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Komiyama C, Kuwabara M, Harima A, Kanie T, Yamaguchi T, Kodama T. Digital Health Interventions for Atherosclerotic Cardiovascular Disease: The Current Impact and Future Directions for Prevention and Management. J Atheroscler Thromb 2025; 32:395-404. [PMID: 39894471 PMCID: PMC11973520 DOI: 10.5551/jat.rv22032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2024] [Accepted: 12/18/2024] [Indexed: 02/04/2025] Open
Abstract
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, including in Japan, where the aging population intensifies its impact. This review evaluated the potential impact of digital healthcare on the prevention and management of ASCVD, covering both primary and secondary prevention strategies. Digital health tools, such as risk assessment applications remote monitoring, lifestyle modification support, and remote rehabilitation, have shown promise in improving patient engagement, adherence, and outcomes. However, while digital health interventions demonstrate significant benefits, challenges persist, including interoperability issues, privacy concerns, low digital literacy among older adults, and limited health insurance coverage for digital interventions. Through an analysis of recent advancements and case studies, this review demonstrates the need for user-centered design, enhanced regulatory frameworks, and expanded insurance support to facilitate the effective integration of digital health in ASCVD care. Furthermore, emerging technologies such as personalized healthcare modules offer promising directions for tailored and impactful care. Addressing these barriers is critical to unleashing the full potential of digital healthcare to reduce the burden of ASCVD and enhance patient outcomes.
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Affiliation(s)
| | - Masanari Kuwabara
- Department of Cardiology, Toranomon Hospital, Tokyo, Japan
- Division of Public Health and Division of Cardiovascular Medicine, Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Ayako Harima
- Department of Cardiology, Toranomon Hospital, Tokyo, Japan
| | - Takayoshi Kanie
- Department of Cardiology, St. Luke’s International Hospital, Tokyo, Japan
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Lakkireddy D, Angiolillo DJ, Charlton‐Ouw K, Jefferson B, Peeran S, Bisharat M, Ortega‐Paz L, Harxhi A, Kaul S, Michaud E, Juan S, Woods B, Damaraju CV, Fontana G, Bonaca MP. Rationale and Design of a Study to Assess the Engagement and Usefulness of the Care4Today Connect Digital Health Application for Disease Management in Coronary Artery Disease and Peripheral Artery Disease (iPACE-CVD Study). Clin Cardiol 2024; 47:e70039. [PMID: 39663755 PMCID: PMC11635118 DOI: 10.1002/clc.70039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/27/2024] [Accepted: 10/16/2024] [Indexed: 12/13/2024] Open
Abstract
INTRODUCTION Coronary artery disease (CAD) and peripheral artery disease (PAD) increase the risks of cardiovascular events and death. Digital health technologies are rapidly expanding to improve healthcare quality and access. The Care4Today Connect (C4T CAD-PAD) mobile application is designed to help patients with CAD and/or PAD improve medication adherence, learn about their disease, make lifestyle modifications, and enhance healthcare provider (HCP) connection via an HCP-facing portal. HYPOTHESIS & METHODS The prospective, single-arm, multicenter, noninterventional iPACE-CVD (innovative Patient compAnion impaCting health outcomEs: a CardioVascular Digital health program) study (ClinicalTrials.gov identifier: NCT06052319) is evaluating engagement and usefulness of the application for patients with CAD and/or PAD in clinical settings. Application access is provided with a code from patients' HCPs. Key features include medication and health experience tracking. The application is available in English and Spanish and for iOS and Android devices. Engagement is defined as the proportion of patients who use the application for ≥ 10 weeks during the 3-month study period. Application use is defined as the number of patients using ≥ 1 application feature(s) each week. Usefulness is determined by the percentage of engaged patients who complete the My Feedback Matters survey with a satisfaction response score of > 2 (on a 5-point scale, where 1 = strongly disagree and 5 = strongly agree) for at least three of the six questions. RESULTS A total of 271 participants were enrolled between November 29, 2023, and May 15, 2024. The study concluded on August 15, 2024. CONCLUSION This study will help enhance the application for subsequent studies. TRIAL REGISTRATION NCT06052319.
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Affiliation(s)
| | | | | | | | - Syed Peeran
- Coastal Cardiothoracic and Vascular SurgeryPortsmouth Regional HospitalPortsmouthNew HampshireUSA
| | - Mohannad Bisharat
- Ashchi Heart and Vascular Center and HCA Florida Memorial HospitalJacksonvilleFloridaUSA
| | - Luis Ortega‐Paz
- Division of CardiologyUniversity of Florida College of MedicineJacksonvilleFloridaUSA
| | - Ante Harxhi
- Janssen Scientific Affairs, LLC, a Johnson & Johnson CompanyTitusvilleNew JerseyUSA
| | - Simrati Kaul
- Janssen Scientific Affairs, LLC, a Johnson & Johnson CompanyTitusvilleNew JerseyUSA
| | - Evelyne Michaud
- Janssen Scientific Affairs, LLC, a Johnson & Johnson CompanyTitusvilleNew JerseyUSA
| | - Stephanie Juan
- Janssen Scientific Affairs, LLC, a Johnson & Johnson CompanyTitusvilleNew JerseyUSA
| | - Breeana Woods
- Johnson & Johnson Technology SolutionsRaritanNew JerseyUSA
| | - CV Damaraju
- Janssen Scientific Affairs, LLC, a Johnson & Johnson CompanyTitusvilleNew JerseyUSA
| | - Gregory Fontana
- Cardiovascular Institute of Los Robles Health SystemHCA Healthcare Research InstituteThousand OaksCaliforniaUSA
| | - Marc P. Bonaca
- CPC Clinical Research, Department of MedicineUniversity of ColoradoAuroraColoradoUSA
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Iino H, Kizaki H, Imai S, Hori S. Medication Management Initiatives Using Wearable Devices: Scoping Review. JMIR Hum Factors 2024; 11:e57652. [PMID: 39602520 PMCID: PMC11612519 DOI: 10.2196/57652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 08/28/2024] [Accepted: 09/13/2024] [Indexed: 11/29/2024] Open
Abstract
Background Wearable devices (WDs) have evolved beyond simple fitness trackers to sophisticated health monitors capable of measuring vital signs, such as heart rate and blood oxygen levels. Their application in health care, particularly medication management, is an emerging field poised to significantly enhance patient adherence to treatment regimens. Despite their widespread use and increasing incorporation into clinical trials, a comprehensive review of WDs in terms of medication adherence has not been conducted. Objective This study aimed to conduct a comprehensive scoping review to evaluate the impact of WDs on medication adherence across a variety of diseases, summarizing key research findings, outcomes, and challenges encountered. Methods Adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a structured search was conducted across MEDLINE, Web of Science, and Embase databases, covering the literature from January 1, 2010, to September 30, 2022. The search strategy was based on terms related to WDs and medication adherence, specifically focusing on empirical studies to ensure the inclusion of original research findings. Studies were selected based on their relevance to medication adherence, usage of WDs in detecting medication-taking actions, and their role in integrated medication management systems. Results We screened 657 articles and identified 18 articles. The identified studies demonstrated the diverse applications of WDs in enhancing medication adherence across diseases such as Parkinson disease, diabetes, and cardiovascular conditions. The geographical distribution and publication years of these studies indicate a growing interest in this research area. The studies were divided into three types: (1) studies reporting a correlation between data from WDs or their usage and medication adherence or drug usage as outcomes, (2) studies using WDs to detect the act of medication-taking itself, and (3) studies proposing an integrated medication management system that uses WDs in managing medication. Conclusions WDs are increasingly being recognized for their potential to enhance medication management and adherence. This review underscores the need for further empirical research to validate the effectiveness of WDs in real-life settings and explore their use in predicting adherence based on activity rhythms and activities. Despite technological advancements, challenges remain regarding the integration of WDs into routine clinical practice. Future research should focus on leveraging the comprehensive data provided by WDs to develop personalized medication management strategies that can improve patient outcomes.
