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/lp/jmir-publications/designing-developing-evaluating-and-implementing-a-smartphone-hk070mXGwM
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- JMIR Publications
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- Copyright © The Author(s). Licensed under Creative Commons Attribution cc-by 4.0
- ISSN
- 2291-5222
- DOI
- 10.2196/38740
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Abstract
Background: Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation. Objective: The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care. Methods: We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study. Results: The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al considerations—user-centered design and privacy and security—that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes. Conclusions: Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns. (JMIR Mhealth Uhealth 2022;10(10):e38740) doi: 10.2196/38740 KEYWORDS conceptual framework; conversational agent; chatbot; mobile health; mHealth; digital health; mobile phone contrast, AI algorithms, particularly neural networks, may Introduction develop decisions that are not explainable or understood by the end user, a phenomenon referred to as the black box [17]. In Background health care settings, the black box effect may lead to biased or Conversational agents (CAs) are computer programs that use erroneous decision-making and patient harm [18], which may text, speech, and other input modalities to enable communication limit the use of AI. A new field of explainable AI is currently with users [1]. They can be accessed through a variety of ways, emerging that aims to provide justification for algorithm such as social media platforms (eg, Facebook Messenger), predictions and increase system transparency, although the websites, and smartphone apps, or deployed using stand-alone validity of results for individual patients should be carefully digital devices (eg, Alexa, Google Assistant, and Siri). The considered [19]. interactive nature of CAs makes them acceptable to a diverse CAs can be deployed using a variety of digital devices, including group of users [2-4] and a preferred tool in a number of smartphones. The widespread availability of smartphones in disciplines, including customer service, retail, and e-commerce high-income countries and increasingly in low- and [5-7]. middle-income countries [20] makes them an ideal interface to In health care, CAs are increasingly used to assist in various deliver CA interventions. Smartphones offer users the possibility tasks, such as patient education, self-management of chronic of continuous and dynamic monitoring of health conditions in conditions, and routine task automation (eg, appointment a private space and at the time of their convenience [21] not booking), and support health professionals’ decision-making only of subjective, self-reported data but also of objective, for diagnosis and triage [3,8-10]. More recently, CAs have seen sensor-based data. Furthermore, smartphones allow for the large-scale implementation with the introduction of Babylon’s delivery of interventions according to user needs [22]. CA artificial intelligence (AI)–based symptom checker CA to the interventions are complex and often require lengthy, costly UK National Health Service and to Rwanda’s National Health design and development processes led by multidisciplinary Insurance Scheme [11]. CAs have the potential to support health teams of health care professionals, computer scientists, and app care delivery, improve access to health care services, and developers, which may limit the number of teams able to engage automate tasks [12], and they may also reduce health in CA development, particularly in low- and middle-income professionals’ workload [13]. countries. However, mobile health (mHealth) interventions, particularly SMS text messages delivered using mobile phones, CAs vary in complexity and capability. There are 3 design are effective in delivering health care interventions, especially dimensions used to classify CAs: purpose, communication in low-resource settings [23,24]. channels, and response generation architecture [6]. According to purpose, CAs can be classified into task- or goal-oriented Several frameworks for the design and development of mHealth CAs, which respond to a limited number of tasks within a interventions currently exist, offering guidance at every step of prespecified domain, or non–task- or non–goal-oriented CAs, the cycle, from the conceptualization of user needs [25,26] to which are potentially able to respond to an unrestricted variety the development of the digital health intervention [25-27]. These of user requests [6]. Communication channels can commonly frameworks focus on generic, app-based interventions without be divided into 2 main types: text-based or voice-based CAs. a conversational interface. However, Zhang et al [28] described Response generation architecture can be broadly classified into a framework for the development of AI-based CAs to deliver 3 groups: rule-based and retrieval-based CAs, which produce behavior change interventions that may require significant a response by selecting it from a pool of predetermined deployment of resources, including a large, multidisciplinary responses either following simple rules to match phrases or team, and close supervision of the AI algorithms to prevent identifying specific keywords in the text [6,14,15], and unintended and potentially harmful effects on the users. generative-based CAs, which use AI algorithms to develop a However, to date, no conceptual framework for the design, contextual response informed by the system’s previous and development, and evaluation of rule-based CAs has been ongoing learning [6,14-16]. Although all 3 groups may involve published despite a growing interest in the use of CAs in health the use of AI algorithms [6], rule-based CAs allow developers care settings. greater control over the conversation content and flow, which is a useful feature when developing CAs for health care. By https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al implementation of smartphone-delivered, rule-based, Objectives goal-oriented, and text-based CAs for health care. CAs constitute a specific type of digital intervention characterized by the use of a conversational interface, often led Methods by an agent with a distinct personality as evidenced by its tone of speech, method of interaction, and visual representation, We developed the Designing, Developing, Evaluating, and which is often associated with higher levels of engagement with Implementing a Smartphone-Delivered, Rule-Based the user. These features and the ubiquity of smartphones support Conversational Agent (DISCOVER) conceptual framework the need for a framework that is accessible to large as well as according to the methodology described by Jabareen [29], smaller research teams with limited resources to guide CA consisting of the iterative, qualitative analysis of development, including the distinct design and development multidisciplinary data based on the grounded theory method. It challenges of CAs such as the creation of dialogs and the look comprises 8 interlinked steps aimed at integrating and analyzing and personality of the agent, grounded in current best evidence. the data and developing and validating the conceptual Therefore, this research aimed to develop a conceptual framework [29] (Figure 1). framework for the design, development, evaluation, and Figure 1. The 8 phases of the methodology by Jabareen [29] for conceptual framework development. studies. This step involved grouping the concepts extracted from Step 1 both literature reviews into overarching domains. We conducted 2 literature reviews. The first review aimed to Step 5 and Step 6 summarize the current literature on conceptual frameworks for the design, development, and evaluation of mHealth The next 2 steps involved linking the overarching domains and interventions, and the second review focused on developing the first iteration of the conceptual framework. smartphone-delivered, rule-based CAs. A description of these Step 7 and Step 8 literature reviews can be found in Multimedia Appendix 1 The conceptual framework was further amended based on [5,30-62] and