We validated actigraphy for detecting sleep and wakefulness versus polysomnography (PSG).
Actigraphy and polysomnography were simultaneously collected during sleep laboratory admissions. All studies ...involved 8.5 h time in bed, except for sleep restriction studies. Epochs (30-sec; n = 232,849) were characterized for sensitivity (actigraphy = sleep when PSG = sleep), specificity (actigraphy = wake when PSG = wake), and accuracy (total proportion correct); the amount of wakefulness after sleep onset (WASO) was also assessed. A generalized estimating equation (GEE) model included age, gender, insomnia diagnosis, and daytime/nighttime sleep timing factors.
Controlled sleep laboratory conditions.
Young and older adults, healthy or chronic primary insomniac (PI) patients, and daytime sleep of 23 night-workers (n = 77, age 35.0 ± 12.5, 30F, mean nights = 3.2).
N/A.
Overall, sensitivity (0.965) and accuracy (0.863) were high, whereas specificity (0.329) was low; each was only slightly modified by gender, insomnia, day/night sleep timing (magnitude of change < 0.04). Increasing age slightly reduced specificity. Mean WASO/night was 49.1 min by PSG compared to 36.8 min/night by actigraphy (β = 0.81; CI = 0.42, 1.21), unbiased when WASO < 30 min/night, and overestimated when WASO > 30 min/night.
This validation quantifies strengths and weaknesses of actigraphy as a tool measuring sleep in clinical and population studies. Overall, the participant-specific accuracy is relatively high, and for most participants, above 80%. We validate this finding across multiple nights and a variety of adults across much of the young to midlife years, in both men and women, in those with and without insomnia, and in 77 participants. We conclude that actigraphy is overall a useful and valid means for estimating total sleep time and wakefulness after sleep onset in field and workplace studies, with some limitations in specificity.
The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource ...(NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.
We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.
The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.
The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.
Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological ...advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.
To test the utility of an integrated clinical pathway for obstructive sleep apnea (OSA) diagnosis and continuous positive airway pressure (CPAP) treatment using portable monitoring devices.
...Randomized, open-label, parallel group, unblinded, multicenter clinical trial comparing home-based, unattended portable monitoring for diagnosis and autotitrating CPAP (autoPAP) compared with in-laboratory polysomnography (PSG) and CPAP titration.
Seven American Academy of Sleep Medicine (AASM) accredited sleep centers.
Consecutive new referrals, age 18 yr or older with high probability of moderate to severe OSA (apnea-hypopnea index AHI ≥ 15) identified by clinical algorithm and Epworth Sleepiness Scale (ESS) score ≥ 12.
Home-based level 3 testing followed by 1 wk of autoPAP with a fixed pressure CPAP prescription based on the 90% pressure from autotitration of PAP therapy (autoPAP) device (HOME) compared with attended, in-laboratory studies (LAB).
CPAP acceptance, time to treatment, adherence at 1 and 3 mo; changes in ESS, and functional outcomes.
Of 373 participants, approximately one-half in each study arm remained eligible (AHI ≥ 15) to continue in the study. At 3 mo, PAP usage (nightly time at pressure) was 1 hr greater: 4.7 ± 2.1 hr (HOME) compared with 3.7 ± 2.4 hr (LAB). Adherence (percentage of night used ≥ 4 hr) was 12.6% higher: 62.8 ± 29.2% compared with 49.4 ± 36.1% in the HOME versus LAB. Acceptance of PAP therapy, titration pressures, effective titrations, time to treatment, and ESS score change did not differ between arms.
A home-based strategy for diagnosis and treatment compared with in-laboratory PSG was not inferior in terms of acceptance, adherence, time to treatment, and functional improvements.
http://www.ClinicalTrials.gov; Identifier: NCT: 00642486.
While actigraphy is considered objective, the process of setting rest intervals to calculate sleep variables is subjective. We sought to evaluate the reproducibility of actigraphy-derived measures of ...sleep using a standardized algorithm for setting rest intervals.
Observational study.
Community-based.
A random sample of 50 adults aged 18-64 years free of severe sleep apnea participating in the Sueño sleep ancillary study to the Hispanic Community Health Study/Study of Latinos.
N/A.
Participants underwent 7 days of continuous wrist actigraphy and completed daily sleep diaries. Studies were scored twice by each of two scorers. Rest intervals were set using a standardized hierarchical approach based on event marker, diary, light, and activity data. Sleep/wake status was then determined for each 30-sec epoch using a validated algorithm, and this was used to generate 11 variables: mean nightly sleep duration, nap duration, 24-h sleep duration, sleep latency, sleep maintenance efficiency, sleep fragmentation index, sleep onset time, sleep offset time, sleep midpoint time, standard deviation of sleep duration, and standard deviation of sleep midpoint. Intra-scorer intraclass correlation coefficients (ICCs) were high, ranging from 0.911 to 0.995 across all 11 variables. Similarly, inter-scorer ICCs were high, also ranging from 0.911 to 0.995, and mean inter-scorer differences were small. Bland-Altman plots did not reveal any systematic disagreement in scoring.
With use of a standardized algorithm to set rest intervals, scoring of actigraphy for the purpose of generating a wide array of sleep variables is highly reproducible.
Abstract
Study objectives:
To investigate cross-sectional associations of neighborhood social environment (social cohesion, safety) with objective measures of sleep duration, timing, and ...disturbances.
Methods:
A racially/ethnically diverse population of men and women (N = 1949) aged 54 to 93 years participating in the Multi-Ethnic Study of Atherosclerosis Sleep and Neighborhood Ancillary studies. Participants underwent 1-week actigraphy between 2010 and 2013. Measures of sleep duration, timing, and disruption were averaged over all days. Neighborhood characteristics were assessed via questionnaires administered to participants and an independent sample within the same neighborhood and aggregated at the neighborhood (census tract, N = 783) level using empirical Bayes estimation. Multilevel linear regression models were used to assess the association between the neighborhood social environment and each sleep outcome.
