Chatbots for Sexual Health Improvement: A Systematic Review

Abstract

There is a rising interest in chatbots dedicated to enhancing sexual health. However, there is limited research on the effectiveness of these chatbots, and the current literature lacks sufficient exploration of gaps and patterns in this field. In this review, we provided an overview of the state-of-the-art research conducted on sexual health chatbots, with the goal of identifying prevalent trends, design patterns, and features. In addition, we investigated existing research gaps, challenges, and shortcomings in the landscape of sexual health chatbots. Further, we proposed potential enhancements and directions for future research and development to create more effective chatbots in this field. A systematic search and screening of the literature from the past decade (2013–2023), extracted from seven databases, yielded a total of 1040 studies, out of which 29 articles were included in the final review following screening. The findings suggest that chatbots are usable and effective tools in sexual health education, persuasion, and assistance that are appreciated for their confidentiality, efficiency, and 24/7 availability. However, their performance is hindered by limitations such as restricted scope of knowledge and challenges in understanding user inputs. Additionally, constraints such as text-only input/output modalities and a predominant reliance on the English language limit their accessibility and acceptability. There is also a crucial need for more research in low-income or lower-middle-income countries, where individuals require increased sexual health education and support.

Keywords:

• Conversational agent
• chatbot
• sexual health
• systematic review
• HIV

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was undertaken, in part, thanks to funding from the Canada Research Chairs (CRC) Program. We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant. We acknowledge the support of Mitacs through the Mitacs Accelerate program. The research is conducted as part of the Dalhousie Persuasive Computing Lab.

Notes on contributors

Hemad Fetrati

Hemad Fetrati is a graduate student and researcher in the Persuasive Computing Lab at Dalhousie University, Canada. His research focuses on conversational AI and its applications in healthcare, aiming to improve patient outcomes and experiences.

Gerry Chan

Gerry Chan is a postdoctoral researcher in the Persuasive Computing Lab at Dalhousie University, Canada. Gerry received his PhD in 2022 in Information Technology from Carleton University. His research interests are in the social and motivational aspects of games with a focus on player matching mechanisms and collaborative play.

Rita Orji

Rita Orji is Canada Research Chair in Persuasive Technology and Computer Science Professor at Dalhousie University. She directs the Persuasive Computing Lab. Her research focuses on user-centered approaches to designing technologies to improve lives and support people to achieve various self-improvement goals.