Health AI Central
Share:
Peer-Reviewed Publication
Psychophysiology2024;61(6):e14533.June 1, 2024Journal Article

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

Kirstin Aschbacher1, Mara Mather2,3,4, Paul Lehrer5, Richard Gevirtz6, Elissa Epel7, Nicholas C Peiper1,8
1Meru Health, San Mateo, California, USA.
2Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA.
3Department of Psychology, University of Southern California, Los Angeles, California, USA.
4Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.
5Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Monmouth Junction, New Jersey, USA.
6Department of Clinical Psychology, California School of Professional Psychology, Alliant International University, San Diego, California, USA.
7Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, California, USA.
8Department of Epidemiology and Population Health, University of Louisville, Louisville, Kentucky, USA.
PubMed DOI

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 algorith…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.