The impact of Chatbot-Assisted Self Assessment (CASA) on intentions for sexual health screening in people from minoritised ethnic groups at risk of sexually transmitted infections
Tom Nadarzynski
A # * , Nicky Knights A # , Deborah Husbands A , Cynthia A. Graham B , Carrie D. Llewellyn C , Tom Buchanan A , Ian Montgomery D , Nuha Khlafa A , Jana Tichackova A , Riliwan Odeyemi A , Samantha Johnson A , Neomi Jesuthas A , Syeda Tahia A and Damien Ridge A
A
B
C
D
Handling Editor: Dan Wu
Abstract
Sexually transmitted infections (STIs) present a significant global public health issue, with disparities in STI rates often observed across ethnic groups. The study investigates the impact of Chatbot-Assisted Self Assessment (CASA) on the intentions for sexual health screening within minoritised ethnic groups (MEGs) at risk of STIs as well as the subsequent use of a chatbot for booking STI screening.
A simulation within-subject design was utilised to evaluate the effect of CASA on intentions for STI/HIV screening, concern about STIs, and attitudes towards STI screening. Screening intentions served as the dependent variable, while demographic and behavioural factors related to STI/HIV risk were the independent variables. ANCOVA tests were conducted to measure the impact of CASA on these perceptions.
Involving 548 participants (54% women, 66% black, average age = 30 years), the study found that CASA positively influenced screening intentions t(547) = −10.3, P < 0.001], concerns about STIs t(544) = −4.96, P < 0.001, and attitudes towards sexual health screening [t(543) = −4.36, P < 0.001. Positive attitudes towards CASA were observed (mean, 13.30; s.d., 6.73; range, −17 to 21). About 72% of users who booked STI screening appointments via chatbot were from MEGs.
CASA increased motivations for STI screening intentions among ethnically diverse communities. The intervention’s non-judgemental nature and the chatbot’s ability to emulate sexual history-taking were critical in fostering an environment conducive to behavioural intention change. The study’s high acceptability indicates the potential for broader application in digital health interventions. However, the limitation of not tracking actual post-intervention behaviour warrants further investigation into CASA’s real-world efficacy.
Keywords: AI, artificial intelligence, behaviour change, chatbot, natural language processing, risk assessment, self-assessment, sexual health, sexually transmitted disease, sexually transmitted infections, virtual assistant.
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