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Al-Dalakta A, Tabaja C, Motairek I, El Hajjar AH, Agarwal N, St John J, Laffin LJ. Blood Pressure Time in Target Range and its Impact on Clinical Outcomes. Curr Cardiol Rep 2024:10.1007/s11886-024-02111-9. [PMID: 39141252 DOI: 10.1007/s11886-024-02111-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/28/2024] [Indexed: 08/15/2024]
Abstract
PURPOSE OF REVIEW To examine the concept of time in target range for blood pressure (BP) management, exploring its calculation methods, implications for patient outcomes, and potential use in patient care. RECENT FINDINGS Recent post-hoc analyses of clinical trials and observational studies highlight the importance of BP time in target range in predicting cardiovascular outcomes. Higher time in target range correlates with reduced risks of major adverse cardiovascular events including heart failure, stroke, myocardial infarction and all-cause mortality. Additionally, longer time in target range decreases the risk of incident atrial fibrillation and risk of developing dementia. BP time in target range is a novel metric offering valuable insights into BP control and its impact on clinical outcomes. Higher time in target range is consistently associated with better cardiovascular outcomes across various patient populations. However, the clinical application of BP time in target range requires further investigation through prospective clinical trials and real-world studies. Integrating wearable devices for continuous BP monitoring could enhance the practical utility of BP time in target range in hypertension management.
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Affiliation(s)
- Astefanos Al-Dalakta
- Department of Internal Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Chadi Tabaja
- Department of Internal Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Issam Motairek
- Department of Internal Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Abdel Hadi El Hajjar
- Department of Internal Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Neel Agarwal
- Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Julie St John
- Cleveland Clinic, C5Research, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Luke J Laffin
- Cleveland Clinic, C5Research, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
- Section of Preventive Cardiology and Rehabilitation, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code JB1, Cleveland, OH, 44195, USA.
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Rosa RP, Rosace G, Trovato V. Recent Advancements in Acrylic Fabric Applications: A Comprehensive Review and Future Trends. Polymers (Basel) 2024; 16:2111. [PMID: 39125138 PMCID: PMC11314402 DOI: 10.3390/polym16152111] [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: 06/28/2024] [Revised: 07/19/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Acrylic fibres, as synthetic polymers, have been used extensively in the textile industry to create a wide variety of products, ranging from apparel and home furnishings to car rooftops and carbon fibres. Their widespread application is attributed to a combination of desirable properties, including a soft, wool-like texture, chemical stability, and robust mechanical characteristics. Furthermore, the chemical structure of acrylic fibres can be modified to imbue them with additional features, such as antimicrobial properties, fire resistance, conductivity, water repellency, and ultraviolet protection. This review explores the technological methods employed to functionalise acrylic fibres and discusses future trends in their development.
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Affiliation(s)
- Raphael Palucci Rosa
- Department of Engineering and Applied Sciences, University of Bergamo, Viale Marconi 5, 24044 Dalmine, BG, Italy;
| | - Giuseppe Rosace
- Department of Engineering and Applied Sciences, University of Bergamo, and Local INSTM Unit, Viale Marconi 5, 24044 Dalmine, BG, Italy;
| | - Valentina Trovato
- Department of Engineering and Applied Sciences, University of Bergamo, Viale Marconi 5, 24044 Dalmine, BG, Italy;
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Liu ZD, Li Y, Zhang YT, Zeng J, Chen ZX, Liu JK, Miao F. HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement. IEEE J Biomed Health Inform 2024; 28:3882-3894. [PMID: 38687656 DOI: 10.1109/jbhi.2024.3395445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.
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Acharya M, Ali MM, Bogulski CA, Pandit AA, Mahashabde RV, Eswaran H, Hayes CJ. Association of Remote Patient Monitoring with Mortality and Healthcare Utilization in Hypertensive Patients: a Medicare Claims-Based Study. J Gen Intern Med 2024; 39:762-773. [PMID: 37973707 PMCID: PMC11043264 DOI: 10.1007/s11606-023-08511-x] [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: 05/09/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Hypertension management is complex in older adults. Recent advances in remote patient monitoring (RPM) have warranted evaluation of RPM use and patient outcomes. OBJECTIVE To study associations of RPM use with mortality and healthcare utilization measures of hospitalizations, emergency department (ED) utilization, and outpatient visits. DESIGN A retrospective cohort study. PATIENTS Medicare beneficiaries aged ≥65 years with an outpatient hypertension diagnosis between July 2018 and September 2020. The first date of RPM use with a corresponding hypertension diagnosis was recorded (index date). RPM non-users were documented from those with an outpatient hypertension diagnosis; a random visit was selected as the index date. Six months prior continuous enrollment was required. MAIN MEASURES Outcomes studied within 180 days of index date included (i) all-cause mortality, (ii) any hospitalization, (iii) cardiovascular-related hospitalization, (iv) non-cardiovascular-related hospitalization, (v) any ED, (vi) cardiovascular-related ED, (vii) non-cardiovascular-related ED, (viii) any outpatient, (ix) cardiovascular-related outpatient, and (x) non-cardiovascular-related outpatient. Patient demographics and clinical variables were collected from baseline and index date. Propensity score matching (1:4) and Cox regression were performed. Hazard ratios (HR) and 95% confidence intervals (CI) are reported. KEY RESULTS The matched sample had 16,339 and 63,333 users and non-users, respectively. Cumulative incidences of mortality outcome were 2.9% (RPM) and 4.3% (non-RPM), with a HR (95% CI) of 0.66 (0.60-0.74). RPM users had lower hazards of any [0.78 (0.75-0.82)], cardiovascular-related [0.79 (0.73-0.87)], and non-cardiovascular-related [0.79 (0.75-0.83)] hospitalizations. No significant association was observed between RPM use and the three ED measures. RPM users had higher hazards of any [1.10 (1.08-1.11)] and cardiovascular-related outpatient visits [2.17 (2.13-2.19)], while a slightly lower hazard of non-cardiovascular-related outpatient visits [0.94 (0.93-0.96)]. CONCLUSIONS RPM use was associated with substantial reductions in hazards of mortality and hospitalization outcomes with an increase in cardiovascular-related outpatient visits.
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Affiliation(s)
- Mahip Acharya
- Institute for Digital Health & Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Mir M Ali
- Institute for Digital Health & Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Cari A Bogulski
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Ambrish A Pandit
- Divison of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Ruchira V Mahashabde
- Divison of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Hari Eswaran
- Institute for Digital Health & Innovation, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Corey J Hayes
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
- Center for Mental Healthcare and Outcomes Research, Central Arkansas Veterans Healthcare Systems, North Little Rock, AR, USA.
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Sheng Y, Bond R, Jaiswal R, Dinsmore J, Doyle J. Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial. J Med Internet Res 2024; 26:e46287. [PMID: 38546724 PMCID: PMC11009852 DOI: 10.2196/46287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 10/25/2023] [Accepted: 01/29/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. OBJECTIVE The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. METHODS Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. RESULTS Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity). CONCLUSIONS The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.
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Affiliation(s)
- Yiyang Sheng
- NetwellCASALA, Dundalk Institution of Technology, Dundalk, Ireland
| | - Raymond Bond
- School of Computing, Ulster University, Jordanstown, United Kingdom
| | - Rajesh Jaiswal
- School of Enterprise Computing and Digital Transformation, Technological University Dublin, Dublin, Ireland
| | - John Dinsmore
- Trinity Centre for Practice and Healthcare Innovation, School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland
| | - Julie Doyle
- NetwellCASALA, Dundalk Institution of Technology, Dundalk, Ireland
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Kohjitani H, Koshimizu H, Nakamura K, Okuno Y. Recent developments in machine learning modeling methods for hypertension treatment. Hypertens Res 2024; 47:700-707. [PMID: 38216731 DOI: 10.1038/s41440-023-01547-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/22/2023] [Accepted: 11/09/2023] [Indexed: 01/14/2024]
Abstract
Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.
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Affiliation(s)
- Hirohiko Kohjitani
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Hiroshi Koshimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuki Nakamura
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [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] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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Virzì GM, Morisi N, Milan Manani S, Tantillo I, Gonzàlez Barajas JD, Villavicencio BD, Castiglione C, Alfano G, Donati G, Zanella M. Scheduling of Remote Monitoring for Peritoneal Dialysis Patients. J Clin Med 2024; 13:406. [PMID: 38256540 PMCID: PMC10816117 DOI: 10.3390/jcm13020406] [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: 12/22/2023] [Revised: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Peritoneal dialysis (PD) is performed as a home-based treatment and in this context, telemedicine has been proven helpful for improving clinicians' surveillance and maintaining PD patients in their home setting. The new e-health devices make remote patient monitoring (RPM) for automated peritoneal dialysis (APD) treatment possible, evaluating the data at the end of every treatment and adapting the prescription at distance if necessary. This paper aims to share a method for improving clinical surveillance and enabling PD patients to receive their treatment at home. In the present case series, we delineate the clinical protocol of the Vicenza PD Center regarding patient characteristics, timing, and the purpose of the APD-RPM. We present the Vicenza PD Center's experience, illustrating its application through three case reports as exemplars. Telemedicine helps to carefully allocate healthcare resources while removing the barriers to accessing care. However, there is a risk of data overload, as some data might not be analyzed because of an increased workload for healthcare professionals. A proactive physician's attitude towards the e-health system has to be supported by clinical instructions and legislative rules. International and national guidelines may suggest which patients should be candidates for RPM, which parameters should be monitored, and with what timing. According to our experience, we suggest that the care team should define a workflow that helps in formulating a correct approach to RPM, adequately utilizing resources. The workflow has to consider the different needs of patients, in order to assure frequent remote control for incident or unstable patients, while prevalent and stable patients can perform their home treatment more independently, helped by periodic and deferred clinical supervision.
