Abstract: 

Heart rate variability biofeedback (HRV-B) is a clinically effective therapy in which patients can improve their mental and physical well-being through real-time monitoring of the heart-rate and specialized breathing techniques. HRV-B can improve health outcomes in a number of medical or wellness-related conditions, ranging from depression and anxiety, to cardiovascular disease, asthma, cancer fatigue, women’s health, better sleep, peak athletic performance, and stress resilience. The effectiveness of HRV-B in treating these conditions is due to how it modulates the nervous system connections linking the brain and heart, particularly the baroreflex. HRV-B is now entering digital health and wellness; however, traditional metrics and algorithms were designed for research or in-person clinical care. Hence, Dr. Aschbacher brings her unique integrative experience as a Head of Data Science, an Adjunct Associate Professor at UCSF, and a licensed, HRV-certified Psychotherapist in order to present a novel algorithm that runs real-time in an app, provides user feedback that is actionable and immediate, and personalizes the challenge for each user’s unique physiology. You will learn: 1) who benefits most from HRV-B, 2) how to model it from a data science perspective, and 3) why it represents a unique market differentiator. We present a novel algorithm that combines nonlinear machine learning models with time series analytic and signal processing techniques in order to derive an algorithmic strategy to provide real-time HRV biofeedback. The pipeline employed is built using interbeat interval data collected from several thousand users of a digital mental health program, and involves SQL, python, and javascript code run in a Google Cloud infrastructure. Finally, we employ user-centered design and behavior change principles to build the precision models in a manner intended to achieve clinically meaningful outcomes. This presentation is geared to provide audience members at all levels with an accessible introduction to HRV-B as a treatment tool, with the data algorithmic portions focused toward an intermediate level of proficiency.

Bio: 

Kirstin Aschbacher is a Data Scientist, with a background in PsychoNeuroImmunology Research from her days as an Associate Professor at the University of California, San Francisco (UCSF), Department of Psychology, Weill Institute for Neurosciences, and the Division of Cardiology. She has a PhD in Clinical Psychology and is also a licensed Psychologist with a certificate in HRV Biofeedback. She uses her cross-functional skill-sets to drive innovative, AI-based products that enhance user well-being and stress-resilience. In her current role as Senior Director of Health Data Science at Meru Health, she has focused on HRV Biofeedback and Precision Care algorithms.