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Nourse R, Dingler T, Kelly J, Kwasnicka D, Maddison R. The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions. J Med Internet Res 2023; 25:e44265. [PMID: 38109188 PMCID: PMC10758944 DOI: 10.2196/44265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 06/07/2023] [Accepted: 06/29/2023] [Indexed: 12/19/2023] Open
Abstract
The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.
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Affiliation(s)
- Rebecca Nourse
- School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
| | - Tilman Dingler
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Jaimon Kelly
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Dominika Kwasnicka
- NHMRC CRE in Digital Technology to Transform Chronic Disease Outcomes, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Wroclaw, Poland
| | - Ralph Maddison
- School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
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2
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Vu T, Petty T, Yakut K, Usman M, Xue W, Haas FM, Hirsh RA, Zhao X. Real-time arrhythmia detection using convolutional neural networks. Front Big Data 2023; 6:1270756. [PMID: 38058406 PMCID: PMC10696646 DOI: 10.3389/fdata.2023.1270756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023] Open
Abstract
Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.
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Affiliation(s)
- Thong Vu
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
| | - Tyler Petty
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
| | - Kemal Yakut
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Muhammad Usman
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Wei Xue
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Francis M. Haas
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Robert A. Hirsh
- Department of Anesthesiology, Cooper University Hospital, Camden, NJ, United States
| | - Xinghui Zhao
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
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3
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Fawaz A, Ferraresi A, Isidoro C. Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making. J Pers Med 2023; 13:1590. [PMID: 38003905 PMCID: PMC10672164 DOI: 10.3390/jpm13111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.
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Affiliation(s)
| | | | - Ciro Isidoro
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, 28100 Novara, Italy; (A.F.); (A.F.)
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Shaikh Y, Gibbons MC. Pathophysiologic Basis of Connected Health Systems. J Med Internet Res 2023; 25:e42405. [PMID: 37733435 PMCID: PMC10557002 DOI: 10.2196/42405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 03/29/2023] [Accepted: 08/13/2023] [Indexed: 09/22/2023] Open
Abstract
Since the start of the COVID-19 pandemic, there has been a rapid transition to telehealth across the United States, primarily involving virtual clinic visits. Additionally, the proliferation of consumer technologies related to health reveals that for many people health and care in the contemporary world extends beyond the boundaries of a clinical interaction and includes sensors and devices that facilitate health in personal environments. The ideal connected environment is networked and intelligent, personalized to promote health and prevent disease. The combination of sensors, devices, and intelligence constitutes a connected health system around an individual that is optimized to improve and maintain health, deliver care, and predict and reduce risk of illness. Just as modern medicine uses the pathophysiology of disease as a framework for the basis of pharmacologic therapy, a similar clinically reasoned approach can be taken to organize and architect technological elements into therapeutic systems. In this work, we introduce a systematic methodology for the design of connected health systems grounded in the pathophysiologic basis of disease. As the digital landscape expands with the ubiquity of health devices, it is pivotal to enable technology-agnostic clinical reasoning to guide the integration of technological innovations into systems of health and care delivery that extend beyond the boundaries of a clinical interaction. Applying clinical reasoning in a repeatable and systematic way to organizing technology into therapeutic systems can yield potential benefits including expanding the study of digital therapeutics from individual devices to networked technologies as therapeutic interventions; empowering physicians who are not technological experts to still play a significant role in using clinical reasoning for architecting therapeutic networks of sensors and devices; and developing platforms to catalog and share combinations of technologies that can form therapeutic networks and connected health systems.
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Affiliation(s)
- Yahya Shaikh
- The MITRE Corporation, Windsor Mill, MD, United States
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5
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Ahmed S, Irfan S, Kiran N, Masood N, Anjum N, Ramzan N. Remote Health Monitoring Systems for Elderly People: A Survey. Sensors (Basel) 2023; 23:7095. [PMID: 37631632 PMCID: PMC10458487 DOI: 10.3390/s23167095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/03/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023]
Abstract
This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.
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Affiliation(s)
- Salman Ahmed
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Saad Irfan
- Department of Information Engineering Technology, National Skills University, Islamabad 44000, Pakistan;
| | - Nasira Kiran
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK; (N.K.); (N.R.)
| | - Nayyer Masood
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Nadeem Anjum
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan; (N.M.); (N.A.)
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK; (N.K.); (N.R.)
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6
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Zhang Q, Zhou D. Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction. Sensors (Basel) 2023; 23:5723. [PMID: 37420885 DOI: 10.3390/s23125723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 07/09/2023]
Abstract
BACKGROUND Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. METHODS A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. RESULTS With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. CONCLUSIONS Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.
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Affiliation(s)
- Qingxue Zhang
- Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Purdue School of Engineering and Technology, 723 W. Michigan St., Indianapolis, IN 46202, USA
| | - Dian Zhou
- Department of Electrical and Computer Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA
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Siddiqi MH, Idris M, Alruwaili M. FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis. Healthcare (Basel) 2023; 11:1713. [PMID: 37372831 DOI: 10.3390/healthcare11121713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/04/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.
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Affiliation(s)
| | | | - Madallah Alruwaili
- College of Computer and Information Sciences, Jouf University, Sakaka 73211, Saudi Arabia
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8
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Reviewing Federated Machine Learning and Its Use in Diseases Prediction. Sensors (Basel) 2023; 23:s23042112. [PMID: 36850717 PMCID: PMC9958993 DOI: 10.3390/s23042112] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 05/31/2023]
Abstract
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12-24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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9
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors (Basel) 2023; 23:s23020828. [PMID: 36679626 PMCID: PMC9865666 DOI: 10.3390/s23020828] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Zhang JR, Wu YE, Huang YF, Zhang SQ, Pan WL, Huang JX, Huang QP. Effectiveness of smart health-based rehabilitation on patients with poststroke dysphagia: A brief research report. Front Neurol 2023; 13:1110067. [PMID: 36698875 PMCID: PMC9868154 DOI: 10.3389/fneur.2022.1110067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023] Open
Abstract
Objective This study aimed to evaluate the effectiveness of smart health-based rehabilitation on patients with poststroke dysphagia (PSD). Methods We recruited 60 PSD patients and randomly allocated them to the intervention (n = 30) and control (n = 30) groups. The former received the smart health-based rehabilitation for 12 weeks, whereas the latter received routine rehabilitation. Water swallow test (WST), standardized swallowing assessment (SSA), swallow quality-of-life questionnaire (SWAL-QOL), stroke self-efficacy questionnaire (SSEQ), perceived social support scale (PSSS) and nutritional measurements including body weight, triceps skinfold thickness (TSF), total protein (TP), serum albumin (ALB) and serum prealbumin (PA) in both groups were measured. Results When the baseline WST, SSA, SWAL-QOL, SSEQ, PSSS and nutritional measurements were examined, there was no significant difference between the intervention group and the control group (P > 0.05). After rehabilitation interventions, the WST and SSA scores in the intervention group were significantly lower than those in the control group (P < 0.01). The SWAL-QOL, SSEQ and PSSS scores in the intervention group were significantly higher than in the control group (P < 0.01). Compared with the control group, the intervention group showed an increase in the serum levels of PA (P < 0.01). However, no statistically significant difference existed between the intervention group and the control group in terms of body weight, TSF, TP or ALB (P > 0.05). Conclusions Overall, our data revealed that smart health-based rehabilitation is significantly beneficial to the swallowing function, quality of life, self-efficacy, and social support for PSD patients when compared with routine rehabilitation. However, nutritional measurements were not significantly improved in such patients under the smart health-based rehabilitation when compared the routine rehabilitation. In the future, it is necessary to extend the intervention time to further evaluate the long-term efficacy of smart health-based rehabilitation on nutritional measurements of PSD patients.
