Conversational Agents in Health Education: Protocol for a Scoping Review

Powell, Leigh; Nizam, Mohammed Zayan; Nour, Radwa; Zidoun, Youness; Sleibi, Randa; Kaladhara, Sreelekshmi; Suwaidi, Hanan Al; Zary, Nabil

doi:10.2196/31923

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…There is a great diversity of chatbots available today, ranging in abilities, features, and complexity. A hierarchy of chatbots has been proposed [ 4 , 5 ], which classifies chatbots according to their ability to use algorithms or artificial intelligence or both to recognize the context of language as it is written in a real-time conversation, called natural language processing (NLP), and respond with greater specificity. Chatbots higher on the classification scale have more advanced NLP, promoting a greater likelihood of mimicking an actual conversation.…”

Section: Introductionmentioning

confidence: 99%

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Powell¹,

Nour²,

Sleibi³

et al. 2023

JMIR Hum Factors

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Background Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health. Objective This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team. Methods This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants’ experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot? Results A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%). Conclusions This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.

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Section: Introductionmentioning

confidence: 99%

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Powell¹,

Nour²,

Sleibi³

et al. 2023

JMIR Hum Factors

Self Cite

View full text Add to dashboard Cite

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Chatting with artificial intelligence to combat antibiotic resistance: Opportunities and challenges

Abavisani,

Khoshrou,

Karbas Foroushan

et al. 2024

Current Research in Biotechnology

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Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation (Preprint)

Powell¹,

Nour²,

Sleibi³

et al. 2022

Preprint

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BACKGROUND The COVID-19 pandemic highlighted the pros and cons of current health communication approaches. Social media became a primary medium by which people received information. Misinformation, disinformation, and conspiracy theories undermined the propagation of sound health information during the pandemic, so much so that the WHO called it an infodemic. Inaccurate and false information severely impacts public health, delaying individual health choices to take preventative measures, and influences vaccine uptake. Using social media for health communication is increasing in popularity due in part to the ability to reach more significant numbers of people. The use of chatbots in healthcare has significantly increased in recent years. Chatbots enable users to have humanlike conversations on various topics and can vary widely in their complexity and functionality. OBJECTIVE This paper aims to describe the development and feasibility testing of a low-tech chatbot called VWise. VWise is designed to engage participants in correcting misinformation around COVID-19 vaccinations by utilizing Motivational Interviewing (MI) as a behavior change model. METHODS We developed personas for both the bot and the potential participants. Using our participant personas, the research team engaged in mock conversations. Transcriptions of the conversations were coded to identify phases of MI and where conversations diverged from these phases. Several iterations of sample dialogues were created. We selected to use ManyChat, a cloud-based platform, for its easy-to-use interface. Relevant participant responses were first stored into variables and mapped to a pre-configured Google Sheet that became our data set for analysis. No identifying data, generated by ManyChat was included in our data set. Responses to qualitative questions were deductively coded by two independent researchers. VWise was pilot tested on a group of 33 participants. RESULTS Out of 33 participants, 17(51%) chose to continue the conversation with VWise till the end as a mark of engagement. When they were asked about the influence the conversation had on them, 10 (6 -fully vaccinated, 4- partially vaccinated) expressed positive opinion, 1 partially-vaccinated expressed a neutral opinion, and 6 participants did not answer (5-fully vaccinated, 1-non-vaccinated). A validated tool, Chatbot Usability Questionnaire (CUQ) was included at the very end of the conversation. Of the 17 people who concluded the chat, 13(76.5%) participants filled out the CUQ. The mean score was 70.9, (SD±19.4), median score was 78.1, with the lowest and highest scores being 34.4 and 95.3 respectively. CONCLUSIONS This study presented the development and feasibility testing of VWise, a low-tech chatbot aimed at addressing COVID-19 vaccine misinformation through engaging participants in the behavior change process led by MI techniques. A high level of engagement with the bot was demonstrated. Our study highlights that low-tech bots are a viable option for use in health communication and in the promotion of behavior change. Our conversational model, based on MI, was successful is observing change talk and resistance in participants, furthering the argument that NLP is not absolutely necessary to produce observations of readiness for behavior change.

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Conversational Agents in Health Education: Protocol for a Scoping Review

Cited by 3 publications

References 23 publications

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

Chatting with artificial intelligence to combat antibiotic resistance: Opportunities and challenges

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation (Preprint)

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