The Use of Digital Health in the Detection and Management of COVID-19

How, why and when are delayed (back-up) antibiotic prescriptions used in primary care? A realist review integrating concepts of uncertainty in healthcare

BACKGROUND

Antimicrobial resistance is a global patient safety priority and inappropriate antimicrobial use is a key contributing factor. Evidence have shown that delayed (back-up) antibiotic prescriptions (DP) are an effective and safe strategy for reducing unnecessary antibiotic consumption but its use is controversial.

METHODS

We conducted a realist review to ask why, how, and in what contexts general practitioners (GPs) use DP. We searched five electronic databases for relevant articles and included DP-related data from interviews with healthcare professionals in a related study. Data were analysed using a realist theory-driven approach - theorising which context(s) influenced (mechanisms) resultant outcome(s) (context-mechanism-outcome-configurations: CMOCs).

RESULTS

Data were included from 76 articles and 41 interviews to develop a program theory comprising nine key and 56 related CMOCs. These explain the reasons for GPs' tolerance of risk to different uncertainties and how these may interact with GPs' work environment, self-efficacy and perceived patient concordance to make using DP as a safety-net or social tool more or less likely, at a given time-point. For example, when a GP uses clinical scores or diagnostic tests: a clearly high or low score/test result may mitigate scientific uncertainty and lead to an immediate or no antibiotic decision; an intermediary result may provoke hermeneutic (interpretation-related) uncertainty and lead to DP becoming preferred and used as a safety net. Our program theory explains how DP can be used to mitigate some uncertainties but also provoke or exacerbate others.

CONCLUSION

This review explains how, why and in what contexts GPs are more or less likely to use DP, as well as various uncertainties GPs face which DP may mitigate or provoke. We recommend that efforts to plan and implement interventions to optimise antibiotic prescribing in primary care consider these uncertainties and the contexts when DP may be (dis)preferred over other interventions to reduce antibiotic prescribing. We also recommend the following and have included example activities for: (i) reducing demand for immediate antibiotics; (ii) framing DP as an 'active' prescribing option; (iii) documenting the decision-making process around DP; and (iv) facilitating social and system support.

Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks

Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.

Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar

BACKGROUND

Public health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event.

METHODS

This cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS' contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action.

RESULTS

The probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %-0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79).

CONCLUSION

EIOS proved useful in the early warning of public health threats.

Feasibility Study on the Use of Infrared Cameras for Skin Cancer Detection under a Proposed Data Degradation Model

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Precision public health, the key for future outbreak management: A scoping review

Background

Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.

Objective

We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.

Methods and analysis

Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.

Results

A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).

Conclusions

PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective

Digital health has transformed the healthcare landscape by leveraging technology to improve patient outcomes and access to medical services. The COVID-19 pandemic has highlighted the urgent need for digital healthcare solutions that can mitigate the impact of the outbreak while ensuring patient safety. In this chapter, we delve into how digital health technologies such as telemedicine, mobile apps, and wearable devices can provide personalized care, reduce healthcare provider burden, and lower healthcare costs. We also explore the creation of a greenway of digital healthcare that safeguards patient confidentiality, enables efficient communication, and ensures cost-effective payment systems. This chapter showcases the potential of digital health to revolutionize healthcare delivery while ensuring patient well-being and medical staff satisfaction.

Role of mobile health applications in prevention and detection of pandemic disease: A population perspective

Background

The Saudi government and the MOH launched six mobile application help in tracking positive cases, get medical consultation from home, and vaccination for coronavirus disease 2019 (COVID-19). Our study was conducted to evaluate the role of mobile health applications in the prevention and detection of pandemic disease from population perspectives.

Methods

A cross-sectional descriptive exploratory research design was utilized in this study. Based on the sample size calculation (described below), we recruited a convenience sample of 462 participants from the Northern Border Region according to the set of inclusion and exclusion criteria: Anyone over 12 years of age, including both genders and both Saudi citizens and non-Saudi citizens, were eligible to participate during the period from March 2022 to the end of July.

Results

In total 462 were participated, and 79.2% of them were females. There was a statistically significant difference between educational level and the overall score of public satisfaction with the ease of use of mobile health applications as well as overall satisfaction with the services provided by mobile health apps during the COVID-19 pandemic. Additionally, there was a statistically significant difference between gender and the role of mobile applications in the prevention of COVID-19 (p = 0.028).

Conclusion

The study found that most participants agree that the mobile health applications launched by the Saudi government and Ministry of Health have been successful in aiding the anticipation and early detection of COVID-19 cases, as well as facilitating access to healthcare services. Over half of the participants strongly agree that these mobile applications have been very effective and beneficial for their health and have helped save time.

Design, development and evaluation of a mobile-based self-care application for patients with COVID-19 not requiring hospitalization; a study of Northwest of Iran

BACKGROUND

Given the effective role of a mobile applications in disease management, disease monitoring, and self-care in patients with COVID-19 disease, we aimed to design, development and evaluation of a self-care Mobile app for COVID-19 patients not requiring hospitalization.

METHODS

The design, development and evaluation the usability of the self-care and education mobile app for patients with COVID-19 disease were conducted in two main phases at 2021 in Northwest of IRAN; (1) Determine the features and capabilities and (2) Design, development and evaluation of self-care mobile App. JAVA programming languages and Android Operating System were used and selected to design and development of a mobile app. There were 25 participants who conducted evaluations of the mobile app's usability and impact using the mobile health app usability a Questionnaire of User Interface Satisfaction was administered to assess the usability of the developed application. The results were analyzed via Excel 2013.

