Changes in Continuous, Long-Term Heart Rate Variability and Individualized Physiological Responses to Wellness and Vacation Interventions Using a Wearable Sensor

Author: Abhishek Pratap1,2, Steve Steinhubl3, Elias Chaibub Neto1, Stephan W Wegerich4, Christine Tara Peterson5, Lizzy Weiss6, Sheila Patel5,7, Deepak Chopra5,6, Paul J Mills5
Affiliation: <sup>1</sup> Sage Bionetworks, Seattle, WA, United States. <sup>2</sup> Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States. <sup>3</sup> Scripps Translational Science Institute, La Jolla, CA, United States. <sup>4</sup> PhysIQ, Chicago, IL, United States. <sup>5</sup> Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, United States. <sup>6</sup> The Chopra Foundation, Carlsbad, CA, United States. <sup>7</sup> Chopra Global, New York, NY, United States.
Conference/Journal: Front Cardiovasc Med
Date published: 2020 Jul 31
Other: Volume ID: 7 , Pages: 120 , Special Notes: doi: 10.3389/fcvm.2020.00120. , Word Count: 246

There are many approaches to maintaining wellness, including taking a simple vacation to attending highly structured wellness retreats, which typically regulate the attendee's personal time and activities. In a healthy English-speaking cohort of 112 women and men (aged 30-80 years), this study examined the effects of participating in either a 6-days intensive wellness retreat based on Ayurvedic medicine principles or unstructured 6-days vacation at the same wellness center setting. Heart rate variability (HRV) was monitored continuously using a wearable ECG sensor patch for up to 7 days prior to, during, and 1-month following participation in the interventions. Additionally, salivary cortisol levels were assessed for all participants at multiple times during the day. Continual HRV monitoring data in the real-world setting was seen to be associated with demographic [HRVALF: βAge = 0.98 (95% CI = 0.96-0.98), false discovery rate (FDR) < 0.001] and physiological characteristics [HRVPLF: β = 0.98 (95% CI = 0.98-1), FDR =0.005] of participants. HRV features were also able to quantify known diurnal variations [HRVLF/HF: βACT:night vs. early-morning = 2.69 (SE = 1.26), FDR < 0.001] along with notable inter- and intraperson heterogeneity in response to intervention. A statistically significant increase in HRVALF [β = 1.48 (SE = 1.1), FDR < 0.001] was observed for all participants during the resort visit. Personalized HRV analysis at an individual level showed a distinct individualized response to intervention, further supporting the utility of using continuous real-world tracking of HRV at an individual level to objectively measure responses to potentially stressful or relaxing settings.

Keywords: cardiovascular medicine; digital health; intervention; remote monitoring; stress; wearable; wellness.

PMID: 32850982 PMCID: PMC7411743 DOI: 10.3389/fcvm.2020.00120