Surveillance estimates indicate an alarming resurgence of new HIV/AIDS cases among young men who have sex with men (YMSM), highlighting the importance of re-examining prevention modalities and behavior change strategies. After diagnosis, persons living with HIV continue to engage in sexual and drug use behaviors that increase the risk for transmission of HIV. By continuing to engage in unprotected sex, HIV+ YMSM put themselves at risk of transmitting HIV to their partners, becoming re-infected with another strain of HIV, and/or acquiring other STIs. Individuals who communicate with sex partners about condom use and sexual health topics, including disclosure of HIV status, have been found to be more likely to engage in condom use behaviors. However, disclosure represents a challenging situation which is a universal and recurrent stressor in the lives of HIV+ persons. Despite being recognized as a critical component of prevention interventions directed at MSM, there is currently no stand-alone, easily delivered disclosure intervention. Moreover, there is a need to design interventions that engage the targeted population without the requirement of extensive training of facilitators who traditionally provide in person role plays, a method that will always e limited by lack of availability and scalability. Developing an ecologically relevant, artificially intelligent (AI) technology tool to offer the opportunity for knowledge acquisition and skill implementation, in a safe and engaging manner, is highly innovative, relevant and needed. This tool would be able to address the context (e.g., the emotionally charged experience of disclosure), content (the highly stigmatizing nature of an HIV diagnosis), and possible consequences (both positive and negative). Such a tool offers the added benefit of constant availability for the user to practice and receive feedback for each trial of practice while providig the possibility of a rewarding and reinforcing technology that shapes targeted behaviors to be more effective. The central goal of this proposal, titled, "Tough Talks: A disclosure intervention for HIV+ YMSM," is to create a safe, internet-delivered software program that will offer intelligen virtual character-driven scenarios that are engaging, interactive, ecologically sensitive, and user-centric in content and design, all focused on increasing mastery of important skills associated with disclosure of HIV status. In Phase I, Tough Talks will be a novel prevention tool that will address sexual health communication with sex partners in the context of addressing disclosure of HIV status. If this paradigm is successful, this intervention would represent a highl customizable solution that could be expanded to address HIV disclosure among MSM to family and friends or adapted to deal with disclosure among other HIV infected populations or for other contagious and/or stigmatizing conditions where disclosure is challenging.

Public Health Relevance

Reducing sexual risk among HIV-infected YMSM, the only risk group experiencing a significant increase in HIV incidence, begins with negotiation of safer sex behaviors with current and potential sex partners. Individuals who can communicate with sex partners about sexual health topics, including disclosure of HIV status, are more likely to engage in condom use behaviors. Therefore, HIV status disclosure represents an important underdeveloped public health strategy to address ongoing sexual risk behaviors. Despite being recognized as a critical component of prevention interventions, there is currently no stand-alone, easily delivered HIV disclosure intervention. Developing an ecologically relevant, artificially intelligent (AI) technology tool to offer the opportunity for knowledge acquisition and skill implementation as it relates to disclosure of HIS status, in a safe and engaging manner, is highly innovative, relevant and needed. Tough Talks will be a novel and high impact prevention tool to address sexual health communication with sex partners in the context of disclosing HIV status.

Agency
National Institute of Health (NIH)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43MH104102-01
Application #
8731751
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Delcarmen-Wiggins, Rebecca
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Virtually Better, Inc.
Department
Type
DUNS #
City
Decatur
State
GA
Country
United States
Zip Code
30033