Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation Relative to Polysomnography and Actigraphy: Importance of Generalized Versus Personalized Scoring

Study Objectives: Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and performance. In this study, we tested the performance of a novel device, alongside other commercial wearables, against in-lab and at-home polysomnography (PSG). Methods: 36 healthy adults were assessed across 77 nights while wearing the Happy Ring, as well as the Actiwatch, Fitbit, Whoop, and Oura Ring devices. Subjects participated in a single night of in-lab PSG and 2 nights of at-home PSG. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. Epoch-by-epoch analyses compared the wearable de-vices to both in-lab and at-home PSG. The Happy Ring utilized two machine-learning derived scor-ing algorithms: a generalized algorithm that applied broadly to all users, and a personalized algorithm that adapted to the data of individual subjects. Results: Compared to in-lab PSG, the generalized and personalized algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The other wearable devices also demonstrated good sensitivity (89%-94%) but lower specificity (19%-54%), relative to the Happy Ring. Accuracy was 91% for generalized and 92% for personalized algorithms, compared to other devices that ranged from 84%-88%. The generalized algorithm demonstrated an accuracy of 67%, 85%, and 85% for light, deep, and REM sleep, respectively. The personalized algorithm was 81%, 95%, and 92% accurate for light, deep, and REM sleep, re-spectively. Conclusions: The Happy Ring performed well at home and in the lab, especially regarding sleep-wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.

RestEaze: An Emerging Technology to Characterize Leg Movements During Sleep

Several sleep disorders are characterized by periodic leg movements during sleep including Restless Leg Syndrome, and can indicate disrupted sleep in otherwise healthy individuals. Current technologies to measure periodic leg movements during sleep are limited. Polysomnography and some home sleep tests use surface electromyography to measure electrical activity from the anterior tibilias muscle. Actigraphy uses 3-axis accelerometers to measure movement of the ankle. Electromyography misses periodic leg movements that involve other leg muscles and is obtrusive because of the wires needed to carry the signal. Actigraphy based devices require large amplitude movements of the ankle to detect leg movements (missing the significant number of more subtle leg movements) and can be worn in multiple configurations precluding precision measurement. These limitations have contributed to their lack of adoption as a standard of care for several sleep disorders. In this study, we develop the RestEaze sleep assessment tool as an ankle-worn wearable device that combines capacitive sensors and a 6-axis inertial measurement unit to precisely measure periodic leg movements during sleep. This unique combination of sensors and the form-factor of the device addresses current limitations of periodic leg movements during sleep measurement techniques. Pilot data collected shows high correlation with polysomnography across a heterogeneous participant sample and high usability ratings. RestEaze shows promise in providing ecologically valid, longitudinal measures of leg movements that will be useful for clinicians, researchers, and patients to better understand sleep.

Assessment of Sleep Duration Based on Ankle and Wrist Actigraphy in Hospitalized Older Patients

Poor sleep at time of hospitalization is associated with undesirable outcomes. Most studies performed in the hospital assess sleep by self-report, while only few rely on actigraphy. Although wrist actigraphy is commonly used for sleep assessment in field studies, in-hospital assessment may be challenging and cumbersome due to other more necessary monitoring devices that are often attached to patients’ upper limbs, that may in turn affect interpretation of wrist activity-data. Placement on the ankle may be a viable solution. In this pilot study, we aimed to compare total sleep time (TST) using concomitant wrist and ankle actigraphy as well as self-report. Twenty-one older adults (65+) hospitalized in medical units wore ankle and wrist actigraphy devices and subjectively estimated their TST for an average of (2.15±1.01) nights. A total of 45 nights were available for analysis. Average TST in minutes was 332.06±81.58, 427.05±97.74 and 374.28±124.96 based on wrist, ankle, and self-report, respectively. Repeated measure mixed models analysis was performed adjusting for age, gender, and sleep medications. TST was significantly lower using wrist compared to ankle actigraphy (F(2,102)=7.63, p=0.0008), and both were not different from self-report. No significant within subjects variation and no interaction between device and repeated measures were found. Despite differences between ankle and wrist assessments, all three provide consistent TST estimation within subjects. Self-report provides a stable and accessible assessment of TST, representing a good approximation of ankle and wrist actigraphy. Findings provide preliminary support for the use of ankle actigraphy for sleep assessment in hospital settings.