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LeBaron V, Homdee N, Ogunjirin E, Patel N, Blackhall L, Lach J. Describing and visualizing the patient and caregiver experience of cancer pain in the home context using ecological momentary assessments. Digit Health 2023; 9:20552076231194936. [PMID: 37654707 PMCID: PMC10467200 DOI: 10.1177/20552076231194936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/28/2023] [Indexed: 09/02/2023] Open
Abstract
Background Pain continues to be a difficult and pervasive problem for patients with cancer, and those who care for them. Remote health monitoring systems (RHMS), such as the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), can utilize Ecological Momentary Assessments (EMAs) to provide a more holistic understanding of the patient and family experience of cancer pain within the home context. Methods Participants used the BESI-C system for 2-weeks which collected data via EMAs deployed on wearable devices (smartwatches) worn by both patients with cancer and their primary family caregiver. We developed three unique EMA schemas that allowed patients and caregivers to describe patient pain events and perceived impact on quality of life from their own perspective. EMA data were analyzed to provide a descriptive summary of pain events and explore different types of data visualizations. Results Data were collected from five (n = 5) patient-caregiver dyads (total 10 individual participants, 5 patients, 5 caregivers). A total of 283 user-initiated pain event EMAs were recorded (198 by patients; 85 by caregivers) over all 5 deployments with an average severity score of 5.4/10 for patients and 4.6/10 for caregivers' assessments of patient pain. Average self-reported overall distress and pain interference levels (1 = least distress; 4 = most distress) were higher for caregivers (x ¯ 3.02, x ¯ 2.60 , respectively ) compared to patients (x ¯ 2.82, x ¯ 2.25, respectively) while perceived burden of partner distress was higher for patients (i.e., patients perceived caregivers to be more distressed, x ¯ 3.21, than caregivers perceived patients to be distressed, x ¯ 2.55 ). Data visualizations were created using time wheels, bubble charts, box plots and line graphs to graphically represent EMA findings. Conclusion Collecting data via EMAs is a viable RHMS strategy to capture longitudinal cancer pain event data from patients and caregivers that can inform personalized pain management and distress-alleviating interventions.
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Affiliation(s)
- Virginia LeBaron
- University of Virginia School of Nursing, Charlottesville, VA, USA
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Emmanuel Ogunjirin
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, USA
| | - Nyota Patel
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, USA
| | - Leslie Blackhall
- University of Virginia School of Medicine, Charlottesville, VA, USA
| | - John Lach
- The George Washington University School of Engineering & Applied Science, Washington, DC, USA
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12
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Deng J, Huang S, Wang L, Deng W, Yang T. Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes. Int J Environ Res Public Health 2022; 19:16742. [PMID: 36554622 PMCID: PMC9779490 DOI: 10.3390/ijerph192416742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Smart health is considered to be a new phase in the application of information and communication technologies (ICT) in healthcare that can improve its efficiency and sustainability. However, based on our literature review on the concept of smart health, there is a lack of a comprehensive perspective on the concept of smart health and a framework for how to link the drivers and outcomes of smart health. This paper aims to interweave the drivers and outcomes in a multi-dimensional framework under the input-process-output (IPO) logic of the "system view" so as to promote a deeper understanding of the model of smart health. In addition to the collection of studies, we used the modified Delphi method (MDM) to invite 10 experts from different fields, and the views of the panelists were analyzed and integrated through a three-round iterative process to reach a consensus on the elements included in the conceptual framework. The study revealed that smart health contains five drivers (community, technology, policy, service, and management) and eight outcomes (efficient, smart, sustainable, planned, trustworthy, safe, equitable, health-beneficial, and economic). They all represent a unique aspect of smart health. This paper expands the research horizon of smart health, shifting from a single technology to multiple perspectives, such as community and management, to guide the development of policies and plans in order to promote smart health.
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Affiliation(s)
- Jianwei Deng
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
- Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China
| | - Sibo Huang
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
- Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China
| | - Liuan Wang
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Wenhao Deng
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
- Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China
| | - Tianan Yang
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
- Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China
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13
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. Sensors (Basel) 2022; 22:s22197472. [PMID: 36236570 PMCID: PMC9573761 DOI: 10.3390/s22197472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 05/13/2023]
Abstract
Today's world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a "job" or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
- Correspondence: ; Tel.: +1-(581)624-9394
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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14
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Ghouali S, Onyema EM, Guellil MS, Wajid MA, Clare O, Cherifi W, Feham M. Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy. IEEE Open J Eng Med Biol 2022; 3:124-133. [PMID: 36712318 PMCID: PMC9870271 DOI: 10.1109/ojemb.2022.3192780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/22/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.