RESULTS

The model of developing a mobile app as an Information System was the Waterfall model. The smartphone application based on a set of capabilities and features was designed and consists of two main parts: the login screen for user registration, and the main home menu. The user interface includes three main pages or activities; (a) Main Menu for quick access to all of the pages, (b) Symptom management and monitoring to monitor the signs and symptoms during the illness, and (c) Set Reminders and Alarms to notify patients. The users' mean score of the application usability was calculated as 7.91 out of 9 indicating a good level of satisfaction.

CONCLUSION

This app can be a guideline and a useful tools for managing and monitoring symptoms, reminding medications, and implementing self-care instructions in outpatients. The authors suggest evaluating the efficacy and functionality test of mobile-based applications for COVID-19 in clinical trial studies.

Facilitators and barriers of mHealth interventions during the Covid-19 pandemic: systematic review

BACKGROUND

With the spread of Covid-19 disease, health interventions related to the control, prevention, and treatment of this disease and other diseases were given real attention. The purpose of this systematic review is to express facilitators and barriers of using mobile health (mHealth) interventions during the Covid-19 pandemic.

METHODS

In this systematic review, original studies were searched using keywords in the electronic database of PubMed until August 2022. The objectives and outcomes of these studies were extracted. Finally, to identify the facilitators and barriers of mHealth interventions, a qualitative content analysis was conducted based on the strengths, weaknesses, opportunities, and threats (SWOT) analysis method with Atlas.ti 8 software. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT).

RESULTS

In total, 1598 articles were identified and 55 articles were included in this study. Most of the studies used mobile applications to provide and receive health services during the Covid-19 pandemic (96.4%). The purpose of the applications was to help prevention (17), follow-up (15), treatment (12), and diagnosis (8). Using SWOT analysis, 13 facilitators and 18 barriers to patients' use of mHealth services were identified.

CONCLUSION

Mobile applications are very flexible technologies that can be customized for each person, patient, and population. During the Covid-19 pandemic, the applications designed due to lack of interaction, lack of time, lack of attention to privacy, and non-academic nature have not met their expectations of them.

Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study

BACKGROUND

Some of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021.

OBJECTIVE

This study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH).

METHODS

This cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19-related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level.

RESULTS

Regression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19-confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=-24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors.

CONCLUSIONS

The use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level.

Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review

BACKGROUND

In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive.

OBJECTIVE

This systematic review aimed to summarize the existing peer-reviewed literature evaluating the triage accuracy (directing users to appropriate services based on their presenting symptoms) and diagnostic accuracy of OSCs aimed at lay users for general health concerns.

METHODS

Searches were conducted in MEDLINE, Embase, CINAHL, Health Management Information Consortium (HMIC), and Web of Science, as well as the citations of the studies selected for full-text screening. We included peer-reviewed studies published in English between January 1, 2010, and February 16, 2022, with a controlled and quantitative assessment of either or both triage and diagnostic accuracy of OSCs directed at lay users. We excluded tools supporting health care professionals, as well as disease- or specialty-specific OSCs. Screening and data extraction were carried out independently by 2 reviewers for each study. We performed a descriptive narrative synthesis.

RESULTS

A total of 21,296 studies were identified, of which 14 (0.07%) were included. The included studies used clinical vignettes, medical records, or direct input by patients. Of the 14 studies, 6 (43%) reported on triage and diagnostic accuracy, 7 (50%) focused on triage accuracy, and 1 (7%) focused on diagnostic accuracy. These outcomes were assessed based on the diagnostic and triage recommendations attached to the vignette in the case of vignette studies or on those provided by nurses or general practitioners, including through face-to-face and telephone consultations. Both diagnostic accuracy and triage accuracy varied greatly among OSCs. Overall diagnostic accuracy was deemed to be low and was almost always lower than that of the comparator. Similarly, most of the studies (9/13, 69 %) showed suboptimal triage accuracy overall, with a few exceptions (4/13, 31%). The main variables affecting the levels of diagnostic and triage accuracy were the severity and urgency of the condition, the use of artificial intelligence algorithms, and demographic questions. However, the impact of each variable differed across tools and studies, making it difficult to draw any solid conclusions. All included studies had at least one area with unclear risk of bias according to the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool.

CONCLUSIONS

Although OSCs have potential to provide accessible and accurate health advice and triage recommendations to users, more research is needed to validate their triage and diagnostic accuracy before widescale adoption in community and health care settings. Future studies should aim to use a common methodology and agreed standard for evaluation to facilitate objective benchmarking and validation.

TRIAL REGISTRATION

PROSPERO CRD42020215210; https://tinyurl.com/3949zw83.

Predictors of desire to limit childbearing among reproductive age women in high fertility regions in Ethiopia. A multilevel mixed effect analysis

BACKGROUND

A high fertility rate can have a number of expensive consequences for developing nations, such as limiting economic growth, adversely impacting women and their children's health, and reducing access to quality education, nutrition, and employment. The problem is more obvious in Ethipia's high fertility regions. Therefore, this study aimed to assess predictors of desire to limit childbearing among reproductive age women in high fertility regions in Ethiopia.

METHODS

The analysis was based on secondary data using the 2016 Ethiopian Demographic and Health Survey. Stata version 14 software was used for analysis. A multi-level mixed-effect logistic regression analysis was fitted. Adjusted Odds Ratio at 95% confidence interval was used to show the strength and direction of the association. Statistical significance was declared at a P- value less than 0.05.

