sleep start, sleep end, bed exit time, and rest interval duration. However, WASO estimates are poorly correlated between the two devices. Emfit may offer methodological advantages in situations where actigraphy is challenging to implement.

Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography

BACKGROUND

Wearable devices have tremendous potential for large-scale longitudinal measurement of sleep, but their accuracy needs to be validated. We compared the performance of the multisensor Oura ring (Oura Health Oy, Oulu, Finland) to polysomnography (PSG) and a research actigraph in healthy adolescents.

METHODS

Fifty-three adolescents (28 females; aged 15-19 years) underwent overnight PSG monitoring while wearing both an Oura ring and Actiwatch 2 (Philips Respironics, USA). Measurements were made over multiple nights and across three levels of sleep opportunity (5 nights with either 6.5 or 8h, and 3 nights with 9h). Actiwatch data at two sensitivity settings were analyzed. Discrepancies in estimated sleep measures as well as sleep-wake, and sleep stage agreements were evaluated using Bland-Altman plots and epoch-by-epoch (EBE) analyses.

RESULTS

Compared with PSG, Oura consistently underestimated TST by an average of 32.8 to 47.3 minutes (Ps < 0.001) across the different TIB conditions; Actiwatch 2 at its default setting underestimated TST by 25.8 to 33.9 minutes. Oura significantly overestimated WASO by an average of 30.7 to 46.3 minutes. It was comparable to Actiwatch 2 at default sensitivity in the 6.5, and 8h TIB conditions. Relative to PSG, Oura significantly underestimated REM sleep (12.8 to 19.5 minutes) and light sleep (51.1 to 81.2 minutes) but overestimated N3 by 31.5 to 46.8 minutes (Ps < 0.01). EBE analyses demonstrated excellent sleep-wake accuracies, specificities, and sensitivities - between 0.88 and 0.89 across all TIBs.

CONCLUSION

The Oura ring yielded comparable sleep measurement to research grade actigraphy at the latter's default settings. Sleep staging needs improvement. However, the device appears adequate for characterizing the effect of sleep duration manipulation on adolescent sleep macro-architecture.

Poor Self-Reported Sleep is Related to Regional Cortical Thinning in Aging but not Memory Decline-Results From the Lifebrain Consortium

We examined whether sleep quality and quantity are associated with cortical and memory changes in cognitively healthy participants across the adult lifespan. Associations between self-reported sleep parameters (Pittsburgh Sleep Quality Index, PSQI) and longitudinal cortical change were tested using five samples from the Lifebrain consortium (n = 2205, 4363 MRIs, 18–92 years). In additional analyses, we tested coherence with cell-specific gene expression maps from the Allen Human Brain Atlas, and relations to changes in memory performance. “PSQI # 1 Subjective sleep quality” and “PSQI #5 Sleep disturbances” were related to thinning of the right lateral temporal cortex, with lower quality and more disturbances being associated with faster thinning. The association with “PSQI #5 Sleep disturbances” emerged after 60 years, especially in regions with high expression of genes related to oligodendrocytes and S1 pyramidal neurons. None of the sleep scales were related to a longitudinal change in episodic memory function, suggesting that sleep-related cortical changes were independent of cognitive decline. The relationship to cortical brain change suggests that self-reported sleep parameters are relevant in lifespan studies, but small effect sizes indicate that self-reported sleep is not a good biomarker of general cortical degeneration in healthy older adults.

Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults

The purpose of the present work is to examine, on a clinically diverse population of older adults (N = 46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh's algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for a fully automatic identification of sleep period time (SPT) and within the identified sleep period, the sleep-wake classification. SPT detected by ACT-S1 did not differ statistically from using PSG-EEG (bias = -9.98 min; correlation 0.89). In sleep-wake classification on 30-s epochs within the identified sleep period, the new ACT-S1 presented similar or slightly higher accuracy (83-87%), precision (86-89%) and F1 score (90-92%), significantly higher specificity (39-40%), and significantly lower, but still high, sensitivity (96-97%) compared to Sadeh's algorithm, which achieved 99% sensitivity as the only measure better than ACT-S1's. Total sleep times (TST) estimated with ACT-S1 and Sadeh's algorithm were higher, but still highly correlated to PSG-EEG's TST. Sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 were not significantly different from PSG-EEG, while the same sleep quality metrics derived by Sadeh's algorithm differed significantly from PSG-EEG. Agreement between ACT-S1 and PSG-EEG reached was highest when analyzing the subset of subjects with least disrupted sleep (N = 28). These results provide evidence of promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults. Future longitudinal validations across specific medical conditions are needed. The algorithm's performance may further improve with integrating multi-sensor information.

Daytime sleepiness and napping in nursing-home eligible community dwelling older adults: A mixed methods study

Objectives

To describe perceptions and beliefs about daytime sleepiness and napping along with subjective and objective daytime sleep characteristics in nursing-home eligible community dwelling older adults.

Methods

A mixed methods study; we conducted semi-structured interviews and measured sleep variables via Actigraphy, sleep diary, and Epworth Sleepiness Scale (ESS). Napping was defined as >10 minutes; anything less was considered dozing.

