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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Lluva-Plaza S, Jiménez-Martín A, Gualda-Gómez D, Villadangos-Carrizo JM, García-Domínguez JJ. Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place. SENSORS (BASEL, SWITZERLAND) 2023; 23:8646. [PMID: 37896739 PMCID: PMC10611293 DOI: 10.3390/s23208646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/12/2023] [Accepted: 10/14/2023] [Indexed: 10/29/2023]
Abstract
Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.
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Affiliation(s)
- Sergio Lluva-Plaza
- Department of Electronics, University of Alcalá, 28801 Madrid, Spain; (S.L.-P.); (A.J.-M.); (J.M.V.-C.)
| | - Ana Jiménez-Martín
- Department of Electronics, University of Alcalá, 28801 Madrid, Spain; (S.L.-P.); (A.J.-M.); (J.M.V.-C.)
| | - David Gualda-Gómez
- Department of Signal Theory and Communications, University Rey Juan Carlos, 28943 Fuenlabrada, Spain;
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Pathak K, Marwaha JS, Tsai TC. The role of digital technology in surgical home hospital programs. NPJ Digit Med 2023; 6:22. [PMID: 36750629 PMCID: PMC9904247 DOI: 10.1038/s41746-023-00750-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 01/10/2023] [Indexed: 02/09/2023] Open
Abstract
Home hospital (HH), a care delivery model of providing hospital-grade care to patients in their homes, has become increasingly common in medical settings, though surgical uptake has been limited. HH programs have been shown to be safe and effective in a variety of medical contexts, with increased usage of this care pathway during the COVID-19 pandemic. Though surgical patients have unique clinical considerations, surgical Home Hospital (SHH) programs may have important benefits for this population. Various technologies exist for the delivery of hospital care in the home, such as clinical risk prediction models and remote patient monitoring platforms. Here, we use institutional experiences at Brigham and Women's Hospital (BWH) to discuss the utility of technology in enabling SHH programs and highlight current limitations. Additionally, we comment on the importance of data interoperability, access for all patients, and clinical workflow design in successfully implementing SHH programs.
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Affiliation(s)
- Kavya Pathak
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Jayson S. Marwaha
- grid.239395.70000 0000 9011 8547Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Thomas C. Tsai
- grid.62560.370000 0004 0378 8294Division of General and Gastrointestinal Surgery, Brigham and Women’s Hospital, Boston, MA USA
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Ma B, Yang J, Wong FKY, Wong AKC, Ma T, Meng J, Zhao Y, Wang Y, Lu Q. Artificial intelligence in elderly healthcare: A scoping review. Ageing Res Rev 2023; 83:101808. [PMID: 36427766 DOI: 10.1016/j.arr.2022.101808] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/26/2022] [Accepted: 11/20/2022] [Indexed: 11/24/2022]
Abstract
The ageing population has led to a surge in the adoption of artificial intelligence (AI) technologies in elderly healthcare worldwide. However, in the advancement of AI technologies, there is currently a lack of clarity about the types and roles of AI technologies in elderly healthcare. This scoping review aimed to provide a comprehensive overview of AI technologies in elderly healthcare by exploring the types of AI technologies employed, and identifying their roles in elderly healthcare based on existing studies. A total of 10 databases were searched for this review, from January 1 2000 to July 31 2022. Based on the inclusion criteria, 105 studies were included. The AI devices utilized in elderly healthcare were summarised as robots, exoskeleton devices, intelligent homes, AI-enabled health smart applications and wearables, voice-activated devices, and virtual reality. Five roles of AI technologies were identified: rehabilitation therapists, emotional supporters, social facilitators, supervisors, and cognitive promoters. Results showed that the impact of AI technologies on elderly healthcare is promising and that AI technologies are capable of satisfying the unmet care needs of older adults and demonstrating great potential in its further development in this area. More well-designed randomised controlled trials are needed in the future to validate the roles of AI technologies in elderly healthcare.
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Affiliation(s)
- Bingxin Ma
- School of Nursing, Tianjin Medical University, Tianjin, China
| | - Jin Yang
- School of Nursing, Tianjin Medical University, Tianjin, China
| | | | | | - Tingting Ma
- School of Nursing, Tianjin Medical University, Tianjin, China
| | - Jianan Meng
- School of Nursing, Tianjin Medical University, Tianjin, China
| | - Yue Zhao
- School of Nursing, Tianjin Medical University, Tianjin, China.
| | - Yaogang Wang
- School of Public Health, Tianjin Medical University, Tianjin, China; School of Integrative Medicine, Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Institute of Health Data Science at Peking University, Beijing, China.
| | - Qi Lu
- School of Nursing, Tianjin Medical University, Tianjin, China.
