Affiliation:
1. Meru Health San Mateo California USA
2. Leonard Davis School of Gerontology University of Southern California Los Angeles California USA
3. Department of Psychology University of Southern California Los Angeles California USA
4. Department of Biomedical Engineering University of Southern California Los Angeles California USA
5. Department of Psychiatry, Robert Wood Johnson Medical School Rutgers University Monmouth Junction New Jersey USA
6. Department of Clinical Psychology, California School of Professional Psychology Alliant International University San Diego California USA
7. Department of Psychiatry and Behavioral Sciences University of California, San Francisco San Francisco California USA
8. Department of Epidemiology and Population Health University of Louisville Louisville Kentucky USA
Abstract
AbstractHeart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) requires computing and providing real‐time HRVB metrics in a personalized and user‐friendly fashion. To address these gaps, this study validates a real‐time HRVB feedback algorithm and characterizes the association of the main algorithmic summary metric—HRVB amplitude—with demographic, psychological, and health factors. We analyzed HRVB data from 5158 participants in a therapist‐supported DMHI incorporating slow‐paced breathing to treat depression or anxiety symptoms. A real‐time feedback metric of HRVB amplitude and a gold‐standard research metric of low‐frequency (LF) power were computed for each session and then averaged within‐participants over 2 weeks. We provide HRVB amplitude values, stratified by age and gender, and we characterize the multivariate associations of HRVB amplitude with demographic, psychological, and health factors. Real‐time HRVB amplitude correlated strongly (r = .93, p < .001) with the LF power around the respiratory frequency (~0.1 Hz). Age was associated with a significant decline in HRVB (β = −0.46, p < .001), which was steeper among men than women, adjusting for demographic, psychological, and health factors. Resting high‐ and low‐frequency power, body mass index, hypertension, Asian race, depression symptoms, and trauma history were significantly associated with HRVB amplitude in multivariate analyses (p's < .01). Real‐time HRVB amplitude correlates highly with a research gold‐standard spectral metric, enabling automated biofeedback delivery as a potential treatment component of DMHIs. Moreover, we identify demographic, psychological, and health factors relevant to building an equitable, accurate, and personalized biofeedback user experience.
Funder
National Institute of Mental Health