RESULTS

The overall desire to limit childbearing in high fertility regions in Ethiopia was 37.7% (95% CI: 36.28, 39.17). Age; 25-34 (AOR = 3.74; 95% CI: 2.97, 4.73), 35-49 years (AOR = 14; 95% CI: 10.85, 18.06), women education; Primary education (AOR = 0.73; 95% CI: 0.61, 0.88), secondary and higher (AOR = 0.29; 95% CI: 0.19, 0.43), from the community level variables Oromia National Regional state (AOR = 5.86; 95% CI: 2.82, 12.23), high proportion of community level poverity (AOR = 0.67; 95% CI: 0.45, 0.98), and high proportion of community level media exposure (AOR = 1.53; 95% CI: 1.07, 2.19) were statistically significant factors for desire to limit childbearing in high fertility regions of Ethiopia.

CONCLUSION

Nearly four in ten women had the desire to limit childbearing in high fertility regions in Ethiopia. Thus, to fulfill the women's desire to limit childbearing, Ministry of Health and health facilities are needed to increase financial support strategies and Family planning programs that enable pregnant women from poor households to use health services. In addition, increasing community level media exposure are important interventions.

Emerging data inputs for infectious diseases surveillance and decision making

Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.

Correlating global trends in COVID-19 cases with online symptom checker self-assessments

BACKGROUND

Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user's symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced.

OBJECTIVES

Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections.

MATERIALS AND METHODS

In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset's spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries.

RESULTS

We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries.

CONCLUSION

We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.

The role of mobile health in prevention, diagnosis, treatment and self-care of COVID-19 from the healthcare professionals' perspectives

Background

To facilitate disease management, understanding the attitude of healthcare professionals regarding the use of this tool can help mobile health (mHealth) program developers develop appropriate interventions.

Aims

To assess the perspective of healthcare professionals regarding the contribution of mobile-based interventions in the prevention, diagnosis, self-care, and treatment (PDST) of COVID-19.

Methods

This is a survey study conducted in 2020 in Iran with 81 questions. In this study mHealth functionalities were categorized into four dimensions including innovative, monitoring and screening, remote services, and education and decision-making. The data were analyzed using descriptive statistics, ANOVA, and the Kruskal-Wallis test to compare the attitudes of the different job groups.

Results

In total, 123 providers participated, and 87.4% of them reported that mHealth technology is moderate to most helpful for the management of COVID-19. Healthcare professionals believed that mHealth technology could be most helpful in self-care and least helpful in the diagnosis of COVID-19. Regarding the functionalities of the mobile application, the results showed that the use of patient decision aids can be most helpful in self-care and the use of computer games can be least helpful in treatment. The participants believed that mHealth is more effective in monitoring and screening dimensions and less effective in providing remote services.

Conclusions

This study showed that healthcare professionals believed that mHealth technology could have a better contribution to self-care for patients with COVID-19. Therefore, it is better to plan and invest more in the field of self-care to help patients to combat COVID-19. The results of this study revealed which mhealth functionalities work better in four domains of prevention, treatment, self-care, and diagnosis of COVID-19. This can help healthcare authorities to implement appropriate IT-based interventions to combat COVID-19.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Enabling Value Co-Creation in Healthcare through Blockchain Technology

The COVID-19 pandemic highlighted the need to manage complex relations within the healthcare ecosystem. The role of new technologies in achieving this goal is a topic of current interest. Among them, blockchain technology is experiencing widespread application in the healthcare context. The present work investigates how this technology fosters value co-creation paths in the new digital healthcare ecosystems. To this end, a multiple case study has been conducted examining the development and application of blockchain by 32 healthcare tech companies. The results show blockchain technology adoption's current and potential impacts on value co-creation regarding data and resource sharing, patient participation, and collaboration between professionals. Three main areas of activity emerge from the case studies where blockchain implementation brings significant benefits for value co-creation: improving service interaction, impacting actors' engagement, and fostering ecosystem transparency.

Insights from Review and Content Analysis of Current COVID-19 Mobile Apps and Recommendations for Future Pandemics

A number of mobile health apps related to coronavirus infectious disease 2019 (COVID-19) have been developed, but research into app content analytics for effective surveillance and management is still in its preliminary stages. The present study aimed to identify the purpose and functions of the currently available COVID-19 apps using content analysis. The secondary aim was to propose directions for the future development of apps that aid infectious disease surveillance and control with a focus on enhancing the app content and quality. Prior to conducting an app search in the App Store and the Google Play Store, we reviewed previous studies on COVID-19 apps found in Google Scholar and PubMed to examine the main purposes of the apps. Using the five selected keywords based on the review, we searched the two app stores to retrieve eligible COVID-19 apps including those already addressed in the reviewed literature. We conducted descriptive and content analyses of the selected apps. We classified the purpose types of the COVID-19 apps into the following five categories: Information provision, tracking, monitoring, mental health management, and engagement. We identified 890 apps from the review articles and the app stores: 47 apps met the selection criteria and were included in the content analysis. Among the selected apps, iOS apps outnumbered Android apps, 27 apps were government-developed, and most of the apps were created in the United States. The most common function for the iOS apps (63.6%) and Android apps (62.5%) was to provide COVID-19-related knowledge. The most common function among the tracking apps was to notify users of contact with infected people by the iOS apps (40.9%) and Android apps (37.5%). About 29.5% of the iOS apps and 25.0% of the Android apps were used to record symptoms and self-diagnose. Significantly fewer apps targeted mental health management and engagement. Six iOS apps (6/44, 13.6%) and four Android apps (4/24, 16.7%) provided behavioral guidelines about the pandemic. Two iOS apps (2/44, 4.5%) and two Android apps (2/24, 8.3%) featured communication functions. The present content analysis revealed that most of the apps provided unilateral information and contact tracing or location tracking. Several apps malfunctioned. Future research and development of COVID-19 apps or apps for other emerging infectious diseases should address the quality and functional improvements, which should begin with continuous monitoring and actions to mitigate any technical errors.

