Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review

Effectiveness of Smart Mama application on postpartum depression, anxiety, and maternal-infant bonding among women during the postnatal period: a randomized controlled trial

BACKGROUND

Postpartum depression and anxiety are significant public health concerns that have serious well documented negative effects on mothers and their families. However, they often remain under-recognized because of limited in-person interactions, time restrictions, lack of adequate support, and pervasive stigmatization. This study investigated the effectiveness of the Smart Mama application on postpartum depression, anxiety levels, and maternal-infant bonding at 12 weeks postpartum.

MATERIALS AND METHODS

This prospective parallel-group randomized controlled trial included 148 participants from March 1, 2023, to March 31, 2024. Those who agreed to participate were randomly assigned to receive the Smart Mama intervention (n = 74) or routine care (n = 74), using permuted stratified block randomization. The primary outcome was assessed using the Edinburgh Postnatal Depression Scale (EPDS). The secondary outcomes were evaluated using the State-Trait Anxiety Inventory (STAI) and Maternal-Infant Bonding Scale (MIBS) at baseline and the 12-week follow-up using validated standardized tools.

RESULTS

Compared with the control group, the Smart Mama intervention group showed a significant reduction in postpartum depressive symptoms (P for time × group interaction = 0.04), with a reduction in the EPDS mean score from 9.03 (standard deviation, 2.47) to 5.61 (3.3), whereas the control group showed a change from 9.01 (2.75) to 7.16 (3.1) at 12 weeks post-intervention. Similarly, the Smart Mama intervention led to a significantly greater decrease in both state and trait anxiety levels (both P for time × group interaction < 0.05) compared to the control group. No statistically significant effect on maternal-infant bonding was observed between the intervention and control group (P for time × group interaction = 0.25).

CONCLUSION AND IMPLICATIONS

The Smart Mama intervention significantly reduced postpartum depressive symptoms and anxiety. This study provides empirical evidence and novel insights into the effectiveness of mobile device applications. By integrating a holistic approach, Smart Mama represents a promising and innovative solution for enhancing maternal health outcomes, empowering self-care activities, and overcoming barriers to accessibility.

TRIAL REGISTRATION

The study was registered in the University Hospital Medical Information Network (UMIN) Clinical Trial Registry (ID: (UMIN000050065) on January 19, 2023 ( https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000056562 ).

A comparison of self-reported COVID-19 symptoms between android and iOS CoronaCheck app users

This study explored differences in COVID-19 infections and symptoms between Android and iOS users using data from the CoronaCheck app. This cross-sectional analysis included 23,063 global users (20,753 Android and 2310 iOS) from April 2020 to February 2023. Participants reported COVID-19 symptoms and contact risks, with data analyzed to adjust for age, sex, education, and country. Android users were generally younger, more often male, had a lower educational level, and reported more symptoms on average (2.1 vs. 1.6) than iOS users. Android users also had higher suspected COVID-19 infection rates (24% vs. 11%), with an adjusted odds ratio of 2.21 (95% CI: 1.93-2.54). These findings suggest platform-based differences in COVID-19 infection rates and symptom reporting, highlighting potential biases in mobile health research. Adjusting for device operating systems may be crucial in improving the reliability of population-based health data collected through mobile platforms.

Exploring Heart Disease-Related mHealth Apps in India: Systematic Search in App Stores and Metadata Analysis

BACKGROUND

Smartphone mobile health (mHealth) apps have the potential to enhance access to health care services and address health care disparities, especially in low-resource settings. However, when developed without attention to equity and inclusivity, mHealth apps can also exacerbate health disparities. Understanding and creating solutions for the disparities caused by mHealth apps is crucial for achieving health equity. There is a noticeable gap in research that comprehensively assesses the entire spectrum of existing health apps and extensively explores apps for specific health priorities from a health care and public health perspective. In this context, with its vast and diverse population, India presents a unique context for studying the landscape of mHealth apps.

OBJECTIVE

This study aimed to create a comprehensive dataset of mHealth apps available in India with an initial focus on heart disease (HD)-related apps.

METHODS

We collected individual app data from apps in the "medical" and "health and fitness" categories from the Google Play Store and the Apple App Store in December 2022 and July 2023, respectively. Using natural language processing techniques, we selected HD apps, performed statistical analysis, and applied latent Dirichlet allocation for clustering and topic modeling to categorize the resulting HD apps.

RESULTS

We collected 118,555 health apps from the Apple App Store and 108,945 health apps from the Google Play Store. Within these datasets, we found that approximately 1.7% (1990/118,555) of apps on the Apple App Store and 0.5% (548/108,945) on the Google Play Store included support for Indian languages. Using monograms and bigrams related to HD, we identified 1681 HD apps from the Apple App Store and 588 HD apps from the Google Play Store. HD apps make up only a small fraction of the total number of health apps available in India. About 90% (1496/1681 on Apple App Store and 548/588 on Google Play Store) of the HD apps were free of cost. However, more than 70% (1329/1681, 79.1% on Apple App Store and 423/588, 71.9% on Google Play Store) of HD apps had no reviews and rating-scores, indicating low overall use.

CONCLUSIONS

Our study proposed a robust method for collecting and analyzing metadata from a wide array of mHealth apps available in India through the Apple App Store and Google Play Store. We revealed the limited representation of India's linguistic diversity within the health and medical app landscape, evident from the negligible presence of Indian-language apps. We observed a scarcity of mHealth apps dedicated to HD, along with a lower level of user engagement, as indicated by reviews and app ratings. While most HD apps are financially accessible, uptake remains a challenge. Further research should focus on app quality assessment and factors influencing user adoption.

A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

Workplace Deceptions During the Pandemic: Differences in Conspiracy Beliefs, Psychological Functioning, and Covid-19 Experiences

The global pandemic has disrupted virtually all countries on health, psychological functioning, and economies, to name a few. Accurate information has also fallen victim to the pandemic, which has been rife with misinformation and conspiracy theories. The current study investigated Covid-19 deceptions related to employment. With complete anonymity via MTurk, 389 participants from the United States rated their likelihood of deception regarding hypothetical four workplace scenarios. The first set of analyses examined differences between high and low risk of deceptions for each scenario based on participants' self-appraisals. The largest differences were found for general conspiracy beliefs and affective disorders, specifically major depression and generalized anxiety. The second set of analyses focused across the workplace scenarios on two operationalized groups with Likely-Deceptive (n = 189) vastly outnumbering Likely-Genuine (n = 55). Personal experiences with Covid-19 dramatically increased deceptions. Testing positive for Covid-19 increased the odds of being in the Likely-Deceptive by twelve-fold. Two discriminant models examined cognitive misbeliefs and psychological functioning. When both were combined, depression and Covid-19 misinformation produced the strongest structure coefficients followed closely by general conspiracy beliefs and generalized anxiety. The far-ranging implications of these findings are discussed.

Patient satisfaction and digital health in primary health care: a scoping review protocol

Introduction

Using digital health in primary health care (PHC) contributes to reducing costs and travel time, achieving global development goals, improving access, quality and longitudinality of care, and managing health crises. Its evaluation must go beyond the technical-operational aspects to include patient satisfaction, a key element in assessing the quality of care.

