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Objective During the coronavirus disease pandemic, enforced restrictions prevented face-to-face consultations for patients requiring non-emergency medical treatment. In response, there was a rise in telemedical practices, such as teledermatology. This study aimed at understanding the pan-world experiences of patients and healthcare staff who adapted to teledermatology in the coronavirus disease era. Methods This study made use of an online survey presented to dermatology professionals using social media and WhatsApp groups. Professionals who applied teledermatology between March and June 2020 were targeted. The survey was designed to identify respondent demographics and the preferred platforms for digital consultations. The most common diagnoses and rates of referral for further evaluation were recorded. Lastly, a platform was provided for practitioners to report their own and their patient's perspectives on the advantages and operational challenges of teledermatology. Data were collated and analyzed in Microsoft Excel. Results In total, 653 stakeholders participated, representing countries worldwide. Facebook and WhatsApp services were the most popular mediums of digital consultation. Diagnoses of ailments, such as acne and eczema, as well as skin-related infections, were most common. Of the cases referred for biopsy, 10 patients were subsequently diagnosed with cutaneous malignancies. Practitioners and patients not only reported personal benefit from adopting teledermatology, but also reported concerns regarding data privacy and the levels of technological literacy required. Conclusions Teledermatology proved an innovative clinical response to unprecedented challenges. However, further policy development and technological advancement aimed at increasing the diagnostic power of digital consultations are needed to support the continuation of teledermatology in the post-pandemic world.

Objective To summarize the current status of, and the current expert opinions, recommendation and evidence associated with the use and implementation of electronic health (eHealth), telemedicine, and/or telehealth to provide healthcare services for chronic disease patients during the COVID-19 pandemic. Materials and methods We searched four electronic databases (PubMed, Google Scholar, Science Direct, and Web of Science Core Collection) to identify relevant articles published between 2019 and 2020. Searches were restricted to English language articles only. Two independent reviewers screened the titles, abstracts, and keywords for relevance. The potential eligible articles, papers with no abstract, and those that fall into the uncertain category were read in full text independently. The reviewers met and discussed which articles to include in the final review and reached a consensus. Results We identified 51 articles of which 25 articles met the inclusion criteria. All included articles indicated the promising potential of eHealth, telehealth, and/or telemedicine solutions in delivering healthcare services to patients living with chronic diseases/conditions during the COVID-19 pandemic. We synthesized the main findings into ten usages and eight recommendations concerning the different activities for delivering healthcare services remotely for those living with chronic diseases/conditions in the era of COVID-19. Discussion and conclusions There is limited evidence available about the effectiveness of such solutions. Further research is required during this pandemic to improve the credibility of evidence on telemedicine, telehealth, and/or eHealth-related outcomes for those living with chronic diseases.

In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Resilience, a person's mental ability to deal with challenging situations adaptively, is an important life skill. Supporting students in building psychological resilience and coping during crises (with the COVID-19 pandemic being a prime example) is crucial. Very few mobile applications (apps) for mental health explicitly report behavioral change techniques. Moreover, only a handful of the apps that support resilience are gamified, or use smartphone sensors readily available in modern smartphones for health self-management, or were designed for use by a nonclinical population. This study describes the design of a prototype for a gamified, theory-based mobile app that utilizes the Internet of Things to provide personalized data and enhance undergraduate students’ resilience. A total of 74 participants evaluated the prototype and completed an online questionnaire during the COVID-19 lockdowns. The questionnaire included questions examining the design's feasibility for supporting resilience and questions on the System Usability Scale evaluating its usability. Regarding the evaluation of the prototype on improving psychological resilience, positive responses (M = 3.76 out of 5, SD = 0.82) were received for all functions (goal setting for studying, socializing and physical exercise, progress monitoring using sensors or self-reporting, reflection, motivational badges). The System Usability Scale returned an evaluation score of 72.9, indicating a satisfactory degree of usability. The resilience app is a promising proof of concept. Combining Internet of Things capabilities with active user interaction while incorporating behavior change techniques in a gamified environment was well accepted by students. Implications for the design of gamified environments for well-being are drawn. Future research will empirically validate its design using quasi-experimental methods.