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Affiliation(s)
- Grazia Maria Virzì
- Department of Nephrology, Dialysis and Transplantation, San Bortolo Hospital, 36100 Vicenza, Italy; (G.M.V.); (I.T.); (M.Z.)
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
| | - Niccolò Morisi
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
- Surgical, Medical and Dental Department of Morphological Sciences, Section of Nephrology, University of Modena and Reggio Emilia, 41121 Modena, Italy; (G.A.); (G.D.)
| | - Sabrina Milan Manani
- Department of Nephrology, Dialysis and Transplantation, San Bortolo Hospital, 36100 Vicenza, Italy; (G.M.V.); (I.T.); (M.Z.)
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
| | - Ilaria Tantillo
- Department of Nephrology, Dialysis and Transplantation, San Bortolo Hospital, 36100 Vicenza, Italy; (G.M.V.); (I.T.); (M.Z.)
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
| | - José David Gonzàlez Barajas
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
- Departiment of Nephrology, University of Guadalajara Health Sciences Center, Guadalajara 44100, Mexico
| | - Bladimir Diaz Villavicencio
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
- Departiment of Nephrology, University of Guadalajara Health Sciences Center, Guadalajara 44100, Mexico
| | - Claudia Castiglione
- Department of Medicine, Section of Nephrology, University of Verona, 37129 Verona, Italy;
| | - Gaetano Alfano
- Surgical, Medical and Dental Department of Morphological Sciences, Section of Nephrology, University of Modena and Reggio Emilia, 41121 Modena, Italy; (G.A.); (G.D.)
| | - Gabriele Donati
- Surgical, Medical and Dental Department of Morphological Sciences, Section of Nephrology, University of Modena and Reggio Emilia, 41121 Modena, Italy; (G.A.); (G.D.)
| | - Monica Zanella
- Department of Nephrology, Dialysis and Transplantation, San Bortolo Hospital, 36100 Vicenza, Italy; (G.M.V.); (I.T.); (M.Z.)
- IRRIV—International Renal Research Institute Vicenza Foundation, 36100 Vicenza, Italy; (N.M.); (J.D.G.B.); (B.D.V.)
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Truong ATL, Tan SB, Wang GZ, Yip AWJ, Egermark M, Yeung W, Lee VV, Chan MY, Kumar KS, Tan LWJ, Vijayakumar S, Blasiak A, Wang LYT, Ho D. CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial). EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:41-49. [PMID: 38264697 PMCID: PMC10802822 DOI: 10.1093/ehjdh/ztad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/11/2023] [Accepted: 09/06/2023] [Indexed: 01/25/2024]
Abstract
Aims Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the feasibility of a larger randomized controlled trial (RCT), evaluating the efficacy of CURATE.AI-assisted dose titration intervention. We will also collect preliminary efficacy and safety data and explore stakeholder feedback in the early design process. Methods and results In this open-label, randomized, pilot feasibility trial, we aim to recruit 45 participants with primary hypertension. Participants will be randomized in 1:1:1 ratio into control (no intervention), home blood pressure monitoring (active control; HBPM), or CURATE.AI arms (intervention; HBPM and CURATE.AI-assisted dose titration). The home treatments include 1 month of two-drug anti-hypertensive regimens. Primary endpoints assess the logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility, and define the progression criteria for the RCT in a 'traffic light system'. Secondary endpoints assess preliminary efficacy [e.g. mean change in office blood pressures (BPs)] and safety (e.g. hospitalization events) associated with each treatment protocol. Participants with both baseline and post-treatment BP measurements will form the intent-to-treat analysis. Following their involvement with the CURATE.AI intervention, feedback from CURATE.AI participants and healthcare providers will be collected via exit survey and interviews. Conclusion Findings from this study will inform about potential refinements of the current treatment protocols before proceeding with a larger RCT, or potential expansion to collect additional information. Positive results may suggest the potential efficacy of CURATE.AI to improve BP control. Trial registration number NCT05376683.
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Affiliation(s)
- Anh T L Truong
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Shi-Bei Tan
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Golda Z Wang
- Department of Pharmacy, Alexandra Hospital, Singapore 15996, Singapore
| | - Alexander W J Yip
- Department of Medicine, Alexandra Hospital, Singapore 159964, Singapore
- Department of Healthcare Redesign, Alexandra Hospital, Singapore 159964, Singapore
| | - Mathias Egermark
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
| | - Wesley Yeung
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - V Vien Lee
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Mark Y Chan
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - Kirthika S Kumar
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Lester W J Tan
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Smrithi Vijayakumar
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
| | - Laureen Y T Wang
- Department of Medicine, Alexandra Hospital, Singapore 159964, Singapore
- Department of Healthcare Redesign, Alexandra Hospital, Singapore 159964, Singapore
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
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Wang Y, Wang X, Nie S, Meng K, Lin Z. Recent Progress of Wearable Triboelectric Nanogenerator-Based Sensor for Pulse Wave Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 24:36. [PMID: 38202897 PMCID: PMC10780409 DOI: 10.3390/s24010036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/24/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Today, cardiovascular diseases threaten human health worldwide. In clinical practice, it has been concluded that analyzing the pulse waveform can provide clinically valuable information for the diagnosis of cardiovascular diseases. Accordingly, continuous and accurate monitoring of the pulse wave is essential for the prevention and detection of cardiovascular diseases. Wearable triboelectric nanogenerators (TENGs) are emerging as a pulse wave monitoring biotechnology due to their compelling characteristics, including being self-powered, light-weight, and wear-resistant, as well as featuring user-friendliness and superior sensitivity. Herein, a comprehensive review is conducted on the progress of wearable TENGs for pulse wave monitoring. Firstly, the four modes of operation of TENG are briefly described. Secondly, TENGs for pulse wave monitoring are classified into two categories, namely wearable flexible film-based TENG sensors and textile-based TENG sensors. Next, the materials, fabrication methods, working mechanisms, and experimental performance of various TENG-based sensors are summarized. It concludes by comparing the characteristics of the two types of TENGs and discussing the potential development and challenges of TENG-based sensors in the diagnosis of cardiovascular diseases and personalized healthcare.
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Affiliation(s)
- Yiming Wang
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (Y.W.); (X.W.); (S.N.)
| | - Xiaoke Wang
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (Y.W.); (X.W.); (S.N.)
| | - Shijin Nie
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (Y.W.); (X.W.); (S.N.)
| | - Keyu Meng
- School of Electronic and Information Engineering, Changchun University, Changchun 130022, China;
| | - Zhiming Lin
- School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; (Y.W.); (X.W.); (S.N.)
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11
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Yao C, Sun T, Huang S, He M, Liang B, Shen Z, Huang X, Liu Z, Wang H, Liu F, Chen HJ, Xie X. Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring. ACS NANO 2023; 17:24242-24258. [PMID: 37983291 DOI: 10.1021/acsnano.3c09766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since the correlations of pulse waveforms to BP are highly individualized due to the diversity of the patients' physiological characteristics, wearable sensors based on universal designs and algorithms often fail to derive BP accurately when applied on individual patients. Herein, a wearable triboelectric pulse sensor based on a biomimetic nanopillar layer was developed and coupled with Personalized Machine Learning (ML) to provide accurate and continuous monitoring of BP. Flexible conductive nanopillars as the triboelectric layer were fabricated through soft lithography replication of a cicada wing, which could effectively enhance the sensor's output performance to detect weak signal characteristics of pulse waveform for BP derivation. The sensors were coupled with a personalized Partial Least-Squares Regression (PLSR) ML to derive unknown BP based on individual pulse characteristics with reasonable accuracy, avoiding the issue of individual variability that was encountered by General PLSR ML or formula algorithms. The cuffless and intelligent design endow this ML-sensor as a highly promising platform for the care and treatments of hypertensive patients.
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Affiliation(s)
- Chuanjie Yao
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Tiancheng Sun
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Shuang Huang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Mengyi He
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Baoming Liang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhiran Shen
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Xinshuo Huang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjie Liu
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - HaoLin Wang
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Fanmao Liu
- The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou 510080, China
| | - Hui-Jiuan Chen
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Xi Xie
- State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
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12
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Xu W, Liu F, Li Q, Li L, Liu X. Integrated Analysis of miRNA and mRNA Regulation Network in Hypertension. Biochem Genet 2023; 61:2566-2579. [PMID: 37165183 DOI: 10.1007/s10528-023-10389-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 04/18/2023] [Indexed: 05/12/2023]
Abstract
Hypertension is the most common chronic disease. Early diagnosis is helpful for early medical intervention. The miRNAs and the messenger RNAs (mRNAs) network may be valuable disease diagnosis markers. We aimed to explore the diagnostic value of the miRNA-mRNA network for hypertension patients. Data of miRNAs and mRNAs expression were obtained from the Gene Expression Omnibus database. The weighted gene co-expression network analysis was performed to screen hypertension-related gene modules, and these genes undergone functional enrichment analysis using "clusterProfiler" package. Differential expression analysis was applied on miRNAs expression profiles using "limma" package. TargetScanHuman and miRDB databases were used to select target mRNAs. Cytoscape software was used to visualize the miRNA-mRNA regulation network. P value < 0.05 was considered statistically significant after t test. There were 123 screened mRNAs which were enriched in 161 Gene ontology (GO) terms and 14 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Thirty-five differentially expressed miRNAs (DEMs) are found in the GSE75670. Totally 36 miRNA-mRNA pairs were obtained after the integrated analysis, and three mRNAs and the hsa-miRNA-5589-5p were identified as key joints. Hub genes, KIAA0513, ARID3A and LRPAP1, and key hsa-miRNA-5589-5p are potential diagnostic biomarkers for hypertension. Our findings are promising in the clinical application, conducive to early detection and prompt intervention of hypertension.