A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment

With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

Neuropsychological effects of COVID-19: A review

OBJECTIVE

The purpose of this review is to examine review literature on the psychological effects of the COVID-19 pandemic.

METHODS

Studies were identified by an online search of the PubMed database. We selected studies published from January to May 2020 (during the COVID-19 emergency).

RESULTS

We found that psychological effects of COVID-19 remain serious among the most of the population, in particular for people with mental disorders, adolescents, healthcare workers, and the general population that experienced high levels of stress, anxiety, and depression symptoms, with possible long-term psychological implications.

CONCLUSION

Findings revealed that living in urban areas, having economic stability, and living with parents were protective factors against anxiety for youth groups, whereas a risk factor was represented by the presence of COVID-19 infection that involved family members.

Total Quality and Innovation Management in Healthcare (TQIM-H) for an Effective Innovation Development: A Conceptual Framework and Exploratory Study

To thrive, an organization must adapt to the fast and constant change in the economic environment caused by an aging society, technological changes, and the pandemic crisis. Innovation becomes important for the adaptation of industries. Healthcare is one of them. Innovation development in hospitals is effective and acceptable when its management is effective and aligns with the healthcare quality context since quality is a philosophy of work in life-related settings. To the best of our knowledge, quality management and innovation management in healthcare have never been integrated. Therefore, this research aimed to create an integrated framework of quality and innovation management in healthcare (TQIM-H). To establish the effectiveness of applying TQIM-H for the development of effective healthcare innovation, this study developed a TQIM-H conceptual framework using multiple methodologies including a literature review, multiple case studies analysis, Delphi study with healthcare experts, Technology Acceptance Model (TAM), and triangulation with an external dataset. We constructed a TQIM-H conceptual framework, consisting of seven dimensions, that can be used in developing innovation projects in hospitals and which agrees with safety and quality principles in hospitals.

Data Management and Privacy Policy of COVID-19 Contact-Tracing Apps: Systematic Review and Content Analysis

BACKGROUND

COVID-19 digital contact-tracing apps were created to assist public health authorities in curbing the pandemic. These apps require users' permission to access specific functions on their mobile phones, such as geolocation, Bluetooth or Wi-Fi connections, or personal data, to work correctly. As these functions have privacy repercussions, it is essential to establish how contact-tracing apps respect users' privacy.

OBJECTIVE

This study aimed to systematically map existing contact-tracing apps and evaluate the permissions required and their privacy policies. Specifically, we evaluated the type of permissions, the privacy policies' readability, and the information included in them.

METHODS

We used custom Google searches and existing lists of contact-tracing apps to identify potentially eligible apps between May 2020 and November 2021. We included contact-tracing or exposure notification apps with a Google Play webpage from which we extracted app characteristics (eg, sponsor, number of installs, and ratings). We used Exodus Privacy to systematically extract the number of permissions and classify them as dangerous or normal. We computed a Permission Accumulated Risk Score representing the threat level to the user's privacy. We assessed the privacy policies' readability and evaluated their content using a 13-item checklist, which generated a Privacy Transparency Index. We explored the relationships between app characteristics, Permission Accumulated Risk Score, and Privacy Transparency Index using correlations, chi-square tests, or ANOVAs.

RESULTS

We identified 180 contact-tracing apps across 152 countries, states, or territories. We included 85.6% (154/180) of apps with a working Google Play page, most of which (132/154, 85.7%) had a privacy policy document. Most apps were developed by governments (116/154, 75.3%) and totaled 264.5 million installs. The average rating on Google Play was 3.5 (SD 0.7). Across the 154 apps, we identified 94 unique permissions, 18% (17/94) of which were dangerous, and 30 trackers. The average Permission Accumulated Risk Score was 22.7 (SD 17.7; range 4-74, median 16) and the average Privacy Transparency Index was 55.8 (SD 21.7; range 5-95, median 55). Overall, the privacy documents were difficult to read (median grade level 12, range 7-23); 67% (88/132) of these mentioned that the apps collected personal identifiers. The Permission Accumulated Risk Score was negatively associated with the average App Store ratings (r=-0.20; P=.03; 120/154, 77.9%) and Privacy Transparency Index (r=-0.25; P<.001; 132/154, 85.7%), suggesting that the higher the risk to one's data, the lower the apps' ratings and transparency index.

CONCLUSIONS

Many contact-tracing apps were developed covering most of the planet but with a relatively low number of installs. Privacy-preserving apps scored high in transparency and App Store ratings, suggesting that some users appreciate these apps. Nevertheless, privacy policy documents were difficult to read for an average audience. Therefore, we recommend following privacy-preserving and transparency principles to improve contact-tracing uptake while making privacy documents more readable for a wider public.

Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak

BACKGROUND

Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing.

OBJECTIVE

We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations.

METHODS

Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords.

RESULTS

We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors' keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion.

CONCLUSIONS

mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.

Advanced materials used in wearable health care devices and medical textiles in the battle against coronavirus (COVID-19): A review

The novel coronavirus disease (COVID-19) has generated great confusion around the world, affecting people's lives and producing a large number of deaths. The development of portable and wearable devices is of great importance in several fields such as point-of-care medical applications and environmental monitoring. Wearable devices with an ability to collect various types of physiological records are progressively becoming incorporated into everyday life of people. Physiological indicators are essential health indicators and their monitoring could efficiently enable early discovery of disease. This would also help decrease the number of extra severe health problems, in disease avoidance, and lower the overall public sector health cost. Protective clothing is nowadays a main part of textiles classified as technical or industrial textiles. Protective clothing aims to protect its wearer from the harsh environmental impacts that may result in injury or death. Providing protection for the common population has also been taken seriously considering the anticipated disaster due to virus attacks. This review highlights the properties of the materials that are used in wearable health care device and medical textiles.