Multimedia Appendix 2 [5,30-62]. Multimedia discussions among the research team members and feedback Appendix 3 presents the search strategy used to retrieve the from colleagues collected in a seminar. We subsequently applied studies for the review of CAs. the conceptual framework to develop a rule-based, text-based, Step 2 and Step 3 smartphone-delivered CA prototype (Precilla) designed to The screening of retrieved citations was performed in 2 stages, support healthy lifestyle changes and educate participants about independently and in parallel, by DD and LM. The same 2 diabetes. The development, feasibility, and acceptability of reviewers extracted data from all the included studies Precilla have been reported elsewhere [63,64]. independently and in parallel. At all stages of screening and The feedback received from team members and colleagues and data extraction, the results were compared, and discrepancies the lessons learned during the application study led to the were resolved by consensus between the reviewers. refinement of concepts and domain labels, definitions, order, and grouping that were derived in the current version of Step 4 DISCOVER presented in this paper. The data analysis followed qualitative meta-synthesis to systematically summarize the findings across all the included https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al [63,64] presents the steps to develop CA Precilla mapped to the Ethical Considerations steps of the current version of the conceptual framework. This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2018-11-032). The 2 literature searches retrieved a total of 55 studies, of which 41 (75%) described conceptual frameworks for the design, Results development, and evaluation of mHealth interventions and 14 (25%) were clinical trials evaluating smartphone- and rule-based A Framework for Guiding the Design, Development, CAs. The findings from these reviews are presented in Evaluation, and Implementation of Multimedia Appendices 1 and 2. The “Characteristics of Smartphone-Delivered, Rule-Based CAs in Health included studies” tables are presented in Multimedia Appendix Care: Overview 6 [47-58], Multimedia Appendix 7 [5,32,65-67], and Multimedia Appendix 8 [3,30,31,33,34,68-80]. The conceptual framework development was informed by the 2 literature reviews and iterative consultations within the The initial framework contained 8 steps. They were subsequently research team. Further refinements were also informed by the condensed into 5 steps augmented by 2 overarching themes development of our CA prototype (Precilla) [63,64] as well as relevant to all phases of the development process. Further by presentations at clinical seminars and conferences. refinements led to the framework presented in this paper Multimedia Appendix 4 outlines the methodology applied in consisting of an iterative process of design, development, the development of the DISCOVER framework according to evaluation, and implementation steps, each comprising several each step described by Jabareen [29]. Multimedia Appendix 5 components, as presented in Figure 2 and described in the following sections. Figure 2. The DISCOVER conceptual framework for the design, development, and evaluation of rule-based, smartphone-based conversational agents in health care. evaluation—completing a thorough needs assessment, defining Step 1: Design the aim, and characterizing the end user and objectives—which, The first stage comprised 4 interlinked steps encapsulating the in turn, determine the parameters to be tested and reported. The initial conceptual work of identifying the health care focus of CA goal was described in 64% (35/55) of the papers in our the CA, target users, multidisciplinary team members, and the reviews [3,5,25-28,65,68-76,81-99]. CA delivery interface. Needs Assessment Defining the Goal The design process should commence with an in-depth needs assessment to understand existing gaps that may be filled by Overview the CA. These may be informed by a literature review A clearly defined goal is the first step in the design process and [83,90,91,96,100] to assess potential research areas and the the foundation that will guide the development and evaluation needs and challenges of the target population, including not of the CA. This step consists of 3 interlinked areas of https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al only patients but also caregivers, health care providers (HCPs), Studies have consistently shown that CAs displaying empathy, and other experts [25,26,87,89,95,98]. Researchers should also relational behavior, and self-disclosure enhance the user involve end users in this initial phase by using surveys and a experience [78,105] and increase the working alliance [109]. variety of qualitative methods [70,87] such as in-depth Conversely, users would notice if the CA did not convey interviews and focus group discussions to gather their views. empathy [69]. The Aim Acceptability may be further enhanced if the CA design acknowledges the specific cultural or demographic traits of the Aligned with the needs assessment, the design team should target population [73] or offers options to personalize the formulate clear, attainable, and relevant objectives to drive the interface (eg, offering a male and female persona) [31,73]. CA design and development process. It is important to consider Alternatively, CAs may explicitly disclose their identity [70] the CA temporal profile, which characterizes 4 types of CAs to reduce user expectations about their capabilities. Finally, CA according to the type and frequency of CA-user dialogs [101]. personality should align with its intended function. For example, The CA temporal profile will also determine the type of health care CAs often display one of two personality types: a objectives included, broadly classified as short term or long more approachable, empathic coach-like personality, particularly term [101]. A short-term objective refers to an outcome to be if delivering behavior change interventions [77] supporting completed as soon as the interaction with the CA ends, such as self-management of chronic disorders [73,78] and mental health medication reminders [30]. A long-term goal would involve conditions [3], or a health care professional persona to several CA-user interactions being completed over a period, as emphasize the legitimacy of the CA and its content [1]. in mental health interventions to promote mental well-being in the general population [3] or young people with cancer [70]. Tone and Language Complex CA interventions may include short- and long-term The language recommended for text-based interventions should goals, such as CA Vik [30] providing medication reminders be encouraging, positive, friendly, polite, and light-hearted and (short-term goal) and health education (long-term goal) to may include light humor while at the same time being formal patients with breast cancer. Furthermore, Kowatsch et al [73] [110]. To maintain the flow of the conversation, it may be used prompts and reminder SMS text messages to enhance advisable to use visual cues such as successive moving dots children’s discipline and routine, which are essential for the signaling that the CA is “typing” the next message. self-management of asthma. The text should be written in clear, short sentences using simple Determining the End User language and avoid scientific jargon. The National Institutes of The next important design consideration is to determine the Health recommends that patient education materials be written target population. An initial assessment should establish whether at or below the sixth-grade reading level (ages of 11 and 12 the CA will be offered to healthy users or individuals with a years) to reach a diverse range of individuals with varying levels specific medical condition, caregivers, or HCPs. It is important of literacy [111]. The readability of the text can be assessed to generate a detailed and accurate portrayal of the target user, using a scale such as the Flesch-Kincaid grade level to determine including gender, age group, cultural beliefs and socioeconomic its suitability [112]. Furthermore, the CA should use the target concerns, digital and health literacy, access to digital devices, population’s native language in its communications [75] and, and smartphone