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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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Huerne K, Eisenberg MJ. Advancing telemedicine in cardiology: A comprehensive review of evolving practices and outcomes in a postpandemic context. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:96-110. [PMID: 38765624 PMCID: PMC11096655 DOI: 10.1016/j.cvdhj.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
Telemedicine, telehealth, e-Health, and other related terms refer to the exchange of medical information or medical care from one site to another through electronic communication between a patient and a health care provider. As telemedicine infrastructure has changed since the coronavirus disease 2019 (COVID-19) pandemic, this review provides an overview of telemedicine use and effectiveness in cardiology, with emphasis on coronary artery disease in the postpandemic context. Prepandemic studies tend to report statistically insignificant or modest improvements in cardiovascular disease outcome from telemedicine use to usual care. In contrast, postpandemic studies tend to report positive outcomes or comparable acceptance of telemedicine use to usual care. Today, telemedicine can effectively replace in person follow-up visits to produce comparable (but not necessarily superior) outcomes in cardiovascular disease management. A benefit of telemedicine is the potential reduction in follow-up time or time to intervention, which may lead to earlier detection and prevention of adverse events. Nonetheless, barriers remain to effective telemedicine implementation in the postpandemic context. Ensuring accessible and user-friendly telemedicine devices, maintaining adherence to remote rehabilitation procedures, and normalizing use of telemedicine in routine follow-up visits are examples. Current knowledge gaps include the true economic cost of telemedicine infrastructure, feasibility of use in specific cardiology contexts, and sex/gender differences in telemedicine use. Future telemedicine developments will need to address these concerns before acceptance of telemedicine as the new standard of care.
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Affiliation(s)
- Katherine Huerne
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Mark J. Eisenberg
- Departments of Medicine and of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
- Division of Cardiology, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
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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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Coss NA, Gaitán JM, Adans-Dester CP, Carruthers J, Fanarjian M, Sassano C, Manuel SP, Perakslis E. Does clinical research account for diversity in deploying digital health technologies? NPJ Digit Med 2023; 6:187. [PMID: 37816886 PMCID: PMC10564850 DOI: 10.1038/s41746-023-00928-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/20/2023] [Indexed: 10/12/2023] Open
Abstract
Digital health technologies (DHTs) should expand access to clinical research to represent the social determinants of health (SDoH) across the population. The frequency of reporting participant SDoH data in clinical publications is low and is not known for studies that utilize DHTs. We evaluated representation of 11 SDoH domains in 126 DHT-enabled clinical research publications and proposed a framework under which these domains could be captured and subsequently reported in future studies. Sex, Race, and Education were most frequently reported (in 94.4%, 27.8%, and 20.6% of publications, respectively). The remaining 8 domains were reported in fewer than 10% of publications. Medical codes were identified that map to each of the proposed SDoH domains and the resulting resource is suggested to highlight that existing infrastructure could be used to capture SDoH data. An opportunity exists to increase reporting on the representation of SDoH among participants to encourage equitable and inclusive research progress through DHT-enabled clinical studies.
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Affiliation(s)
| | | | | | | | | | | | - Solmaz P Manuel
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, USA
| | - Eric Perakslis
- HumanFirst, Inc., San Francisco, CA, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
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5
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Virani SS, Newby LK, Arnold SV, Bittner V, Brewer LC, Demeter SH, Dixon DL, Fearon WF, Hess B, Johnson HM, Kazi DS, Kolte D, Kumbhani DJ, LoFaso J, Mahtta D, Mark DB, Minissian M, Navar AM, Patel AR, Piano MR, Rodriguez F, Talbot AW, Taqueti VR, Thomas RJ, van Diepen S, Wiggins B, Williams MS. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol 2023; 82:833-955. [PMID: 37480922 DOI: 10.1016/j.jacc.2023.04.003] [Citation(s) in RCA: 163] [Impact Index Per Article: 81.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
AIM The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease." METHODS A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.