Leadership Style and Hospital Performance: Empirical Evidence From Indonesia

The COVID-19 pandemic created new conditions for the functioning of all organizations. Suddenly, there was a problem with the lack of appropriate leadership styles models in health care organizations (hospitals), which are particularly vulnerable to disruptions in a pandemic. Hospitals, in particular, have become exposed to organizational and managerial problems. The article aims to propose an appropriate leadership style model that will guarantee a high level of hospital efficiency, taking into account a pandemic situation in the example of private hospitals in Indonesia. Organizational identification is promoted as a mediating variable due to the high level of this variable in explaining hospital performance in Indonesia based on preliminary studies. During research used a structural equation model using 394 samples at the unit leadership level in private hospitals in Indonesia. The results of this study explain that there is an impact between innovative leadership and strategic leadership styles on hospital performance. Private hospitals in Indonesia need to improve themselves to use the most appropriate leadership style model based on the needs of the hospital itself.

Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health

Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.

Adoption of Health Mobile Apps during the COVID-19 Lockdown: A Health Belief Model Approach

This study aimed to investigate the adoption of the Sehha, Mawid, and Tetamman mobile health applications during the COVID-19 pandemic in Saudi Arabia. The present study investigated factors influencing app use intention based on the Health Belief Model (HBM) approach. This study was conducted using a sample of 176 participants from the Riyadh and Makkah regions during the lockdown in May 2020. This study uses structural equation modeling for data collected using SmartPLS 3.3.9 (GmbH, Oststeinbek, Germany) to examine the effect of constructs on the model. The most important predictor was the perceived benefits of the mobile health apps, followed by self-efficacy. The perceived barriers and cues to action have no significant effect on behavioral intention. The perceived benefits and self-efficacy as keys can provide an overview to the government and to health organizations for taking into account the most important factors of the adoption of mobile health apps, meaning that the developer must adjust to the characteristics of the community of people that need applications that provide many benefits and have an impact.

Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic

Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic.

Virtual Infection Prevention and Control in Low- and Middle-Income Countries

COVID-19 brought new challenges and opportunities for infection prevention and control. Virtual infection prevention and control (VIPC), although nascent, is rapidly becoming a viable and necessary strategy for combatting the COVID-19 pandemic. Benefits of VIPC include extending the impact of globally scarce infectious disease providers and public health practitioners, allowing coordination between disparate professionals to more effectively combat infectious disease, and increasing access to and quality of healthcare. Although mainly applied in developed countries, VIPC may play its greatest role in low- and middle-income countries (LMICs) with fewer healthcare resources. We conducted a brief literature search of VIPC in LMICs and found that many studies describe solutions in developed countries or describe planned or theoretical solutions. Few studies describe actual VIPC implementation in LMICs, except for China. Literature from related fields, for example, virtual critical care, and from developed countries is more robust and provides a roadmap for future research on VIPC in LMICs. Further research into strategies and outcomes related to VIPC in LMICs is necessary.

Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation

Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.

COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland

Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources.

Identifying data elements and key features of a mobile-based self-care application for patients with COVID-19 in Iran

Mobile Health applications have shown different usages in the COVID-19 pandemic, which consisted of empowering patient's awareness, promoting patient's self-care, and self-monitor behaviors. The purpose of this study is to identify key features and capabilities of a mobile-based application for self-care and self-management of people with COVID-19 disease. This study was a descriptive-analytical study that was conducted in two main phases in 2020. In the first phase, a literature review study was performed. In the second phase, using the information obtained from the review of similar articles, a questionnaire was designed to validate identified requirements. Based on the results of the first phase, 53 data elements and technical key features for mobile-based self-care application for people with COVID-19 were identified. According to the statistical population, 11 data elements for demographic requirements, 11 data elements for clinical requirements, 15 data elements for self-care specifications, and 16 features for the technical capability of this app were determined. Most of the items were selected by infectious and internal medicine specialists (94%). This study supports that the use of mobile-based applications can play an important role in the management of this disease. Software design and development could help manage and improve patients' health status.

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

Molecularly imprinted polymer as a synthetic receptor mimic for capacitive impedimetric selective recognition of Escherichia coli K-12

We fabricated an electrochemical molecularly imprinted polymer (MIP) chemosensor for rapid identification and quantification of E. coli strain using 2-aminophenyl boronic acid as the functional monomer. This strain is a modified Gram-negative strain of Escherichia coli bacterium, an ordinary human gut component. The E. coli strongly interacts with a boronic acid because of porous and flexible polymers of the cell wall. The SEM imaging showed that the bacteria template was partially entrapped within the polymeric matrix in a single step. Moreover, this imaging confirmed E. coli K-12 cell template extraction effectiveness. The prepared MIP determined the E. coli K-12 strain up to 2.9 × 10⁴ cells mL^-1. The interference study performed in the presence of E. coli variants expressing different surface appendages (type 1 fimbriae or Antigen 43 protein) or Shewanella oneidensis MR1, another Gram-negative bacteria, demonstrated that the bacterial surface composition notably impacts sensing properties of the bacteria imprinted polymer.

