Biometrics from a Wearable Device Reveals Temporary Effects of COVID-19 Vaccines on Cardiovascular, Respiratory, and Sleep Physiology

Although vaccines against SARS-CoV-2 have been proven safe and effective, transient side-effects lasting 24-48 hours post-vaccination have been reported. To better understand the subjective and objective response to COVID-19 vaccination, we conducted a retrospective analysis on 69619 subscribers to a wrist-worn biometric device (WHOOP Inc, Boston, MA, USA) who received either the AstraZeneca, Janssen/Johnson & Johnson, Moderna, or Pfizer/BioNTech vaccine. The WHOOP device measures resting heart rate (RHR), heart rate variability (HRV), respiratory rate (RR), and sleep architecture, and these physiological measures were normalized to the same day of the week, one week prior to vaccination. Averaging across vaccines, RHR, RR, and percent sleep derived from light sleep were elevated on the first night following vaccination and returned to baseline within four nights post-vaccination. When statistical differences were observed between doses on the first night post-vaccination, larger deviations in physiological measures were observed following the first dose of AstraZeneca and the second dose of Moderna and Pfizer/BioNTech. When statistical differences were observed between age groups or gender on the first night post-vaccination, larger deviations in physiological measures were observed in younger populations and in females (compared to males). When combining self-reported symptoms (fatigue, muscle aches, headache, chills, or fever) with the objectively measured physiological parameters, we found that self-reporting fever or chills had the strongest association with deviations in physiological measures following vaccination. In summary, these results suggest that COVID-19 vaccines temporarily affect cardiovascular, respiratory, and sleep physiology, and that dose, gender, and age affect the physiological response to vaccination.

Ketamine: More than Just NMDA Blocker

Ketamine has been extensively used in the medical field for more than 50 years, but its exact mechanism of action remains unknown. It\'s used to induce dissociative anesthesia (a state of profound analgesia, amnesia with light sleep, immobility, and a sense of disassociation from one\'s own body and surroundings). Clinical studies on ketamine as a dissociative anesthetic, a model for psychosis, and as a rapidly acting antidepressant have sparked great interest in understanding its effects at the molecular and cellular level. It exerts uncompetitive inhibitory effects on NMDARs (N-Methyl-D-asperate) and may preferentially affect the function of NMDARs in interneurons. The hypnotic effects of this drug are attributed to its blocking action on NMDA and HCN1 receptors; however, both positive and negative modulation of choline, amine, and opioid systems appears to occur. It is likely that ketamine\'s effect on chronic pain and depression far outlasts its actual levels. This could be due to the hyperglutamatergic state induced by ketamine causing a secondary increase in structural synaptic connectivity. The authors of this review have attempted to highlight the action of ketamine not only on NMDA receptors but also on a variety of biochemical processes and functions found in intercellular environments, which may explain its diverse role in many diseases.

Monitoring of Anesthesia by Bispectral Analysis of EEG Signals

Background. In intensive care, monitoring the depth of anesthesia during surgical procedures is a key element in the success of the medical operation and postoperative recovery. However, despite the development of anesthesia thanks to technological and pharmacological advances, its side effects such as underdose or overdose of hypnotics remain a major problem. Observation and monitoring must combine clinical observations (loss of consciousness and reactivity) with tools for real-time measurement of changes in the depth of anesthesia. Methodology. In this work, we will develop a noninvasive method for calculating, monitoring, and controlling the depth of general anesthesia during surgery. The objective is to reduce the effects of pharmacological usage of hypnotics and to ensure better quality recovery. Thanks to the overall activity of sets of neurons in the brain, we have developed a BIS technique based on bispectral analysis of the electroencephalographic signal EEG. Discussion. By collecting the electrical voltages from the brain, we distinguish light sleep from deep sleep according to the values of the BIS indicator (ranging from 0 : sleep to 100 : wake) and also control it by acting on the dosage of propofol and sevoflurane. We showed that the BIS value must be maintained during the operation and the anesthesia at a value greater than 60. Conclusion. This study showed that the BIS technology led to an optimization of the anesthetic management, the adequacy of the hypnotic dosage, and a better postoperative recovery.