Objective

To identify and map patient satisfaction (expectations, desires, cultural values) about the adoption of digital health strategies and assess their impact on the quality of care in PHC.

Methods

The review will follow the recommendations proposed by the Joanna's Briggs Institute (JBI) manual, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the methodology proposed by Arksey and O'Malley and Levac et al. and will be conducted in nine stages. The search will be conducted in health studies databases (MEDLINE via PubMed, EMBASE, CINAHL, Web of Science, and BVS), gray literature, and preprint repositories (Google Scholar and MedRxiv). Two reviewers will select the studies, and the third will analyze possible conflicts. The inclusion criteria comprise studies that have been made available in their entirety, whether they are primary studies or short communications, as well as the following materials extracted from the gray literature: preprints, manuals, government documents, books, guidelines, theses and dissertations. Exclusion criteria include literature reviews, abstracts, books, conference archives, letters to the editor, duplicates and opinion articles. Data will be analyzed by content analysis and inferential statistics. This protocol is registered on the Open Science Framework (OSF) under DOI 10.17605/OSF.IO/PUJDB.

Results

The study aims to understand aspects related to the expectations, desires, and cultural values of patients from different countries, as well as the strengths and critical nodes of the use of digital health on the quality of care in PHC.

Effectiveness of a Mobile Health Application for Educating Outpatients about Bowel Preparation

Colonoscopy is an essential method for diagnosing and treating colorectal cancer, relying on effective bowel preparation to thoroughly examine the large intestinal mucosa. Traditional education involves printed instructions and verbal explanations but does not guarantee clear patient understanding. Poor bowel preparation can obscure mucosal visibility, delaying cancer diagnosis and treatment. A mobile medical model using Android devices for bowel preparation education was tested in a single-blind, randomized trial. This trial enrolled outpatients undergoing colonoscopy at the Endoscopy Center for Diagnostic and Treatment between 27 October 2021 and 31 December 2022. This study introduced the ColonClean app alongside traditional methods. After examination, endoscopists rated the preparation quality using the Aronchick scale. A data analysis was conducted using SPSS 25.0 to determine if there was a significant improvement in bowel preparation quality between the control group (traditional method) and the experimental group (traditional method plus the ColonClean app). Forty patients were recruited in each group. In the experimental group, all ratings were "fair", with 75% receiving an "excellent" or "good" rating, showing statistical significance (p = 0.016). The ColonClean app improves bowel preparation quality more effectively than traditional care instructions.

Effectiveness and intention to use a COVID-19 self-management app for epidemiological investigation: a web-based survey study

Introduction

Numerous COVID-19-related apps were widely used during the COVID-19 pandemic. Among them, those supporting epidemiological investigations were particularly useful. This study explored the effectiveness of apps that support epidemiological investigations, factors influencing users' intention to use them, and ways to encourage their use.

Methods

We developed and evaluated the KODARI app to demonstrate its importance in epidemiological investigations. After adapting a questionnaire based on an existing evaluation framework for COVID-19-related apps, we collected data from 276 participants through an online survey conducted between April 28 and May 25, 2023. We conducted two independent sample t-tests to determine the differences between each variable according to demographic characteristics and a multiple regression analysis to identify factors affecting intention to use.

Results

Users were generally satisfied with the KODARI. We observed differences in sex, age, marital status, occupational characteristics, and experience with epidemiological investigation. Females rated the app's information accuracy higher than males. Males had a higher intention to use than females. Participants aged under 35 years rated information accuracy and transparency highly, whereas single participants rated information accuracy higher than married participants. Occupational groups with frequent interactions with others evaluated their self-determination regarding the application. The app's self-determination was highly valued among participants with experience in epidemiological investigations. By investigating the factors affecting the intention to use the app, we confirmed that effectiveness, self-determination, and usability significantly affected the intention to use.

Discussion

This study demonstrated the effectiveness of app supporting epidemiological investigations, identified meaningful factors that influence intention to use, and confirmed the applicability of our new framework by considering the specificity of infectious disease situations such as COVID-19. This study provides a new basis for future epidemiological studies.

Mobile Apps for Common Noncommunicable Disease Management: Systematic Search in App Stores and Evaluation Using the Mobile App Rating Scale

Background

The success of mobile apps in improving the lifestyle of patients with noncommunicable diseases through self-management interventions is contingent upon the emerging growth in this field. While users of mobile health (mHealth) apps continue to grow in number, little is known about the quality of available apps that provide self-management for common noncommunicable diseases such as diabetes, hypertension, and obesity.

Objective

We aimed to investigate the availability, characteristics, and quality of mHealth apps for common noncommunicable disease health management that included dietary aspects (based on the developer's description), as well as their features for promoting health outcomes and self-monitoring.

Methods

A systematic search of English-language apps on the Google Play Store (Google LLC) and Apple App Store (Apple Inc) was conducted between August 7, 2022, and September 13, 2022. The search terms used included weight management, obesity, diabetes, hypertension, cardiovascular diseases, stroke, and diet. The selected mHealth apps' titles and content were screened based on the description that was provided. Apps that were not designed with self-management features were excluded. We analyzed the mHealth apps by category and whether they involved health care professionals, were based on scientific testing, and had self-monitoring features. A validated and multidimensional tool, the Mobile App Rating Scale (MARS), was used to evaluate each mHealth app's quality based on a 5-point Likert scale from 1 (inadequate) to 5 (excellent).

Results

Overall, 42 apps were identified. Diabetes-specific mHealth apps accounted for 7% (n=3) of the market, hypertension apps for 12% (n=5), and general noncommunicable disease management apps for 21% (n=9). About 38% (n=16) of the apps were for managing chronic diseases, while 74% (n=31) were for weight management. Self-management features such as weight tracking, BMI calculators, diet tracking, and fluid intake tracking were seen in 86% (n=36) of the apps. Most mHealth apps (n=37, 88%) did not indicate whether there was involvement of health professionals in app development. Additionally, none of the apps reported scientific evidence demonstrating their efficacy in managing health. The overall mean MARS score was 3.2 of 5, with a range of 2.0 to 4.1. Functionality was the best-rated category (mean score 3.9, SD 0.5), followed by aesthetics (mean score 3.2, SD 0.9), information (mean score 3.1, SD 0.7), and engagement (mean score 2.9, SD 0.6).

Conclusions

The quality of mHealth apps for managing chronic diseases was heterogeneous, with roughly half of them falling short of acceptable standards for both quality and content. The majority of apps contained scant information about scientific evidence and the developer's history. To increase user confidence and accomplish desired health outcomes, mHealth apps should be optimized with the help of health care professionals. Future studies on mHealth content analysis should focus on other diseases as well.

Mobile Apps for COVID-19: A Systematic Review of Reviews

BACKGROUND

One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic.

METHODS

We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022.

RESULTS

Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% (n = 13) of the studies were published in 2021, and 33% (n = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous).

CONCLUSIONS

Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.

A Web-Based COVID-19 Tool for Testing Residents in Retirement Homes: Development Study

BACKGROUND

Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.

