State of the science and recommendations for using wearable technology in sleep and circadian research.

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.

A 4-week morning light treatment with stable sleep timing for individuals with fibromyalgia: a randomized controlled trial.

OBJECTIVES: Fibromyalgia is characterized by chronic widespread pain, mood, and sleep disturbance. Pharmacological treatments have modest efficacy and are associated with negative side effects, and alternative approaches are needed. Morning bright light treatment may assist in the management of fibromyalgia as it can reduce depressive symptoms, improve sleep, and advance circadian timing. METHODS: Sixty people with fibromyalgia (58 women, mean age 41.8 ± 13.3 years) were enrolled in a study comparing 4 weeks of a 1-hour daily morning bright light treatment (active treatment) to a morning dim light treatment (comparison treatment). Both light treatments included behavioral procedures to stabilize sleep timing. The morning bright light treatment was expected to produce larger improvements in pain and function than the dim light treatment and larger improvements in potential mediators (mood, sleep, and circadian timing). RESULTS: Both the bright and dim light treatment groups achieved significant but similar levels of improvement in pain intensity, pain interference, physical function, depressive symptoms, and sleep disturbance. Overall, the sample on average displayed a clinically meaningful improvement in the Fibromyalgia Impact Questionnaire-Revised score (mean reduction of 11.2 points), comparable to that reported following physical exercise treatments. Minimal side effects were observed. CONCLUSIONS: Findings indicate that the effects of a morning bright light treatment did not exceed those of a comparison dim light treatment; yet the changes on average in both conditions revealed clinically meaningful improvements. Future research is warranted to identify what elements of this trial may have contributed to the observed effects.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

BACKGROUND: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD: The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS: Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION: Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

Distinct Circadian Assessments From Wearable Data Reveal Social Distancing Promoted Internal Desynchrony Between Circadian Markers.

Mobile measures of human circadian rhythms (CR) are needed in the age of chronotherapy. Two wearable measures of CR have recently been validated: one that uses heart rate to extract circadian rhythms that originate in the sinoatrial node of the heart, and another that uses activity to predict the laboratory gold standard and central circadian pacemaker marker, dim light melatonin onset (DLMO). We first find that the heart rate markers of normal real-world individuals align with laboratory DLMO measurements when we account for heart rate phase error. Next, we expand upon previous work that has examined sleep patterns or chronotypes during the COVID-19 lockdown by studying the effects of social distancing on circadian rhythms. In particular, using data collected from the Social Rhythms app, a mobile application where individuals upload their wearable data and receive reports on their circadian rhythms, we compared the two circadian phase estimates before and after social distancing. Interestingly, we found that the lockdown had different effects on the two ambulatory measurements. Before the lockdown, the two measures aligned, as predicted by laboratory data. After the lockdown, when circadian timekeeping signals were blunted, these measures diverged in 70% of subjects (with circadian rhythms in heart rate, or CRHR, becoming delayed). Thus, while either approach can measure circadian rhythms, both are needed to understand internal desynchrony. We also argue that interventions may be needed in future lockdowns to better align separate circadian rhythms in the body.

A multidimensional approach to sleep health in multiple sclerosis.

BACKGROUND: Although sleep disturbances are common among people with Multiple Sclerosis (PwMS), understanding of their impact has been stymied by limitations in approaches to sleep measurement within this population. The aim of this study was to comprehensively phenotype sleep patterns in PwMS through application of an emerging seven-domain framework that includes sleep duration, continuity, timing, quality, rhythmicity, regularity, and sleepiness. METHODS: Sleep domains were estimated from wrist-worn accelerometry, Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index responses. Extreme sleep values within each domain were constructed using previously published guidelines. A composite score of extreme values was calculated for each participant. Associations between sleep domains and severity of MS symptoms were explored (pain, fatigue, depressive symptoms, and cognitive dysfunction). RESULTS: Among n = 49 participants, median total sleep time was 456.3 min. Median time spent awake after sleep onset was 37 min. Sleepiness, abnormal sleep timing, and poor sleep quality affected 33%, 35%, and 45% of participants, respectively. Seventy-six percent had ≥2 sleep domains in extreme ranges. PwMS had longer sleep duration and decreased sleep regularity compared to a non-MS historical cohort of older men. Greater daytime sleepiness, poorer sleep quality, and higher composite sleep health score were associated with more depressive symptoms, and lower sleep rhythmicity was associated with higher fatigue. Associations were observed between measures of cognitive function and sleep fragmentation, duration, quality, rhythmicity, and composite score. CONCLUSION: Application of a seven-domain sleep health framework that captures the dynamic and multifaceted aspects of sleep is feasible in PwMS, and offers potential for an improved understanding of the scope and impact of sleep disturbances in PwMS.

