Purpose: Heart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) is hindered by the lack of normative data for real-time HRVB metrics, without which, biofeedback risks reduced engagement and efficacy.Methods: We analyzed HRVB data from 5,158 participants in a therapist-supported DMHI incorporating slow-paced breathing to treat depression or anxious symptoms. A real-time metric of HRVB amplitude, shared with users, and a gold-standard research metric of low-frequency (LF) power were computed for each session, and averaged within-participant over 2 weeks. Descriptive statistics characterized age- and gender-based norms for HRVB metrics, while multivariate regression analyses confirmed the effects adjusting for demographic, psychological, and health factors.Results: Real-time HRVB amplitude correlated strongly (r=.93, p<.001) with the LF research gold-standard. Age was associated with a significant decline in HRVB across the lifespan (p<.001), which was steeper among men than women, adjusting for demographic, psychological and health factors. Moreover, 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). Conclusions: Real-time HRVB metrics correlate highly with research gold-standard metrics, enabling automated biofeedback delivery as a treatment component of DMHIs. However, without a precision care algorithm to tailor feedback difficulty, automated HRVB will likely result in suboptimal efficacy and heightened attrition, particularly among older adults. Moreover, we identify clinical and health factors (BMI, hypertension) relevant to building an equitable, accurate algorithm.