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Affiliation(s)
- Haru Iino
- Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan
| | - Hayato Kizaki
- Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan
| | - Shungo Imai
- Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan
| | - Satoko Hori
- Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan
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Garba DL, Jacobsen AP, Blumenthal RS, Martinez MW, Ndumele CE, Coslick AM, Barouch LA. Assessing athletes beyond routine screening: Incorporating essential factors to optimize cardiovascular health and performance. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2024; 44:100413. [PMID: 38947733 PMCID: PMC11214176 DOI: 10.1016/j.ahjo.2024.100413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/27/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
The American Heart Association (AHA) has devised Life's Essential 8, a set of eight evidence-based health behaviors that play a crucial role in optimizing cardiovascular health and overall well-being. In addition to Life's Essential 8, enhanced screening for Cardiovascular-Kidney-Metabolic (CKM) Syndrome risk factors into routine athlete screening also provides a more comprehensive approach for ensuring athlete safety and long-term health. Incorporating Life's Essential 8 and CKM Syndrome metrics into athlete health evaluations will improve the sports performance of athletes and help optimize their long-term health.
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Affiliation(s)
- Deen L. Garba
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Alan P. Jacobsen
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Roger S. Blumenthal
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Matthew W. Martinez
- Chanin T. Mast Center for Hypertrophic Cardiomyopathy, Atlantic Health System, Morristown Medical Center, Morristown, NJ, USA
| | - Chiadi E. Ndumele
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Alexis M. Coslick
- Sports Medicine, Departments of Physical Medicine and Rehabilitation and Orthopaedics, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Lili A. Barouch
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
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Liang F, Yang X, Peng W, Zhen S, Cao W, Li Q, Xiao Z, Gong M, Wang Y, Gu D. Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 43:100817. [PMID: 38456090 PMCID: PMC10920052 DOI: 10.1016/j.lanwpc.2023.100817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/04/2023] [Accepted: 05/23/2023] [Indexed: 03/09/2024]
Abstract
Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.
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Affiliation(s)
- Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, 22 Qixiangtai Rd, Tianjin 300070, People's Republic of China
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, 22 Qixiangtai Rd, Tianjin 300070, People's Republic of China
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University, 251 Ningda Road, Xining City 810016, People's Republic of China
- Qinghai Provincial Key Laboratory of Prevention and Control of Glucolipid Metabolic Diseases with Traditional Chinese Medicine, Xining 810008, People's Republic of China
| | - Shihan Zhen
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Wenzhe Cao
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Qian Li
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Zhiyi Xiao
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, No. 1023-1063, Shatai South Road, Guangzhou 510515, People's Republic of China
| | - Youfa Wang
- The First Affiliated Hospital of Xi'an Jiaotong University Public Health Institute, Global Health Institute, School of Public Health, International Obesity and Metabolic Disease Research Center, Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - Dongfeng Gu
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
- School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
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Isakadze N, Kim CH, Marvel FA, Ding J, MacFarlane Z, Gao Y, Spaulding EM, Stewart KJ, Nimbalkar M, Bush A, Broderick A, Gallagher J, Molello N, Commodore-Mensah Y, Michos ED, Dunn P, Hanley DF, McBee N, Martin SS, Mathews L. Rationale and Design of the mTECH-Rehab Randomized Controlled Trial: Impact of a Mobile Technology Enabled Corrie Cardiac Rehabilitation Program on Functional Status and Cardiovascular Health. J Am Heart Assoc 2024; 13:e030654. [PMID: 38226511 PMCID: PMC10926786 DOI: 10.1161/jaha.123.030654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/01/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Cardiac rehabilitation (CR) is an evidence-based, guideline-recommended intervention for patients recovering from a cardiac event, surgery or procedure that improves morbidity, mortality, and functional status. CR is traditionally provided in-center, which limits access and engagement, most notably among underrepresented racial and ethnic groups due to barriers including cost, scheduling, and transportation access. This study is designed to evaluate the Corrie Hybrid CR, a technology-based, multicomponent health equity-focused intervention as an alternative to traditional in-center CR among patients recovering from a cardiac event, surgery, or procedure compared with usual care alone. METHODS The mTECH-Rehab (Impact of a Mobile Technology Enabled Corrie CR Program) trial will randomize 200 patients who either have diagnosis of myocardial infarction or who undergo coronary artery bypass grafting surgery, percutaneous coronary intervention, heart valve repair, or replacement presenting to 4 hospitals in a large academic health system in Maryland, United States, to the Corrie Hybrid CR program combined with usual care CR (intervention group) or usual care CR alone (control group) in a parallel arm, randomized controlled trial. The Corrie Hybrid CR program leverages 5 components: (1) a patient-facing mobile application that encourages behavior change, patient empowerment, and engagement with guideline-directed therapy; (2) Food and Drug Administration-approved smart devices that collect health metrics; (3) 2 upfront in-center CR sessions to facilitate personalization, self-efficacy, and evaluation for the safety of home exercise, followed by a combination of in-center and home-based sessions per participant preference; (4) a clinician dashboard to track health data; and (5) weekly virtual coaching sessions delivered over 12 weeks for education, encouragement, and risk factor modification. The primary outcome is the mean difference between the intervention versus control groups in distance walked on the 6-minute walk test (ie, functional capacity) at 12 weeks post randomization. Key secondary and exploratory outcomes include improvement in a composite cardiovascular health metric, CR engagement, quality of life, health factors (including low-density lipoprotein-cholesterol, hemoglobin A1c, weight, diet, smoking cessation, blood pressure), and psychosocial factors. Approval for the study was granted by the local institutional review board. Results of the trial will be published once data collection and analysis have been completed. CONCLUSIONS The Corrie Hybrid CR program has the potential to improve functional status, cardiovascular health, and CR engagement and advance equity in access to cardiac rehabilitation. REGISTRATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT05238103.
