Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

Subjective and Objective Measurement of Sleep Quality and Activity in Glaucoma

PRCIS

Glaucoma patients exhibit worse indices of sleep function by both objective and subjective metrics compared with controls.

PURPOSE

The purpose of this study is to characterize the sleep parameters and physical activity levels of glaucoma patients compared with controls.

PATIENTS AND METHODS

A total of 102 patients with a diagnosis of glaucoma in at least 1 eye and 31 control subjects were enrolled in the study. Participants completed the Pittsburgh Sleep Quality Index (PSQI) during enrollment and then wore wrist actigraphs for 7 consecutive days to characterize circadian rhythm, sleep quality, and physical activity. The primary outcomes of the study were subjective and objective metrics of sleep quality using the PSQI and actigraphy devices, respectively. The secondary outcome was physical activity, measured by the actigraphy device.

RESULTS

From the PSQI survey, glaucoma patients had higher (worse) scores compared with controls for sleep latency, sleep duration, and subjective sleep quality, whereas scores for sleep efficiency were lower (better), suggesting more time spent in bed asleep. By actigraphy, time in bed was significantly higher in glaucoma patients as was time awake after sleep onset. Interdaily stability, quantifying the synchronization to the 24-hour light-dark cycle, was lower in glaucoma patients. There were no other significant differences between glaucoma and control patients with regard to rest-activity rhythms or physical activity metrics. In contrast to the survey data, findings from the actigraphy demonstrated that there were no significant associations between the study group and controls regarding sleep efficiency, onset latency, or total sleep time.

CONCLUSIONS

In this study, patients with glaucoma demonstrated several subjective and objective differences in sleep function when compared with controls, whereas physical activity metrics were similar.

Transition to shift work: Sleep patterns, activity levels, and physiological health of early-career paramedics

The physiological impact of transitioning from full-time study to work in occupations that involve high-stress environments and shift work may plausibly impact sleep patterns and quality. There are limited studies focusing on the transition to shift work in graduate paramedics. This study aimed to assess early metabolic markers of health, activity, and sleep quality during the first 5 months of rostered shift work in a cohort of 28 graduate paramedics. Participants were tested for 4-week blocks before starting shift work (baseline), and during their first and fifth month of shift work. In each block, sleep and activity levels were monitored 24 h/day (workdays and nonworking days) using a wrist-worn actigraph. During shift work, the number of sleep episodes increased by 16.7% (p = .02) and self-reporting of poor sleep quality increased by 35.4% (p = .05); however, overall sleep quantity and sleep efficiency did not differ. Sleep metrics recorded during nonwork days were not different to baseline with exception of reduced sleep duration recorded the night before returning to work (5.99 ± 1.66 hours Month 1; 5.72 ± 1.06 hours Month 5). Sedentary behavior increased by 4.8% across the study, attributable to a significant decline in light exercise (p = .05). No changes were recorded in vigorous physical activity, average steps recorded per day, fasting blood glucose levels, systolic and diastolic blood pressure, weight, or waist circumference. These results warrant further large-scale and longitudinal studies to gauge any physiological implications for ongoing paramedic health.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Reconstruction of Pulse Wave and Respiration from Wrist Accelerometer During Sleep

OBJECTIVE

Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts -- a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects' wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep.

METHODS

Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths.

RESULTS

The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events.

CONCLUSION

During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration.

SIGNIFICANCE

The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects' normal sleep environment.

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Respiratory rate and changes in respiratory activity provide important markers of health and fitness. Assessing the breathing signal without direct respiratory sensors can be very helpful in large cohort studies and for screening purposes. In this paper, we demonstrate that long-term nocturnal acceleration measurements from the wrist yield significantly better respiration proxies than four standard approaches of ECG (electrocardiogram) derived respiration. We validate our approach by comparison with flow-derived respiration as standard reference signal, studying the full-night data of 223 subjects in a clinical sleep laboratory. Specifically, we find that phase synchronization indices between respiration proxies and the flow signal are large for five suggested acceleration-derived proxies with [Formula: see text] for males and [Formula: see text] for females (means ± standard deviations), while ECG-derived proxies yield only [Formula: see text] for males and [Formula: see text] for females. Similarly, respiratory rates can be determined more precisely by wrist-worn acceleration devices compared with a derivation from the ECG. As limitation we must mention that acceleration-derived respiration proxies are only available during episodes of non-physical activity (especially during sleep).

