This paper is in the following e-collection/theme issue:

Theme Issue 2022: Chatbots and COVID-19 (42) JMIR Theme Issue: COVID-19 Special Issue (2452) Chatbots and Conversational Agents (531) Artificial Intelligence (1371) Digital Health Reviews (1289)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43112, first published .
General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

Authors of this article:

Wendell Adrian Lim1 Author Orcid Image ;   Razel Custodio1 Author Orcid Image ;   Monica Sunga1 Author Orcid Image ;   Abegail Jayne Amoranto1 Author Orcid Image ;   Raymond Francis Sarmiento1 Author Orcid Image
Article Authors Cited by (1) Tweetations (7) Metrics

    Review

    National Telehealth Center, National Institutes of Health, University of the Philippines Manila, Manila, Philippines

    Corresponding Author:

    Wendell Adrian Lim, PTRP

    National Telehealth Center

    National Institutes of Health

    University of the Philippines Manila

    670 Padre Faura Street, Ermita

    Manila, 1000

    Philippines

    Phone: 63 9269819254

    Email: wolim@up.edu.ph


    Background: A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews.

    Objective: This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework.

    Methods: We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines.

    Results: Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user.

    Conclusions: The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response.

    J Med Internet Res 2024;26:e43112

    doi:10.2196/43112

    Keywords

    COVID-19; health chatbot; conversational agent in health care; artificial intelligence; systematic review; mobile phone 


    Background

    The COVID-19 pandemic is regarded as one of the most critical public health crises to have affected societies worldwide [1,2]. In December 2019, the first human cases of COVID-19 were reported in Wuhan, China [3]. A few months later, in March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic [4]. Since then, it has caused massive disruptions and catastrophes not only in the health care sector but also in education, transportation, the economy, and politics [1,5]. During this period, we observed a paradigm shift toward digital transformation and a surge in the use of web-based technologies to ensure safe access and provision of needed services [6,7]. The worldwide effects of the COVID-19 pandemic, especially in the health sector, have caused health authorities such as the WHO to turn to digital technologies, such as the use of chatbots [8]. Early in the pandemic, chatbots were considered an instant resource about the latest COVID-19 health advice to the public [9]. Chatbots are conversational agents powered by artificial intelligence (AI) that enable interaction between end users and machines, with the intervention of natural language processing (NLP) [6,7,10,11]. Currently, chatbots are already in use to tackle various health concerns, particularly in the realm of mental health, which has the most extensive literature available [8]. Chatbots can also currently perform many labor-intensive tasks at lower costs across other fields, such as customer service, pedagogy, and personal assistance [12]. During a pandemic, however, chatbots can be particularly useful for informing people, promoting behavior change, offering mental health assistance, and diagnosing and tracking symptoms [8]. Therefore, a surge in the use of COVID-19–related chatbots occurred around the world. Many of these COVID-19 chatbots were implemented for public health responses and information sharing, such as the WHO Health Alert via WhatsApp and Facebook [12] and the HealthBuddy of the WHO Regional Office for Europe [13]. Other uses of chatbots at the time of the COVID-19 pandemic were in the field of education, such as Iggy and Pounce [14]; business and employment, such as Benefits Bot [15]; mental health, such as Woebot [16]; and customer service, such as the Rapid Response Virtual Agent [17].

    The existing literature investigated the design characteristics of chatbots in general, including their frequency of interaction and consequential design implications [18-20]. Notably, the design of a chatbot could affect user engagement, which in turn could affect its usefulness to its intended audience [18]. It is also critical to note that the design of chatbots evolves with time to perform new functions [19]. Despite this, we noticed a lack of comprehensive characterization and analysis of the chatbots explicitly designed to address the negative effects of the COVID-19 pandemic. Prior chatbot reviews mainly covered conversational agents developed before the pandemic [4,11,21]. Given the recent surge in COVID-19–related chatbot development and deployment, we believe that an up-to-date review that evaluates the characteristics and design of chatbots deployed in response to the pandemic is crucial. Analyzing the design of these technologies and their innovative applications can track recent developments in chatbot technology and assist chatbot developers and implementers in making design decisions for their conversational agents.

    Objectives

    To address this gap, we aimed to determine the general characteristics of the existing COVID-19 chatbots worldwide. We also intended to examine the system blueprints of these conversational agents using the adapted and modified design taxonomy framework by Nißen et al [21].

    This chatbot design framework was based on a mixed methods research study, in which 120 pre–COVID-19 chatbots with various purposes were analyzed to develop a design taxonomy. The framework characterized relationships among users and chatbots with different time horizons based on 5 design perspectives (eg, temporal profile, appearance, intelligence, interaction, and context), 22 design dimensions, and 61 design characteristics [21] (Table 1). In the interest of our work, we did not cover the front-end user interface, which is 1 of the 22 design dimensions in the design taxonomy by Nißen et al [21]. The front-end user interface dimension addresses user access, which can already be covered in the description of the chatbot’s general characteristics. Using this chatbot design framework, we will be able to see the design trend of COVID-19 chatbots.

    Table 1. Modified design taxonomy for COVID-19 chatbots.
    Layer, design perspective, and design dimensionsDesign characteristics
    Chatbot

    		Temporal profile


    		Time horizonShort term, medium term, long term, and life long


    		Frequency of interactions1-time only and multiple times


    		Duration of interactionShort, medium, and long


    		Consecutiveness of interactionsUnrelated and related

    		Appearance


    		RoleExpert, facilitator, and peer


    		Communication styleTask oriented, and socially or chat oriented


    		Avatar representationDisembodied and embodied

    		Intelligence


    		Intelligence frameworkRule-based, hybrid, and artificially intelligent


    		IQRule-based knowledge only, text understanding, and other alternative sources


    		Personality adaptabilityPrincipal self and adaptive self


    		Socioemotional behaviorNot present and present


    		Service integrationNone, external data, media resources, and multiple
    Chatbot and user

    		Interaction


    		Communication modalityText only and text+voice


    		Interaction modalityGraphical and interactive


    		User assistance designReactive, proactive, and reciprocal


    		PersonalizationStatic and adaptive


    		Additional human supportNone and yes


    		GamificationNot gamified and gamified
    User

    		Context


    		Application domainBusiness, health care, education, and daily life


    		Motivation or purposeProductivity, entertainment, utility, informational, and coaching


    		Collaboration goalNongoal oriented and goal oriented

    Search Strategies

    We compiled published papers and journals on COVID-19 chatbots, targeting the social facets of the pandemic response, such as health, education, and lifestyle management, as described in our objectives. We examined databases such as PubMed, Scopus, CINAHL, ProQuest, as well as Google Scholar and ResearchGate to help us capture the gray literature. We also used the following search string for this systematic review: (“Covid” OR “Covid-19” OR “Covid19” OR “coronavirus” OR “2019nCoV” OR “SARS-COV-2”) AND (“Chatbot” OR “conversational agent” OR “virtual assistant”).

    Study Selection

    We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for this systematic review (Multimedia Appendix 1). The results of our study selection are shown in Figure 1. After eliminating duplicates and screening titles and abstracts, we developed a set of inclusion and exclusion criteria to determine the most relevant articles. We included studies that matched the following statements: (1) papers that extensively discussed the characteristics, design, and behavior of a COVID-19–specific chatbot, not limited to health; (2) papers that described a chatbot that underwent a clinical trial during the time of the pandemic; and (3) papers that detailed the development and conceptualization of a COVID-19 chatbot. Alternatively, we excluded publications that consisted of the following: (1) studies that were not written in English; (2) studies without full texts; (3) studies that were published before March 2020 and after April 30, 2022; (4) qualitative studies related to COVID-19 chatbots or conversational agents; (5) studies that mentioned chatbots related to other public health emergencies of international concern such as swine influenza, Ebola, polio, Zika virus, and Kivu Ebola; (6) other systematic reviews or meta-analyses about COVID-19 chatbots; and (7) opinion papers, commentary, news, and editorials on COVID-19 chatbots. The papers were concurrently scrutinized through intensive reading and analysis by 2 research associates and 1 senior researcher from April to June 2022. Papers published after April 30, 2022, were not included to not disrupt the paper analysis and statistics retrieved by the researchers.

