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Henry B, Merz M, Hoang H, Abdulkarim G, Wosik J, Schoettker P. Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits. Blood Press 2024; 33:2304190. [PMID: 38245864 DOI: 10.1080/08037051.2024.2304190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.
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Affiliation(s)
- Benoit Henry
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maxime Merz
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Harry Hoang
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ghaith Abdulkarim
- Neuro-Informatics Laboratory, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jedrek Wosik
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Patrick Schoettker
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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2
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Kaveh R, Schwendeman C, Pu L, Arias AC, Muller R. Wireless ear EEG to monitor drowsiness. Nat Commun 2024; 15:6520. [PMID: 39095399 PMCID: PMC11297174 DOI: 10.1038/s41467-024-48682-7] [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: 10/01/2023] [Accepted: 05/09/2024] [Indexed: 08/04/2024] Open
Abstract
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
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Affiliation(s)
- Ryan Kaveh
- University of California Berkeley, Berkeley, CA, 94708, USA.
| | | | - Leslie Pu
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Ana C Arias
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Rikky Muller
- University of California Berkeley, Berkeley, CA, 94708, USA.
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3
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M VR, GNK G, D R, T VP, Rao GN. Neuro Receptor Signal Detecting and Monitoring Smart Devices for Biological Changes in Cognitive Health Conditions. Ann Neurosci 2024; 31:225-233. [PMID: 39156625 PMCID: PMC11325689 DOI: 10.1177/09727531231206888] [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: 07/20/2023] [Accepted: 09/19/2023] [Indexed: 08/20/2024] Open
Abstract
Background Currently, wearable sensors significantly impact health care through continuous monitoring and event prediction. The types and clinical applications of wearable technology for the prevention of mental illnesses, as well as associated health authority rules, are covered in the current review. Summary The technologies behind wearable ECG monitors, biosensors, electronic skin patches, neural interfaces, retinal prosthesis, and smart contact lenses were discussed. We described how sensors will examine neuronal impulses using verified machine-learning algorithms running in real-time. These sensors will closely monitor body signals and demonstrate continuous sensing with wireless functionality. The wearable applications in the following medical fields were covered in our review: sleep, neurology, mental health, anxiety, depression, Parkinson's disease, epilepsy, seizures, and schizophrenia. These mental health conditions can cause serious issues, even death. Inflammation brought on by mental health problems can worsen hypothalamic-pituitary-adrenal axis dysfunction and interfere with certain neuroregulatory systems such as the neural peptide Y, serotonergic, and cholinergic systems. Severe depressive disorder symptoms are correlated with elevated Interleukin (IL-6) levels. On the basis of previous and present data collected utilizing a variety of sensory modalities, researchers are currently investigating ways to identify or detect the current mental state. Key message This review explores the potential of various mental health monitoring technologies. The types and clinical uses of wearable technology, such as ECG monitors, biosensors, electronic skin patches, brain interfaces, retinal prostheses, and smart contact lenses, were covered in the current review will be beneficial for patients with mental health problems like Alzheimer, epilepsy, dementia. The sensors will closely monitor bodily signals with wireless functionality while using machine learning algorithms to analyse neural impulses in real time.
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Affiliation(s)
- Vivek Reddy M
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Ganesh GNK
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Rudhresh D
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Vaishnavi Parimala T
- Department of Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Gaddam Narasimha Rao
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
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4
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Dong T, Zhu W, Yang Z, Matos Pires NM, Lin Q, Jing W, Zhao L, Wei X, Jiang Z. Advances in heart failure monitoring: Biosensors targeting molecular markers in peripheral bio-fluids. Biosens Bioelectron 2024; 255:116090. [PMID: 38569250 DOI: 10.1016/j.bios.2024.116090] [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: 10/11/2023] [Revised: 01/10/2024] [Accepted: 01/28/2024] [Indexed: 04/05/2024]
Abstract
Cardiovascular diseases (CVDs), especially chronic heart failure, threaten many patients' lives worldwide. Because of its slow course and complex causes, its clinical screening, diagnosis, and prognosis are essential challenges. Clinical biomarkers and biosensor technologies can rapidly screen and diagnose. Multiple types of biomarkers are employed for screening purposes, precise diagnosis, and treatment follow-up. This article provides an up-to-date overview of the biomarkers associated with the six main heart failure etiology pathways. Plasma natriuretic peptides (BNP and NT-proBNP) and cardiac troponins (cTnT, cTnl) are still analyzed as gold-standard markers for heart failure. Other complementary biomarkers include growth differentiation factor 15 (GDF-15), circulating Galactose Lectin 3 (Gal-3), soluble interleukin (sST2), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α). For these biomarkers, the electrochemical biosensors have exhibited sufficient sensitivity, detection limit, and specificity. This review systematically summarizes the latest molecular biomarkers and sensors for heart failure, which will provide comprehensive and cutting-edge authoritative scientific information for biomedical and electronic-sensing researchers in the field of heart failure, as well as patients. In addition, our proposed future outlook may provide new research ideas for researchers.
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Affiliation(s)
- Tao Dong
- Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Transduction, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engincering, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China; X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China; Department of Microsystems- IMS, Faculty of Technology, Natural Sciences and Maritime Sciences, University of South-Eastern Norway-USN, P.O. Box 235, Kongsberg, 3603, Norway
| | - Wangang Zhu
- Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Transduction, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engincering, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China; X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhaochu Yang
- Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Transduction, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engincering, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Nuno Miguel Matos Pires
- Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Transduction, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, School of Mechanical Engincering, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Qijing Lin
- X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Weixuan Jing
- X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Libo Zhao
- X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xueyong Wei
- X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhuangde Jiang
- X Multidisciplinary Research Institute, Faculty of Instrumentation Science and Technology, State Key Laboratory for Manufacturing Systems Engineering, International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi'an Jiaotong University, Xi'an, 710049, China
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Roy A, Zenker S, Jain S, Afshari R, Oz Y, Zheng Y, Annabi N. A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2404225. [PMID: 38970527 DOI: 10.1002/adma.202404225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.
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Affiliation(s)
- Arpita Roy
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Shea Zenker
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Saumya Jain
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ronak Afshari
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yavuz Oz
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuting Zheng
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Nasim Annabi
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
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6
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Juyal A, Bisht S, Singh MF. Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring. Blood Press Monit 2024:00126097-990000000-00112. [PMID: 38958493 DOI: 10.1097/mbp.0000000000000711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.
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Affiliation(s)
- Anubhuti Juyal
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Shradha Bisht
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Mamta F Singh
- Department of Pharmacology, College of Pharmacy, COER University, Roorkee, Uttarakhand, India
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7
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Phan V, Song K, Silva RS, Silbernagel KG, Baxter JR, Halilaj E. Seven Things to Know About Exercise Classification With Inertial Sensing Wearables. IEEE J Biomed Health Inform 2024; 28:3411-3421. [PMID: 38381640 PMCID: PMC11284806 DOI: 10.1109/jbhi.2024.3368042] [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] [Indexed: 02/23/2024]
Abstract
OBJECTIVE Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. METHODS We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. RESULTS Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. CONCLUSIONS Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.
