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Al Younis SM, Hadjileontiadis LJ, Stefanini C, Khandoker AH. Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review. Front Bioeng Biotechnol 2023; 11:1261022. [PMID: 37920244 PMCID: PMC10619666 DOI: 10.3389/fbioe.2023.1261022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/09/2023] [Indexed: 11/04/2023] Open
Abstract
The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy.
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Affiliation(s)
- Sona M. Al Younis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Leontios J. Hadjileontiadis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Cesare Stefanini
- Creative Engineering Design Lab at the BioRobotics Institute, Applied Experimental Sciences Scuola Superiore Sant'Anna, Pontedera (Pisa), Italy
| | - Ahsan H. Khandoker
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
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Hermsen S, Verbiest V, Buijs M, Wentink E. Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study. JMIR Hum Factors 2023; 10:e44850. [PMID: 37566450 PMCID: PMC10457698 DOI: 10.2196/44850] [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: 12/06/2022] [Revised: 04/13/2023] [Accepted: 06/21/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive. OBJECTIVE This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience? METHODS Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences. RESULTS Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants. CONCLUSIONS This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted.
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Affiliation(s)
| | | | | | - Eva Wentink
- OnePlanet Research Center, Wageningen, Netherlands
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3
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Bender BF, Berry JA. Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2023; 5:e230002. [PMID: 37274061 PMCID: PMC10237513 DOI: 10.20900/agmr20230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.
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Affiliation(s)
| | - Jasmine A. Berry
- Robotics Institute, University of Michigan, College of Engineering, Ann Arbor, MI 48109, USA
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Ge TJ, Rahimzadeh VN, Mintz K, Park WG, Martinez-Martin N, Liao JC, Park SM. Passive monitoring by smart toilets for precision health. Sci Transl Med 2023; 15:eabk3489. [PMID: 36724240 DOI: 10.1126/scitranslmed.abk3489] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Smart toilets are a key tool for enabling precision health monitoring in the home, but such passive monitoring has ethical considerations.
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Affiliation(s)
- T Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Kevin Mintz
- Stanford Center for Biomedical Ethics, Stanford University, Stanford, CA 94305, USA
| | - Walter G Park
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Seung-Min Park
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.,Molecular Imaging Program at Stanford, Stanford University School of Medicine, CA 94305 USA
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5
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Schutte AE. The promise and pitfalls of novel cuffless blood pressure devices. Eur Heart J 2022; 43:4222-4223. [DOI: 10.1093/eurheartj/ehac474] [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: 11/13/2022] Open
Affiliation(s)
- Aletta E Schutte
- School of Population Health, University of New South Wales, Kensington Campus, High Street , Sydney, NSW 2052 , Australia
- The George Institute for Global Health, Level 5/1, King Street, Newtown , NSW 2042 , Australia
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6
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Jang YI, Sim JY, Yang JR, Kwon NK. Improving heart rate variability information consistency in Doppler cardiogram using signal reconstruction system with deep learning for Contact-free heartbeat monitoring. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Bhatia A, Ewald G, Maddox T. The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Protocol for a Single-Center Prospective Trial. JMIR Res Protoc 2022; 11:e32873. [PMID: 35771609 PMCID: PMC9284360 DOI: 10.2196/32873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/05/2021] [Accepted: 12/08/2021] [Indexed: 11/24/2022] Open
Abstract
Background Heart failure remains a leading cause of mortality and a major driver of health care utilization. Despite numerous medical advances in heart failure, associated hospitalizations continue to increase, owing largely to suboptimal outpatient management. Remote patient monitoring (RPM) aims to further address this current need in heart failure care by providing data to clinical teams to act pre-emptively to address clinical decompensation. However, to date, RPM approaches using noninvasive home-based patient sensors have failed to demonstrate clinical efficacy. Objective The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial aims to address current noninvasive RPM limitations. Nov-RPM-HF will evaluate a clinician co-designed RPM platform using emerging data collection and presentation tools for heart failure management. These tools include a ballistocardiograph to monitor nocturnal patient biometrics, clinical alerts for abnormal biometrics, and longitudinal data presentation for clinician review. Methods Nov-RPM-HF is a 100-patient single-center prospective trial, evaluating patients over 6 months. The outcomes will include patient adherence to data collection, patient/clinician-perceived utility of the RPM platform, medication changes including the titration of guideline-directed medical therapy to target doses, heart failure symptoms/performance status, and unplanned heart failure hospitalizations or emergency department visits. Results This prospective trial began enrollment in March 2020 and anticipates enrollment completion by June 2022, with trial completion by December 2022. Conclusions This trial protocol aims to provide a systematic framework for the evaluation of heart failure RPM strategies, which are currently heavily used but seldom robustly studied. The trial results will help to inform the role of noninvasive RPM as a viable clinical management strategy in heart failure care. International Registered Report Identifier (IRRID) DERR1-10.2196/32873
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Affiliation(s)
- Ankit Bhatia
- Division of Cardiology, Washington University School of Medicine, Saint Louis, MO, United States.,Healthcare Innovation Lab, BJC Healthcare, Washington University School of Medicine, Saint Louis, MO, United States.,The Christ Hospital Health Network, Cincinnati, OH, United States
| | - Gregory Ewald
- Division of Cardiology, Washington University School of Medicine, Saint Louis, MO, United States
| | - Thomas Maddox
- Division of Cardiology, Washington University School of Medicine, Saint Louis, MO, United States.,Healthcare Innovation Lab, BJC Healthcare, Washington University School of Medicine, Saint Louis, MO, United States
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8
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Giorgi D, Bastiani L, Morales MA, Pascali MA, Colantonio S, Coppini G. Cardio-metabolic risk modeling and assessment through sensor-based measurements. Int J Med Inform 2022; 165:104823. [PMID: 35763936 DOI: 10.1016/j.ijmedinf.2022.104823] [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: 02/07/2022] [Revised: 05/13/2022] [Accepted: 06/20/2022] [Indexed: 10/17/2022]
Abstract
OBJECTIVE Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors. METHODS We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables. RESULTS Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%. CONCLUSIONS Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.
