A computational future for preventing HIV in minority communities: How advanced technology can improve implementation of effective programs Review uri icon

Overview

MeSH Major

  • Computing Methodologies
  • HIV Infections
  • Health Plan Implementation
  • Health Promotion
  • Minority Groups

abstract

  • African Americans and Hispanics in the United States have much higher rates of HIV than non-minorities. There is now strong evidence that a range of behavioral interventions are efficacious in reducing sexual risk behavior in these populations. Although a handful of these programs are just beginning to be disseminated widely, we still have not implemented effective programs to a level that would reduce the population incidence of HIV for minorities. We proposed that innovative approaches involving computational technologies be explored for their use in both developing new interventions and in supporting wide-scale implementation of effective behavioral interventions. Mobile technologies have a place in both of these activities. First, mobile technologies can be used in sensing contexts and interacting to the unique preferences and needs of individuals at times where intervention to reduce risk would be most impactful. Second, mobile technologies can be used to improve the delivery of interventions by facilitators and their agencies. Systems science methods including social network analysis, agent-based models, computational linguistics, intelligent data analysis, and systems and software engineering all have strategic roles that can bring about advances in HIV prevention in minority communities. Using an existing mobile technology for depression and 3 effective HIV prevention programs, we illustrated how 8 areas in the intervention/implementation process can use innovative computational approaches to advance intervention adoption, fidelity, and sustainability.

publication date

  • June 2013

Research

keywords

  • Review

Identity

Language

  • eng

PubMed Central ID

  • PMC3746769

Digital Object Identifier (DOI)

  • 10.1097/QAI.0b013e31829372bd

PubMed ID

  • 23673892

Additional Document Info

start page

  • S72

end page

  • 84

volume

  • 63

number

  • SUPPL. 1