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Affiliation(s)
- Vu Phan
- Mechanical Engineering Department, Carnegie Mellon University Pittsburgh, PA, USA
| | - Ke Song
- Orthopaedic Surgery Department, University of Pennsylvania, Philadelphia, PA, USA
| | - Rodrigo Scattone Silva
- Postgraduate Program in Rehabilitation Sciences, Federal University of Rio Grande do Norte, Santa Cruz, Brazil
| | | | - Josh R. Baxter
- Orthopaedic Surgery Department, University of Pennsylvania, Philadelphia, PA, USA
| | - Eni Halilaj
- Mechanical Engineering Department, Carnegie Mellon University Pittsburgh, PA, USA
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Vento V, Kuntz S, Lejay A, Chakfe N. Evolutionary trends and innovations in cardiovascular intervention. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1384008. [PMID: 38756327 PMCID: PMC11098563 DOI: 10.3389/fmedt.2024.1384008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
Cardiovascular diseases remain a global health challenge, prompting continuous innovation in medical technology, particularly in Cardiovascular MedTech. This article provides a comprehensive exploration of the transformative landscape of Cardiovascular MedTech in the 21st century, focusing on interventions. The escalating prevalence of cardiovascular diseases and the demand for personalized care drive the evolving landscape, with technologies like wearables and AI reshaping patient-centric healthcare. Wearable devices offer real-time monitoring, enhancing procedural precision and patient outcomes. AI facilitates risk assessment and personalized treatment strategies, revolutionizing intervention precision. Minimally invasive procedures, aided by robotics and novel materials, minimize patient impact and improve outcomes. 3D printing enables patient-specific implants, while regenerative medicine promises cardiac regeneration. Augmented reality headsets empower surgeons during procedures, enhancing precision and awareness. Novel materials and radiation reduction techniques further optimize interventions, prioritizing patient safety. Data security measures ensure patient privacy in the era of connected healthcare. Modern technologies enhance traditional surgeries, refining outcomes. The integration of these innovations promises to shape a healthier future for cardiovascular procedures, emphasizing collaboration and research to maximize their transformative potential.
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Affiliation(s)
- Vincenzo Vento
- Vascular Surgery Department, Lancisi Cardiovascular Center, Ancona, Italy
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, Strasbourg, France
| | - Salomé Kuntz
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, Strasbourg, France
- Department of Vascular Surgery, Kidney Transplantation and Innovation, University Hospital of Strasbourg, Strasbourg, France
| | - Anne Lejay
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, Strasbourg, France
- Department of Vascular Surgery, Kidney Transplantation and Innovation, University Hospital of Strasbourg, Strasbourg, France
| | - Nabil Chakfe
- Department of Vascular Surgery and Kidney Transplantation, University Hospital of Strasbourg, Strasbourg, France
- Department of Vascular Surgery, Kidney Transplantation and Innovation, University Hospital of Strasbourg, Strasbourg, France
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Lazarou E, Exarchos TP. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices. AIMS Neurosci 2024; 11:76-102. [PMID: 38988886 PMCID: PMC11230864 DOI: 10.3934/neuroscience.2024006] [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: 12/27/2023] [Revised: 03/22/2024] [Accepted: 04/08/2024] [Indexed: 07/12/2024] Open
Abstract
Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
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Affiliation(s)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Dept of Informatics, Ionian University, GR49132, Corfu, Greece
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10
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Neri L, Gallelli I, Dall'Olio M, Lago J, Borghi C, Diemberger I, Corazza I. Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting. Bioengineering (Basel) 2024; 11:222. [PMID: 38534496 DOI: 10.3390/bioengineering11030222] [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: 01/12/2024] [Revised: 02/03/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Wearable devices represent a new approach for monitoring key clinical parameters, such as ECG signals, for research and health purposes. These devices could outcompete medical devices in terms of affordability and use in out-clinic settings, allowing remote monitoring. The major limitation, especially when compared to implantable devices, is the presence of artifacts. Several authors reported a relevant percentage of recording time with poor/unusable traces for ECG, potentially hampering the use of these devices for this purpose. For this reason, it is of the utmost importance to develop a simple and inexpensive system enabling the user of the wearable devices to have immediate feedback on the quality of the acquired signal, allowing for real-time correction. METHODS A simple algorithm that can work in real time to verify the quality of the ECG signal (acceptable and unacceptable) was validated. Based on simple statistical parameters, the algorithm was blindly tested by comparison with ECG tracings previously classified by two expert cardiologists. RESULTS The classifications of 7200 10s-signal samples acquired on 20 patients with a commercial wearable ECG monitor were compared. The algorithm has an overall efficiency of approximately 95%, with a sensitivity of 94.7% and a specificity of 95.3%. CONCLUSIONS The results demonstrate that even a simple algorithm can be used to classify signal coarseness, and this could allow real-time intervention by the subject or the technician.
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Affiliation(s)
- Luca Neri
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | | | | | - Jessica Lago
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
- IRCCS AOU, Policlinico di S. Orsola, 40138 Bologna, Italy
| | - Igor Diemberger
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
- IRCCS AOU, Policlinico di S. Orsola, 40138 Bologna, Italy
| | - Ivan Corazza
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
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11
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Na Y, Kim C, Kim K, Kim TH, Kwon SH, Kang IS, Jung YW, Kim TW, Cho DH, An J, Lee JK, Park J. Quarter-Annulus Si-Photodetector with Equal Inner and Outer Radii of Curvature for Reflective Photoplethysmography Sensors. BIOSENSORS 2024; 14:109. [PMID: 38392028 PMCID: PMC10886646 DOI: 10.3390/bios14020109] [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: 01/11/2024] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/24/2024]
Abstract
Reflection-type photoplethysmography (PPG) pulse sensors used in wearable smart watches, true wireless stereo, etc., have been recently considered a key component for monitoring biological signals such as heart rate, SPO3, and blood pressure. Typically, the optical front end (OFE) of these PPG sensors is heterogeneously configured and packaged with light sources and receiver chips. In this paper, a novel quarter-annulus photodetector (NQAPD) with identical inner and outer radii of curvature has been developed using a plasma dicing process to realize a ring-type OFE receiver, which maximizes manufacturing efficiency and increases the detector collection area by 36.7% compared to the rectangular PD. The fabricated NQAPD exhibits a high quantum efficiency of over 90% in the wavelength of 500 nm to 740 nm and the highest quantum efficiency of 95% with a responsivity of 0.41 A/W at the wavelength of 530 nm. Also, the NQAPD is shown to increase the SNR of the PPG signal by 5 to 7.6 dB compared to the eight rectangular PDs. Thus, reflective PPG sensors constructed with NQAPD can be applied to various wearable devices requiring low power consumption, high performance, and cost-effectiveness.