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Affiliation(s)
- Daniela Giorgi
- CNR Institute of Information Science and Technologies, Via G. Moruzzi 1, Pisa 56124, Italy.
| | - Luca Bastiani
- CNR Institute of Clinical Physiology, Via G. Moruzzi 1, Pisa 56124, Italy.
| | | | | | - Sara Colantonio
- CNR Institute of Information Science and Technologies, Via G. Moruzzi 1, Pisa 56124, Italy.
| | - Giuseppe Coppini
- CNR Institute of Information Science and Technologies, Via G. Moruzzi 1, Pisa 56124, Italy.
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Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review. APPLIED SYSTEM INNOVATION 2022. [DOI: 10.3390/asi5030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.
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10
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Smart toilets for monitoring COVID-19 surges: passive diagnostics and public health. NPJ Digit Med 2022; 5:39. [PMID: 35354937 PMCID: PMC8967843 DOI: 10.1038/s41746-022-00582-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/23/2022] [Indexed: 11/08/2022] Open
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11
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Scherrenberg M, Marinus N, Giallauria F, Falter M, Kemps H, Wilhelm M, Prescott E, Vigorito C, De Kluiver E, Cipriano G, Dendale P, Hansen D. The need for long-term personalized management of frail CVD patients by rehabilitation and telemonitoring: a framework. Trends Cardiovasc Med 2022:S1050-1738(22)00023-8. [PMID: 35121082 DOI: 10.1016/j.tcm.2022.01.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/24/2022] [Accepted: 01/29/2022] [Indexed: 10/19/2022]
Abstract
Due to advances in cardiovascular medicine and preventive cardiology, patients benefit from a better prognosis, even in case of significant disease burden such as acute and chronic coronary syndromes, advanced valvular heart disease and chronic heart failure. These advances have allowed CVD patients to increase their life expectancy, but on the other hand also experience aging-related syndromes such as frailty. Despite being underrecognized, frailty is a critical, common, and co-existent condition among older CVD patients, leading to exercise intolerance and compromised adherence to cardiovascular rehabilitation. Moreover, frail patients need a different approach for CR and are at very high risk for adverse events, but yet are underrepresented in conventional CR. Fortunately, recent advances have been made in technology, allowing remote monitoring, coaching and supervision of CVD patients in secondary prevention programs with promising benefits. Similarly, we hypothesized that such programs should also be implemented to treat frailty in CVD patients. However, considering frail patients' particular needs and challenges, telerehabilitation interventions should thus be appropriately adapted. Our purpose is to provide, for the first time and based on expert opinions, a framework of how such a cardiac telerehabilitation program could be developed and implemented to manage a prevention and rehabilitation program for CVD patients with frailty.
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Affiliation(s)
- Martijn Scherrenberg
- Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; UHasselt, Faculty of Medicine and Life Sciences, Diepenbeek, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Belgium
| | - Nastasia Marinus
- UHasselt, Faculty of Rehabilitation Sciences, BIOMED-REVAL, Hasselt, Belgium
| | | | - Maarten Falter
- Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; UHasselt, Faculty of Medicine and Life Sciences, Diepenbeek, Belgium; Faculty of Medicine, Department of Cardiology, KULeuven, Herestraat 49, 3000, Leuven, Belgium
| | - Hareld Kemps
- Department of Cardiology, Máxima Medical Center, The Netherlands; Department of Industrial Design, Technical University Eindhoven, The Netherlands
| | - Matthias Wilhelm
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Eva Prescott
- Department of Cardiology, Bispebjerg University Hospital, University of Copenhagen, Copenhagen, NW, Denmark
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University of Naples
| | | | | | - Paul Dendale
- Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; UHasselt, Faculty of Medicine and Life Sciences, Diepenbeek, Belgium
| | - Dominique Hansen
- Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium; UHasselt, Faculty of Rehabilitation Sciences, BIOMED-REVAL, Hasselt, Belgium.