The Psychoanalysis of Sleep-Paralysis – Perspectives on Oneirology

Dream is the reflection of our unconscious mind when we are in deep slumber. The duration of dream varies from 14 sec - 40sec and are characterized by Rapid Eye Movement (REM). Dreams occur in the transition period from light sleep to deep sleep or from deep sleep to light sleep. The associated study of dreams is known as Oneirology. It is sometimes associated by physical bodily movement

Awareness in the void: a micro-phenomenological exploration of conscious dreamless sleep

This paper presents a pilot study that explores instances of objectless awareness during sleep: conscious experiences had during sleep that prima facie lack an object of awareness. This state of objectless awareness during sleep has been widely described by Indian contemplative traditions and has been characterised as a state of consciousness-as-such; while in it, there is nothing to be aware of, one is merely conscious (cf. Evans-Wentz, 1960; Fremantle, 2001; Ponlop, 2006). While this phenomenon has received different names in the literature, such as ‘witnessing-sleep’ and ‘clear light sleep’ among others, the specific phenomenological profile of this state has not yet been rigorously studied. This paper aims at presenting a preliminary investigation of objectless consciousness during sleep using a novel tool in qualitative research that can guide future research. Five participants experiencing objectless consciousness during sleep were interviewed following the Micro-phenomenological Interview technique (MPI; Petitmengin, 2005, 2006). All participants reported an experience they had during sleep in which there was no scenery and no dream. This period labelled as ‘No Scenery/Void’ was either preceded by the dissolution of a lucid dream or by other forms of conscious mentation. The analysis of the results advances four experiential dimensions during this state of void, namely (1) Perception of absence, (2) Self-perception, (3) Perception of emotions, and (4) Perception of awareness. While the results are primarily explorative, they refer to themes found in the literature to describe objectless sleep and point at potential avenues of research. The results from this study are taken as indications to guide future operationalisations of this phenomenon.

Amplitude-integrated EEG amplitudes differ between wakefulness and sleep states in children

Introduction: Interpretation of pediatric amplitude-integrated EEG (aEEG) is hindered by the lack of knowledge on physiological background patterns in children. The aim of this study was to assess the amplitudes and bandwidths of background patterns during wakefulness and sleep in children from long-term EEGs. Methods: Forty long-term EEGs from patients < 18 years of age without or only solitary interictal epileptiform discharges were converted into aEEGs. Upper and lower amplitudes (μV) of the C3 - C4, P3 - P4, C3 - P3, C4 - P4, and Fp1 - Fp2 channels were measured during wakefulness and sleep. Bandwidths (BW, μV) were calculated, and sleep states assessed during the episodes of interest. A sensitivity analysis excluded patients who received antiepileptic drugs. Results: Median age was 9.9 years (interquartile range 6.1 - 14.7). All patients displayed continuous background patterns. Amplitudes and BW differed between wakefulness (C3 - C4 channel: upper 35 (27 - 49), lower 13 (10 - 19), BW 29 (21 - 34)) and sleep. During sleep, episodes with high amplitudes (upper 99 (71 - 125), lower 35 (25 - 44), BW 63 (44 - 81)) corresponded to sleep states N2 - N4. These episodes were interrupted by low amplitudes that were the dominating background pattern towards the morning (upper 39 (30 - 51), lower 16 (11 - 20), BW 23 (19 - 31), sleep states REM, N1, and N2). With increasing age, amplitudes and bandwidths declined. The sensitivity analysis yielded no differences in amplitude values or bandwidths. Conclusion: aEEG amplitudes and bandwidths were low during wakefulness and light sleep and high during deep sleep in stable children undergoing 24 hour EEG recordings. aEEG values were not altered by antiepileptic drugs in this study.

Sex differences in sleep and influence of the menstrual cycle on women’s sleep in junior endurance athletes

Previous research shows that female athletes sleep better according to objective parameters but report worse subjective sleep quality than male athletes. However, existing sleep studies did not investigate variations in sleep and sleep stages over longer periods and have, so far, not elucidated the role of the menstrual cycle in female athletes’ sleep. To address these methodological shortcomings, we investigated sex differences in sleep and sleep stages over 61 continuous days in 37 men and 19 women and examined the role of the menstrual cycle and its phases in 15 women. Sleep was measured by a non-contact radar, and menstrual bleeding was self-reported. Associations were investigated with multilevel modeling. Overall, women tended to report poorer subjective sleep quality (p = .057), but objective measurements showed that women obtained longer sleep duration (p < .001), more light (p = .013) and rapid eye movement sleep (REM; hours (h): p < .001, %: p = .007), shorter REM latency (p < .001), and higher sleep efficiency (p = .003) than men. R2 values showed that sleep duration, REM and REM latency were especially affected by sex. Among women, we found longer time in bed (p = .027) and deep sleep (h: p = .036), and shorter light sleep (%: p = .021) during menstrual bleeding vs. non-bleeding days; less light sleep (h: p = .040), deep sleep (%: p = .013) and shorter REM latency (p = .011) during the menstrual than pre-menstrual phase; and lower sleep efficiency (p = .042) and more deep sleep (%: p = .026) during the follicular than luteal phase. These findings indicate that the menstrual cycle may impact the need for physiological recovery, as evidenced by the sleep stage variations. Altogether, the observed sex differences in subjective and objective sleep parameters may be related to the female athletes’ menstrual cycle. The paper provides unique data of sex differences in sleep stages and novel insights into the role of the menstrual cycle in sleep among female athletes.