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Affiliation(s)
- Nino Isakadze
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Chang H Kim
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Francoise A Marvel
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Jie Ding
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Zane MacFarlane
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Yumin Gao
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Erin M Spaulding
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
- Johns Hopkins University School of Nursing Baltimore MD USA
- Welch Center for Prevention, Epidemiology, and Clinical Research Johns Hopkins University Baltimore MD USA
| | - Kerry J Stewart
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Mansi Nimbalkar
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Alexandra Bush
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Ashley Broderick
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Jeanmarie Gallagher
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Nancy Molello
- Johns Hopkins Center for Health Equity Baltimore MD USA
| | - Yvonne Commodore-Mensah
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
- Johns Hopkins University School of Nursing Baltimore MD USA
- Johns Hopkins Center for Health Equity Baltimore MD USA
| | - Erin D Michos
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
| | - Patrick Dunn
- Center for Health Technology and Innovation, American Heart Association Dallas TX USA
- Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA
| | - Daniel F Hanley
- Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA
- Division of Neurosurgery, Department of Surgery Johns Hopkins University School of Medicine Baltimore MD USA
- Department of Anesthesiology and Critical Care Medicine Johns Hopkins University School of Medicine Baltimore MD USA
| | - Nichol McBee
- Ginsburg Institute for Health Equity, Nemours Children's Health Orlando FL USA
- Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA
| | - Seth S Martin
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
- Johns Hopkins Center for Health Equity Baltimore MD USA
| | - Lena Mathews
- Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA
- Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH Center) Baltimore MD USA
- Johns Hopkins Center for Health Equity Baltimore MD USA
- Welch Center for Prevention, Epidemiology, and Clinical Research Johns Hopkins University Baltimore MD USA
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Kaplan A, Boivin M, Bouchard J, Kim J, Hayes S, Licskai C. The emerging role of digital health in the management of asthma. Ther Adv Chronic Dis 2023; 14:20406223231209329. [PMID: 38028951 PMCID: PMC10657529 DOI: 10.1177/20406223231209329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
The most common reasons seen for lack of asthma control include misconceptions about disease control, low controller treatment adherence, poor inhaler technique, and the resulting underuse of controllers and overuse of short-acting beta2 agonists (SABAs). Narrowing these care gaps may be achieved through well-designed patient education that considers the patient's motivation, beliefs, and capabilities regarding their asthma and its management and empowers the patient to become an active participant in treatment decisions. Digital health technologies (DHTs) and digital therapeutic (DT) devices provide new opportunities to monitor treatment behaviors, improve communication between healthcare providers and patients, and generate data that inform educational interactions. DHT and DT have been proven effective in enhancing patient self-management in other chronic conditions, particularly diabetes. Accelerated integration of DHT and DT into the management of asthma patients is facilitated by the use of digital inhalers that employ sensor technology ("smart" inhalers). These devices efficiently provide real-time feedback on controller adherence, SABA use, and inhaler technique that have the strong potential to optimize asthma control.
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Affiliation(s)
- Alan Kaplan
- Department of Family and Community Medicine, University of Toronto, 14872 Yonge Street, Aurora, Toronto, ON L4G 1N2, Canada
- Family Physician Airways Group of Canada, Markham, ON, Canada
| | | | | | - James Kim
- Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Christopher Licskai
- Division of Respirology, Department of Medicine, Western University, London, ON, Canada
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Stremmel C, Breitschwerdt R. Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review. JMIR Cardio 2023; 7:e44983. [PMID: 37647103 PMCID: PMC10500361 DOI: 10.2196/44983] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 06/12/2023] [Accepted: 08/07/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND The digital transformation of our health care system has experienced a clear shift in the last few years due to political, medical, and technical innovations and reorganization. In particular, the cardiovascular field has undergone a significant change, with new broad perspectives in terms of optimized treatment strategies for patients nowadays. OBJECTIVE After a short historical introduction, this comprehensive literature review aimed to provide a detailed overview of the scientific evidence regarding digitalization in the diagnostics and therapy of cardiovascular diseases (CVDs). METHODS We performed an extensive literature search of the PubMed database and included all related articles that were published as of March 2022. Of the 3021 studies identified, 1639 (54.25%) studies were selected for a structured analysis and presentation (original articles: n=1273, 77.67%; reviews or comments: n=366, 22.33%). In addition to studies on CVDs in general, 829 studies could be assigned to a specific CVD with a diagnostic and therapeutic approach. For data presentation, all 829 publications were grouped into 6 categories of CVDs. RESULTS Evidence-based innovations in the cardiovascular field cover a wide medical spectrum, starting from the diagnosis of congenital heart diseases or arrhythmias and overoptimized workflows in the emergency care setting of acute myocardial infarction to telemedical care for patients having chronic diseases such as heart failure, coronary artery disease, or hypertension. The use of smartphones and wearables as well as the integration of artificial intelligence provides important tools for location-independent medical care and the prevention of adverse events. CONCLUSIONS Digital transformation has opened up multiple new perspectives in the cardiovascular field, with rapidly expanding scientific evidence. Beyond important improvements in terms of patient care, these innovations are also capable of reducing costs for our health care system. In the next few years, digital transformation will continue to revolutionize the field of cardiovascular medicine and broaden our medical and scientific horizons.
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Seneviratne MG, Connolly SB, Martin SS, Parakh K. Grains of Sand to Clinical Pearls: Realizing the Potential of Wearable Data. Am J Med 2023; 136:136-142. [PMID: 36351523 DOI: 10.1016/j.amjmed.2022.10.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/15/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022]
Abstract
Despite the rapid growth of wearables as a consumer technology sector and a growing evidence base supporting their use, they have been slow to be adopted by the health system into clinical care. As regulatory, reimbursement, and technical barriers recede, a persistent challenge remains how to make wearable data actionable for clinicians-transforming disconnected grains of wearable data into meaningful clinical "pearls". In order to bridge this adoption gap, wearable data must become visible, interpretable, and actionable for the clinician. We showcase emerging trends and best practices that illustrate these 3 pillars, and offer some recommendations on how the ecosystem can move forward.
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Affiliation(s)
| | | | - Seth S Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Department of Medicine, Johns Hopkins, Baltimore, MD
| | - Kapil Parakh
- Google Research, Washington, DC; Georgetown School of Medicine, Washington, DC
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Isakadze N, Molello N, MacFarlane Z, Gao Y, Spaulding EM, Commodore Mensah Y, Marvel FA, Khoury S, Marine JE, Michos ED, Spragg D, Berger RD, Calkins H, Cooper LA, Martin SS. The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study. JMIR Hum Factors 2022; 9:e38048. [DOI: 10.2196/38048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/28/2022] [Accepted: 08/02/2022] [Indexed: 11/05/2022] Open
Abstract
Background
Smartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity.
Objective
We aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions.
Methods
We conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study.
Results
The top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support.
Conclusions
We co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators.