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12
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Harrington N, Bui QM, Wei Z, Hernandez-Pacheco B, DeYoung PN, Wassell A, Duwaik B, Desai AS, Bhatt DL, Agnihotri P, Owens RL, Coleman TP, King KR. Passive longitudinal weight and cardiopulmonary monitoring in the home bed. Sci Rep 2021; 11:24376. [PMID: 34934065 PMCID: PMC8692625 DOI: 10.1038/s41598-021-03105-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/22/2021] [Indexed: 01/01/2023] Open
Abstract
Home health monitoring has the potential to improve outpatient management of chronic cardiopulmonary diseases such as heart failure. However, it is often limited by the need for adherence to self-measurement, charging and self-application of wearables, or usage of apps. Here, we describe a non-contact, adherence-independent sensor, that when placed beneath the legs of a patient's home bed, longitudinally monitors total body weight, detailed respiratory signals, and ballistocardiograms for months, without requiring any active patient participation. Accompanying algorithms separate weight and respiratory signals when the bed is shared by a partner or a pet. Validation studies demonstrate quantitative equivalence to commercial sensors during overnight sleep studies. The feasibility of detecting obstructive and central apneas, cardiopulmonary coupling, and the hemodynamic consequences of non-sustained ventricular tachycardia is also established. Real-world durability is demonstrated by 3 months of in-home monitoring in an example patient with heart failure and ischemic cardiomyopathy as he recovers from coronary artery bypass grafting surgery. BedScales is the first sensor to measure adherence-independent total body weight as well as longitudinal cardiopulmonary physiology. As such, it has the potential to create a multidimensional picture of chronic disease, learn signatures of impending hospitalization, and enable optimization of care in the home.
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Affiliation(s)
- Nicholas Harrington
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Quan M Bui
- Division of Cardiovascular Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Zhe Wei
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Brandon Hernandez-Pacheco
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Pamela N DeYoung
- Department of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrew Wassell
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Bayan Duwaik
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Akshay S Desai
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Deepak L Bhatt
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Parag Agnihotri
- Population Health Services Organization, University of California San Diego, La Jolla, CA, 92093, USA
| | - Robert L Owens
- Department of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Todd P Coleman
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA
| | - Kevin R King
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, 9500 Gilman Dr. MC 0412, La Jolla, CA, 92093, USA.
- Division of Cardiovascular Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
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Kataria S, Ravindran V. Harnessing of real world data and real world evidence using digital tools: utility and potential models in rheumatology practice. Rheumatology (Oxford) 2021; 61:502-513. [PMID: 34528081 DOI: 10.1093/rheumatology/keab674] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/23/2021] [Indexed: 11/12/2022] Open
Abstract
The diversity of diseases in rheumatology and variability in disease prevalence necessitates greater data parity in disease presentation, treatment responses including adverse events to drugs and various co-morbidities. Randomized Controlled Trials (RCTs) are the gold standard for drug development and performance evaluation. However, when the drug is applied outside the controlled environment the outcomes may differ in patient population. In this context, the need to understand the macro and micro changes involved in disease evolution and progression becomes important and so is the need for harvesting and harnessing the Real-World Data (RWD) from various resources to use them in generating Real World Evidence (RWE). Digital tools with potential relevance to rheumatology can be potentially leveraged to obtain greater patient insights, greater information on disease progression and disease micro processes and even in the early diagnosis of diseases. Since the patients spend only a minuscule proportion of their time in hospital or in a clinic, using the modern digital tools to generate realistic, bias proof RWD in non-invasive patient friendly manner becomes critical. In this review we have appraised different digital mediums and mechanisms for collecting RWD and proposed digital care models for generating RWE in rheumatology.
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14
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Malik AR, Boger J. Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study. JMIR Rehabil Assist Technol 2021; 8:e25996. [PMID: 34057420 PMCID: PMC8204244 DOI: 10.2196/25996] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/07/2021] [Accepted: 04/17/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiovascular diseases are a leading cause of death worldwide and result in significant economic costs to health care systems. The prevalence of cardiovascular conditions that require monitoring is expected to increase as the average age of the global population continues to rise. Although an accurate cardiac assessment can be performed at medical centers, frequent visits for assessment are not feasible for most people, especially those with limited mobility. Monitoring of vital signs at home is becoming an increasingly desirable, accessible, and practical alternative. As wearable devices are not the ideal solution for everyone, it is necessary to develop parallel and complementary approaches. OBJECTIVE This research aims to develop a zero-effort, unobtrusive, cost-effective, and portable option for home-based ambient heart rate monitoring. METHODS The prototype seat cushion uses load cells to acquire a user's ballistocardiogram (BCG). The analog signal from the load cells is amplified and filtered by a signal-conditioning circuit before being digitally recorded. A pilot study with 20 participants was conducted to analyze the prototype's ability to capture the BCG during five real-world tasks: sitting still, watching a video on a computer screen, reading, using a computer, and having a conversation. A novel algorithm based on the continuous wavelet transform was developed to extract the heart rate by detecting the largest amplitude values (J-peaks) in the BCG signal. RESULTS The pilot study data showed that the BCG signals from all five tasks had sufficiently large portions to extract heart rate. The continuous wavelet transform-based algorithm for J-peak detection demonstrated an overall accuracy of 91.4% compared with electrocardiography. Excluding three outliers that had significantly noisy BCG data, the algorithm achieved 94.6% accuracy, which was aligned with that of wearable devices. CONCLUSIONS This study suggests that BCG acquired through a seat cushion is a viable alternative to wearable technologies. The prototype seat cushion presented in this study is an example of a relatively accessible, affordable, portable, and unobtrusive zero-effort approach to achieve frequent home-based ambient heart rate monitoring.
