Wireless Heart Rate Variability in Assessing Community COVID-19

Abstract

According to the Johns Hopkins Coronavirus Resources Center, the number of confirmed COVID-19 cases exceeds 170,558,922 worldwide today with more than 3,546,881 fatalities (2021). The pandemic's massive health and well-being issues have already impacted the lives of millions globally, including spikes in mortality and morbidity. Many nations were unprepared for these significant consequences, revealing the critical need for standard public health principles of population assessment, intervention, and treatment. This editorial will address an innovative use of Heart Rate Variability (HRV), which is a non-invasive, inexpensive, and sensitive measure of inflammatory processes and immunomodulation (Kovatchev et al., 2003; Ahmad et al., 2009; Leitzke et al., 2020; Owens, 2020), among other health and well-being parameters. Specifically, the vagus nerve maintains tonic inhibitory control of proinflammatory cytokines via acetylcholine release into the reticuloendothelial system (e.g., spleen, gastrointestinal tract, heart, liver), mediating the inflammatory reflex through the cholinergic anti-inflammatory pathway (Dantzer and Kelley, 2007). HRV has been described in this special HRV Horizons 2030 Frontiers series as follows: “HRV offers insights into humoral, neural, and neurovisceral processes in health and disorders of brain, body, and behavior but has yet to be fully potentiated in the digital age” (Owens, 2020). Building on a growing body of HRV data (Rangon et al., 2020; Whitelaw et al., 2020; Hirten et al., 2021), we propose use of a wearable high fidelity Oura sensor ring (https://ouraring.com/blog/category/research-validation/) to acquire HRV, in addition to other physiological indicators, to track both pre-illness longitudinal baseline and an ongoing longitudinal Community assessment of those indicators associated with COVID-19 using algorithmic analysis and actionable feedback. While various aspects of this proposal have been used by investigators producing promising results at UC San Francisco (Smarr et al., 2020), UC San Diego/Scripps Research (Whitelaw et al., 2020), Stanford School of Medicine (Perez et al., 2019), Mt. Sinai's Icahn School of Medicine (Hirten et al., 2021), and others (Chung, 2020; Hasty et al., 2020), we propose a synthetic approach that incorporates the advantages of the most promising, actionable and practical elements to elucidate how HRV can act as a predictor of COVID19 infection. The use of longitudinal HRV data acquired by a personal device, transferred by smart phone application and analyzed by high throughput cloud-based machine learning algorithm represents an innovative, inexpensive, easily deployable, and scalable method for both individual use for health behavior maintenance and for communication and decision support with clinical and public health professionals in communities and larger jurisdictions. HRV research has produced extensive literature, with a recent PubMed search of the term “heart rate variability” producing more than 50,000 citations (Malik and Camm, 2004; Shaffer and Ginsberg, 2019), with rapidly evolving neuroscientific HRV studies (Holzman and Bridgett, 2017). Major theoretical contributions have been made by Porges' Polyvagal Theory (Porges, 2011); Grossman's biobehavioral studies of cardiac vagal tone (Grossman and Taylor, 2007); Owens, Critchley and associates studies of HRV as a remote digital biomarker (Owens et al., 2018); and Thayer's neurovisceral integration approach (Thayer and Lane, 2020), all show the important role of HRV as a physiological indicator of inflammatory and immune system activity. Briefly, HRV is the instantaneous variation in the inter-beat interval (IBI) of the electrocardiogram. HRVs relation to many disease states and human psychophysiological functions has been studied extensively. Perhaps counterintuitively, greater variability in IBI, measured as the time between adjacent R to R peaks in the ECG, is positively correlated to fewer and/or lesser negative health or well-being consequences in many diseases and conditions. These constant allostatic variations can be seen as analogous to the over 22,000 course corrections necessary for Apollo 8 to land on the moon (McEwen, 2017). Recent reviews have described the wide variety of applications of HRV in both medical and psychosocial settings (Drury et al., 2019). In particular, the Thayer group showed that HRV is related to inflammatory processes in humans (Williams et al., 2019; Jarczok et al., 2021) and identified an HRV related cholinergic anti-inflammatory pathway (Thayer and Fischer, 2009). Investigators have explored the use of HRV in medical conditions, including infectious and immune related disorders, in both human and animal studies showing various HRV parameters to be related to infection and immune system function (Fairchild, 2013; Herry et al., 2016; Pavlov and Tracey, 2019; Pavlov et al., 2020). Based on this body of basic and applied HRV research, we wish to urgently propose using HRV monitoring as an element of a larger framework of truly personalized health (Drury, 2019; Hood et al., 2019). HRV screening, analysis and feedback can be applied immediately to the present COVID-19 pandemic. A recent report by Jarczok et al. (2019) has presented proof of concept of HRV as a marker of health risks in human adults. We propose applying the Jarczok et al. method to IBI data obtained from personal devices such as the Oura ring, Apple Watch, Fitbit, and the Polar strap, among others, facilitating scalability, accessibility, economy and high fidelity data acquisition. The Apple Heart Study conducted by Stanford University's School of Medicine demonstrated the feasibility of using wearable technology, specifically the Apple Watch, to examine cardiovascular data for atrial fibrillation. They point out “this is just the beginning, as this study opens the door to further research into wearable technologies and how they...