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Affiliation(s)
- S Ghouali
- Faculty of Sciences and TechnologyMustapha Stambouli University Mascara 29000 Algeria
| | - E M Onyema
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Adjunct Faculty, Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai 602105 India
| | - M S Guellil
- Faculty of Economics, Business and Management Sciences, MCLDL LaboratoryUniversity of Mascara Mascara 29000 Algeria
| | - M A Wajid
- Department of Computer ScienceAligarh Muslim University Aligarh 202002 India
| | - O Clare
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
| | - W Cherifi
- InnoDev (Dev Software) Tlemcen 13000 Algeria
| | - M Feham
- STIC Lab, Faculty of TechnologyUniversity of Tlemcen Tlemcen 13000 Algeria
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15
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Mulita F, Verras GI, Anagnostopoulos CN, Kotis K. A Smarter Health through the Internet of Surgical Things. Sensors (Basel) 2022; 22:s22124577. [PMID: 35746359 PMCID: PMC9231158 DOI: 10.3390/s22124577] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 05/14/2023]
Abstract
(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the most important studies evaluating the IoT concept within surgical practice, focusing on Telesurgery and surgical Telementoring. (2) Methods: We conducted a systematic review of the current literature, focusing on the Internet of Surgical Things in Telesurgery and Telementoring. Forty-eight (48) studies were included in this review. As secondary research questions, we also included brief overviews of the use of IoT in image-guided surgery, and patient Telemonitoring, by systematically analyzing fourteen (14) and nineteen (19) studies, respectively. (3) Results: Data from 219 patients and 757 healthcare professionals were quantitively analyzed. Study designs were primarily observational or based on model development. Palpable advantages from the IoT incorporation mainly include less surgical hours, accessibility to high quality treatment, and safer and more effective surgical education. Despite the described technological advances, and proposed benefits of the systems presented, there are still identifiable gaps in the literature that need to be further explored in a systematic manner. (4) Conclusions: The use of the IoT concept within the surgery domain is a widely incorporated but less investigated concept. Advantages have become palpable over the past decade, yet further research is warranted.
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Affiliation(s)
- Francesk Mulita
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Department of Surgery, General University Hospital of Patras, 26504 Rio, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
| | | | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
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16
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Syed AS, Sierra-Sosa D, Kumar A, Elmaghraby A. A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition. Sensors (Basel) 2022; 22:s22072547. [PMID: 35408163 PMCID: PMC9002977 DOI: 10.3390/s22072547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/16/2022] [Accepted: 03/24/2022] [Indexed: 01/12/2023]
Abstract
Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.
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Affiliation(s)
- Abbas Shah Syed
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
- Correspondence:
| | - Daniel Sierra-Sosa
- Department of Computer Science and Information Technology, Hood College, Frederick, MD 21701, USA;
| | - Anup Kumar
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| | - Adel Elmaghraby
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
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17
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Gurugubelli VS, Fang H, Shikany JM, Balkus SV, Rumbut J, Ngo H, Wang H, Allison JJ, Steffen LM. A review of harmonization methods for studying dietary patterns. Smart Health (Amst) 2022; 23:100263. [PMID: 35252528 PMCID: PMC8896407 DOI: 10.1016/j.smhl.2021.100263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Data harmonization is the process by which each of the variables from different research studies are standardized to similar units resulting in comparable datasets. These data may be integrated for more powerful and accurate examination and prediction of outcomes for use in the intelligent and smart electronic health software programs and systems. Prospective harmonization is performed when researchers create guidelines for gathering and managing the data before data collection begins. In contrast, retrospective harmonization is performed by pooling previously collected data from various studies using expert domain knowledge to identify and translate variables. In nutritional epidemiology, dietary data harmonization is often necessary to construct the nutrient and food databases necessary to answer complex research questions and develop effective public health policy. In this paper, we review methods for effective data harmonization, including developing a harmonization plan, which common standards already exist for harmonization, and defining variables needed to harmonize datasets. Currently, several large-scale studies maintain harmonized nutrient databases, especially in Europe, and steps have been proposed to inform the retrospective harmonization process. As an example, data harmonization methods are applied to several U.S longitudinal diet datasets. Based on our review, considerations for future dietary data harmonization include user agreements for sharing private data among participating studies, defining variables and data dictionaries that accurately map variables among studies, and the use of secure data storage servers to maintain privacy. These considerations establish necessary components of harmonized data for smart health applications which can promote healthier eating and provide greater insights into the effect of dietary patterns on health.
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Affiliation(s)
| | - Hua Fang
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
- Corresponding author. Tel.: +0-508-910-6411;
| | - James M Shikany
- Division of Preventive Medicine, University of Alabama at Birmingham, 1720 University Blvd, Birmingham, 35294, Alabama, USA
| | - Salvador V Balkus
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Joshua Rumbut
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
| | - Hieu Ngo
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Honggang Wang
- University of Massachusetts Dartmouth, 285 Old Westport Rd, North Dartmouth, 02747, Massachusetts, USA
| | - Jeroan J Allison
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 N Lake Ave, Worcester, 01655, Massachusetts, USA
| | - Lyn M. Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, 55455, Minnesota, USA
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Cristiano A, Musteata S, De Silvestri S, Bellandi V, Ceravolo P, Cesari M, Azzolino D, Sanna A, Trojaniello D. Older Adults' and Clinicians' Perspectives on a Smart Health Platform for the Aging Population: Design and Evaluation Study. JMIR Aging 2022; 5:e29623. [PMID: 35225818 PMCID: PMC8922154 DOI: 10.2196/29623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/14/2022] Open
Abstract
Background Over recent years, interest in the development of smart health technologies aimed at supporting independent living for older populations has increased. The integration of innovative technologies, such as the Internet of Things, wearable technologies, artificial intelligence, and ambient-assisted living applications, represents a valuable solution for this scope. Designing such an integrated system requires addressing several aspects (eg, equipment selection, data management, analytics, costs, and users’ needs) and involving different areas of expertise (eg, medical science, service design, biomedical and computer engineering). Objective The objective of this study is 2-fold; we aimed to design the functionalities of a smart health platform addressing 5 chronic conditions prevalent in the older population (ie, hearing loss, cardiovascular diseases, cognitive impairments, mental health problems, and balance disorders) by considering both older adults’ and clinicians’ perspectives and to evaluate the identified smart health platform functionalities with a small group of older adults. Methods Overall, 24 older adults (aged >65 years) and 118 clinicians were interviewed through focus group activities and web-based questionnaires to elicit the smart health platform requirements. Considering the elicited requirements, the main functionalities of smart health platform were designed. Then, a focus group involving 6 older adults was conducted to evaluate the proposed solution in terms of usefulness, credibility, desirability, and learnability. Results Eight main functionalities were identified and assessed—cognitive training and hearing training (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 6/6, 100%; learnability: 6/6, 100%), monitoring of physiological parameters (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 6/6, 100%; learnability: 5/6, 83%), physical training (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 5/6, 83%; learnability: 2/6, 33%), psychoeducational intervention (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 4/6, 67%; learnability: 2/6, 33%), mood monitoring (usefulness: 4/6, 67%; credibility: 4/6, 67%; desirability: 3/6, 50%; learnability: 5/6, 50%), diet plan (usefulness: 5/6, 83%; credibility: 4/6, 67%; desirability: 1/6, 17%; learnability: 2/6, 33%), and environment monitoring and adjustment (usefulness: 1/6, 17%; credibility: 1/6, 17%; desirability: 0/6, 0%; learnability: 0/6, 0%). Most of them were highly appreciated by older participants, with the only exception being environment monitoring and adjustment. The results showed that the proposed functionalities met the needs and expectations of users (eg, improved self-management of patients’ disease and enhanced patient safety). However, some aspects need to be addressed (eg, technical and privacy issues). Conclusions The presented smart health platform functionalities seem to be able to meet older adults’ needs and desires to enhance their self-awareness and self-management of their medical condition, encourage healthy and independent living, and provide evidence-based support for clinicians’ decision-making. Further research with a larger and more heterogeneous pool of stakeholders in terms of demographics and clinical conditions is needed to assess system acceptability and overall user experience in free-living conditions.