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Affiliation(s)
- Ahmed Raza Malik
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Jennifer Boger
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Research Institute for Aging, Waterloo, ON, Canada
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Quesada O, Shandhi MMH, Beach S, Dowling S, Tandon D, Heller J, Etemadi M, Roy S, Gonzalez Velez JM, Inan OT, Klein L. Use of Ballistocardiography to Monitor Cardiovascular Hemodynamics in Preeclampsia. WOMEN'S HEALTH REPORTS 2021; 2:97-105. [PMID: 33937907 PMCID: PMC8080913 DOI: 10.1089/whr.2020.0127] [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] [Accepted: 03/08/2021] [Indexed: 11/29/2022]
Abstract
Objective: Pregnancy requires a complex physiological adaptation of the maternal cardiovascular system, which is disrupted in women with pregnancies complicated by preeclampsia, putting them at higher risk of future cardiovascular events. The measurement of body movements in response to cardiac ejection via ballistocardiogram (BCG) can be used to assess cardiovascular hemodynamics noninvasively in women with preeclampsia. Methods: Using a previously validated, modified weighing scale for assessment of cardiovascular hemodynamics through measurement of BCG and electrocardiogram (ECG) signals, we collected serial measurements throughout pregnancy and postpartum and analyzed data in 30 women with preeclampsia and 23 normotensive controls. Using BCG and ECG signals, we extracted measures of cardiac output, J-wave amplitude × heart rate (J-amp × HR). Mixed-effect models with repeated measures were used to compare J-amp × HRs between groups at different time points in pregnancy and postpartum. Results: In normotensive controls, the J-amp × HR was significantly lower early postpartum (E-PP) compared with the second trimester (T2; p = 0.016) and third trimester (T3; p = 0.001). Women with preeclampsia had a significantly lower J-amp × HR compared with normotensive controls during the first trimester (T1; p = 0.026). In the preeclampsia group, there was a trend toward an increase in J-amp × HR from T1 to T2 and then a drop in J-amp × HR at T3 and further drop at E-PP. Conclusions: We observe cardiac hemodynamic changes consistent with those reported using well-validated tools. In pregnancies complicated by preeclampsia, the maximal force of contraction is lower, suggesting lower cardiac output and a trend in hemodynamics consistent with the hyperdynamic disease model of preeclampsia.
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Affiliation(s)
- Odayme Quesada
- Women's Heart Center, The Christ Hospital Heart Vascular and Lung Institute, Cincinnati, Ohio, USA.,Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Los Angeles, California, USA
| | - Md Mobashir Hasan Shandhi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Shire Beach
- Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA
| | - Sean Dowling
- Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA
| | - Damini Tandon
- Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA
| | - James Heller
- Department of Obstetrics and Gynecology, University of California San Francisco, San Francisco, California, USA
| | - Mozziyar Etemadi
- Department of Obstetrics and Gynecology, University of California San Francisco, San Francisco, California, USA
| | - Shuvo Roy
- Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA
| | | | - Omer T Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Liviu Klein
- Division of Cardiology, Department of Internal Medicine, University of California San Francisco, San Francisco, California, USA
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16
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Bakogiannis C, Tsarouchas A, Mouselimis D, Lazaridis C, Theofillogianakos EK, Billis A, Tzikas S, Fragakis N, Bamidis PD, Papadopoulos CE, Vassilikos VP. A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study. JMIR Mhealth Uhealth 2021; 9:e24271. [PMID: 33847599 PMCID: PMC8080140 DOI: 10.2196/24271] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/05/2020] [Accepted: 03/02/2021] [Indexed: 01/01/2023] Open
Abstract
Background Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. Mobile health (mHealth)–based interventions taking advantage of smartphone technology have shown particular promise in increasing the quality of self-care among these patients, and in turn improving the outcomes of their disease. Objective The objective of this study was to co-develop with physicians, patients with HF, and their caregivers a patient-oriented mHealth app, perform usability assessment, and investigate its effect on the quality of life of patients with HF and rate of hospitalizations in a pilot study. Methods The development of an mHealth app (The Hellenic Educational Self-care and Support Heart Failure app [ThessHF app]) was evidence based, including features based on previous clinically tested mHealth interventions and selected by a panel of HF expert physicians and discussed with patients with HF. At the end of alpha development, the app was rated by mHealth experts with the Mobile Application Rating Scale (MARS). The beta version was tested by patients with HF, who rated its design and content by means of the Post-Study System Usability Questionnaire (PSSUQ). Subsequently, a prospective pilot study (THESS-HF [THe Effect