Results

Final sample (n = 40) was primarily female (85%), Black (100%), with a mean age of 72 ± 9.5 years. Few (25%) reported daytime sleepiness (ESS >10). However, average duration of napping per day was 33.1 ± 11.5 minutes with a nap frequency of 2.5 ± 1.5 naps.

Conclusion

Our sample napped frequently throughout the day, yet the majority reported no daytime sleepiness. These older adults did not always recognize napping or how much they napped.

Comparison of single-channel EEG, actigraphy, and sleep diary in cognitively normal and mildly impaired older adults

Study Objectives

Multiple methods for monitoring sleep-wake activity have identified sleep disturbances as risk factors for Alzheimer disease (AD). In order to identify the level of agreement between different methods, we compared sleep parameters derived from single-channel EEG (scEEG), actigraphy, and sleep diaries in cognitively normal and mildly impaired older adults.

Methods

Two hundred ninety-three participants were monitored at home for up to six nights with scEEG, actigraphy, and sleep diaries. Total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO) were calculated using each of these methods. In 109 of the 293 participants, the ratio of cerebrospinal fluid concentrations of phosphorylated tau (p-tau) and amyloid-β-42 (Aβ42) was used as a biomarker for AD pathology.

Results

Agreement was highest for TST across instruments, especially in cognitively normal older adults. Overall, scEEG and actigraphy appeared to have greater agreement for multiple sleep parameters than for scEEG and diary or actigraphy and diary. Levels of agreement between scEEG and actigraphy overall decreased in mildly impaired participants and those with biomarker evidence of AD pathology, especially for measurements of TST.

Conclusions

Caution should be exercised when comparing scEEG and actigraphy in individuals with mild cognitive impairment or with AD pathology. Sleep diaries may capture different aspects of sleep compared to scEEG and actigraphy. Additional studies comparing different methods of measuring sleep-wake activity in older adults are necessary to allow for comparison between studies using different methods.

Active Today, Replenished Tomorrow? How Daily Physical Activity Diminishes Next-Morning Depletion

BACKGROUND

Physical activity is a salient input to psychological health and well-being. Recent applied psychology research suggests that physical activity of a greater intensity is particularly important for recovery from work-related effort expenditure. However, whether and how moderate-to-vigorous levels of physical activity influence recovery outside of working populations remains unclear. Further, the process through which this relationship unfolds on a day-to-day basis has yet to be mapped.

METHOD

We conducted a 10-day daily diary study in a sample of 66 college students that incorporated objective measurements of physical activity and sleep to address these research gaps.

RESULTS

We found that higher levels of daily moderate-to-vigorous physical activity were associated with leisure-time psychological detachment from daily school demands, which in turn related to longer duration sleep that diminished next-morning depletion.

DISCUSSION

We discuss how our findings advance a dynamic perspective of the intersection of physical activity and recovery from day-to-day that can be applied outside of working populations.

Subjective and objective sleep among air ambulance personnel

The present study aimed to investigate the effects of shift work on sleep among pilots and Helicopter Emergency Medical Service crew members (HCM) in the Norwegian Air Ambulance. Sleep was assessed by diaries and actigraphy during a workweek (24 h duty for 7 consecutive days) in the winter season and a workweek during the summer season in pilots and HCM (N = 50). Additionally, differences in sleep were studied between the week before work, the workweek, and the week after work in both seasons. Results indicated that bedtime was later (p <.001) and time spent in bed (p <.05) was shorter during the summer, compared to the winter, season. The workers delayed the sleep period in the workweek, compared to the week before (winter: p <.001, summer: p <.001) and the week after (winter: p <.05-.001, summer: p <.001). They spent more time in bed during the workweek, compared to the week before (winter: p <.001, summer: p <.01) and after (winter: p <.001, summer: p =.37). Further, the workers had longer wake after sleep onset during the workweek, compared to the week before (winter: p <.001, summer: p <.01) and the week after (winter: p <.01, summer: p <.01). Finally, the workers had lower sleep efficiency during the workweek recorded by actigraphy compared to the week before (winter: p <.01, summer: p <.001) and the week after (winter: p <.01, summer: p <.001). According to the sleep diaries the total sleep time was 7:17 h in the winter and 7:03 h in the summer season. Overall, the sleep was somewhat affected during the workweek, with delayed sleep period, longer wake after sleep onset, and lower sleep efficiency compared to when off work. However, the workers spent more time in bed during the workweek compared to the weeks off, and they obtained over 7 h of sleep in both workweeks. Our findings suggest that the pilots and the HCM sleep well during the workweek, although it affected their sleep to some extent.