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Johnson T, Isakazde N, Mathews L, Gao Y, MacFarlane Z, Spaulding EM, Martin SS, Marvel FA. Building a hybrid virtual cardiac rehabilitation program to promote health equity: Lessons learned. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:158-160. [PMID: 36046432 PMCID: PMC9422060 DOI: 10.1016/j.cvdhj.2022.06.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Xiong P, Lee SMY, Chan G. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Front Cardiovasc Med 2022; 9:860032. [PMID: 35402563 PMCID: PMC8990170 DOI: 10.3389/fcvm.2022.860032] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/18/2022] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Affiliation(s)
- Ping Xiong
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
| | - Ging Chan
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
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Liu ES, Hung CC, Chiang CH, Tsai YC, Fu YJ, Ko YL, Wang CL, Lai WY, Tsai FT, Kuo FY, Huang WC. Quality care in ST-segment elevation myocardial infarction. J Chin Med Assoc 2022; 85:268-275. [PMID: 34999635 DOI: 10.1097/jcma.0000000000000687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Over the past decades, the treatment of ST-segment elevation myocardial infarction (STEMI) has been redefined with the incorporation of evidence from multiple clinical trials. Recommendations from guidelines are updated regularly to reduce morbidity and mortality. However, heterogeneous care systems, physician perspectives, and patient behavior still lead to a disparity between evidence and clinical practice. The quality of care has been established and become an integral part of modern healthcare in order to increase the likelihood of desired health outcomes and adhere to professional knowledge. For patients with STEMI, measuring the quality of care is a multifactorial and multidimensional process that cannot be estimated solely based on patients' clinical outcomes. The care of STEMI is similar to the concept of "the chain of survival" that emphasizes the importance of seamless integration of five links: early recognition and diagnosis, timely reperfusion, evidence-based medications, control of cholesterol, and cardiac rehabilitation. Serial quality indicators, reflecting the full spectrum of care, have become a widely used tool for assessing performance. Comprehension of every aspect of quality assessment and indicators might be too demanding for a physician. However, it is worthwhile to understand the concepts involved in quality improvement since every physician wants to provide better care for their patients. This article reviews a fundamental approach to quality care in STEMI.
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Affiliation(s)
- En-Shao Liu
- Department of critical care medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC
| | - Cheng Chung Hung
- Department of critical care medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC
| | - Cheng-Hung Chiang
- Department of critical care medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC
| | - Yi-Ching Tsai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yun-Ju Fu
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yu-Lin Ko
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chia-Lin Wang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Wei-Yi Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Fu-Ting Tsai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Feng-You Kuo
- Department of critical care medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC
| | - Wei-Chun Huang
- Department of critical care medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Physical Therapy, Fooyin University, Kaohsiung, Taiwan, ROC
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC
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Mauch J, Thachil V, Tang WHW. Diagnostics and Prevention: Landscape for Technology Innovation in Precision Cardiovascular Medicine. ADVANCES IN CARDIOVASCULAR TECHNOLOGY 2022:603-624. [DOI: 10.1016/b978-0-12-816861-5.00004-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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Brownstein AJ, Derenbecker R, Gao Y, Ding J, Varghese B, Isakadze N, Spaulding EM, Marvel FA, Martin SS. Application of the very high risk criterion and evaluation of cholesterol guideline adherence in acute myocardial infarction patients at an urban academic medical center. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 13:100082. [PMID: 38560081 PMCID: PMC10978217 DOI: 10.1016/j.ahjo.2021.100082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 12/23/2021] [Indexed: 04/04/2024]
Abstract
Objective The 2018 AHA/ACC cholesterol guidelines recommend considering non-statin agents among very high-risk (VHR) patients with LDL-C ≥ 70 mg/dL after maximizing statin therapy. We aimed to evaluate the prevalence of VHR status in acute myocardial infarction (AMI) patients at hospital discharge and the adherence to guideline-directed cholesterol therapy (GDCT) within one-year follow-up post-AMI. Methods We performed a retrospective analysis of patients who suffered a type 1 AMI between October 2015 and March 2019, and then were followed at our institution for 1 year after hospital discharge. We calculated the percentage of patients at VHR and among those with follow up lipid panels, we determined the proportion able to achieve GDCT. Results The mean age of the 331 AMI patients was 61.0 (SD 11.9) years and 33.6% were women. Overall, 268 (81.0%) patients were categorized as having VHR at discharge. Among patients at VHR, a lipid panel was rechecked in 153 individuals (57.1%) within 1 year of discharge, with the median time to lipid recheck being 22.4 weeks (interquartile range: 10.9-40.7 weeks). Among those with a lipid panel re-check, 100 (65.4%) of patients achieved GDCT. Conclusions Approximately 4 out of 5 AMI patients were considered VHR per the 2018 AHA/ACC guidelines, only about half had follow up lipid panels in the year following AMI, and about two-thirds of those with follow up lipid panels achieved GDCT.
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Affiliation(s)
- Adam J. Brownstein
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Derenbecker
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yumin Gao
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jie Ding
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bibin Varghese
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nino Isakadze
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Erin M. Spaulding
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Nursing, Baltimore, MD, USA
| | - Francoise A. Marvel
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S. Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
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Bhardwaj V, Spaulding EM, Marvel FA, LaFave S, Yu J, Mota D, Lorigiano TJ, Huynh PP, Shan R, Yesantharao PS, Lee MA, Yang WE, Demo R, Ding J, Wang J, Xun H, Shah L, Weng D, Wongvibulsin S, Carter J, Sheidy J, McLin R, Flowers J, Majmudar M, Elgin E, Vilarino V, Lumelsky D, Leung C, Allen JK, Martin SS, Padula WV. Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery. Med Care 2021; 59:1023-1030. [PMID: 34534188 PMCID: PMC8516712 DOI: 10.1097/mlr.0000000000001636] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone. METHODS A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty. RESULTS The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone. CONCLUSIONS Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.
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Affiliation(s)
- Vinayak Bhardwaj
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, US
| | - Erin M. Spaulding
- Johns Hopkins University School of Nursing, Baltimore, MD, US
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Francoise A. Marvel
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Sarah LaFave
- Johns Hopkins University School of Nursing, Baltimore, MD, US
| | - Jeffrey Yu
- Johns Hopkins Health System, Baltimore, MD, US
- Dept. of Pharmaceutical & Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, US
| | - Daniel Mota
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, US
- Dimock Center, Baltimore, MD, US
| | | | - Pauline P. Huynh
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Rongzi Shan
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Pooja S. Yesantharao
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Matthias A. Lee
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, US
| | - William E. Yang
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Ryan Demo
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, US
| | - Jie Ding
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, US
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Jane Wang
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Helen Xun
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Lochan Shah
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Daniel Weng
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Shannon Wongvibulsin
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | | | | | | | | | - Maulik Majmudar
- Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | | | - Valerie Vilarino
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD, US
| | - David Lumelsky
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD, US
| | | | - Jerilyn K. Allen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, US
- Johns Hopkins University School of Nursing, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
| | - Seth S. Martin
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, US
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University School of Medicine, Baltimore, MD, US
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, US
| | - William V. Padula
- Dept. of Pharmaceutical & Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, US
- Leonard D. Schaeffer Center for Health Economics & Policy, University of Southern California, Los Angeles, CA
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Weng D, Ding J, Sharma A, Yanek L, Xun H, Spaulding EM, Osuji N, Huynh PP, Ogunmoroti O, Lee MA, Demo R, Marvel FA, Martin SS. Heart rate trajectories in patients recovering from acute myocardial infarction: A longitudinal analysis of Apple Watch heart rate recordings. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:270-281. [PMID: 35265918 PMCID: PMC8890343 DOI: 10.1016/j.cvdhj.2021.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Background Using mobile health, vital signs such as heart rate (HR) can be used to assess a patient’s recovery process from acute events including acute myocardial infarction (AMI). Objective We aimed to characterize clinical correlates associated with HR change in the subacute period among patients recovering from AMI. Methods HR measurements were collected from 91 patients (4447 HR recordings) enrolled in the MiCORE study using the Apple Watch and Corrie smartphone application. Mixed regression models were used to estimate the associations of patient-level characteristics during hospital admission with HR changes over 30 days postdischarge. Results The mean daily HR at admission was 78.0 beats per minute (bpm) (95% confidence interval 76.1 to 79.8), declining 0.2 bpm/day (-0.3 to -0.1) under a linear model of HR change. History of coronary artery bypass graft, history of depression, or being discharged on anticoagulants was associated with a higher admission HR. Having a history of hypertension, type 2 diabetes mellitus (T2DM), or hyperlipidemia was associated with a slower decrease in HR over time, but not with HR during admission. Conclusion While a declining HR was observed in AMI patients over 30 days postdischarge, patients with hypertension, T2DM, or hyperlipidemia showed a slower decrease in HR relative to their counterparts. This study demonstrates the feasibility of using wearables to model the recovery process of patients with AMI and represents a first step in helping pinpoint patients vulnerable to decompensation.
