Topic Editors

Dr. Antonis Billis
Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Robotics and Computer Technology Lab, University of Seville, Seville, Spain
Robotics and Computer Technology Lab, University of Seville, 41012 Seville, Spain

eHealth and mHealth: Challenges and Prospects, 2nd Volume

Abstract submission deadline
30 June 2024
Manuscript submission deadline
30 September 2024
Viewed by
1788

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

  • artificial intelligence
  • computer vision
  • image processing
  • medical imaging
  • decision support system
  • diagnostic aid system
  • machine learning
  • deep learning
  • ambient assisted living
  • gamification
  • wearable medical devices
  • biomedical signal processing
  • physiological signal processing
  • accident prevention systems
  • detection of abnormal situations

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biomedicines
biomedicines
4.7 3.7 2013 15.4 Days CHF 2600 Submit
Computers
computers
2.8 4.7 2012 17.7 Days CHF 1800 Submit
Information
information
3.1 5.8 2010 18 Days CHF 1600 Submit
International Journal of Environmental Research and Public Health
ijerph
- 5.4 2004 29.6 Days CHF 2500 Submit
Journal of Personalized Medicine
jpm
3.4 2.6 2011 17.8 Days CHF 2600 Submit

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Published Papers (1 paper)

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Brief Report
Prevalence and Predictors of Long COVID in Patients Accessing a National Digital Mental Health Service
Int. J. Environ. Res. Public Health 2023, 20(18), 6756; https://doi.org/10.3390/ijerph20186756 - 13 Sep 2023
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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 Volume)
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