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Affiliation(s)
- Yeeun Na
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Chaehwan Kim
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Keunhoi Kim
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Tae Hyun Kim
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Soo Hyun Kwon
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Il-Suk Kang
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
| | - Young Woo Jung
- Sensor & Package Business Division, Partron Co., Ltd., Hwaseong-si 18449, Gyeonggi-do, Republic of Korea; (Y.W.J.); (T.W.K.)
| | - Tae Won Kim
- Sensor & Package Business Division, Partron Co., Ltd., Hwaseong-si 18449, Gyeonggi-do, Republic of Korea; (Y.W.J.); (T.W.K.)
| | - Deok-Ho Cho
- Research Department, Sigetronics Inc., Wanju-gun 55314, Jeollabuk-do, Republic of Korea;
| | - Jihwan An
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang-si 37673, Gyeongsangbuk-do, Republic of Korea;
| | - Jong-Kwon Lee
- Department of System Semiconductor Engineering, Cheongju University, Cheongju-si 28503, Chungcheongbuk-do, Republic of Korea
| | - Jongcheol Park
- Nano Convergence Technology Division, National Nano Fab Center, Yuseong-gu, Daejeon 34141, Republic of Korea; (Y.N.); (C.K.); (K.K.); (T.H.K.); (S.H.K.); (I.-S.K.)
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Dahiya ES, Kalra AM, Lowe A, Anand G. Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:1318. [PMID: 38400474 PMCID: PMC10893166 DOI: 10.3390/s24041318] [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: 11/28/2023] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions.
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Affiliation(s)
| | - Anubha Manju Kalra
- Institute of Biomedical Technologies (IBTec), Auckland University of Technology, Auckland 1010, New Zealand; (E.S.D.); (A.L.); (G.A.)
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13
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Babu M, Lautman Z, Lin X, Sobota MHB, Snyder MP. Wearable Devices: Implications for Precision Medicine and the Future of Health Care. Annu Rev Med 2024; 75:401-415. [PMID: 37983384 DOI: 10.1146/annurev-med-052422-020437] [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] [Indexed: 11/22/2023]
Abstract
Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Ziv Lautman
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Department of Bioengineering, Stanford University School of Medicine, Stanford, California, USA
| | - Xiangping Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Milan H B Sobota
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
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14
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Yip W, Fu H, Jian W, Liu J, Pan J, Xu D, Yang H, Zhai T. Universal health coverage in China part 2: addressing challenges and recommendations. Lancet Public Health 2023; 8:e1035-e1042. [PMID: 38000883 DOI: 10.1016/s2468-2667(23)00255-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/26/2023]
Abstract
This report analyses the underlying causes of China's achievements and gaps in universal health coverage over the past 2 decades and proposes policy recommendations for advancing universal health coverage by 2030. Although strong political commitment and targeted financial investment have produced positive outcomes in reproductive, maternal, newborn, and child health and infectious diseases, a fragmented and hospital-centric delivery system, rising health-care costs, shallow benefit coverage of health insurance schemes, and little integration of health in all policies have restricted China's ability to effectively prevent and control chronic disease and provide adequate financial risk protection, especially for lower-income households. Here, we used a health system conceptual framework and we propose a set of feasible policy recommendations that draw from international experiences and first-hand knowledge of China's unique institutional landscape. Our six recommendations are: instituting a primary care-focused integrated delivery system that restructures provider incentives and accountability mechanisms to prioritise prevention; leveraging digital tools to support health behaviour change; modernising information campaigns; improving financial protection through insurance reforms; promoting a health in all policy; and developing a domestic monitoring framework with refined tracer indicators that reflects China's disease burden.
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Affiliation(s)
- Winnie Yip
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Hongqiao Fu
- Department of Health Policy and Management, School of Public Health, Peking University Health Science Center, Beijing, China.
| | - Weiyan Jian
- Department of Health Policy and Management, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jay Pan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; School of Public Administration, Sichuan University, Chengdu, China
| | - Duo Xu
- Institute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing, China
| | - Hanmo Yang
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Tiemin Zhai
- China National Health Development Research Center, Beijing, China
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15
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Piet A, Jablonski L, Daniel Onwuchekwa JI, Unkel S, Weber C, Grzegorzek M, Ehlers JP, Gaus O, Neumann T. Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review. Bioengineering (Basel) 2023; 10:1321. [PMID: 38002444 PMCID: PMC10669651 DOI: 10.3390/bioengineering10111321] [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: 10/11/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management.
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Affiliation(s)
- Artur Piet
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany
| | - Lennart Jablonski
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany
| | | | - Steffen Unkel
- Department of Digital Health Sciences and Biomedicine, University of Siegen, 57076 Siegen, Germany
- Department of Medical Statistics, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Christian Weber
- Department of Digital Health Sciences and Biomedicine, University of Siegen, 57076 Siegen, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany
- Department of Knowledge Engineering, University of Economics in Katowice, 40-287 Katowice, Poland
| | - Jan P. Ehlers
- Department of Didactics and Educational Research in Health Science, Witten/Herdecke University, 58455 Witten, Germany
| | - Olaf Gaus
- Department of Digital Health Sciences and Biomedicine, University of Siegen, 57076 Siegen, Germany
| | - Thomas Neumann
- Department of Digital Health Sciences and Biomedicine, University of Siegen, 57076 Siegen, Germany
- Faculty of Economics and Management, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
- University Department of Neurology, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
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16
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Hafid A, Gunnarsson E, Ramos A, Rödby K, Abtahi F, Bamidis PD, Billis A, Papachristou P, Seoane F. Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities. SENSORS (BASEL, SWITZERLAND) 2023; 23:9208. [PMID: 38005593 PMCID: PMC10675781 DOI: 10.3390/s23229208] [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: 10/21/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023]
Abstract
The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.
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Affiliation(s)
- Abdelakram Hafid
- Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden; (E.G.); (A.R.); (K.R.); (F.S.)
- School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden
| | - Emanuel Gunnarsson
- Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden; (E.G.); (A.R.); (K.R.); (F.S.)
| | - Alberto Ramos
- Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden; (E.G.); (A.R.); (K.R.); (F.S.)
- UDIT—University of Design, Innovation and Technology, 28016 Madrid, Spain
| | - Kristian Rödby
- Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden; (E.G.); (A.R.); (K.R.); (F.S.)
| | - Farhad Abtahi
- Institute for Clinical Science, Intervention and Technology, Karolinska Institutet, 141 83 Stockholm, Sweden;
- Department of Medical Care Technology, Karolinska University Hospital, 141 57 Huddinge, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, 141 57 Huddinge, Sweden
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (P.D.B.); (A.B.)
| | - Antonis Billis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (P.D.B.); (A.B.)
| | - Panagiotis Papachristou
- Academic Primary Health Care Center, Region Stockholm, 104 31 Stockholm, Sweden;
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden
| | - Fernando Seoane
- Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business Swedish School of Textiles, University of Borås, 503 32 Borås, Sweden; (E.G.); (A.R.); (K.R.); (F.S.)