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Virani SS, Newby LK, Arnold SV, Bittner V, Brewer LC, Demeter SH, Dixon DL, Fearon WF, Hess B, Johnson HM, Kazi DS, Kolte D, Kumbhani DJ, LoFaso J, Mahtta D, Mark DB, Minissian M, Navar AM, Patel AR, Piano MR, Rodriguez F, Talbot AW, Taqueti VR, Thomas RJ, van Diepen S, Wiggins B, Williams MS. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation 2023; 148:e9-e119. [PMID: 37471501 DOI: 10.1161/cir.0000000000001168] [Citation(s) in RCA: 436] [Impact Index Per Article: 218.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
AIM The "2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease" provides an update to and consolidates new evidence since the "2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease" and the corresponding "2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease." METHODS A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost-value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.
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Affiliation(s)
| | | | | | | | | | | | - Dave L Dixon
- Former Joint Committee on Clinical Practice Guideline member; current member during the writing effort
| | - William F Fearon
- Society for Cardiovascular Angiography and Interventions representative
| | | | | | | | - Dhaval Kolte
- AHA/ACC Joint Committee on Clinical Data Standards
| | | | | | | | - Daniel B Mark
- Former Joint Committee on Clinical Practice Guideline member; current member during the writing effort
| | | | | | | | - Mariann R Piano
- Former Joint Committee on Clinical Practice Guideline member; current member during the writing effort
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Loureiro M, Parola V, Duarte J, Mendes E, Oliveira I, Coutinho G, Martins MM, Novo A. Interventions for Caregivers of Heart Disease Patients in Rehabilitation: Scoping Review. NURSING REPORTS 2023; 13:1016-1029. [PMID: 37606457 PMCID: PMC10443275 DOI: 10.3390/nursrep13030089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/29/2023] [Accepted: 07/24/2023] [Indexed: 08/23/2023] Open
Abstract
Map the interventions/components directed to the caregivers of heart disease patients in cardiac rehabilitation programs that promote their role and health. METHODS The Joanna Briggs Institute method was used to guide this scoping review. Two independent reviewers assessed articles for relevance and extracted and synthesized data. Inclusion criteria comprised articles published in English, Spanish, and Portuguese since 1950. The following databases were searched: CINAHL Complete (Via EBSCO), Medline (via PubMed), Scopus, PEDro, and Repositórios Científicos de Acesso Aberto de Portugal (RCAAP). RESULTS From 351 articles retrieved, 10 were included in the review. The interventions identified directed to the caregiver were: educational interventions and lifestyle changes; physical exercise; psychological interventions/stress management; and a category "Other" with training interventions in basic life support, elaboration of guidelines/recommendations, and training for the role of caregiver. CONCLUSIONS It was found that most of the related cardiac rehabilitation interventions are aimed at the dyad heart failure patient and their caregivers/family. Including specific interventions targeting caregivers improves the caregiver's health and empowers them. Patient care planning should include interventions specifically aimed at them that result in health gains for caregivers and patients, striving to improve the quality of care. This study was not registered.
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Affiliation(s)
- Maria Loureiro
- Instituto Ciências Biomédicas Abel Salazar, Cintesis-NursID, Centro Hospitalar e Universitário de Coimbra, 3000-602 Coimbra, Portugal
| | - Vítor Parola
- Nursing School of Coimbra (ESEnfC), The Health Sciences Research Unit: Nursing (UICISA:E), Portugal Centre for Evidence-Based Practice: A Joanna Briggs Institute Centre of Excellence, 3004-011 Coimbra, Portugal
| | - João Duarte
- Instituto Ciências Biomédicas Abel Salazar, Cintesis-NursID, Centro Hospitalar e Universitário de Coimbra, 3000-602 Coimbra, Portugal
| | - Eugénia Mendes
- Instituto Politécnico de Bragança-Escola Superior de Saúde, Cintesis-NursID, 5300-121 Bragança, Portugal
| | - Isabel Oliveira
- Escola Superior de Saúde Norte Cruz Vermelha Portuguesa Oliveira de Azeméis, Center for Health Studies and Research of the University of Coimbra, 3040-156 Coimbra, Portugal
| | - Gonçalo Coutinho
- Faculdade de Medicina de Coimbra, Centro Hospitalar e Universitário de Coimbra, 3000-602 Coimbra, Portugal
| | | | - André Novo
- Instituto Politécnico de Bragança-Escola Superior de Saúde, Cintesis-NursID, 5300-121 Bragança, Portugal