    ‎
    Figure 1. Process flowchart of study selection. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram for new systematic reviews which included searches of databases, registers, and other sources.

    Overview

    We identified 1106 papers from the databases (n=788, 71.25%) and websites (n=318, 28.75%). We then removed 92 duplicates (n=45, 49% in databases and n=47, 51% in websites) before the study screening and performed title and abstract screening. From the remaining 1014 papers in the databases and websites, we sought 98 (9.66%) for full-text retrieval. Then, of the 98 papers, we reviewed the full text of 58 (59%) from database sources and 3 (3%) from websites using the study inclusion criteria. After the final screening, we included 56 papers in this study.

    General Characteristics

    The general characteristics of a COVID-19 chatbot describe its structural and technical information. These comprise the app’s name, country and continent of origin, maturity level, and the platform where it was deployed. Details of the general characteristics of COVID-19 chatbots are provided in Multimedia Appendix 2 [10,22-76]. A summary is presented in Table 2.

    Table 2. Summary of the general characteristics of the COVID-19 chatbots (N=56).
    Characteristic and categoryValues, n (%)
    Region or continent

    		Asia24 (43)

    		Europe15 (27)

    		North America7 (13)

    		Africa4 (7)

    		South America3 (5)

    		Oceania3 (5)
    Maturity level

    		Released26 (46)

    		Beta stage11 (20)

    		Conceptualization10 (18)

    		Alpha stage5 (9)

    		Prealpha stage2 (4)

    		Prototype2 (4)
    Platform

    		SMS text messaging apps17 (30)

    		Websites16 (29)

    		Android operating system12 (21)

    		Others (eg, iOS and Google cloud)9 (16)

    Design Taxonomy

    Temporal Profile Perspective

    Table 3 shows the distribution of studies in relation to their temporal profiles, which refer to the time-dependent characteristics of a chatbot and can be further classified according to the time horizon, frequency of interaction, duration of the exchange, and consecutiveness of interactions [21]. Our results indicated that a substantial proportion of COVID-19 chatbot interactions (26/56, 46%) is categorized under the medium term, as the interaction of these chatbots can go beyond a single conversation and may last for days and weeks. With regard to frequency of interactions, more than half of the COVID-19 chatbots (31/56, 55%) interacted with their users multiple times instead of a single occurrence [21]. Regarding interaction duration, most COVID-19 chatbots had medium-length conversations with the user (22/56, 39%).

    Regarding the consecutiveness of interactions, nearly half of the chatbots (25/56, 45%) could remember information from previous user interactions, whereas the remaining chatbots (8/56, 14%) were found to perform the opposite function toward past interactions with the user.

    Table 3. Temporal profile perspective of the COVID-19 chatbots (N=56).
    Design dimension and design characteristicValues, n (%)Study
    Time horizon

    		Short term11 (20)[28,34,40,41,45,47-51,59]

    		Medium term26 (46)[31,33,36,39,42-44,46,54,56-58,60,63,64,66-76]

    		Long term2 (4)[35,38]

    		Not stated17 (30)[10,22-27,29,30,32,37,52,53,55,61,62,65]
    Frequency of interaction

    		1 time only8 (14)[28,34,40,41,45,47,48,59]

    		Multiple times31 (55)[26,31,33,35,36,38,39,42,44,46,49,54,56,57,60-76]

    		Not stated17 (30)[10,22-25,27,29,30,32,37,43,50-53,55,58]
    Duration of interaction

    		Short14 (25)[28,34,40-42,45-51,57,59]

    		Medium22 (39)[31,33,35,36,39,44,54,56,60,63,64,66-76]

    		Not stated20 (36)[10,22-27,29,30,32,37,38,43,52,53,55,58,61,62,65]
    Consecutiveness of interactions

    		Related25 (45)[26,31,33,35,36,39,48,49,54,60-68,70-76]

    		Unrelated8 (14)[28,34,40,41,43,45,46,69]

    		Not stated23 (41)[10,22-25,27,29,30,32,37,38,42,44,47,50-53,55-59]
    Appearance Perspective

    In Table 4, we characterize the appearance of COVID-19 chatbots (ie, the chatbot’s overall look and feel) into 3 design dimensions: role, primary communication style, and avatar representation [21]. Regarding chatbot roles, our results showed that most conversational agents for COVID-19 were considered experts (33/56, 59%). Expert chatbots have specific skills or expertise in a specific field and provide answers or reactions to the user’s query, keywords, or actions [21]. In contrast, facilitator chatbots assist users in reaching a certain objective or executing a particular task [21]. In our analysis, 27% (15/56) were facilitator COVID-19 chatbots. Finally, peer chatbots aim to be a “sparring partner” for the user, and they value socioemotional behavior as part of their design. Of the 56 chatbots, only 8 (14%) COVID-19 chatbots served as peers.

    Regarding the communication style of COVID-19 chatbots, we found that 63% (35/56) of them were task oriented. Task-oriented chatbots prioritize task efficiency and achievement of goals at the most minimal cost and time possible [21]. In contrast, socially oriented chatbots prioritize social engagement and personalization of interactions with their users [21]. These comprised only 34% (19/56) of COVID-19 chatbots.

    Chatbots can also have a visual or character representation (embodied avatars), or they may not have one at all (disembodied avatars) [21]. We found that more than half of the COVID-19 chatbots (32/56, 57%) did not have any visual or character representation. Only 34% (19/56) of the COVID-19 chatbots had embodied avatars.

    Table 4. Appearance perspective of the COVID-19 chatbots (N=56).
    Design dimension and design characteristicValues, n (%)Study
    Role

    		Expert33 (59)[10,22-24,26,29-34,38,39,41,43,47,50,51,55-58,63,64,66-73,75,76]

    		Facilitator15 (27)[25,27,35-37,40,45,53,54,59-62,65,72]

    		Peer8 (14)[28,42,44,46,48,49,52,74]
    Primary communication style

    		Task oriented35 (63)[23-26,29,30,32-34,37,39,40,43,45,46,54-69,71,72,75,76]

    		Socially or chat oriented19 (34)[10,22,27,28,35,36,38,41,42,44,47-52,70,73,74]

    		Not specified2 (4)[31,53]
    Avatar representation

    		Disembodied32 (57)[10,23-26,29,30,32-34,36,37,39-41,52,56,60,61,63-75]

    		Embodied19 (34)[22,27,28,35,38,42,44-51,53,54,57,62,76]

    		Not specified5 (9)[31,43,55,58,59]
    Intelligence Perspective

    The intelligence perspective is characterized by the functionalities of a chatbot’s inner working mechanisms, which build on an intelligence framework and quotient, personality adaptability, socioemotional behavior, and service integration [21]. As depicted in Table 5, we found that a large number of COVID-19 chatbots (26/56, 46%) were artificially intelligent. Artificially intelligent chatbots use their NLP capabilities to learn from each conversation with the user [21]. Meanwhile, a high proportion of COVID-19 chatbots (25/56, 45%) could respond to users according to set rules and by understanding an individual’s textual input.

    With regard to personality adaptability, more than half of the COVID-19 chatbots (31/56, 55%) have a principal self-adaptability, meaning they operate on a structured flow. In contrast, a significant number of COVID-19 chatbots (24/56, 43%) were adaptive because these chatbots adjust their responses depending on the user input [21].

    Moreover, 57% (32/56) of the COVID-19 chatbots did not demonstrate socioemotional behavior toward their clients. Among the existing COVID-19 chatbots, only 36% (20/56) could express empathic reactions to the users.