OBJECTIVE

We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the "testing strategy," is based on dividing facility residents into groups and then testing no more than 1 group per day.

METHODS

We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution.

RESULTS

Considering the number of residents and staff and other practical constraints of the facilities, the proposed application computes the optimal trade-off testing strategy and suggests the corresponding grouping and testing schedule for residents. The current version of the application is deployed on the server of the Where2Test project and is accessible on their website. The application is open source, and all contents are offered in English and German. We provide comprehensive instructions and guidelines for easy use and understanding of the application's functionalities. The application was launched in July 2022, and it is currently being tested in RHs in Saxony, Germany.

CONCLUSIONS

Recommended testing strategies by our application are tailored to each RH and the goals set by the managers. We advise the users of the application that the proposed model and approach focus on the expected scenarios, that is, the expected risk of infection, and they do not guarantee the avoidance of worst-case scenarios.

The resistance toward COVID-19 contact tracing apps: A study of psychological reactance among young adults in Italy

Western governments' attempts to encourage young adults to adopt COVID-19 contact tracing apps (CTAs) have been unsuccessful. Drawing on psychological reactance theory, we propose that government-imposed containment measures (e.g., lockdowns, curfews) may cause young adults to resist CTAs. We investigate how and when threats to freedom posed by government-imposed containment measures to young adults reduce their CTA adoption intentions. We conducted a survey of young adults during the second general lockdown (March‒April 2021) in Italy. The results show that when young Italian adults focus on the restrictive nature of government-imposed containment measures, their sense of freedom is threatened. Threats to freedom produce psychological states of either helplessness or reactance, depending on if young Italian adults think they can recover their freedom. Helpless young adults are motivated to adopt CTAs because they seek guidance from containment measures. Reactant young adults resist CTAs because they exhibit aversive psychological states toward containment measures. These results offer relevant insights for policymakers. They shed light on young Italian adults' resistance toward CTAs. They also inform governments on how to interact more efficiently with young adults if a novel pandemic should occur.

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review

BACKGROUND

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

OBJECTIVE

This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

METHODS

We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.

RESULTS

We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.

CONCLUSIONS

This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Investigating Individuals' Preferences in Determining the Functions of Smartphone Apps for Fighting Pandemics: Best-Worst Scaling Survey Study

BACKGROUND

Smartphone apps have been beneficial in controlling and preventing the COVID-19 pandemic. However, there is a gap in research surrounding the importance of smartphone app functions from a user's perspective. Although the insights and opinions of different stakeholders, such as policymakers and medical professionals, can influence the success of a public health policy, any strategy will face difficulty in achieving the expected effect if it is not based on a method that users can accept.

OBJECTIVE

This study aimed to assess the importance of a hypothetical smartphone app's functions for managing health during a pandemic based on the perspective of user preferences.

METHODS

A cross-sectional and web-based survey using the best-worst scaling (BWS) method was used to investigate the general population's preferences for important smartphone app functions. Participants were recruited from a professional surveying company's web-based surveying panel. The attributes of the BWS questionnaire were developed based on a robust process, including literature review, interviews, and expert discussion. A balanced incomplete block design was used to construct the choice task to ensure the effectiveness of the research design. Count analysis, conditional logit model analysis, and mixed logit analysis were used to estimate preference heterogeneity among respondents.

RESULTS

The responses of 2153 participants were eligible for analysis. Nearly 55% (1192/2153) were female, and the mean age was 31.4 years. Most participants (1765/2153, 81.9%) had completed tertiary or higher education, and approximately 70% (1523/2153) were urban residents. The 3 most vital functions according to their selection were "surveillance and monitoring of infected cases," "quick self-screening," and "early detection of infected cases." The mixed logit regression model identified significant heterogeneity in preferences among respondents, and stratified analysis showed that some heterogeneities varied in respondents by demographics and COVID-19-related characteristics. Participants who preferred to use the app were more likely to assign a high weight to the preventive functions than those who did not prefer to use it. Conversely, participants who showed lower willingness to use the app tended to indicate a higher preference for supportive functions than those who preferred to use it.

CONCLUSIONS

This study ranks the importance of smartphone app features that provide health care services during a pandemic based on the general population's preferences in China. It provides empirical evidence for decision-makers to develop eHealth policies and strategies that address future public health crises from a person-centered care perspective. Continued use of apps and smart investment in digital health can help improve health outcomes and reduce the burden of disease on individuals and communities.

The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: systematic review and meta-analysis

CONTEXT

Arterial Hypertension (AH) and Diabetes Mellitus (DM) are diseases that are getting worse all over the world. Linked to this advance, is the growing digital health market with numerous mobile health applications, which aim to help patients and professionals in the proper management of chronic diseases. The aim of this study was to analyze, through a systematic review and meta-analysis, the effectiveness of using mobile health applications in monitoring AH and/or DM in the adult and elderly population.

METHODS

The systematic review and meta-analysis was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Metanalyses guidelines and involved searching five databases - Medline/PubMed, Embase, CINAHL, Virtual Library in Health and Cochrane Library. The review included randomized and cohort clinical trials testing the effects of the intervention on changing biochemical parameters and clinical efficacy in people treated for AH and/or DM. The quality of the selected studies was assessed based on the evaluation criteria of the Joanna Briggs Institute tool. The random effects meta-analysis method was used to explain effect distribution between studies, by Stata® software (version 11.0) and publication bias was examined by visual inspection of graphs and Egger test.

RESULTS

We included 26 studies in the systematic review and 17 in the meta-analysis. These studies were published between 2014 to 2022 in 14 countries. Were reported improvement in knowledge and self-management of AH and DM, social motivation with treatment and behavioral change, reduction in glycated hemoglobin values, fasting glucose and blood pressure, improvement in adherence to drug treatment, among others. The result of the meta-analysis showed that there is evidence that the use of mobile applications can help reduce glycated hemoglobin by 0.39% compared to the usual care group.

CONCLUSIONS

Monitoring and self-monitoring of behaviors and health care related to AH and DM in adults and the elderly through mobile applications, has clinically significant effectiveness in reducing glycated hemoglobin levels. Future studies should provide more evidence and recommendations for best practices and development of digital health interventions.

TRIAL REGISTRATION

PROSPERO. International Prospective Registry of Systematic Reviews. CRD42022361928.

Observational study of organisational responses of 17 US hospitals over the first year of the COVID-19 pandemic

OBJECTIVES

The COVID-19 pandemic has required significant modifications of hospital care. The objective of this study was to examine the operational approaches taken by US hospitals over time in response to the COVID-19 pandemic.

DESIGN, SETTING AND PARTICIPANTS

This was a prospective observational study of 17 geographically diverse US hospitals from February 2020 to February 2021.

OUTCOMES AND ANALYSIS

We identified 42 potential pandemic-related strategies and obtained week-to-week data about their use. We calculated descriptive statistics for use of each strategy and plotted percent uptake and weeks used. We assessed the relationship between strategy use and hospital type, geographic region and phase of the pandemic using generalised estimating equations (GEEs), adjusting for weekly county case counts.

