Connected Health Technology for Cardiovascular Disease Prevention and Management.
Digital medicine
Innovation
Mobile health
Journal
Current treatment options in cardiovascular medicine
ISSN: 1092-8464
Titre abrégé: Curr Treat Options Cardiovasc Med
Pays: United States
ID NLM: 9815942
Informations de publication
Date de publication:
18 May 2019
18 May 2019
Historique:
entrez:
20
5
2019
pubmed:
20
5
2019
medline:
20
5
2019
Statut:
epublish
Résumé
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. 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.
Identifiants
pubmed: 31104157
doi: 10.1007/s11936-019-0729-0
pii: 10.1007/s11936-019-0729-0
pmc: PMC7263827
mid: NIHMS1042544
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
29Subventions
Organisme : NHLBI NIH HHS
ID : F30 HL142131
Pays : United States
Organisme : NIA NIH HHS
ID : T32 AG000182
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007309
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002550
Pays : United States
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