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Affiliation(s)
- Alessia Cristiano
- Center for Advanced Technology in Health and Wellbeing, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Stela Musteata
- Center for Advanced Technology in Health and Wellbeing, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Sara De Silvestri
- Center for Advanced Technology in Health and Wellbeing, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Valerio Bellandi
- Department of Computer Science, Università Degli Studi di Milano, Milan, Italy
| | - Paolo Ceravolo
- Department of Computer Science, Università Degli Studi di Milano, Milan, Italy
| | - Matteo Cesari
- Geriatric Unit, Istituto di Ricovero e Cura a Carattere Scientifico Istituti Clinici Scientifici Maugeri, Università Degli Studi di Milano, Milan, Italy
| | - Domenico Azzolino
- Geriatric Unit, Istituto di Ricovero e Cura a Carattere Scientifico Istituti Clinici Scientifici Maugeri, Università Degli Studi di Milano, Milan, Italy
| | - Alberto Sanna
- Center for Advanced Technology in Health and Wellbeing, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Diana Trojaniello
- Center for Advanced Technology in Health and Wellbeing, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
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19
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Shimizu Y, Ishizuna A, Osaki S, Hashimoto T, Tai M, Tanibe T, Karasawa K. The Social Acceptance of Smart Health Services in Japan. Int J Environ Res Public Health 2022; 19:1298. [PMID: 35162321 DOI: 10.3390/ijerph19031298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/19/2022] [Accepted: 01/22/2022] [Indexed: 02/04/2023]
Abstract
In recent years, smart health (s-Health) services have gained momentum worldwide. The s-Health services obtain personal information and aim to provide efficient health and medical services based on these data. In Japan, active efforts to implement these services have increased, but there is a lack of social acceptance. This study examined social acceptance concerning various factors such as trust in the city government, perceived benefits, perceived necessity, perceived risk, and concern about interventions for individuals. An online survey was conducted, and Japanese participants (N = 720) were presented with a vignette depicting a typical s-Health service overview. The results of structural equation modeling showed that trust was positively related to perceived benefit and necessity and negatively related to perceived risk and concern about interventions for individuals. Perceived benefit and trust were positively related to social acceptance, and perceived risk was negatively related to acceptance. The model obtained in this study can help implement s-Health services in public. Empirical studies that contribute to improving public health by investigating the social acceptance of s-Health services should be conducted in the future.
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20
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Anderson MS, Bankole A, Homdee N, Mitchell BA, Byfield GE, Lach J. Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study. JMIR Aging 2021; 4:e30353. [PMID: 34874886 PMCID: PMC8691404 DOI: 10.2196/30353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/14/2021] [Accepted: 09/26/2021] [Indexed: 01/26/2023] Open
Abstract
Background Caregiver burden associated with dementia-related agitation is one of the most common reasons for a community-dwelling person living with dementia to transition to a care facility. The Behavioral and Environmental Sensing and Intervention (BESI) for the Dementia Caregiver Empowerment system uses sensing technology, smartwatches, tablets, and data analytics to detect and predict agitation in persons living with dementia and to provide just-in-time notifications and dyad-specific intervention recommendations to caregivers. The BESI system has shown that there is a valid relationship between dementia-related agitation and environmental factors and that caregivers prefer a home-based monitoring system. Objective The aim of this study is to obtain input from caregivers of persons living with dementia on the value, usability, and acceptability of the BESI system in the home setting and obtain their insights and recommendations for the next stage of system development. Methods A descriptive qualitative design with thematic analysis was used to analyze 10 semistructured interviews with caregivers. The interviews comprised 16 questions, with an 80% (128/160) response rate. Results Postdeployment caregiver feedback about the BESI system and the overall experience were generally positive. Caregivers acknowledged the acceptability of the system by noting the ease of use and saw the system as a fit for them. Functionality issues such as timeliness in agitation notification and simplicity in the selection of agitation descriptors on the tablet interface were identified, and caregivers indicated a desire for more word options to describe agitation behaviors. Agitation intervention suggestions were well received by the caregivers, and the resulting decrease in the number and severity of agitation events helped confirm that the BESI system has good value and acceptability. Thematic analysis suggested several subjective experiences and yielded the themes of usefulness and helpfulness. Conclusions This study determined preferences for assessing caregiver strain and burden, explored caregiver acceptance of the technology system (in-home sensors, actigraph or smart watch technology, and tablet devices), discerned caregiver insights on the burden and stress of caring for persons living with dementia experiencing agitation in dementia, and solicited caregiver input and recommendations for system changes. The themes of usefulness and helpfulness support the use of caregiver knowledge and experience to inform further development of the technology.
