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Zuccotti G, Marsilio M, Fiori L, Erba P, Destro F, Zamana C, Folgori L, Mandelli A, Braghieri D, Guglielmetti C, Pisarra M, Magnani L, Infante G, Dilillo D, Fabiano V, Carlucci P, Zoia E, Pelizzo G, Calcaterra V. Leveraging User-Friendly Mobile Medical Devices to Facilitate Early Hospital Discharges in a Pediatric Setting: A Randomized Trial Study Protocol. CHILDREN (BASEL, SWITZERLAND) 2024; 11:683. [PMID: 38929262 PMCID: PMC11201467 DOI: 10.3390/children11060683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/24/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Mobile technology is increasingly prevalent in healthcare, serving various purposes, including remote health monitoring and patient self-management, which could prove beneficial to early hospital discharges. AIMS This study investigates the transitional care program experience facilitating early discharges in a pediatric setting through the use of an easy-to-use mobile medical device (TytoCare™, TytoCare Ltd., Natanya, Israel). OUTCOMES This study aims to assess the effectiveness of telehomecare in achieving complete resolution of diseases without readmission, compare the length of stay between intervention and standard care groups, and gather user and professional experiences. METHODS A randomized open-label, controlled pilot study enrolled 102 children, randomly assigned to the telehomecare (TELE) group (n = 51, adopting early hospital discharge with continued home monitoring) or the standard-of-care (STAND) group (n = 51). Primary outcomes include complete disease resolution without readmission. Secondary objectives include recording a shorter length of stay in the intervention group. Surveys on user and professional experiences were conducted. A group of 51 children declining telemedicine services (NO-TELE) was also included. RESULTS In the TELE group, 100% of children achieved complete disease resolution without readmission, with a median duration of stay of 4 days, significantly shorter than the 7 days in the STAND group (p = 0.01). The telemedicine system demonstrated efficient performance and high satisfaction levels. The NO-TELE group showed no significant differences in demographics or digital technology competence. Perceived benefits of telemedicine included time and cost savings, reduced hospital stays, and technology utility and usability. CONCLUSIONS This study demonstrates that user-friendly mobile medical devices effectively facilitate early hospital discharges in a pediatric setting. These devices serve as a bridge between home and hospital, optimizing care pathways.
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Affiliation(s)
- Gianvincenzo Zuccotti
- Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy; (D.B.); (V.F.); (G.P.)
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Marta Marsilio
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy; (M.M.); (C.G.); (M.P.); (L.M.); (G.I.)
| | - Laura Fiori
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Paola Erba
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Francesca Destro
- Pediatric Surgery Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (F.D.); (C.Z.)
| | - Costantino Zamana
- Pediatric Surgery Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (F.D.); (C.Z.)
| | - Laura Folgori
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Anna Mandelli
- Intensive Care Unit, Buzzi Children’s Hospital, 20154 Milan, Italy; (A.M.); (E.Z.)
| | - Davide Braghieri
- Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy; (D.B.); (V.F.); (G.P.)
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Chiara Guglielmetti
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy; (M.M.); (C.G.); (M.P.); (L.M.); (G.I.)
| | - Martina Pisarra
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy; (M.M.); (C.G.); (M.P.); (L.M.); (G.I.)
| | - Letizia Magnani
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy; (M.M.); (C.G.); (M.P.); (L.M.); (G.I.)
| | - Gabriele Infante
- Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy; (M.M.); (C.G.); (M.P.); (L.M.); (G.I.)
| | - Dario Dilillo
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Valentina Fabiano
- Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy; (D.B.); (V.F.); (G.P.)
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Patrizia Carlucci
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
| | - Elena Zoia
- Intensive Care Unit, Buzzi Children’s Hospital, 20154 Milan, Italy; (A.M.); (E.Z.)
| | - Gloria Pelizzo
- Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy; (D.B.); (V.F.); (G.P.)
- Pediatric Surgery Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (F.D.); (C.Z.)
| | - Valeria Calcaterra
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milan, Italy; (L.F.); (P.E.); (L.F.); (D.D.); (P.C.); (V.C.)
- Department of Internal Medicine, University of Pavia, 27100 Pavia, Italy
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Gao Z, Chee CS, Omar Dev RD, Gao J. Comprehensive analysis of college students' autonomous fitness behavior-a narrative review. Front Sports Act Living 2024; 6:1406810. [PMID: 38835705 PMCID: PMC11148380 DOI: 10.3389/fspor.2024.1406810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024] Open
Abstract
Although the physical health of college students is increasingly receiving attention, their autonomous fitness behavior has not been thoroughly investigated. This narrative review conducted a comprehensive literature search through databases such as PubMed, PsycINFO, Web of Science, and the China National Knowledge Infrastructure (CNKI), reviewing studies published up to December 2023. We explored the constructs of autonomy, fitness behavior, and agency, and discussed their integration within the autonomous fitness model. Our findings indicate a lack of comprehensive studies exploring the multifaceted factors influencing autonomous fitness behaviors. Future research should strive to deepen conceptual understanding and further explore the complex dynamics of the transition from autonomy to persistence, employing technological and interdisciplinary methodological perspectives to enhance understanding and promote sustainable fitness habits.
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Affiliation(s)
- Zhendong Gao
- Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
| | - Chen Soon Chee
- Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
| | - Roxana Dev Omar Dev
- Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
| | - Jianhong Gao
- Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Malaysia
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Sideridou M, Kouidi E, Hatzitaki V, Chouvarda I. Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology. SENSORS (BASEL, SWITZERLAND) 2024; 24:2037. [PMID: 38610249 PMCID: PMC11013996 DOI: 10.3390/s24072037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/09/2024] [Accepted: 03/18/2024] [Indexed: 04/14/2024]
Abstract
Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely.
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Affiliation(s)
- Maria Sideridou
- Lab of Computing, Medical Informatics, and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Evangelia Kouidi
- School of Physical Education and Sport Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (E.K.); (V.H.)
| | - Vassilia Hatzitaki
- School of Physical Education and Sport Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (E.K.); (V.H.)
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integr Med Res 2024; 13:101024. [PMID: 38384497 PMCID: PMC10879672 DOI: 10.1016/j.imr.2024.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.
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Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Myeong Soo Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - David Moher
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
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Avoke D, Elshafeey A, Weinstein R, Kim CH, Martin SS. Digital Health in Diabetes and Cardiovascular Disease. Endocr Res 2024; 49:124-136. [PMID: 38605594 DOI: 10.1080/07435800.2024.2341146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). PURPOSE OF THE REVIEW In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. CONCLUSION We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.
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Affiliation(s)
- Dorothy Avoke
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Robert Weinstein
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Chang H Kim
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S Martin
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Son YS, Kwon KH. Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review. Mhealth 2023; 10:7. [PMID: 38323151 PMCID: PMC10839508 DOI: 10.21037/mhealth-23-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/24/2023] [Indexed: 02/08/2024] Open
Abstract
Background Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices. Methods Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage. Results Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle. Conclusions Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.
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Affiliation(s)
- Youn Sun Son
- Division of Beauty Arts Care, Department of Practical Arts, Graduate School of Culture and Arts, Dongguk University, Seoul, Korea
| | - Ki Han Kwon
- College of General Education, Kookmin University, Seoul, Korea
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