Peng, M., Wickersham, J., Altice, F., Shrestha, R., Azwa, I., Zhou, X., Ab Halim, M., Ikhtiaruddin, H., Tee, V., Kamarulzaman, A., & Ni, Z.
JMIR Formative Research, June 2022.
Background: Mobile technologies are increasingly deployed to support the practice of medicine, nursing, and public health, including HIV testing and prevention. Chatbots using artificial intelligence (AI) are novel mHealth strategies that can promote HIV testing and prevention among men who have sex with men (MSM) in Malaysia, a hard-to-reach population at elevated risk for HIV, yet little is known about features important to this key population.
Objective: To identify the barriers and facilitators of Malaysian MSM’s acceptance of an AI-chatbot designed to assist HIV testing, in relation to its perceived benefits, limitations, and preferred features among potential users.
Methods: We conducted five online focus group interviews with 31 MSM in Malaysia between July and September 2021. Interviews were first recorded, transcribed, coded, and thematically analyzed using NVIVO 9 (QSR International). The Unified Theory of Acceptance and Use of Technology (UTAUT) was used to guide data analysis to map emerging themes related to the barriers and facilitators of chatbot acceptance onto the four domains of UTAUT, specifically performance expectancy, effort expectancy, facilitating conditions, and social influence.
Results: Multiple barriers and facilitators influencing MSM’s acceptance of an AI-chatbot were identified for each domain. Performance expectancy (i.e., the perceived usefulness of the AI-chatbot) was influenced by MSM’s concerns about the AI-chatbot’s ability to deliver accurate information, its effectiveness in information dissemination and problem-solving, and its ability to provide emotional support and raise health awareness. Convenience, cost, and technical errors influenced the AI-chatbot’s effort expectancy (i.e., the perceived ease of use). Efficient linkage to healthcare professionals and HIV self-testing were reported as facilitating conditions of MSM’s receptiveness to using an AI-chatbot to access HIV testing. Social influence factors influencing acceptability of mobile technology addressing HIV in Malaysia included the socio-political climate in Malaysia, including privacy concerns, pervasive stigma against homosexuality, and the criminalization of same-sex sexual behaviors. Key design strategies that could enhance MSM’s acceptance of an HIV-prevention AI-chatbot included anonymous user setting, embedding the chatbot in MSM-friendly online platforms, and providing within-app guiding questions and options related to HIV testing, prevention, and treatment.
Conclusions: This study provides important insights into key features and potential implementation strategies central to designing an AI-chatbot as a culturally sensitive digital health tool to prevent stigmatized health conditions in vulnerable and systematically marginalized populations. Such features not only are crucial to designing effective user-centered and culturally situated mHealth intervention for MSM in Malaysia, but also illuminate the importance of incorporating social stigma considerations into health technology implementation strategies.