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Affiliation(s)
- Martha Smith Anderson
- Department of Health Care Innovation and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, United States
| | - Azziza Bankole
- Department of Psychiatry and Behavioral Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA, United States
| | - Nutta Homdee
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Brook A Mitchell
- Virginia Tech Carilion School of Medicine, Roanoke, VA, United States
| | - Grace E Byfield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - John Lach
- Department of Electrical and Computer Engineering, The George Washington University, Washington, DC, United States
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21
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Parah SA, Kaw JA, Bellavista P, Loan NA, Bhat GM, Muhammad K, de Albuquerque VHC. Efficient Security and Authentication for Edge-Based Internet of Medical Things. IEEE Internet Things J 2021; 8:15652-15662. [PMID: 35582243 PMCID: PMC8956370 DOI: 10.1109/jiot.2020.3038009] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 10/17/2020] [Accepted: 10/20/2020] [Indexed: 05/11/2023]
Abstract
Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content. The proposed approach is based on novel left data mapping (LDM), pixel repetition method (PRM), RC4 encryption, and checksum computation. The input image of size [Formula: see text] is upscaled by using PRM that guarantees reversibility with lesser computational complexity. The binary secret data are encrypted using the RC4 encryption algorithm and then the encrypted data are grouped into 3-bit chunks and converted into decimal equivalents. Before embedding, these decimal digits are encoded by LDM. To embed the shifted data, the cover image is divided into [Formula: see text] blocks and then in each block, two digits are embedded into the counter diagonal pixels. For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels. A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection. The average peak signal to noise ratio (PSNR) of the stego-images obtained is 41.95 dB for a very high embedding capacity of 2.25 bits per pixel. Furthermore, the embedding time is less than 0.2 s. Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper. All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT-based smart health.
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Affiliation(s)
- Shabir A Parah
- Department of Electronics and Instrumentation TechnologyUniversity of Kashmir Srinagar 190006 India
| | - Javaid A Kaw
- Department of Electronics and Instrumentation TechnologyUniversity of Kashmir Srinagar 190006 India
| | - Paolo Bellavista
- Department of Computer Science and EngineeringUniversity of Bologna 40136 Bologna Italy
| | - Nazir A Loan
- Department of Electronics and Instrumentation TechnologyUniversity of Kashmir Srinagar 190006 India
| | - G M Bhat
- Department of Electronics EngineeringInstitute of Technology, University of Kashmir (Zakura Campus) Srinagar 190006 India
| | - Khan Muhammad
- Department of SoftwareSejong University Seoul 143-747 South Korea
| | - Victor Hugo C de Albuquerque
- LAPISCOFederal Institute of Education, Science and Technology of Ceará Fortaleza 60811-905 Brazil
- ARMTEC Tecnologia em Robótica Fortaleza 60811-341 Brazil
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22
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Hannan A, Shafiq MZ, Hussain F, Pires IM. A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction. Sensors (Basel) 2021; 21:s21196692. [PMID: 34641012 PMCID: PMC8512175 DOI: 10.3390/s21196692] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 10/02/2021] [Accepted: 10/03/2021] [Indexed: 11/16/2022]
Abstract
Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today's time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.
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Affiliation(s)
- Abdul Hannan
- Knowledge Unit of System and Technology, University of Management and Technology, Sialkot 51310, Pakistan
- Correspondence: (A.H.); (F.H.); (I.M.P.)
| | - Muhammad Zohaib Shafiq
- Department of Computer Science and Engineering, Università di Bologna, 40126 Bologna, Italy;
| | - Faisal Hussain
- Al-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore 54890, Pakistan
- Correspondence: (A.H.); (F.H.); (I.M.P.)
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
- Escola de Ciências e Tecnologias, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
- Correspondence: (A.H.); (F.H.); (I.M.P.)
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Syed AS, Sierra-Sosa D, Kumar A, Elmaghraby A. A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling. Sensors (Basel) 2021; 21:s21196653. [PMID: 34640974 PMCID: PMC8512095 DOI: 10.3390/s21196653] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/19/2021] [Accepted: 10/04/2021] [Indexed: 02/01/2023]
Abstract
Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.
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Affiliation(s)
- Abbas Shah Syed
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
- Correspondence:
| | - Daniel Sierra-Sosa
- Department of Computer Science and Information Technology, Hood College, Frederick, MD 21701, USA;
| | - Anup Kumar
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| | - Adel Elmaghraby
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
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24
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Ferre M, Batista E, Solanas A, Martínez-Ballesté A. Smart Health-Enhanced Early Mobilisation in Intensive Care Units. Sensors (Basel) 2021; 21:5408. [PMID: 34450850 DOI: 10.3390/s21165408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/04/2021] [Accepted: 08/06/2021] [Indexed: 12/04/2022]
Abstract
Critically ill patients that stay in Intensive Care Units (ICU) for long periods suffer from Post-Intensive Care Syndrome or ICU Acquired Weakness, whose effects can decrease patients’ quality of life for years. To prevent such issues and aiming at shortening intensive care treatments, Early Mobilisation (EM) has been proposed as an encouraging technique: the literature includes numerous examples of the benefits of EM on the prevention of post-operative complications and adverse events. However, the appropriate application of EM programmes entails the use of scarce resources, both human and technical. Information and Communication Technologies can play a key role in reducing cost and improving the practice of EM. Although there is rich literature on EM practice and its potential benefits, there are some barriers that must be overcome, and technology, i.e., the use of sensors, robotics or information systems, can contribute to that end. This article reviews the literature and analyses on the use of technology in the area of EM, and moreover, it proposes a smart health-enhanced scenario.
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25
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Bayahya AY, Alhalabi W, AlAmri SH. Smart Health System to Detect Dementia Disorders Using Virtual Reality. Healthcare (Basel) 2021; 9:healthcare9070810. [PMID: 34203116 PMCID: PMC8307494 DOI: 10.3390/healthcare9070810] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/06/2021] [Accepted: 02/25/2021] [Indexed: 11/16/2022] Open
Abstract
Smart health technology includes physical sensors, intelligent sensors, and output advice to help monitor patients’ health and adjust their behavior. Virtual reality (VR) plays an increasingly larger role to improve health outcomes, being used in a variety of medical specialties including robotic surgery, diagnosis of some difficult diseases, and virtual reality pain distraction for severe burn patients. Smart VR health technology acts as a decision support system in the diseases diagnostic test of patients as they perform real world tasks in virtual reality (e.g., navigation). In this study, a non-invasive, cognitive computerized test based on 3D virtual environments for detecting the main symptoms of dementia (memory loss, visuospatial defects, and spatial navigation) is proposed. In a recent study, the system was tested on 115 real patients of which thirty had a dementia, sixty-five were cognitively healthy, and twenty had a mild cognitive impairment (MCI). The performance of the VR system was compared with Mini-Cog test, where the latter is used to measure cognitive impaired patients in the traditional diagnosis system at the clinic. It was observed that visuospatial and memory recall scores in both clinical diagnosis and VR system of dementia patients were less than those of MCI patients, and the scores of MCI patients were less than those of the control group. Furthermore, there is a perfect agreement between the standard methods in functional evaluation and navigational ability in our system where P-value in weighted Kappa statistic= 100% and between Mini-Cog-clinical diagnosis vs. VR scores where P-value in weighted Kappa statistic= 93%.