Clinical accuracy and agreement between tympanic and forehead body temperature measurements for screening of patients with COVID-19

AIM

To investigate the accuracy, reliability and agreement between infrared forehead thermometers versus infrared tympanic thermometers temperature, a cross-sectional study was conducted in April 2020.

METHODS

The forehead and tympanic temperatures of 615 subjects were measured simultaneously in three exposed SARS-COV-2 groups at one hospital in Iran, during April 2020. These comparisons were evaluated by Bland-Altman Plot, repeatability, Passing-Bablok regression and Lin's concordance correlation coefficient. The receiver operating characteristic (ROC) analysis was done to describe the discrimination accuracy of a diagnostic test. The study adhered to STROBE checklist for cross-sectional studies.

RESULTS

A Bland-Altman plot indicated that the limits of agreement between the forehead and tympanic temperature were -0.259 to +0.19°C. Passing-Bablok regression analysis illustrated that the infrared forehead was not linearly related to tympanic temperatures (reference method), with a slope estimate that was significantly different from 1.00. The infrared forehead thermometer showed poor precision and lower accuracy than the tympanic. The forehead temperature readings had 60.0% sensitivity and 44.4% specificity (p > .05) to predict disease.

CONCLUSION

According to the results of study, there is no evidence that the assessment of temperature by infrared forehead thermometer could discriminate between the two groups (positive and negative).

Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics

INTRODUCTION

This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources.

METHODS

HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols.

RESULTS

A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3-4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making.

CONCLUSIONS

This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.

Exploring digital health interventions to support community health workers in low-and-middle-income countries during the COVID-19 pandemic: a scoping review protocol

INTRODUCTION

COVID-19 has significantly affected community health workers' (CHWs) performance as they are expected to perform pandemic-related tasks along with routine essential healthcare services. A plausible way to optimise CHWs' functioning during this pandemic is to couple the efforts of CHWs with digital tools. So far, no systematic evidence is available on the use of digital health interventions to support CHWs in low-middle-income countries (LMICs) amid the COVID-19 pandemic. The article describes a protocol for a scoping review of primary research studies that aim to map evidence on the use of unique digital health interventions to support CHWs during COVID-19 in LMICs.

METHODS AND ANALYSIS

Our methodology has been adapted from scoping review guidelines provided by Arksey and O'Malley, Levac et al. and the Joanna Briggs Institute. Our search strategy has been developed for the following four main electronic databases: Excerpta Medica Database, Medical Literature Analysis and Retrieval System Online, Cochrane Central Register of Controlled Trials and Cumulated Index to Nursing and Allied Health Literature. Google Scholar and reference tracking will be used for supplementary searches. Each article will be screened against eligibility criteria by two independent researchers at the title and abstract and full-text level. The review will include studies that targeted digital health interventions at CHWs' level to provide support in delivering COVID-19-related and other essential healthcare services. A date limit of 31 December 2019 to the present date will be placed on the search and English language articles will be included.

ETHICS AND DISSEMINATION

Formal ethical approval is not required, as primary data will not be collected in this study. The results from our scoping review will provide valuable insight into the use of digital health interventions to optimise CHWs' functioning and will reveal current knowledge gaps in research. The results will be disseminated through journal publications and conference presentations.

The Contribution of Digital Health in the Response to Covid-19 in Vietnam

Emerging from early of 2020, the COVID-19 pandemic has become one of the most serious health crisis globally. In response to such threat, a wide range of digital health applications has been deployed in Vietnam to strengthen surveillance, risk communication, diagnosis, and treatment of COVID-19. Digital health has brought enormous benefits to the fight against COVID-19, however, numerous constrains in digital health application remain. Lack of strong governance of digital health development and deployment; insufficient infrastructure and staff capacity for digital health application are among the main drawbacks. Despite several outstanding problems, digital health is expected to contribute to reducing the spread, improving the effectiveness of pandemic control, and adding to the dramatic transformation of the health system the post-COVID era.

Implementation of a virtual ward as a response to the COVID-19 pandemic

Objective The aim of this study was to describe and evaluate the implementation of a virtual ward as a COVID-19 hospital avoidance response strategy and identify opportunities for improvement and future applicability. Methods A mixed-method observational study was conducted of a centralised virtual ward, which operated in a large metropolitan Australian health service from 23 March to 1 June 2020. Results In total, 238 unique patients were admitted to the virtual ward, accounting for 264 individual admission episodes and 2451 virtual bed days. Twenty (7.6%) episodes resulted in transfer to hospital and 136 patients provided responses to feedback surveys and reported their experience as very good (61.7%, n=87) or good (34.8%, n=49). Implementation success was high, with the model widely accepted and adopted across the health service. The service delivery model was considered to be low-cost in comparison to inpatient hospital-based care. Conclusions Overall, as a rapidly developed and implemented low-tech model of care, the virtual ward was found to provide an effective, accessible and low-cost solution to managing low-acuity COVID-19-positive patients in the community. This model should be considered in future pandemics as a hospital-avoidance response, with the ability to minimise patient-to-healthcare worker transmission, reduce personal protective equipment use and enhance patient adherence with isolation requirements. Targeted remote telemonitoring should be considered as a future modification to improve patient care. What is known about this topic? Virtual wards aim to reduce hospital demand by providing hospital-level care in community settings such as the patients' home. The COVID-19 pandemic has seen a rapid increase in the utilisation of virtual wards as an acute healthcare response that facilitates contactless care of infectious patients. Despite this rapid adoption, there is limited literature on the effectiveness of virtual ward models of care in a pandemic context. What does this paper add? This study provides a detailed description of the implementation of a virtual ward in a large metropolitan health service. It evaluates the effectiveness of the virtual ward as a COVID-19 response strategy and identifies opportunities for improvement and future applicability. This study contributes to the growing body of literature on the COVID-19 healthcare response and virtual wards. What are the implications for practitioners? This study details the implementation of a virtual ward and highlights potential facilitators and barriers to successful implementation and sustained applicability. Findings provide a comparative benchmark for other health services implementing virtual wards as a pandemic response strategy.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature

BACKGROUND

Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic.