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Affiliation(s)
- Weijuan Xu
- Department of Geriatrics, Zibo Central Hospital, No 54 Gongqingtuan West Road, 255036, Zibo, People's Republic of China
| | - Feng Liu
- Department of Emergency, Zibo Central Hospital, No 54 Gongqingtuan West Road, 255036, Zibo, People's Republic of China
| | - Qinghua Li
- Department of Emergency, Zibo Central Hospital, No 54 Gongqingtuan West Road, 255036, Zibo, People's Republic of China
| | - Li Li
- Department of Geriatrics, Zibo Central Hospital, No 54 Gongqingtuan West Road, 255036, Zibo, People's Republic of China.
| | - Xiqiang Liu
- Department of Geriatrics, Zibo Central Hospital, No 54 Gongqingtuan West Road, 255036, Zibo, People's Republic of China
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13
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Liu Z, Li H, Li W, Zhuang D, Zhang F, Ouyang W, Wang S, Bertolaccini L, Alskaf E, Pan X. Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening. Quant Imaging Med Surg 2023; 13:8657-8668. [PMID: 38106309 PMCID: PMC10722034 DOI: 10.21037/qims-23-970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/27/2023] [Indexed: 12/19/2023]
Abstract
Background As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model. Methods The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms. Results A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994). Conclusions This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.
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Affiliation(s)
- Zeye Liu
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hang Li
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Wenchao Li
- Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Huazhong Fuwai Hospital, Pediatric Cardiac Surgery, Zhengzhou, China
| | - Donglin Zhuang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fengwen Zhang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Wenbin Ouyang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Shouzheng Wang
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Ebraham Alskaf
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, UK
| | - Xiangbin Pan
- Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, China
- Key Laboratory of Innovative Cardiovascular Devices, Chinese Academy of Medical Sciences, Beijing, China
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
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14
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Kario K, Tomitani N, Wang TD, Park S, Li Y, Shin J, Tsoi K, Chen CH, Cheng HM, Siddique S, Turana Y, Buranakitjaroen P, Van Huynh M, Nailes J, Sison J, Soenarta AA, Sogunuru GP, Sukonthasarn A, Tay JC, Teo BW, Verma N, Zhang Y, Schlaich M, Nagai M, Fujiwara T, Hoshide S, Chia YC, Wang JG. Home blood pressure-centered approach - from digital health to medical practice: HOPE Asia Network consensus statement 2023. Hypertens Res 2023; 46:2561-2574. [PMID: 37605071 DOI: 10.1038/s41440-023-01397-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/20/2023] [Accepted: 07/12/2023] [Indexed: 08/23/2023]
Abstract
Recent innovations in digital technology have enabled the simultaneous accumulation, and the linking and analysis of time-series big data relating to several factors that influence blood pressure (BP), including biological indicators, physical activity, and environmental information. Various approaches can be used to monitor BP: in the office/clinic; at home; 24-h ambulatory recording; or with wearable and cuffless devices. Of these, home BP monitoring is a reliable and convenient method, and is recommended for hypertension management by current national and international guidelines. This recommendation is based on evidence showing that home BP is an important predictor of cardiovascular, cerebrovascular and kidney disease in patients with hypertension. In addition, lifetime personalized health record (PHR)-based home BP with telemonitoring combined with co-interventions has been shown to lower BP more effectively than the traditional approach based on office BP. Thus, home BP represents a key metric for personalized anticipation medicine, from digital healthcare to digital medicine. This paper summarizes the latest evidence on home BP monitoring and proposes a Hypertension Cardiovascular Outcome Prevention and Evidence in Asia (HOPE Asia) Network consensus on a home BP-centered approach to the management of hypertension.
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Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.
| | - Naoko Tomitani
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Tzung-Dau Wang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- Division of Hospital Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Sungha Park
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, South Korea
| | - Yan Li
- Centre for Epidemiological Studies and Clinical Trials and Center for Vascular Evaluations, Shanghai Key Lab of Hypertension, Shanghai Institute of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jinho Shin
- Faculty of Cardiology Service, Hanyang University Medical Center, Seoul, South Korea
| | - Kelvin Tsoi
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Chen-Huan Chen
- Department of Internal Medicine, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
| | - Hao-Min Cheng
- Institute of Public Health and Community Medicine Research Center, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Ph.D. Program of Interdisciplinary Medicine (PIM), National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
- Division of Faculty Development, Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
| | | | - Yuda Turana
- Department of Neurology. School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia
| | - Peera Buranakitjaroen
- Division of Hypertension, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Minh Van Huynh
- Department of Internal Medicine, University of Medicine and Pharmacy, Hue University, Hue City, Vietnam
| | - Jennifer Nailes
- Department of Preventive and Community Medicine and Research Institute for Health Sciences, University of the East Ramon Magsaysay Memorial Medical Center Inc., Quezon City, Philippines
| | - Jorge Sison
- Section of Cardiology, Department of Medicine, Medical Center Manila, Manila, Philippines
| | - Arieska Ann Soenarta
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, University of Indonesia-National Cardiovascular Center, Harapan Kita, Jakarta, Indonesia
| | - Guru Prasad Sogunuru
- Fortis Hospitals, Chennai, Tamil Nadu, India
- College of Medical Sciences, Kathmandu University, Bharatpur, Nepal
| | - Apichard Sukonthasarn
- Cardiology Division, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Jam Chin Tay
- Department of General Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Boon Wee Teo
- Division of Nephrology Department of Medicine, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Narsingh Verma
- Department of Physiology, King George's Medical University, Lucknow, India
| | - Yuqing Zhang
- Divisions of Hypertension and Heart Failure, Fu Wai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Markus Schlaich
- Dobney Hypertension Centre, Medical School - Royal Perth Hospital Unit and Royal Perth Hospital Research Foundation, The University of Western Australia, Perth, WA, Australia
| | - Michiaki Nagai
- Department of Internal Medicine, General Medicine and Cardiology, Hiroshima City Asa Hospital, Hiroshima, Japan
| | - Takeshi Fujiwara
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Satoshi Hoshide
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Yook-Chin Chia
- Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Bandar Sunway, Selangor, Malaysia
- Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Ji-Guang Wang
- Department of Hypertension, Centre for Epidemiological Studies and Clinical Trials, the Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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15
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Kario K. Digital hypertension towards to the anticipation medicine. Hypertens Res 2023; 46:2503-2512. [PMID: 37612370 DOI: 10.1038/s41440-023-01409-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/25/2023]
Abstract
"Digital Hypertension" is a new research field proposed by the Japanese Society of Hypertension that integrates digital technology into hypertension management and proactively promotes research activities. This novel approach includes the development of new technologies for better BP management, such as sensors for detecting environmental factors that affect BP, information processing, and machine learning. To facilitate "Digital Hypertension," a more sophisticated BP monitoring system capable of measuring an individual's BP more frequently in various situations would be required. With the use of these technologies, hypertension management could shift from the current "dots" management based on office BP readings during clinic visits to a "line" management system based on seamless home BP or individual BP data taken by a wearable BP monitoring device. DTx is the innovation to change hypertension management from "dots" to "line", completely achieved by wearable BP.
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Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, School of Medicine, Jichi Medical University, Tochigi, Japan.
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16
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Qureshi FM, Golan R, Ghomeshi A, Ramasamy R. An Update on the Use of Wearable Devices in Men's Health. World J Mens Health 2023; 41:785-795. [PMID: 36792091 PMCID: PMC10523121 DOI: 10.5534/wjmh.220205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 02/01/2023] Open
Abstract
Men's health represents an often-overlooked aspect of public health. Men have higher mortality rates worldwide and are more negatively affected by chronic conditions such as obesity and heart disease, as well as addiction to alcohol and tobacco. Men also have health issues such as prostate cancer and male sexual dysfunction which only affect them. Because of the skewed burden of morbidity and mortality on men, it is imperative from a public health perspective to make a concerted effort to specifically improve men's health. The use of wearable devices in medical practice presents a novel avenue to invest in men's health in a safe, easily scalable, and economic fashion. Wearable devices are now ubiquitous in society, and their use in the healthcare setting is only increasing with time. There are commercially available devices such as smart watches which are available to lay people and healthcare professionals alike to improve overall health and wellness, and there are also purpose-built wearable devices which are used to track or treat a specific disease. In our review of the literature, we found that while research in the field of wearable devices is still in its early stages, there is ample evidence that wearable devices can greatly improve men's health in the long-term.
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Affiliation(s)
- Farhan M Qureshi
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
- Medical Scientist Training Program, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Roei Golan
- Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA
| | - Armin Ghomeshi
- Department of Urology, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Ranjith Ramasamy
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.
