1
|
Souza GS, Furtado BKA, Almeida EB, Callegari B, Pinheiro MDCN. Enhancing public health in developing nations through smartphone-based motor assessment. Front Digit Health 2024; 6:1345562. [PMID: 38835672 PMCID: PMC11148357 DOI: 10.3389/fdgth.2024.1345562] [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: 11/28/2023] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Several protocols for motor assessment have been validated for use on smartphones and could be employed by public healthcare systems to monitor motor functional losses in populations, particularly those with lower income levels. In addition to being cost-effective and widely distributed across populations of varying income levels, the use of smartphones in motor assessment offers a range of advantages that could be leveraged by governments, especially in developing and poorer countries. Some topics related to potential interventions should be considered by healthcare managers before initiating the implementation of such a digital intervention.
Collapse
Affiliation(s)
- Givago Silva Souza
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
| | | | | | - Bianca Callegari
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil
- Instituto de Ciências da Saúde, Universidade Federal do Pará, Belém, Brazil
| | | |
Collapse
|
2
|
Moses JC, Adibi S, Wickramasinghe N, Nguyen L, Angelova M, Islam SMS. Non-invasive blood glucose monitoring technology in diabetes management: review. Mhealth 2023; 10:9. [PMID: 38323150 PMCID: PMC10839510 DOI: 10.21037/mhealth-23-9] [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: 02/14/2023] [Accepted: 10/07/2023] [Indexed: 02/08/2024] Open
Abstract
Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.
Collapse
Affiliation(s)
- Jeban Chandir Moses
- School of Information Technology, Deakin University, Melbourne, VIC, Australia
| | - Sasan Adibi
- School of Information Technology, Deakin University, Melbourne, VIC, Australia
| | - Nilmini Wickramasinghe
- School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia
| | - Lemai Nguyen
- Department of Information Systems and Business Analytics, Deakin Business School, Deakin University, Melbourne, VIC, Australia
| | - Maia Angelova
- School of Information Technology, Deakin University, Melbourne, VIC, Australia
- Aston Digital Futures Institute, College of Physical Sciences and Engineering, Aston University, Birmingham, UK
| | | |
Collapse
|
3
|
Smith MK, Staynor JMD, El-Sallam A, Ebert JR, Ackland TR. Longitudinal concordance of body composition and anthropometric assessment by a novel smartphone application across a 12-week self-managed weight loss intervention. Br J Nutr 2023; 130:1260-1266. [PMID: 36700352 DOI: 10.1017/s0007114523000259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Smartphone applications (SPA) now offer the ability to provide accessible in-home monitoring of relevant individual health biomarkers. Previous cross-sectional validations of similar technologies have reported acceptable accuracy with high-grade body composition assessments; this research assessed longitudinal agreement of a novel SPA across a self-managed weight loss intervention of thirty-eight participants (twenty-one males, seventeen females). Estimations of body mass (BM), body fat percentage (BF%), fat-free mass (FFM) and waist circumference (WC) from the SPA were compared with ground truth (GT) measures from a dual-energy X-ray absorptiometry scanner and expert technician measurement. Small mean differences (MD) and standard error of estimate (SEE) were observed between method deltas (ΔBM: MD = 0·12 kg, SEE = 2·82 kg; ΔBF%: MD = 0·06 %, SEE = 1·65 %; ΔFFM: MD = 0·17 kg, SEE = 1·65 kg; ΔWC: MD = 1·16 cm, SEE = 2·52 cm). Concordance correlation coefficient (CCC) assessed longitudinal agreement between the SPA and GT methods, with moderate concordance (CCC: 0·55-0·73) observed for all measures. The novel SPA may not be interchangeable with high-accuracy medical scanning methods yet offers significant benefits in cost, accessibility and user comfort, in conjunction with the ability to monitor body shape and composition estimates over time.