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Affiliation(s)
- Daniel Weng
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jie Ding
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Apurva Sharma
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Lisa Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Biostatistics, Epidemiology, and Data Management Core Faculty, Baltimore, Maryland
| | - Helen Xun
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Erin M. Spaulding
- Johns Hopkins University School of Nursing, Baltimore, Maryland
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Ngozi Osuji
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Pauline P. Huynh
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Oluseye Ogunmoroti
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Matthias A. Lee
- Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland
| | - Ryan Demo
- Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland
| | - Francoise A. Marvel
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Seth S. Martin
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Address reprint requests and correspondence: Dr Seth S. Martin, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Carnegie 591, 600 N Wolfe St, Baltimore, MD 21287.
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Abstract
PURPOSE OF REVIEW Despite cutting edge acute interventions and growing preventive strategies supported by robust clinical trials, cardiovascular disease (CVD) has stubbornly persisted as a leading cause of death in the United States and globally. The American Heart Association recognizes mobile health technologies (mHealth) as an emerging strategy in the mitigation of CVD risk factors, with significant potential for improving population health. The purpose of this review is to highlight and summarize the latest available literature on mHealth applications and provide perspective on future directions and barriers to implementation. RECENT FINDINGS While available randomized controlled trials and systematic reviews tend to support efficacy of mHealth, published literature includes heterogenous approaches to similar problems with inconsistent results. Some of the strongest recent evidence has been focused on the use of wearables in arrhythmia detection. Systematic reviews of mHealth approaches demonstrate benefit when applied to risk factor modification in diabetes, cigarette smoking cessation, and physical activity/weight loss, while also showing promise in multi risk factor modification via cardiac rehabilitation. SUMMARY Evidence supports efficacy of mHealth in a variety of applications for CVD prevention and management, but continued work is needed for further validation and scaling. Future directions will focus on platform optimization, data and sensor consolidation, and clinical workflow integration. Barriers include application heterogeneity, lack of reimbursement structures, and inequitable access to technology. Policies to promote access to technology will be critical to evidence-based mHealth technologies reaching diverse populations and advancing health equity.
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Affiliation(s)
- Michael Kozik
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine
- Ciccarone Center for the Prevention of Cardiovascular Disease, Digital Health Innovation Laboratory, Johns Hopkins University School of Medicine
- Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH)
| | - Nino Isakadze
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine
- Ciccarone Center for the Prevention of Cardiovascular Disease, Digital Health Innovation Laboratory, Johns Hopkins University School of Medicine
- Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Seth S. Martin
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine
- Ciccarone Center for the Prevention of Cardiovascular Disease, Digital Health Innovation Laboratory, Johns Hopkins University School of Medicine
- Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Marvel FA, Spaulding EM, Lee MA, Yang WE, Demo R, Ding J, Wang J, Xun H, Shah LM, Weng D, Carter J, Majmudar M, Elgin E, Sheidy J, McLin R, Flowers J, Vilarino V, Lumelsky DN, Bhardwaj V, Padula WV, Shan R, Huynh PP, Wongvibulsin S, Leung C, Allen JK, Martin SS. Digital Health Intervention in Acute Myocardial Infarction. Circ Cardiovasc Qual Outcomes 2021; 14:e007741. [PMID: 34261332 PMCID: PMC8288197 DOI: 10.1161/circoutcomes.121.007741] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/06/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control. METHODS This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score-adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group. RESULTS Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26-0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching. CONCLUSIONS Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03760796.
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Affiliation(s)
- Francoise A. Marvel
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Erin M. Spaulding
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Nursing, Baltimore, MD (E.M.S., W.V.P., J.K.A.)
- The Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (E.M.S., S.S.M.)
| | - Matthias A. Lee
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD (M.A.L., R.Y., S.S.M.)
| | - William E. Yang
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Ryan Demo
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD (M.A.L., R.Y., S.S.M.)
| | - Jie Ding
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.)
| | - Jane Wang
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
| | - Helen Xun
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Lochan M. Shah
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Daniel Weng
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | | | - Maulik Majmudar
- Massachusetts General Hospital, Boston (J.C., M.M.)
- Harvard Medical School, Boston, MA (M.M.)
| | - Eric Elgin
- Reading Hospital, West Reading, PA (E.E., J.S., R.M., J.F.)
| | - Julie Sheidy
- Reading Hospital, West Reading, PA (E.E., J.S., R.M., J.F.)
| | - Renee McLin
- Reading Hospital, West Reading, PA (E.E., J.S., R.M., J.F.)
| | | | - Valerie Vilarino
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.)
- Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD (V.V., D.N.L.)
| | - David N. Lumelsky
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD (V.V., D.N.L.)
| | - Vinayak Bhardwaj
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
- Johns Hopkins University School of Nursing, Baltimore, MD (E.M.S., W.V.P., J.K.A.)
- The Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (E.M.S., S.S.M.)
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD (M.A.L., R.Y., S.S.M.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.)
- Massachusetts General Hospital, Boston (J.C., M.M.)
- Harvard Medical School, Boston, MA (M.M.)
- Reading Hospital, West Reading, PA (E.E., J.S., R.M., J.F.)
- Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD (V.V., D.N.L.)
- Department of Pharmaceutical and Health Economics, School of Pharmacy (W.V.P.)
- University of Southern California, Los Angeles, CA (W.V.P.)
- Leonard D. Schaeffer Center for Health Economics and Policy, University of Southern California, Los Angeles, CA (W.V.P.)
- Johns Hopkins Health System, Baltimore, MD (C.L.)
| | - William V. Padula
- Johns Hopkins University School of Nursing, Baltimore, MD (E.M.S., W.V.P., J.K.A.)
- Department of Pharmaceutical and Health Economics, School of Pharmacy (W.V.P.)
- University of Southern California, Los Angeles, CA (W.V.P.)