penetration rate. If the intervention is if needed, the conversational content may be translated to one educational, a knowledge test should be implemented [73]. The or more languages, particularly if the CA will be deployed in acceptability of CAs by the target population and the perceived multiethnic, multilanguage societies. risk of using a CA for health care matters should be evaluated, With regard to the tone of the conversation, despite the particularly for severe or highly stigmatizing conditions [102] text-based nature of the CA, it may be advisable to simulate such as mental health disorders [103,104]. more casual, verbal speech while avoiding the use of “textese” Creating the CA Identity [113], a form of abbreviated written or typed language characterized by unconventional spelling and grammar (eg, This step involves determining the CA’s name, appearance, “tonite” instead of “tonight”) and abbreviations and contractions tone of communication, language, and other characteristics that (eg, “pls” instead of “please” or “wanna” for “want to”) [114]. define its identity. This step was discussed in 25% (14/55) of Furthermore, words written in full capital letters should be the papers in our reviews [5,31,32,66,69,70,72-78]. avoided as they equate to shouting [110]. CA Personality Emojis may be used to articulate emotions or other expressions User interaction with CAs appears to be enhanced when the CA more efficiently than text [70]. However, emojis are vulnerable displays a well-defined, positive, and empathic personality to varied interpretations across cultures and contexts and should [105,106]. In general, giving a name and profile picture to the be used mindfully. Fadhil et al [115] noted a context-specific CA may enhance its social presence and user acceptance [107], nature of emojis whereby they increased efficacy in a mental although its effect appears to be small [106]. In health care health intervention but did not help in promoting physical settings, using a human-like avatar rendering realistic features, well-being. including medical attire, may increase user satisfaction [105], although avatars displaying highly realistic features may upset CAs designed to address sensitive topics such as HIV and AIDS, users and decrease engagement, an experience referred to as sexually transmitted infections, or mental health disorders may the “uncanny valley” [108]. emphasize the confidential nature of the messages or include https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al code words to protect users’ privacy. This is particularly relevant Communication Modalities in low- and middle-income settings, where family members Aligned with the framework focus, text would be the CA’s main may share a single smartphone [116]. input and output modality. Messages should be brief, fit the mobile screen without scrolling [69], and be of an adequate font Selecting the Delivery Interface size to allow for comfortable reading. Moreover, if the CA Human Involvement targets populations for whom reading might be challenging, such as older adults or visually impaired individuals, Conditional to the CA’s aim, the design and development team text-to-speech assistive technology may be incorporated into may consider a “hybrid” intervention where the interaction with the app. the CA would be complemented by regular interactions with HCPs offering timely feedback on a self-management technique Visual aids such as images or videos are useful to adapt content or regular support and motivation [33,72,73]. Alternatively, as to audiences with lower educational attainment [121], deliver presented in the study by Stasinaki et al [79], the CA may be personal narratives relevant to the end users (eg, young people fitted with multiple channels, where the user can converse with with cancer), or decrease the amount of textual information the CA in one channel and directly with an HCP in another. [76]. When using multimedia content, it is important to use high-resolution files to avoid pixelated or blurred images. Peer support is recognized to play an important role in Furthermore, if pictures are obtained from the web, developers promoting adherence to self-management interventions [117] should abide by copyright regulations and either source the and a further point of human involvement to be considered. The pictures from free stock photo repositories, acquire the image CA intervention may include an additional communication rights, or produce the images in-house. channel for users to interact, share experiences, and receive peer support. For example, Wang et al [75] developed a WeChat Assembling a Multidisciplinary Team intervention to support smoking cessation where the CA not The composition of the design and development team would only responded to individual users but also acted as a group be based on the objectives of the intervention. In addition to the moderator. inclusion of health professionals with the relevant expertise, it Delivery Channel is recommended to include end users as well [69,70]. For CAs may be delivered through a variety of channels, such as example, a CA to support a lifestyle intervention in overweight stand-alone apps [3,73] and existing messaging platforms adolescents was developed by a multidisciplinary team including [68,71,75] such as Facebook Messenger, Telegram, WeChat, computer scientists, physicians, a psychotherapist, and diet and and WhatsApp, or embedded in a website [69]. Each channel sports experts [72]. End-user involvement in the intervention possesses its own set of complexities, and the decision regarding design is critical to ensure that it aligns with user needs. User the delivery channel should be based on the target population involvement was reported in a large number of studies in our needs and the expertise of the CA development team [118]. If review (36/55, 65%); for example, young people with cancer the research team does not include app developers or computer participated in focus groups to refine the content of a CA aimed scientists, the CA may be embedded in a messaging platform at delivering positive psychology to enhance well-being [70], or may be developed using a CA development platform that and young patients with asthma and their parents were part of offers templates or other design solutions for individuals with a multidisciplinary team of experts who developed a CA to no previous programming knowledge [5,118], such as Chatfuel, improve cognitive and behavioral skills [73]. In general, studies ManyChat, and others. CAs are generally web-based, and some that mentioned the composition of their multidisciplinary teams of these platforms are free of charge. Alternatively, if the team often reported computer scientists and physicians as key expertise or project budget allows, the CA may be delivered members [72-75], although other health professionals such as through a stand-alone app. This approach offers design physiotherapists [78], psychologists [3], and music therapists flexibility, such as a variety of data collection sources including [76] may be included as well. smartphone sensors, health programming interfaces, connected Step 2: Development medical devices, and patient self-reported data [119]. The combination of subjective patient reports with objective, Developing the Content real-time data may reduce users’ responsibility to update their Content development may involve determining the sources of progress and at the same time receive relevant, dynamic information, adapting content to the target audience, defining coaching based on the current data [120], which in turn may the behavior change theories and techniques guiding the increase adherence to the intervention. intervention [28,94], and establishing error management and safety-netting strategies [26-28, 30, 67-70, 73, 75, 77, 79, 80, In addition, factors associated with the target population may 82, 83, 85, 87, 90, 92-97, 99, 100]. also affect the selection of the most suitable delivery channel and operating system (eg, Android or Apple’s iOS). For Evidence-Based Information example, Kamita et al [71] implemented their CA on the All health-related information included in the CA should be messaging platform “LINE” as it was the most popular social derived from reputable sources and adequately referenced. network service in Japan, and