Collapse
Affiliation(s)
- Marc K Smith
- School of Human Sciences (Exercise and Sport Science), The University of Western Australia, WA, Australia
- Body Composition Technologies Pty Ltd, South Perth, WA, Australia
| | | | - Amar El-Sallam
- Advanced Human Imaging LTD, South Perth, WA, Australia
- School of Computer Science and Software Engineering, The University of Western Australia, WA, Australia
| | - Jay R Ebert
- School of Human Sciences (Exercise and Sport Science), The University of Western Australia, WA, Australia
| | - Tim R Ackland
- School of Human Sciences (Exercise and Sport Science), The University of Western Australia, WA, Australia
| |
Collapse
|
4
|
Islam SMS, Daryabeygi-Khotbehsara R, Ghaffari MP, Uddin R, Gao L, Xu X, Siddiqui MU, Livingstone KM, Siopis G, Sarrafzadegan N, Schlaich M, Maddison R, Huxley R, Schutte AE. Burden of Hypertensive Heart Disease and High Systolic Blood Pressure in Australia from 1990 to 2019: Results From the Global Burden of Diseases Study. Heart Lung Circ 2023; 32:1178-1188. [PMID: 37743220 DOI: 10.1016/j.hlc.2023.06.853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/23/2023] [Accepted: 06/28/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND There is a dearth of comprehensive studies examining the burden and trends of hypertensive heart disease (HHD) and high systolic blood pressure (SBP) among the Australian population. We aimed to explore the burden of HHD and high SBP, and how they changed over time from 1990 to 2019 in Australia. METHODS We analysed data from the Global Burden of Disease study in Australia. We assessed the prevalence, mortality, disability-adjusted life-years (DALY), years lived with disability (YLD) and years of life lost (YLL) attributable to HHD and high SBP. Data were presented as point estimates with 95% uncertainty intervals (UI). We compared the burden of HHD and high SBP in Australia with World Bank defined high-income countries and six other comparator countries with similar sociodemographic characteristics and economies. RESULTS From 1990 to 2019, the burden of HHD and high SBP in Australia reduced. Age standardised prevalence rate of HHD was 119.3 cases per 100,000 people (95% UI 86.6-161.0) in 1990, compared to 80.1 cases (95% UI 57.4-108.1) in 2019. Deaths due to HDD were 3.4 cases per 100,000 population (95% UI 2.6-3.8) in 1990, compared to 2.5 (95% UI 1.9-3.0) in 2019. HHD contributed to 57.2 (95% UI 46.6-64.7) DALYs per 100,000 population in 1990 compared to 38.4 (95% UI 32.0-45.2) in 2019. Death rates per 100,000 population attributable to high SBP declined significantly over time for both sexes from 1990 (155.6 cases; 95% UI 131.2-177.0) to approximately one third in 2019 (53.8 cases; 95% UI 43.4-64.4). Compared to six other countries in 2019, the prevalence of HHD was highest in the USA (274.3%) and lowest in the UK (52.6%), with Australia displaying the third highest prevalence. Australia ranked second in term of lowest rates of deaths and third for lowest DALYs respectively due to high SBP. From 1990-2019, Australia ranked third best for reductions in deaths and DALYs due to HHD and first for reductions in deaths and DALYs due to high SBP. CONCLUSION Over the past three decades, the burden of HHD in Australia has reduced, but its prevalence remains relatively high. The contribution of high SBP to deaths, DALYs and YLLs also reduced over the three decades.
Collapse
Affiliation(s)
| | | | | | - Riaz Uddin
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Vic, Australia
| | - Lan Gao
- School of Health and Social Development, Faculty of Health, Deakin University, Geelong, Vic, Australia
| | - Xiaoyue Xu
- School of Population Health, University of New South Wales, Sydney, NSW, Australia; The George Institute for Global Health, Sydney, NSW, Australia
| | - Muhammad Umer Siddiqui
- Department of Internal Medicine, Thomas Jefferson University Hospital Philadelphia, PA, USA
| | | | - George Siopis
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Vic, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Vic, Australia
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Markus Schlaich
- Dobney Hypertension Centre, Medical School-Royal Perth Hospital Unit, The University of Western Australia, Perth, WA, Australia
| | - Ralph Maddison
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, Vic, Australia
| | - Rachel Huxley
- Faculty of Health, Deakin University, Geelong, Vic, Australia
| | - Aletta E Schutte
- School of Population Health, University of New South Wales, Sydney, NSW, Australia; The George Institute for Global Health, Sydney, NSW, Australia
| |
Collapse
|
5
|
Zhang F, Zhu S, Chen S, Hao Z, Fang Y, Zou H, Cai Y, Cao B, Zhang K, Cao H, Chen Y, Hu T, Wang Z. Application of machine learning for risky sexual behavior interventions among factory workers in China. Front Public Health 2023; 11:1092018. [PMID: 37601175 PMCID: PMC10437811 DOI: 10.3389/fpubh.2023.1092018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 07/11/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Assessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models. Methods The survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12. Results About a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%). Discussion Machine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services.
Collapse
Affiliation(s)
- Fang Zhang
- Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong, China
| | - Shiben Zhu
- Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Siyu Chen
- Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Ziyu Hao
- Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Yuan Fang
- Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong, China
| | - Huachun Zou
- School of Public Health, Sun Yat-sen University, Shenzhen, China
- Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | - Yong Cai
- School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Bolin Cao
- School of Media and Communication, Shenzhen University, Shenzhen, China
| | - Kechun Zhang
- Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - He Cao
- Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Yaqi Chen
- Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Tian Hu
- Longhua District Center for Disease Control and Prevention, Shenzhen, China
| | - Zixin Wang
- Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
6
|
Lin CY, Ratan ZA, Pakpour AH. Collection of smartphone and internet addiction. BMC Psychiatry 2023; 23:427. [PMID: 37316810 DOI: 10.1186/s12888-023-04915-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/16/2023] Open
Abstract
The enigma of smartphone and internet addiction has plagued academics for the last decade, now scholars believe this behavior might have a substantial effect on human health and social issues. However, there are literature gaps. Thus, BMC Psychiatry works with us to launch the special collection "Smartphone and Internet Addiction".
Collapse
Affiliation(s)
- Chung-Ying Lin
- Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, 1 University Rd, Tainan, 701401, Taiwan.
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Occupational Therapy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Biostatistics Consulting Center, National Cheng Kung University Hospital, National Cheng Kung University, Tainan, Taiwan.
| | - Zubair Ahmed Ratan
- School of Health and Society, University of Wollongong, Wollongong, NSW, 2500, Australia
| | - Amir H Pakpour
- Department of Nursing, School of Health and Welfare, Jönköping University, Jönköping, Sweden
| |
Collapse
|
7
|
Nagino K, Okumura Y, Yamaguchi M, Sung J, Nagao M, Fujio K, Akasaki Y, Huang T, Hirosawa K, Iwagami M, Midorikawa-Inomata A, Fujimoto K, Eguchi A, Okajima Y, Kakisu K, Tei Y, Yamaguchi T, Tomida D, Fukui M, Yagi-Yaguchi Y, Hori Y, Shimazaki J, Nojiri S, Morooka Y, Yee A, Miura M, Ohno M, Inomata T. Diagnostic Ability of a Smartphone App for Dry Eye Disease: Protocol for a Multicenter, Open-Label, Prospective, and Cross-sectional Study. JMIR Res Protoc 2023; 12:e45218. [PMID: 36912872 PMCID: PMC10131757 DOI: 10.2196/45218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND Dry eye disease (DED) is one of the most common ocular surface diseases. Numerous patients with DED remain undiagnosed and inadequately treated, experiencing various subjective symptoms and a decrease in quality of life and work productivity. A mobile health smartphone app, namely, the DEA01, has been developed as a noninvasive, noncontact, and remote screening device, in the context of an ongoing paradigm shift in the health care system, to facilitate a diagnosis of DED. OBJECTIVE This study aimed to evaluate the capabilities of the DEA01 smartphone app to facilitate a DED diagnosis. METHODS In this multicenter, open-label, prospective, and cross-sectional study, the test method will involve using the DEA01 smartphone app to collect and evaluate DED symptoms, based on the Japanese version of the Ocular Surface Disease Index (J-OSDI), and to measure the maximum blink interval (MBI). The standard method will then involve a paper-based J-OSDI evaluation of subjective symptoms of DED and tear film breakup time (TFBUT) measurement in an in-person encounter. We will allocate 220 patients to DED and non-DED groups, based on the standard method. The primary outcome will be the sensitivity and specificity of the DED diagnosis according to the test method. Secondary outcomes will be the validity and reliability of the test method. The concordance rate, positive and negative predictive values, and the likelihood ratio between the test and standard methods will be assessed. The area under the curve of the test method will be evaluated using a receiver operating characteristic curve. The internal consistency of the app-based J-OSDI and the correlation between the app-based J-OSDI and paper-based J-OSDI will be assessed. A DED diagnosis cutoff value for the app-based MBI will be determined using a receiver operating characteristic curve. The app-based MBI will be assessed to determine a correlation between a slit lamp-based MBI and TFBUT. Adverse events and DEA01 failure data will be collected. Operability and usability will be assessed using a 5-point Likert scale questionnaire. RESULTS Patient enrollment will start in February 2023 and end in July 2023. The findings will be analyzed in August 2023, and the results will be reported from March 2024 onward. CONCLUSIONS This study may have implications in identifying a noninvasive, noncontact route to facilitate a diagnosis of DED. The DEA01 may enable a comprehensive diagnostic evaluation within a telemedicine setting and facilitate early intervention for undiagnosed patients with DED confronting health care access barriers. TRIAL REGISTRATION Japan Registry of Clinical Trials jRCTs032220524; https://jrct.niph.go.jp/latest-detail/jRCTs032220524. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/45218.
Collapse
Affiliation(s)
- Ken Nagino
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Yamaguchi
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Morsani College of Medicine, University of South Florida, Tampa, FL, United States
| | - Masashi Nagao
- Department of Orthopedics, Juntendo University Faculty of Medicine, Tokyo, Japan.,Medical Technology Innovation Center, Juntendo University, Tokyo, Japan.,Graduate School of Health and Sports Science, Juntendo University, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsuko Eguchi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yukinobu Okajima
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Koji Kakisu
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yuto Tei
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Takefumi Yamaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Daisuke Tomida
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Masaki Fukui
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Yukari Yagi-Yaguchi
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Yuichi Hori
- Department of Ophthalmology, Toho University Omori Medical Center, Tokyo, Japan
| | - Jun Shimazaki
- Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Alan Yee
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mizu Ohno
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.,AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
| |
Collapse
|
8
|
Delir Haghighi P, Burstein F. Advances in E-Health and Mobile Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:8621. [PMID: 36433218 PMCID: PMC9697701 DOI: 10.3390/s22228621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
E-health as a new industrial phenomenon and a field of research integrates medical informatics, public health and healthcare business, aiming to facilitate the provision of more accessible healthcare services, such as remote health monitoring, reducing healthcare costs and enhancing patient experience [...].
Collapse
|
9
|
Impact of Hypertension on COVID-19 Burden in Kidney Transplant Recipients: An Observational Cohort Study. Viruses 2022; 14:v14112409. [PMID: 36366507 PMCID: PMC9698847 DOI: 10.3390/v14112409] [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: 10/02/2022] [Revised: 10/23/2022] [Accepted: 10/27/2022] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND COVID-19 severity is determined by cardiometabolic risk factors, which can be further aggravated by chronic immunosuppression in kidney transplant recipients (KTRs). We aimed to verify the main risk factors related to hypertension (HTN) that contribute to COVID-19 progression and mortality in that population. METHODS Retrospective analysis of 300 KTRs from March 2020 to August 2020 in a single center. We compared the main outcomes between HTN (n = 225) and non-HTN (n = 75), including admission to the intensive care unit (ICU), development of acute kidney injury (AKI), need for invasive mechanical ventilation or oxygen, and mortality. RESULTS Of the patients in the study, 57.3% were male, 61.3% were white, the mean age was 52.5 years, and 75% had HTN. Pre-existing HTN was independently associated with higher rates of mortality (32.9%, OR = 1.96, p = 0.036), transfer to the ICU (50.7%, OR = 1.94, p = 0.017), and AKI with hemodialysis (HD) requirement (40.4%, OR = 2.15, p = 0.011). In the hypertensive group, age, diabetes mellitus, heart disease, smoking, glycemic control before admission, C-reactive protein, lactate dehydrogenase, lymphocytes, and D-dimer were significantly associated with COVID-19 progression and mortality. Both lower basal and previous estimated glomerular filtration rates posed KTRs with HTN at greater risk for HD requirement. CONCLUSIONS Therefore, the early identification of factors that predict COVID-19 progression and mortality in KTRs affected by COVID-19 contributes to therapeutic decisions, patient flow management, and allocation of resources.
Collapse
|
10
|
Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review. APPLIED SYSTEM INNOVATION 2022. [DOI: 10.3390/asi5030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.
Collapse
|