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Zhao Y, Yu L, Fan X, Pang MYC, Tsui KL, Wang H. Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:8008. [PMID: 37766060 PMCID: PMC10535689 DOI: 10.3390/s23188008] [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: 08/16/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
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Affiliation(s)
- Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China;
| | - Lisha Yu
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China;
| | - Marco Y. C. Pang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
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Zhao Y, Xu F, Fan X, Wang H, Tsui KL, Guan Y. Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11136. [PMID: 36078847 PMCID: PMC9518405 DOI: 10.3390/ijerph191711136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/26/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with noninvasive and cost-effective merits, have been widely used to monitor various health conditions. However, the dynamic and heterogeneous pattern of ECG signals makes relevant feature construction and predictive model development a challenging task. In this study, we aim to develop an integrated approach for one-day-forward wellness prediction in the community-dwelling elderly using single-lead short ECG signal data via multiple-features construction and predictive model implementation. Vital signs data from the elderly were collected via station-based equipment on a daily basis. After data preprocessing, a set of features were constructed from ECG signals based on the integration of various models, including time and frequency domain analysis, a wavelet transform-based model, ensemble empirical mode decomposition (EEMD), and the refined composite multiscale sample entropy (RCMSE) model. Then, a machine learning based predictive model was established to map the l-day lagged features to wellness condition. The results showed that the approach developed in this study achieved the best performance for wellness prediction in the community-dwelling elderly. In practice, the proposed approach could be useful in the timely identification of elderly people who might have health risks, and could facilitating decision-making to take appropriate interventions.
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Affiliation(s)
- Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China
| | - Fan Xu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yurong Guan
- Department of Computer Science, Huanggang Normal University, Huanggang 438000, China
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Predicting Prognostic Effects of Acupuncture for Depression Using the Electroencephalogram. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:1381683. [PMID: 35280515 PMCID: PMC8906952 DOI: 10.1155/2022/1381683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022]
Abstract
Depression is considered to be a major public health problem with significant implications for individuals and society. Patients with depression can be with complementary therapies such as acupuncture. Predicting the prognostic effects of acupuncture has a big significance in helping physicians make early interventions for patients with depression and avoid malignant events. In this work, a novel framework of predicting prognostic effects of acupuncture for depression based on electroencephalogram (EEG) recordings is presented. Specifically, EEG, as a widely used measurement to evaluate the therapeutic effects of acupuncture, is utilized for predicting prognostic effects of acupuncture. Max-relevance and min-redundancy (mRMR), with merits of removing redundant information among selected features and remaining high relevance between selected features and response variable, is employed to select important lead-rhythm features extracted from EEG recordings. Then, according to the subject Hamilton Depression Rating Scale (HAMD) scores before and after acupuncture for eight weeks, the reduction rate of HAMD score is calculated as a measure of the prognostic effects of acupuncture. Finally, five widely used machine learning methods are utilized for building the predicting models of prognostic effects of acupuncture for depression. Experimental results show that nonlinear machine learning methods have better performance than linear ones on predicting prognostic effects of acupuncture using EEG recordings. Especially, the support vector machine with Gaussian kernel (SVM-RBF) can achieve the best and most stable performance using the mRMR with both evaluating criteria of FCD and FCQ for feature selection. Both mRMR-FCD and mRMR-FCQ obtain the same best performance, where the accuracy and F1 score are 84.61% and 86.67%, respectively. Moreover, lead-rhythm features selected by mRMR-FCD and mRMR-FCQ are analyzed. The top seven selected lead-rhythm features have much higher mRMR evaluating scores, which guarantee the good predicting performance for machine learning methods to some degree. The presented framework in this work is effective in predicting the prognostic effects of acupuncture for depression. It can be integrated into an intelligent medical system and provide information on the prognostic effects of acupuncture for physicians. Informed prognostic effects of acupuncture for depression in advance and taking interventions can greatly reduce the risk of malignant events for patients with mental disorders.