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Affiliation(s)
- Areej Y. Bayahya
- Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Correspondence: or
| | - Wadee Alhalabi
- Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Virtual Reality Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sultan H. AlAmri
- Department of Family Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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26
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Amagasa S, Kojin H, Kamada M, Fukuoka Y, Inoue S. [Evaluation of physical activity using smartphones and wearable devices in healthcare: Current situation and future perspectives]. Nihon Koshu Eisei Zasshi 2021; 68:585-596. [PMID: 34121060 DOI: 10.11236/jph.20-143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract With the growing popularity of mobile health (mHealth) devices, including smartphones and wearable devices, information and communications technology has gained high importance in healthcare settings. This study aimed to summarize the current trends in physical activity research wherein mHealth devices are used and provide perspectives for future research. Until recently, questionnaire surveys were primarily used to evaluate physical activity. While questionnaire surveys are effective for subjective evaluation, the use of mHealth devices enables large-scale, real-time, objective evaluation of physical activity. In addition, mHealth devices automatically collect and aggregate data. This allows researchers to perform retrospective analysis of a wide range of indicators of physical activity and health. Particularly, the use of smartphones is highly likely to contribute to large-scale monitoring and health interventions because of their ubiquity. Even though there are fewer users of wearable devices (wrist-worn devices) than those of smartphones, using wearable devices allows for the evaluation of 24-hour movement patterns. The use of wearable devices helps perform further precise analysis that focuses not only on the total amount of physical activity but also on the quality, including measures of intensity, duration, frequency, type, and time. Moreover, some wrist-worn devices measure physiological information such as heart rate and may also provide location information. Combining such data with information from an accelerometer associated with a device may allow for further specific and detailed evaluation of physical activity. The validity of physical activity assessment using major mHealth devices has been confirmed in several studies and is comparable to that of pedometers and accelerometers developed for research purposes. Evaluation of physical activity using mHealth devices involves issues related to the representativeness of the target population and continuity of data, as well as the need for ethical considerations based on privacy policies. While mHealth devices may be used by individuals as a health management tool, it is also expected that the evaluation of physical activity using mHealth devices will be performed in various settings such as epidemiological and clinical studies on physical activity, as well as community services wherein indicators of physical activity are used.
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Affiliation(s)
- Shiho Amagasa
- Department of Preventive Medicine and Public Health, Tokyo Medical University.,Department of Health and Social Behavior, School of Public Health, Graduate School of Medicine, The University of Tokyo
| | - Hiroyuki Kojin
- Department of Preventive Medicine and Public Health, Tokyo Medical University.,Department of Quality and Patient Safety, University of Yamanashi Hospital
| | - Masamitsu Kamada
- Department of Health Education and Health Sociology, School of Public Health, Graduate School of Medicine, The University of Tokyo
| | - Yutaka Fukuoka
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Kogakuin University
| | - Shigeru Inoue
- Department of Preventive Medicine and Public Health, Tokyo Medical University
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27
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Carriere J, Shafi H, Brehon K, Pohar Manhas K, Churchill K, Ho C, Tavakoli M. Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic. Front Artif Intell 2021; 4:613637. [PMID: 33733232 PMCID: PMC7907599 DOI: 10.3389/frai.2021.613637] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/08/2021] [Indexed: 01/16/2023] Open
Abstract
The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.
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Affiliation(s)
- Jay Carriere
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Hareem Shafi
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Katelyn Brehon
- School of Public Health, University of Alberta, Edmonton, AB, Canada
| | - Kiran Pohar Manhas
- Neurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
| | - Katie Churchill
- Department of Occupational Therapy, University of Alberta, Edmonton, AB, Canada.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chester Ho
- Neurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada.,Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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Badawi HF, Laamarti F, El Saddik A. Devising Digital Twins DNA Paradigm for Modeling ISO-Based City Services. Sensors (Basel) 2021; 21:s21041047. [PMID: 33557039 PMCID: PMC7913799 DOI: 10.3390/s21041047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/23/2021] [Accepted: 02/01/2021] [Indexed: 11/18/2022]
Abstract
Digital twins (DTs) technology has recently gained attention within the research community due to its potential to help build sustainable smart cities. However, there is a gap in the literature: currently no unified model for city services has been proposed that can guarantee interoperability across cities, capture each city’s unique characteristics, and act as a base for modeling digital twins. This research aims to fill that gap. In this work, we propose the DT-DNA model in which we design a city services digital twin, with the goal of reflecting the real state of development of a city’s services towards enhancing its citizens’ quality of life (QoL). As it was designed using ISO 37120, one of the leading international standards for city services, the model guarantees interoperability and allows for easy comparison of services within and across cities. In order to test our model, we built DT-DNA sequences of services in both Quebec City and Boston and then used a DNA alignment tool to determine the matching percentage between them. Results show that the DT-DNA sequences of services in both cities are 46.5% identical. Ground truth comparisons show a similar result, which provides a preliminary proof-of-concept for the applicability of the proposed model and framework. These results also imply that one city performs better than the other. Therefore, we propose an algorithm to compare cities based on the proposed DT-DNA and, using Boston and Quebec City as a case study, demonstrate that Boston has better services towards enhancing QoL for its citizens.
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Affiliation(s)
- Hawazin Faiz Badawi
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (F.L.); (A.E.S.)
- Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Mecca 24381, Saudi Arabia
- Correspondence:
| | - Fedwa Laamarti
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (F.L.); (A.E.S.)
| | - Abdulmotaleb El Saddik
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (F.L.); (A.E.S.)
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Hurley NC, Spatz ES, Krumholz HM, Jafari R, Mortazavi BJ. A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. ACM Trans Comput Healthc 2021; 2:9. [PMID: 34337602 PMCID: PMC8320445 DOI: 10.1145/3417958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/01/2020] [Indexed: 10/22/2022]
Abstract
Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.
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30
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Tanwar G, Chauhan R, Singh M, Singh D. Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms. Sensors (Basel) 2020; 20:E7068. [PMID: 33321780 DOI: 10.3390/s20247068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/22/2020] [Accepted: 11/27/2020] [Indexed: 01/03/2023]
Abstract
Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.