Potential use of actigraphy to measure sleep in monkeys: comparison with behavioral analysis from videography

Sleep is indispensable for human health, with sleep disorders initiating a cascade of negative consequences. As our closest phylogenetic relatives, non-human primates (NHPs) are invaluable for comparative sleep studies and exhibit tremendous potential for improving our understanding of human sleep and related disorders. Previous work on measuring sleep in NHPs has mostly used electroencephalography or videography. In this study, simultaneous videography and actigraphy were applied to observe sleep patterns in 10 cynomolgus monkeys ( Macaca fascicularis) over seven nights (12 h per night). The durations of wake, transitional sleep, and relaxed sleep were scored by analysis of animal behaviors from videography and actigraphy data, using the same behavioral criteria for each state, with findings then compared. Here, results indicated that actigraphy constituted a reliable approach for scoring the state of sleep in monkeys and showed a significant correlation with that scored by videography. Epoch-by-epoch analysis further indicated that actigraphy was more suitable for scoring the state of relaxed sleep, correctly identifying 97.57% of relaxed sleep in comparison with video analysis. Only 34 epochs (0.13%) and 611 epochs (2.30%) were differently interpreted as wake and transitional sleep compared with videography analysis. The present study validated the behavioral criteria and actigraphy methodology for scoring sleep, which can be considered as a useful and a complementary technique to electroencephalography and/or videography analysis for sleep studies in NHPs.

Optimizing actigraphic estimates of polysomnographic sleep features in insomnia disorder

Study Objectives

Actigraphy is a useful tool for estimating sleep, but less accurately distinguishes sleep and wakefulness in patients with insomnia disorder (ID) than in good sleepers. Specific algorithm parameter settings have been suggested to improve the accuracy of actigraphic estimates of sleep onset or nocturnal sleep and wakefulness in ID. However, a direct comparison of how different algorithm parameter settings affect actigraphic estimates of sleep features has been lacking. This study aimed to define the optimal algorithm parameter settings for actigraphic estimates of polysomnographic sleep features in people suffering from ID and matched good sleepers.

Methods

We simultaneously recorded actigraphy and polysomnography without sleep diaries during 210 laboratory nights of people with ID (n = 58) and matched controls (CTRL) without sleep complaints (n = 56). We analyzed cross-validation errors using 150 algorithm parameter configurations and Bland–Altman plots of sleep features using the optimal settings.

Results

Optimal sleep onset latency and total sleep time (TST) errors were lower in CTRL (8.9 ± 2.1 and 16.5 ± 2.1 min, respectively) than in ID (11.7 ± 0.8 and 29.1 ± 3.4 min). The sleep–wake algorithm, a period duration of 5 min, and a wake sensitivity threshold of 40 achieved optimal results in ID and near-optimal results in CTRL. Bland–Altman plots were nearly identical for ID and controls for all common all-night sleep features except for TST.

Conclusion

This systematic evaluation shows that actigraphic sleep feature estimation can be improved by using uncommon parameter settings. One specific parameter setting provides (near-)optimal estimation of sleep onset and nocturnal sleep across ID and controls.

An Actigraphy-Based Validation Study of the Sleep Disorder Inventory in the Nursing Home

Background: Disrupted sleep is common among nursing home patients with dementia and is associated with increased agitation, depression, and cognitive impairment. Detecting and treating sleep problems in this population are therefore of great importance, albeit challenging. Systematic observation and objective recordings of sleep are time-consuming and resource intensive and self-report is often unreliable. Commonly used proxy-rated scales contain few sleep items, which affects the reliability of the raters' reports. The present study aimed to adapt the proxy-rated Sleep Disorder Inventory (SDI) to a nursing home context and validate it against actigraphy. Methods: Cross-sectional study of 69 nursing home patients, 68% women, mean age 83.5 (SD 7.1). Sleep was assessed with the SDI, completed by nursing home staff, and with actigraphy (Actiwatch II, Philips Respironics). The SDI evaluates the frequency, severity, and distress of seven sleep-related behaviors. Internal consistency of the SDI was evaluated by Cronbach's alpha. Spearman correlations were used to evaluate the convergent validity between actigraphy and the SDI. Test performance was assessed by calculating the sensitivity, specificity, and predictive values, and by ROC curve analyses. The Youden's Index was used to determine the most appropriate cut-off against objectively measured sleep disturbance defined as <6 h nocturnal total sleep time (TST) during 8 h nocturnal bed rest (corresponding to SE <75%). Results: The SDI had high internal consistency and convergent validity. Three SDI summary scores correlated moderately and significantly with actigraphically measured TST and wake-after-sleep-onset. A cut-off score of five or more on the SDI summed product score (sum of the products of the frequency and severity of each item) yielded the best sensitivity, specificity, predictive values, and Youden's Index. Conclusion: We suggest a clinical cut-off for the presence of disturbed sleep in institutionalized dementia patients to be a SDI summed product score of five or more. The results suggest that the SDI can be clinically useful for the identification of disrupted sleep when administered by daytime staff in a nursing home context. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03357328.

Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation

Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis.Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most highly-valued devices. Software apps have also been compared with regards to the market.Expert opinion: Thanks to technological advances, the reliability and accuracy of sensors has been significantly increased in recent years. According to validation studies, some actigraphs present a sensibility higher than 90%. However, the market analysis reveals that many hardware devices have not been validated, and especially software devices should be studied before their clinical use.