    With regard to service integration, there was an equal distribution of the number of COVID-19 chatbots that were limited to only processing external data and those that could process both external data and broadcast resources (21/56, 38%).

    Table 5. Intelligence perspective of the COVID-19 chatbots (N=56).
    Design dimension and design characteristicsValues, n (%)Study
    Intelligence framework

    		Rule based14 (25)[25,27,30,32,36,37,40,46,60,61,65,69,72,74]

    		Hybrid14 (25)[10,22,54-57,63,66-68,70,71,73,76]

    		Artificially intelligent26 (46)[23,24,26,28,29,33-35,38,39,41-45,47-53,59,62,64,75]

    		Not stated2 (4)[31,58]
    IQ

    		Rule-based knowledge only14 (25)[25,27,30,32,34,36,40,41,43,46,65,68,69,72]

    		Text understanding14 (25)[10,22-24,26,28,29,33,35,37-39,42,71]

    		Both25 (45)[44,45,47-56,59-64,66,67,70,73-76]

    		Not stated3 (5)[31,57,58]
    Personality adaptability

    		Principal self31 (55)[22-27,29,30,32,34,37,39-41,43-46,53-61,65,68,69,72]

    		Adaptive self24 (43)[10,28,33,35,36,38,42,47-52,62-64,66,67,70,71,73-76]

    		Not stated1 (2)[31]
    Socioemotional behavior

    		Not present32 (57)[10,23-26,29,30,32-34,37,39-41,43,45,55,59-69,71,72,75,76]

    		Present20 (36)[22,27,28,35,36,38,42,44,46,48-52,54,57,58,70,73,74]

    		Not stated4 (7)[31,47,53,56]
    Service integration

    		None2 (4)[55,61]

    		External data21 (38)[10,23-27,30-34,39,40,45,60,65,71,72,74-76]

    		Media resource3 (5)[41,46,57]

    		Multiple21 (38)[22,28,29,35-38,48,50,51,54,59,62-64,66-70,73]

    		Not stated9 (16)[42-44,47,49,52,53,56,58]
    Interaction Perspective

    The domain encompasses the design dimensions of the users’ communication with the COVID-19 chatbot. As depicted in Table 6, of the 56 COVID-19 chatbots, 38 (68%) were text based, where the end user and the bot interacted and communicated through text or internet messaging. The interactive modality was the most predominant (39/56, 70%), where end users can enter free texts to communicate with a chatbot [21].

    Regarding user assistance design, more than half of the COVID-19 chatbots (31/59, 54%) were reactive, meaning they only reacted to user input. Concerning personalization, most COVID-19 chatbots (36/56, 64%) were adaptive, meaning they could change their meaning and adjust their responses based on user needs and circumstances [21]. Moreover, most COVID-19 chatbots did not receive any additional human support (44/56, 79%) or have any gaming elements (42/56, 75%).

    Table 6. Interaction perspective of the COVID-19 chatbots (N=56).
    Design dimension and design characteristicsValues, n (%)Study
    Communication modality

    		Text only38 (68)[22,23,25,26,29,30,32-41,43,45-48,51,54,55,57,60-66,68,69,71,72,74,75]

    		Text and voice16 (29)[10,24,27,28,42,44,49,50,52,53,56,59,67,70,73,76]

    		Not specified2 (4)[31,58]
    Interaction modality

    		Graphical13 (23)[22,25,27,32,36,40,46,57,62,65,68,69,72]

    		Interactive39 (70)[10,23,24,26,28-30,33-35,37-39,41,42,44,45,47-54,56,59-61,63,64,66,67,70,71,73-76]

    		Not specified4 (7)[31,43,55,58]
    User assistance design

    		Reactive30 (54)[10,23-30,32-37,40,45,46,52,53,55,56,58-61,65,68,69,72]

    		Proactive1 (2)[71]

    		Reciprocal23 (41)[22,38,39,41-44,47-51,54,62-64,66,67,70,73-76]

    		Not specified2 (4)[31,57]
    Personalization

    		Static19 (34)[23-26,30,34,36,37,40,46,55,57,58,60,61,65,68,69,72]

    		Adaptive36 (64)[10,22,27-29,32,33,35,38,39,41-45,47-54,56,59,62-64,66,67,70,71,73-76]

    		Not specified1 (2)[31]
    Additional human support

    		Yes10 (18)[10,39,43,53,58,62,67,68,72,73]

    		No44 (79)[22-30,32-38,40-42,44-52,54-57,60,61,63-66,69-71,74-76]

    		Not specified2 (4)[31,59]
    Gamification

    		Gamified6 (11)[42,47,48,54,57,73]

    		Not gamified42 (75)[10,22-30,32-41,43-46,53,55,56,60,62-72,74-76]

    		Not specified8 (14)[31,49-52,58,59,61]
    Context Perspective

    Context refers to the motivation of users to interact with the chatbot, and it is reflected in the application domain, the user motivation to engage with the chatbot, and the collaboration goal of the chatbot. As depicted in Table 7, the vast majority of COVID-19 chatbots were in the health care domain (46/56, 82%). Consequently, we found 20% (11/56) of the COVID-19 chatbots in the education domain, and there was a smaller percentage of chatbots designed activities of daily living (6/56, 11%). Among all the COVID-19 chatbots, 11% (6/56) fell under multiple application domains.

    We also found that most COVID-19 chatbots (35/56, 63%) were purposely designed to provide information to the user. Our results also showed that there was an almost equal distribution of COVID-19 chatbots designed for utility (18/56, 32%) and coaching purposes (15/56, 27%). However, some COVID-19 chatbot researchers designed their conversational agents for productivity (6/56, 11%) and entertainment (4/56, 7%) purposes. It is also noteworthy that several COVID-19 chatbots (16/56, 29%) were designed for multiple purposes.

    Table 7. Context perspective of the COVID-19 chatbots (N=56).
    Design dimension and design characteristicValues, n (%)Study
    Application domaina

    		Health care46 (82)[10,22-29,31-46,48,50,51,55,58,59,61,63-76]

    		Education11 (20)[27,30,44,47,48,53,56,57,60,62,65]

    		Daily life6 (11)[42,44,46,49,52,54]
    Motivation or purposeb

    		Productivity6 (11)[48,54,60,62,65,75]

    		Entertainment4 (7)[44,49,54,57]

    		Utility18 (32)[10,22,25,31,33,34,37-40,48,54,59,61,62,67,68,76]

    		Informational35 (63)[1-3,6,7,10,12-19,21-68,73,75-77]

    		Coaching15 (27)[23,27,35,36,42,44,46,52,54,57,61,70,72-74]
    Collaboration goal

    		Goal oriented56 (100)[10,22-76]

    aIn total, 11% (6/56) of the chatbots fall under multiple application domains.

    bIn total, 29% (16/56) of the chatbots are designed to for multiple purposes.

    Finally, all the COVID-19 chatbots were goal oriented, which means that the chatbots helped users achieve a predefined goal [78].


    Overview

    Chatbots have a huge impact on augmenting the public health response worldwide because of their wide accessibility, functionality, practicality, and fast dissemination of information in addressing the debilitating effects of COVID-19 [79]. This systematic review successfully classified 56 COVID-19 chatbots worldwide based on their general characteristics and systems design, which was based on the design taxonomy by Nißen et al [21]. After further analysis of the collated COVID-19 chatbots, our research yielded the following findings.