RESULTS

We found heterogeneity in strategy uptake over time, some of which was associated with geographic region and phase of pandemic. We identified a body of strategies that were both commonly used and sustained over time, for example, limiting staff in COVID-19 rooms and increasing telehealth capacity, as well as those that were rarely used and/or not sustained, for example, increasing hospital bed capacity.

CONCLUSIONS

Hospital strategies during the COVID-19 pandemic varied in resource intensity, uptake and duration of use. Such information may be valuable to health systems during the ongoing pandemic and future ones.

User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review

BACKGROUND

Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low.

OBJECTIVE

Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS.

METHODS

The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, ScienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022.

RESULTS

A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS.

CONCLUSIONS

Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a research framework and identified research gaps for future research on this topic.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Digital Application for Promoting Evidence-Based Children's Oral Health to Control Early Childhood Caries: Randomized Control Trial on Parental Acceptance and Efficacy

BACKGROUND

Early childhood caries (ECC) remains a major global health problem. Various measures to prevent it have been implemented in the past, including those using digital applications.

AIM

To evaluate the acceptance and efficacy of a digital application (FU-APP) based on evidence-based caries control recommendations for parents of children aged 6-72 months.

METHODS

Part 1, prospective questionnaire-based survey to test FU-APP (usage, acceptance, content information, usefulness, and satisfaction) filled out by parents (n = 22); Part 2, two-armed (test n = 20; control n = 23) care-based, randomized controlled trial, where the test arm received instructions verbally and via FU-APP, and the control arm received them only verbally. At baseline and follow-up (4 weeks), intraoral clinical indices (plaque index-API and caries-dmft) were recorded.

RESULTS

FU-APP was considered by parents to be a suitable tool for gaining knowledge about oral health practices for their children (all criteria >86%). No differences in the dmft levels were expected. However, API was significantly better at the follow-up in the test-arm (p = 0.01), with no differences in the control-arm (p = 0.72).

CONCLUSION

A digital application can serve as an innovative tool to promote evidence-based oral hygiene recommendations among parents of children to control ECC. Its long-term usability and functionality should be tested.

Telemedicine technologies and applications in the era of COVID-19 pandemic: A systematic review

INTRODUCTION

Telemedicine services saw substantial surges in their use during the COVID-19 pandemic due to the lockdowns and characteristics of the pandemic. Therefore, the authors aimed to systematically review the telemedicine services provided during the COVID-19 pandemic and their potential applications.

METHODS

The authors searched PubMed, Scopus, and Cochrane databases on September 14, 2021. Then, the retrieved records underwent two-step title/abstract and full-text screening processes, and the eligible articles were included for qualitative synthesis.

RESULTS

The review of studies demonstrated that the telephone is listed 38 times, making it the most common technology used in telemedicine. Video conferencing is also mentioned in 29 articles, as well as other technologies: Mobile-health (n = 15), Virtual reality (n = 7). According to the findings of the present study, Tele-follow-up (n = 24), Tele-consulting (n = 20), Virtual visits (20), and Tele-monitoring (n = 18) were the most widely used telemedicine applications.

CONCLUSION

Telemedicine has been an effective approach to COVID-19 management. Telemedicine technology is going to play a key role in the future of health medicine, patient consultation, and many other extended applications of health care in remote rural locations.

Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study

BACKGROUND

Access to data is crucial for decision-making; this fact has become more evident during the pandemic. Data collected using mobile apps can positively influence diagnosis and treatment, the supply chain, and the staffing resources of health care facilities. Developers and health care professionals have worked to create apps that can track a person's COVID-19 status. For example, these apps can monitor positive COVID-19 test results and vaccination status. Regrettably, people may be concerned about sharing their data with government or private sector organizations that are developing apps. Understanding user perceptions is essential; without substantial user adoption and the use of mobile tracing apps, benefits cannot be achieved.

OBJECTIVE

This study aimed to assess the factors that positively and negatively affect the use of COVID-19 tracing apps by examining individuals' perceptions about sharing data on mobile apps, such as testing regularity, infection, and immunization status.

METHODS

The hypothesized research model was tested using a cross-sectional survey instrument. The survey contained 5 reflective constructs and 4 control variables selected after reviewing the literature and interviewing health care professionals. A digital copy of the survey was created using Qualtrics. After receiving approval, data were collected from 367 participants through Amazon Mechanical Turk (MTurk). Participants of any gender who were 18 years or older were considered for inclusion to complete the anonymized survey. We then analyzed the theoretical model using structural equation modeling.

RESULTS

After analyzing the quality of responses, 325 participants were included. Of these 325 participants, 216 (66.5%) were male and 109 (33.5%) were female. Among the participants in the final data set, 72.6% (236/325) were employed. The results of structural equation modeling showed that perceived vulnerability (β=0.688; P<.001), self-efficacy (β=0.292; P<.001), and an individual's prior infection with COVID-19 (β=0.194; P=.002) had statistically significant positive impacts on the intention to use mobile tracing apps. Privacy concerns (β=-0.360; P<.001), risk aversion (β=-0.150; P=.09), and a family member's prior infection with COVID-19 (β=-0.139; P=.02) had statistically significant negative influences on a person's intention to use mobile tracing apps.

CONCLUSIONS

This study illustrates that various user perceptions affect whether individuals use COVID-19 tracing apps. By working collaboratively on legislation and the messaging provided to potential users before releasing an app, developers, health care professionals, and policymakers can improve the use of tracking apps. Health care professionals need to emphasize disease vulnerability to motivate people to use mobile tracing apps, which can help reduce the spread of viruses and diseases. In addition, more work is needed at the policy-making level to protect the privacy of users, which in return can increase user engagement.

Mobile Health Applications in the COVID-19 Pandemic: A Scoping Review of the Reviews

Background: Mobile health (m-Health) is a combination of electronic communications and medical information technology that has helped patients and health care workers during the COVID-19 outbreak. This study aimed to aggregate and highlight findings from existing review studies about applications of m-health to prevent COVID-19. Methods: This scoping review was conducted based on the Arksey and O'Malley framework after searching the PubMed, Web of Science, and Scopus databases from March 2020 to February 2022. Keywords for the search included the English words "Mobile health";" mobile apps"; "corona disease"; "COVID-19"; and "review." Screening of articles was done in 4 stages. Results: Out of 37,569 papers found in the search, after the screening and review process, 22 articles were finally selected. From the analysis of the studies, 2 main categories emerged with the titles of "primary preventive applications" and "secondary preventive applications." Conclusion: M-health is used in both primary and secondary prevention. The m-health tools can be effective in controlling the spread of COVID-19 and improving the treatment process of this disease by providing various pieces of training related to COVID-19 as well as installing various programs to monitor the condition of patients. Also, m-Health can provide services through the exchange of treatment data between health care providers or between patients and health care providers, as well as provide appropriate training for the remote care needs of patients with COVID-19.

Description of apps targeting stroke patients: A review of apps store

Background

As a principal cause of mortality and disability worldwide, stroke imposes considerable burdens on society and effects on the lives of patients, families, and communities. Owing to their growing global popularity, health-related applications (apps) offer a promising approach to stroke management but show a knowledge gap regarding mobile apps for stroke survivors.