of a Specialized Smartphone app on Heart Failure patients’ quality of self-care, quality of life and hospitalization rate]) was performed to investigate the effect of app use on patients with HF over a 3-month follow-up period. The primary endpoint was patients’ quality of life, which was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the 5-level EQ-5D version (EQ-5D-5L). The secondary endpoints were the European Heart Failure Self-care Behavior Scale (EHFScBS) score and the hospitalization rate. Results A systematic review of mHealth-based HF interventions and expert panel suggestions yielded 18 separate app features, most of which were incorporated into the ThessHF app. A total of 14 patients and 5 mHealth experts evaluated the app. The results demonstrated a very good user experience (overall PSSUQ score 2.37 [SD 0.63], where 1 is the best, and a median MARS score of 4.55/5). Finally, 30 patients (male: n=26, 87%) participated in the THESS-HF pilot study (mean age 68.7 [SD 12.4] years). A significant increase in the quality of self-care was noted according to the EHFScBS, which increased by 4.4% (SD 7.2%) (P=.002). The mean quality of life increased nonsignificantly after 3 months according to both KCCQ (mean increase 5.8 [SD 15] points, P=.054) and EQ-5D-5L (mean increase 5.6% [SD 15.6%], P=.06) scores. The hospitalization rate for the follow-up duration was 3%. Conclusions The need for telehealth services and remote self-care management in HF is of vital importance, especially in periods such as the COVID-19 pandemic. We developed a user-friendly mHealth app to promote remote self-care support in HF. In this pilot study, the use of the ThessHF app was associated with an increase in the quality of self-care. A future multicenter study will investigate the effect of the app use on long-term outcomes in patients with HF.
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Affiliation(s)
- Constantinos Bakogiannis
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anastasios Tsarouchas
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Mouselimis
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Charalampos Lazaridis
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Antonios Billis
- Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stergios Tzikas
- Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Fragakis
- Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christodoulos E Papadopoulos
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilios P Vassilikos
- Cardiovascular Prevention and Digital Cardiology Lab, Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Third Cardiology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
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17
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Dos Santos Silva A, Almeida H, da Silva HP, Oliveira A. Design and evaluation of a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes. Sci Rep 2021; 11:6222. [PMID: 33737660 PMCID: PMC7973576 DOI: 10.1038/s41598-021-85697-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/02/2021] [Indexed: 11/17/2022] Open
Abstract
Multiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.
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Affiliation(s)
- Aline Dos Santos Silva
- FEUP-Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias, 4200-465, Porto, Portugal.
| | - Hugo Almeida
- ISCA-Instituto Superior de Contabilidade e Administração, Universidade de Aveiro, R. Associação Humanitária dos Bombeiros Voluntários, 3810-902, Aveiro, Portugal
| | - Hugo Plácido da Silva
- IT-Instituto de Telecomunicações, IST-Instituto Superior Técnico, Torre Norte-Piso 10, Av. Rovisco Pais, 1049-001, Lisboa, Portugal
| | - António Oliveira
- OLI-Sistemas Sanitários, S.A., Travessa Do Milão, 3800-235, Aveiro, Portugal
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18
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Silva-Cardoso J, Juanatey JRG, Comin-Colet J, Sousa JM, Cavalheiro A, Moreira E. The Future of Telemedicine in the Management of Heart Failure Patients. Card Fail Rev 2021; 7:e11. [PMID: 34136277 PMCID: PMC8201465 DOI: 10.15420/cfr.2020.32] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Telemedicine (TM) is potentially a way of escalating heart failure (HF) multidisciplinary integrated care. Despite the initial efforts to implement TM in HF management, we are still at an early stage of its implementation. The coronavirus disease 2019 pandemic led to an increased utilisation of TM. This tendency will probably remain after the resolution of this threat. Face-to-face medical interventions are gradually transitioning to the virtual setting by using TM. TM can improve healthcare accessibility and overcome geographic inequalities. It promotes healthcare system efficiency gains, and improves patient self-management and empowerment. In cooperation with human intervention, artificial intelligence can enhance TM by helping to deal with the complexities of multicomorbidity management in HF, and will play a relevant role towards a personalised HF patient approach. Artificial intelligence-powered/telemedical/heart team/multidisciplinary integrated care may be the next step of HF management. In this review, the authors analyse TM trends in the management of HF patients and foresee its future challenges within the scope of HF multidisciplinary integrated care.
