Shannon Wongvibulsin1, Seth S Martin2, Steven R Steinhubl3, Evan D Muse4,5. 1. Department of Biomedical Engineering, Johns Hopkins University, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 2. Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 3. Scripps Research Translational Institute, 3344 N. Torrey Pines Ct, Suite 300, La Jolla, San Diego, CA, 92037, USA. 4. Scripps Research Translational Institute, 3344 N. Torrey Pines Ct, Suite 300, La Jolla, San Diego, CA, 92037, USA. emuse@scripps.edu. 5. Division of Cardiovascular Disease, Scripps Clinic-Scripps Health, La Jolla, San Diego, CA, USA. emuse@scripps.edu.
Abstract
PURPOSE OF THE REVIEW: Advances in computing power and wireless technologies have reshaped our approach to patient monitoring. Medical grade sensors and apps that were once restricted to hospitals and specialized clinic are now widely available. Here, we review the current evidence supporting the use of connected health technologies for the prevention and management of cardiovascular disease in an effort to highlight gaps and future opportunities for innovation. RECENT FINDINGS: Initial studies in connected health for cardiovascular disease prevention and management focused primarily on activity tracking and blood pressure monitoring but have since expanded to include a full panoply of novel sensors and pioneering smartphone apps with targeted interventions in diet, lipid management and risk assessment, smoking cessation, cardiac rehabilitation, heart failure, and arrhythmias. While outfitting patients with sensors and devices alone is infrequently a lasting solution, monitoring programs that include personalized insights based on patient-level data are more likely to lead to improved outcomes. Advances in this space have been driven by patients and researchers while healthcare systems remain slow to fully integrate and adequately adapt these new technologies into their workflows. Cardiovascular disease prevention and management continue to be key focus areas for clinicians and researchers in the connected health space. Exciting progress has been made though studies continue to suffer from small sample size and limited follow-up. Efforts that combine home patient monitoring, engagement, and personalized feedback are the most promising. Ultimately, combining patient-level ambulatory sensor data, electronic health records, and genomics using machine learning analytics will bring precision medicine closer to reality.
PURPOSE OF THE REVIEW: Advances in computing power and wireless technologies have reshaped our approach to patient monitoring. Medical grade sensors and apps that were once restricted to hospitals and specialized clinic are now widely available. Here, we review the current evidence supporting the use of connected health technologies for the prevention and management of cardiovascular disease in an effort to highlight gaps and future opportunities for innovation. RECENT FINDINGS: Initial studies in connected health for cardiovascular disease prevention and management focused primarily on activity tracking and blood pressure monitoring but have since expanded to include a full panoply of novel sensors and pioneering smartphone apps with targeted interventions in diet, lipid management and risk assessment, smoking cessation, cardiac rehabilitation, heart failure, and arrhythmias. While outfitting patients with sensors and devices alone is infrequently a lasting solution, monitoring programs that include personalized insights based on patient-level data are more likely to lead to improved outcomes. Advances in this space have been driven by patients and researchers while healthcare systems remain slow to fully integrate and adequately adapt these new technologies into their workflows. Cardiovascular disease prevention and management continue to be key focus areas for clinicians and researchers in the connected health space. Exciting progress has been made though studies continue to suffer from small sample size and limited follow-up. Efforts that combine home patient monitoring, engagement, and personalized feedback are the most promising. Ultimately, combining patient-level ambulatory sensor data, electronic health records, and genomics using machine learning analytics will bring precision medicine closer to reality.
Entities:
Keywords:
Digital medicine; Innovation; Mobile health
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