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Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Consumer sleep technologies have rapidly evolved from wrist-worn activity trackers to multisensory products. These technologies reflect a widespread interest in sleep health, and their ubiquitous ownership allows for remote sleep monitoring. Therefore, these technologies may play a valuable role in telemedicine. However, clinical usefulness remains contested and is limited by a lack of transparency in data acquisition and analysis, and uncertain accuracy of consumer sleep technology-derived sleep metrics. Collaboration between manufacturers, sleep scientists, and clinical sleep medicine teams is required to produce useable, verified technologies that can enhance and personalize the care of patients with sleep disorders.

Restless legs syndrome (RLS) is a sensorimotor disorder that can cause significant discomfort, impaired quality of life, poor mood, and disturbed sleep. Because the disorder is chronic and associated with multiple comorbidities, RLS can be seen in an inpatient or perioperative setting. Certain characteristics of the hospitalized or surgical context can exacerbate or unmask RLS. Importantly, RLS and the associated discomfort and insomnia can prolong hospital stay and negatively impact outcomes. RLS medications should be continued during the hospital admission when possible. Avoidance of excessive phlebotomy and medications known to trigger RLS is helpful. Patients should increase activity when acceptable.

ABSTRACTMany pathogenic and commensal organisms are multidrug resistant due to exposure to various antibiotics. Often, this antimicrobial resistance is encoded by integrons that occur on plasmids or that are integrated into the bacterial chromosome. Integrons are commonly associated with bacterial genera in the familyEnterobacteriaceae. We determined that class 1 integrases were present in approximately 46% of the isolates from the familyEnterobacteriaceae; class 2 integrases were present only among Escherichia coliand Salmonellaisolates. Seven percent of veterinary isolates were positive for class 3 integrase by DNA-DNA hybridization but could not be confirmed to be positive by PCR. None of the veterinary isolates possessed the class 4 integrase gene. The distribution of these integrase genes was variable within the members of the family Enterobacteriaceaewhen some or all integrase classes were absent from a particular genus. There was also considerable variability in the distribution of these integrases within a species, depending on the animal host. Unlike the class 1 integrases, the other integrase class, intI2, appears to be more restricted in its distribution among the members of the family Enterobacteriaceae. There is also considerable variability in the distribution of the class 1 integrases withinE. colistrains isolated from different food animals. The class 1 integrases are the most widely disseminated of the four classes among the members of the family Enterobacteriaceaefrom both the clinical and normal flora of animals. This is the first report to closely examine the distribution of class 2 integrases in members of the family Enterobacteriaceaeisolated in the United States.

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase noninvasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 h, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.

Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the “Social Rhythms” iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method.

•A seven-domain framework was used to evaluate the sleep health of people with MS.•Objective sleep domains: duration, continuity, timing, regularity, rhythmicity.•Subjective sleep domains: daytime sleepiness and perception of sleep quality.•Seventy-six percent of the sample had extreme values for ≥2 sleep domains.

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.