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Kim D, Bian H, Chang CK, Dong L, Margrett J. In-Home Monitoring Technology for Aging in Place: Scoping Review. Interact J Med Res 2022; 11:e39005. [PMID: 36048502 PMCID: PMC9478817 DOI: 10.2196/39005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/15/2022] [Accepted: 07/31/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND For successful aging-in-place strategy development, in-home monitoring technology is necessary as a new home modification strategy. Monitoring an older adult's daily physical activity at home can positively impact their health and well-being by providing valuable information about functional, cognitive, and social health status. However, it is questionable how these in-home monitoring technologies have changed the traditional residential environment. A comprehensive review of existing research findings should be utilized to characterize recent relative technologies and to inform design considerations. OBJECTIVE The main purpose of this study was to classify recent smart home technologies that monitor older adults' health and to architecturally describe these technologies as they are used in older adults' homes. METHODS The scoping review method was employed to identify key characteristics of in-home monitoring technologies for older adults. In June 2021, four databases, including Web of Science, IEEE Xplore, ACM Digital Library, and Scopus, were searched for peer-reviewed articles pertaining to smart home technologies used to monitor older adults' health in their homes. We used two search strings to retrieve articles: types of technology and types of users. For the title, abstract, and full-text screening, the inclusion criteria were original and peer-reviewed research written in English, and research on monitoring, detecting, recognizing, analyzing, or tracking human physical, emotional, and social behavior. The exclusion criteria included theoretical, conceptual, or review papers; studies on wearable systems; and qualitative research. RESULTS This scoping review identified 30 studies published between June 2016 and 2021 providing overviews of in-home monitoring technologies, including (1) features of smart home technologies and (2) sensor locations and sensor data. First, we found six functions of in-home monitoring technology among the reviewed papers: daily activities, abnormal behaviors, cognitive impairment, falls, indoor person positioning, and sleep quality. Most of the research (n=27 articles) focused on functional monitoring and analysis, such as activities of daily living, instrumental activities of daily living, or falls among older adults; a few studies (n=3) covered social interaction monitoring. Second, this scoping review also found 16 types of sensor technologies. The most common data types encountered were passive infrared motion sensors (n=21) and contact sensors (n=19), which were used to monitor human behaviors such as bodily presence and time spent on activities. Specific locations for each sensor were also identified. CONCLUSIONS This wide-ranging synthesis demonstrates that in-home monitoring technologies within older adults' homes play an essential role in aging in place, in that the technology monitors older adults' daily activities and identifies various health-related issues. This research provides a key summarization of in-home monitoring technologies that can be applied in senior housing for successful aging in place. These findings will be significant when developing home modification strategies or new senior housing.
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Affiliation(s)
- Daejin Kim
- Department of Interior Design, Iowa State University, Ames, IA, United States
| | - Hongyi Bian
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Carl K Chang
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Liang Dong
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States
| | - Jennifer Margrett
- Department of Human Development and Family Studies, Iowa State University, Ames, IA, United States
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Lam L, Fadrique L, Bin Noon G, Shah A, Morita PP. Evaluating Challenges and Adoption Factors for Active Assisted Living Smart Environments. Front Digit Health 2022; 4:891634. [PMID: 35712229 PMCID: PMC9197685 DOI: 10.3389/fdgth.2022.891634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
While there have been rapid advancements in individual technologies such as Internet of Things (IoT) and Active Assisted Living (AAL) to address challenges related to an aging population, there remain large gaps in how these technologies can be integrated into the broader ecosystem to support older adults in aging in place. This research provides an overview of 15 solutions available to date around the globe and compares key factors for adoption in each solution, including user acceptance, privacy and security, accessibility, and interoperability. To scale these solutions sustainably and universally, the development and implementation of standards for key factors for adoption in AAL environments is critical. There is also a need for increased and sustainable funding to complement research priorities, to continue advancing AAL technologies.
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Affiliation(s)
- Lena Lam
- School of Health, University of Waterloo, Waterloo, ON, Canada
| | - Laura Fadrique
- School of Health, University of Waterloo, Waterloo, ON, Canada
| | - Gaya Bin Noon
- School of Health, University of Waterloo, Waterloo, ON, Canada
| | - Aakanksha Shah
- School of Health, University of Waterloo, Waterloo, ON, Canada
| | - Plinio Pelegrini Morita
- School of Health, University of Waterloo, Waterloo, ON, Canada
- Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- *Correspondence: Plinio Pelegrini Morita
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Offermann J, Wilkowska W, Poli A, Spinsante S, Ziefle M. Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments. SENSORS 2021; 21:s21248297. [PMID: 34960390 PMCID: PMC8704554 DOI: 10.3390/s21248297] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 01/25/2023]
Abstract
Diverse sensor-based technologies can be used to track (older and frail) people’s movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and health-related changes. Typical examples are passive infrared sensors (PIR), humidity and temperature sensors (H&T) as well as magnetic sensors (MAG). So far, it is not known whether and to what extent these three specific sensor types are perceived and accepted differently by future users. Therefore, the present study analyzed the perception of benefits and barriers as well as acceptance of these specific sensor-based technologies using an online survey (reaching N=312 German participants). The results show technology-related differences, especially regarding the perception of benefits. Furthermore, the participants estimated the costs of these sensors to be higher than they are, but at the same time showed a relatively high willingness to pay for the implementation of sensor-based technologies in their home environment. The results enable the derivation of guidelines for both the technical development and the communication and information of assisting sensor-based technologies and systems.
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Affiliation(s)
- Julia Offermann
- Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany; (W.W.); (M.Z.)
- Correspondence:
| | - Wiktoria Wilkowska
- Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany; (W.W.); (M.Z.)
| | - Angelica Poli
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy; (A.P.); (S.S.)
| | - Susanna Spinsante
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy; (A.P.); (S.S.)
| | - Martina Ziefle
- Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany; (W.W.); (M.Z.)
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Méndez JI, Meza-Sánchez AV, Ponce P, McDaniel T, Peffer T, Meier A, Molina A. Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System. SENSORS 2021; 21:s21237864. [PMID: 34883868 PMCID: PMC8659873 DOI: 10.3390/s21237864] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 01/30/2023]
Abstract
Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.
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Affiliation(s)
- Juana Isabel Méndez
- School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico; (J.I.M.); (A.M.)
| | | | - Pedro Ponce
- School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico; (J.I.M.); (A.M.)
- Correspondence:
| | - Troy McDaniel
- The Polytechnic School, Arizona State University, Mesa, AZ 85212, USA;
| | - Therese Peffer
- Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA;
| | - Alan Meier
- Energy and Efficiency Institute, University of California, Davis, CA 95616, USA;
| | - Arturo Molina
- School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico; (J.I.M.); (A.M.)
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