METHODS

We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis.

RESULTS

A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers.

CONCLUSION

Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

Knowledge, attitudes, and practices of primary health care professionals in coping with COVID-19 in Brazil: a cross-sectional study

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has posed challenges to healthcare professionals, who needed to quickly adjust impacts on their work processes. Primary health care has become key to fighting the pandemic, as most mild cases seek primary care services as their point of first contact.

Objectives

To ascertain the knowledge, attitudes, and practices of health professionals working in primary health care in Brazil early in the COVID-19 pandemic.

Methods

Cross-sectional study of a convenience sample. An online questionnaire was made available from May 1 through May 31, 2020. The sole criterion for inclusion was a response rate greater than 30 respondents per Brazilian state. Data were treated descriptively and statistically.

Results

Overall, 293 responses were obtained, and the states of Paraná (n = 86), Mato do Grosso do Sul (n = 50) and São Paulo (n = 48) were included in the study. There was a predominance of female respondents (89.1%). Physical therapy (31.6%) and nursing (12.4%) were the most represented occupations. Respondents generally reported moderate knowledge (54.3%) and preparedness (57.6%), with daily information seeking (63.5%) in handbooks and technical guidance publications (89.6%). There was no statistically significant difference between states for the variables knowledge (p = 0.28) and preparedness (p = 0.19).

Conclusions

The participating states showed similar, positive results regarding knowledge, attitudes, and practices. Previous experiences seem to generate cumulative knowledge; however, greater readiness in training professionals is needed in emergency situations.

Systematic Review and Meta-Analysis of the Diagnostic Accuracy of Mobile-Linked Point-of-Care Diagnostics in Sub-Saharan Africa

Mobile health devices are emerging applications that could help deliver point-of-care (POC) diagnosis, particularly in settings with limited laboratory infrastructure, such as Sub-Saharan Africa (SSA). The advent of Severe acute respiratory syndrome coronavirus 2 has resulted in an increased deployment and use of mHealth-linked POC diagnostics in SSA. We performed a systematic review and meta-analysis to evaluate the accuracy of mobile-linked point-of-care diagnostics in SSA. Our systematic review and meta-analysis were guided by the Preferred Reporting Items requirements for Systematic Reviews and Meta-Analysis. We exhaustively searched PubMed, Science Direct, Google Scholar, MEDLINE, and CINAHL with full text via EBSCOhost databases, from mHealth inception to March 2021. The statistical analyses were conducted using OpenMeta-Analyst software. All 11 included studies were considered for the meta-analysis. The included studies focused on malaria infections, Schistosoma haematobium, Schistosoma mansoni, soil-transmitted helminths, and Trichuris trichiura. The pooled summary of sensitivity and specificity estimates were moderate compared to those of the reference representing the gold standard. The overall pooled estimates of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of mobile-linked POC diagnostic devices were as follows: 0.499 (95% CI: 0.458–0.541), 0.535 (95% CI: 0.401–0.663), 0.952 (95% CI: 0.60–1.324), 1.381 (95% CI: 0.391–4.879), and 0.944 (95% CI: 0.579–1.538), respectively. Evidence shows that the diagnostic accuracy of mobile-linked POC diagnostics in detecting infections in SSA is presently moderate. Future research is recommended to evaluate mHealth devices’ diagnostic potential using devices with excellent sensitivities and specificities for diagnosing diseases in this setting.

State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review

BACKGROUND

During the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data.

OBJECTIVE

The aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies.

METHODS

A systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps' compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies.

RESULTS

Overall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app.

CONCLUSIONS

Contact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized.

Virtually Serving the Underserved: Resident Perceptions of Telemedicine Use While Training During Coronavirus Disease 2019

Background: To adapt during the coronavirus disease 2019 (COVID-19) pandemic, a large safety-net hospital in Atlanta, Georgia and other hospitals across the United Sates, increased telemedicine (TM) for outpatient visits. The impact on resident physicians, including minority providers, had not yet been reported. We aimed to assess how primary care residents perceived increased TM in clinics during the pandemic, and assess resident burnout. Materials and Methods: An online survey was sent to 60 internal medicine (IM) and 18 family medicine (FM) residents who used TM to treat underserved patients, from March to May 2020, at the onset of the COVID-19 pandemic. The questionnaire included questions on resident demographics, satisfaction with TM, screening capability for COVID-19, effectiveness for patient education on COVID-19, impact on patient interactions and time management. Burnout was assessed with the Abbreviated Maslach Burnout Inventory. Results: A majority (62 of 78) eligible residents (79.5%) responded to the survey. Ninety-one percent of residents agreed that TM was a secure alternative to face-to-face encounters. Seventy-nine percent used it to screen for signs/symptoms of COVID-19 and 93% provided patient education on COVID-19. Average visit length decreased by 10-20 min with TM. Post-TM, scores for overall burnout were decreased (p = 0.0003) and less residents in total exhibited burnout (p = 0.0156). Discussion and Conclusions: IM and FM residents viewed TM as an efficient way to screen for and provide education on COVID-19, as well as a secure alternative to face-to-face encounters. With increased used of TM during COVID-19, there was decreased burnout among primary care residents.