Methods

This review was conducted across the Android and iOS app stores in September-December 2022 to identify and describe all apps targeting stroke survivors. Apps were included if they were designed for stroke management and contained at least one of the following components: medication taking, risk management, blood pressure management, and stroke rehabilitation. Apps were excluded if they were unrelated to health, not in Chinese or English, or the targeted users were healthcare professionals. The included apps were downloaded, and their functionalities were investigated.

Results

The initial search yielded 402 apps, with 115 eligible after title and description screening. Some apps were later excluded due to duplicates, registration problems, or installation failures. In total, 83 apps were included for full review and evaluated by three independent reviewers. Educational information was the most common function (36.1%), followed by rehabilitation guidance (34.9%), communication with healthcare providers (HCPs), and others (28.9%). The majority of these apps (50.6%) had only one functionality. A minority had contributions from an HCP or patients.

Conclusion

With the widespread accessibility and availability of smartphone apps across the mHealth landscape, an increasing number of apps targeting stroke survivors are being released. One of the most important findings is that the majority of the apps were not specifically geared toward older adults. Many of the currently available apps lack healthcare professionals' and patients' involvement in their development, and most offer limited functionality, thus requiring further attention to the development of customized apps.

Country- and app-level factors affecting the adoption and evaluation of COVID-19 mobile apps

BACKGROUND

Countries across the globe have released many COVID-19 mobile apps. However, there is a lack of systematic empirical investigation into the factors affecting the adoption and evaluation of COVID-related apps. This study explores what factors at the country level and the app levels would influence the adoption and evaluation of COVID-19 apps.

METHODS

We collected data on 267 COVID-19 apps in App Store and Google Play. The number of installs, ratings, reviews and rating scores were used as indicators of adoption and evaluation. Country-level predictors include the number of infected cases and the political system (i.e., democratic vs. non-democratic). App-level predictors include developer (i.e., government vs. non-government) and functions. Four app functions were coded for analysis: providing health information, contact tracing, home monitoring, and consultation. Negative binomial regression and OLS (Ordinary Least Square) regression were used to analyze the data.

RESULTS

Our analyses show that apps developed by countries with more infected cases (B = 0.079, CI (Confidence Interval) = 0.000, 0.158; P = .049) and by non-governmental institutions (B=-0.369, CI=-0.653, -0.083; P = .01) received more positive rating scores. Apps with home monitoring function received lower rating scores (B=-0.550, CI=-0.971, -0.129; P = .01). Regarding adoption, apps developed by governments were more likely to be installed (IRR (Incident Rate Ratio) = 8.156, CI = 3.389, 19.626; P < .001), to be rated (IRR = 26.036, CI = 7.331, 92.468; P < .001), and to receive user comments (IRR = 12.080, CI = 3.954, 37.568; p < .001). Apps with functions of contact tracing or consultation were more likely to be installed (IRR = 4.533, CI = 2.072, 9.918; p < .001; IRR = 4.885, CI = 1.970, 12.111; p < .001), to be rated (IRR = 11.634, CI = 3.486, 38.827; p < .001; IRR = 17.194, CI = 5.309, 55.680; p < .001), and to receive user comments (IRR = 5.688, CI = 2.052, 5.770; p < .001; IRR = 16.718, CI = 5.363, 52.113; p < .001). Apps with home monitoring functions were less likely to be rated (IRR = 0.206, CI = 0.047, 0.896; P = .04) but more likely to receive user comments (IRR = 3.874, CI = 1.044, 14.349; P = .04). Further analysis shows that apps developed in democratic countries (OR (Odd Ratio) = 3.650, CI = 1.238, 10.758; P = .02) or by governments (OR = 7.987, CI = 4.106, 15.534, P < .001) were more likely to include the function of contact tracing.

CONCLUSION

This study systematically investigates factors affecting the adoption and evaluation of COVID-19 apps. Evidence shows that government-developed apps and the inclusion of contact tracing and consultation app functions strongly predict app adoption.

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.

Predicting User Satisfaction of Mobile Healthcare Services using Machine Learning

Outbreak of the COVID-19 leads to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that Logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with Bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and Logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey

OBJECTIVES

To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups.

METHODS

An online cross-sectional survey was conducted during June-September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher's exact tests. Three open-ended questions were included to elicit participants' views; thematic analysis was conducted.

RESULTS

Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18-44 (46.1%), more people aged 60+ (74.5%) and more people aged 45-60 (57.6%) used a COVID-19 related app (P < .001). Qualitative data suggest people viewed technologies (especially social media) as a 'double-edged sword': helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19.

CONCLUSIONS

Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.

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.

Emerging Artificial Intelligence-Empowered mHealth: Scoping Review

BACKGROUND

Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.

OBJECTIVE

Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.

METHODS

Using Arksey and O'Malley's 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as "mobile healthcare," "wearable medical sensors," "smartphones", and "AI." We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.

RESULTS

We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.

CONCLUSIONS

The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

The Association between mHealth App Use and Healthcare Satisfaction among Clients at Outpatient Clinics: A Cross-Sectional Study in Inner Mongolia, China

Mobile health (mHealth) applications (apps) have been developed in hospital settings to allocate and manage medical care services, which is one of the national strategies to improve health care in China. Little is known about the comprehensive effects of hospital-based mHealth app use on client satisfaction. The aim of this study was to determine the relationship between the full range of mHealth app use and satisfaction domains among clients attending outpatient clinics. A cross-sectional survey was conducted from January to February 2021 in twelve tertiary hospitals in Inner Mongolia. After the construction of the mHealth app use, structural equation modeling was used for data analysis. Of 1889 participants, the standardized coefficients β on environment/convenience, health information, and medical service fees were 0.11 (p < 0.001), 0.06 (p = 0.039), and 0.08 (p = 0.004), respectively. However, app use was not significantly associated with satisfaction of doctor−patient communication (β = 0.05, p = 0.069), short-term outcomes (β = 0.05, p = 0.054), and general satisfaction (β = 0.02, p = 0.429). Clients of the study hospitals were satisfied with the services, but their satisfaction was not much associated with mHealth use. The limitation of the mHealth system should be improved to enhance communication and engagement among clients, doctors, and healthcare givers, as well as to pay more attention to health outcomes and satisfaction of clients.

Extending the Unified Theory of Acceptance and Use of Technology for COVID-19 Contact Tracing Application by Malaysian Users

The Malaysian government has mobilized its strength to confront the current COVID-19 pandemic and has sought to develop and implement a digital contact tracking application, making it an integral part of the exit strategy from the lockdown. These applications record which users have been near one another. When a user is confirmed with COVID-19, app users who have recently been near this person are notified. The effectiveness of these applications is determined by the users’ willingness to install and use them. Therefore, this research aims at identifying the factors that would stimulate or slow down the adoption of a contact-tracing app. It proposes solutions to mitigate the impact of the factors affecting the user’s acceptance of COVID-19 Digital Contact Tracing Apps. A quantitative approach was followed in this research, where an electronic survey was spread in Malaysia, for the objective of data collection, considering the previous discussion of the results. Then, using PLS-SEM, the collected data were analyzed statistically. The findings of this study indicate that the unified theory of acceptance and use of technology (UTAUT) factors (Performance Expectancy, Effort Expectancy, Social Influence, Facilities Condition) were significant predictors of MySejahtera application adoption among citizens in Malaysia. On the other hand, the factors of app-related privacy concern were found to be insignificant for MySejahtera application adoption.