    General Characteristics

    Regarding the general characteristics of the existing COVID-19 chatbots, we found that most studies on COVID-19 chatbot design and development were conducted in Asia and Europe. This indicates the need to conduct and implement more studies on COVID-19 chatbot research in other continents. Our research also revealed that most chatbots were accessible on websites, internet messaging apps (eg, Facebook Messenger, Viber, Telegram, WeChat, and LINE), and the Android operating system. This implies the ease of development and accessibility of web-based and internet messaging app chatbots as well as the popularity of social media in this generation [76]. Future research should focus on the design and development of pandemic-related chatbots for less popular native platforms such as iOS and Google Cloud to cater to a wider variety of people. Studies on the evaluation of the effectiveness of such chatbots in mitigating the negative effects of a pandemic should also be conducted to enrich the body of knowledge on chatbot designs.

    Design Taxonomy

    Overview

    A recent scoping review pooled the existing conversational agents combating the negative effects of COVID-19 and classified them based on their applications [6]. In this study, we updated the number of existing COVID-19 chatbots and supplemented the current literature with the design trends of existing COVID-19 conversational agents. We adopted and modified the chatbot design taxonomy of Nißen et al [21] and classified each COVID-19 chatbot based on 5 design perspectives.

    Temporal Profile Perspective

    From the temporal profile perspective, nearly half of the COVID-19 chatbots (26/56, 46%) interacted with the user for several weeks (medium term). This implies that most COVID-19 chatbots did not establish a deep, long-lasting relationship and companionship with the user, which is observed in chatbots with a long-term time horizon [21]. COOPERA (COVID-19: Operation for Personalized Empowerment to Render Smart Prevention and Care Seeking), for example, provides individualized self-care support, real-time follow-up of the users’ health condition, and the epidemiological situation in the users’ area for up to several weeks [31].

    Moreover, COVID-19 chatbots tended to interact with their users for >1 time, which was seen in 55% (31/56) of these chatbots. For instance, Indonesian researchers deployed ARIGAselfCareNursingBot, a teleassessment chatbot, for 5 months in a wound care clinic [56]. Furthermore, 45% (25/56) of COVID-19 chatbots could remember information from previous user interactions. COOPERA, as discussed previously, could monitor users’ health conditions daily based on their previous answers about their physical and health conditions [31].

    However, it is notable that a significant proportion of COVID-19 chatbot papers did not explicitly report the time horizon (17/56, 30%), frequency (17/56, 30%), duration (20/56, 36%), and consecutiveness of interactions (23/56, 41%) of their chatbot. This may show that there is a need for researchers and computer programmers to have a deeper dive into chatbot design specifications in their respective manuscripts to help future research in the identification of the most cost-efficient design for a pandemic-responsive chatbot.

    Appearance Perspective

    From the appearance perspective, we found that most COVID-19 chatbots (33/56, 59%) were considered experts. For example, Chloe provides Canadians with up-to-date information regarding COVID-19 by answering users’ inquiries about the pandemic [22]. In contrast, only 27% (15/56) of the COVID-19 chatbots assumed a facilitator role. An example of this is Atena, a chatbot that delivers digital mental health intervention and suggests effective coping strategies to improve the mental well-being of university students [36]. A screenshot showing a sample of conversation from the Atena chatbot is shown in Figure 2 [36].

    Our research also found that most COVID-19 chatbots (35/56, 63%) were task oriented in terms of primary communication style. To illustrate, Symptoma assesses the end user’s risk of COVID-19 based on the symptoms the person inputted or answered in the textbox, without considering the user’s emotional needs [34]. This is in contrast with socially oriented chatbots, which represent only 34% (19/56) of the literature examined. An example is Bella, which demonstrates empathy and emotional expressions based on the user’s inputs to personalize every interaction [28].

    Finally, our analysis showed that more than half of the existing COVID-19 chatbots (32/56, 57%) were disembodied, meaning they did not have any character representation. Chatbots with avatars could strengthen their social presence, which may reinforce a good social relationship with the users [79]. This finding may suggest that the immediate priority of designers at the height of the COVID-19 pandemic is to fulfill their functional efficiency while deprioritizing social engagement that may lengthen development cycles.

    ‎
    Figure 2. Screenshot of the Atena chatbot.
    Intelligence Perspective

    From the intelligence perspective, a significant number of COVID-19 chatbots (26/56, 46%) were artificially intelligent. One example is Drever, an educational assistant developed in Japan using an AI engine that acts as a teacher capable of answering questions from students [62]. Similarly, Real Conversation is an AI-based chatbot in India that provides COVID-19 information while assisting with customer queries [64].

    Meanwhile, a high number of COVID-19 chatbots (25/56, 45%) could respond to users according to a set of rules and by understanding the textual input of the user. For example, CoronaGo is a health care chatbot embedded in a website that uses NLP to understand user intention but also has a rule-based system architecture to track the COVID-19 situation in India efficiently [66]. This means that most COVID-19 chatbots maximize their capabilities in understanding the users’ intentions in a timely manner, which is critical for a chatbot designed for a pandemic.

    Most COVID-19 chatbots (31/56, 55%) operate on a structured flow, for example, Chatbot-Mental Wellness, a chatbot developed in India that provides guided activities in simple exercises, meditation, and positive affirmation [46], although nearly half (24/56, 43%) of the chatbots can adjust their responses depending on user input. An example is the JuggleChat, an educational assistant chatbot that addresses students’ specific needs dependent on their input [47].

    Our analysis also showed that most COVID-19 chatbots (32/56, 57%) did not present socioemotional behavior to the users, whereas only 36% (20/56) of COVID-19 chatbots could express empathic reactions to the consumers. This suggests that expectations for chatbots to exhibit social behaviors that are customary in human-to-human communication should always be considered as there is a growing demand for humanlike conversational engagement styles [80]. Some empathic chatbots include Chasey, a character-based conversation agent for COVID-19, who responds to users with phrases such as “Don’t worry my friend, everything will be fine.” and “Get well soon, pal!” [51].

    Finally, COVID-19 chatbots that are limited to only processing external data and those that can process both external data and media resources were of equal distribution (21/56, 38%). Such chatbots include Mano, an interactive chatbot that provides up-to-date COVID-19 information from reliable sources including, but not limited to, the WHO and predicts pandemic effects in the Mano River Union region [76], and Aapka Chikitsak, a medical bot that provides its users with quality information from the National Health Portal and engaging media as a means to deliver telehealth services in India during the pandemic [70].

    Interaction Perspective

    From the interaction perspective, most COVID-19 chatbots (38/56, 68%) reach out and communicate with the user through text or internet messaging and even allow the user to respond via free text inputs. This is essential as limiting interaction options may indicate a disregard for the users’ inputs [81]. One example is Elena+, a smartphone-based and conversational agent that delivers pandemic lifestyle care interventions that communicate with the user through textual input and is currently being expanded into Spanish and Tamil languages to reach larger audiences [35]. Another example is Wakamola, a chatbot that recognizes unstructured messages and is used to encourage confined individuals to modify their lifestyle practices [37].

    In addition, more than half of the COVID-19 chatbots (30/56, 54%) reacted only to user input rather than steering the conversation. This may imply that chatbot designers should aim to design constructive, goal-oriented interactions [82]. Similar to a proof-of-concept study implemented at the University of Trento in Italy, Atena was able to react to user inputs only [36], unlike the lone proactive Alia that can steer conversation [71].

    Moreover, most COVID-19 chatbots (36/56, 64%) had adaptive personalization. This shows that pandemic designers concentrated on the chatbots’ capacity to adapt to users’ choices, needs, and circumstances, which is consistent with an earlier study [81]. Cory COVID-Bot, for instance, which was developed to engage with difficult-to-reach populations directly, aims to resolve public ambiguity and uncertainty regarding community health guidelines. With its sophisticated features, an algorithm dynamically determines which questions to ask a user and when to solve them, encompassing recommendations on COVID-19 safety and behavior change interventions [38].