Wang et al [75] selected WeChat, Sources of evidence-based information include comprehensive the most common messaging app in Hong Kong. literature reviews; clinical practice guidelines; Cochrane systematic reviews; and reputable organization websites such https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al as the World Health Organization, MEDLINE Plus, and the reminders to share positive results and progress, and fixed Centers for Disease Control and Prevention in the United States answers to frequently asked questions or requests. or the National Health Service Health A to Z in the United Behavior Change Theories Kingdom [65]. For example, Kowatsch et al [73] used evidence CAs are increasingly used to promote behavior change [1,129]. from multiple sources such as published literature on the Behavior change interventions are complex [130] and often improvement of asthma management in children [122], comprise one or more behavior change techniques (BCTs) to technology acceptance research [123], and user-CA working induce change. In our assessment, 4% (2/55) of the studies used alliances [124] to inform their intervention for asthma a behavior change theory to guide the intervention design, management. including the Health Action Process Approach [78] and the Managing Errors technology acceptance model [71]. In addition, 13% (7/55) of Another important aspect of content development is to ensure the studies [31,72-75,77,80] reported the use of specific BCTs an adequate understanding of user requests, particularly for such as goal setting, self-monitoring, tracking and feedback, potentially serious or life-threatening health conditions. social support, use of rewards, and anticipated regret. Safeguards to be implemented within the dialog include the For example, a study described a multicomponent behavior request for clarification if the CA receives an unfamiliar input change intervention incorporating several BCTs, such as goal or directing the user to contact an HCP or a human administrator setting, self-monitoring, stimulus control, and behavioral [125,126]. These strategies were included in TensioBot, an contract, to support a healthy lifestyle for adolescents with intervention to facilitate self-measurement of blood pressure obesity [34,79]. Furthermore, including group chats where peers where, after obtaining confirmation of a blood pressure or HCPs offer relevant information and emotional support may measurement value outside the normal range, the CA alerted also assist in promoting positive behavior change, such as using the attending physician [68]. Important strategies to manage a CA-led WeChat peer group to promote smoking cessation unintended errors include using validated data entry fields; [75]. limiting the data input to predetermined number ranges, words, or characters; or including predefined options for the user to Optional Add-ons select. Depending on the purpose of the CA, it may be appropriate to integrate data from external devices such as glucometers [131] Safety Netting or activity trackers [119]. Alternatively, access to smartphone In general, health care CAs should include a disclaimer clearly sensor data [132] may facilitate passive monitoring of the user’s stating that the intervention “does not replace healthcare activity [79] or determine novel digital biomarkers to assess the provider’s advice.” Furthermore, in the case of health conditions user’s mood [133] or disease status [134]. The use of smartphone associated with rapid deterioration of patient status leading to sensors for passive monitoring may further allow for real-time medical emergencies, such as cardiovascular conditions, information sharing with HCPs, caregivers, or peers, a feature diabetes, chronic pulmonary disorders, or mental health that may be particularly useful to monitor older people living conditions that increase the risk of suicide, information should alone, who may be at higher risk of falling, or individuals with be included to assist users in managing an emergency situation, severe chronic illnesses and multiple hospital admissions. such as the provision of emergency services or crisis helpline telephone numbers [127], links to contact their primary Building the Conversation Flow physician, or clear advice on first aid treatments such as offering A good CA is eloquent and knowledgeable and, thus, requires a sugary drink to manage a hypoglycemic event in a person a meticulously crafted script. Conversation flow building was with diabetes [128]. discussed in 35% (19/55) of the papers in our literature search [3,27,28,30-32,65,73,78,79,82,85,87,92-96,99]. Types of Messages The content and style of the messages should be aligned with Providing Suitable Answer Options the health condition and CA aim. Broadly, the messages may For a good conversation flow, the predefined answer options be educational [30,78] or motivational [34,77,79] or deliver should be sufficient and appropriate to align with the user intent, reminders to perform a self-management task [68], input data defined as the user goals or intentions in each conversation turn. [77], comply with preset tasks [73], take a medication, or attend Constructing a mind map outlining the different facets associated an HCP appointment [68]. For CAs tasked with engaging with with a topic (eg, medication adherence) and the likely the user during clinic visits, it may be useful to include a status influencing factors (lifestyle components or emotional state) report or summary of the consultation [126]. would help predict the most relevant answer options to provide to the user [135]. CAs assuming a coach-like persona might emphasize sympathy, empathy, and participants’ achievements [78]. Interventions Selecting a Mapping Tool attempting to modify users’ behavior may deliver messages A mind map is a diagram representing concepts, ideas, or tasks with higher emotional content, as reported in the study by generated from a key concept, which is generally represented Carfora et al [80], where only emotional messages led users to in the center of the graph [136]. Mind maps are an effective reduce red meat consumption. In addition, the Wang et al [75] method of brainstorming [137] that can be applied to building CA used 4 types of messages to deliver a smoking cessation the conversation flows. Several web-based programs and intervention: group announcements, health-related information, platforms are available to organize the conversation flow, https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al including tools specifically designed to build the CA larger development team that includes computer scientists and conversation, such as SAP Conversational AI [138] or app developers. MobileCoach [35]. Conversation flows may also be built using Using Engagement Strategies nonspecific mind mapping software such as Xmind [139]. Mind Strategies to keep the users engaged for the intended duration mapping is useful to assist in recording the flow of conversations of the intervention are particularly important in health care between different topics or different user interactions. A settings. These aspects were discussed in 11% (6/55) of the well-constructed conversation flow leads the conversation, studies in our reviews [3,30,31,73,78,79]. Reported strategies guides the user, and can address all relevant questions about its included notifications, weekly summaries, reminders, purpose. Furthermore, interactivity, personalization, and motivational statements, persuasive techniques, a high frequency consistent messaging have been noted as valued qualities [140]. of messages to promote habit formation, and daily Personalizing Content and Delivery encouragement. In addition, CA-specific engagement strategies Interventions should be tailored to individual participant needs included building rapport and attachment with the user [72,73] [110]. When compared with generic CAs, context, situational, or adding gamified components to incentivize CA use for or individually aware agents promote a more positive user rewards and points [73,79]. experience [132]. Personalized interventions include addressing Step 3: Evaluation and Implementation the user by their name or nickname [141]; delivering notifications and reminders tailored to individual needs [110], Evaluation such as medication or appointment