- Institute for Clinical Science, Intervention and Technology, Karolinska Institutet, 141 83 Stockholm, Sweden;
- Department of Medical Care Technology, Karolinska University Hospital, 141 57 Huddinge, Sweden
- Department of Clinical Physiology, Karolinska University Hospital, 141 57 Huddinge, Sweden
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17
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Hosseinzadeh S, Sajadi Tabar SS. Smartwatches in healthcare medicine: assistance and monitoring; a scoping review. BMC Med Inform Decis Mak 2023; 23:248. [PMID: 37924029 PMCID: PMC10625201 DOI: 10.1186/s12911-023-02350-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: 07/12/2023] [Accepted: 10/22/2023] [Indexed: 11/06/2023] Open
Abstract
Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed/Medline, Scopus, Embase, Web of Science, ERIC and Google Scholar. In our search strategy, 761 articles were returned. The exclusion/inclusion criteria were applied. Finally, 35 articles were selected for extracting data. These included six studies on stress monitoring, six on movement disorders, three on sleep tracking, three on blood pressure, two on heart disease, six on covid pandemic, three on safety and six on validation. The use of smartwatches has been found to be effective in diagnosing the symptoms of various diseases. In particular, smartwatches have shown promise in detecting heart diseases, movement disorders, and even early signs of COVID-19. Nevertheless, it should be emphasised that there is an ongoing discussion concerning the reliability of smartwatch diagnoses within healthcare systems. Despite the potential advantages offered by utilising smartwatches for disease detection, it is imperative to approach their data interpretation with prudence. The discrepancies in detection between smartwatches and their algorithms have important implications for healthcare use. The accuracy and reliability of the algorithms used are crucial, as well as high accuracy in detecting changes in health status by the smartwatches themselves. This calls for the development of medical watches and the creation of AI-hospital assistants. These assistants will be designed to help with patient monitoring, appointment scheduling, and medication management tasks. They can educate patients and answer common questions, freeing healthcare providers to focus on more complex tasks.
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Affiliation(s)
- Mohsen Masoumian Hosseini
- Department of E-Learning in Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- CyberPatient Research Affiliate, Interactive Health International, Department of the surgery, University of British Columbia, Vancouver, Canada
| | - Seyedeh Toktam Masoumian Hosseini
- CyberPatient Research Affiliate, Interactive Health International, Department of the surgery, University of British Columbia, Vancouver, Canada.
- Department of Nursing, School of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
| | - Karim Qayumi
- Professor at Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Shahriar Hosseinzadeh
- CyberPatient Research Coordinator, Interactive Health International, Department of Surgery, University of British Columbia, Vancouver, Canada
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18
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Lee MA, Song M, Bessette H, Roberts Davis M, Tyner TE, Reid A. Use of wearables for monitoring cardiometabolic health: A systematic review. Int J Med Inform 2023; 179:105218. [PMID: 37806179 DOI: 10.1016/j.ijmedinf.2023.105218] [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: 05/09/2023] [Revised: 08/28/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023]
Abstract
INTRODUCTION Cardiometabolic disorders (CMD) such as hyperglycemia, obesity, hypertension, and dyslipidemia are the leading causes of mortality and significant public health concerns worldwide. With the advances in wireless technology, wearables have become popular for health promotion, but its impact on cardiometabolic health is not well understood. PURPOSE A systematic literature review aimed to describe the features of wearables used for monitoring cardiometabolic health and identify the impact of using wearables on those cardiometabolic health indicators. METHODS A systematic search of PubMed, CINAHL, Academic Search Complete, and Science and Technology Collection databases was performed using keywords related to CMD risk indicators and wearables. The wearables were limited to sensors for blood pressure (BP), heart rate (HR), electrocardiogram (ECG), glucose, and cholesterol. INCLUDED STUDIES 1) were published from 2016 to March 2021 in English, 2) focused on wearables external to the body, and 3) examined wearable use by individuals in daily life (not by health care providers). Protocol, technical, and non-empirical studies were excluded. RESULTS Out of 53 studies, the types of wearables used were smartwatches (45.3%), patches (34.0%), chest straps (22.6%), wristbands (13.2%), and others (9.4%). HR (58.5%), glucose (28.3%), and ECG (26.4%) were the predominant indicators. No studies tracked BP or cholesterol. Additional features of wearables included physical activity, respiration, sleep, diet, and symptom monitoring. Twenty-two studies primarily focused on the use of wearables and reported direct impacts on cardiometabolic indicators; seven studies used wearables as part of a multi-modality approach and presented outcomes affected by a primary intervention but measured through CMD-sensor wearables; and 24 validated the precision and usability of CMD-sensor wearables. CONCLUSION The impact of wearables on cardiometabolic indicators varied across the studies, indicating the need for further research. However, this body of literature highlights the potential of wearables to promote cardiometabolic health.
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Affiliation(s)
- Mikyoung A Lee
- Texas Woman's University, College of Nursing, Dallas, TX, United States.
| | - MinKyoung Song
- Oregon Health & Science University, School of Nursing, Portland, OR, United States.
| | - Hannah Bessette
- Oregon Health & Science University, School of Nursing, Portland, OR, United States
| | - Mary Roberts Davis
- Oregon Health & Science University, School of Nursing, Portland, OR, United States
| | - Tracy E Tyner
- Texas Woman's University, College of Nursing, Dallas, TX, United States
| | - Amy Reid
- Texas Woman's University, College of Nursing, Dallas, TX, United States
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Ullah M, Hamayun S, Wahab A, Khan SU, Rehman MU, Haq ZU, Rehman KU, Ullah A, Mehreen A, Awan UA, Qayum M, Naeem M. Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective. Curr Probl Cardiol 2023; 48:101922. [PMID: 37437703 DOI: 10.1016/j.cpcardiol.2023.101922] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 06/27/2023] [Indexed: 07/14/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.
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Affiliation(s)
- Muneeb Ullah
- Department of Pharmacy, Kohat University of Science and technology (KUST), Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
| | - Shah Hamayun
- Department of Cardiology, Pakistan Institute of Medical Sciences (PIMS), Islamabad, 04485 Punjab, Pakistan
| | - Abdul Wahab
- Department of Pharmacy, Kohat University of Science and technology (KUST), Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
| | - Shahid Ullah Khan
- Department of Biochemistry, Women Medical and Dental College, Khyber Medical University, Abbottabad, 22080, Khyber Pakhtunkhwa, Pakistan
| | - Mahboob Ur Rehman
- Department of Cardiology, Pakistan Institute of Medical Sciences (PIMS), Islamabad, 04485 Punjab, Pakistan
| | - Zia Ul Haq
- Department of Public Health, Institute of Public Health Sciences, Khyber Medical University, Peshawar 25120, Pakistan
| | - Khalil Ur Rehman
- Department of Chemistry, Institute of chemical Sciences, Gomel University, Dera Ismail Khan, KPK, Pakistan
| | - Aziz Ullah
- Department of Chemical Engineering, Pukyong National University, Busan 48513, Republic of Korea
| | - Aqsa Mehreen
- Department of Biological Sciences, National University of Medical Sciences (NUMS) Rawalpindi, Punjab, Pakistan
| | - Uzma A Awan
- Department of Biological Sciences, National University of Medical Sciences (NUMS) Rawalpindi, Punjab, Pakistan
| | - Mughal Qayum
- Department of Pharmacy, Kohat University of Science and technology (KUST), Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
| | - Muhammad Naeem
- Department of Biological Sciences, National University of Medical Sciences (NUMS) Rawalpindi, Punjab, Pakistan.