    Furthermore, the vast majority of COVID-19 chatbots did not have any additional human support nor gamification (44/56, 79% and 42/56, 75%, respectively). These design elements for chatbot interaction show that COVID-19 chatbots are designed in such a way that most COVID-19 chatbots straightforwardly interact with the user and identify their intentions as soon as possible. Previous research suggests that conversational agents should possess a humanlike quality or assistive nature, particularly within telemedicine platforms [82]. In addition, incorporating games can help evoke personality traits and enhance thoroughness [83]. Notably, a proposed conversational agent from a university hospital in the United Kingdom targets these 2 design characteristics [73]. The in-app features will be gamified as a motivational strategy for older people in self-isolation to alleviate problems with mental health. Moreover, it is planned that this chatbot will have a social support function to facilitate cognitive behavioral therapy sessions.

    Context Perspective

    From the context perspective, almost all COVID-19 chatbots (46/56, 82%) fell under the health care application domain. We expected this because COVID-19 is a public health concern. Some of these chatbots were used for personal risk assessment [10], monitoring of COVID-19 exposure [22], and dissemination of health information regarding the pandemic [26].

    Aside from health care, 20% (11/56) of COVID-19 chatbots were used in the education domain. For instance, Student Interactive Assistant Android Application with Chatbot is an academic chatbot developed during the COVID-19 pandemic that assists students interactively concerning their academic requirements [30]. Only a minority of COVID-19 chatbots (6/56, 11%) were used for other purposes such as assisting with daily life. In Morocco, for instance, a chatbot is being developed with the intention of aiding students daily with their mental health [52].

    Our results also showed that most COVID-19 chatbots (35/56, 63%) were designed to provide information to the users. For example, a chatbot developed in France informs and educates people about COVID-19 vaccines and reduces vaccine hesitancy [32]. There were also COVID-19 chatbots designed for utility and coaching purposes, but these types comprised only 32% (18/56) and 27% (15/56) of COVID-19 chatbots, respectively. For example, a chatbot developed by the University of California was used as a COVID-19 screening tool for health employees entering their hospital before their clinical shift [40]. Regarding coaching, Managing Stress at University instructs Indian University students on stress management during the COVID-19 pandemic by providing 3 different patterns of tailored explanations (ie, belief-based, goal-based, and belief- and goal-based explanations) on students’ intentions to change the recommended behaviors [27].

    Although most chatbots (40/56, 71%) served only a single purpose, there were several COVID-19 chatbots (16/56, 29%) that were multipurpose in nature. For instance, COOPERA, as discussed previously, can be used to monitor COVID-19 symptoms, provide self-care instructions, and suggest preventive actions to avoid infections [31].

    The use of COVID-19 chatbots across numerous application domains and contexts demonstrates the pandemic’s impact on other elements of life. Nonhealth industries could investigate the use of a conversational agent to efficiently solve inquiries and issues without the need for human contact. This is critical for mitigating the rapid transmission of a global pandemic. Nevertheless, our results show that the bulk of the COVID-19 chatbots were developed to disseminate updated information regarding the COVID-19 pandemic and to track virus-related symptoms and other health-related concerns. As a result, automation of knowledge extraction from reputable sources may be required for information dissemination chatbots to be deployed efficiently and effectively as knowledge sources [78].

    Limitations

    To the best of our knowledge, this is the first review to systematically analyze the general characteristics and system designs of existing COVID-19 chatbots worldwide, thereby providing developers with directions for designing chatbots in response to a pandemic. Although pioneering, this study also has its limitations. First, we limited our search to peer-reviewed studies published in English with unrestricted access to their full texts. Second, the team did not perform ancestral searches on existing scoping and systematic reviews of COVID-19 chatbots. Third, meta-analysis was not performed in this systematic review, meaning that the effectiveness of COVID-19 chatbots in mitigating the effects of the pandemic was not analyzed. We acknowledge that the latter is critical to understanding the relationship between the current design of the chatbots and their efficacy in reducing the impacts of COVID-19, which may lead to a broader perspective on the management of a global pandemic. Finally, owing to the heterogeneity of the methodology and study designs included in this review, quality assessment, risk of bias, and critical appraisal of the papers were not conducted.

    Recommendations

    Our results indicate that there is a need to supplement COVID-19 chatbot research in the United States, Africa, and Australia as well as design and development studies on less well-known platforms for chatbots (eg, iOS and Google Cloud). A meta-analysis of existing COVID-19 chatbots is also recommended because there is a lack of studies evaluating the effectiveness of such chatbots. In addition, researchers and computer programmers are suggested to use the chatbot design framework by Nißen et al [21] for the purposes of having a uniform framework and for the identification of a cost-efficient design for a chatbot designed for a pandemic. Our findings also indicate that COVID-19 chatbots are used in various application areas aside from health care, which suggests the need for future investigations into other potential uses of chatbots during a pandemic. Moreover, aside from the design-centric approach toward the development of such chatbots, future studies must also evoke the assessment of user satisfaction and engagement with this type of chatbot as these are essential factors to consider for better uptake and use of these types of technology. Accordingly, it is imperative to appraise the influence of these types of conversational agents in the clinical practice guidelines and expert-client relationships [84]. In addition, our results show that several studies have “not stated” elements in multiple categories of the framework. This is because we noticed that the authors did not report the entire design specifications of their chatbot. It would be ideal for the authors to include all the intricate details of the design and development of chatbots to help future research on the most cost-efficient design for a chatbot made to address the negative effects of a pandemic.

    Conclusions

    This paper describes the general characteristics and design taxonomy of the existing COVID-19 chatbots. The conceptualization, development, implementation, and use of these conversational agents emerged to mitigate the effects of a global pandemic in societies worldwide. This study successfully summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives using a chatbot design taxonomy, which may serve as a starting point for helping health and nonhealth chatbot communities on how to build and deploy a future-proof chatbot archetype that would meet the needs of the public amid the growing demand for better services as part of a more robust pandemic response. This would also direct researchers to explore methods on how to develop comprehensive evaluation and analytical methods for conversational agents in health care.

    Acknowledgments

    This study was supported by the Philippine Council for Health Research and Development of the Department of Science and Technology under the project entitled, “DISPO: Design, Development, and Implementation of a Natural Language Processing Enabled Virtual Assistant for Depression and Anxiety among People with COVID-19.”

    Authors' Contributions

    WAL, RC, and MS contributed substantially to the study’s conception and design, with inputs from RFS and AJA. RFS served as the principal investigator for the entire project. WAL, RC, and MS made substantial contributions to data collection, analysis, and interpretation of the study data, with inputs from RFS. RC drafted the manuscript, and this paper was revised critically for important intellectual content by all authors. All authors approved the final version of the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

    DOCX File , 62 KB
    Multimedia Appendix 2

    General characteristics of the COVID-19 chatbots.

