Background Mobile phone apps have been leveraged to combat the spread of COVID-19. However, little is known about these technologies’ characteristics, technical features, and various applications in health care when responding to this public health crisis. The lack of understanding has led developers and governments to make poor choices about apps’ designs, which resulted in creating less useful apps that are overall less appealing to consumers due to their technical flaws. Objective This review aims to identify, analyze, and categorize health apps related to COVID-19 that are currently available for consumers in app stores; in particular, it focuses on exploring their key technical features and classifying the purposes that these apps were designed to serve. Methods A review of health apps was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The Apple Store and Google Play were searched between April 20 and September 11, 2020. An app was included if it was dedicated for this disease and was listed under the health and medical categories in these app stores. The descriptions of these apps were extracted from the apps’ web pages and thematically analyzed via open coding to identify both their key technical features and overall purpose. The characteristics of the included apps were summarized and presented with descriptive statistics. Results Of the 298 health apps that were initially retrieved, 115 met the inclusion criteria. A total of 29 technical features were found in our sample of apps, which were then categorized into five key purposes of apps related to COVID-19. A total of 77 (67%) apps were developed by governments or national authorities and for the purpose of promoting users to track their personal health (9/29, 31%). Other purposes included raising awareness on how to combat COVID-19 (8/29, 27%), managing exposure to COVID-19 (6/29, 20%), monitoring health by health care professionals (5/29, 17%), and conducting research studies (1/29, 3.5%). Conclusions This study provides an overview and taxonomy of the health apps currently available in the market to combat COVID-19 based on their differences in basic technical features and purpose. As most of the apps were provided by governments or national authorities, it indicates the essential role these apps have as tools in public health crisis management. By involving most of the population in self-tracking their personal health and providing them with the technology to self-assess, the role of these apps is deemed to be a key driver for a participatory approach to curtail the spread of COVID-19. Further effort is required from researchers to evaluate these apps’ effectiveness and from governmental organizations to increase public awareness of these digital solutions.

Background: Health chatbots are rising in popularity and capability for fighting the novel SARS-CoV-2 coronavirus (COVID-19). Objectives: This study aims to review the current literature on COVID-19 related chatbots in healthcare, identify and characterize these emerging technologies and their applications for combating COVID-19, and describe related challenges. Methods: The authors conducted a scoping review of peer-reviewed literature on COVID-19, guided by the Arksey and O’Malley framework. PubMed/MEDLINE and Google Scholar were searched over a period between January and September 2020 by using the keywords “COVID* chatbot”, “virtual assistant”, “AI enabled platform COVID” and associated synonyms. Relevant studies’ references were checked for further articles. The content of these studies was screened and thematically analyzed by the two authors. Results: Out of 543 articles initially identified, 9 were eligible for inclusion. Studies describing chatbots’ development and architecture (n=6) were the most common, and only 3 empirical studies on the user experience were identified. Our scoping review identified five key applications of the current health chatbots, which were: disseminating health information and knowledge; self-triage and personal risk assessment; monitoring exposure and notifications; tracking COVID-19 symptoms and health aspects; and combating misinformation and fake news. Furthermore, these technologies can accomplish the following tasks: ask and answer questions; create health records and history of use; complete forms and generate reports; and take simple actions. Nonetheless, the use of health chatbots poses many challenges both at the level of the social system (i.e., consumers’ acceptability) as well as the technical system (i.e., design and usability). Conclusion: Using health chatbots to combat COVID-19 is a practice still in its infancy. We believe that our work will help researchers in this domain gain better understanding of this novel technology’s design and applications, which are needed for continuous improvement in the health chatbots’ functionalities and their usefulness to fight COVID-19.