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17
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Hu JR, Park DY, Agarwal N, Herzig M, Ormseth G, Kaushik M, Giao DM, Turkson-Ocran RAN, Juraschek SP. The Promise and Illusion of Continuous, Cuffless Blood Pressure Monitoring. Curr Cardiol Rep 2023; 25:1139-1149. [PMID: 37688763 PMCID: PMC10842120 DOI: 10.1007/s11886-023-01932-4] [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] [Accepted: 07/31/2023] [Indexed: 09/11/2023]
Abstract
PURPOSE OF REVIEW Blood pressure (BP) fluctuations outside of clinic are increasingly recognized for their role in the development of cardiovascular disease, syncope, and premature death and as a promising target for tailored hypertension treatment. However, current cuff-based BP devices, including home and ambulatory devices, are unable to capture the breadth of BP variability across human activities, experiences, and contexts. RECENT FINDINGS Cuffless, wearable BP devices offer the promise of beat-to-beat, continuous, noninvasive measurement of BP during both awake and sleep periods with minimal patient inconvenience. Importantly, cuffless BP devices can characterize BP variability, allowing for the identification of patient-specific triggers of BP surges in the home environment. Unfortunately, the pace of evidence, regulation, and validation testing has lagged behind the pace of innovation and direct consumer marketing. We provide an overview of the available technologies and devices for cuffless BP monitoring, considerations for the calibration and validation of these devices, and the promise and pitfalls of the cuffless BP paradigm.
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Affiliation(s)
- Jiun-Ruey Hu
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Dae Yong Park
- Department of Medicine, Cook County Health, Chicago, IL, USA
| | - Nikita Agarwal
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Matthew Herzig
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - George Ormseth
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Milan Kaushik
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Ruth-Alma N Turkson-Ocran
- Section for Research, Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Stephen P Juraschek
- Section for Research, Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- , 330 Brookline Avenue, CO-1309, #204, MA, 02215, USA.
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18
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Kario K. Evidence for the surge blood pressure resonance hypothesis as a trigger for cardiovascular disease events. Hypertens Res 2023; 46:2065-2069. [PMID: 37322131 DOI: 10.1038/s41440-023-01346-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.
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19
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Juraschek SP, Turkson-Ocran RAN, Cluett JL. Peak home blood pressure: new heights for SMBP. Hypertens Res 2023; 46:2124-2125. [PMID: 37296311 PMCID: PMC10250172 DOI: 10.1038/s41440-023-01345-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Affiliation(s)
- Stephen P Juraschek
- General Medicine-Research, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Ruth-Alma N Turkson-Ocran
- General Medicine-Research, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jennifer L Cluett
- General Medicine-Research, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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20
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Kario K, Tomitani N, Fujiwara T, Okawara Y, Kanegae H, Hoshide S. Peak home blood pressure as an earlier and strong novel risk factor for stroke: the practitioner-based nationwide J-HOP study extended. Hypertens Res 2023; 46:2113-2123. [PMID: 37076610 PMCID: PMC10113967 DOI: 10.1038/s41440-023-01297-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 04/21/2023]
Abstract
While home blood pressure (BP) measurement is recommended for hypertension management, the clinical implications of peak home BP values have not been well studied. This study investigated the association between pathological threshold or frequency of peak home BP and cardiovascular events in patients with ≥1 cardiovascular risk factor. The Japan Morning Surge-Home Blood Pressure (J-HOP) study enrolled participants from 2005-2012 with extended follow-up from December 2017 to May 2018, which generated the dataset for this analysis. Average peak home systolic BP (SBP) was defined as average of the highest three BP values on 14-day measurement period. Patients were divided into quintiles of peak home BP, and the risk of stroke, coronary artery disease (CAD) and atherosclerotic cardiovascular disease (ASCVD; stroke+CAD) was determined. In 4231 patients (mean 65 years) followed for 6.2 years there were 94 strokes and 124 CAD events. The adjusted hazard ratio (HR) (95% CI) for the risk of stroke and ASCVD in patients with average peak home SBP in the highest versus lowest quintile was 4.39 (1.85-10.43) and 2.04 (1.24-3.36), respectively. Risk was greatest for stroke in the first 5 years: HR 22.66 (2.98-172.1). The pathological threshold of average peak home SBP for 5-year stroke risk was 176 mmHg. There was a linear association between the number of times peak home SBP > 175 mmHg and stroke risk. Peak home BP was a strong risk factor for stroke, especially within the first 5 years. We propose exaggerated peak home SBP > 175 mmHg as an early and strong novel risk factor for stroke.
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Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.
| | - Naoko Tomitani
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Takeshi Fujiwara
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Yukie Okawara
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | | | - Satoshi Hoshide
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
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21
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Minuz P, Albini FL, Imbalzano E, Izzo R, Masi S, Pengo MF, Pucci G, Scalise F, Salvetti M, Tocci G, Cicero A, Iaccarino G, Savoia C, Sechi L, Parati G, Borghi C, Volpe M, Ferri C, Grassi G, Muiesan ML. Telemedicine and Digital Medicine in the Clinical Management of Hypertension and Hypertension-Related Cardiovascular Diseases: A Position Paper of the Italian Society of Arterial Hypertension (SIIA). High Blood Press Cardiovasc Prev 2023; 30:387-399. [PMID: 37594686 PMCID: PMC10600275 DOI: 10.1007/s40292-023-00595-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 07/25/2023] [Indexed: 08/19/2023] Open
Abstract
High blood pressure is the leading cause of death and disability globally and an important treatable risk factor for cardiovascular, cerebrovascular and chronic kidney diseases. Digital technology, including mobile health solutions and digital therapy, is expanding rapidly in clinical medicine and has the potential to improve the quality of care and effectiveness of drug treatment by making medical interventions timely, tailored to hypertensive patients' needs and by improving treatment adherence. Thus, the systematic application of digital technologies could support diagnosis and awareness of hypertension and its complications, ultimately leading to improved BP control at the population level. The progressive implementation of digital medicine in the national health systems must be accompanied by the supervision and guidance of health authorities and scientific societies to ensure the correct use of these new technologies with consequent maximization of the potential benefits. The role of scientific societies in relation to the rapid adoption of digital technologies, therefore, should encompass the entire spectrum of activities pertaining to their institutional role: information, training, promotion of research, scientific collaboration and advice, evaluation and validation of technological tools, and collaboration with regulatory and health authorities.
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Affiliation(s)
- Pietro Minuz
- Department of Medicine, University of Verona, Medicina Generale C, Policlinico GB Rossi, Piazzale LA Scuro 10, 37134, Verona, Italy.
| | | | - Egidio Imbalzano
- Hypertension Unit, Division of Internal Medicine, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Raffaele Izzo
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Stefano Masi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- Institute of Cardiovascular Science, University College London, London, UK
| | - Martino F Pengo
- Department of Cardiovascular, Neural and Metabolic Sciences, IRCCS Istituto Auxologico Italiano, Ospedale San Luca, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Giacomo Pucci
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
- Unit of Internal Medicine, "Santa Maria" Terni Hospital, Terni, Italy
| | - Filippo Scalise
- Center for the Study of Hypertension and Vascular Diseases-Clinical Institute Verano Brianza, Policlinico di Monza, Monza, Italy
| | - Massimo Salvetti
- Department of Clinical and Experimental Sciences, University of Brescia and Emergency Medicine ASST Spedali Civili di Brescia, Brescia, Italy
| | - Giuliano Tocci
- Division of Cardiology, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Azienda Ospedaliero-Universitaria Sant'Andrea, Rome, Italy
| | - Arrigo Cicero
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Guido Iaccarino
- Center for Research on Hypertension and Related Conditions, Federico II University of Naples, Naples, Italy
| | - Carmine Savoia
- Clinical and Molecular Medicine, University of Rome Sapienza and IRCCS San Raffaele Roma, Rome, Italy
| | - Leonardo Sechi
- Department of Life, Health, and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Gianfranco Parati
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- IRCCS, Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Massimo Volpe
- Clinical and Molecular Medicine, University of Rome Sapienza and IRCCS San Raffaele Roma, Rome, Italy
| | - Claudio Ferri
- Department of Life, Health, and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Guido Grassi
- Clinica Medica, University of Milano-Bicocca, Milan, Italy
| | - Maria Lorenza Muiesan
- Department of Clinical and Experimental Sciences, University of Brescia and Emergency Medicine ASST Spedali Civili di Brescia, Brescia, Italy
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22
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Li S, Wang H, Ma W, Qiu L, Xia K, Zhang Y, Lu H, Zhu M, Liang X, Wu XE, Liang H, Zhang Y. Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array. SCIENCE ADVANCES 2023; 9:eadh0615. [PMID: 37566652 PMCID: PMC10421034 DOI: 10.1126/sciadv.adh0615] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/11/2023] [Indexed: 08/13/2023]
Abstract
Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.
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Affiliation(s)
- Shuo Li
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Haomin Wang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Wei Ma
- Department of Cardiovascular Disease, Peking University First Hospital, Beijing 100084, PR China
| | - Lin Qiu
- Department of Cardiovascular Disease, Peking University First Hospital, Beijing 100084, PR China
| | - Kailun Xia
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Yong Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Haojie Lu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Mengjia Zhu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Xiaoping Liang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Xun-En Wu
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Huarun Liang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Yingying Zhang
- Key Laboratory of Organic Optoelectronics and Molecular Engineering of the Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
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23
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Petek BJ, Al-Alusi MA, Moulson N, Grant AJ, Besson C, Guseh JS, Wasfy MM, Gremeaux V, Churchill TW, Baggish AL. Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 82:245-264. [PMID: 37438010 PMCID: PMC10662962 DOI: 10.1016/j.jacc.2023.04.054] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 07/14/2023]
Abstract
The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.