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31
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LeBaron V, Bennett R, Alam R, Blackhall L, Gordon K, Hayes J, Homdee N, Jones R, Martinez Y, Ogunjirin E, Thomas T, Lach J. Understanding the Experience of Cancer Pain From the Perspective of Patients and Family Caregivers to Inform Design of an In-Home Smart Health System: Multimethod Approach. JMIR Form Res 2020; 4:e20836. [PMID: 32712581 PMCID: PMC7481872 DOI: 10.2196/20836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 07/11/2020] [Accepted: 07/25/2020] [Indexed: 01/20/2023] Open
Abstract
Background Inadequately managed pain is a serious problem for patients with cancer and those who care for them. Smart health systems can help with remote symptom monitoring and management, but they must be designed with meaningful end-user input. Objective This study aims to understand the experience of managing cancer pain at home from the perspective of both patients and family caregivers to inform design of the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system. Methods This was a descriptive pilot study using a multimethod approach. Dyads of patients with cancer and difficult pain and their primary family caregivers were recruited from an outpatient oncology clinic. The participant interviews consisted of (1) open-ended questions to explore the overall experience of cancer pain at home, (2) ranking of variables on a Likert-type scale (0, no impact; 5, most impact) that may influence cancer pain at home, and (3) feedback regarding BESI-C system prototypes. Qualitative data were analyzed using a descriptive approach to identity patterns and key themes. Quantitative data were analyzed using SPSS; basic descriptive statistics and independent sample t tests were run. Results Our sample (n=22; 10 patient-caregiver dyads and 2 patients) uniformly described the experience of managing cancer pain at home as stressful and difficult. Key themes included (1) unpredictability of pain episodes; (2) impact of pain on daily life, especially the negative impact on sleep, activity, and social interactions; and (3) concerns regarding medications. Overall, taking pain medication was rated as the category with the highest impact on a patient’s pain (=4.79), followed by the categories of wellness (=3.60; sleep quality and quantity, physical activity, mood and oral intake) and interaction (=2.69; busyness of home, social or interpersonal interactions, physical closeness or proximity to others, and emotional closeness and connection to others). The category related to environmental factors (temperature, humidity, noise, and light) was rated with the lowest overall impact (=2.51). Patients and family caregivers expressed receptivity to the concept of BESI-C and reported a preference for using a wearable sensor (smart watch) to capture data related to the abrupt onset of difficult cancer pain. Conclusions Smart health systems to support cancer pain management should (1) account for the experience of both the patient and the caregiver, (2) prioritize passive monitoring of physiological and environmental variables to reduce burden, and (3) include functionality that can monitor and track medication intake and efficacy; wellness variables, such as sleep quality and quantity, physical activity, mood, and oral intake; and levels of social interaction and engagement. Systems must consider privacy and data sharing concerns and incorporate feasible strategies to capture and characterize rapid-onset symptoms.
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Affiliation(s)
- Virginia LeBaron
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - Rachel Bennett
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - Ridwan Alam
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Leslie Blackhall
- University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Kate Gordon
- Virginia Commonwealth University Health, Richmond, VA, United States
| | - James Hayes
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Nutta Homdee
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Randy Jones
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - Yudel Martinez
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Emmanuel Ogunjirin
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Tanya Thomas
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - John Lach
- The George Washington University School of Engineering & Applied Science, Washington, DC, United States
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32
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Javed AR, Sarwar MU, Khan S, Iwendi C, Mittal M, Kumar N. Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition. Sensors (Basel) 2020; 20:E2216. [PMID: 32295298 DOI: 10.3390/s20082216] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 11/16/2022]
Abstract
Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.
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LeBaron V, Hayes J, Gordon K, Alam R, Homdee N, Martinez Y, Ogunjirin E, Thomas T, Jones R, Blackhall L, Lach J. Leveraging Smart Health Technology to Empower Patients and Family Caregivers in Managing Cancer Pain: Protocol for a Feasibility Study. JMIR Res Protoc 2019; 8:e16178. [PMID: 31815679 PMCID: PMC6928698 DOI: 10.2196/16178] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/01/2019] [Accepted: 11/05/2019] [Indexed: 12/11/2022] Open
Abstract
Background An estimated 60%-90% of patients with cancer experience moderate to severe pain. Poorly managed cancer pain negatively affects the quality of life for both patients and their family caregivers and can be a particularly challenging symptom to manage at home. Mobile and wireless technology (“Smart Health”) has significant potential to support patients with cancer and their family caregivers and empower them to safely and effectively manage cancer pain. Objective This study will deploy a package of sensing technologies, known as Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), and evaluate its feasibility and acceptability among patients with cancer-family caregiver dyads. Our primary aims are to explore the ability of BESI-C to reliably measure and describe variables relevant to cancer pain in the home setting and to better understand the dyadic effect of pain between patients and family caregivers. A secondary objective is to explore how to best share collected data among key stakeholders (patients, caregivers, and health care providers). Methods This descriptive two-year pilot study will include dyads of patients with advanced cancer and their primary family caregivers recruited from an academic medical center outpatient palliative care clinic. Physiological (eg, heart rate, activity) and room-level environmental variables (ambient temperature, humidity, barometric pressure, light, and noise) will be continuously monitored and collected. Behavioral and experiential variables will be actively collected when the caregiver or patient interacts with the custom BESI-C app on their respective smart watch to mark and describe pain events and answer brief, daily ecological momentary assessment surveys. Preliminary analysis will explore the ability of the sensing modalities to infer and detect pain events. Feasibility will be assessed by logistic barriers related to in-home deployment, technical failures related to data capture and fidelity, smart watch wearability issues, and patient recruitment and attrition rates. Acceptability will be measured by dyad perceptions and receptivity to BESI-C through a brief, structured interview and surveys conducted at deployment completion. We will also review summaries of dyad data with participants and health care providers to seek their input regarding data display and content. Results Recruitment began in July 2019 and is in progress. We anticipate the preliminary results to be available by summer 2021. Conclusions BESI-C has significant potential to monitor and predict pain while concurrently enhancing communication, self-efficacy, safety, and quality of life for patients and family caregivers coping with serious illness such as cancer. This exploratory research offers a novel approach to deliver personalized symptom management strategies, improve patient and caregiver outcomes, and reduce disparities in access to pain management and palliative care services. International Registered Report Identifier (IRRID) DERR1-10.2196/16178
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Affiliation(s)
- Virginia LeBaron
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - James Hayes
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Kate Gordon
- Virginia Commonwealth University Health, Richmond, VA, United States
| | - Ridwan Alam
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Nutta Homdee
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Yudel Martinez
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Emmanuel Ogunjirin
- University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States
| | - Tanya Thomas
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - Randy Jones
- University of Virginia School of Nursing, Charlottesville, VA, United States
| | - Leslie Blackhall
- University of Virginia School of Medicine, Charlottesville, VA, United States
| | - John Lach
- The George Washington University School of Engineering & Applied Science, Washington, DC, United States
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Esteves M, Esteves M, Abelha A, Machado J. A Proof of Concept of a Mobile Health Application to Support Professionals in a Portuguese Nursing Home. Sensors (Basel) 2019; 19:E3951. [PMID: 31547445 DOI: 10.3390/s19183951] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 11/24/2022]
Abstract
Over the past few years, the rapidly aging population has been posing several challenges to healthcare systems worldwide. Consequently, in Portugal, nursing homes have been getting a higher demand, and health professionals working in these facilities are overloaded with work. Moreover, the lack of health information and communication technology (HICT) and the use of unsophisticated methods, such as paper, in nursing homes to clinically manage residents lead to more errors and are time-consuming. Thus, this article proposes a proof of concept of a mobile health (mHealth) application developed for the health professionals working in a Portuguese nursing home to support them at the point-of-care, namely to manage and have access to information and to help them schedule, perform, and digitally record their tasks. Additionally, clinical and performance business intelligence (BI) indicators to assist the decision-making process are also defined. Thereby, this solution aims to introduce technological improvements into the facility to improve healthcare delivery and, by taking advantage of the benefits provided by these improvements, lessen some of the workload experienced by health professionals, reduce time-waste and errors, and, ultimately, enhance elders’ quality of life and improve the quality of the services provided.
