Peer Group Focused eHealth Strategies to Promote HIV Prevention, Testing, and Care Engagement.

PURPOSE OF REVIEW: Electronic communication platforms are increasingly used to support all steps of the HIV care cascade (an approach defined as eHealth). Most studies have employed individual-level approaches in which participants are connected with information, reminders, or a healthcare worker. Recent growth in use of social media platforms, which create digital communities, has created an opportunity to leverage virtual peer-to-peer connection to improve HIV prevention and care. In this article, we describe the current landscape of peer group eHealth interventions in the HIV field, based on a review of published literature, an online survey of unpublished ongoing work, and discussions with practitioners in the field in an in-person workshop. RECENT FINDINGS: We identified 45 published articles and 12 ongoing projects meeting our inclusion criteria. Most reports were formative or observational; only three randomized evaluations of two interventions were reported. Studies indicated that use of peer group eHealth interventions is acceptable and has unique potential to influence health behaviors, but participants reported privacy concerns. Evaluations of health outcomes of peer group eHealth interventions show promising data, but more rigorous evaluations are needed. Development of group eHealth interventions presents unique technological, practical, and ethical challenges. Intervention design must consider privacy and data sovereignty concerns, and respond to rapid changes in platform use. Innovative development of open-source tools with high privacy standards is needed.

Effects of digital chatbot on gender attitudes and exposure to intimate partner violence among young women in South Africa.

BACKGROUND: South Africa has among the highest rates of intimate partner violence (IPV) globally, with young women at heightened risk due to inequitable gender roles, limited relationship skills, and inadequate social support. Despite an urgent need for violence prevention in low- and middle-income settings, most efficacious approaches are time-intensive and costly to deliver. Digital, interactive chatbots may help young women navigate safer relationships and develop healthier gender beliefs and skills. METHODS: Young women (18-24 years old) across South Africa were recruited via Facebook for participation in an individually randomised controlled trial (n = 19,643) during the period of June 2021-September 2021. Users were randomly allocated, using a pipeline algorithm, to one of four trial arms: Pure Control (PC) had no user engagement outside of study measures; Attention Treatment (T0) provided didactic information about sexual health through a text-based chatbot; Gamified Treatment (T1) was a behaviourally-informed gamified text-based chatbot; Narrative Treatment (T2) was a behaviourally-informed drama delivered through pre-recorded voice notes. All chatbots were delivered in WhatsApp, through which users were invited to complete brief "quizzes" comprising adapted versions of validated scales. Primary outcomes were short-form adaptations of scales for gender attitudes (Gender Relations Scale) and past-month IPV (WHO Multi-country Study Instrument). Secondary outcomes were identification of unhealthy relationship behaviours (Intimate Partner Violence Attitudes Scale) and brief screener for depressive symptoms (Patient Health Questionnaire). A direct chat link to a trained counsellor was a safety measure (accessed by 4.5% of the sample). We estimated treatment effects using ordinary least squares and heteroskedasticity robust standard errors. FINDINGS: The trial retained 11,630 (59.2%) to the primary endpoint of gender attitudes. Compared to control, all treatments led to moderate and significant changes in attitudes towards greater gender equity (Cohen's D = 0.10, 0.29, 0.20 for T0, T1, and T2, respectively). The gamified chatbot (T1) had modest but significant effects on IPV: 56% of young women reported past-month IPV, compared to 62% among those without treatment (marginal effects = -0.07, 95%CI = -0.09to-0.05). The narrative treatment (T2) had no effect on IPV exposure. T1 increased identification of unhealthy relationship behaviours at a moderate and significant level (Cohen's D = 0.25). Neither T1 nor T2 had a measurable effect on depressive symptoms as measured by the brief screener. Interpretation: A behaviourally-informed, gamified chatbot increased gender equitable attitudes and was protective for IPV exposure among young women in South Africa. These effects, while modest in magnitude, could represent a meaningful impact given potential to scale the low-cost intervention.

How do psychobehavioural variables shed light on heterogeneity in COVID-19 vaccine acceptance? Evidence from United States general population surveys on a probability panel and social media.

OBJECTIVES: To (1) understand what behaviours, beliefs, demographics and structural factors predict US adults' intention to get a COVID-19 vaccination, (2) identify segments of the population ('personas') who share similar factors predicting vaccination intention, (3) create a 'typing tool' to predict which persona people belong to and (4) track changes in the distribution of personas over time and across the USA. DESIGN: Three surveys: two on a probability-based household panel (NORC's AmeriSpeak) and one on Facebook. SETTING: The first two surveys were conducted in January 2021 and March 2021 when the COVID-19 vaccine had just been made available in the USA. The Facebook survey ran from May 2021 to February 2022. PARTICIPANTS: All participants were aged 18+ and living in the USA. OUTCOME MEASURES: In our predictive model, the outcome variable was self-reported vaccination intention (0-10 scale). In our typing tool model, the outcome variable was the five personas identified by our clustering algorithm. RESULTS: Only 1% of variation in vaccination intention was explained by demographics, with about 70% explained by psychobehavioural factors. We identified five personas with distinct psychobehavioural profiles: COVID Sceptics (believe at least two COVID-19 conspiracy theories), System Distrusters (believe people of their race/ethnicity do not receive fair healthcare treatment), Cost Anxious (concerns about time and finances), Watchful (prefer to wait and see) and Enthusiasts (want to get vaccinated as soon as possible). The distribution of personas varies at the state level. Over time, we saw an increase in the proportion of personas who are less willing to get vaccinated. CONCLUSIONS: Psychobehavioural segmentation allows us to identify why people are unvaccinated, not just who is unvaccinated. It can help practitioners tailor the right intervention to the right person at the right time to optimally influence behaviour.