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Affiliation(s)
- Bradley J Petek
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Mostafa A Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nathaniel Moulson
- Division of Cardiology and Sports Cardiology BC, University of British Columbia, Vancouver, British Columbia, Canada
| | - Aubrey J Grant
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Cyril Besson
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - J Sawalla Guseh
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Meagan M Wasfy
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vincent Gremeaux
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - Timothy W Churchill
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aaron L Baggish
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland.
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24
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Zhuo E, Wang Z, Chen X, Zou J, Fang Y, Zhuo J, Li Y, Zhang J, Gong Z. Wearable Smart Fabric Based on Hybrid E-Fiber Sensor for Real-Time Finger Motion Detection. Polymers (Basel) 2023; 15:2934. [PMID: 37447578 DOI: 10.3390/polym15132934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023] Open
Abstract
Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection.
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Affiliation(s)
- Erhan Zhuo
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Ziwen Wang
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Xiaochen Chen
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Junhao Zou
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Yuan Fang
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Jiekai Zhuo
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Yicheng Li
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
| | - Jun Zhang
- Laboratory for Artificial Intelligence in Design, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Zidan Gong
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
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25
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Minen MT, George A, Katara A, Lebowitz N, Snyder IC, Busis NA, Lipchitz JM. Headache clinicians' perspectives on the remote monitoring of patients' electronic diary data: A qualitative study. Headache 2023; 63:917-925. [PMID: 37313636 PMCID: PMC11238271 DOI: 10.1111/head.14519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVE We assessed headache clinicians' viewpoints on potential remote access to patients' digital headache diary data and the practicalities of data utilization. BACKGROUND With the ubiquitous nature of electronic medical records and the existence of remote monitoring (RM) for many medical conditions, there is now the potential for remote symptom monitoring for patients with headache disorders. While patients are asked to utilize headache diaries, clinicians may or may not have access to the data before patient visits, and their perspectives regarding this emerging technology are currently unknown. METHODS After recruiting participants from the National Institutes of Health Pain Consortium Network, the American Headache Society Special Interest Section listservs, and Twitter and Facebook social media platforms, we conducted 20 semi-structured qualitative interviews of headache providers across the United States from various types of institutions and asked them their perspectives on remote access to patient headache diary data. We transcribed the interviews, which were then coded by two independent coders. Themes and sub-themes were developed using inductive content analysis. RESULTS All clinicians felt the RM data needed to be integrated into the electronic medical record. Six themes emerged from the interviews: (i) Clinician perspectives on how RM could be beneficial but at other times could create obstacles/challenges, (ii) operationally, data integration could benefit headache care, (iii) there should be initial logistical considerations for bringing RM into clinical care, (iv) education may need to be provided to both patients and clinicians, (v) there are likely research benefits associated with RM, and (vi) additional suggestions for considering potential integration of RM into practice. CONCLUSIONS While headache clinicians had mixed opinions on the benefits/challenges that RM presents to patient care, patient satisfaction, and visit time, new ideas emerged that may help advance the field.
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Affiliation(s)
- Mia T Minen
- Department of Neurology, NYU Langone Health, New York, New York, USA
| | - Alexis George
- Department of Neurology, NYU Langone Health, New York, New York, USA
| | - Aarti Katara
- Barnard College of Columbia University, New York, New York, USA
| | - Naomi Lebowitz
- Barnard College of Columbia University, New York, New York, USA
| | | | - Neil A Busis
- Department of Neurology, NYU Langone Health, New York, New York, USA
| | - Jessica M Lipchitz
- Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
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26
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Shiwani MA, Chico TJA, Ciravegna F, Mihaylova L. Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies. SENSORS (BASEL, SWITZERLAND) 2023; 23:5752. [PMID: 37420916 DOI: 10.3390/s23125752] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.
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Affiliation(s)
- Muhammad Ali Shiwani
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
| | - Timothy J A Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Sheffield S10 2RX, UK
| | - Fabio Ciravegna
- Dipartimento di Informatica, Università di Torino, 10124 Turin, Italy
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
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27
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Sel K, Mohammadi A, Pettigrew RI, Jafari R. Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation. NPJ Digit Med 2023; 6:110. [PMID: 37296218 DOI: 10.1038/s41746-023-00853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Amirmohammad Mohammadi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA.
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28
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Volckens J, Haynes EN, Croisant SP, Cui Y, Errett NA, Henry HF, Horney JA, Kwok RK, Magzamen S, Rappold AG, Ravichandran L, Reinlib L, Ryan PH, Shaughnessy DT. Health Research in the Wake of Disasters: Challenges and Opportunities for Sensor Science. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:65002. [PMID: 37389972 PMCID: PMC10312369 DOI: 10.1289/ehp12270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/24/2023] [Accepted: 06/15/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Disaster events adversely affect the health of millions of individuals each year. They create exposure to physical, chemical, biological, and psychosocial hazards while simultaneously exploiting community and individual-level vulnerabilities that allow such exposures to exert harm. Since 2013, the National Institute of Environmental Health Sciences (NIEHS) has led the development of the Disaster Research Response (DR2) program and infrastructure; however, research exploring the nature and effects of disasters on human health is lacking. One reason for this research gap is the challenge of developing and deploying cost-effective sensors for exposure assessment during disaster events. OBJECTIVES The objective of this commentary is to synergize the consensus findings and recommendations from a panel of experts on sensor science in support of DR2. METHODS The NIEHS convened the workshop, "Getting Smart about Sensors for Disaster Response Research" on 28 and 29 July 2021 to discuss current gaps and recommendations for moving the field forward. The workshop invited full discussion from multiple viewpoints, with the goal of identifying recommendations and opportunities for further development of this area of research. The panel of experts included leaders in engineering, epidemiology, social and physical sciences, and community engagement, many of whom had firsthand experience with DR2. DISCUSSION The primary finding of this workshop is that exposure science in support of DR2 is severely lacking. We highlight unique barriers to DR2, such as the need for time-sensitive exposure data, the chaos and logistical challenges that ensue from a disaster event, and the lack of a robust market for sensor technologies in support of environmental health science. We highlight a need for sensor technologies that are more scalable, reliable, and versatile than those currently available to the research community. We also recommend that the environmental health community renew efforts in support of DR2 facilitation, collaboration, and preparedness. https://doi.org/10.1289/EHP12270.
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Affiliation(s)
- John Volckens
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Erin N. Haynes
- Department of Epidemiology and Environmental Health, University of Kentucky, Lexington, Kentucky, USA
| | - Sharon P. Croisant
- Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, Texas, USA
| | - Yuxia Cui
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Nicole A. Errett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Heather F. Henry
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | | | - Richard K. Kwok
- National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Ana G. Rappold
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Chapel Hill, North Carolina, USA
| | - Lingamanaidu Ravichandran
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Les Reinlib
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Patrick H. Ryan
- Department of Environmental Health, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Daniel T. Shaughnessy
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
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29
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Kario K, Hoshide S, Mogi M. Topics 2023 in Hypertension Research leading to guidelines in Asia. Hypertens Res 2023; 46:1357-1362. [PMID: 37271784 DOI: 10.1038/s41440-023-01285-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 06/06/2023]
Affiliation(s)
- Kazuomi Kario
- Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.
| | - Satoshi Hoshide
- Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Masaki Mogi
- Department of Pharmacology, Ehime University Graduate School of Medicine, Ehime, Japan
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30
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Sheikh AB, Sobotka PA, Garg I, Dunn JP, Minhas AMK, Shandhi MMH, Molinger J, McDonnell BJ, Fudim M. Blood Pressure Variability in Clinical Practice: Past, Present and the Future. J Am Heart Assoc 2023; 12:e029297. [PMID: 37119077 PMCID: PMC10227216 DOI: 10.1161/jaha.122.029297] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Recent advances in wearable technology through convenient and cuffless systems will enable continuous, noninvasive monitoring of blood pressure (BP), heart rate, and heart rhythm on both longitudinal 24-hour measurement scales and high-frequency beat-to-beat BP variability and synchronous heart rate variability and changes in underlying heart rhythm. Clinically, BP variability is classified into 4 main types on the basis of the duration of monitoring time: very-short-term (beat to beat), short-term (within 24 hours), medium-term (within days), and long-term (over months and years). BP variability is a strong risk factor for cardiovascular diseases, chronic kidney disease, cognitive decline, and mental illness. The diagnostic and therapeutic value of measuring and controlling BP variability may offer critical targets in addition to lowering mean BP in hypertensive populations.