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    1. Chakraborty I, Maity P. COVID-19 outbreak: migration, effects on society, global environment and prevention. Sci Total Environ. Aug 01, 2020;728:138882. [FREE Full text] [CrossRef] [Medline]
    2. COVID-19 Map. Johns Hopkins Coronavirus Resource Center. URL: https://coronavirus.jhu.edu/map.html [accessed 2023-12-05]
    3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. Feb 15, 2020;395(10223):497-506. [FREE Full text] [CrossRef] [Medline]
    4. Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res. Oct 22, 2020;22(10):e20346. [FREE Full text] [CrossRef] [Medline]
    5. Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed. Mar 19, 2020;91(1):157-160. [FREE Full text] [CrossRef] [Medline]
    6. Almalki M, Azeez F. Health chatbots for fighting COVID-19: a scoping review. Acta Inform Med. Dec 2020;28(4):241-247. [FREE Full text] [CrossRef] [Medline]
    7. Albites-Tapia A, Gamboa-Cruzado J, Almeyda-Ortiz J, Lázaro AM. Chatbots for the detection of COVID-19: a systematic review of the literature. Int J Adv Comput Sci Appl. 2022;13(4):91-98. [FREE Full text] [CrossRef]
    8. White BK, Martin A, White JA. User experience of COVID-19 chatbots: scoping review. J Med Internet Res. Dec 27, 2022;24(12):e35903. [FREE Full text] [CrossRef] [Medline]
    9. Weeks R, Cooper L, Sangha P, Sedoc J, White S, Toledo A, et al. Chatbot-delivered COVID-19 vaccine communication message preferences of young adults and public health workers in Urban American communities: qualitative study. J Med Internet Res. Jul 06, 2022;24(7):e38418. [FREE Full text] [CrossRef] [Medline]
    10. Battineni G, Chintalapudi N, Amenta F. AI chatbot design during an epidemic like the novel coronavirus. Healthcare (Basel). Jun 03, 2020;8(2):154. [FREE Full text] [CrossRef] [Medline]
    11. Nagarhalli T, Vaze V, Rana NK. A review of current trends in the development of chatbot systems. In: Proceedings of the 6th International Conference on Advanced Computing and Communication Systems. Presented at: ICACCS '20; March 6-7, 2020, 2020;706-710; Coimbatore, India. URL: https://ieeexplore.ieee.org/document/9074420 [CrossRef]
    12. WHO health alert brings COVID-19 facts to billions via WhatsApp. World Health Organization. URL: https:/​/www.​who.int/​news-room/​feature-stories/​detail/​who-health-alert-brings-covid-19-facts-to-billions-via-whatsapp [accessed 2021-04-26]
    13. HealthBuddy: a new chatbot to engage with communities in Europe and Central Asia on COVID-19. World Health Organization. 2020. URL: https:/​/www.​euro.who.int/​en/​health-topics/​health-emergencies/​coronavirus-covid-19/​news/​news/​2020/​5/​healthbuddy-a-new-chatbot-to-engage-with-communities-in-europe-and-central-asia-on-covid-19 [accessed 2022-06-06]
    14. Pappano L. College chatbots, with names like Iggy and pounce, are here to help. The New York Times. 2020. URL: https://www.nytimes.com/2020/04/08/education/college-ai-chatbots-students.html [accessed 2023-12-05]
    15. Bebo, the benefits bot. Bebo. URL: https://whowteam.github.io/bebo/ [accessed 2022-06-06]
    16. Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. Sep 01, 2018;25(9):1248-1258. [FREE Full text] [CrossRef] [Medline]
    17. Hao K. The pandemic is emptying call centers. AI Chatbots are swooping in. MIT Technology Review. May 14, 2020. URL: https:/​/www.​technologyreview.com/​2020/​05/​14/​1001716/​ai-chatbots-take-call-center-jobs-during-coronavirus-pandemic/​ [accessed 2023-12-05]
    18. Gnewuch U, Morana S, Maedche A. Towards designing cooperative and social conversational agents for customer service. In: Proceedings of the 38th International Conference on Information Systems. Presented at: ICIS '17; December 10-13, 2017, 2017;1-14; Seoul, South Korea. URL: https:/​/web.​archive.org/​web/​20220803152206id_/​https:/​/aisel.​aisnet.org/​cgi/​viewcontent.​cgi?article=1312&context=icis2017
    19. Seering J, Luria M, Kaufman GF, Hammer J. Beyond dyadic interactions: considering chatbots as community members. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Presented at: CHI '19; May 4-9, 2019, 2019;1-13; Scotland, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300680 [CrossRef]
    20. Adamopoulou E, Moussiades L. An overview of chatbot technology. In: Proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations. Presented at: AIAI '20; June 5–7, 2020, 2020;373-383; Neos Marmaras, Greece. URL: https://link.springer.com/chapter/10.1007/978-3-030-49186-4_31 [CrossRef]
    21. Nißen M, Selimi D, Janssen A, Cardona DR, Breitner MH, Kowatsch T, et al. See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons. Comput Human Behav. Feb 2022;127:107043. [FREE Full text] [CrossRef]
    22. Siedlikowski S, Noël LP, Moynihan SA, Robin M. Chloe for COVID-19: evolution of an intelligent conversational agent to address infodemic management needs during the COVID-19 pandemic. J Med Internet Res. Sep 21, 2021;23(9):e27283. [FREE Full text] [CrossRef] [Medline]
    23. Lee H, Kang J, Yeo J. Medical specialty recommendations by an artificial intelligence chatbot on a smartphone: development and deployment. J Med Internet Res. May 06, 2021;23(5):e27460. [FREE Full text] [CrossRef] [Medline]
    24. Pandey R, Gautam V, Pal R, Bandhey H, Dhingra LS, Misra V, et al. A machine learning application for raising WASH awareness in the times of COVID-19 pandemic. Sci Rep. Jan 17, 2022;12(1):810. [FREE Full text] [CrossRef] [Medline]
    25. Jain A, Yadav K, Alharbi HF, Tiwari S. IoT and AI enabled three-phase secure and non-invasive COVID 19 diagnosis system. Comput Mater Contin. 2022;71(1):423-438. [FREE Full text] [CrossRef]
    26. Mukhtar G, Almurtadha Y, Algarni F, Abdullah M, Felemban E, Alsharafi AM, et al. Mining the chatbot brain to improve COVID-19 bot response accuracy. Comput Mater Contin. 2022;70(2):2619-2638. [FREE Full text] [CrossRef]
    27. Nelekar S, Abdulrahman A, Gupta M, Richards D. Effectiveness of embodied conversational agents for managing academic stress at an Indian University (ARU) during COVID‐19. Br J Educ Technol. Dec 20, 2021;53(3):491-511. [FREE Full text] [CrossRef]
    28. Loveys K, Sagar M, Pickering I, Broadbent E. A digital human for delivering a remote loneliness and stress intervention to at-risk younger and older adults during the COVID-19 pandemic: randomized pilot trial. JMIR Ment Health. Nov 08, 2021;8(11):e31586. [FREE Full text] [CrossRef] [Medline]
    29. Hong YJ, Piao M, Kim J, Lee JH. Development and evaluation of a child vaccination chatbot real-time consultation messenger service during the COVID-19 pandemic. Appl Sci. Dec 20, 2021;11(24):12142. [FREE Full text] [CrossRef]
    30. Sweidan SZ, Abu Laban SS, Alnaimat NA, Darabkh KA. SIAAA‐C: a student interactive assistant android application with chatbot during COVID‐19 pandemic. Comput Appl Eng Educ. Apr 16, 2021;29(6):1718-1742. [FREE Full text] [CrossRef]
    31. Yoneoka D, Kawashima T, Tanoue Y, Nomura S, Ejima K, Shi S, et al. Early SNS-based monitoring system for the COVID-19 outbreak in Japan: a population-level observational study. J Epidemiol. Aug 05, 2020;30(8):362-370. [FREE Full text] [CrossRef] [Medline]
    32. Altay S, Hacquin AS, Chevallier C, Mercier H. Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions. J Exp Psychol Appl. Mar 2023;29(1):52-62. [CrossRef] [Medline]
    33. Hassan A, Ali MD, Ahammed R, Bourouis S, Khan MM. Development of NLP-integrated intelligent web system for E-mental health. Comput Math Methods Med. Dec 13, 2021;2021:1546343-1546320. [FREE Full text] [CrossRef] [Medline]