Background The coronavirus disease 2019 pandemic has led to an increase in remote consultations in health care. This study aimed to assess the acceptance of video consultation as an alternative to face-to-face in-office visits in general practice (GP) and to investigate its drivers and barriers. Methods A cross-sectional study was conducted in Germany during the coronavirus disease 2019 pandemic from December 2020 to April 2021. Participants were recruited among patients in 16 GP surgeries. Assessed were sociodemographic and medical data as well as information and communications technology related data. Acceptance of video consultation and its predictors were determined using a modified questionnaire based on a short version of the renowned unified theory of acceptance and use of technology model. Results In total, 371 participants were included in the data analysis. Acceptance of video consultation was moderate. A hierarchical regression revealed acceptance was significantly predicted by the PHQ-2, taking no regular medication, computer proficiency, knowledge about digital health care solutions, no prior use of video consultation, and the unified theory of acceptance and use of technology predictors performance expectancy, effort expectancy, and social influence. The extended unified theory of acceptance and use of technology model explained significantly more variance than the restricted unified theory of acceptance and use of technology model in acceptance of video consultation. Conclusions In this study computer proficiency, existing knowledge about digital health care solutions and depressive symptoms functioned as drivers to acceptance, no prior use of video consultation could be identified as a potential barrier. Patients with regular medication have been particularly receptive to video consultation. The study confirmed the validity of the unified theory of acceptance and use of technology model in determining acceptance of video consultation. Considering that there is growing demand and acceptance for different approaches to engage with health care providers, additional steps should be taken to establish video consultation as a genuine alternative.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications’ objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications—namely healthcare apps, smartwatches, and remote temperature scanners—are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

During the coronavirus disease 2019 (COVID-19) pandemic, it is difficult for travelers to choose suitable nature-based leisure travel destinations because many factors are related to health risks and are highly uncertain. This research proposes a type-II fuzzy approach with explainable artificial intelligence to overcome this difficulty. First, an innovative type-II alpha-cut operations fuzzy collaborative intelligence method was used to derive the fuzzy priorities of factors critical for nature-based leisure travel destination selection. Subsequently, a type-II fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje method, which is also novel, was employed to evaluate and compare the overall performance of nature-based leisure travel destinations. Furthermore, several measures were taken to enhance the explainability of the selection process and result. The effectiveness of the proposed type-II fuzzy approach was evaluated in a regional experiment conducted in Taichung City, Taiwan, during the COVID-19 pandemic.

The COVID-19 pandemic has affected people’s lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.

Background Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

Testing is crucial for early detection, isolation, and treatment of coronavirus disease (COVID-19)-infected individuals. However, in resource-constrained countries such as the Philippines, test kits have limited availability. As of 11 April 2020, there are 11 testing centers in the country that have been accredited by the Department of Health (DOH) to conduct testing. In this paper, we use nonlinear programming (NLP) to determine the optimal percentage allocation of COVID-19 test kits among accredited testing centers in the Philippines that gives an equitable chance to all infected individuals to be tested. Heterogeneity in testing accessibility, population density of municipalities, and the capacity of testing facilities are included in the model. Our results show that the range of optimal allocation per testing center are as follows: Research Institute for Tropical Medicine (4.17–6.34%), San Lazaro Hospital (14.65–24.03%), University of the Philippines-National Institutes of Health (16.25–44.80%), Lung Center of the Philippines (15.8–26.40%), Baguio General Hospital Medical Center (0.58–0.76%), The Medical City, Pasig City (5.96–25.51%), St. Luke’s Medical Center, Quezon City (1.09–6.70%), Bicol Public Health Laboratory (0.06–0.08%), Western Visayas Medical Center (0.71–4.52%), Vicente Sotto Memorial Medical Center (1.02–2.61%), and Southern Philippines Medical Center (≈ 0.01 % ). Our results can serve as a guide to the authorities in distributing the COVID-19 test kits. These can also be used for proposing additional testing centers and utilizing the available test kits properly and equitably, which helps in “flattening” the epidemic curve.