Telemedicine for hypertension management: where we stand, where we are headed

Hypertension is the leading cause of cardiovascular disease worldwide. Telemedicine may support doctors and health care professionals to raise awareness, increase detection, and improve the management of hypertension, by enhancing the connection with their patients. Given the growing popularity of telemedicine, the objective of the present review paper is to present the typical applications of telemedicine in hypertension management and available recommendations for use and summarize the evidence of their clinical efficacy before and during COVID-19 and the future trends and perspectives. Blood pressure telemonitoring (BPT), which enables remote transmission of BP and additional information on a patient’s health status from different settings to a healthcare facility, is the most common application of telemedicine for hypertension management. BPT is an integral component of a complex and multifaceted intervention, which includes video consultation, education on lifestyle and risk factors, antihypertensive medication review and management, and multidisciplinary team care. Several randomized controlled studies documented larger BP reduction and enhanced BP control with telemedicine compared to usual care. Telemedicine also helps optimize antihypertensive medications, improve treatment adherence, reduce office visits and resort to laboratory tests, and improve quality of life. At the time of COVID-19, telemedicine has helped to maintain adequate BP control in hypertensive patients under home confinement. Consequently, telemedicine is generally recommended to ensure continuity of care for hypertensive patients with uncontrolled BP, older patients, those at high risk of developing cardiovascular diseases, those with multiple comorbidities, medically underserved people, or patients isolated due to pandemics or national emergencies. Telemedicine applications relying on smart wearables, cuffless BP monitors, multiparametric devices, ambient sensors, and tools integrated with machine learning algorithms are particularly promising for telemedicine’s future development and diffusion since they may provide continuous surveillance of patients and remarkable support decision tools for doctors.