BackgroundSelf-quantification is seen as an emerging paradigm for health care self-management. Self-quantification systems (SQS) can be used for tracking, monitoring, and quantifying health aspects including mental, emotional, physical, and social aspects in order to gain self-knowledge. However, there has been a lack of a systematic approach for conceptualising and mapping the essential activities that are undertaken by individuals who are using SQS in order to improve health outcomes.In this paper, we propose a new model of personal health information self-quantification systems (PHI-SQS). PHI-SQS model describes two types of activities that individuals go through during their journey of health self-managed practice, which are 'self-quantification' and 'self-activation'.ObjectivesIn this paper, we aimed to examine thoroughly the first type of activity in PHI-SQS which is 'self-quantification'. Our objectives were to review the data management processes currently supported in a representative set of self-quantification tools and ancillary applications, and provide a systematic approach for conceptualising and mapping these processes with the individuals' activities.MethodWe reviewed and compared eleven self-quantification tools and applications (Zeo Sleep Manager, Fitbit, Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, uBiome, Digifit, BodyTrack, and Wikilife), that collect three key health data types (Environmental exposure, Physiological patterns, Genetic traits). We investigated the interaction taking place at different data flow stages between the individual user and the self-quantification technology used.FindingsWe found that these eleven self-quantification tools and applications represent two major tool types (primary and secondary self-quantification systems). In each type, the individuals experience different processes and activities which are substantially influenced by the technologies' data management capabilities.ConclusionsSelf-quantification in personal health maintenance appears promising and exciting. However, more studies are needed to support its use in this field. The proposed model will in the future lead to developing a measure for assessing the effectiveness of interventions to support using SQS for health self-management (e.g., assessing the complexity of self-quantification activities, and activation of the individuals).

Background: Consumers’ willingness to use health chatbots can eventually determine if the adoption of health chatbots will succeed in delivering healthcare services for combating COVID-19. However, little research to date has empirically explored influential factors of consumer willingness toward using these novel technologies, and the effect of individual differences in predicting this willingness. Objectives: This study aims to explore (a) the influential factors of consumers’ willingness to use health chatbots related to COVID-19, (b) the effect of individual differences in predicting willingness, and (c) the likelihood of using health chatbots in the near future as well as the challenges/barriers that could hinder peoples’ motivations. Methods: An online survey was conducted which comprised of two sections. Section one measured participants’ willingness by evaluating the following six factors: performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating conditions, and emotions. Section two included questions on demographics, the likelihood of using health chatbots in the future, and concerns that could impede such motivation. Results: A total of 166 individuals provided complete responses. Although 40% were aware of health chatbots and only 24% had used them before, about 84% wanted to use health chatbots in the future. The strongest predictors of willingness to use health chatbots came from the intrinsic motivation factor whereas the next strongest predictors came from the performance efficacy factor. Nearly 39.5% of participants perceived health chatbots to have human-like features such as consciousness and free will, but no emotions. About 38.4% were uncertain about the ease of using health chatbots. Conclusion: This study contributes toward theoretically understanding factors influencing peoples’ willingness to use COVID-19-related health chatbots. The findings also show that the perception of chatbots’ benefits outweigh the challenges.

BackgroundSelf-quantification (SQ) is a way of working in which, by using tracking tools, people aim to collect, manage, and reflect on personal health data to gain a better understanding of their own body, health behavior, and interaction with the world around them. However, health SQ lacks a formal framework for describing the self-quantifiers’ activities and their contextual components or constructs to pursue these health related goals. Establishing such framework is important because it is the first step to operationalize health SQ fully. This may in turn help to achieve the aims of health professionals and researchers who seek to make or study changes in the self-quantifiers’ health systematically.ObjectiveThe aim of this study was to review studies on health SQ in order to answer the following questions: What are the general features of the work and the particular activities that self-quantifiers perform to achieve their health objectives? What constructs of health SQ have been identified in the scientific literature? How have these studies described such constructs? How would it be possible to model these constructs theoretically to characterize the work of health SQ?MethodsA systematic review of peer-reviewed literature was conducted. A total of 26 empirical studies were included. The content of these studies was thematically analyzed using Activity Theory as an organizing framework.ResultsThe literature provided varying descriptions of health SQ as data-driven and objective-oriented work mediated by SQ tools. From the literature, we identified two types of SQ work: work on data (ie, data management activities) and work with data (ie, health management activities). Using Activity Theory, these activities could be characterized into 6 constructs: users, tracking tools, health objectives, division of work, community or group setting, and SQ plan and rules. We could not find a reference to any single study that accounted for all these activities and constructs of health SQ activity.ConclusionsA Health Self-Quantification Activity Framework is presented, which shows SQ tool use in context, in relation to the goals, plans, and competence of the user. This makes it easier to analyze issues affecting SQ activity, and thereby makes it more feasible to address them. This review makes two significant contributions to research in this field: it explores health SQ work and its constructs thoroughly and it adapts Activity Theory to describe health SQ activity systematically.