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Affiliation(s)
- Abu Baker Sheikh
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Paul A. Sobotka
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
| | - Ishan Garg
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Jessilyn P. Dunn
- Department of Biomedical EngineeringDuke UniversityDurhamNCUSA
- Department of Biostatistics & BioinformaticsDuke UniversityDurhamNCUSA
| | | | | | | | - Barry J. McDonnell
- Department of Biomedical ResearchCardiff Metropolitan UniversitySchool of Sport and Health SciencesCardiffUnited Kingdom
| | - Marat Fudim
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
- Duke Clinical Research InstituteDurhamNCUSA
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31
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Avolio A. The reality and serendipity of cuffless blood pressure monitoring. Hypertens Res 2023:10.1038/s41440-023-01269-z. [PMID: 37016027 DOI: 10.1038/s41440-023-01269-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 03/12/2023] [Indexed: 04/06/2023]
Affiliation(s)
- Alberto Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
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32
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Lu Y, Linderman GC, Mahajan S, Liu Y, Huang C, Khera R, Mortazavi BJ, Spatz ES, Krumholz HM. Quantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study. Circ Cardiovasc Qual Outcomes 2023; 16:e009258. [PMID: 36883456 DOI: 10.1161/circoutcomes.122.009258] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 01/09/2023] [Indexed: 03/09/2023]
Abstract
BACKGROUND Visit-to-visit variability (VVV) in blood pressure values has been reported in clinical studies. However, little is known about VVV in clinical practice and whether it is associated with patient characteristics in real-world setting. METHODS We conducted a retrospective cohort study to quantify VVV in systolic blood pressure (SBP) values in a real-world setting. We included adults (age ≥18 years) with at least 2 outpatient visits between January 1, 2014 and October 31, 2018 from Yale New Haven Health System. Patient-level measures of VVV included SD and coefficient of variation of a given patient's SBP across visits. We calculated patient-level VVV overall and by patient subgroups. We further developed a multilevel regression model to assess the extent to which VVV in SBP was explained by patient characteristics. RESULTS The study population included 537 218 adults, with a total of 7 721 864 SBP measurements. The mean age was 53.4 (SD 19.0) years, 60.4% were women, 69.4% were non-Hispanic White, and 18.1% were on antihypertensive medications. Patients had a mean body mass index of 28.4 (5.9) kg/m2 and 22.6%, 8.0%, 9.7%, and 5.6% had a history of hypertension, diabetes, hyperlipidemia, and coronary artery disease, respectively. The mean number of visits per patient was 13.3, over an average period of 2.4 years. The mean (SD) intraindividual SD and coefficient of variation of SBP across visits were 10.6 (5.1) mm Hg and 0.08 (0.04). These measures of blood pressure variation were consistent across patient subgroups defined by demographic characteristics and medical history. In the multivariable linear regression model, only 4% of the variance in absolute standardized difference was attributable to patient characteristics. CONCLUSIONS The VVV in real-world practice poses challenges for management of patients with hypertension based on blood pressure readings in outpatient settings and suggest the need to go beyond episodic clinic evaluation.
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Affiliation(s)
- Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, CT (Y.L., S.M., R.K., E.S.S., H.M.K.)
| | - George C Linderman
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Department of Applied Mathematics, Yale University, New Haven, CT (G.C.L.)
| | - Shiwani Mahajan
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, CT (Y.L., S.M., R.K., E.S.S., H.M.K.)
| | - Yuntian Liu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
| | - Chenxi Huang
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
| | - Rohan Khera
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, CT (Y.L., S.M., R.K., E.S.S., H.M.K.)
| | - Bobak J Mortazavi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX (B.M.)
- Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX (B.M.)
| | - Erica S Spatz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, CT (Y.L., S.M., R.K., E.S.S., H.M.K.)
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, CT (Y.L., G.C.L., S.M., Y.L., C.H., R.K., B.M., E.S.S., H.M.K.)
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, CT (Y.L., S.M., R.K., E.S.S., H.M.K.)
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (H.M.K.)
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Nakagami H, Otsuka H, Akiyama H. Monitoring with wearable devices will clarify the association between indoor temperature and blood pressure. Hypertens Res 2023:10.1038/s41440-023-01261-7. [PMID: 36977899 DOI: 10.1038/s41440-023-01261-7] [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: 01/24/2023] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/30/2023]
Abstract
The association of blood pressure and temperature is well known in seasonal observation, and low temperature in the winter season is often considered a cause of high blood pressure. The current evidence for short-term studies of temperature and blood pressure is based on the daily observation, however continuous monitoring with wearable devices will allow us to evaluate the rapid effect of cold temperature exposure on blood pressure. In a Japanese, prospective intervention study from 2014 to 2019 (the Smart Wellness Housing survey), approximately 90% of Japanese lived in cold houses (indoor temperature less than 18 °C). Importantly, the indoor temperature was associated with the increase of morning systolic blood pressure. We recently addressed the sympathetic nervous activation of individuals in both their houses and a highly insulated and airtight model house in the winter season using portable electrocardiography equipment. A few subjects showed a morning surge in sympathetic activity, which was more intense at their cold houses, which suggests the importance of the indoor environment in the management of early morning hypertension. In near future, real-time monitoring with wearable devices will provide important information for a better life-environment, leading to risk reduction of morning surge and cardiovascular events.
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Affiliation(s)
- Hironori Nakagami
- Department of Health Development and Medicine, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, Japan.
| | - Hiroki Otsuka
- Asahi Kasei Construction Materials Corporation, 1-105 Kanda Jimbocho, Chiyoda Ward, Tokyo, Japan
| | - Hitoshi Akiyama
- Asahi Kasei Construction Materials Corporation, 1-105 Kanda Jimbocho, Chiyoda Ward, Tokyo, Japan
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34
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Economic evaluation of a new blood pressure target for hypertensive patients in Taiwan. Hypertens Res 2023; 46:784-786. [PMID: 36604476 DOI: 10.1038/s41440-022-01125-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/08/2022] [Indexed: 01/07/2023]
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35
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Han M, Lee YR, Park T, Ihm SH, Pyun WB, Burkard T, Cho MC, Camafort M, Yang E, Stergiou GS, Lee HY, Seo JM. Feasibility and measurement stability of smartwatch-based cuffless blood pressure monitoring: A real-world prospective observational study. Hypertens Res 2023; 46:922-931. [PMID: 36781979 DOI: 10.1038/s41440-023-01215-z] [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: 11/05/2022] [Revised: 01/04/2023] [Accepted: 01/20/2023] [Indexed: 02/15/2023]
Abstract
Cuffless wearable devices are currently being developed for long-term monitoring of blood pressure (BP) in patients with hypertension and in apparently healthy people. This study evaluated the feasibility and measurement stability of smartwatch-based cuffless BP monitoring in real-world conditions. Users of the first smartwatch-based cuffless BP monitor approved in Korea (Samsung Galaxy Watch) were invited to upload their data from using the device for 4 weeks post calibration. A total of 760 participants (mean age 43.7 ± 11.9, 80.3% men) provided 35,797 BP readings (average monitoring 22 ± 4 days [SD]; average readings 47 ± 42 per participant [median 36]). Each participant obtained 1.5 ± 1.3 readings/day and 19.7% of the participants obtained measurements every day. BP showed considerable variability, mainly depending on the day and time of the measurement. There was a trend towards higher BP levels on Mondays than on other days of the week and on workdays than in weekends. BP readings taken between 00:00 and 04:00 tended to be the lowest, whereas those between 12:00 and 16:00 the highest. The average pre-post calibration error for systolic BP (difference in 7-day BP before and after calibration), was 6.8 ± 5.6 mmHg, and was increased with higher systolic BP levels before calibration. Smartwatch-based cuffless BP monitoring is feasible for out-of-office monitoring in the real-world setting. The stability of BP measurement post calibration and the standardization and optimal time interval for recalibration need further investigation.
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Affiliation(s)
- Minju Han
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea.,Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Young-Ro Lee
- Department of Electrical and Computer Engineering, College of Engineering, Seoul National University, Seoul, South Korea
| | - Taeyoung Park
- Department of Applied Statistics and Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Sang-Hyun Ihm
- Division of Cardiology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Wook Bum Pyun
- Division of Cardiology, Department of Internal Medicine, Ewha Woman's University Seoul Hospital, Seoul, South Korea
| | - Thilo Burkard
- Medical Outpatient and Hypertension Clinic, ESH Hypertension Centre of Excellence, University Hospital Basel, Basel, Switzerland
| | - Myeong-Chan Cho
- Department of Internal Medicine, College of Medicine, Chungbuk National University, Cheongju, Korea
| | - Miguel Camafort
- ESH Excellence Hypertension Center, Department of Internal Medicine, Geriatrics Section, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Eugene Yang
- Division of Cardiology, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - George S Stergiou
- Hypertension Center STRIDE-7, National and Kapodistrian University of Athens, School of Medicine, Third Department of Medicine, Sotiria Hospital, Athens, Greece
| | - Hae Young Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea. .,Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.
| | - Jong-Mo Seo
- Department of Electrical and Computer Engineering, College of Engineering, Seoul National University, Seoul, South Korea
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36
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Short- to long-term blood pressure variability: Current evidence and new evaluations. Hypertens Res 2023; 46:950-958. [PMID: 36759660 DOI: 10.1038/s41440-023-01199-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/05/2023] [Accepted: 01/19/2023] [Indexed: 02/11/2023]
Abstract
Increased blood pressure (BP) variability and the BP surge have been reported to be associated with increased cardiovascular risk independently of BP levels and can also be a trigger of cardiovascular events. There are multiple types of BP variation: beat-to-beat variations related to breathing and the autonomic nervous system, diurnal BP variation and nocturnal dipping related to sleep and physical activity over a 24-hr period, day-to-day BP variability with anomalous readings within a several-day period, visit-to-visit BP variability between outpatient visits, and seasonal variations. BP variability is also associated with the progression to hypertension from prehypertension and the progression of chronic kidney disease and cognitive impairments. Our research group proposed the "resonance hypothesis of blood pressure surge" as a new etiological hypothesis of BP variability and surges; i.e., the concept that when the time phases of surges and hypertension-inducing environmental influences coincide, resonance occurs and is amplified into a larger "dynamic surge" that triggers the onset of cardiovascular disease. New devices to assess BP variability as well as new therapeutic interventions to reduce BP variability are being developed. Although there are still issues to be addressed (including measurement accuracy), cuffless devices and information and communication technology (ICT)-based BP monitoring devices have been developed and validated. These new devices will be useful for the individualized optimal management of BP. However, evidence regarding the usefulness of therapeutic interventions to control BP variability is still lacking.
