title: A computational future for preventing HIV in minority communities: how advanced technology can improve implementation of effective programs.
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.
authors.affiliation: Department of Public Health Sciences, Miller School of Medicine, University of Miami, Coral Gables, FL 33136, USA. chbrown@med.miami.edu
authors.forename: C Hendricks
authors.initials: CH
authors.lastname: Brown
authors.forename: David C
authors.initials: DC
authors.lastname: Mohr
authors.forename: Carlos G
authors.initials: CG
authors.lastname: Gallo
authors.forename: Christopher
authors.initials: C
authors.lastname: Mader
authors.forename: Lawrence
authors.initials: L
authors.lastname: Palinkas
authors.forename: Gina
authors.initials: G
authors.lastname: Wingood
authors.forename: Guillermo
authors.initials: G
authors.lastname: Prado
authors.forename: Sheppard G
authors.initials: SG
authors.lastname: Kellam
authors.forename: Hilda
authors.initials: H
authors.lastname: Pantin
authors.forename: Jeanne
authors.initials: J
authors.lastname: Poduska
authors.forename: Robert
authors.initials: R
authors.lastname: Gibbons
authors.forename: John
authors.initials: J
authors.lastname: McManus
authors.forename: Mitsunori
authors.initials: M
authors.lastname: Ogihara
authors.forename: Thomas
authors.initials: T
authors.lastname: Valente
authors.forename: Fred
authors.initials: F
authors.lastname: Wulczyn
authors.forename: Sara
authors.initials: S
authors.lastname: Czaja
authors.forename: Geoff
authors.initials: G
authors.lastname: Sutcliffe
authors.forename: Juan
authors.initials: J
authors.lastname: Villamar
authors.forename: Christopher
authors.initials: C
authors.lastname: Jacobs
mesh_terms: D001741:Black or African American; D040421:Cell Phone; D003205:Computing Methodologies; D005544:Forecasting; D015658:HIV Infections / Q000208:ethnology / Q000517:prevention & control*; D006284:Health Plan Implementation; D006293:Health Promotion / Q000379:methods*; D006630:Hispanic or Latino; D006801:Humans; D008913:Minority Groups; D015397:Program Evaluation; D040242:Risk Reduction Behavior; D012725:Sexual Behavior; D014481:United States