Results:
Neighborhood social environment characterized by higher levels of social cohesion and safety were associated with longer sleep duration and earlier sleep midpoint. Each 1 standard deviation higher neighborhood social environment score was associated with 6.1 minutes longer 95% confidence interval (CI): 2.0, 10.2 sleep duration and 6.4 minutes earlier (CI: 2.2, 10.6) sleep midpoint after adjustment for age, sex, race, socioeconomic status, and marital status. These associations persisted after adjustment for other risk factors. Neighborhood social factors were not associated with sleep efficiency or sleep fragmentation index.
Conclusions:
A more favorable neighborhood social environment is associated with longer objectively measured sleep duration and earlier sleep timing. Intervening on the neighborhood environment may improve sleep and subsequent health outcomes.
Abstract
Study Objectives
We investigated associations between actigraphy-assessed sleep measures and cognitive function in people with and without HIV using different analytical approaches to better ...understand these associations and highlight differences in results obtained by these approaches.
Methods
Cognitive and 7-day/night actigraphy data were collected from people with HIV (PWH) and lifestyle-similar HIV-negative individuals from HIV and sexual health clinics in the United Kingdom/Ireland. A global cognitive T-score was obtained averaging the standardized individual cognitive test scores accounting for sociodemographics. Average and SD of 11 sleep measures over 7 days/nights were obtained. Rank regression, partial least-squares (PLS) regression, random forest, sleep dimension construct, and latent class analysis (LCA) were applied to evaluate associations between global T-scores and sleep measures.
Results
In 344 PWH (median age 57 years, 86% males), average sleep duration, efficiency, and wake after sleep onset were not associated with global T-scores according to rank regression (p = 0.51, p = 0.09, p = 0.16, respectively). In contrast, global T-scores were associated with average and SD of length of nocturnal awakenings, SD of maintenance efficiency, and average out-of-bed time when analyzed by PLS regression and random forest. No associations were found when using sleep dimensions or LCA. Overall, findings observed in PWH were similar to those seen in HIV-negative individuals (median age 61 years, 67% males).
Conclusions
Using multivariable analytical approaches, measures of sleep continuity, timing, and regularity were associated with cognitive performance in PWH, supporting the utility of newer methods of incorporating multiple standard and novel measures of sleep-wake patterns in the assessment of health and functioning.
Background
Previous studies have documented a high prevalence of atrial fibrillation (AF) in individuals with obstructive sleep apnea (OSA). Central sleep apnea (CSA) has been associated with AF in ...patients with heart failure. However, data from prospective cohorts are sparse and few studies have distinguished the associations of obstructive sleep apnea from CSA with AF in population studies.
Methods and Results
We assessed the association of obstructive sleep apnea and CSA with incident AF among 2912 individuals without a history of AF in the SHHS (Sleep Heart Health Study), a prospective, community‐based study of existing (“parent”) cohort studies designed to evaluate the cardiovascular consequences of sleep disordered breathing. Incident AF was documented by 12‐lead ECG or assessed by the parent cohort. obstructive sleep apnea was defined by the obstructive apnea‐hypopnea index (OAHI). CSA was defined by a central apnea index ≥5 or the presence of Cheyne Stokes Respiration. Logistic regression was used to assess the association between sleep disordered breathing and incident AF. Over a mean of 5.3 years of follow‐up, 338 cases of incident AF were observed. CSA was a predictor of incident AF in all adjusted models and was associated with 2‐ to 3‐fold increased odds of developing AF (central apnea index ≥5 odds ratio OR, 3.00, 1.40–6.44; Cheyne–Stokes respiration OR, 1.83, 0.95–3.54; CSA or Cheyne–Stokes respiration OR, 2.00, 1.16–3.44). In contrast, OAHI was not associated with incident AF (OAHI per 5 unit increase OR, 0.97, 0.91–1.03; OAHI 5 to <15 OR, 0.84, 0.59–1.17; OAHI 15 to <30 OR, 0.93, 0.60–1.45; OAHI ≥30 OR, 0.76, 0.42–1.36).
Conclusions
In a prospective, community‐based cohort, CSA was associated with incident AF, even after adjustment for cardiovascular risk factors.
Background: Understanding variation in physical activity (PA) and sleep is necessary to develop novel intervention strategies targeting adolescents' health behaviors. We examined the extent to which ...PA and sleep vary by aspects of the physical environment. Participants: We performed a cross-sectional analysis of 669 adolescents in the Project Viva cohort. Methods: We estimated total PA, sleep duration, sleep efficiency, and sleep midpoint timing from wrist accelerometers. We used multivariable linear regression models and generalized estimated equations to assess associations of PA and sleep with season and daily weather conditions obtained from the National Oceanic and Atmospheric Administration archive. Results: Mean age was 12.9 (SD 0.6) years; 51% were female and 68% were white. Mean sleep duration was 466 (SD 42) min per night and total PA was 1,652 (SD 431) counts per min per day. Sleep midpoint time was 41 (95% CI: 27 to 54) min later in summer, 28 (95% CI: −41 to −14) min earlier in spring, and 29 (95% CI: −43 to −15) min earlier in autumn compared to winter. Higher temperature and longer day length both were associated with small reductions of nightly sleep duration. Adolescents were less physically active during winter and on rainy and short sunlight days. There was an inverse U-shaped relationship between PA and mean temperature. Conclusions: Season was associated with large changes in sleep timing, and smaller changes in other sleep and PA measurements. Given the importance of sleep and circadian alignment, future health behavioral interventions may benefit by targeting "season-specific" interventions.
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