Developing and testing of an Early Warning mobileHealth screening and risk scoring application for preventing heath worker in-hospital transmission of Covid-19: A feasibility study

BACKGROUND

Hospitals have been identified as very high-risk places for Covid-19 transmission between health workers and non-covid-19 patients. Health care workers are the most at risk population to contract and transmit the infection especially to the already vulnerable non-Covid-19 patients. In Low-income countries, routine testing is not feasible due to the high cost of testing therefore presenting the risk of un-controlled transmission within the non Covid-19 treatment wards. This challenge necessitated the development of an affordable intermediary screening tool that would enable early identification of potentially infected health care workers and for early real time DNA-PCR testing prioritization. This would limit the contact time of potentially infected health care workers with the patients but also efficiently utilize the limited testing kits.

OBJECTIVE

To describe an early warning in-hospital risk analysis mobile application for screening Covid-19 and determine the feasibility and user friendliness among health care workers.

METHODS

Materials and methods: Using the WHO, Ministry of Health of Uganda guidelines on the case definition of Covid-19, we developed a screening questionnaire tool for risk assessment of Covid-19. Specific signs and symptoms were weighted based on how prevalent they were among Covid-19 patients and subsequently an algorithm developed for the various case scenarios of Covid-19. Risk sores were computed based on the symptoms and contact history and a daily risk category assigned based on the risk score. The questionnaire, flow charts and algorithms were then integrated into an android mobile application. Following the launch, 100 health care workers in the directorate of surgical services used the application for one month and the feasibility and consistence of use as well as user friendliness was evaluated. Health care workers would submit their daily risk scores and high-risk staff would be selected for testing and further intervention including treatment.

RESULTS

Results: The primary result of this research project was the development of a mobile based daily early warning system for in-hospital transmission of Covid-19. Overall, the EWAS mobile application was found to be feasible with over 69% of the health care workers having logged more than 67% of the required times. Over 93% of the participants reported the tool as easy to use.

CONCLUSIONS

Conclusion: The EWAS mobile application is a feasible and user-friendly daily risk scoring tool for preventing in-hospital transmission of Covid-19. Despite it, not being designed to be a diagnostic but a screening tool, there is a need to evaluate its sensitivity in predicting persons likely to have contracted Covid-19.

CLINICALTRIAL

A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic

BACKGROUND

With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19.

OBJECTIVE

In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases.

METHODS

It used more than a hundred countries' data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R.

RESULTS

The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value ≤ 0.001), nurses and midwives (NCC = 0.17; p-value ≤ 0.001), hospital beds (NCC = 0.20; p-value ≤ 0.001), life expectancy of both sexes (NCC = 0.22; p-value ≤ 0.001), life expectancy of female (NCC = 0.27; p-value ≤ 0.001), and life expectancy of male (NCC = 0.19; p-value ≤ 0.001). COVID-19 deaths were found to be reduced with increased medical personnel and hospital beds. Interestingly, no association between the comorbidities and severity of COVID-19 was found excluding asthma, cancer, Alzheimer's, and smoking.

CONCLUSIONS

Enhancing healthcare facilities and early imposing the control measures could be valuable to prevent the COVID-19 pandemic. No association between COVID-19 and other comorbidities warranted further investigation at the pathobiological level.

Barriers to Use of Remote Monitoring Technologies Used to Support Patients With COVID-19: Rapid Review

Background

The COVID-19 pandemic has acted as a catalyst for the development and adoption of a broad range of remote monitoring technologies (RMTs) in health care delivery. It is important to demonstrate how these technologies were implemented during the early stages of this pandemic to identify their application and barriers to adoption, particularly among vulnerable populations.

Objective

The purpose of this knowledge synthesis was to present the range of RMTs used in delivering care to patients with COVID-19 and to identify perceived benefits of and barriers to their use. The review placed a special emphasis on health equity considerations.

Methods

A rapid review of published research was conducted using Embase, MEDLINE, and QxMD for records published from the inception of COVID-19 (December 2019) to July 6, 2020. Synthesis involved content analysis of reported benefits of and barriers to the use of RMTs when delivering health care to patients with COVID-19, in addition to health equity considerations.

Results

Of 491 records identified, 48 publications that described 35 distinct RMTs were included in this review. RMTs included use of existing technologies (eg, videoconferencing) and development of new ones that have COVID-19–specific applications. Content analysis of perceived benefits generated 34 distinct codes describing advantages of RMTs, mapped to 10 themes overall. Further, 52 distinct codes describing barriers to use of RMTs were mapped to 18 themes. Prominent themes associated with perceived benefits included a lower burden of care (eg, for hospitals, health care practitioners; 28 records), reduced infection risk (n=33), and support for vulnerable populations (n=14). Prominent themes reflecting barriers to use of RMTs included equity-related barriers (eg, affordability of technology for users, poor internet connectivity, poor health literacy; n=16), the need for quality “best practice” guidelines for use of RMTs in clinical care (n=12), and the need for additional resources to develop and support new technologies (n=11). Overall, 23 of 48 records commented on equity characteristics that stratify health opportunities and outcomes, including general characteristics that vary over time (eg, age, comorbidities; n=17), place of residence (n=11), and socioeconomic status (n=7).

Conclusions

Results of this rapid review highlight the breadth of RMTs being used to monitor and inform treatment of COVID-19, the potential benefits of using these technologies, and existing barriers to their use. Results can be used to prioritize further efforts in the implementation of RMTs (eg, developing “best practice” guidelines for use of RMTs and generating strategies to improve equitable access for marginalized populations).