A method for characterizing daily physiology from widely used wearables.

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.

Objective sleep duration and timing predicts completion of in vitro fertilization cycle.

PURPOSE: To examine associations between objectively measured sleep duration and sleep timing with odds of completion of an in vitro fertilization (IVF) cycle. METHODS: This prospective cohort study enrolled 48 women undergoing IVF at a large tertiary medical center between 2015 and 2017. Sleep was assessed by wrist-worn actigraphy, 1-2 weeks prior to initiation of the IVF cycle. Reproductive and IVF cycle data and demographic and health information were obtained from medical charts. Sleep duration, midpoint, and bedtime were examined in relation to IVF cycle completion using logistic regression models, adjusted for age and anti-Müllerian hormone levels. A sub-analysis excluded women who worked non-day shifts to control for circadian misalignment. RESULTS: The median age of all participants was 33 years, with 29% of women >35 years. Ten women had an IVF cycle cancelation prior to embryo transfer. These women had shorter sleep duration, more nocturnal awakenings, lower sleep efficiency, and later sleep timing relative to those who completed their cycle. Longer sleep duration was associated with lower odds of uncompleted IVF cycle (OR = 0.88; 95%CI 0.78, 1.00, per 20-min increment of increased sleep duration). Women with later sleep midpoint and later bedtime had higher odds of uncompleted cycle relative to those with earlier midpoint and earlier bedtime; OR = 1.24; 95%CI 1.09, 1.40 and OR = 1.33; 95%CI 1.17, 1.53 respectively, for 20-min increments. These results were independent of age, anti-Müllerian hormone levels, or sleep duration, and remained significant after exclusion of shift-working women. CONCLUSIONS: Shorter sleep duration and later sleep timing increase the odds of uncompleted cycles prior to embryo transfer.

Genomic heterogeneity affects the response to Daylight Saving Time.

Circadian clocks control the timing of many physiological events in the 24-h day. When individuals undergo an abrupt external shift (e.g., change in work schedule or travel across multiple time zones), circadian clocks become misaligned with the new time and may take several days to adjust. Chronic circadian misalignment, e.g., as a result of shift work, has been shown to lead to several physical and mental health problems. Despite the serious health implications of circadian misalignment, relatively little is known about how genetic variation affects an individual's ability to entrain to abrupt external changes. Accordingly, we used the one-hour advance from the onset of daylight saving time (DST) as a natural experiment to comprehensively study how individual heterogeneity affects the shift of sleep/wake cycles in response to an abrupt external time change. We found that individuals genetically predisposed to a morning tendency adjusted to the advance in a few days, while genetically predisposed evening-inclined individuals had not shifted. Observing differential effects by genetic disposition after a one-hour advance underscores the importance of heterogeneity in adaptation to external schedule shifts. These genetic differences may affect how individuals adjust to jet lag or shift work as well.

Predicting circadian phase across populations: a comparison of mathematical models and wearable devices.

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.

Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians.

While 24-h total sleep time (TST) is established as a critical driver of major depression, the relationships between sleep timing and regularity and mental health remain poorly characterized because most studies have relied on either self-report assessments or traditional objective sleep measurements restricted to cross-sectional time frames and small cohorts. To address this gap, we assessed sleep with a wearable device, daily mood with a smartphone application and depression through the 9-item Patient Health Questionnaire (PHQ-9) over the demanding first year of physician training (internship). In 2115 interns, reduced TST (b = -0.11, p < 0.001), later bedtime (b = 0.068, p = 0.015), along with increased variability in TST (b = 0.4, p = 0.0012) and in wake time (b = 0.081, p = 0.005) were associated with more depressive symptoms. Overall, the aggregated impact of sleep variability parameters and of mean sleep parameters on PHQ-9 were similar in magnitude (both r2 = 0.01). Within individuals, increased TST (b = 0.06, p < 0.001), later wake time (b = 0.09, p < 0.001), earlier bedtime (b = - 0.07, p < 0.001), as well as lower day-to-day shifts in TST (b = -0.011, p < 0.001) and in wake time (b = -0.004, p < 0.001) were associated with improved next-day mood. Variability in sleep parameters substantially impacted mood and depression, similar in magnitude to the mean levels of sleep parameters. Interventions that target sleep consistency, along with sleep duration, hold promise to improve mental health.

The prevalence and impact of pre-existing sleep disorder diagnoses and objective sleep parameters in patients hospitalized for COVID-19.