reminders; and notifications The evaluation of digital interventions, including CAs, starts for missed activities or unread messages [30,78]. For example, early in the development process and comprises several iterative an intervention promoting self-management of chronic pain steps. To ensure the validity of the results, the process must use offered personalized content based on the user’s type and a robust methodology that is adequate for the intervention design duration of pain and personal interests [78]. [15]. In digital health interventions, a commonly used evaluation methodology is the multiphase optimization strategy by Collins An important caveat involves the design of interventions et al [145,146]. offering personalized advice based on user measurements, such as suggesting a treatment based on individually reported data The CA evaluation follows 3 distinct stages representing the (eg, blood glucose levels or blood pressure readings), as these intervention development process. The initial iterations of the interventions may require regulatory oversight and be considered CA may be evaluated using one or more usability testing a “mobile medical application” [142]. methods [147] aiming to produce a minimum viable prototype. Once this working prototype is ready, pilot and randomized Selecting Appropriate Message Timing and Frequency trials may ensue to assess the effectiveness of the CA [148]. The timing and frequency of messages are important Several aspects of CA evaluation were discussed in 36% (20/55) components when planning the intervention and may be of the studies in our reviews [25, 26,28, 33, 71,73, 83, 85, 86, determined by the intervention scope as well as user preference. 88, 89, 91-95, 97-100]. Earlier studies on SMS text messaging interventions have suggested a preference for weekly messaging [113]. However, The evaluation design may include one or more aspects of the different intervention types may require a more adaptive CA functionalities, including clinical or technical attributes and message delivery system, such as smoking cessation programs user experience. The outcomes should be clearly defined and that often require an increased volume of messages close to the include widely used and validated outcome measurement tools desired quit date [143] or high-risk behavior prevention whenever possible to improve the comparability and programs targeting binge drinking or inappropriate sexual reproducibility of the research results. Examples of outcome behaviors timing their messages to when the risky behavior is measurement tools include the Patient Health Questionnaire-9 expected to occur, for example, on a Friday night [116,141]. [149] to screen for depression, the Flourishing Scale [150] to Therefore, strategies for message delivery and frequency could assess psychological well-being, the Brief Pain Inventory [151] be adapted to suit the CA intervention. to assess pain intensity and its interference in activities of daily living, and the Working Alliance Inventory-Short Revised [152] Just-in-time adaptive interventions (JITAIs) leverage smartphone to evaluate the CA-user working alliance. sensor data to “provide the right type (or amount) of support at the right time” [22]. Smartphone sensor data would determine Usability Testing and even predict “states of vulnerability” (susceptibility to The evaluation of the CA should start early in the development negative health outcomes) [144] and “states of receptivity” (the cycle [153]. In the initial stages, formative evaluation aims to capacity to receive, process, and use the intervention) [120] in assess the viability of the digital tool by assessing its usability, the user when the intervention may be required and more useful. usefulness, and user experience [154] using one or more This novel approach may be particularly useful for behavior qualitative or quantitative research designs. Qualitative methods change interventions supporting a healthy lifestyle, such as include surveys, interviews, focus group discussions, and “think increasing physical activity or adhering to a healthier diet, or aloud” protocols [147] in which users express their opinions supporting substance use remission [22,120]. Nevertheless, about the product as they use it. Quantitative methods include researchers considering this approach should take into account closed-ended questionnaires, task completion assessments, and human and economic resources as JITAI design may require a A/B testing [147,155]. An A/B test, split test, or controlled https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al experiment compares two or more versions of a product to of screens opened if the CA also includes other functions [70]. evaluate the intervention components that perform better or are Chaix et al [30] measured use duration, interest in various preferred by the user [155]. This stage relates to the screening educational contents, and level of interactivity as indicators of and confirming stages in the multiphase optimization strategy engagement. Nevertheless, researchers should consider the [145,146], which use a fractional factorial design to assess which challenges of defining engagement with digital interventions, components should be included in the digital intervention and which may include other user-related variables such as the the best dosages to use in a more cost-effective fashion. Finally, severity or stage of the disease as well as the long-term microrandomized trials are another novel methodology that is engagement with the CA [163]. particularly useful for assessing and optimizing the delivery of Other aspects of CA use, such as underused or missing topics JITAIs [156]. Microrandomized trials allow the randomization or CA functionalities not working as intended, may also be of multiple components to occur at multiple times triggered by assessed. CA use analytics are often embedded in host platforms. predefined decision points [156] and have been used to evaluate Commercial platforms such as ManyChat [164] may offer a CA interventions, as reported by Kramer et al [119,157]. variety of built-in analytics tools such as the number of times the CA is accessed. Some of these platforms offer free-of-charge Efficacy and Effectiveness of the CA Intervention services. For health care CAs, the open-source MobileCoach Once initial evaluations have determined the components that platform [35] offers flexible, customizable use analytics. should be included in the intervention and the frequency of administration, a traditional randomized trial design should be Qualitative Evaluation implemented to assess the effectiveness of the CA intervention Acceptability refers to the “affective attitudes towards a new compared with current best practices [145,146,148]. Given the digital health intervention” [165]. It is a dynamic concept complexities and cost that a full-powered randomized controlled comprising the intention to engage with the novel CA, the actual trial often entails, researchers may consider conducting a pilot interaction with the CA, and the postengagement satisfaction study to refine the study methodology or assess the feasibility [165]. of the study design and participant recruitment strategies, among Acceptability is a subjective term that is generally assessed other aspects [158]. For example, Casas et al [77] conducted a using questionnaires or other qualitative methods such as focus pilot study to preliminarily assess a CA aimed at coaching groups or interviews. For example, Kowatsch et al [73] participants to make healthier food choices, whereas Greer et evaluated the acceptance of a CA to support asthma al [70] evaluated a CA delivering a positive psychological self-management using a 7-point Likert scale (strongly intervention to young people with cancer. agree-strongly disagree) for perceived usefulness, ease of use, User Engagement and Acceptability enjoyment, and use intention, and Echeazarra et al [68] used a survey with questions on ease of use, preference for the CA Overview over existing methods, CA usefulness for its intended purpose, Digital health interventions often report high rates of participant and whether the user had stopped using it as measures of attrition, which may limit the validity of research findings and, acceptability and satisfaction. Furthermore, Gabrielli et