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Diodato S, Bardacci Y, El Aoufy K, Belli S, Bambi S. Early myopericarditis diagnosed in a 31-year-old patient using smartwatch technology: A case report. Int Emerg Nurs 2023; 71:101365. [PMID: 37797416 DOI: 10.1016/j.ienj.2023.101365] [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: 06/27/2023] [Revised: 09/06/2023] [Accepted: 09/23/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION Smartwatches, wrist-mounted devices with computing capacity able to connect with other devices via short-range wireless networking, are today commonly used by the general population to monitor their health status using specific applications. Currently, these devices offer new possibilities in remote health care monitoring and integration with other applications, through alert notifications, collection of personal data by a variety of sensors and the storage of these data. Several companies are introducing smartwatches with "health status" monitoring software with multiple functions, i.e. electrocardiogram (ECG) sensors. Recently, detection of atrial fibrillation based on heart rate monitoring by optical sensors resulted to be feasible and reliable when using the Apple Watch® and its corresponding application. Indeed, previous case reports highlighted its sensitivity in detecting morphological changes typical of the Acute Coronary Syndrome. CASE REPORT We report the case of a healthcare worker, who experienced chest pain and diffuse myalgia, detected ECG alterations in the ST segment, and reached the Emergency Department Myopericarditis was diagnosed and treated promptly to prevent complications. DISCUSSION Acute viral myocarditis and pericarditis are clinical conditions, usually characterized by 21 a benign course that does not require medical evaluation. However, ventricular arrhythmias are also common in viral myocarditis, and the latter is associated with a large proportion of sudden cardiac deaths in the young population without previous structural heart disease. In this case report, smartwatch technology allowed the preventive implementation of interventions against potentially life-threatening complications. Further developments in smartwatch technology could lead to more sensitive and specific diagnostic algorithms for conditions that require immediate medical intervention.
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Affiliation(s)
- Samuele Diodato
- Emergency and Trauma Intensive Care Unit, Careggi University Hospital, Florence, Italy
| | - Yari Bardacci
- Emergency and Trauma Intensive Care Unit, Careggi University Hospital, Florence, Italy.
| | - Khadija El Aoufy
- Department of Experimental and Clinical Medicine, University of Florence, Italy
| | - Simone Belli
- Emergency and Trauma Intensive Care Unit, Careggi University Hospital, Florence, Italy
| | - Stefano Bambi
- Department of Health Science, University of Florence, Italy
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21
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Estepp JR. Sensing haemodynamics via wearables in sync. Nat Biomed Eng 2023; 7:1210-1211. [PMID: 37848558 DOI: 10.1038/s41551-023-01103-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Affiliation(s)
- Justin R Estepp
- 711th Human Performance Wing, Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, USA.
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22
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Franklin D, Tzavelis A, Lee JY, Chung HU, Trueb J, Arafa H, Kwak SS, Huang I, Liu Y, Rathod M, Wu J, Liu H, Wu C, Pandit JA, Ahmad FS, McCarthy PM, Rogers JA. Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography. Nat Biomed Eng 2023; 7:1229-1241. [PMID: 37783757 PMCID: PMC10653655 DOI: 10.1038/s41551-023-01098-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 08/18/2023] [Indexed: 10/04/2023]
Abstract
Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.
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Affiliation(s)
- Daniel Franklin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada.
| | - Andreas Tzavelis
- Medical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | | | | | - Jacob Trueb
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Hany Arafa
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Sung Soo Kwak
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Ivy Huang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Yiming Liu
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Megh Rathod
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Jonathan Wu
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Haolin Liu
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Onatrio, Canada
| | - Changsheng Wu
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Jay A Pandit
- Scripps Research Translational Institute, San Diego, CA, USA
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Bluhm Cardiovascular Institute, Northwestern University, Chicago, IL, USA
| | - John A Rogers
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA.
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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23
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Geangu E, Smith WAP, Mason HT, Martinez-Cedillo AP, Hunter D, Knight MI, Liang H, del Carmen Garcia de Soria Bazan M, Tse ZTH, Rowland T, Corpuz D, Hunter J, Singh N, Vuong QC, Abdelgayed MRS, Mullineaux DR, Smith S, Muller BR. EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory-Visual Statistics and Autonomic Nervous System Function 'in the Wild'. SENSORS (BASEL, SWITZERLAND) 2023; 23:7930. [PMID: 37765987 PMCID: PMC10534696 DOI: 10.3390/s23187930] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants' egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning.
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Affiliation(s)
- Elena Geangu
- Psychology Department, University of York, York YO10 5DD, UK; (A.P.M.-C.); (M.d.C.G.d.S.B.)
| | - William A. P. Smith
- Department of Computer Science, University of York, York YO10 5DD, UK; (W.A.P.S.); (J.H.); (M.R.S.A.); (B.R.M.)
| | - Harry T. Mason
- School of Physics, Engineering and Technology, University of York, York YO10 5DD, UK; (H.T.M.); (D.H.); (N.S.); (S.S.)
| | | | - David Hunter
- School of Physics, Engineering and Technology, University of York, York YO10 5DD, UK; (H.T.M.); (D.H.); (N.S.); (S.S.)
| | - Marina I. Knight
- Department of Mathematics, University of York, York YO10 5DD, UK; (M.I.K.); (D.R.M.)
| | - Haipeng Liang
- School of Engineering and Materials Science, Queen Mary University of London, London E1 2AT, UK; (H.L.); (Z.T.H.T.)
| | | | - Zion Tsz Ho Tse
- School of Engineering and Materials Science, Queen Mary University of London, London E1 2AT, UK; (H.L.); (Z.T.H.T.)
| | - Thomas Rowland
- Protolabs, Halesfield 8, Telford TF7 4QN, UK; (T.R.); (D.C.)
| | - Dom Corpuz
- Protolabs, Halesfield 8, Telford TF7 4QN, UK; (T.R.); (D.C.)
| | - Josh Hunter
- Department of Computer Science, University of York, York YO10 5DD, UK; (W.A.P.S.); (J.H.); (M.R.S.A.); (B.R.M.)
| | - Nishant Singh
- School of Physics, Engineering and Technology, University of York, York YO10 5DD, UK; (H.T.M.); (D.H.); (N.S.); (S.S.)
| | - Quoc C. Vuong
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Mona Ragab Sayed Abdelgayed
- Department of Computer Science, University of York, York YO10 5DD, UK; (W.A.P.S.); (J.H.); (M.R.S.A.); (B.R.M.)
| | - David R. Mullineaux
- Department of Mathematics, University of York, York YO10 5DD, UK; (M.I.K.); (D.R.M.)
| | - Stephen Smith
- School of Physics, Engineering and Technology, University of York, York YO10 5DD, UK; (H.T.M.); (D.H.); (N.S.); (S.S.)
| | - Bruce R. Muller
- Department of Computer Science, University of York, York YO10 5DD, UK; (W.A.P.S.); (J.H.); (M.R.S.A.); (B.R.M.)
