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Advances in Digital Health and the Implications for Health and Wellbeing in the Time of COVID-19

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (3 April 2023) | Viewed by 5368

Special Issue Editors

Prof. Dr. Louise Moody

E-Mail Website
Guest Editor
Centre for Arts, Memory and Communities, Coventry University, Coventry CV1 5FB, UK
Interests: improving health; wellbeing; online and app-based health interventions
Dr. Hayley Wright

E-Mail Website
Guest Editor
Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK
Interests: cognitive function; sexual wellbeing; ageing; stroke and brain injury; long-term conditions; digital self-management; neuropsychology; cognitive psychology

Special Issue Information

Dear Colleagues,

Telehealth has the potential to play a role in the prevention, diagnosis, treatment, and management of physical and mental health conditions. According to McKinsey, telehealth usage surged at the start of the COVID-19 pandemic, as consumers and providers sought ways to safely access and deliver healthcare. As a result of the pandemic, the willingness of both users and providers to use telehealth increased, and changes occurred in associated regulations. Telehealth offered a ‘bridge to care’, playing a role in the management of the challenges presented by COVID-19, minimizing the risk of transmission through direct physical contact, and providing continuous care in the community.

In this Special Issue, we consider the progress of telehealth since the initial COVID-19 spike alongside the implications for telehealth and virtual health going forward. Research in this area might include advanced technical developments, trials of novel solutions, meta-analyses, digital interventions, and innovative technologies (such as virtual reality and augmented reality). Papers may also consider the application of these methods and technologies in particular groups, including populations with long-term conditions, deprived communities, children, and older people. There are a range of design, delivery and ethical challenges for research and practical implementation within this context.

Papers addressing these, as well as related topics, are invited to contribute to this Special Issue, especially those combining a high academic standard with a practical focus on user needs and involvement during the pandemic.

Prof. Dr. Louise Moody
Dr. Hayley Wright
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • telehealth
  • tele-medicine
  • virtual health
  • COVID-19
  • technology acceptance and adoption
  • digital health
  • self-management
  • mental health and wellbeing
  • long-term conditions.

Published Papers (2 papers)

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Research

18 pages, 1639 KiB  
Article
Understanding the Antecedents and Effects of mHealth App Use in Pandemics: A Sequential Mixed-Method Investigation
by Xiaoling Jin, Zhangshuai Yuan and Zhongyun Zhou
Int. J. Environ. Res. Public Health 2023, 20(1), 834; https://doi.org/10.3390/ijerph20010834 - 2 Jan 2023
Cited by 6 | Viewed by 2595
Abstract
Pandemics such as COVID-19 pose serious threats to public health and disrupt the established systems for obtaining healthcare services. Mobile health (mHealth) apps serve the general public as a potential method for coping with these exogenous challenges. However, prior research has rarely discussed [...] Read more.
Pandemics such as COVID-19 pose serious threats to public health and disrupt the established systems for obtaining healthcare services. Mobile health (mHealth) apps serve the general public as a potential method for coping with these exogenous challenges. However, prior research has rarely discussed the antecedents and effects of mHealth apps and their use as a coping method during pandemics. Based on the technology acceptance model, empowerment theory, and event theory, we developed a research model to examine the antecedents (technology characteristics and event strength) and effects (psychological empowerment) of mHealth apps and their use. We tested this research model through a sequential mixed-method investigation. First, a quantitative study based on 402 Chinese mHealth users who used the apps during the COVID-19 pandemic was conducted to validate the theoretical model. A follow-up qualitative study of 191 online articles and reviews on mHealth during the COVID-19 pandemic was conducted to cross-validate the results and explain the unsupported findings of the quantitative study. The results show that (1) the mHealth app characteristics (perceived usefulness and perceived ease of use) positively affect mHealth app use; (2) mHealth app use positively affects the psychological empowerment of mHealth users; and (3) the characteristics of pandemic events (event criticality and event disruption) have positive moderating effects on the relationship between mHealth app characteristics and mHealth app use. This study explains the role of mHealth apps in the COVID-19 pandemic on the micro-level, which has implications for the ways in which mHealth apps are used in response to public pandemics. Full article
(This article belongs to the Special Issue Advances in Digital Health and the Implications for Health and Wellbeing in the Time of COVID-19)
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14 pages, 1344 KiB  
Article
Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning
by Haipeng Liu, Jiangtao Wang, Yayuan Geng, Kunwei Li, Han Wu, Jian Chen, Xiangfei Chai, Shaolin Li and Dingchang Zheng
Int. J. Environ. Res. Public Health 2022, 19(17), 10665; https://doi.org/10.3390/ijerph191710665 - 26 Aug 2022
Cited by 3 | Viewed by 2075
Abstract
Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis [...] Read more.
Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients. Full article
(This article belongs to the Special Issue Advances in Digital Health and the Implications for Health and Wellbeing in the Time of COVID-19)
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