al [69] more importantly, the effectiveness of the intervention. facilitated a participatory design workshop where suggestions Therefore, the assessment of the CA-led intervention should be for improvement were provided via open-ended questions, and complemented by regular evaluations of end-user adherence to Ly et al [3] conducted semistructured interviews on the benefits, as well as engagement with and acceptability of the intervention. opportunities, and challenges associated with the CA for mental Several assessment methods are commonly used, including health. Yan et al [166] described a very involved process of quantitative, data-driven analyses and qualitative assessments evaluation of an mHealth intervention to promote physical of users’ opinions. activity. A focus group discussion was organized whereby each Data-Driven Analyses SMS text message was displayed and participants were required to respond either with “Yes, I like it” or “No, let’s change it to The definition of adherence to digital health interventions refers make it better.” This voting was then followed by a discussion to the extent to which a user has interacted with the intervention in which suboptimal messages were improved and the strengths [159]. This term may be used to define the degree to which a of effective messages were noted. Finally, participants may also user interacts with the CA (greater adherence equals more time be questioned about their willingness to recommend the engaging with the intervention) or the degree to which the conversation to others, which is a good indicator of satisfaction user-CA interaction complies with the prescribed and acceptability [70]. recommendation (intended use of the intervention) [159]. In health care interventions, the concept of “intended use” is Several aspects of user engagement and acceptability may be preferred, and it should be clearly defined during the CA design measured using one of several app quality rating tools, of which and development stage for the subsequent adherence the most commonly used one is the Mobile App Rating Scale measurements to be meaningful. Increased adherence to an [167]. The use of standardized, validated rating scales may intervention may be related to its increased effectiveness improve the reproducibility of this research area and facilitate [75,160], although the data are not conclusive [3,161,162]. the reporting of trial results, although they are not specific for CAs. User engagement with the CA may be evaluated using data metrics such as the times the user opened the app, time spent interacting with the CA, the extent of the dialog, or the number https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al and the Conformité Européenne mark in the European Union Economic Evaluation (EU) [181]. The economic evaluation includes not only the affordability of the project but also the cost-benefits associated with developing Cross-Cutting Considerations the CA. These analyses should consider the end-user perspective The themes described in this section are relevant throughout all as well as the potential benefits for the health care system in the design stages referred to in the previous sections. general [168,169]. Digital health interventions appear to be cost-effective [170], although reports often present varying, User-Centered Design and Co-design inconclusive results [171]. Although it is often mentioned that User-centered design refers to design practices that include the one of the potential advantages of digital health interventions, end users’ views to guide the process, either in a passive, particularly in the long term, may be a significant decrease in consultive manner or as active participants in the design process health care costs [172], the upfront expenses of developing the (co-design) [182]. Several approaches to user-centered design digital intervention might be substantial. For example, Kowatsch have been described. They share the general principles of et al [73] reported upfront expenses of approximately US involving users during the design process, although the steps $250,000 to develop a CA to support asthma self-management involved in the process and the type and extent of end-user in young patients. The development costs will vary conditional involvement may differ. They include but may not be limited to the type and functionalities of the CA, the use of a messaging to human-centered design [183,184] and design thinking [185] platform or development as a stand-alone app, and the number (often considered synonyms), user-centered design [186], of team members, among other aspects. Despite the increasing co-design [182], and participatory action research [187]. importance of conducting economic evaluations of digital health End users include patients, caregivers, HCPs, or other relevant care interventions, only 2% (1/55) of the studies included in stakeholders. There are several benefits of including end users our reviews reported economic evaluation data [73]. Recent as part of the CA development team, such as a better documents from the World Health Organization [168] and the understanding of users’ and communities’ needs, development International Training and Education Center for Health [169] of culturally sensitive products, and improved communication at the University of Washington in the United States, as well between the different stakeholders [188,189]. This, in turn, may as a recent review [171], present a practical overview of how increase compliance with the intervention and improve to perform economic evaluations. health-related outcomes [190]. For example, to develop a CA Implementation to promote positivity and well-being in young people after Once the effectiveness of the CA intervention has been cancer treatment, Greer et al [70] conducted interviews and determined in rigorous clinical trials, the research team should focus groups with young adults treated for cancer to refine the consider implementing the intervention in the broader informational content. population. Implementation research aims to integrate research During the evaluation stage, thinking-out-loud usability testing and practice [173] and understand the users and context in which is another example of a user-centered design methodology in an intervention would be implemented. The research methods, the design of digital health interventions, including CAs [191]. including pragmatic trials, participatory action research, and mixed methods studies, aim to assess the intervention The role of user-centered design in the development of digital “acceptability, adoption, appropriateness, feasibility, fidelity, health interventions has been repeatedly emphasized by several implementation cost, coverage, and sustainability” [174-177]. frameworks included in our review (36/55, 65%) Important considerations include the need to upgrade the [25,26,36-39,41-46,62,69,70,73,76,82-85,87-100,110]. systems to adapt to higher traffic, personnel to provide long-term Privacy and Security system maintenance and updates, and the costs these changes may incur [25,26]. Furthermore, the team should consider CA Overview intervention commercialization strategies, including engaging Safeguarding the privacy and security of CA users’ data is HCPs, health insurers, or governmental organizations if aligned essential and should be a part of the entire design and with the health care focus of the intervention [26]. development cycle. Health information is considered personal, sensitive information that should be protected at all times. The Finally, the team should be aware of and comply with the current level of data protection should align with the data collected by regulatory frameworks for digital health interventions. the CA, if any. Therefore, the functionalities of the CA will Increasingly, countries are developing national policy determine the type of sensitive data to be collected and guide frameworks to regulate the evaluation, use, and the inclusion of data protection software such as firewalls and commercialization of digital health interventions [178], encryption. particularly if the intervention is considered a digital therapeutic [179]. Digital therapeutics refer to “evidence-based therapeutic In general, developers should minimize the amount of personal interventions that are driven by high-quality