Ethical, Legal, and Sociocultural Issues in the Use of Mobile Technologies and Call Detail Records Data for Public Health in the East African Region: Scoping Review

BACKGROUND

The exponential scale and pace of real-time data generated from mobile phones present opportunities for new insights and challenges across multiple sectors, including health care delivery and public health research. However, little attention has been given to the new ethical, social, and legal concerns related to using these mobile technologies and the data they generate in Africa.

OBJECTIVE

The objective of this scoping review was to explore the ethical and related concerns that arise from the use of data from call detail records and mobile technology interventions for public health in the context of East Africa.

METHODS

We searched the PubMed database for published studies describing ethical challenges while using mobile technologies and related data in public health research between 2000 and 2020. A predefined search strategy was used as inclusion criteria with search terms such as "East Africa," "mHealth," "mobile phone data," "public health," "ethics," or "privacy." We screened studies using prespecified eligibility criteria through a two-stage process by two independent reviewers. Studies were included if they were (1) related to mobile technology use and health, (2) published in English from 2000 to 2020, (3) available in full text, and (4) conducted in the East African region. We excluded articles that (1) were conference proceedings, (2) studies presenting an abstract only, (3) systematic and literature reviews, (4) research protocols, and (5) reports of mobile technology in animal subjects. We followed the five stages of a published framework for scoping reviews recommended by Arksey and O'Malley. Data extracted included title, publication year, target population, geographic region, setting, and relevance to mobile health (mHealth) and ethics. Additionally, we used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Extension for Scoping Reviews checklist to guide the presentation of this scoping review. The rationale for focusing on the five countries in East Africa was their geographic proximity, which lends itself to similarities in technology infrastructure development.

RESULTS

Of the 94 studies identified from PubMed, 33 met the review inclusion criteria for the final scoping review. The 33 articles retained in the final scoping review represent studies conducted in three out of five East African countries: 14 (42%) from Uganda, 13 (39%) from Kenya, and 5 (16%) from Tanzania. Three main categories of concerns related to the use of mHealth technologies and mobile phone data can be conceptualized as (1) ethical issues (adequate informed consent, privacy and confidentiality, data security and protection), (2) sociocultural issues, and (3) regulatory/legal issues.

CONCLUSIONS

This scoping review identified major cross-cutting ethical, regulatory, and sociocultural concerns related to using data from mobile technologies in the East African region. A comprehensive framework that accounts for the critical concerns raised would be valuable for guiding the safe use of mobile technology data for public health research purposes.

Monitoring Symptoms of COVID-19: Review of Mobile Apps

BACKGROUND

Mobile health (mHealth) apps have facilitated symptom monitoring of COVID-19 symptoms globally and have been used to share data with health care professionals and support disease prediction, prevention, management, diagnostics, and improvements in treatments and patient education.

OBJECTIVE

The aim of this review is to evaluate the quality and functionality of COVID-19 mHealth apps that support tracking acute and long-term symptoms of COVID-19.

METHODS

We systematically reviewed commercially available mHealth apps for COVID-19 symptom monitoring by searching Google Play and Apple iTunes using search terms such as "COVID-19," "Coronavirus," and "COVID-19 and symptoms." All apps underwent three rounds of screening. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), an informatics functionality scoring system, and the Center for Disease Control and World Health Organization symptom guidelines. The MARS is a 19-item standardized tool to evaluate the quality of mHealth apps on engagement, functionality, aesthetics, and information quality. Functionality was quantified across the following criteria: inform, instruct, record (collect, share, evaluate, and intervene), display, guide, remind or alert, and communicate. Interrater reliability between the reviewers was calculated.

RESULTS

A total of 1017 mobile apps were reviewed, and 20 (2%) met the inclusion criteria. The majority of the 20 included apps (n=18, 90%) were designed to track acute COVID-19 symptoms, and only 2 (10%) addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.89 out of 5, and the highest domain score for functionality (4.2). The most common functionality among all apps was the instruct function (n=19, 95%). The most common symptoms included in the apps for tracking were fever and dry cough (n=18, 90%), aches and pains (n=17, 85%), difficulty breathing (n=17, 85%), tiredness, sore throat, headache, loss of taste or smell (n=16, 80%), and diarrhea (n=15, 75%). Only 2 (10%) apps specifically tracked long-term symptoms of COVID-19. The top 4 rated apps overall were state-specific apps developed and deployed for public use.

CONCLUSIONS

Overall, mHealth apps designed to monitor symptoms of COVID-19 were of high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19 and evidence-based educational materials; they should also include a feature that would allow patients to communicate their symptoms to specific caregivers or their own health care team. App developers should also follow updated technical and clinical guidelines from the Center for Disease Control and the World Health Organization.

Digital Marketing: A Unique Multidisciplinary Approach towards the Elimination of Viral Hepatitis

New technologies are supported by the global implementation of the internet. These improvements have deeply affected various disciplines of sciences and consequently changed services such as daily business, particularly health sectors. Innovative digital marketing strategies utilize the channels of social media and retrieved user data to analyze and improve relevant services. These multidisciplinary innovations can assist specialists, physicians and researchers in diagnostic, prophylaxis and treatment issues in the health sector. Accordingly, compared to recent decades, health decision makers are more accurate and trustful in defining new strategies. Interestingly, using social media and mobile health apps in current pandemics of SARS-CoV-2 could be an important instance of the key role of these platforms at the local and global level of health policies. These digital technologies provide platforms to connect public health sectors and health politicians for communicating and spreading relevant information. Adding influencers and campaigns to this toolbox strengthens the implementation of public health programs. In 2016, the WHO adopted a global program to eliminate viral hepatitis by 2030. Recent constructive measures that have been used in the battle against COVID-19 could be adopted for the elimination of viral hepatitis program. The presented evidence in our narrative review demonstrates that the application of digital marketing tools to create campaigns on social media, armed with professional influencers, can efficiently consolidate this program. The application of different strategies in using these popular tools will raise the public awareness about viral hepatitis. Subsequently, the availability of an effective vaccine for HBV and antiviral medication for HCV can motivate the audience to take steps towards prophylaxis and screening methods against these infectious illnesses. The encouragement of health policy makers to apply digital communication technologies and comprehensive roadmaps to implement this global program will certainly decrease the burden of viral hepatitis worldwide.

COVID-19 Mobile Health Apps: An Overview of Mobile Applications in Indonesia

Background

Mobile health applications (mHealth apps) have been widely used for various purposes for mitigating the COVID-19 pandemic, such as self-assessment, contact tracing, disseminating information, minimizing exposure, and reducing face-to-face health consultation. The objective of this study is to systematically review COVID-19 related mHealth apps and highlight gaps to inform the development of future mHealth initiatives in Indonesia.