Extent of sleep problems and relationship with severity of chronic pain using three validated sleep assessment tools

Objective Chronic pain can impact on sleep, but the extent and nature of sleep problems in patients with chronic pain are incompletely clear. Several validated tools are available for sleep assessment but they each capture different aspects. We aimed to describe the extent of sleep issues in patients with chronic non-malignant pain using three different validated sleep assessment tools and to determine the relationship of sleep issues with pain severity recorded using the Brief Pain Inventory (BPI), a commonly used self-assessment tool in pain clinics. The BPI has a single question on the interference of pain on sleep and we also compared this with the validated sleep tools. Design Prospective, cross-sectional study. Setting Pain management clinic at a large teaching hospital in the United Kingdom. Subjects Adult patients (with chronic non-malignant pain of at least 3 months’ duration) attending clinic during a 2-month period. Methods Participants completed the Pittsburgh Sleep Quality Index (PSQI), the Pain and Sleep Questionnaire-3 (PSQ-3) and the Verran Snyder-Halpern (VSH) sleep scale, plus the BPI. Duration and type of pain, current medications and demographic data were recorded. Results We recruited 51 patients and 82% had poor sleep quality as shown by PSQIscores above five. PSQI ( p = 0.0002), PSQ-3 ( p = 0.0032), VSH sleep efficiency ( p = 0.012), sleep disturbance ( p = 0.0014) and waking after sleep onset ( p = 0.0005) scores were associated with worse BPI pain scores. BPI sleep interference scores concurred broadly with the validated sleep tools. Median [range] sleep duration was 5.5 [3.0–10.0] hours and was also related to pain score ( p = 0.0032). Conclusion Chronic pain has a marked impact on sleep regardless of the assessment tool used. The sleep interference question in the BPI could be used routinely for initial identification of sleep problems in patients with chronic pain.

Behavioral and postural analyses establish sleep-like states for mosquitoes that can impact interactions with hosts

Sleep is an evolutionarily conserved process that has been described in different animal systems. For insects, sleep characterization has been primarily achieved using behavioral and electrophysiological correlates in a few systems. Sleep in mosquitoes, which are important vectors of disease-causing pathogens, has not been directly examined. This is surprising as circadian rhythms, which have been well studied in mosquitoes, influence sleep in other systems. In this study, we characterized sleep in mosquitoes using body posture analysis and behavioral correlates, and quantified the effect of sleep deprivation on sleep rebound and host landing. Body and appendage position metrics revealed a clear distinction between the posture of mosquitoes in their putative sleep and awake states for multiple species, which correlates with a reduction in responsiveness to host cues. Sleep assessment informed by these posture analyses indicated significantly more sleep during periods of low activity. Nighttime and daytime sleep deprivation resulting from the delivery of vibration stimuli induced sleep rebound in the subsequent phase in day and night active mosquitoes, respectively. Lastly, sleep deprivation suppressed host landing in both laboratory and field settings when mosquitoes would normally be active. These results suggest that quantifiable sleep states occur in mosquitoes, and highlight the potential epidemiological importance of mosquito sleep.

Racial-ethnic Differences in Actigraphy, Questionnaire, and Polysomnography Indicators of Healthy Sleep: The Multi-Ethnic Study of Atherosclerosis

A paradigm shift in sleep science argues for a systematic, multidimensional approach to investigate sleep’s association with disease and mortality and to address sleep disparities. We utilized the comprehensive sleep assessment of the Multi-Ethnic Study of Atherosclerosis (2010- 2013), a cohort of U.S. White, Black, Chinese, and Hispanic adults and older adults (n=1,736; mean age=68.3), to draw 13 sleep dimensions and create composite Sleep Health Scores to quantify multidimensional sleep health disparities. After age and sex adjustment in linear regression, compared to White participants, Black participants showed the greatest global sleep disparity, then Hispanic and Chinese participants. We estimated relative ‘risk’ of obtaining favorable sleep compared to White adults at the component level by race/ethnicity (lower is worse). The largest disparities were in objectively-measured sleep timing regularity (RRBlack [95% CI]: 0.37 [0.29,0.47], RRHispanic: 0.64 [0.52,0.78], RRChinese: 0.70 [0.54,0.90]) and duration regularity (RRBlack: 0.55 [0.47,0.65], RRHispanic: 0.76 [0.66,0.88], RRChinese: 0.74 [0.61,0.90]), after sex and age adjustment. Disparities in duration and continuity were also apparent, and Black adults were additionally disadvantaged in %N3 (slow wave sleep), sleepiness, and sleep timing (24-hour placement). Sleep timing regularity, duration regularity, duration, and continuity may comprise a multidimensional cluster of targets to reduce racial-ethnic sleep disparities.

