eHealth and mHealth: Challenges and Prospects, 2nd Edition

Topic Information

Dear Colleagues,

We live in an era in which the rise of information technologies has spread to all areas of society. This evolution has gradually affected the healthcare field throughout the 21st century, although advances have mainly been focused on supervised systems where the intervention of healthcare specialists was necessary. However, the global pandemic in 2020 and part of 2021 left a large part of the population without regular face-to-face healthcare. This was a very important push against the clock for the digital transformation of medicine and represents an unprecedented situation in history that can be used by governments and health centres to further research and advancements in the field of e-Health and m-Health. The main focus of this topic is to bring together works from different branches of research, integrated in different journals of this publishing house, in order to showcase all kinds of medical advances linked to new technologies, studies and future challenges, computer-aided diagnostic systems (CADs), wearable devices or unobtrusive ambient sensors for detecting daily life patterns and/or anomalies, accident prevention systems, rehabilitation-oriented technologies, biological and physiological signal processing, and medical image processing, to name a few. The application of new technologies to the medical field to reduce the workload of healthcare staff must remain at the forefront, empowering chronic patients through self-management, helping in the diagnosis of diseases and accelerating the diagnostic process. It is envisioned that some of the described works may represent forerunners of future developments in the field of e-Health and m-Health.

Dr. Antonis Billis
Dr. Manuel Dominguez-Morales
Prof. Dr. Anton Civit
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
International Journal of Environmental Research and Public Health ijerph	-	7.3	2004	24.3 Days	CHF 2500	Submit
Journal of Personalized Medicine jpm	-	4.1	2011	16.7 Days	CHF 2600	Submit
Healthcare healthcare	2.4	3.5	2013	20.5 Days	CHF 2700	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit

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Published Papers (2 papers)

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17 pages, 1343 KiB  

Open AccessReview

The State of the Art of Digital Twins in Health—A Quick Review of the Literature

by Leonardo El-Warrak and Claudio M. de Farias

Computers 2024, 13(9), 228; https://doi.org/10.3390/computers13090228 - 11 Sep 2024

Cited by 1 | Viewed by 2116

Abstract

A digital twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. Virtual models can integrate with all the latest technologies, such as the Internet of Things [...] Read more.

A digital twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. Virtual models can integrate with all the latest technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI). Digital twins have applications in a wide range of sectors, from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. By analysing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects. Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. This article explores how digital twins are being used in healthcare. This article also introduces some discussions on the impact of this use and future research and technology development projections for the use of digital twins in the healthcare sector. Full article

(This article belongs to the Topic eHealth and mHealth: Challenges and Prospects, 2nd Edition)

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8 pages, 488 KiB  

Open AccessBrief Report

Prevalence and Predictors of Long COVID in Patients Accessing a National Digital Mental Health Service

by Lauren G. Staples, Olav Nielssen, Blake F. Dear, Madelyne A. Bisby, Alana Fisher, Rony Kayrouz and Nickolai Titov

Int. J. Environ. Res. Public Health 2023, 20(18), 6756; https://doi.org/10.3390/ijerph20186756 - 13 Sep 2023

Cited by 3 | Viewed by 2277

Abstract

MindSpot is a national mental health service that provides assessments and treatment to Australian adults online or via telephone. Since the start of 2020, questions related to the mental health impacts of COVID-19 have been routinely administered. The objective of the current study [...] Read more.

MindSpot is a national mental health service that provides assessments and treatment to Australian adults online or via telephone. Since the start of 2020, questions related to the mental health impacts of COVID-19 have been routinely administered. The objective of the current study is to report the prevalence and predictors of self-reported “long COVID” in patients completing an assessment at the MindSpot Clinic between 5 September 2022 and 7 May 2023 (n = 17,909). Consistent with the World Health Organization definition, we defined long COVID as the occurrence of ongoing physical or mental health symptoms three months after a COVID-19 infection. We conducted a descriptive univariate analysis of patients who reported: no COVID-19 diagnosis (n = 6151); a current or recent (within 3 months) COVID-19 infection (n = 2417); no symptoms three months post-COVID-19 infection (n = 7468); or COVID-related symptoms at least three months post-infection (n = 1873). Multivariate logistic regression was then used to compare patients with and without symptoms three months post-COVID to identify potential predictors for long COVID. The prevalence of long COVID was 10% of the total sample (1873/17909). Patients reporting symptoms associated with long COVID were older, more likely to be female, and more likely to be depressed and report a reduced ability to perform their usual tasks. Sociodemographic factors, including cultural background, education, and employment, were examined. These results provide evidence of the significant prevalence of symptoms of long COVID in people using a national digital mental health service. Reporting outcomes in an Australian context and in specific sub-populations is important for public health planning and for supporting patients. Full article

(This article belongs to the Topic eHealth and mHealth: Challenges and Prospects, 2nd Edition)

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