Methods

A systematic search strategy using a PRISMA flowchart was used to identify mHealth apps available in Google Play and Apple Play stores. We searched mHealth apps using certain specific terms related to COVID-19 outbreaks. The inclusion criteria were apps-based smartphone users related to COVID-19 using local language, free of cost, available in the Google Play and Apple Play Stores, and supported by the Indonesian government. We excluded games, apps on infectious diseases unrelated to COVID-19 specifically, and apps with non-Bahasa Indonesia (Indonesian language). The selected mHealth apps were assessed based on two measures: (1) the WHO guidelines on digital health intervention and (2) the four dimensions of the mHealth technology fit framework. In addition, user feedback from experienced and non-experienced users was conducted to evaluate four dimensions of the apps.

Results

A total of 339 mHealth apps were generated from the initial search, remaining seven selected apps that met inclusion criteria. The results highlighted that mHealth apps reviewed had still not been widely used by the general public. The applications were purposed to disseminate information, conduct a self-risk assessment, provide an online community forum, and telemedicine or teleconsultation regarding COVID-19. Data services, including data storage, aggregation, and data exchange, are available in most apps. The rarest function found was contact tracing and assisting health management and health workers, such as the availability of testing facilities, reporting test results, and prescribing medication. The main issues reported were the lack of data security and data privacy protection, integration and infrastructures, usability, and usefulness.

Conclusion

Our study highlighted the necessity to improve mHealth apps' functions related to assisting health workers and the function of digital contact tracing. An effort to increase public awareness regarding the use of mHealth is also necessary to streamline the function of this innovation. Policymakers must consider usefulness, usability, integration, and infrastructure issues to improve their mHealth function.

Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application "MorChana" in Thailand: UTAUT2 Approach

COVID-19 contact-tracing mobile applications have been some of the most important tools during the COVID-19 pandemic. One preventive measure that has been incorporated to help reduce the virus spread is the strict implementation of utilizing a COVID-19 tracing application, such as the MorChana mobile application of Thailand. This study aimed to evaluate the factors affecting the actual usage of the MorChana mobile application. Through the integration of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2), latent variables such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), perceived risk (PCR), self-efficacy (SEF), privacy (PR), trust (TR), and understanding COVID-19 (U) were considered to measure the intention to use MorChana (IU) and the actual usage (AU) of the mobile application. This study considered 907 anonymous participants who voluntarily answered an online self-administered survey collected via convenience sampling. The results show that IU presented the highest significant effect on AU, followed by HB, HM, PR, FC, U, SEF, PE, EE, TR, and SI. This is evident due to the strict implementation of using mobile applications upon entering any area of the vicinity. Moreover, PCR was not seen to be a significant latent factor affecting AU. This study is the first to have evaluated mobile contact tracing in Thailand. The integrated framework can be applied and extended to determine factors affecting COVID-19 tracing applications in other countries. Moreover, the findings of this study could be applied to other health-related mobile applications worldwide.

Effect of Using a Technology-Based (Mobile Health) Nursing Protocol on Positive COVID-19 Patients' Dyspnea and Level of Activity

Nursing faced a challenge in caring for COVID-19 patients in terms of keeping their chests clear from secretions, which required much effort from already-exhausted patients, along with the tasks of minimizing dyspnea and cough, strengthening immunity status, and improving their activity levels. The present study aimed to evaluate improvement of dyspnea and activity levels after 1 week of applying a mobile health nursing protocol for positive COVID-19 patients. The study was conducted in quarantine hospitals in Cairo on 90 COVID-19-positive patients who agreed to participate in the study, were fully conscious, were themselves able to use mobile phone applications (or their accompanying relatives were), and were not in need of artificial ventilation. A self-administered online questionnaire was utilized via Google forms to collect data. Patients who agreed to participate in the study received a video on their WhatsApp application containing audio and visual explanations of how they would apply the nursing intervention protocol. The study's findings showed that the best level of dyspnea was only 10.59% of the sample prior to applying the nursing protocol and increased to 20.00% of the sample after applying the nursing protocol. Also, the highest level of activity was only 50.59% pre-protocol and increased post-protocol to become 70.59%.

A Novel Tool for Real-Time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

Background

The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application.

Objective

This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak.

Methods

Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data.

Results

Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61.

Conclusions

We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.

A Mobile Phone App to Support Adherence to Daily HIV Pre-exposure Prophylaxis Engagement Among Young Men Who Have Sex With Men and Transgender Women Aged 15 to 19 Years in Thailand: Pilot Randomized Controlled Trial

Background

Widespread smartphone use provides opportunities for mobile health HIV prevention strategies among at-risk populations.

Objective

This study aims to investigate engagement in a theory-based (information–motivation–behavioral skills model) mobile phone app developed to support HIV pre-exposure prophylaxis (PrEP) adherence among Thai young men who have sex with men (YMSM) and young transgender women (YTGW) in Bangkok, Thailand.

Methods

A randomized controlled trial was conducted among HIV-negative YMSM and YTGW aged 15-19 years initiating daily oral PrEP. Participants were randomized to receive either youth-friendly PrEP services (YFS) for 6 months, including monthly contact with site staff (clinic visits or telephone follow-up) and staff consultation access, or YFS plus use of a PrEP adherence support app (YFS+APP). The target population focus group discussion findings and the information–motivation–behavioral skills model informed app development. App features were based on the 3Rs—risk assessment of self-HIV acquisition risk, reminders to take PrEP, and rewards as redeemable points. Dried blood spots quantifying of tenofovir diphosphate were collected at months 3 and 6 to assess PrEP adherence. Tenofovir diphosphate ≥350-699 fmol/punch was classified as fair adherence and ≥700 fmol/punch as good adherence. Data analysis on app use paradata and exit interviews were conducted on the YFS+APP arm after 6 months of follow-up.

Results

Between March 2018 and June 2019, 200 participants with a median age of 18 (IQR 17-19) years were enrolled. Overall, 74% (148/200) were YMSM; 87% (87/100) of participants who received YFS+APP logged in to the app and performed weekly HIV acquisition risk assessments (log-in and risk assessment [LRA]). The median duration between the first and last log-in was 3.5 (IQR 1.6-5.6) months, with a median frequency of 6 LRAs (IQR 2-10). Moreover, 22% (22/100) of the participants in the YFS+APP arm were frequent users (LRA≥10) during the 6-month follow-up period. YMSM were 9.3 (95% CI 1.2-74.3) times more likely to be frequent app users than YTGW (P=.04). Frequent app users had higher proportions (12%-16%) of PrEP adherence at both months 3 and 6 compared with infrequent users (LRA<10) and the YFS arm, although this did not reach statistical significance. Of the 100 participants in the YFS+APP arm, 23 (23%) were interviewed. The risk assessment function is perceived as the most useful app feature. Further aesthetic adaptations and a more comprehensive rewards system were suggested by the interviewees.

Conclusions

Higher rates of PrEP adherence among frequent app users were observed; however, this was not statistically significant. A short app use duration of 3 months suggests that they may be useful in establishing habits in taking daily PrEP, but not long-term adherence. Further studies on the specific mechanisms of mobile phone apps that influence health behaviors are needed.