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Affiliation(s)
- José Silva-Cardoso
- Faculty of Medicine, University of PortoPorto, Portugal
- São João University Hospital CentrePorto, Portugal
- CINTESIS, Centre for Health Technology and Services Research, Faculty of Medicine, University of PortoPorto, Portugal
| | | | - Josep Comin-Colet
- Bio-Heart Cardiovascular Diseases Research Group, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de LlobregatBarcelona, Spain
- Community Heart Failure Program, Cardiology Department, Bellvitge University Hospital, L’Hospitalet de LlobregatBarcelona, Spain
- Department of Clinical Sciences, School of Medicine, University of BarcelonaBarcelona, Spain
| | - José Maria Sousa
- São João University Hospital CentrePorto, Portugal
- CINTESIS, Centre for Health Technology and Services Research, Faculty of Medicine, University of PortoPorto, Portugal
| | - Ana Cavalheiro
- CINTESIS, Centre for Health Technology and Services Research, Faculty of Medicine, University of PortoPorto, Portugal
- Department of Physical Rehabilitation, Centro Hospitalar do PortoPorto, Portugal
| | - Emília Moreira
- Faculty of Medicine, University of PortoPorto, Portugal
- CINTESIS, Centre for Health Technology and Services Research, Faculty of Medicine, University of PortoPorto, Portugal
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19
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Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy. INTERNATIONAL JOURNAL OF HEART FAILURE 2021; 3:31-50. [PMID: 36263114 PMCID: PMC9536717 DOI: 10.36628/ijhf.2020.0023] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/19/2020] [Accepted: 11/06/2020] [Indexed: 12/11/2022]
Abstract
Despite clinical advances in its treatment, heart failure (HF) is associated with significant adverse clinical outcomes and is among the greatest drivers of healthcare utilization. Outpatient management of HF remains suboptimal, with gaps in the provision of evidence-based therapies, and difficulties in predicting and managing clinical decompensation. Remote patient monitoring (RPM) has the potential to address these issues, and thus has been of increasing interest to HF clinicians and health systems. Economic incentives, including increasing RPM reimbursement and HF readmission penalties, are also spurring increased interest in RPM. This review establishes a framework for evaluating RPM based on its various components: 1) patient data collection, 2) data transmission, analysis, and presentation, and 3) care team review and clinical action. The existing evidence regarding RPM in HF management is also reviewed. Based on the data, we identify RPM features associated with clinical efficacy and describe emerging digital tools that have the promise of addressing current needs.
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20
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Chang IS, Mak S, Armanfard N, Boger J, Grace SL, Arcelus A, Chessex C, Mihailidis A. Quantification of Resting-State Ballistocardiogram Difference Between Clinical and Non-Clinical Populations for Ambient Monitoring of Heart Failure. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2020; 8:2700811. [PMID: 33094034 PMCID: PMC7571868 DOI: 10.1109/jtehm.2020.3029690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/14/2020] [Accepted: 10/05/2020] [Indexed: 11/12/2022]
Abstract
A ballistocardiogram (BCG) is a versatile bio-signal that enables ambient remote monitoring of heart failure (HF) patients in a home setting, achieved through embedded sensors in the surrounding environment. Numerous methods of analysis are available for extracting physiological information using the BCG; however, most have been developed based on non-clinical subjects. While the difference between clinical and non-clinical populations are expected, quantification of the difference may serve as a useful tool. In this work, the differences in resting-state BCGs of the two cohorts in a sitting posture were quantified. An instrumented chair was used to collect the BCG from 29 healthy adults and 26 NYHA HF class I and II patients while seated without any stress test for five minutes. Five 20-second epochs per subject were used to calculate the waveform fluctuation metric at rest (WFMR). The WFMR was obtained in two steps. The ensemble average of the segmented BCG heartbeats within an epoch were calculated first. Mean square errors (MSE) between different ensemble average pairs were then retrieved. The MSEs were averaged to produce the WFMR. The comparison showed that the clinical cohort had higher fluctuation than the non-clinical population and had at least 82.2% separation, suggesting that greater errors may result when existing algorithms were used. The WFMR acts as a bridge that may enable important features, including the addition of error margins in parameter estimation and ways to devise a calibration strategy when resting-state BCG is unstable.
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Affiliation(s)
- Isaac Sungjae Chang
- Institute of Biomaterials and Biomedical Engineering, University of TorontoONM5S 3G9Canada
| | - Susanna Mak
- Division of CardiologyDepartment of MedicineMount Sinai HospitalTorontoONM5G 1X5Canada
| | - Narges Armanfard
- Department of Electrical and Computer EngineeringMcGill UniversityMontrealQCH3A 0G4Canada
| | - Jennifer Boger
- Department of Systems Design EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada.,Research Institute for AgingWaterlooONN2J 0E2Canada
| | - Sherry L Grace
- Faculty of HealthYork UniversityTorontoONM3J IP3Canada.,Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Amaya Arcelus
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Caroline Chessex
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Alex Mihailidis
- Toronto Rehabilitation Institute, University Health NetworkTorontoONM5T 2S8Canada
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21
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Sadek I, Heng TTS, Seet E, Abdulrazak B. A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study. J Med Internet Res 2020; 22:e18297. [PMID: 32945773 PMCID: PMC7532465 DOI: 10.2196/18297] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/10/2020] [Accepted: 07/26/2020] [Indexed: 01/26/2023] Open
Abstract
Background At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient’s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. Conclusions Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection.