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37
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Brignole M, Rivasi G, Fedorowski A, Ståhlberg M, Groppelli A, Ungar A. Tests for the identification of reflex syncope mechanism. Expert Rev Med Devices 2023; 20:109-119. [PMID: 36814102 DOI: 10.1080/17434440.2023.2174428] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
INTRODUCTION Treatment efficacy of reflex syncope is mainly related to the mechanism underlying syncope rather than its etiology or clinical presentation. The predominant mechanism underlying reflex syncope can be assigned to hypotensive or to bradycardic phenotypes. AREAS COVERED Methodology and diagnostic criteria of the most useful tests for the identification of hypotensive and bradycardic phenotypes are discussed. Diagnostic tests for the hypotensive phenotype include office blood pressure measurement with active standing test, home, and wearable blood pressure monitoring, 24-h ambulatory blood pressure monitoring and tilt table test. Diagnostic tests for the bradycardic phenotype include carotid sinus massage, tilt table test and prolonged ECG monitoring. EXPERT OPINION In reflex syncope, the documentation of bradycardia/asystole during a syncopal episode does not rule out the possibility that a preceding or parallel hypotensive reflex plays an important role. Similarly, even when a hypotensive mechanism is established, the possibility of an associated cardioinhibitory reflex should be investigated. Investigating the mechanism of reflex syncope is mandatory in patients with severe recurrent episodes, with the final aim to develop a personalized treatment strategy. Recent trials have demonstrated the benefits of personalized mechanism-based therapy, thus highlighting the importance of a comprehensive assessment of the mechanisms underlying syncope.
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Affiliation(s)
- Michele Brignole
- Department of Cardiology, S. Luca Hospital, IRCCS, Istituto Auxologico Italiano, Milan, Italy
| | - Giulia Rivasi
- Division of Geriatric and Intensive Care Medicine, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Artur Fedorowski
- Department of Cardiology, Karolinska University Hospital, and Department of Medicine, Karolinska Institute, Sweden
| | - Marcus Ståhlberg
- Department of Cardiology, Karolinska University Hospital, and Department of Medicine, Karolinska Institute, Sweden
| | - Antonella Groppelli
- Department of Cardiology, S. Luca Hospital, IRCCS, Istituto Auxologico Italiano, Milan, Italy
| | - Andrea Ungar
- Division of Geriatric and Intensive Care Medicine, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy
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Li Y, Jiang Y, Tang Y. Is remote blood pressure monitoring and management a better approach for patients with hypertension? A narrative review. J Clin Hypertens (Greenwich) 2023; 25:121-126. [PMID: 36602447 PMCID: PMC9903197 DOI: 10.1111/jch.14624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/12/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023]
Abstract
Hypertension is a global public health problem which affects the physical and mental health of individuals. The management of hypertension is a long-term process that requires the cooperation of both doctors and patients, and the blood pressure variability is closely related to the clinical prognosis. In recent years, the development of telemedicine has promoted better blood pressure monitoring and management for patients, as well as better medical intervention and health education for patients by medical staff. This article provides a review of remote blood pressure monitoring and management.
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Affiliation(s)
- Yan Li
- Department of General MedicineChongqing University Central HospitalChongqing Emergency Medical CenterChongqingChina
| | - Yi Jiang
- Department of General MedicineChongqing University Central HospitalChongqing Emergency Medical CenterChongqingChina
| | - Yuping Tang
- Department of General MedicineChongqing University Central HospitalChongqing Emergency Medical CenterChongqingChina
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39
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Hu JR, Martin G, Iyengar S, Kovell LC, Plante TB, Helmond NV, Dart RA, Brady TM, Turkson-Ocran RAN, Juraschek SP. Validating cuffless continuous blood pressure monitoring devices. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2023; 4:9-20. [PMID: 36865583 PMCID: PMC9971997 DOI: 10.1016/j.cvdhj.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the promise of continuous beat-to-beat measurement of BP. These devices take advantage of a variety of principles to determine BP, including (1) pulse arrival time, (2) pulse transit time, (3) pulse wave analysis, (4) volume clamping, and (5) applanation tonometry. Because BP is calculated indirectly, these devices require calibration with cuff-based devices at regular intervals. Unfortunately, the pace of regulation of these devices has failed to match the speed of innovation and direct availability to patient consumers. There is an urgent need to develop a consensus on standards by which cuffless BP devices can be tested for accuracy. In this narrative review, we describe the landscape of cuffless BP devices, summarize the current status of validation protocols, and provide recommendations for an ideal validation process for these devices.
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Affiliation(s)
- Jiun-Ruey Hu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut
| | - Gabrielle Martin
- University of Massachusetts Chan School of Medicine, Worcester, Massachusetts
| | - Sanjna Iyengar
- University of Massachusetts Chan School of Medicine, Worcester, Massachusetts
| | - Lara C. Kovell
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan School of Medicine, Worcester, Massachusetts
| | - Timothy B. Plante
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont
| | - Noud van Helmond
- Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota
- Department of Anesthesiology, Cooper University Hospital, Camden, New Jersey
| | - Richard A. Dart
- Center for Precision Medicine, Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin
| | - Tammy M. Brady
- Department of Pediatrics, Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Stephen P. Juraschek
- Department of Medicine, Beth Israel Lahey Clinic/Harvard Medical School, Boston, Massachusetts
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40
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Wan B, Hu Z, Garg H, Cheng Y, Han M. An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets. COMPLEX INTELL SYST 2023; 9:1-34. [PMID: 36694862 PMCID: PMC9853511 DOI: 10.1007/s40747-022-00953-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 12/08/2022] [Indexed: 01/21/2023]
Abstract
It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.
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Affiliation(s)
- Benting Wan
- Shenzhen Research Institute, Jiangxi University of Finance and Economics, Shenzhen, 518000 China
- School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013 China
| | - Zhaopeng Hu
- Shenzhen Research Institute, Jiangxi University of Finance and Economics, Shenzhen, 518000 China
- School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013 China
| | - Harish Garg
- School of Mathematics, Thapar Institute of Engineering and Technology (Deemed University), Patiala, Punjab 147004 India
- Department of Mathematics, Graphic Era Deemed to Be University, Dehradun, Uttarakhand 248002 India
- Applied Science Research Center, Applied Science Private University, Amman, 11931 Jordan
| | - Youyu Cheng
- Shenzhen Research Institute, Jiangxi University of Finance and Economics, Shenzhen, 518000 China
| | - Mengjie Han
- School of Information and Engineering, Dalarna University, 79188 Falun, Sweden
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Sel K, Mohammadi A, Pettigrew RI, Jafari R. Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure. RESEARCH SQUARE 2023:rs.3.rs-2423200. [PMID: 36711741 PMCID: PMC9882661 DOI: 10.21203/rs.3.rs-2423200/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need to be trained with a significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would reduce reliance on ground truth information. We achieve this by building Taylor's approximation for the gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series regression models tested on the same datasets, we retain a high correlation (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Amirmohammad Mohammadi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA,Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA,School of Engineering Medicine, Texas A&M University, Houston, TX, USA,Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA,corresponding author:
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Blood-Pressure-Monitoring Smartphone Applications: Ushering in a New Era of Wearable Cardiac Devices? Comment on Vischer et al. Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements-A Pilot Study. Diagnostics 2022, 12, 749. Diagnostics (Basel) 2023; 13:diagnostics13020290. [PMID: 36673099 PMCID: PMC9858022 DOI: 10.3390/diagnostics13020290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
Systemic arterial hypertension in adults is generally defined as a systolic blood pressure (SBP) of >140 mmHg and/or a diastolic blood pressure (DBP) of >90 mmHg [...]
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Logan AG. Clinical Value of Ambulatory Blood Pressure Monitoring in CKD. Am J Kidney Dis 2023; 81:10-12. [PMID: 36153151 DOI: 10.1053/j.ajkd.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Alexander G Logan
- Department of Medicine, Mount Sinai Hospital, Division of Nephrology, University Health Network, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Institute of Health Policy, Management and Evaluation, University of Toronto; and Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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Long-term stability of over-the-counter cuffless blood pressure monitors: a proposal. HEALTH AND TECHNOLOGY 2023; 13:53-63. [PMID: 36713070 PMCID: PMC9870659 DOI: 10.1007/s12553-023-00726-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 10/17/2022] [Accepted: 01/04/2023] [Indexed: 01/25/2023]
Abstract
Blood pressure is an important cardiovascular parameter. Currently, the cuff-based sphygmomanometer is a popular, reliable, measurement method, but blood pressure monitors without cuffs have become popular and are now available without a prescription. Blood pressure monitors must be approved by regulatory authorities. Current cuffless blood pressure (CL-BP) monitors are not suitable for at-home management and prevention of hypertension. This paper proposes simple criteria for over-the-counter CL-BP monitoring. First, the history of the sphygmomanometer and current standard blood pressure protocol are reviewed. The main components of CL-BP monitoring are accuracy during the resting condition, accuracy during dynamic blood pressure changes, and long-term stability. In this proposal we recommend intermittent measurement to ensure that active measurement accuracy mirrors resting condition accuracy. A new experimental protocol is proposed to maintain long-term stability. A medically approved automated sphygmomanometer was used as the standard device in this study. The long-term accuracy of the test device is based on the definition of propagation error, i.e., for an oscillometric automated sphygmomanometer (5 ± 8 mmHg) ± the error for the test device static accuracy (-0.12 ± 5.49 mmHg for systolic blood pressure and - 1.17 ± 5.06 mmHg for diastolic blood pressure). Thus, the long-term stabilities were - 3.38 ± 7.1 mmHg and - 1.38 ± 5.4 mmHg, which satisfied propagation error. Further research and discussion are necessary to create standards for use by manufacturers; such standards should be readily evaluated and ensure high-quality evidence. Supplementary information The online version contains supplementary material available at 10.1007/s12553-023-00726-6.