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Chadborn NH, Blair K, Creswick H, Hughes N, Dowthwaite L, Adenekan O, Pérez Vallejos E. Citizens' Juries: When Older Adults Deliberate on the Benefits and Risks of Smart Health and Smart Homes. Healthcare (Basel) 2019; 7:E54. [PMID: 30939848 DOI: 10.3390/healthcare7020054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 03/25/2019] [Accepted: 03/27/2019] [Indexed: 11/29/2022] Open
Abstract
Background: Technology-enabled healthcare or smart health has provided a wealth of products and services to enable older people to monitor and manage their own health conditions at home, thereby maintaining independence, whilst also reducing healthcare costs. However, despite the growing ubiquity of smart health, innovations are often technically driven, and the older user does not often have input into design. The purpose of the current study was to facilitate a debate about the positive and negative perceptions and attitudes towards digital health technologies. Methods: We conducted citizens’ juries to enable a deliberative inquiry into the benefits and risks of smart health technologies and systems. Transcriptions of group discussions were interpreted from a perspective of life-worlds versus systems-worlds. Results: Twenty-three participants of diverse demographics contributed to the debate. Views of older people were felt to be frequently ignored by organisations implementing systems and technologies. Participants demonstrated diverse levels of digital literacy and a range of concerns about misuse of technology. Conclusion: Our interpretation contrasted the life-world of experiences, hopes, and fears with the systems-world of surveillance, efficiencies, and risks. This interpretation offers new perspectives on involving older people in co-design and governance of smart health and smart homes.
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Mauldin TR, Canby ME, Metsis V, Ngu AHH, Rivera CC. SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors (Basel) 2018; 18:E3363. [PMID: 30304768 DOI: 10.3390/s18103363] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/04/2018] [Accepted: 10/04/2018] [Indexed: 11/17/2022]
Abstract
This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.
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Mavragani A, Sampri A, Sypsa K, Tsagarakis KP. Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era. JMIR Public Health Surveill 2018; 4:e24. [PMID: 29530839 PMCID: PMC5869181 DOI: 10.2196/publichealth.8726] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/15/2017] [Accepted: 01/13/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND With the internet's penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma-a common respiratory disease-is present. OBJECTIVE This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. METHODS Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term "asthma," forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. RESULTS Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years' queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=-.82, P=.001) and current asthma (r=-.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=-.78, P=.003) and current asthma (r=-.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. CONCLUSIONS Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma.
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Affiliation(s)
- Amaryllis Mavragani
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
| | - Alexia Sampri
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
| | - Karla Sypsa
- Department of Pharmacy and Forensic Science, King's College London, University of London, London, United Kingdom
| | - Konstantinos P Tsagarakis
- Business and Environmental Technology Economics Lab, Department of Environmental Engineering, Democritus University of Thrace, Xanthi, Greece
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Chen SCI. Technological Health Intervention in Population Aging to Assist People to Work Smarter not Harder: Qualitative Study. J Med Internet Res 2018; 20:e3. [PMID: 29301736 PMCID: PMC5773817 DOI: 10.2196/jmir.8977] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/30/2017] [Accepted: 10/30/2017] [Indexed: 11/13/2022] Open
Abstract
Background Technology-based health care has been promoted as an effective tool to enable clinicians to work smarter. However, some health stakeholders believe technology will compel users to work harder by creating extra work. Objective The objective of this study was to investigate how and why electronic health (eHealth) has been applied in Taiwan and to suggest implications that may inspire other countries facing similar challenges. Methods A qualitative methodology was adopted to obtain insightful inputs from deeper probing. Taiwan was selected as a typical case study, given its aging population, advanced technology, and comprehensive health care system. This study investigated 38 stakeholders in the health care ecosystem through in-depth interviews and focus groups, which provides an open, flexible, and enlightening way to study complex, dynamic, and interactive situations through informal conversation or a more structured, directed discussion. Results First, respondents indicated that the use of technology can enable seamless patient care and clinical benefits such as flexibility in time management. Second, the results suggested that a leader’s vision, authority, and management skills might influence success in health care innovation. Finally, the results implied that both internal and external organizational governance are highly relevant for implementing technology-based innovation in health care. Conclusions This study provided Taiwanese perspectives on how to intelligently use technology to benefit health care and debated the perception that technology prevents human interaction between clinicians and patients.
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Affiliation(s)
- Sonia Chien-I Chen
- Connected Health Innovation Centre, Department of Leadership and Management, Ulster University, Newtownabbey, United Kingdom.,Ministry of Science and Technology, Taipei, Taiwan
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Chang J, Yao W, Li X. A Context-Aware S-Health Service System for Drivers. Sensors (Basel) 2017; 17:s17030609. [PMID: 28304330 PMCID: PMC5375895 DOI: 10.3390/s17030609] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 03/08/2017] [Accepted: 03/10/2017] [Indexed: 12/03/2022]
Abstract
As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver’s health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver’s health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced.
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Affiliation(s)
- Jingkun Chang
- Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Wenbin Yao
- Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Xiaoyong Li
- The Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
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Abstract
OBJECTIVES To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. METHODS A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. RESULTS The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. CONCLUSIONS The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.
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