Movement-based sleep-wake detection devices (i.e., actigraphy devices) were first developed in the early 1970s and have repeatedly been validated against polysomnography, which is considered the “gold-standard” of sleep measurement. Indeed, they have become important tools for objectively inferring sleep in free-living conditions. Standard actigraphy devices are rooted in accelerometry to measure movement and make predictions, via scoring algorithms, as to whether the wearer is in a state of wakefulness or sleep. Two important developments have become incorporated in newer devices. First, additional sensors, including measures of heart rate and heart rate variability and higher resolution movement sensing through triaxial accelerometers, have been introduced to improve upon traditional, movement-based scoring algorithms. Second, these devices have transcended scientific utility and are now being manufactured and distributed to the general public. This review will provide an overview of: (1) the history of actigraphic sleep measurement, (2) the physiological underpinnings of heart rate and heart rate variability measurement in wearables, (3) the refinement and validation of both standard actigraphy and newer, multisensory devices for real-world sleep-wake detection, (4) the practical applications of actigraphy, (5) important limitations of actigraphic measurement, and lastly (6) future directions within the field.

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Introduction The sudden COVID-19 pandemic and the uncertainty surrounding the virus has led to increased worry and fear. How this fear is associated with sleep remains unknown. Methods 419 US adults completed online surveys about sleep and COVID-19 experiences. Participants were asked about agreement with statements endorsing beliefs that they were currently infected, that they would someday be infected, that they fear testing, and that they are worried about a family member becoming infected. They were asked to rate agreement with statements regarding changes to sleep during the pandemic. Ordinal logistic regressions with degree of agreement with statements about sleep changes were outcomes, agreement with statements about COVID infection beliefs as predictor, and age, sex, and race/ethnicity as covariates were examined. Results Those who believed they were infected were 65% less likely to be keeping a regular schedule (p=0.001), 61% less likely to report improved sleep (p=0.009), 2.9 times as likely to report worse sleep (p=0.001), 2.7 times as likely to report difficulty falling asleep (p=0.002), 2.1 times as likely to report sleep maintenance problems (p=0.03), 2.9 times as likely to report sleepiness (p=0.001). Those who believed they would be infected in the future were 83% less likely to report improved sleep (p=0.005), 7.49 times as likely to report worse sleep (p=0.001), 5.3 times as likely to report difficulty falling asleep (p=0.003), 4.1 times as likely to report sleep maintenance problems (p=0.01), and 5.7 times as likely to report sleepiness (0.003). Those that feared testing were 5.7 times as likely to report more sleepiness (p=0.03). Those that worried about family were 80% less likely to be keeping a regular schedule (p=0.01), 75% less likely to report improved sleep (p=0.02), 4.5 times as likely to report worse sleep (p=0.02), 4.5 times as likely to report sleep maintenance problems (p=0.01), and 8.3 times as likely to report sleepiness (p=0.001). Conclusion Those who believed they were infected reported worsening sleep, though the degree was even greater among those who anticipated infection for themselves or a family member. Worries about COVID-19 may result in more adverse impact on sleep than potential infection itself. Support (if any):

Introduction The novel Coronavirus has caused major disruptions to sleep and cognitive function of many individuals. The present study evaluated the degree to which daytime cognitive dysfunction may be related to worsening sleep. Methods Data from adults aged 18 and older in the 2020 Coronavirus and Impact on Dreams (CovID) study were used. Individuals were asked “how would you rate your sleep quality overall” and answered with “very good, fairly good, fairly bad, or very bad.” Subjects were asked if it takes “30 minutes or more to fall asleep” and answered with how frequently that happened. In addition, participants were asked how often they wake up during the night. Participants were then asked how much they think their sleep problems have contributed to daytime functioning and answered on a scale of 0 to 5, from “not at all” to “very much,” respectively. Results 46% of the sample did not report problems with daytime cognitive function, while 33%, 12%, and 9% reported mild, moderate, and severe problems, respectively. Those who reported that they have kept a regular schedule during the pandemic were 83% less likely to report greater daytime cognitive dysfunction (95%CI:0.08,0.37, p<0.0005). Those who indicated that overall their sleep worsened or improved did not demonstrate a difference in likelihood of daytime cognitive problems. Regarding specific sleep experiences, those who reported more problems falling asleep due to the pandemic were 8.2 times more likely to report daytime cognitive dysfunction (95%CI:3.53,19.07, p<0.0005) and those who reported more problems with morning awakenings were 5.7 times more likely (95%CI:2.10,15.56, p<0.001). Those who reported that they were sleepier as a result of the pandemic were 9.3 times as likely to report daytime cognitive dysfunction (95%CI:3.53,24.46, p<0.0005) and those who reported taking more naps were 4.4 times more likely (95%CI:1.90,10.40, p<0.001). Conclusion In general, people who reported increased sleepiness, a less regular schedule, more insomnia, and more napping were more likely to experience daytime cognitive dysfunction during the COVID-19 pandemic. Support (if any) R01MD011600, R01DA051321