- Leonard D. Schaeffer Center for Health Economics and Policy, University of Southern California, Los Angeles, CA (W.V.P.)
| | - Rongzi Shan
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
| | - Pauline P. Huynh
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Shannon Wongvibulsin
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
| | - Curtis Leung
- Johns Hopkins Health System, Baltimore, MD (C.L.)
| | - Jerilyn K. Allen
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
- Johns Hopkins University School of Nursing, Baltimore, MD (E.M.S., W.V.P., J.K.A.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.)
| | - Seth S. Martin
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.)
- Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.)
- The Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (E.M.S., S.S.M.)
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD (M.A.L., R.Y., S.S.M.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.)
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20
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Shah LM, Ding J, Spaulding EM, Yang WE, Lee MA, Demo R, Marvel FA, Martin SS. Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction. J Cardiovasc Transl Res 2021; 14:951-961. [PMID: 33999374 PMCID: PMC8127845 DOI: 10.1007/s12265-021-10098-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/04/2021] [Indexed: 02/07/2023]
Abstract
Increasing evidence suggests that digital health interventions (DHIs) are an effective tool to reduce hospital readmissions by improving adherence to guideline-directed therapy. We investigated whether sociodemographic characteristics influence use of a DHI targeting 30-day readmission reduction after acute myocardial infarction (AMI). Covariates included age, sex, race, native versus loaner iPhone, access to a Bluetooth-enabled blood pressure monitor, and disease severity as marked by treatment with CABG. Age, sex, and race were not significantly associated with DHI use before or after covariate adjustment (fully adjusted OR 0.98 (95%CI: 0.95-1.01), 0.6 (95%CI: 0.29-1.25), and 1.22 (95% CI: 0.60-2.48), respectively). Being married was associated with high DHI use (OR 2.12; 95% CI 1.02-4.39). Our findings suggest that DHIs may have a role in achieving equity in cardiovascular health given similar use by age, sex, and race. The presence of a spouse, perhaps a proxy for enhanced caregiver support, may encourage DHI use.
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Affiliation(s)
- Lochan M Shah
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jie Ding
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Erin M Spaulding
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Nursing, Baltimore, MD, USA
- Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH), an AHA SFRN Center for Health Technology and Innovation, Baltimore, MD, USA
| | - William E Yang
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthias A Lee
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ryan Demo
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Francoise A Marvel
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University School of Nursing, Baltimore, MD, USA
| | - Seth S Martin
- Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH), an AHA SFRN Center for Health Technology and Innovation, Baltimore, MD, USA.
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
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21
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Li Y, Lin Y, Bai H. Effects of a structured team nursing model on the efficacy and quality of cardiopulmonary resuscitation in myocardial infarction patients undergoing PCI. Am J Transl Res 2021; 13:3129-3137. [PMID: 34017481 PMCID: PMC8129222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 01/21/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE This study aimed to evaluate the effects of a structured team nursing model on the efficacy and quality of cardiopulmonary resuscitation (CPR) in acute myocardial infarction patients undergoing percutaneous coronary intervention (PCI). METHODS With the random number table, 130 myocardial infarction patients undergoing PCI were divided into two groups, including the control group (n=65) receiving routine emergency resuscitation and nursing care, and the study group (n=65) receiving a structured team care model. The efficacy of CPR, cardiac function, exercise tolerance, ability of daily living activities, quality of life, complication rate and nursing satisfaction were compared between the two groups. RESULTS The door-to-balloon time, length of stay at the emergency department, duration of balloon dilation, bedtime and hospital stay in the study group were shorter than those in the control group (P<0.05). The study group showed lower LVEDD and LVESD and higher LVEF than the control group after nursing (P<0.05). The extend of physical limitation, angina stability, level of disease awareness, number of angina attacks, and treatment satisfaction scores in the 6-MWT, MBI, and SAQ scales in the study group after nursing were higher than those in the control group (P<0.05). The complication rate in the study group (7.69%) was lower than that in the control group (20.00%) (P<0.05). The study group had higher satisfaction with operational skills, teamwork, clinical practice, rescue awareness, orderliness, and timeliness than the control group (P<0.05). CONCLUSION Structured team nursing model is helpful to improve the timeliness and quality of CRP, shorten the treatment time, improve patients' cardiac function and exercise tolerance, improve self-care ability and quality of life, reduce the occurrence of complications, and enhance the patient-nurse relationship.
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Affiliation(s)
- Yangyujing Li
- Department of Emergency, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology Wuhan 430014, Hubei Province, China
| | - Yin Lin
- Department of Emergency, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology Wuhan 430014, Hubei Province, China
| | - Haitao Bai
- Department of Emergency, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology Wuhan 430014, Hubei Province, China
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22
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Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen LY, Couderc J, Cronin EM, Estep JD, Grieten L, Lane DA, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov PG, Ribeiro AL, Rich RE, Russo AM, Slotwiner D, Steinberg JS, Svennberg E. 2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society. J Arrhythm 2021; 37:271-319. [PMID: 33850572 PMCID: PMC8022003 DOI: 10.1002/joa3.12461] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 02/06/2023] Open
Abstract
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.
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Affiliation(s)
| | | | | | | | - Yufeng Hu
- Taipei Veterans General HospitalTaipeiTaiwan
| | | | | | | | | | | | | | | | - Alex Page
- University of RochesterRochesterNYUSA
| | - Rod Passman
- Northwestern University Feinberg School of MedicineChicagoILUSA
| | | | | | | | | | - Antonio Luiz Ribeiro
- Faculdade de MedicinaCentro de TelessaúdeHospital das Clínicasand Departamento de Clínica MédicaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
| | | | | | - David Slotwiner
- Cardiology DivisionNewYork‐Presbyterian Queensand School of Health Policy and ResearchWeill Cornell MedicineNew YorkNYUSA
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23
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Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen LY, Couderc J, Cronin EM, Estep JD, Grieten L, Lane DA, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov PG, Ribeiro AL, Rich RE, Russo AM, Slotwiner D, Steinberg JS, Svennberg E. 2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society. Ann Noninvasive Electrocardiol 2021; 26:e12795. [PMID: 33513268 PMCID: PMC7935104 DOI: 10.1111/anec.12795] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 02/06/2023] Open
Abstract
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.
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Affiliation(s)
| | | | | | | | - Yufeng Hu
- Taipei Veterans General HospitalTaipeiTaiwan
| | | | | | | | | | | | | | | | - Alex Page
- University of RochesterRochesterNYUSA
| | - Rod Passman
- Northwestern University Feinberg School of MedicineChicagoILUSA
| | | | | | | | | | - Antonio Luiz Ribeiro
- Faculdade de MedicinaCentro de Telessaúde, Hospital das Clínicas, and Departamento de Clínica MédicaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
| | | | | | - David Slotwiner
- Cardiology DivisionNewYork‐Presbyterian Queens, and School of Health Policy and ResearchWeill Cornell MedicineNew YorkNYUSA
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24
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Varma N, Cygankiewicz I, Turakhia M, Heidbuchel H, Hu Y, Chen LY, Couderc J, Cronin EM, Estep JD, Grieten L, Lane DA, Mehra R, Page A, Passman R, Piccini J, Piotrowicz E, Piotrowicz R, Platonov PG, Ribeiro AL, Rich RE, Russo AM, Slotwiner D, Steinberg JS, Svennberg E. 2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:7-48. [PMID: 36711170 PMCID: PMC9708018 DOI: 10.1093/ehjdh/ztab001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.