Physical Activity, Sedentary Behavior, and Sleep Quality in Adults with Primary Hypertension and Obesity before and after an Aerobic Exercise Program: EXERDIET-HTA Study

Background: The purposes of the study were to: analyze, by objective (accelerometry) and subjective (International Physical Activity Questionnaire, IPAQ) methodologies, the physical activity (PA) and sedentary behavior (SB) in healthy adults (HEALTHY, n = 30) and individuals with primary hypertension (HTN) and overweight/obesity (n = 218); assess the effects of an aerobic exercise intervention on physical activity (PA), sedentary behavior (SB), and sleep quality in the HTN group; and evaluate the relationship between objectively measured and subjectively reported PA and SB. Methods: The measurements were performed before a 16-week exercise intervention period in both HEALTHY and HTN groups and after the intervention period only in the HTN group, randomized to attention control or exercise training (ExT) subgroups. Results: The HEALTHY group showed more moderate-to-vigorous PA (p < 0.05) and better sleep quality (p < 0.05) than the HTN group, but no difference in SB. After the intervention, HTN participants’ PA and SB, objectively measured by accelerometry, were unchanged, but increased PA and decreased SB (p < 0.05) were observed through IPAQ in ExT. The intervention was effective in improving sleep quality in HTN participants. Conclusions: The differences in moderate-to-vigorous PA and SB may be useful in defining the health profile of a population. The supervised aerobic exercise program was effective in increasing PA, reducing SB, and improving sleep quality in overweight/obese adults with HTN. Accelerometer-measured and self-reported data were not comparable, but complementary.

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.

Innovations in research and clinical care using patient-generated health data

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Central Sleep Apnea Alters Neuronal Excitability and Increases the Randomness in Sleep-Wake Transitions

OBJECTIVE

While most studies on Central Sleep Apnea (CSA) have focused on breathing and metabolic disorders, the neuronal dysfunction that causes CSA remains largely unknown. Here, we investigate the underlying neuronal mechanism of CSA by studying the sleep-wake dynamics as derived from hypnograms.

METHODS

We analyze sleep data of seven groups of subjects: healthy adults (n = 48), adults with obstructive sleep apnea (OSA) (n = 29), adults with CSA (n = 25), healthy children (n = 40), children with OSA (n = 18), children with CSA (n = 73) and CSA children treated with CPAP (n = 10). We calculate sleep-wake parameters based on the probability distributions of wake-bout durations and sleep-bout durations. We compare these parameters with results obtained from a neuronal model that simulates the interplay between sleep- and wake-promoting neurons.

RESULTS

We find that sleep arousals of CSA patients show a characteristic time scale (i.e., exponential distribution) in contrast to the scale-invariant (i.e., power-law) distribution that has been reported for arousals in healthy sleep. Furthermore, we show that this change in arousal statistics is caused by triggering more arousals of similar durations, which through our model can be related to a higher excitability threshold in sleep-promoting neurons in CSA patients.

CONCLUSIONS

We propose a neuronal mechanism to shed light on CSA pathophysiology and a method to discriminate between CSA and OSA. We show that higher neuronal excitability thresholds can lead to complex reorganization of sleep-wake dynamics.

SIGNIFICANCE

The derived sleep parameters enable a more specific evaluation of CSA severity and can be used for CSA diagnosis and monitor CSA treatment.

Detection and analysis of pulse waves during sleep via wrist-worn actigraphy

The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

Actigraphy-based sleep analysis in sedentary and overweight/obese adults with primary hypertension: data from the EXERDIET-HTA study

PURPOSE

The aim of this study was to analyze actigraphy-based sleep quantity and quality in sedentary and overweight/obese adults with primary hypertension (HTN) divided by sex and cardiorespiratory fitness (CRF) and to assess the association of sleep parameters with body composition, blood pressure (BP), and CRF.

METHODS

This is a cross-sectional design utilizing data from the EXERDIET-HTA study conducted in 154 non-physically, obese adults with HTN (53.3 ± 7.8 years). Sleep parameters (total bedtime; total sleep time, TST; and sleep efficiency = (TST/total bedtime) × 100)) were calculated from raw accelerometer data (ActiGraph GT3X+). Peak oxygen uptake (V̇O_2peak) determined the CRF. Blood pressure was assessed with the 24-h ambulatory BP monitoring. The distributions of V̇O_2peak were divided into tertiles (low, medium, and high CRF) in each sex. Series of linear regression analyses were conducted between sleep, fitness, and health-related variables.

RESULTS

Short sleep duration (6.2 h) both on weekdays and weekends, poor sleep quality (< 85% of efficiency), and no significant differences in sleep variables between women and men, nor among CRF groups, were observed. The short sleeping pattern was negatively associated (P < 0.05) with mean and night systolic BP (mmHg, β = - 0.2), and sleep efficiency with waist circumference (cm, β = - 0.08, P = 0.05).

CONCLUSIONS

Actigraphy-based sleep analysis reinforces that sleep disorders, such as short sleep duration and poor sleep quality, are associated with high BP and abdominal obesity in sedentary adults with overweight/obesity and HTN. Sleep pattern did not appear to be related with CRF level in this population.

Real-time Mobile Monitoring of the Dynamic Associations Among Motor Activity, Energy, Mood, and Sleep in Adults With Bipolar Disorder

IMPORTANCE

Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems.

OBJECTIVES

To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders.

DESIGN, SETTING, AND PARTICIPANTS

This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018.

MAIN OUTCOMES AND MEASURES

Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks.

RESULTS

Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (β = -0.018, P = .04). Bidirectional associations were observed between motor activity (β = 0.176; P = .03) and subjective energy level (β = 0.027; P = .03) as well as between motor activity (β = -0.027; P = .04) and sleep duration (β = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy.

CONCLUSIONS AND RELEVANCE

These findings suggest that interventions focused on motor activity and energy may have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.