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Affiliation(s)
- Ibrahim Sadek
- AMI-Lab, Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, QC, Canada.,Research Centre on Aging, Sherbrooke, QC, Canada.,Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
| | - Terry Tan Soon Heng
- Department of Otolaryngology, Woodlands Health Campus and Khoo Teck Puat Hospital, Singapore, Singapore
| | - Edwin Seet
- Department of Anaesthesia, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Bessam Abdulrazak
- AMI-Lab, Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, QC, Canada.,Research Centre on Aging, Sherbrooke, QC, Canada
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22
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Chen Y, Du J, Sun X, Li Q, Qin M, Xiao Q, Bryan M. Treatment of Left Ventricular Circulation Disorder: Application of Echocardiography Information Data Monitoring. JMIR Med Inform 2020; 8:e19110. [PMID: 32936076 PMCID: PMC7527912 DOI: 10.2196/19110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 04/30/2020] [Accepted: 05/01/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cardiac hypertrophy induced by pressure overload is one of the important causes of heart failure and sudden cardiac death. At present, there are few studies on the outcome of left ventricular hypertrophy and left ventricular function after complete pressure load removal. OBJECTIVE This study aims to better simulate the changes of left ventricular structure and function during the process of left ventricular pressure overload and deloading, and to explore the application of echocardiography in it. METHODS In this study, healthy male (BALB/C) mice were used as research objects to establish an ascending aorta constriction model, to carry out echocardiographic and hemodynamic examinations, to establish an ascending aorta deconstriction model in mice, and to carry out echocardiographic and hemodynamic examinations. RESULTS Compared with the sham operation group, the left ventricular end-systolic diameter (LVESD), left ventricular end-diastolic diameter (LVEDD), interventricular septal (IVS), and left ventricular posterior wall (LVPW) in the constriction operation group were significantly increased (P=.02, P=.02, P=.02, and P=.02, respectively). LVESD, LVEDD, IVS, and LVPW in the early and late constriction groups were significantly decreased, and the degree of decrease in the early group was greater than that in the late group; compared with the sham operation group, left ventricular diastolic pressure in the constriction operation group increased significantly at 9 and 15 weeks after operation (P=.03). Left ventricular systolic pressure at 15 weeks after operation decreased to a certain extent but was higher than that of the sham operation group (P=.02). The maximal rate of the increase of left ventricular pressure at 3 weeks, 9 weeks, and 15 weeks after operation decreased significantly (P=.03, P=.02, and P=.02, respectively). CONCLUSIONS In this study, the ascending aorta coarctation model and descending aorta coarctation model were successfully established, which verifies the value of echocardiography information data monitoring in the treatment of left ventricular circulation disorders and the evaluation of surgical treatment.
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Affiliation(s)
- Yulong Chen
- Department of Ultrasound, Xuzhou Children's Hospital, Xuzhou Medical University, Xuzhou, China
| | - Jianxia Du
- Department of Ultrasound, Xuzhou Children's Hospital, Xuzhou Medical University, Xuzhou, China
| | - Xiao Sun
- Department of Ultrasound, Xuzhou Children's Hospital, Xuzhou Medical University, Xuzhou, China
| | - Qiancheng Li
- Department of Ultrasound, Xuzhou Children's Hospital, Xuzhou Medical University, Xuzhou, China
| | - Ming Qin
- Department of Ultrasound, Xuzhou Children's Hospital, Xuzhou Medical University, Xuzhou, China
| | - Qian Xiao
- Department of Emergency, Xuzhou Central Hospital, Xuzhou, China
| | - Mark Bryan
- Dipartimento di Biomedicina, Universita di Torino, Torino, Italy
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23
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Abstract
Cardiovascular diseases (CVDs) are responsible for more deaths than any other cause, with coronary heart disease and stroke accounting for two-thirds of those deaths. Morbidity and mortality due to CVD are largely preventable, through either primary prevention of disease or secondary prevention of cardiac events. Monitoring cardiac status in healthy and diseased cardiovascular systems has the potential to dramatically reduce cardiac illness and injury. Smart technology in concert with mobile health platforms is creating an environment where timely prevention of and response to cardiac events are becoming a reality.
