Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health

Abstract

Heart 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.