STUDY OBJECTIVES: Obstructive sleep apnea and other sleep disorders overlap with comorbidities associated with poor outcomes related to severe acute respiratory syndrome coronavirus 2 infection. However, the prevalence of obstructive sleep apnea among patients hospitalized for COVID-19 and relationship to outcomes is poorly characterized, and the relevance of other sleep disorders remains unknown. The objective of this study was to identify the prevalence of pre-existing sleep disorders and association with outcomes related to severe COVID-19 illness. METHODS: Patients with severe acute respiratory syndrome coronavirus 2 infection admitted to the University of Michigan Hospital System were included. Electronic medical records were queried for sleep disorders diagnostic codes. Data were extracted from polysomnography and home sleep testing in a subgroup with previous diagnostic testing at our center. Logistic regression was used to examine the association of sleep disorders with mechanical ventilation requirement, treatment with vasopressors, and death and Cox proportional hazards regression for time to discharge. RESULTS: Among n = 572 adult patients hospitalized for COVID-19, 113 (19.8%) patients had obstructive sleep apnea, 4 patients had central sleep apnea (0.7%), 5 had hypoventilation (0.9%), 63 had insomnia (11.0%), and 22 had restless legs syndrome or periodic limb movements disorder (3.9%). After adjusting for age, sex, body mass index, and race, no significant relationship was apparent between sleep disorders diagnoses or indices of sleep-disordered breathing severity and outcomes. CONCLUSIONS: This is the first study to determine the prevalence of obstructive sleep apnea and other sleep disorders in a well-characterized cohort of patients hospitalized for COVID-19. Once hospitalized, a significant contribution of sleep disorders to outcomes was not identified. Therefore, future evaluations should focus on earlier outcomes, such as infection or clinical manifestations after exposure to severe acute respiratory syndrome coronavirus 2.

The effects of COVID-19 stay-at-home order on sleep, health, and working patterns: a survey study of US health care workers.

STUDY OBJECTIVES: By March 2020, COVID-19 forced much of the world to stay at home to reduce the spread of the disease. Whereas some health care workers transitioned to working from home, many continued to report to work in person as essential employees. We sought to explore changes in sleep, health, work, and mood in health care workers during the stay-at-home orders. METHODS: We developed a cross-sectional online survey administered to health care workers. The survey assessed changes in sleep, work, screen time, media exposure, diet, exercise, substance use, and mood. The survey data were collected between March 28, 2020, and April 29, 2020. RESULTS: A total of 834 of 936 individuals completed the entire survey. Respondents were from 41 US states. Mood after the stay-at-home orders worsened, and screen time and substance use increased. Total sleep time shortened in those continuing to work in person (P < .001), whereas it was unchanged in those working from home (P = .73). Those working from home went to bed later, woke up later, and worked fewer hours. Reduced total sleep time and increased screen time before bed were associated with worse mood and screen time. Longer sleep time was associated with better mood. CONCLUSIONS: Health care workers' mood worsened regardless of whether work was in person or remote, although total sleep time was shorter for those working in person. Those working from home may have shifted their sleep time to be more in line with their endogenous circadian phase. Peer or other support services may be indicated to address sleep, mood, and health behaviors among health care workers during these unprecedented times.

The influence of opioids and nonopioid central nervous system active medications on central sleep apnea: a case-control study.

STUDY OBJECTIVES: Opioids are known to contribute to central sleep apnea (CSA), but the influence of nonopioid central nervous system active medications (CNSAMs) on CSA remains unclear. In light of the hypothesized impact of nonopioid CNSAMs on respiration, we examined the relationships between the use of opioids only, nonopioid CNSAMs alone, and their combination with CSA. METHODS: Among all adults who underwent polysomnography testing at the University of Michigan's sleep laboratory between 2013 and 2018 (n = 10,606), we identified 212 CSA cases and randomly selected 300 controls. Participants were classified into four groups based on their medication use: opioids alone, nonopioid CNSAMs only, their combination, and a reference group, including those who did not use any of these medications. We defined CSA as a binary outcome and as a continuous variable using central apnea index data. Logistic and linear regression were used to examine associations between medication use, CSA diagnosis, and central apnea index. RESULTS: Study participants included 58% men, and mean age was 50 (± 14 standard deviation years. Nearly half of the study participants did not use opioids or nonopioid CNSAMs, 6% used opioids alone, 27% nonopioid CNSAMs alone, and 16% used a combination of these medications. In adjusted analyses, opioids-only users had a nearly twofold increase in CSA odds, whereas those who used a combination of opioids and nonopioid CNSAMs had fivefold higher odds of CSA relative to the reference group. In contrast, the use of nonopioid CNSAMs alone had protective associations with CSA. CONCLUSIONS: This report showed increased odds of CSA, particularly among patients with sleep complaints who were prescribed opioids in combination with nonopioid CNSAMs compared with those who did not use any of these medications.

Current and Future Roles of Consumer Sleep Technologies in Sleep Medicine.

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.