Background: A vast amount of mobile apps have been developed during the past few months in an attempt to “flatten the curve” of the increasing number of COVID-19 cases.
Objective: This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19.
Methods: We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation.
Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic.
Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors.

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COVID-19 Mobile Apps: A Systematic Review of the Literature.

Patients diagnosed with coronavirus disease 2019 (COVID-19) associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently experience symptom burden post-acute infection or post-hospitalisation. We aimed to identify optimal strategies for follow-up care that may positively impact the patient's quality of life (QoL). A European Respiratory Society (ERS) Task Force convened and prioritised eight clinical questions. A targeted search of the literature defined the timeline of “long COVID” as 1–6 months post-infection and identified clinical evidence in the follow-up of patients. Studies meeting the inclusion criteria report an association of characteristics of acute infection with persistent symptoms, thromboembolic events in the follow-up period, and evaluations of pulmonary physiology and imaging. Importantly, this statement reviews QoL consequences, symptom burden, disability and home care follow-up. Overall, the evidence for follow-up care for patients with long COVID is limited. Follow-up care of patients infected with SARS-CoV-2 is crucial and may improve their quality of life. More evidence and research is emerging to understand the causes, mechanisms and risks of long COVID consequences. https://bit.ly/3J1WMWy

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European Respiratory Society statement on long COVID follow-up

Background: Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting.
Objective: The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review.
Methods: This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed.
Results: This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps.
Conclusions: This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.

Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis.

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy In this context, the use of infoveillance tools has become a primary necessity Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected Methods: RSVs of the query coronavirus + covid during February 1-December 4, 2020 (period 1), and February 20-May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020 The survey covered Italian regions and cities, and countries and cities worldwide The search category was set to all categories Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution X i = X ( σ i , x ¯ i ) was used to represent the trend of daily variations of x i j = R S V s i j  When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected Welch's t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset Two RSVs were considered statistical confident when t 15  A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold) The percentage increase Δ was used to quantify the difference between two values Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (97% during period 1 and 109% during period 2) However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets ( M a x | Δ | = + 625 % for Italian regions, and M a x | Δ | = + 175 % for countries worldwide) Furthermore, only RSVs of countries worldwide did not exceed confidence threshold Finally, the large amount of anomalies registered in Italian and international cities' RSVs made these datasets unusable for any kind of statistical inference Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries' data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected Further research is needed to evaluate the effectiveness of this method

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Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Background: Social distancing and school suspension due to the coronavirus pandemic (COVID-19) may have a negative impact on children’s behavior and well-being. Problematic smartphone use (PSU), problematic social media use (PSMU) and perceived weight stigma (PWS) are particularly important issues for children, yet we have a poor understanding of how these may have been affected by lockdowns and physical isolation resulting from COVID-19. This research aimed to understand how these psychosocial and behavioral variables may be associated with psychological distress, and how these associations may have changed during the COVID-19 pandemic. Methods: A total of 489 children completed a three-wave longitudinal study from January 2020 to June 2020. The first wave was conducted before the COVID-19 outbreak. The second wave was conducted during the outbreak. The third wave was conducted during post-COVID-19 lockdown. Questionnaires measured psychological distress, PSU, PSMU, and PWS. Results: PSU, PSMU, PWS and psychological distress were all significantly associated with each other. PSU was significantly higher during outbreak. PWS was significantly higher before outbreak. We found an increased association between PSMU and PWS across three waves in all three models. The association between PSU and depression/anxiety decreased across three waves; however, association between PSMU and depression/anxiety increased across three waves. Conclusions: COVID-19 initiated school suspension and associated lockdowns appear to have exacerbated PSU and depression among children. However, PWS was reduced during this period. Children should use smartphones and social media safely and cautiously, and be aware of the potential exposure to weight stigmatization.

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Problematic Use of Internet-Related Activities and Perceived Weight Stigma in Schoolchildren: A Longitudinal Study Across Different Epidemic Periods of COVID-19 in China

During the burst of the coronavirus pandemic, in early-midst 2020, public health authorities worldwide considered appropriate identification, isolation and contact tracing as the most appropriate strategy for infection containment. This work presents an outbreak response tool, designed for public health authorities to effectively track suspect, probable and confirmed incidence cases in a pandemic by means of a mobile app used by citizens to provide immediate feedback. It is developed based on an already existing personal health record app, which has been extended to properly accommodate specific needs that emerged during the crisis. The aim is to better support human tracers and should not be confused with proximity tracking apps. It respects safety and security regulations, while at the same time it conforms to international standards and widely accepted medical protocols. Issues relevant to privacy concerns, and interoperability with available patient registries and data analytics tools are also examined to better support public healthcare delivery and contain the spread of the infection.

/pdf/an-outbreak-response-tool-to-effectively-support-4ac4kpsluf.pdf

An outbreak response tool to effectively support surveillance of suspect, probable and confirmed incidence cases while staying safe in COVID-19

https://www.mdpi.com/2227-9032/10/3/443/pdf?version=1645862151

Developing an AI-Enabled Integrated Care Platform for Frailty

The ongoing coronavirus pandemic, is affecting the lives of millions of people, while changing our society by establishing new norms for social life, business, and traveling The digital health domain has already tried to respond to the pandemic challenges, by the rapid development and release of mobile apps aiming to “flatten the curve” of the increasing number of COVID-19 cases In this paper, starting from our own developed app to support citizens staying “Safe in COVID-19”, we present our vision on how semantics and data management could highly contribute, along with personal health apps, to the secondary usage of available data in order to support effective disease management, prediction and increase the collective knowledge on the disease Copyright © 2020 for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0)

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Fokion Logothetidis

Papers

COVID-19 Mobile Apps: A Systematic Review of the Literature.

An outbreak response tool to effectively support surveillance of suspect, probable and confirmed incidence cases while staying safe in COVID-19

Developing an AI-Enabled Integrated Care Platform for Frailty

Data management, semantics and personal health apps for staying safe in COVID-19