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Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Med J 2022; 63:S93-S107. [PMID: 35040610 PMCID: PMC8790582 DOI: 10.3349/ymj.2022.63.s93] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. MATERIALS AND METHODS The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. RESULTS A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). CONCLUSION This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Affiliation(s)
- Solam Lee
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yuseong Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Jiseung Ryu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - Young Jun Park
- Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea.
| | - Sang Baek Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
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Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res 2021; 23:e26522. [PMID: 34847057 PMCID: PMC8669587 DOI: 10.2196/26522] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/21/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.
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Affiliation(s)
- Kathrin Seibert
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Domhoff
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Bruch
- Auf- und Umbruch im Gesundheitswesen UG, Bonn, Germany
| | - Matthias Schulte-Althoff
- School of Business and Economics, Department of Information Systems, Freie Universität Berlin, Einstein Center Digital Future, Berlin, Germany
| | - Daniel Fürstenau
- Department of Digitalization, Copenhagen Business School, Frederiksberg, Denmark.,Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Biessmann
- Faculty VI - Informatics and Media, Beuth University of Applied Sciences, Einstein Center Digital Future, Berlin, Germany
| | - Karin Wolf-Ostermann
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
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Viana JN, Edney S, Gondalia S, Mauch C, Sellak H, O'Callaghan N, Ryan JC. Trends and gaps in precision health research: a scoping review. BMJ Open 2021; 11:e056938. [PMID: 34697128 PMCID: PMC8547511 DOI: 10.1136/bmjopen-2021-056938] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/08/2021] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE To determine progress and gaps in global precision health research, examining whether precision health studies integrate multiple types of information for health promotion or restoration. DESIGN Scoping review. DATA SOURCES Searches in Medline (OVID), PsycINFO (OVID), Embase, Scopus, Web of Science and grey literature (Google Scholar) were carried out in June 2020. ELIGIBILITY CRITERIA Studies should describe original precision health research; involve human participants, datasets or samples; and collect health-related information. Reviews, editorial articles, conference abstracts or posters, dissertations and articles not published in English were excluded. DATA EXTRACTION AND SYNTHESIS The following data were extracted in independent duplicate: author details, study objectives, technology developed, study design, health conditions addressed, precision health focus, data collected for personalisation, participant characteristics and sentence defining 'precision health'. Quantitative and qualitative data were summarised narratively in text and presented in tables and graphs. RESULTS After screening 8053 articles, 225 studies were reviewed. Almost half (105/225, 46.7%) of the studies focused on developing an intervention, primarily digital health promotion tools (80/225, 35.6%). Only 28.9% (65/225) of the studies used at least four types of participant data for tailoring, with personalisation usually based on behavioural (108/225, 48%), sociodemographic (100/225, 44.4%) and/or clinical (98/225, 43.6%) information. Participant median age was 48 years old (IQR 28-61), and the top three health conditions addressed were metabolic disorders (35/225, 15.6%), cardiovascular disease (29/225, 12.9%) and cancer (26/225, 11.6%). Only 68% of the studies (153/225) reported participants' gender, 38.7% (87/225) provided participants' race/ethnicity, and 20.4% (46/225) included people from socioeconomically disadvantaged backgrounds. More than 57% of the articles (130/225) have authors from only one discipline. CONCLUSIONS Although there is a growing number of precision health studies that test or develop interventions, there is a significant gap in the integration of multiple data types, systematic intervention assessment using randomised controlled trials and reporting of participant gender and ethnicity. Greater interdisciplinary collaboration is needed to gather multiple data types; collectively analyse big and complex data; and provide interventions that restore, maintain and/or promote good health for all, from birth to old age.