Chronic breathlessness and sleep problems: a population-based survey

ObjectivesThis study aimed to explore the relationship (presence and severity) between chronic breathlessness and sleep problems, independently of diagnoses and health service contact by surveying a large, representative sample of the general population.SettingAnalysis of the 2017 South Australian Health Omnibus Survey, an annual, cross-sectional, face-to-face, multistage, clustered area systematic sampling survey carried out in Spring 2017.Chronic breathlessness was self-reported using the ordinal modified Medical Research Council (mMRC; scores 0 (none) to 4 (housebound)) where breathlessness has been present for more than 3 of the previous 6 months. ‘Sleep problems—ever’ and ‘sleep problem—current’ were assessed dichotomously. Regression models were adjusted for age; sex and body mass index (BMI).Results2900 responses were available (mean age 48.2 years (SD=18.6); 51% were female; mean BMI 27. 1 (SD=5.9)). Prevalence was: 2.7% (n=78) sleep problems—past; 6.8% (n=198) sleep problems—current and breathlessness (mMRC 1–4) was 8.8% (n=254). Respondents with sleep problemspast were more likely to be breathless, older with a higher BMI and sleep problems—present also included a higher likelihood of being female.After adjusting for age, sex and BMI, respondents with chronic breathlessness had 1.9 (95% CI=1.0 to 3.5) times the odds of sleep problems—past and sleep problems—current (adjusted OR=2.3; 95% CI=1.6 to 3.3).ConclusionsThere is a strong association between the two prevalent conditions. Future work will seek to understand if there is a causal relationship using validated sleep assessment tools and whether better managing one condition improves the other.

Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to “measure” sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.

Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association

Obstructive sleep apnea (OSA) is characterized by recurrent complete and partial upper airway obstructive events, resulting in intermittent hypoxemia, autonomic fluctuation, and sleep fragmentation. Approximately 34% and 17% of middle-aged men and women, respectively, meet the diagnostic criteria for OSA. Sleep disturbances are common and underdiagnosed among middle-aged and older adults, and the prevalence varies by race/ethnicity, sex, and obesity status. OSA prevalence is as high as 40% to 80% in patients with hypertension, heart failure, coronary artery disease, pulmonary hypertension, atrial fibrillation, and stroke. Despite its high prevalence in patients with heart disease and the vulnerability of cardiac patients to OSA-related stressors and adverse cardiovascular outcomes, OSA is often underrecognized and undertreated in cardiovascular practice. We recommend screening for OSA in patients with resistant/poorly controlled hypertension, pulmonary hypertension, and recurrent atrial fibrillation after either cardioversion or ablation. In patients with New York Heart Association class II to IV heart failure and suspicion of sleep-disordered breathing or excessive daytime sleepiness, a formal sleep assessment is reasonable. In patients with tachy-brady syndrome or ventricular tachycardia or survivors of sudden cardiac death in whom sleep apnea is suspected after a comprehensive sleep assessment, evaluation for sleep apnea should be considered. After stroke, clinical equipoise exists with respect to screening and treatment. Patients with nocturnally occurring angina, myocardial infarction, arrhythmias, or appropriate shocks from implanted cardioverter-defibrillators may be especially likely to have comorbid sleep apnea. All patients with OSA should be considered for treatment, including behavioral modifications and weight loss as indicated. Continuous positive airway pressure should be offered to patients with severe OSA, whereas oral appliances can be considered for those with mild to moderate OSA or for continuous positive airway pressure–intolerant patients. Follow-up sleep testing should be performed to assess the effectiveness of treatment.

Development of a Sleep Assessment Module in the Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool: New Research Opportunities

Objectives To develop a sleep assessment module in ASA24 to capture self-reported sleep behavior as an optional enhancement to the ASA24 Dietary Assessment Tool for adults. Methods Multiple self-reported sleep assessment tools were considered in module development, including the National Sleep Foundation Sleep Diary, the Activities Completed over Time in 24-hours (ACT24), Munich Chronotype Questionnaire (MCTQ), and the Consensus Sleep Diary (CSD) Core. Priority was given to minimal need for adaptation, questionnaire length to reduce survey fatigue, incorporating plain language, and optimizing for implementation in 24 hour recalls (24HR) and food records. Researchers with expertise in meal timing and sleep were consulted for feedback on content and utility and programmers with expertise in survey design were consulted on implementation. Lastly, the online data collection process and ASA24 System's output data files were tested for accuracy. Results The ASA24 sleep module contains ten questions and can be administered immediately following dietary assessment. Eight CSD Core questions were adapted to assess time in bed, time trying to go to sleep, and length of time to fall asleep; number and duration of nocturnal awakenings; wake time and time out of bed for the day; and perceived sleep quality. Two questions were added to capture sleep quality and comparability of reported sleep to a usual night's sleep. For users completing a 24HR, the module includes two questions on time of awakening and sleep quality immediately preceding the first reported meal; all 10 sleep questions are asked for the sleep period immediately following the last meal (i.e., 12 questions total), allowing for assessment of the impact of diet on sleep. In contrast, a food record is completed on the same day users consume the food, and thus all sleep questions address the sleep window prior to the first meal; a single record can be used to assess the impact of sleep on diet. Consecutive days of records can also be collected to capture sleep pre- and post-eating windows. Conclusions The ASA24 sleep module can assess sleep timing and quality and will be available in Fall 2021. Researchers can soon leverage this novel resource to examine the association of sleep with timing of eating and other chrononutrition variables. Funding Sources This project has been funded by the NIH.