Trial Registration

ClinicalTrials.gov NCT03778892; https://clinicaltrials.gov/ct2/show/NCT03778892

Critical Retrospection of Performance of Emerging Mobile Technologies in Health Data Management

The recent advancement in mobile technologies has led to opening a new paradigm in the field of medical healthcare systems. The development of WBAN sensors, wearable devices, and 5G/6G wireless technology has made real-time monitoring and telecare of the patient feasible. The complex framework to secure sensitive data of the patient and healthcare professionals is critical. The fast computation of health data generated is crucial for disease prediction and trauma-related services; the security of data and financial transactions is also a major concern. Various models, algorithms, and frameworks have been developed to tame critical issues related to healthcare services. The efficiency of these frameworks and models depends on energy and time consumption. Thus, the review of recent emerging technologies in respect of energy and time consumption is required. This paper reviews the developments in recent mobile technologies, their application, and the comparative analysis of their performance parameters to explicitly understand the utility, capacity, and limitations. This will help to understand the shortcomings of the recent technologies for the development of better frameworks with higher performance capabilities as well as higher quality of services.

Achieving the potential of mHealth in medicine requires challenging the ethos of care delivery

Mobile Health (mHealth) interventions have received a mix of praise and excitement, as well as caution and even opposition over recent decades. While the rapid adoption of mHealth solutions due to the COVID-19 pandemic has weakened resistance to integrating these digital approaches into practice and generated renewed interest, the increased reliance on mHealth signals a need for optimizing development and implementation. Despite an historically innovation-resistant medical ethos, mHealth is becoming a normalized supplement to clinical practice, highlighting increased demand. Reaching the full potential of mHealth requires new thinking and investment. The current challenge to broaden mHealth adoption and to ensure equity in access may be overcoming a “design purgatory,” where innovation fails to connect to practice. We recommend leveraging the opportunity presented by the COVID-19 pandemic to disrupt routine practice and with a new focus on theory-driven replicability of mHealth tools and strategies aimed at medical education and professional organizations.

Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework

BACKGROUND

The silent transmission of COVID-19 has led to an exponential growth of fatal infections. With over 4 million deaths worldwide, the need to control and stem transmission has never been more critical. New COVID-19 vaccines offer hope. However, administration timelines, long-term protection, and effectiveness against potential variants are still unknown. In this context, contact tracing and digital contact tracing apps (CTAs) continue to offer a mechanism to help contain transmission, keep people safe, and help kickstart economies. However, CTAs must address a wide range of often conflicting concerns, which make their development/evolution complex. For example, the app must preserve citizens' privacy while gleaning their close contacts and as much epidemiological information as possible.

OBJECTIVE

In this study, we derived a compare-and-contrast evaluative framework for CTAs that integrates and expands upon existing works in this domain, with a particular focus on citizen adoption; we call this framework the Citizen-Focused Compare-and-Contrast Evaluation Framework (C3EF) for CTAs.

METHODS

The framework was derived using an iterative approach. First, we reviewed the literature on CTAs and mobile health app evaluations, from which we derived a preliminary set of attributes and organizing pillars. These attributes and the probing questions that we formulated were iteratively validated, augmented, and refined by applying the provisional framework against a selection of CTAs. Each framework pillar was then subjected to internal cross-team scrutiny, where domain experts cross-checked sufficiency, relevancy, specificity, and nonredundancy of the attributes, and their organization in pillars. The consolidated framework was further validated on the selected CTAs to create a finalized version of C3EF for CTAs, which we offer in this paper.

RESULTS

The final framework presents seven pillars exploring issues related to CTA design, adoption, and use: (General) Characteristics, Usability, Data Protection, Effectiveness, Transparency, Technical Performance, and Citizen Autonomy. The pillars encompass attributes, subattributes, and a set of illustrative questions (with associated example answers) to support app design, evaluation, and evolution. An online version of the framework has been made available to developers, health authorities, and others interested in assessing CTAs.

CONCLUSIONS

Our CTA framework provides a holistic compare-and-contrast tool that supports the work of decision-makers in the development and evolution of CTAs for citizens. This framework supports reflection on design decisions to better understand and optimize the design compromises in play when evolving current CTAs for increased public adoption. We intend this framework to serve as a foundation for other researchers to build on and extend as the technology matures and new CTAs become available.

Including the Public in Public eHealth: The Need for Community Participation in the Development of State-Sponsored COVID-19-Related Mobile Apps

The COVID-19 pandemic has overwhelmed health care systems worldwide, particularly in underresourced communities of color with a high prevalence of pre-existing health conditions. Many state governments and health care entities responded by increasing their capacity for telemedicine and disease tracking and creating mobile apps for dissemination of medical information. Our experiences with state-sponsored apps suggest that because many of these eHealth tools did not include community participation, they inadvertently contributed to widening digital health disparities. We propose that, as eHealth tools continue to expand as a form of health care, more attention needs to be given to their equitable distribution, accessibility, and usage. In this viewpoint collaboratively written by a minority-serving community-based organization and an eHealth academic research team, we present our experience participating in a community advisory board working on the dissemination of the COVID Alert NY mobile app to illustrate the importance of public participation in app development. We also provide practical recommendations on how to involve community representatives in the app development process. We propose that transparency and community involvement in the process of app development ultimately increases buy-in, trust, and usage of digital technology in communities where they are needed most.

The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective

The use of mobile technology and equipment has been found to be successful in the governance of public health. In the context of the coronavirus disease 2019 (COVID-19) pandemic, mobile health (mhealth) apps are expected to play an important role in the governance of public health. This study establishes a structural equation model based on the digital content value chain framework, identifies the main values created by mhealth apps in the prevention and control of COVID-19, and surveys 500 citizens of China. The data were analyzed using an independent t-test and partial least squares structural equations (PLS-SEM). The results showed that people who use mhealth apps are more satisfied with public health governance than those who do not; the healthcare assurance value of mhealth apps and healthcare confidence positively influence the interaction between users and mhealth app functions, the interaction with information, and the interaction with doctors to improve users’ satisfaction with public health governance; and the parasocial relationships between doctors and users of mhealth apps positively affect the interactions between users and doctors to improve users’ satisfaction with public health governance. This study confirms the potential of mhealth apps toward improving public health governance during the COVID-19 pandemic from a new perspective and provides a new theoretical basis whereby mobile technology can contribute toward improving public health governance.

The dawn of digital public health in Europe: Implications for public health policy and practice

The COVID-19 pandemic has highlighted the importance of digital health technologies and the role of effective surveillance systems. While recent events have accelerated progress towards the expansion of digital public health (DPH), there remains significant untapped potential in harnessing, leveraging, and repurposing digital technologies for public health. There is a particularly growing need for comprehensive action to prepare citizens for DPH, to regulate and effectively evaluate DPH, and adopt DPH strategies as part of health policy and services to optimise health systems improvement. As representatives of the European Public Health Association's (EUPHA) Digital Health Section, we reflect on the current state of DPH, share our understanding at the European level, and determine how the application of DPH has developed during the COVID-19 pandemic. We also discuss the opportunities, challenges, and implications of the increasing digitalisation of public health in Europe.