    34. Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Zobel M, et al. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Sci Rep. Nov 04, 2020;10(1):19012. [FREE Full text] [CrossRef] [Medline]
    35. Rexhepi H, Roig AE, Chaudhry T, Ollier J. Elena+ Care for COVID-19, a pandemic lifestyle care intervention: intervention design and study protocol. In: Kane BT, Rexhepi H, Hägglund M, Cajander Å, editors. Personalized Digital Health and Patient-centric Services. Lausanne, Switzerland. Frontiers Media SA; 2022.
    36. Gabrielli S, Rizzi S, Bassi G, Carbone S, Maimone R, Marchesoni M, et al. Engagement and effectiveness of a healthy-coping intervention via chatbot for university students during the COVID-19 pandemic: mixed methods proof-of-concept study. JMIR Mhealth Uhealth. May 28, 2021;9(5):e27965. [FREE Full text] [CrossRef] [Medline]
    37. Asensio-Cuesta S, Blanes-Selva V, Portolés M, Conejero JA, García-Gómez JM. How the Wakamola chatbot studied a university community's lifestyle during the COVID-19 confinement. Health Informatics J. May 27, 2021;27(2):14604582211017944. [FREE Full text] [CrossRef] [Medline]
    38. Van Baal ST, Le S, Fatehi F, Hohwy J, Verdejo-Garcia A. Cory COVID-bot: an evidence-based behavior change chatbot for COVID-19. Stud Health Technol Inform. Jan 14, 2022;289:422-425. [CrossRef] [Medline]
    39. Morales HM, Guedes M, Silva JS, Massuda A. COVID-19 in Brazil-preliminary analysis of response supported by artificial intelligence in municipalities. Front Digit Health. Jun 17, 2021;3:648585. [FREE Full text] [CrossRef] [Medline]
    40. Judson T, Odisho AY, Young JJ, Bigazzi O, Steuer D, Gonzales R, et al. Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic. J Am Med Inform Assoc. Jul 01, 2020;27(9):1450-1455. [FREE Full text] [CrossRef] [Medline]
    41. Morse KE, Ostberg NP, Jones VG, Chan AS. Use characteristics and triage acuity of a digital symptom checker in a large integrated health system: population-based descriptive study. J Med Internet Res. Nov 30, 2020;22(11):e20549. [FREE Full text] [CrossRef] [Medline]
    42. Zhu Y, Wang R, Pu C. "I am chatbot, your virtual mental health adviser." What drives citizens' satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China. Digit Health. Mar 30, 2022;8:20552076221090031. [FREE Full text] [CrossRef] [Medline]
    43. Lai L, Wittbold KA, Dadabhoy FZ, Sato R, Landman AB, Schwamm LH, et al. Digital triage: novel strategies for population health management in response to the COVID-19 pandemic. Healthc (Amst). Dec 2020;8(4):100493. [FREE Full text] [CrossRef] [Medline]
    44. Mateos-Sanchez M, Melo AC, Blanco LS, García AM. Chatbot, as educational and inclusive tool for people with intellectual disabilities. Sustainability. Jan 28, 2022;14(3):1520. [FREE Full text] [CrossRef]
    45. Driss M, Almomani I, Alahmadi L, Alhajjam L, Alharbi R, Alanazi S. COVIBOT: a smart chatbot for assistance and e-awareness during COVID-19 pandemic. In: Proceedings of the 2nd International Conference of Smart Systems and Emerging Technologies. Presented at: SMARTTECH '22; May 22-24, 2022, 2022;124-129; Riyadh, Saudi Arabia. URL: https://ieeexplore.ieee.org/document/9842423 [CrossRef]
    46. Raji VP, Maheswari PU. COVID-19 lockdown in India: an experimental study on promoting mental wellness using a chatbot during the Coronavirus. Int J Ment Health Promot. 2022;19:189-205. [FREE Full text] [CrossRef]
    47. Briel A. Toward an eclectic and malleable multiagent educational assistant. Comput Appl Eng Educ. Aug 31, 2021:1-11. [FREE Full text] [CrossRef]
    48. Hefny WE, Bolock AE, Herbert C, Abdennadher S. Applying the character-based chatbots generation framework in education and healthcare. In: Proceedings of the 9th International Conference on Human-Agent Interaction. Presented at: HAI '21; November 9-11, 2021, 2021;121-129; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3472307.3484172 [CrossRef]
    49. Fu C, Liu C, Ishi CT, Yoshikawa Y, Iio T, Ishiguro H. Using an android robot to improve social connectedness by sharing recent experiences of group members in human-robot conversations. IEEE Robot Autom Lett. Oct 2021;6(4):6670-6677. [FREE Full text] [CrossRef]
    50. Khattab M. Infobotting COVID-19: a case study of ask Nameesa in Egypt. J Afr Media Stud. 2021;13:451-476. [FREE Full text] [CrossRef]
    51. Hefny WE, Bolock AE, Herbert C, Abdennadher S. Chase away the virus: a character-based chatbot for COVID-19. In: Proceedings of the 9th International Conference on Serious Games and Applications for Health. Presented at: SeGAH '21; August 4-6, 2021, 2021;1-8; Dubai, United Arab Emirates. URL: https://ieeexplore.ieee.org/document/9551895 [CrossRef]
    52. Salhi I, Guemmat KE, Qbadou M, Mansouri K. Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation. J Electr Eng Comput Sci. Aug 01, 2021;23(2):1200. [FREE Full text] [CrossRef]
    53. Al-Madi N, Maria KA, Al-Madi MA, Alia MA. An intelligent Arabic chatbot system proposed framework. In: Proceedings of the 2021 International Conference on Information Technology. Presented at: ICIT '21; July 14-15, 2021, 2021;592-597; Amman, Jordan. URL: https://ieeexplore.ieee.org/document/9491699 [CrossRef]
    54. Valtolina S. Charlie: a chatbot to improve the elderly quality of life and to make them more active to fight their sense of loneliness. In: Proceedings of the 14th Biannual Conference of the Italian SIGCHI Chapter. Presented at: CHItaly '21; July 11-13, 2021, 2021;1-5; Bolzano, Italy. URL: https://dl.acm.org/doi/abs/10.1145/3464385.3464726 [CrossRef]
    55. Amer E, Hazem A, Farouk O, Louca A, Mohamed Y, Ashraf M. A proposed chatbot framework for COVID-19. In: Proceedings of the 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference. Presented at: MIUCC '21; May 26-27, 2021, 2021;263-268; Cairo, Egypt. URL: https://ieeexplore.ieee.org/document/9447652 [CrossRef]
    56. Ariga RA. Self care nursing: teleassessment nursing with chatbot application the coronavirus disease-19 pandemic period in North Sumatra. Open Access Maced J Med Sci. Dec 06, 2021;9(G):306-310. [CrossRef]
    57. Holt-Quick C, Warren J, Stasiak K, Williams R, Christie G, Hetrick S, et al. A chatbot architecture for promoting youth resilience. Stud Health Technol Inform. 2021:99-105. [FREE Full text] [CrossRef]
    58. Alkmim MB, Marcolino MS, de Oliveira CR, Borges IN, Cardoso CS, Rocha GM, et al. TeleCOVID-19: a multifaceted strategy from a public Brazilian telehealth service during the COVID-19 pandemic. Stud Health Technol Inform. 2021;19:1-10. [FREE Full text] [CrossRef]
    59. Kim RH. Development of a conversing and body temperature scanning autonomously navigating robot to help screen for COVID-19. arXiv. Preprint posted online June 6, 2021 [FREE Full text] [CrossRef]
    60. Du J, Huang W, Hew KF. Supporting students goal setting process using chatbot: implementation in a fully online course. In: Proceedings of the 2021 IEEE International Conference on Engineering, Technology & Education. Presented at: TALE '21; December 5-8, 2021, 2021;35-41; Wuhan, Chaina. URL: https://ieeexplore.ieee.org/document/9678564 [CrossRef]
    61. Aminuddin R, Noor HM, Ilias NF, Wahab NI. Framework for a mobile application with a chatbot to self-report injuries and carry out contact tracing for athletes and sports trainers. In: Proceedings of the 2021 IEEE Symposium on Industrial Electronics & Applications. Presented at: ISIEA '21; August 1-4, 2021, 2021;1-6; Langkawi Island, Malaysia. URL: https://ieeexplore.ieee.org/document/9509978 [CrossRef]