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24
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Friligkou E, Koller D, Pathak GA, Miller EJ, Lampert R, Stein MB, Polimanti R. Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.02.23293170. [PMID: 37577704 PMCID: PMC10418572 DOI: 10.1101/2023.08.02.23293170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Background Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive. Methods We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts-UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN. Results Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2×10 -6 ) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03). Conclusions Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.
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25
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Drummond CK, Tandon A. Advancing Wearable Technology for Monitoring Heart Activity in Paediatric Populations. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:196-197. [PMID: 37969856 PMCID: PMC10642130 DOI: 10.1016/j.cjcpc.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 06/29/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Colin K. Drummond
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Animesh Tandon
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Pediatric Cardiology, Children’s Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Pediatrics, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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26
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Kim C, Song JH, Kim SH. Validation of Wearable Digital Devices for Heart Rate Measurement During Exercise Test in Patients With Coronary Artery Disease. Ann Rehabil Med 2023; 47:261-271. [PMID: 37536665 PMCID: PMC10475817 DOI: 10.5535/arm.23019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/02/2023] [Accepted: 06/22/2023] [Indexed: 08/05/2023] Open
Abstract
OBJECTIVE To assess the accuracy of recently commercialized wearable devices in heart rate (HR) measurement during cardiopulmonary exercise test (CPX) under gradual increase in exercise intensity, while wearable devices with HR monitors are reported to be less accurate in different exercise intensities. METHODS CPX was performed for patients with coronary artery disease (CAD). Twelve lead electrocardiograph (ECG) was the gold standard and Apple watch 7 (AW7), Galaxy watch 4 (GW4) and Bio Patch Mobicare 200 (MC200) were applied for comparison. Paired absolute difference (PAD), mean absolute percentage error (MAPE) and intraclass correlation coefficient (ICC) were evaluated for each device. RESULTS Forty-four participants with CAD were included. All the devices showed MAPE under 2% and ICC above 0.9 in rest, exercise and recovery phases (MC200=0.999, GW4=0.997, AW7=0.998). When comparing exercise and recovery phase, PAD of MC200 and AW7 in recovery phase were significantly bigger than PAD of exercise phase (p<0.05). Although not significant, PAD of GW4 tended to be bigger in recovery phase, too. Also, when stratified by HR 20, ICC of all the devices were highest under HR of 100, and ICC decreased as HR increased. However, except for ICC of GW4 at HR above 160 (=0.867), all ICCs exceeded 0.9 indicating excellent accuracy. CONCLUSION The HR measurement of the devices validated in this study shows a high concordance with the ECG device, so CAD patients may benefit from the devices during high-intensity exercise under conditions where HR is measured reliably.
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Affiliation(s)
- Chul Kim
- Department of Rehabilitation Medicine, Inje University Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Jun Hyeong Song
- Department of Rehabilitation Medicine, Inje University Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Seung Hyoun Kim
- Department of Rehabilitation Medicine, Inje University Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
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27
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Romero-Tapiador S, Lacruz-Pleguezuelos B, Tolosana R, Freixer G, Daza R, Fernández-Díaz CM, Aguilar-Aguilar E, Fernández-Cabezas J, Cruz-Gil S, Molina S, Crespo MC, Laguna T, Marcos-Zambrano LJ, Vera-Rodriguez R, Fierrez J, Ramírez de Molina A, Ortega-Garcia J, Espinosa-Salinas I, Morales A, Carrillo de Santa Pau E. AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence. Database (Oxford) 2023; 2023:baad049. [PMID: 37465917 PMCID: PMC10354505 DOI: 10.1093/database/baad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 07/20/2023]
Abstract
The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.
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Affiliation(s)
- Sergio Romero-Tapiador
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Blanca Lacruz-Pleguezuelos
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Ruben Tolosana
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Gala Freixer
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Roberto Daza
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Cristina M Fernández-Díaz
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Elena Aguilar-Aguilar
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
- Department of Nursing and Nutrition, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odon, Madrid 28670, Spain
| | - Jorge Fernández-Cabezas
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Silvia Cruz-Gil
- Molecular Oncology and Nutritional Genomics of Cancer Group, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Susana Molina
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Maria Carmen Crespo
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Teresa Laguna
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Ruben Vera-Rodriguez
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Julian Fierrez
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Ana Ramírez de Molina
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Javier Ortega-Garcia
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Isabel Espinosa-Salinas
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Aythami Morales
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Enrique Carrillo de Santa Pau
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
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Lychagov V, Semenov V, Volkova E, Chernakov D, Ahn J, Kim JY. Non-invasive hemoglobin concentration measurements with multi-wavelength reflectance mode PPG sensor and CNN data processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082910 DOI: 10.1109/embc40787.2023.10341173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Possibility of non-invasive hemoglobin concentration measurements with wearable devices have been evaluated. The proposed solution is based on the assumption that PPG waveform shape measured at various wavelengths in the reflectance mode carries information about in-depth distribution of optical pathlength in the tissue. Decomposition of temporal and spectral features of PPG signal have been applied to correct estimation of hemoglobin concentration. The dataset including 840 PPG waveforms from 170 volunteers have been collected for the purpose of neural network training and validation. The achieved performance (MAE~13.6 g/l, R~0.62) is confirmed with the invasive blood test.Clinical Relevance - This paper establishes possibility of non-invasive real time hemoglobin concentration measurements by means of low-cost wearable sensor with accuracy comparable to non-invasive clinical instruments.
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29
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Kong L, Li G, Wang Y, Cheng L, Lin L. Non-contact cardiopulmonary signal monitoring based on magnetic eddy current induction. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:074101. [PMID: 37466408 DOI: 10.1063/5.0148820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/30/2023] [Indexed: 07/20/2023]
Abstract
The magnetic eddy current induction method has become an excellent solution for building home cardiopulmonary monitoring systems because of its non-contact and unobtrusive characteristics, but it has problems such as low precision and complex extraction of cardiopulmonary signals. Therefore, this paper designs a magnetic eddy current sensing system based on a Field Programmable Gate Array that can realize simultaneous real-time monitoring of cardiopulmonary signals. This system adopts a magnetic eddy current sensor design scheme that can improve the amount of cardiopulmonary information in the sensing signal. In addition, it uses a signal acquisition scheme that combines an inductance-to-digital converter (LDC) and oversampling technology to improve the resolution and signal-to-noise ratio of the sensing signal. Moreover, an optimized adaptive discrete wavelet transform algorithm is proposed in this system, which can realize the effective separation and extraction of cardiopulmonary signals in different respiration states. Comparing this system with the medical monitor, the cardiopulmonary signals obtained by the two have good consistency in the time-frequency domain. Under low motion, respiration rate and heart rate detected by this system are within the confidence interval of the 95% limit of agreement; the relative errors are less than 2.63% and 1.37%, respectively; and the accuracy rates are greater than 99.30% and 99.60%, respectively. In addition, an experiment with an asthmatic patient showed that the system still has good detection performance under pathological conditions and can monitor abnormal conditions such as coughing.