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Affiliation(s)
| | | | | | - Hein Heidbuchel
- Antwerp University and University Hospital, Antwerp, Belgium
| | - Yufeng Hu
- Taipei Veterans General Hospital, Taipei, Taiwan
| | | | | | | | | | | | | | | | - Alex Page
- University of Rochester, Rochester, NY, USA
| | - Rod Passman
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | | | | | | | - Antonio Luiz Ribeiro
- Faculdade de Medicina, Centro de Telessaúde, Hospital das Clínicas, and Departamento de Clínica Médica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Andrea M Russo
- Cooper Medical School of Rowan University, Camden, NJ, USA
| | - David Slotwiner
- Cardiology Division, NewYork-Presbyterian Queens, and School of Health, Policy and Research, Weill Cornell Medicine, New York, NY, USA
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25
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Varma N, Cygankiewicz I, Turakhia MP, Heidbuchel H, Hu Y, Chen LY, Couderc JP, Cronin EM, Estep JD, Grieten L, Lane DA, Mehra R, Page A, Passman R, Piccini JP, Piotrowicz E, Piotrowicz R, Platonov PG, Ribeiro AL, Rich RE, Russo AM, Slotwiner D, Steinberg JS, Svennberg E. 2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:4-54. [PMID: 35265889 PMCID: PMC8890358 DOI: 10.1016/j.cvdhj.2020.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.
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Key Words
- ACC, American College of Cardiology
- ACS, acute coronary syndrome
- AED, automated external defibrillator
- AF, atrial fibrillation
- AHA, American Heart Association
- AHRE, atrial high-rate episode
- AI, artificial intelligence
- APHRS, Asia Pacific Heart Rhythm Society
- BP, blood pressure
- CIED, cardiovascular implantable electronic device
- CPR, cardiopulmonary resuscitation
- EHR A, European Heart Rhythm Association
- EMR, electronic medical record
- ESUS, embolic stroke of unknown source
- FDA (U.S.), Food and Drug Administration
- GPS, global positioning system
- HCP, healthcare professional
- HF, heart failure
- HR, heart rate
- HRS, Heart Rhythm Society
- ICD, implantable cardioverter-defibrillator
- ILR, implantable loop recorder
- ISHNE, International Society for Holter and Noninvasive Electrocardiology
- JITAI, just-in-time adaptive intervention
- MCT, mobile cardiac telemetry
- OAC, oral anticoagulant
- PAC, premature atrial complex
- PPG, photoplethysmography
- PVC, premature ventricular complexes
- SCA, sudden cardiac arrest
- TADA, Technology Assissted Dietary Assessment
- VT, ventricular tachycardia
- arrhythmias
- atrial fibrillation
- comorbidities
- digital medicine
- heart rhythm
- mHealth
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Affiliation(s)
| | | | | | - Hein Heidbuchel
- Antwerp University and University Hospital, Antwerp, Belgium
| | - Yufeng Hu
- Taipei Veterans General Hospital, Taipei, Taiwan
| | | | | | | | | | | | | | | | - Alex Page
- University of Rochester, Rochester, NY, USA
| | - Rod Passman
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | | | | | | | - Antonio Luiz Ribeiro
- Faculdade de Medicina, Centro de Telessaúde, Hospital das Clínicas, and Departamento de Clínica Médica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - David Slotwiner
- Cardiology Division, NewYork-Presbyterian Queens, and School of Health Policy and Research, Weill Cornell Medicine, New York, NY, USA
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Varma N, Cygankiewicz I, Turakhia MP, Heidbuchel H, Hu YF, Chen LY, Couderc JP, Cronin EM, Estep JD, Grieten L, Lane DA, Mehra R, Page A, Passman R, Piccini JP, Piotrowicz E, Piotrowicz R, Platonov PG, Ribeiro AL, Rich RE, Russo AM, Slotwiner D, Steinberg JS, Svennberg E. 2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society. Circ Arrhythm Electrophysiol 2021; 14:e009204. [PMID: 33573393 PMCID: PMC7892205 DOI: 10.1161/circep.120.009204] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.
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Affiliation(s)
- Niraj Varma
- Cleveland Clinic, OH (N.V., J.D.E., R.M., R.E.R.)
| | | | | | | | - Yu-Feng Hu
- Taipei Veterans General Hospital, Taiwan (Y.-F.H.)
| | | | | | | | | | | | | | - Reena Mehra
- Cleveland Clinic, OH (N.V., J.D.E., R.M., R.E.R.)
| | - Alex Page
- University of Rochester, NY (J.-P.C., A.P., J.S.S.)
| | - Rod Passman
- Northwestern University Feinberg School of Medicine, Chicago, IL (R. Passman)
| | | | - Ewa Piotrowicz
- National Institute of Cardiology, Warsaw, Poland (E.P., R. Piotrowicz)
| | | | | | - Antonio Luiz Ribeiro
- Faculdade de Medicina, Centro de Telessaúde, Hospital das Clínicas, and Departamento de Clínica Médica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (A.L.R.)
| | | | - Andrea M. Russo
- Cooper Medical School of Rowan University, Camden, NJ (A.M.R.)
| | - David Slotwiner
- Cardiology Division, New York-Presbyterian Queens, NY (D.S.)
| | | | - Emma Svennberg
- Karolinska University Hospital, Stockholm, Sweden (E.S.)
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27
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Shan R, Ding J, Weng D, Spaulding EM, Wongvibulsin S, Lee MA, Demo R, Marvel FA, Martin SS. Early blood pressure assessment after acute myocardial infarction: Insights using digital health technology. Am J Prev Cardiol 2020; 3:100089. [PMID: 32964212 PMCID: PMC7497394 DOI: 10.1016/j.ajpc.2020.100089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/01/2020] [Accepted: 09/14/2020] [Indexed: 11/19/2022] Open
Abstract
Objective There is rising interest in digital health in preventive cardiology, particularly for blood pressure (BP) management. In a digital health study of early BP assessment following acute myocardial infarction (AMI), we sought to examine feasibility and the (1) proportion of post-AMI patients with controlled BP and hypotension, and (2) association between prior cardiovascular disease (CVD) and BP post-AMI. Methods In this substudy of the parent Myocardial infarction, COmbined-device, Recovery Enhancement (MiCORE) study, type 1 AMI patients were enrolled between October 2017 and April 2019. Participants self-monitored their BP through 30 days after hospital discharge using an FDA-approved wireless BP monitor connected with a smartphone application. Linear mixed-effects models assessed the association between prior CVD and BP trajectory post-discharge, adjusting for antihypertensive medications and a propensity score inclusive of CVD risk factors. Results Sixty-eight AMI patients (mean age 58 ± 10 years, 75% male, 68% white race, 68% history of hypertension, 24% prior CVD) provided 2638 measurements over 30 days. The percentage of BP control <130/80 mmHg was 59.6% (95% CI: 54.3–64.9%) and <140/90 mmHg was 83.7% (95% CI: 80.3–87.2%). The percentage of systolic BP <90 mmHg was 1.1% (95% CI: 0.17–2.0%) and the percentage of diastolic BP <60 mmHg was 3.9% (95% CI: 2.6–5.2%). Prior CVD was associated with 12.2 mmHg higher mean daily systolic BP during admission (95% CI: 3.5–20.9 mmHg), which persisted over follow-up. There was no association between prior CVD and diastolic BP. Conclusion The digital health program was feasible and ~40% of post-AMI patients who engaged in it had uncontrolled BP according to recent guideline cutpoints, while hypotension occurred rarely. The gap in BP control was especially large in patients in whom AMI represented recurrent CVD. These data suggest an opportunity for more aggressive secondary prevention early after MI as care models integrate digital health. Digital health reveals home blood pressure trends during early recovery after an event. ~40% of early MI patients had mean daily blood pressure exceeding the guideline goal of <130/80 mmHg. Hypotension occurred rarely over 30 days post-MI. The gap in BP control was especially large in patients in whom MI represented recurrent CVD. There is opportunity for more aggressive secondary prevention early after MI as care models integrate digital health.