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Martyn-Nemeth P, Hayman LL. Digital Technology in Cardiovascular Health: Role and Evidence Supporting Its Use. J Cardiovasc Nurs 2023; 38:207-209. [PMID: 37027125 DOI: 10.1097/jcn.0000000000000985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Zhang M, Liu S, Bi Y, Liu J. Comparison of 30-day planned and unplanned readmissions in a tertiary teaching hospital in China. BMC Health Serv Res 2023; 23:213. [PMID: 36879245 PMCID: PMC9988192 DOI: 10.1186/s12913-023-09193-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 02/16/2023] [Indexed: 03/08/2023] Open
Abstract
PURPOSE The purpose of this study was to analyze and compare the clinical characteristics of patients with 30-day planned and unplanned readmissions and to identify patients at high risk for unplanned readmissions. This will facilitate a better understanding of these readmissions and improve and optimize resource utilization for this patient population. METHODS A retrospective cohort descriptive study was conducted at the West China Hospital (WCH), Sichuan University from January 1, 2015, to December 31, 2020. Discharged patients (≥ 18 years old) were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Demographic and related information was collected for each patient. Logistic regression analysis was used to assess the association between unplanned patient characteristics and the risk of readmission. RESULTS We identified 1,118,437 patients from 1,242,496 discharged patients, including 74,494 (6.7%) 30-day planned readmissions and 9,895 (0.9%) unplanned readmissions. The most common diseases of planned readmissions were antineoplastic chemotherapy (62,756/177,749; 35.3%), radiotherapy sessions for malignancy (919/8,229; 11.2%), and systemic lupus erythematosus (607/4,620; 13.1%). The most common diseases of unplanned readmissions were antineoplastic chemotherapy (2038/177,747; 1.1%), age-related cataract (1061/21,255; 5.0%), and unspecified disorder of refraction (544/5,134; 10.6%). There were statistically significant differences between planned and unplanned readmissions in terms of patient sex, marital status, age, length of initial stay, the time between discharge, ICU stay, surgery, and health insurance. CONCLUSION Accurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identifying risk factors for 30-day unplanned readmissions can help develop interventions to reduce readmission rates.
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Affiliation(s)
- Mengjiao Zhang
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yongdong Bi
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jialin Liu
- Information Center, West China Hospital, Sichuan University, Chengdu, China. .,Department of Medical Informatics, West China Medical School, Sichuan, China.
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Kay MC, Miller HN, Askew S, Spaulding EM, Chisholm M, Christy J, Yang Q, Steinberg DM. Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial. AJPM FOCUS 2022; 1:100037. [PMID: 37791242 PMCID: PMC10546506 DOI: 10.1016/j.focus.2022.100037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Introduction The Dietary Approaches to Stop Hypertension dietary pattern is a proven way to manage hypertension, but adherence remains low. Dietary tracking applications offer a highly disseminable way to self-monitor intake on the pathway to reaching dietary goals but require consistent engagement to support behavior change. Few studies use longitudinal dietary self-monitoring data to assess trajectories and predictors of engagement. We used dietary self-monitoring data from participants in Dietary Approaches to Stop Hypertension Cloud (N=59), a feasibility trial to improve diet quality among women with hypertension, to identify trajectories of engagement and explore associations between participant characteristics. Methods We used latent class growth modeling to identify trajectories of engagement with a publicly available diet tracking application and used bivariate and regression analyses to assess the associations of classifications of engagement with participant characteristics. Results We identified 2 latent classes of engagement: consistent engagers and disengagers. Consistent engagers were more likely to be older, more educated, and married or living with a partner. Although consistent engagers exhibited slightly greater changes in Dietary Approaches to Stop Hypertension score, the difference was not significant. Conclusions This study highlights an important yet underutilized methodologic approach for uncovering dietary self-monitoring engagement patterns. Understanding how certain individuals engage with digital technologies is an important step toward designing cost-effective behavior change interventions. Trial registration This study is registered at www.clinicaltrials.gov NCT03215472.