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Affiliation(s)
- John Noel Viana
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
- Australian National Centre for the Public Awareness of Science, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Sarah Edney
- Physical Activity and Nutrition Determinants in Asia (PANDA) programme, Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Shakuntla Gondalia
- Precision Health Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
| | - Chelsea Mauch
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
| | - Hamza Sellak
- Precision Health Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
- Data61, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - Nathan O'Callaghan
- Precision Health Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
| | - Jillian C Ryan
- Precision Health Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Adelaide, South Australia, Australia
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Zompanti A, Sabatini A, Grasso S, Pennazza G, Ferri G, Barile G, Chello M, Lusini M, Santonico M. Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit. SENSORS (BASEL, SWITZERLAND) 2021; 21:2777. [PMID: 33920787 PMCID: PMC8071160 DOI: 10.3390/s21082777] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 01/06/2023]
Abstract
The use of wearable sensors for health monitoring is rapidly growing. Over the past decade, wearable technology has gained much attention from the tech industry for commercial reasons and the interest of researchers and clinicians for reasons related to its potential benefit on patients' health. Wearable devices use advanced and specialized sensors able to monitor not only activity parameters, such as heart rate or step count, but also physiological parameters, such as heart electrical activity or blood pressure. Electrocardiogram (ECG) monitoring is becoming one of the most attractive health-related features of modern smartwatches, and, because cardiovascular disease (CVD) is one of the leading causes of death globally, the use of a smartwatch to monitor patients could greatly impact the disease outcomes on health care systems. Commercial wearable devices are able to record just single-lead ECG using a couple of metallic contact dry electrodes. This kind of measurement can be used only for arrhythmia diagnosis. For the diagnosis of other cardiac disorders, additional ECG leads are required. In this study, we characterized an electronic interface to be used with multiple contactless capacitive electrodes in order to develop a wearable ECG device able to perform several lead measurements. We verified the ability of the electronic interface to amplify differential biopotentials and to reject common-mode signals produced by electromagnetic interference (EMI). We developed a portable device based on the studied electronic interface that represents a prototype system for further developments. We evaluated the performances of the developed device. The signal-to-noise ratio of the output signal is favorable, and all the features needed for a clinical evaluation (P waves, QRS complexes and T waves) are clearly readable.
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Affiliation(s)
- Alessandro Zompanti
- Unit of Electronics for Sensor Systems, Department of Engineering, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (A.S.); (G.P.)
| | - Anna Sabatini
- Unit of Electronics for Sensor Systems, Department of Engineering, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (A.S.); (G.P.)
| | - Simone Grasso
- Unit of Electronics for Sensor Systems, Department of Science and Technology for Humans and the Environment, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (S.G.); (M.S.)
| | - Giorgio Pennazza
- Unit of Electronics for Sensor Systems, Department of Engineering, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (A.S.); (G.P.)
| | - Giuseppe Ferri
- Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy; (G.F.); (G.B.)
| | - Gianluca Barile
- Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy; (G.F.); (G.B.)
| | - Massimo Chello
- Unit of Cardiology, Department of Medicine, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (M.C.); (M.L.)
| | - Mario Lusini
- Unit of Cardiology, Department of Medicine, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (M.C.); (M.L.)
| | - Marco Santonico
- Unit of Electronics for Sensor Systems, Department of Science and Technology for Humans and the Environment, Campus Bio-Medico University of Rome, 00128 Rome, Italy; (S.G.); (M.S.)
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Wang H, Zhao Y, Yu L, Liu J, Zwetsloot IM, Cabrera J, Tsui KL. A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study. J Med Internet Res 2020; 22:e19223. [PMID: 32996887 PMCID: PMC7557449 DOI: 10.2196/19223] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/21/2020] [Accepted: 06/25/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. OBJECTIVE Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. METHODS The system was designed to capture and record older adults' health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users' attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. RESULTS A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants' daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). CONCLUSIONS The proposed health monitoring system provides an example design for monitoring older adults' health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.
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Affiliation(s)
- Hailiang Wang
- Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong, China
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yang Zhao
- Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong, China
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Lisha Yu
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Jiaxing Liu
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Inez Maria Zwetsloot
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
| | - Javier Cabrera
- Department of Statistics and Biostatistics, Rutgers University, New Brunswick, NJ, United States
| | - Kwok-Leung Tsui
- Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong, China
- School of Data Science, City University of Hong Kong, Hong Kong, China
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Serhani MA, T. El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1796. [PMID: 32213969 PMCID: PMC7147367 DOI: 10.3390/s20061796] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 02/01/2023]
Abstract
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.
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Affiliation(s)
- Mohamed Adel Serhani
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Heba Ismail
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Alramzana Nujum Navaz
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
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