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.