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Affiliation(s)
- Rongzi Shan
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
- David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jie Ding
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Daniel Weng
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Erin M. Spaulding
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
- Johns Hopkins University School of Nursing, Baltimore, MD, USA
| | | | - Matthias A. Lee
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Ryan Demo
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Francoise A. Marvel
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Seth S. Martin
- Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
- Corresponding author. Johns Hopkins Hospital, Carnegie 591, 600 North Wolfe Street, Baltimore, MD, 21287, United States.
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Hung G, Yang WE, Marvel FA, Martin SS. Mobile health application platform 'Corrie' personalises and empowers the heart attack recovery patient experience in the hospital and at home for an underserved heart attack survivor. BMJ Case Rep 2020; 13:13/2/e231801. [PMID: 32071124 PMCID: PMC7046423 DOI: 10.1136/bcr-2019-231801] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide with an estimated 17.5 million deaths annually, according to the World Health Organization (WHO). CVD prevention efforts have the potential to prevent the majority of these deaths by supporting guideline-directed medical therapy (GDMT) and lifestyle modification. Mobile health (mHealth) has the potential to address this gap, but has limited evaluation in clinical studies to date. We present the case of a middle-aged patient of low socioeconomic status, with multiple comorbidities, and no prior smartphone experience, who suffered an acute myocardial infarction (MI) and was given the Corrie intervention while hospitalised. The patient demonstrated improvement in lifestyle modification, adherence to GDMT and post-MI recovery through 2.4 years follow-up. This case supports (1) the potential of mHealth interventions to enhance patient experience and outcomes, (2) intuitive design for adoption and improvement in end user experience and (3) the capability of mHealth to reach and empower underserved patients.
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Affiliation(s)
- George Hung
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA .,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - William E Yang
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Francoise A Marvel
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Seth S Martin
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, Maryland, USA
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Yang WE, Spaulding EM, Lumelsky D, Hung G, Huynh PP, Knowles K, Marvel FA, Vilarino V, Wang J, Shah LM, Xun H, Shan R, Wongvibulsin S, Martin SS. Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study. JMIR Mhealth Uhealth 2019; 7:e16391. [PMID: 31841115 PMCID: PMC6937543 DOI: 10.2196/16391] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 11/11/2019] [Accepted: 11/13/2019] [Indexed: 11/13/2022] Open
Abstract
Background As smartphone ownership continues to rise, health care systems and technology companies are driven to develop mobile health (mHealth) interventions as both diagnostic and therapeutic tools. An important consideration during mHealth intervention development is how to achieve health equity despite demographic differences in smartphone ownership. One solution is through the recirculation of loaner smartphones; however, best practices for implementing such programs to optimize security, privacy, scalability, and convenience for participants are not well defined. Objective In this tutorial, we describe how we implemented our novel Corrie iShare program, a 30-day loaner iPhone and smartwatch recirculation program, as part of a multi-center mHealth intervention to improve recovery and access to guideline-directed therapy following acute myocardial infarction. Methods We conducted a prospective study utilizing a smartphone app and leveraged iOS enterprise features as well as cellular data service to automate recirculation. Results Our configuration protocol was shortened from 1 hour to 10 minutes. Of 200 participants, 92 (46.0%) did not own an iPhone and would have been excluded from the study without iShare. Among iShare participants, 72% (66/92) returned their loaned smartphones. Conclusions The Corrie iShare program demonstrates the potential for a sustainable and scalable mHealth loaner program, enabling broader population reach while optimizing user experience. Implementation may face institutional constraints and software limitations. Consideration should be given to optimizing loaner returns.
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Affiliation(s)
- William E Yang
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Erin M Spaulding
- School of Nursing, Johns Hopkins University, Baltimore, MD, United States
| | - David Lumelsky
- Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - George Hung
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | | | - Kellen Knowles
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Francoise A Marvel
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States.,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Valerie Vilarino
- Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Jane Wang
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Lochan M Shah
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Helen Xun
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Rongzi Shan
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Shannon Wongvibulsin
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Seth S Martin
- School of Medicine, Johns Hopkins University, Baltimore, MD, United States.,Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
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31
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Bostrom J, Sweeney G, Whiteson J, Dodson JA. Mobile health and cardiac rehabilitation in older adults. Clin Cardiol 2019; 43:118-126. [PMID: 31825132 PMCID: PMC7021651 DOI: 10.1002/clc.23306] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/22/2019] [Accepted: 11/08/2019] [Indexed: 12/16/2022] Open
Abstract
With the ubiquity of mobile devices, the availability of mobile health (mHealth) applications for cardiovascular disease (CVD) has markedly increased in recent years. Older adults represent a population with a high CVD burden and therefore have the potential to benefit considerably from interventions that utilize mHealth. Traditional facility-based cardiac rehabilitation represents one intervention that is currently underutilized for CVD patients and, because of the unique barriers that older adults face, represents an attractive target for mHealth interventions. Despite potential barriers to mHealth adoption in older populations, there is also evidence that older patients may be willing to adopt these technologies. In this review, we highlight the potential for mHealth uptake for older adults with CVD, with a particular focus on mHealth cardiac rehabilitation (mHealth-CR) and evidence being generated in this field.
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Affiliation(s)
- John Bostrom
- Department of Medicine, New York University School of Medicine, New York, New York
| | - Greg Sweeney
- Rusk Department of Rehabilitation Medicine, New York University Langone Health, New York, New York
| | - Jonathan Whiteson
- Rusk Department of Rehabilitation Medicine, New York University Langone Health, New York, New York
| | - John A Dodson
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University School of Medicine, New York, New York.,Division of Healthcare Delivery Science, Department of Population Health, New York University School of Medicine, New York, New York
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Affiliation(s)
- Françoise A. Marvel
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Seamus P. Whelton
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Roger S. Blumenthal
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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