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Affiliation(s)
- Jeffrey W. Christle
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, California 94305, USA
| | - Steven G. Hershman
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
| | - Jessica Torres Soto
- Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
| | - Euan A. Ashley
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California 94305, USA
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, California 94305, USA
- Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA
- Stanford Center for Digital Health, Stanford University, Stanford, California 94305, USA
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24
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Nakagawa A, Kim J, Nakajima K. Personal Identification Using a Ballistocardiogram During Urination Obtained from a Toilet Seat. ADVANCED BIOMEDICAL ENGINEERING 2020. [DOI: 10.14326/abe.9.233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Arata Nakagawa
- Division of Bio-information Engineering, University of Toyama
| | - Juhyon Kim
- Division of Bio-information Engineering, University of Toyama
| | - Kazuki Nakajima
- Division of Bio-information Engineering, University of Toyama
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25
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Ding EY, Ensom E, Hafer N, Buchholz B, Picard MA, Dunlap D, Rogers E, Lawton C, Koren A, Lilly C, Fitzgibbons TP, McManus DD. Point-of-care technologies in heart, lung, blood and sleep disorders from the Center for Advancing Point-of-Care Technologies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019; 11:58-67. [PMID: 32582870 PMCID: PMC7314358 DOI: 10.1016/j.cobme.2019.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Recent advancements in point-of-care technologies have transformed care for patients with heart, lung, blood, and sleep disorders by providing rapid, cost-effective, and accessible solutions to challenges in the detection and management of many health conditions. However, major barriers exist throughout the technology development process that inhibit the actualization of many promising and potentially successful ideas. The Center for Advancing Point of Care Technologies has established a system for supporting further innovation in this field and bridging the gap between initial idea conception and implementation. We highlight current and emerging point-of-care technologies throughout the development spectrum and emphasize the need for a needs-driven model of health technology development that involve appropriate stakeholders in the process.
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Affiliation(s)
- Eric Y Ding
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Emily Ensom
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Nathaniel Hafer
- UMass Center for Clinical and Translational Science, University of Massachusetts Medical School, Worcester, MA, USA
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bryan Buchholz
- Department of Biomedical Engineering, University of Massachusetts Lowell, Lowell, MA, USA
- Department of Work Environment, University of Massachusetts Lowell, Lowell, MA, USA
| | - Mary Ann Picard
- Massachusetts Medical Device Development Center, University of Massachusetts, Worcester/Lowell, MA, USA
| | - Denise Dunlap
- The Manning School of Business, University of Massachusetts Lowell, Lowell, MA, USA
| | - Eugene Rogers
- Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, Lowell, MA, USA
| | - Carl Lawton
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, M A, USA
| | - Ainat Koren
- Susan and Alan Solomont School of Nursing, University of Massachusetts Lowell, Lowell, MA, USA
| | - Craig Lilly
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Timothy P Fitzgibbons
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
- UMass Center for Clinical and Translational Science, University of Massachusetts Medical School, Worcester, MA, USA
| | - David D McManus
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
- UMass Center for Clinical and Translational Science, University of Massachusetts Medical School, Worcester, MA, USA
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26
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Aydemir VB, Nagesh S, Shandhi MMH, Fan J, Klein L, Etemadi M, Heller JA, Inan OT, Rehg JM. Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography. IEEE Trans Biomed Eng 2019; 67:1303-1313. [PMID: 31425011 DOI: 10.1109/tbme.2019.2935619] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. METHODS 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. RESULTS The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. CONCLUSION This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. SIGNIFICANCE In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.
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Hersek S, Semiz B, Shandhi MMH, Orlandic L, Inan OT. A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning. IEEE J Biomed Health Inform 2019; 24:1296-1309. [PMID: 31369391 DOI: 10.1109/jbhi.2019.2931872] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrations of the chest wall due to the cardiac cycle. While BCG is a more well-known modality, it requires the use of a modified bathroom scale or a force plate and cannot be measured in a wearable setting, whereas SCG signals can be measured using wearable accelerometers placed on the sternum. In this paper, we explore the idea of finding a mapping between zero mean and unit l2-norm SCG and BCG signal segments such that, the BCG signal can be acquired using wearable accelerometers (without retaining amplitude information). We use neural networks to find such a mapping and make use of the recently introduced UNet architecture. We trained our models on 26 healthy subjects and tested them on ten subjects. Our results show that we can estimate the aforementioned segments of the BCG signal with a median Pearson correlation coefficient of 0.71 and a median absolute deviation (MAD) of 0.17. Furthermore, our model can estimate the R-I, R-J and R-K timing intervals with median absolute errors (and MAD) of 10.00 (8.90), 6.00 (5.93), and 8.00 (5.93), respectively. We show that using all three axis of the SCG accelerometer produces the best results, whereas the head-to-foot SCG signal produces the best results when a single SCG axis is used.
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Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography. SENSORS 2019; 19:s19132922. [PMID: 31266256 PMCID: PMC6651596 DOI: 10.3390/s19132922] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 06/25/2019] [Accepted: 06/26/2019] [Indexed: 01/13/2023]
Abstract
This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.
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