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Affiliation(s)
- Melissa C. Kay
- Department of Pediatrics, Duke University, Durham, North Carolina
- Duke Global Digital Health Science Center, Duke University, Durham, North Carolina
| | - Hailey N. Miller
- Duke Global Digital Health Science Center, Duke University, Durham, North Carolina
- School of Nursing, Duke University, Durham, North Carolina
| | - Sandy Askew
- Duke Global Digital Health Science Center, Duke University, Durham, North Carolina
| | - Erin M. Spaulding
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University School of Medicine, Baltimore, Maryland
- 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, Maryland
| | - Miriam Chisholm
- Duke Global Digital Health Science Center, Duke University, Durham, North Carolina
| | - Jacob Christy
- Duke Global Digital Health Science Center, Duke University, Durham, North Carolina
- Medable, Inc., Palo Alto, California
| | - Qing Yang
- School of Nursing, Duke University, Durham, North Carolina
| | - Dori M. Steinberg
- School of Nursing, Duke University, Durham, North Carolina
- Equip Health, Inc., Carlsbad, California
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11
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Patterson K, Davey R, Keegan R, Kunstler B, Woodward A, Freene N. Behaviour change techniques in cardiovascular disease smartphone apps to improve physical activity and sedentary behaviour: Systematic review and meta-regression. Int J Behav Nutr Phys Act 2022; 19:81. [PMID: 35799263 PMCID: PMC9261070 DOI: 10.1186/s12966-022-01319-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/02/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Smartphone apps are increasingly used to deliver physical activity and sedentary behaviour interventions for people with cardiovascular disease. However, the active components of these interventions which aim to change behaviours are unclear. AIMS To identify behaviour change techniques used in smartphone app interventions for improving physical activity and sedentary behaviour in people with cardiovascular disease. Secondly, to investigate the association of the identified techniques on improving these behaviours. METHODS Six databases (Medline, CINAHL Plus, Cochrane Library, SCOPUS, Sports Discus, EMBASE) were searched from 2007 to October 2020. Eligible studies used a smartphone app intervention for people with cardiovascular disease and reported a physical activity and/or sedentary behaviour outcome. The behaviour change techniques used within the apps for physical activity and/or sedentary behaviour were coded using the Behaviour Change Technique Taxonomy (v1). The association of behaviour change techniques on physical activity outcomes were explored through meta-regression. RESULTS Forty behaviour change techniques were identified across the 19 included app-based interventions. Only two studies reported the behaviour change techniques used to target sedentary behaviour change. The most frequently used techniques for sedentary behaviour and physical activity were habit reversal and self-monitoring of behaviour respectively. In univariable analyses, action planning (β =0.42, 90%CrI 0.07-0.78) and graded tasks (β =0.33, 90%CrI -0.04-0.67) each had medium positive associations with increasing physical activity. Participants in interventions that used either self-monitoring outcome(s) of behaviour (i.e. outcomes other than physical activity) (β = - 0.47, 90%CrI -0.79--0.16), biofeedback (β = - 0.47, 90%CrI -0.81--0.15) and information about health consequences (β = - 0.42, 90%CrI -0.74--0.07) as behaviour change techniques, appeared to do less physical activity. In the multivariable model, these predictors were not clearly removed from zero. CONCLUSION The behaviour change techniques action planning and graded tasks are good candidates for causal testing in future experimental smartphone app designs.
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Affiliation(s)
- Kacie Patterson
- Health Research Institute, University of Canberra, Bruce, ACT, 2617, Australia.
| | - Rachel Davey
- Health Research Institute, University of Canberra, Bruce, ACT, 2617, Australia
| | - Richard Keegan
- Research Institute for Sports and Exercise (UCRISE), Faculty of Health, University of Canberra, Bruce, ACT, 2617, Australia
| | - Brea Kunstler
- BehaviourWorks Australia, Monash University, Clayton, Victoria, 3168, Australia
| | - Andrew Woodward
- Faculty of Health, University of Canberra, Bruce, ACT, 2617, Australia
| | - Nicole Freene
- Health Research Institute, University of Canberra, Bruce, ACT, 2617, Australia
- Physiotherapy, Faculty of Health, University of Canberra, Bruce, ACT, 2617, Australia
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12
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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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