software programs and sensitive information collected from users by asking specific to prevent, manage, or treat a medical disorder or disease” [179], questions to avoid oversharing or simply providing may require a provider’s prescription to be accessed [179], and predetermined responses instead of using free text. Furthermore, often require approval from official regulatory bodies such as all CAs should include a privacy policy that is brief and written the Food and Drug Administration in the United States [180] in clear language outlining the data collected and the uses of these data. All data must be encrypted during transit (when the https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al message is being sent) and at rest (when the message has been DISCOVER conceptual framework includes 8 iterative steps delivered) [192]. The platform on which the CA will be arranged in three main groups: (1) design, which includes deployed may also vary according to the CA functionalities. defining the goal, creating an identity, assembling the team, and For example, a CA collecting users’ personal data should not selecting the delivery interface; (2) development, which be deployed on proprietary or messaging platforms as the comprises developing the content and building the conversation platform data management policies may not be clearly reported flow; and (3) evaluation and implementation. User-centered [65] or data sharing with third parties may occur without design and privacy and security were included as cross-cutting informing the user [193]. This might create an ever-increasing considerations, which are relevant at every stage of the digital footprint, potentially allowing for user identification framework. from data aggregation rather than actually identifiable This framework was based on the comprehensive analysis of information [194]. 36 mHealth frameworks, 5 CA taxonomies, and 14 primary A 2020 framework for governing the responsible use of CAs studies reporting on the design and development of rule-based in health care highlighted the importance of safeguarding data health care CAs. The framework was applied in a web-based privacy, including user health data, history of interactions, and pilot study using a CA deployed on Facebook Messenger. The disclosure of user data even if unintended [195]. In addition, existing mHealth frameworks provided general guidelines to the framework highlighted the user’s right to access their develop mHealth interventions for health care, from the personally identifiable information, the requirement of user characterization of the target population to evaluation, with consent before recording or saving health-related data, and the emphasis on the application of user-centered design techniques preclusion of using the stored data as a means of surveillance in all stages of development. Concurrently, the CA taxonomies or to discriminate users against health care privileges or provided focused on several aspects of CA design and evaluation coverage [195]. as well as the impact of design features on CA-user interactions. Compliance With Data Privacy Laws Considering the multifaceted nature of embodied CAs, we decided to focus on CAs that are nonembodied. Health care CAs that collect users’ sensitive data must comply with country-relevant data privacy laws, such as the Health Comparisons With Prior Work Insurance Portability and Accountability Act in the United States The existing frameworks for the design and development of [192] or the General Data Protection Regulation (GDPR) in the mHealth interventions provide detailed guidance in all steps of EU [196]. These laws’ jurisdiction is generally limited to the the intervention development, starting with an understanding issuing country; however, the GDPR applies to any EU citizen of the needs and the profile of the end users through a review within or outside the EU. The GDPR, which went into effect of existing literature or formative research [67], and they in 2018, is an overarching law that aims to enhance the rights emphasize the need for patient and public involvement to make of individuals over their personal data, defined as any data that the intervention as relevant to the target population as possible may allow for the identification of a person on their own or [90,98]. These frameworks also described the importance of combined with other data, including pseudonymized data [196]. conducting iterative evaluations to identify limitations before Alternatively, the Health Insurance Portability and testing the mHealth intervention in a larger-scale trial [28,98,99]. Accountability Act is industry-specific and applies only to However, the literature on the design and development of CAs health-related data [197]. Other countries have adopted their was restricted to the development of taxonomies that were not own data protection laws and regulations. In Singapore, the limited to health care describing CA design platforms [5], Personal Data Protection Act is a baseline regulatory framework classification of CAs according to the approach to conversation informing the collection, distribution, and use of personal data design [67], characteristics of embodied agents [66], or the [198]. impact of CA characteristics on user interactions [32]. Moreover, In addition to the aforementioned GDPR, children’s data are the taxonomy by Denecke et al [65] referred to health care CAs, generally more stringently safeguarded. For example, in the but they focused exclusively on CA evaluation. Therefore, a United States, the Children’s Online Privacy Protection Act conceptual framework guiding the development of health care [199] requires that verifiable parental consent be obtained by CAs was needed to expand previous mHealth frameworks with all digital operators (not restricted to health care) collecting data elements particularly relevant to CAs, such as personality from children (aged <13 years). Similar considerations are development, converting evidence-based content into included within the GDPR and the Singapore Personal Data conversations, and using novel research designs for evaluation. Protection Act, with the caveat that, in some European countries, Furthermore, our framework focused particularly on the parental consent is required for children and adolescents aged development of the CA, including personality, display of <16 years. empathy, and disclosure of its identity as a computer-generated entity without human involvement, and on the development of Discussion dialogs guided by up-to-date evidence-based information sources. Principal Findings This framework described the development of rule-based CAs, We present a new conceptual framework for the design, allowing the research team total control of the conversation and development, evaluation, and implementation of dialog flow. There are several reasons for this. First, our smartphone-delivered, rule-based, and text-based CAs. The framework presents easy-to-follow steps that could be applied https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al by smaller research teams that do not include computer science The DISCOVER framework builds on an analysis of existing or AI specialists or that undertake the CA development project mHealth frameworks and a stringent analysis of rule-based CA under restricted financial resources. Second, we aimed to literature complemented by the team’s demonstration of its provide guidance for the development of goal-oriented CAs applicability in the development of a rule-based CA to support aimed at delivering health education content or simple lifestyle changes in people at risk of developing diabetes. interventions aimed at improving healthy lifestyle choices or Limitations self-management behavior and, therefore, prioritize control over Much of the information provided is anecdotal or derived from the conversation content using a rule-based paradigm, albeit research conducted on SMS text messaging and other mHealth less engaging, over AI algorithms that have yet to become truly interventions because of the scarcity of research on the explainable. evidence-based development of rule-based CAs for health care. Implications for Future Research Therefore, this framework provides an overview of the main Future research should apply the DISCOVER conceptual steps required to develop