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Affiliation(s)
- Li Kong
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
| | - Gang Li
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
| | - Yunyi Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
| | - Leiyang Cheng
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
| | - Ling Lin
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
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30
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Abdullah S, Hafid A, Folke M, Lindén M, Kristoffersson A. PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points. Front Bioeng Biotechnol 2023; 11:1199604. [PMID: 37378045 PMCID: PMC10292016 DOI: 10.3389/fbioe.2023.1199604] [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: 04/03/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat's performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.
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31
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Paulauskaite-Taraseviciene A, Siaulys J, Sutiene K, Petravicius T, Navickas S, Oliandra M, Rapalis A, Balciunas J. Geriatric Care Management System Powered by the IoT and Computer Vision Techniques. Healthcare (Basel) 2023; 11:healthcare11081152. [PMID: 37107987 PMCID: PMC10138364 DOI: 10.3390/healthcare11081152] [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: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.
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Affiliation(s)
| | - Julius Siaulys
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Kristina Sutiene
- Department of Mathematical Modeling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Titas Petravicius
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Skirmantas Navickas
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Marius Oliandra
- Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
| | - Andrius Rapalis
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
| | - Justinas Balciunas
- Faculty of Medicine, Vilnius University, Universiteto 3, 01513 Vilnius, Lithuania
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Bender BF, Berry JA. Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2023; 5:e230002. [PMID: 37274061 PMCID: PMC10237513 DOI: 10.20900/agmr20230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.
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Affiliation(s)
| | - Jasmine A. Berry
- Robotics Institute, University of Michigan, College of Engineering, Ann Arbor, MI 48109, USA
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Nardini S, Corbanese U, Visconti A, Mule JD, Sanguinetti CM, De Benedetto F. Improving the management of patients with chronic cardiac and respiratory diseases by extending pulse-oximeter uses: the dynamic pulse-oximetry. Multidiscip Respir Med 2023; 18:922. [PMID: 38322131 PMCID: PMC10772858 DOI: 10.4081/mrm.2023.922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/21/2023] [Indexed: 02/08/2024] Open
Abstract
Respiratory and cardio-vascular chronic diseases are among the most common noncommunicable diseases (NCDs) worldwide, accounting for a large portion of health-care costs in terms of mortality and disability. Their prevalence is expected to rise further in the coming years as the population ages. The current model of care for diagnosing and monitoring NCDs is out of date because it results in late medical interventions and/or an unfavourable cost-effectiveness balance based on reported symptoms and subsequent inpatient tests and treatments. Health projects and programs are being implemented in an attempt to move the time of an NCD's diagnosis, as well as its monitoring and follow up, out of hospital settings and as close to real life as possible, with the goal of benefiting both patients' quality of life and health system budgets. Following the SARS-CoV-2 pandemic, this implementation received additional impetus. Pulseoximeters (POs) are currently used in a variety of clinical settings, but they can also aid in the telemonitoring of certain patients. POs that can measure activities as well as pulse rate and oxygen saturation as proxies of cardio-vascular and respiratory function are now being introduced to the market. To obtain these data, the devices must be absolutely reliable, that is, accurate and precise, and capable of recording for a long enough period of time to allow for diagnosis. This paper is a review of current pulse-oximetry (POy) use, with the goal of investigating how its current use can be expanded to manage not only cardio-respiratory NCDs, but also acute emergencies with telemonitoring when hospitalization is not required but the patients' situation is debatable. Newly designed devices, both "consumer" and "professional," will be scrutinized, particularly those capable of continuously recording vital parameters on a 24-hour basis and coupling them with daily activities, a practice known as dynamic pulse-oximetry.
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Affiliation(s)
- Stefano Nardini
- Scientific Committee, Italian Multidisciplinary Respiratory Society (SIPI), Milan
| | - Ulisse Corbanese
- Retired - Chief of Department of Anaesthesia and Intensive Care, Hospital of Vittorio Veneto (TV)
| | - Alberto Visconti
- ICT Engineer and Consultant, Italian Multidisciplinary Respiratory Society (SIPI), Milan
| | | | - Claudio M. Sanguinetti
- Chief Editor of Multidisciplinary Respiratory Medicine journal; Member of Steering Committee of Italian Multidisciplinary Respiratory Society (SIPI), Milan
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Kalasin S, Surareungchai W. Challenges of Emerging Wearable Sensors for Remote Monitoring toward Telemedicine Healthcare. Anal Chem 2023; 95:1773-1784. [PMID: 36629753 DOI: 10.1021/acs.analchem.2c02642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Digitized telemedicine tools with the Internet of Things (IoT) started advancing into our daily lives and have been incorporated with commercial wearable gadgets for noninvasive remote health monitoring. The newly established tools have been steered toward a new era of decentralized healthcare. The advancement of a telemedicine wearable monitoring system has attracted enormous interest in the multimodal big data acquisition of real-time physiological and biochemical information via noninvasive methods for any health-related industries. The expectation of telemedicine wearable creation has been focused on early diagnosis of multiple diseases and minimizing the cost of high-tech and invasive treatments. However, only limited progress has been directed toward the development of telemedicine wearable sensors. This Perspective addresses the advancement of these wearable sensors that encounter multiple challenges on the forefront and technological gaps hampering the realization of health monitoring at molecular levels related to smart materials mostly limited to single use, issues of selectivity to analytes, low sensitivity to targets, miniaturization, and lack of artificial intelligence to perform multiple tasks and secure big data transfer. Sensor stability with minimized signal drift, on-body sensor reusability, and long-term continuous health monitoring provides key analytical challenges. This Perspective also focuses on, promotes, and highlights wearable sensors with a distinct capability to interconnect with telemedicine healthcare for physical sensing and multiplex sensing at deeper levels. Moreover, it points out some critical challenges in different material aspects and promotes what it will take to advance the current state-of-art wearable sensors for telemedicine healthcare. Ultimately, this Perspective is to draw attention to some potential blind spots of wearable technology development and to inspire further development of this integrated technology in mitigating multimorbidity in aging societies through health monitoring at molecular levels to identify signs of diseases.