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Khan Mamun MMR, Sherif A. Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010027. [PMID: 36671599 PMCID: PMC9854981 DOI: 10.3390/bioengineering10010027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.
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Affiliation(s)
| | - Ahmed Sherif
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, MS 39406, USA
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Alam S, Zhang M, Harris K, Fletcher LM, Reneker JC. The Impact of Consumer Wearable Devices on Physical Activity and Adherence to Physical Activity in Patients with Cardiovascular Disease: A Systematic Review of Systematic Reviews and Meta-Analyses. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Sabrina Alam
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Mengna Zhang
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Kisa Harris
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Lauren M. Fletcher
- Rowland Medical Library; University of Mississippi Medical Center, Jackson, Mississippi, USA
- John D. Rockefeller Library, Brown University, Providence, Rhode Island, USA
| | - Jennifer C. Reneker
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
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Minamimoto R, Yamada Y, Sugawara Y, Fujii M, Kotabe K, Iso K, Yokoyama H, Kurihara K, Iwasaki T, Horikawa D, Saito K, Kajiwara H, Matsunaga F. Variation in blood pressure and heart rate of radiological technologists in worktime tracked by a wearable device: A preliminary study. PLoS One 2022; 17:e0276483. [PMCID: PMC9671413 DOI: 10.1371/journal.pone.0276483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/07/2022] [Indexed: 11/18/2022] Open
Abstract
The aim of this preliminary study was to measure the systolic BP (SBP) and diastolic BP (DBP) and heart rate (HR) of radiological technologists by WD, and evaluate variation among individuals by worktime, day of the week, job, and workplace. Measurements were obtained using a wristwatch-type WD with optical measurement technology that can measure SBP and DBP every 10 minutes and HR every 30 minutes. SBP, DBP, and HR data obtained at baseline and during work time were combined with the hours of work, day of the week, job, and workplace recorded by the participants in 8 consecutive weeks. We calculated the mean, the ratio to baseline and coefficient of variation [CV(%)] for SBP, DBP, and HR. SBP, DBP, and HR values were significantly higher during work hours than at baseline (p<0.03). The ratio to baseline values ranged from 1.02 to 1.26 for SBP and from 1.07 to 1.30 for DBP. The ratio to baseline for SBP and DBP showed CV(%) of approximately 10% according to the day of the week and over the study period. For HR, ratio to baseline ranged from 0.95 to 1.29. The ratio of mean BP to baseline was >1.2 at the time of starting work, middle and after lunch, and at 14:00. The ratio to baseline of SBP were 1.2 or more for irradiation, equipment accuracy control, registration of patient data, dose verification and conference time, and were also working in CT examination room, treatment planning room, linac room, and the office. CV(%) of BP and HR were generally stable for all workplaces. WD measurements of SBP, DBP, and HR were higher during working hours than at baseline and varied by the individuals, work time, job, and workplace. This method may enable evaluation of unconscious workload in individuals.
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Affiliation(s)
- Ryogo Minamimoto
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
- * E-mail:
| | - Yui Yamada
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Yasuharu Sugawara
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Megumi Fujii
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Kazuki Kotabe
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Kakeru Iso
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hiroki Yokoyama
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Keiichi Kurihara
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Tsubasa Iwasaki
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Daisuke Horikawa
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Kaori Saito
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hironori Kajiwara
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
| | - Futoshi Matsunaga
- Department of Radiology, Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan
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Konstantinidis D, Iliakis P, Tatakis F, Thomopoulos K, Dimitriadis K, Tousoulis D, Tsioufis K. Wearable blood pressure measurement devices and new approaches in hypertension management: the digital era. J Hum Hypertens 2022; 36:945-951. [PMID: 35322181 PMCID: PMC8942176 DOI: 10.1038/s41371-022-00675-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 11/09/2022]
Abstract
Out-of-office blood pressure (BP) measurement is considered an integral component of the diagnostic algorithm and management of hypertension. In the era of digitalization, a great deal of wearable BP measuring devices has been developed. These digital blood pressure monitors allow frequent BP measurements with minimal annoyance to the patient while they do promise radical changes regarding the diagnostic accuracy, as the importance of making an accurate diagnosis of hypertension has become evident. By increasing the number of BP measurements in different conditions, these monitors allow accurate identification of different clinical phenotypes, such as masked hypertension and pathological BP variability, that seem to have a negative impact on cardiovascular prognosis. Frequent measurements of BP and the incorporation of new features in BP variability, both enable well-rounded interpretation of BP data in the context of real-life settings. This article is a review of all different technologies and wearable BP monitoring devices.
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Affiliation(s)
- D Konstantinidis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece.
| | - P Iliakis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece
| | - F Tatakis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece
| | - K Thomopoulos
- Department of Cardiology, Helena Venizelou Hospital, Athens, Greece
| | - K Dimitriadis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece
| | - D Tousoulis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece
| | - K Tsioufis
- First Cardiology Clinic, Medical School, National and Kapodistrian University of Athens, Hippokration Hospital, Athens, Greece
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Rivera E, Clark-Cutaia MN, Schrauben SJ, Townsend RR, Lash JP, Hannan M, Jaar BG, Rincon-Choles H, Kansal S, He J, Chen J, Hirschman KB. Treatment Adherence in CKD and Support From Health care Providers: A Qualitative Study. Kidney Med 2022; 4:100545. [PMID: 36339664 PMCID: PMC9630784 DOI: 10.1016/j.xkme.2022.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Rationale & Objective Adherence to recommended medical treatment is critical in chronic kidney disease (CKD) to prevent complications and progression to kidney failure. Overall adherence to treatment is low in CKD, and as few as 40% of patients with kidney failure receive any documented CKD-related care. The purpose of this study was to explore the experiences of patients with CKD and their adherence to CKD treatment plans, and the role their health care providers played in supporting their adherence. Study Design One-on-one interviews were conducted in 2019-2020 using a semi-structured interview guide. Participants described experiences with adherence to treatment plans and what they did when experiencing difficulty. Setting & Participants Participants were recruited from the Chronic Renal Insufficiency Cohort (CRIC) study. All CRIC participants were older than 21 years with CKD stages 2-4; this sample consisted of participants from the University of Pennsylvania CRIC site. Analytical Approach Interviews were recorded, transcribed, and coded using conventional content analysis. Data were organized into themes using NVivo 12. Results The sample (n = 32) had a mean age of 67 years, 53% were women, 59% were non-White, with a mean estimated glomerular filtration rate of 56.6 mL/min/1.73 m2. From analysis of factors relevant to treatment planning and adherence, following 4 major themes emerged: patient factors (multiple chronic conditions, motivation, outlook), provider factors (attentiveness, availability/accessibility, communication), treatment planning factors (lack of plan, proactive research, provider-focused treatment goals, and shared decision making), and treatment plan responses (disagreeing with treatment, perceived capability deficit, lack of information, and positive feedback). Limitations The sample was drawn from the CRIC study, which may not be representative of the general population with CKD. Conclusions These themes align with Behavioral Learning Theory, which includes concepts of internal antecedents (patient factors), external antecedents (provider factors), behavior (treatment planning factors), and consequences (treatment plan responses). In particular, the treatment plan responses point to innovative potential intervention approaches to support treatment adherence in CKD.
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Affiliation(s)
- Eleanor Rivera
- Department of Population Health Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL
| | | | - Sarah J. Schrauben
- Division of Renal Electrolyte and Hypertension, School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Raymond R. Townsend
- Division of Renal Electrolyte and Hypertension, School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - James P. Lash
- Division of Nephrology, College of Medicine, University of Illinois Chicago, Chicago, IL
| | - Mary Hannan
- Department of Biobehavioral Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL
| | - Bernard G. Jaar
- Division of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | | | - Sheru Kansal
- Division of Nephrology and Hypertension, University Hospital Cleveland Medical Center, University Hospitals, Cleveland, OH
| | - Jiang He
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Jing Chen
- Department of Medicine, School of Medicine, Tulane University, New Orleans, LA
| | - Karen B. Hirschman
- Department of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania, Philadelphia, PA
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Xu S, Kim J, Walter JR, Ghaffari R, Rogers JA. Translational gaps and opportunities for medical wearables in digital health. Sci Transl Med 2022; 14:eabn6036. [PMID: 36223451 DOI: 10.1126/scitranslmed.abn6036] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
A confluence of advances in biosensor technologies, enhancements in health care delivery mechanisms, and improvements in machine learning, together with an increased awareness of remote patient monitoring, has accelerated the impact of digital health across nearly every medical discipline. Medical grade wearables-noninvasive, on-body sensors operating with clinical accuracy-will play an increasingly central role in medicine by providing continuous, cost-effective measurement and interpretation of physiological data relevant to patient status and disease trajectory, both inside and outside of established health care settings. Here, we review current digital health technologies and highlight critical gaps to clinical translation and adoption.
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Affiliation(s)
- Shuai Xu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60611, USA.,Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.,Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.,Sibel Health, Niles, IL 60714, USA
| | - Joohee Kim
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60611, USA
| | - Jessica R Walter
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60611, USA.,Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Roozbeh Ghaffari
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60611, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.,Epicore Biosystems Inc., Cambridge, MA 02139, USA
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60611, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.,Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.,Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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