a rule-based CA. framework to the development of CAs offering behavior change The descriptions and examples presented in the conceptual interventions aimed at different specialties, settings (hospital framework focused on CA interventions for end users to support or outpatient), target groups, and cultures. Moreover, although either a healthy lifestyle or the management of a chronic the use of theories in the design of behavior change interventions condition, as derived from the literature reviews and our is favored and may increase the effectiveness of the intervention experience developing a CA. Nevertheless, the design and [77,200], it is still unclear which behavior change theories or development principles discussed in this study could apply to techniques are better suited for CA-led interventions. other relevant user groups such as caregivers and health care Alternatively, because of the interactive nature of CAs, it would professionals. be appropriate to assess whether behavior change interventions previously proved effective in traditional face-to-face settings Furthermore, this framework is focused on rule-based CAs and, are equally effective when led by a CA. although it may guide researchers in the development of particular aspects of AI CAs, it does not provide guidance on Although the concepts of identity creation, conversational flow, the development of AI-based conversations. In addition, the and delivery are important, their relative relevance to varying economic, social, and behavioral characteristics of different target populations is still unknown. In addition, more research populations may limit its generalizability. on the assessment of health care chatbot interventions can help inform the ideal health-related outcome measures and digital Conclusions data sets required for a comprehensive evaluation. Finally, The interest in and potential for CAs in health care are growing, although this framework is comprehensive and many but guidelines to design, develop, evaluate, and implement these components may apply to AI CAs, a separate framework is interventions are currently lacking. Drawing on published needed to describe specific aspects relevant to AI CAs, such as evidence, the DISCOVER conceptual framework provides the dialog development using machine learning or natural language first attempt to fill this void. The process was divided into 8 processing techniques, voice versus text parsing, and many iterative steps arranged in 3 overarching groups and others. complemented by 2 cross-cutting considerations. Future research should explore aspects of CA development such as the use of Strengths behavior change theories and privacy and safety concerns. This is, to the best of our knowledge, the first conceptual Further evaluation of this framework in diverse health care areas framework outlining the steps required to develop a and settings and for a variety of users is needed to demonstrate smartphone-delivered, rule-based health care CA offering clear its validity. yet comprehensive guidelines to accommodate health care researchers with varying computer science expertise. Acknowledgments This research was supported by the Ageing Research Institute for Society and Education (ARISE), Nanyang Technological University, Singapore. This study was also supported by the Singapore Ministry of Education under the Singapore Ministry of Education Academic Research Fund Tier 1 (RG36/20). This research was conducted as part of the Future Health Technologies program, which was established collaboratively between ETH Zürich and the National Research Foundation, Singapore. This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise program. Authors' Contributions DAD designed the study, extracted the data, conducted the analysis, and wrote the manuscript. LM conducted the analysis and wrote the manuscript. M-HRH, SJ, TK, and RA provided a critical review of the manuscript. LTC conceptualized and designed the study, provided a critical review of the manuscript, and provided supervision at all steps of the research. All authors approved the final version of the manuscript, and they take accountability for all aspects of this work. https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al Conflicts of Interest TK is affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care at the University of Zurich; the Department of Management, Technology and Economics at ETH Zurich; the Future Health Technologies Programme at the Singapore-ETH Centre; and the School of Medicine and Institute of Technology Management at the University of St. Gallen. Centre for Digital Health Interventions is funded in part by CSS, a Swiss health insurer. TK is also a cofounder of Pathmate Technologies, a university spin-off compan y that creates and delivers digital clinical pathways. However, neither CSS nor Pathmate Technologies were involved in this study. SJ is also affiliated with Salesforce Research. However, Salesforce Research was not involved in this study. The other authors declare that they have no conflicts of interest. Multimedia Appendix 1 Literature review of conceptual frameworks for the design, development, and evaluation of mobile health interventions. [DOCX File , 122 KB-Multimedia Appendix 1] Multimedia Appendix 2 Literature review of smartphone-delivered, rule-based conversational agents. [DOCX File , 30 KB-Multimedia Appendix 2] Multimedia Appendix 3 Search strategy for the conversational agent research trial review. [DOCX File , 30 KB-Multimedia Appendix 3] Multimedia Appendix 4 Methodology implemented for conceptual framework development using the conceptual framework development steps described by Jabareen [29]. [DOCX File , 19 KB-Multimedia Appendix 4] Multimedia Appendix 5 Mapping of the steps of the conceptual framework applied to the design, development, and evaluation of Precilla. [DOCX File , 22 KB-Multimedia Appendix 5] Multimedia Appendix 6 Design, development, and evaluation frameworks for mobile health interventions. [DOCX File , 41 KB-Multimedia Appendix 6] Multimedia Appendix 7 Classification systems for conversational agents. [DOCX File , 20 KB-Multimedia Appendix 7] Multimedia Appendix 8 Characteristics of clinical trials on rule-based conversational agents. [DOCX File , 27 KB-Multimedia Appendix 8] References 1. Tudor Car L, Dhinagaran DA, Kyaw BM, Kowatsch T, Joty S, Theng YL, et al. Conversational agents in health care: scoping review and conceptual analysis. J Med Internet Res 2020 Aug 07;22(8):e17158 [FREE Full text] [doi: 10.2196/17158] [Medline: 32763886] 2. Abd-Alrazaq AA, Alajlani M, Ali N, Denecke K, Bewick BM, Househ M. 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[doi: 10.1111/bjhp.12356] [Medline: 30793445] Abbreviations AI: artificial intelligence BCT: behavior change technique CA: conversational agent DISCOVER: Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent EU: European Union https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 22 (page number not for citation purposes) XSL FO RenderX JMIR MHEALTH AND UHEALTH Dhinagaran et al GDPR: General Data Protection Regulation HCP: health care provider JITAI: just-in-time adaptive intervention mHealth: mobile health Edited by L Buis; submitted 13.04.22; peer-reviewed by A Islam, L Agrawal, M Jalan; comments to author 24.05.22; revised version received 02.08.22; accepted 26.08.22; published 04.10.22 Please cite as: Dhinagaran DA, Martinengo L, Ho MHR, Joty S, Kowatsch T, Atun R, Tudor Car L Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework JMIR Mhealth Uhealth 2022;10(10):e38740 URL: https://mhealth.jmir.org/2022/10/e38740 doi: 10.2196/38740 PMID: ©Dhakshenya Ardhithy Dhinagaran, Laura Martinengo, Moon-Ho Ringo Ho, Shafiq Joty, Tobias Kowatsch, Rifat Atun, Lorainne Tudor Car. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.10.2022. 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 JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included. https://mhealth.jmir.org/2022/10/e38740 JMIR Mhealth Uhealth 2022 | vol. 10 | iss. 10 | e38740 | p. 23 (page number not for citation purposes) XSL FO RenderX
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JMIR mHealth and uHealth – JMIR Publications
Published: Oct 4, 2022
Keywords: conceptual framework; conversational agent; chatbot; mobile health; mHealth; digital health; mobile phone