    62. Ito T, Sode TM, Masako S, Keisuke M. The online PBL (project-based learning) education system using AI (artificial intelligence). In: Proceedings of the 23rd International Conference on Engineering and Product Design Education. Presented at: E&PDE '21; September 9-10, 2021, 2021;1-6; Herning, Denmark. URL: https://tinyurl.com/5n7u6cpv [CrossRef]
    63. Chalaguine L, Hunter A. Addressing popular concerns regarding COVID-19 vaccination with natural language argumentation dialogues. In: Proceedings of the 16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Presented at: ECSQARU '21; September 21-24, 2021, 2021;59-73; Prague, Czech Republic. URL: https://link.springer.com/chapter/10.1007/978-3-030-86772-0_5 [CrossRef]
    64. Patgar T, Patel R, Girija S. Real conversation with human-machine 24/7 COVID-19 chatbot based on knowledge graph contextual search. In: Proceedings of the 16th International Conference on Information Processing on Data Science and Computational Intelligence. Presented at: ICInPro '21; October 22-24, 2021, 2021;258-272; Bengaluru, India. URL: https://link.springer.com/chapter/10.1007/978-3-030-91244-4_21 [CrossRef]
    65. Oruche R, Gundlapalli V, Biswal AP, Calyam P, Alarcon ML, Zhang Y, et al. Evidence-based recommender system for a COVID-19 publication analytics service. IEEE Access. 2021;9:79400-79415. [CrossRef]
    66. Pandey A, Janghel RR, Sujatha R, Kumar S, Kumar TS, Chatterjee JM. Coronago website integrated with chatbot for COVID-19 tracking. In: Proceedings of the 2021 International Semantic Intelligence Conference. Presented at: ISIC ’21; February 25-27, 2021, 2021;521-527; Delhi, India. URL: https://ceur-ws.org/Vol-2786/Paper62.pdf
    67. Hossain ME, Najib AU, Islam MZ. Combating domestic violence during COVID-19 pandemic in Bangladesh: using a mobile application integrated with an effective solution. In: Proceedings of the 23rd International Conference on Computer and Information Technology. Presented at: ICCIT '20; December 19-21, 2020, 2020;1-6; Dhaka, Bangladesh. [CrossRef]
    68. Erazo WS, Guerrero GP, Betancourt CC, Salazar IS. Chatbot implementation to collect data on possible COVID-19 cases and release the pressure on the primary health care system. In: Proceedings of the 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference. Presented at: IEMCON '20; November 4-7, 2020, 2020;0302-0307; Vancouver, BC. URL: https://ieeexplore.ieee.org/document/9284846 [CrossRef]
    69. Maniou TA, Veglis A. Employing a chatbot for news dissemination during crisis: design, implementation and evaluation. Future Internet. Jun 30, 2020;12(7):109. [CrossRef]
    70. Bharti U, Bajaj D, Batra H, Lalit S, Lalit S, Gangwani A. Medbot: conversational artificial intelligence powered chatbot for delivering tele-health after COVID-19. In: Proceedings of the 5th International Conference on Communication and Electronics Systems. Presented at: ICCES '19; July 20-22, 2019, 2020;870-875; Coimbatore, India. URL: https://ieeexplore.ieee.org/document/9137944 [CrossRef]
    71. Bahja M, Abuhwaila N, Bahja J. An antenatal care awareness prototype chatbot application using a user-centric design approach. In: Proceedings of the HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence: 22nd HCI International Conference. Presented at: HCII '20; July 19-24, 2020, 2020;20-31; openhagen, Denmark. URL: https://dl.acm.org/doi/abs/10.1007/978-3-030-60117-1_2 [CrossRef]
    72. Gross C, Kohlbrenner D, Clarenbach CF, Ivankay A, Brunschwiler T, Nordmann Y, et al. A telemonitoring and hybrid virtual coaching solution "CAir" for patients with chronic obstructive pulmonary disease: protocol for a randomized controlled trial. JMIR Res Protoc. Oct 22, 2020;9(10):e20412. [FREE Full text] [CrossRef] [Medline]
    73. Meinert E, Milne-Ives M, Surodina S, Lam C. Agile requirements engineering and software planning for a digital health platform to engage the effects of isolation caused by social distancing: case study. JMIR Public Health Surveill. May 06, 2020;6(2):e19297. [FREE Full text] [CrossRef] [Medline]
    74. Rodrigo M. Towards building mental health resilience through storytelling with a chatbot. In: Proceedings of the 29th International Conference on Computers in Education. Presented at: ICCE '21; November 22-26, 2021, 2021; Virtual Event. URL: https://icce2021.apsce.net/wp-content/uploads/2021/12/ICCE2021-Vol.II-PP.-569-574.pdf
    75. Singh S, Kaur M, Tanwar P, Sharma S. Design and development of conversational chatbot for COVID-19 using NLP: an AI application. In: Proceedings of the 6th International Conference on Computing Methodologies and Communication. Presented at: ICCMC '22; March 29-31, 2022, 2022;1654-1658; Erode, India. URL: https://ieeexplore.ieee.org/document/9753893 [CrossRef]
    76. Fagbolu O, Sellu M, Fagbolu TA. Overview of interactive chatbot for modelling, predicting and reporting COVID-19 in Mano river union. Lond J Res Comput Sci Technol. Nov 2021 [FREE Full text]
    77. Ilievski V, Musat C, Hossmann A, Baeriswyl M. Goal-oriented chatbot dialog management bootstrapping with transfer learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. Presented at: IJCAI '18; July 13-19, 2018, 2018;4115-4121; Stockholm, Sweden. URL: https://www.ijcai.org/proceedings/2018/0572.pdf [CrossRef]
    78. Amiri P, Karahanna E. Chatbot use cases in the COVID-19 public health response. J Am Med Inform Assoc. Apr 13, 2022;29(5):1000-1010. [FREE Full text] [CrossRef] [Medline]
    79. Cai D, Li H, Law R. Anthropomorphism and OTA chatbot adoption: a mixed methods study. J Travel Tour Mark. May 05, 2022;39(2):228-255. [CrossRef]
    80. Chaves AP, Gerosa MA. How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. Int J Hum Comput Int. Nov 08, 2020;37(8):729-758. [CrossRef]
    81. Duijst D. Can we improve the user experience of chatbots with personalisation. University of Amsterdam. 2017. URL: https:/​/www.​scribd.com/​document/​413277603/​Can-We-Improve-the-User-Experience-of-Chatbots-With-Personalisation -Master-Thesis-Final [accessed 2023-12-05]
    82. Fadhil A. Beyond patient monitoring: conversational agents role in telemedicine and healthcare support for home-living elderly individuals. arXiv. Preprint posted online March 3, 2018 [FREE Full text]
    83. Dohsaka K, Asai R, Higashinaka R, Minami Y, Maeda E. Effects of conversational agents on activation of communication in thought-evoking multi-party dialogues. IEICE Trans Inf Syst. 2014;E97.D(8):2147-2156. [CrossRef]
    84. Parviainen J, Rantala J. Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care. Med Health Care Philos. Mar 04, 2022;25(1):61-71. [FREE Full text] [CrossRef] [Medline]

    ‎
    AI: artificial intelligence
    COOPERA: COVID-19: Operation for Personalized Empowerment to Render Smart Prevention and Care Seeking
    NLP: natural language processing
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    WHO: World Health Organization

    Edited by T Leung, V Arnold, T Purnat, H Gouda; submitted 30.09.22; peer-reviewed by Z Zandesh, M Rampioni, B Xie; comments to author 23.11.22; revised version received 28.02.23; accepted 11.07.23; published 05.01.24.

    Copyright

    ©Wendell Adrian Lim, Razel Custodio, Monica Sunga, Abegail Jayne Amoranto, Raymond Francis Sarmiento. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.01.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.