A Validation Study of a Commercial Wearable Device to Automatically Detect and Estimate Sleep

The aims of this study were to: (1) compare actigraphy (ACTICAL) and a commercially available sleep wearable (i.e., WHOOP) under two functionalities (i.e., sleep auto-detection (WHOOP-AUTO) and manual adjustment of sleep (WHOOP-MANUAL)) for two-stage categorisation of sleep (sleep or wake) against polysomnography, and; (2) compare WHOOP-AUTO and WHOOP-MANUAL for four-stage categorisation of sleep (wake, light sleep, slow wave sleep (SWS), or rapid eye movement sleep (REM)) against polysomnography. Six healthy adults (male: n = 3; female: n = 3; age: 23.0 ± 2.2 yr) participated in the nine-night protocol. Fifty-four sleeps assessed by ACTICAL, WHOOP-AUTO and WHOOP-MANUAL were compared to polysomnography using difference testing, Bland–Altman comparisons, and 30-s epoch-by-epoch comparisons. Compared to polysomnography, ACTICAL overestimated total sleep time (37.6 min) and underestimated wake (−37.6 min); WHOOP-AUTO underestimated SWS (−15.5 min); and WHOOP-MANUAL underestimated wake (−16.7 min). For ACTICAL, sensitivity for sleep, specificity for wake and overall agreement were 98%, 60% and 89%, respectively. For WHOOP-AUTO, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 90%, 60%, 86% and 63%, respectively. For WHOOP-MANUAL, sensitivity for sleep, wake, and agreement for two-stage and four-stage categorisation of sleep were 97%, 45%, 90% and 62%, respectively. WHOOP-AUTO and WHOOP-MANUAL have a similar sensitivity and specificity to actigraphy for two-stage categorisation of sleep and can be used as a practical alternative to polysomnography for two-stage categorisation of sleep and four-stage categorisation of sleep.

193 Sleep Variability and Affect Dynamics Among College Students during COVID-19 Pandemic

Introduction Sleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently mood-related disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic. Methods College student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state. Results Multilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p&lt;.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps&lt; .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity). Conclusion Sleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities. Support (if any):

260 Beyond Validation Testing: A Sleep Tracker for Longitudinal Data Collection in Operational Environments

Introduction Sleep tracking wearables are increasingly being validation tested against polysomnography (PSG) and actigraphy, but they may not be ideal for long-term epidemiological sleep studies, or for use in operational environments. A given device’s short battery life, limited data storage capacity, inability to detect naps or estimate sleep architecture, or privacy concerns may discourage researchers from using wearables to collect objective sleep data in real-world settings. The Zulu watch (Institutes for Behavior Resources) is designed to collect longitudinal sleep data in operational populations with irregular sleep patterns, such as long-haul pilots or shift workers. It is capable of on-wrist sleep-wake determination, nap detection, on-wrist sleep depth scoring (i.e., interrupted sleep, light sleep, or deep sleep), on-wrist data storage up to 80 sleep intervals, and year-long battery life. Laboratory testing is an important initial step toward establishing the performance of a device for longitudinal real-world sleep evaluation; therefore, the Zulu watch sleep tracking was subjected to testing against gold-standard PSG and actigraphy. Methods Eight healthy young adult participants (30.4±3.2 years; mean±SD) wore a Zulu watch and Philips Respironics Actiwatch 2 simultaneously over a 3-day laboratory PSG sleep study, with 8 hours time-in-bed each night. Overall epoch-by-epoch agreement of sensitivity (for sleep), specificity (for wake), and accuracy of Zulu watch data were tested against PSG and Actiwatch 2. Results Compared with either PSG or actigraphy, both accuracy and sensitivity for Zulu watch sleep-wake determination were &gt;90% while specificity was low (~26% vs. PSG, ~33% vs. actigraphy). Accuracy for sleep scoring vs. PSG was ~87% for interrupted sleep, ~52% for light sleep, and ~49% for deep sleep. Conclusion The Zulu watch showed mixed results but may be a viable candidate for sleep evaluation based on initial laboratory performance testing in healthy adults. The next steps will be to compare the Zulu watch against self-report of sleep in operational and substance use disorder populations. Longitudinal epidemiological sleep studies can become more feasible if technology is tailored to the specific needs of the real-world environment. Support (if any) Medical Technology Enterprise Consortium award MTEC-17-08-Multi-Topic-0104; Office of Naval Research, Code 34.