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Affiliation(s)
- Surachate Kalasin
- Faculty of Science and Nanoscience & Nanotechnology Graduate Program, King Mongkut's University of Technology Thonburi, 10140 Bangkok, Thailand
| | - Werasak Surareungchai
- Pilot Plant Research and Development Laboratory, King Mongkut's University of Technology Thonburi, 10150 Bangkok, Thailand
- School of Bioresource and Technology, King Mongkut's University of Technology Thonburi, 10150 Bangkok, Thailand
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Joseph T, Barrie M, Karimi A, Haque S, Ogunmwonyi I, Ojha U. Contemporary Considerations in the Evolution of Wearable Technology for Arrhythmia Detection. Curr Cardiol Rev 2023; 19:93-99. [PMID: 37697927 PMCID: PMC10636792 DOI: 10.2174/1573403x19666230811093048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/18/2023] [Accepted: 06/13/2023] [Indexed: 09/13/2023] Open
Abstract
Arrhythmias are an increasingly common cause of hospital admissions worldwide. Late detection of arrhythmias is associated with a significantly increased risk of cardiovascular complications. Early identification and management of life-threatening arrhythmias is paramount to reduce mortality. Wearable technologies are now widespread among the general population, providing a continuous output of healthcare data. However, this data are not routinely integrated into clinical practice. Here, we begin by outlining the current landscape in wearable technology for aiding arrhythmia detection; we then consider the clinical impact of wearable technology for both clinicians and patients; we further highlight the latest and emerging trials in wearable technology for arrhythmia detection and finally postulate the wider implications of the expansion of such cardiac devices.
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Affiliation(s)
- Tobin Joseph
- Department of Acute Medicine, Hillingdon Hospital, Uxbridge, United Kingdom
| | - Mahmoud Barrie
- School of Medicine, Imperial College London, London, United Kingdom
| | - Akbar Karimi
- Department of Acute Medicine, Hillingdon Hospital, Uxbridge, United Kingdom
| | - Sharmi Haque
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Innocent Ogunmwonyi
- Department of Medicine, Darent Valley Hospital, Dartford, Kent, United Kingdom
| | - Utkarsh Ojha
- Chelsea and Westminster Hospital, London, United Kingdom
- Royal Brompton and Harefield Hospital, Harefield Hospital, London, United Kingdom
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Galgut O, Le Page P, Mitchell ARJ. Watch for tachycardia. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022. [DOI: 10.1186/s42444-022-00081-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Background
Wearable devices capable of measuring health metrics are becoming increasingly prevalent. Most work has investigated the potential for these devices in the context of atrial fibrillation, our case highlights the potential of wearable devices across a wider range of arrhythmia.
Case presentation
A 51-year-old woman was referred to the cardiology clinic for an assessment of symptoms of intermittent exertional shortness of breath and palpitation. The patient was otherwise fit and well, took limited alcohol and no caffeine, and was a never smoker. There was no family history of heart disease. Physical examination in clinic was unremarkable, and a 12-lead electrocardiogram (ECG), seven-day ambulatory ECG, exercise stress ECG, and trans-thoracic echocardiogram were all normal. During a severe episode the patient recorded an ECG using an Apple Watch (Apple Inc, California, USA). This was forwarded to the patient’s cardiologist, who suspected a broad complex tachycardia and organised an urgent follow-up appointment. A further 72-h Holter ECG monitor showed frequent sustained periods of monomorphic ventricular tachycardia, confirming the watch findings. The patient was started on beta blocker therapy with a rapid improvement in symptoms.
Conclusions
Current smartwatch technology can reliably identify irregular rhythms and can distinguish atrial fibrillation from sinus rhythm, with emerging evidence supporting detection of other cardiovascular diseases, including medical emergencies. There may also be a role for wearable devices in screening young populations for predictors of sudden cardiac death. At present device outputs require clinician interpretation, but in the future patients may present to primary or secondary care with a firm diagnosis of arrhythmia and may already be making wearable device guided behaviour changes.
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Busnatu ȘS, Niculescu AG, Bolocan A, Andronic O, Pantea Stoian AM, Scafa-Udriște A, Stănescu AMA, Păduraru DN, Nicolescu MI, Grumezescu AM, Jinga V. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment. J Pers Med 2022; 12:jpm12101656. [PMID: 36294795 PMCID: PMC9604784 DOI: 10.3390/jpm12101656] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022] Open
Abstract
With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.
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Affiliation(s)
- Ștefan Sebastian Busnatu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
| | - Alexandra Bolocan
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Octavian Andronic
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Alexandru Scafa-Udriște
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Dan Nicolae Păduraru
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Mihnea Ioan Nicolescu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 050044 Bucharest, Romania
- Correspondence:
| | - Viorel Jinga
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
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Wilson F, McHugh C, MacManus C, Baggish A, Tanayan C, Reddy S, Wasfy MM, Reilly RB. Diagnostic Accuracy of a Portable ECG Device in Rowing Athletes. Diagnostics (Basel) 2022; 12:diagnostics12102271. [PMID: 36291961 PMCID: PMC9600971 DOI: 10.3390/diagnostics12102271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/14/2022] [Accepted: 09/18/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Athletes can experience exercise-induced transient arrythmias during high-intensity exercise or competition, which are difficult to capture on traditional Holter monitors or replicate in clinical exercise testing. The aim of this study was to investigate the reliability of a portable single channel ECG sensor and data recorder (PluxECG) and to evaluate the confidence and reliability in interpretation of ECGs recorded using the PluxECG during remote rowing. Methods: This was a two-phase study on rowing athletes. Phase I assessed the accuracy and precision of heart rate (HR) using the PluxECG system compared to a reference 12-lead ECG system. Phase II evaluated the confidence and reliability in interpretation of ECGs during ergometer (ERG) and on-water (OW) rowing at moderate and high intensities. ECGs were reviewed by two expert readers for HR, rhythm, artifact and confidence in interpretation. Results: Findings from Phase I found that 91.9% of samples were within the 95% confidence interval for the instantaneous value of the changing exercising HR. The mean correlation coefficient across participants and tests was 0.9886 (σ = 0.0002, SD = 0.017) and between the two systems at elevated HR was 0.9676 (σ = 0.002, SD = 0.05). Findings from Phase II found significant differences for the presence of artifacts and confidence in interpretation in ECGs between readers’ for both intensities and testing conditions. Interpretation of ECGs for OW rowing had a lower level of reader agreement than ERG rowing for HR, rhythm, and artifact. Using consensus data between readers’ significant differences were apparent between OW and ERG rowing at high-intensity rowing for HR (p = 0.05) and artifact (p = 0.01). ECGs were deemed of moderate-low quality based on confidence in interpretation and the presence of artifacts. Conclusions: The PluxECG device records accurate and reliable HR but not ECG data during exercise in rowers. The quality of ECG tracing derived from the PluxECG device is moderate-low, therefore the confidence in ECG interpretation using the PluxECG device when recorded on open water is inadequate at this time.
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Affiliation(s)
- Fiona Wilson
- Discipline of Physiotherapy, School of Medicine, Trinity College Dublin, D08 W9RT Dublin, Ireland
| | - Cliodhna McHugh
- Discipline of Physiology, School of Medicine, Trinity College Dublin, D02 R590 Dublin, Ireland
- Correspondence:
| | | | - Aaron Baggish
- Cardiovascular Performance Programme, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher Tanayan
- Cardiovascular Performance Programme, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Satyajit Reddy
- Cardiovascular Performance Programme, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Meagan M. Wasfy
- Cardiovascular Performance Programme, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Richard B. Reilly
- Centre for Bioengineering, School of Medicine, Trinity College Dublin, D02 R590 Dublin, Ireland
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