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.
{"title":"Telemedicine for hypertension management: where we stand, where we are headed","authors":"Stefano Omboni","doi":"10.20517/ch.2022.09","DOIUrl":"https://doi.org/10.20517/ch.2022.09","url":null,"abstract":"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.","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"367 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84913539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Ionov, E. Usova, Michil P. Egorov, N. Zvartau, A. Konradi
Aim: Guidelines recommend treating hypertension (HTN) by keeping office blood pressure (BP) within the therapeutic range (TR). However, little is known about the TR of home BP. Therefore, we aimed to find a reliable proportion of home systolic (S) BP in TR (sBPiTR) using a telehealth platform, which facilitates the access to reliable and structured home BP data. Methods: We used the data of HTN patients who participated in BP telemonitoring and counseling for 3 months. Patients had to manually enter their home BP in electronic diaries. Home SBP readings were averaged by the system itself except the very first or every first day of BP monitoring. We divided sBPiTR (110-130 mmHg) by quartiles. A weighted Cohen’s kappa coefficient was used as an estimate of inter-rater reliability between sBPiTR and office/home SBP in TR. We used a binomial logistic regression to test the predictive value of sBPiTR on target office/home SBP achievement. Results: In total, 123 patients were included (median age 54 years; 102 males) with a median office SBP of 140 mmHg. By 3 months, it decreased to 130 mmHg (P < 0.001), with 60% of patients with target office BP and 70% in the upper sBPiTR quartiles. There was a slight agreement between office SBP in TR and sBPiTR of ≥ 50% (k = 0.19, P < 0.035) and fair agreement when countered against home SBP in TR (k = 0.32-0.65, P < 0.0001). Patients with sBPiTR of ≥ 50% were more likely to fall within the office and home SBP TR after adjustment for baseline covariates. Conclusion: The threshold of 50% of home SBP measurements within 110-130 mmHg has a slight agreement with office BP control and a fair agreement with home BP control. This variable may serve as a predictor for the achievement of target SBP both in and out of office. Larger studies are needed to confirm these preliminary results.
{"title":"Home blood pressure in target range as an additional therapeutic goal in hypertensive patients: a telemonitoring-based analysis","authors":"M. Ionov, E. Usova, Michil P. Egorov, N. Zvartau, A. Konradi","doi":"10.20517/ch.2022.12","DOIUrl":"https://doi.org/10.20517/ch.2022.12","url":null,"abstract":"Aim: Guidelines recommend treating hypertension (HTN) by keeping office blood pressure (BP) within the therapeutic range (TR). However, little is known about the TR of home BP. Therefore, we aimed to find a reliable proportion of home systolic (S) BP in TR (sBPiTR) using a telehealth platform, which facilitates the access to reliable and structured home BP data. Methods: We used the data of HTN patients who participated in BP telemonitoring and counseling for 3 months. Patients had to manually enter their home BP in electronic diaries. Home SBP readings were averaged by the system itself except the very first or every first day of BP monitoring. We divided sBPiTR (110-130 mmHg) by quartiles. A weighted Cohen’s kappa coefficient was used as an estimate of inter-rater reliability between sBPiTR and office/home SBP in TR. We used a binomial logistic regression to test the predictive value of sBPiTR on target office/home SBP achievement. Results: In total, 123 patients were included (median age 54 years; 102 males) with a median office SBP of 140 mmHg. By 3 months, it decreased to 130 mmHg (P < 0.001), with 60% of patients with target office BP and 70% in the upper sBPiTR quartiles. There was a slight agreement between office SBP in TR and sBPiTR of ≥ 50% (k = 0.19, P < 0.035) and fair agreement when countered against home SBP in TR (k = 0.32-0.65, P < 0.0001). Patients with sBPiTR of ≥ 50% were more likely to fall within the office and home SBP TR after adjustment for baseline covariates. Conclusion: The threshold of 50% of home SBP measurements within 110-130 mmHg has a slight agreement with office BP control and a fair agreement with home BP control. This variable may serve as a predictor for the achievement of target SBP both in and out of office. Larger studies are needed to confirm these preliminary results.","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"323 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80299296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
“Digital hypertension” is a new information and communication technology (ICT)-based research field of digital healthcare that adds significant value to the management of hypertension by integrating multidimensional and time-series data. It includes the study of pathogenesis and predictive, individualized, and preemptive treatments, and its clinical outcomes can be introduced in telemedicine. The ICT in digital hypertension includes the research and development of blood pressure (BP) monitoring, e.g., wearable, cuff-less BP monitoring, a platform for digital transformation and transmission systems, and artificial intelligence. A recent clinical trial demonstrated the significant BP-lowering effect of digital therapeutics that facilitate lifestyle modification at the individual level via the patient’s smartphone. One of the goals of digital hypertension is personalized anticipation medicine that identifies the timing, place, and behavior that may trigger the onset of a cardiovascular event. This narrative review aims to address and discuss the cutting-edge information on the technology and concept of “digital hypertension”.
{"title":"State-of-the-art rapid review of the current landscape of digital hypertension","authors":"K. Kario, N. Harada, Ayako Okura","doi":"10.20517/ch.2022.02","DOIUrl":"https://doi.org/10.20517/ch.2022.02","url":null,"abstract":"“Digital hypertension” is a new information and communication technology (ICT)-based research field of digital healthcare that adds significant value to the management of hypertension by integrating multidimensional and time-series data. It includes the study of pathogenesis and predictive, individualized, and preemptive treatments, and its clinical outcomes can be introduced in telemedicine. The ICT in digital hypertension includes the research and development of blood pressure (BP) monitoring, e.g., wearable, cuff-less BP monitoring, a platform for digital transformation and transmission systems, and artificial intelligence. A recent clinical trial demonstrated the significant BP-lowering effect of digital therapeutics that facilitate lifestyle modification at the individual level via the patient’s smartphone. One of the goals of digital hypertension is personalized anticipation medicine that identifies the timing, place, and behavior that may trigger the onset of a cardiovascular event. This narrative review aims to address and discuss the cutting-edge information on the technology and concept of “digital hypertension”.","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"222 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73996586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.1515/9780804788410-010
Steven Cassedy
{"title":"7 The Globalized Consumer Network: From Pineapples to Turkey Red Cigarettes to the Bunny Hug","authors":"Steven Cassedy","doi":"10.1515/9780804788410-010","DOIUrl":"https://doi.org/10.1515/9780804788410-010","url":null,"abstract":"","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"104 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86537360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.1515/9780804788410-014
{"title":"Conclusion: Who You Are","authors":"","doi":"10.1515/9780804788410-014","DOIUrl":"https://doi.org/10.1515/9780804788410-014","url":null,"abstract":"","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90255045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.1515/9780804788410-009
{"title":"6 The Networked House and Home","authors":"","doi":"10.1515/9780804788410-009","DOIUrl":"https://doi.org/10.1515/9780804788410-009","url":null,"abstract":"","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82565280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.1515/9780804788410-008
{"title":"5 The Network of Spatialized Time","authors":"","doi":"10.1515/9780804788410-008","DOIUrl":"https://doi.org/10.1515/9780804788410-008","url":null,"abstract":"","PeriodicalId":93536,"journal":{"name":"Connected health","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86985896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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