Dataset Information

Lessons on mobile apps for COVID-19 from China

ABSTRACT: The COVID-19 pandemic has impacted the global society and human life profoundly. Many countries have launched COVID-19 mobile apps with a wide range in how these apps work. While it is hoped that these apps can assist in the fight against COVID-19, many are worried about user privacy. China implemented “health code” systems, which assigned neighborhoods and citizens a specific health code, meant to indicate their risk of having been exposed to COVID-19. The most widely used health code systems were hosted on the popular apps WeChat and Alipay, each with billions of users. Some experts argued that China's use of mobile applications was essential to its successful combat against COVID-19. Included in this study are a summary of mobile technology usage in China, a review of previous studies of mobile technology in healthcare, and a brief survey of some existing mobile applications for COVID-19 that were implemented. Also included are outcomes of interviews with healthcare and public safety experts and a public survey to understand how mobile applications were used in China's response to COVID-19. The interviews revealed four important themes: personal privacy, community involvement, government involvement, and situational specificity. It was found that a key concern was maintaining a balance between collecting and utilizing personal information, as well as protecting this information. In addition, close collaboration between local communities and the national government was essential. Mobile applications assisted in communication and coordination but did not replace the work of people such as delivery drivers and contact tracers. Our results also showed that there was room for improvement, especially accessibility for the elderly or those unfamiliar with technology. Similar results were obtained from our survey. It was interesting to find that the apps were mostly used for “accessing information on COVID-19.” In addition, respondents overwhelmingly identified “information” as the most valuable feature of COVID-19 apps. Both interview and survey results revealed the importance of providing information as a primary function of COVID-19 apps. Based on our findings we distilled four main lessons: mobile applications should assist in existing COVID-19 responses, inform users, protect users’ personal information, and adapt to users’ environments. We recommend that public health officials and app developers take these lessons into consideration when developing COVID-19 related mobile applications. In addition, we encourage researchers to utilize this report as a jumping off point for further research.

SUBMITTER: Zhou S

PROVIDER: S-EPMC8117666 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMobile 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.MethodsA 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.ResultsA 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.ConclusionOur 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.

Project description:BackgroundMobile health (mHealth) apps are rapidly emerging technologies in China due to strictly controlled medical needs during the COVID-19 pandemic while continuing essential services for chronic diseases. However, there have been no large-scale, systematic efforts to evaluate relevant apps.ObjectiveWe aim to provide a landscape of mHealth apps in China by describing and comparing digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and analyze the impact of COVID-19 on mHealth apps.MethodsWe conducted a semilongitudinal survey of 1593 mHealth apps to study the app data flow and clarify usage changes and influencing factors. We selected mHealth apps in app markets, web pages from the Baidu search engine, the 2018 top 100 hospitals with internet hospitals, and online shopping sites with apps that connect to smart devices. For user experience, we recruited residents from a community in southeastern China from October 2019 to November 2019 (before the outbreak) and from June 2020 to August 2020 (after the outbreak) comparing the attention of the population to apps. We also examined associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes using quantile regression models.ResultsRehabilitation medical support was the top-ranked functionality, with a median 1.44 million downloads per app prepandemic and a median 2.74 million downloads per app postpandemic. Among the top 10 functions postpandemic, 4 were related to maternal and child health: pregnancy preparation (ranked second; fold change 4.13), women's health (ranked fifth; fold change 5.16), pregnancy (ranked sixth; fold change 5.78), and parenting (ranked tenth; fold change 4.03). Quantile regression models showed that rehabilitation (P75, P90), pregnancy preparation (P90), bodybuilding (P50, P90), and vaccination (P75) were positively associated with an increase in downloads after the outbreak. In the user experience survey, the attention given to health information (prepandemic: 249/375, 66.4%; postpandemic: 146/178, 82.0%; P=.006) steadily increased after the outbreak.ConclusionsmHealth apps are an effective health care approach gaining in popularity among the Chinese population following the COVID-19 outbreak. This research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference, which may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion.

Project description:BackgroundIn the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap.ObjectiveWith the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic.MethodsIn this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol.ResultsMobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19.ConclusionsMobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

Dataset Information

Lessons on mobile apps for COVID-19 from China

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets