Advertisement

AIDS and Behavior

, Volume 23, Issue 11, pp 3103–3118 | Cite as

Evaluation of Sex Positive! A Video eHealth Intervention for Men Living with HIV

  • Sabina HirshfieldEmail author
  • Martin J. DowningJr.
  • Mary Ann Chiasson
  • Irene S. Yoon
  • Steven T. Houang
  • Richard A. Teran
  • Christian Grov
  • Patrick S. Sullivan
  • Rachel J. Gordon
  • Donald R. Hoover
  • Jeffrey T. Parsons
Original Paper

Abstract

Sex Positive![+] is a two-arm, video-based web intervention aimed at reducing condomless anal sex (CAS) with partners of known and unknown serostatus that was delivered online to a racially and ethnically diverse sample of 830 gay, bisexual, and other men who have sex with men living with HIV. Men in each arm received 6 weekly videos after completing a baseline assessment and 4 weekly booster videos following a 6-month assessment. Follow-up assessments were conducted every 3 months for 1 year. At 3-month follow-up, men in the intervention arm reported significantly reduced risk of having unknown serodiscordant CAS partners than men in the control arm (RR 0.60, 95% CI 0.39–0.92), partially supporting study hypotheses. Aside from this finding, similar reductions in sexual risk behaviors were observed in both arms over the study period. There is much to be learned about video-based web interventions in terms of methodological development and intervention delivery, including frequency and duration of intervention components.

Keywords

eHealth HIV MSM Video Intervention 

Resumen

Sex Positive! [+] es una intervención vía web de dos brazos basado en videos diseñados para reducir el sexo anal sin condón (SASC) con parejas de estado serológico conocido y desconocido. Los videos se distribuyeron vía Internet a una muestra racial y étnicamente diversa de 830 hombres homosexuales, bisexuales y otros hombres que tienen sexo con hombres que viven con VIH. Después de completar una evaluación basal, semanalmente se presentó un video a los hombres de cada brazo durante 6 semanas. Comenzando en el sexto mes, un video de refuerzo se presentó semanalmente durante 4 semanas. Las evaluaciones de seguimiento se realizaron cada tres meses durante un año. A los 3 meses de seguimiento, los hombres en el brazo de intervención reportaron una reducción significativa en el riesgo de tener SASC con parejas de estado serológico desconocido comparados con hombres en el brazo de control (RR = 0,60; IC del 95%: 0,39-0,92), lo cual apoya parcialmente la hipótesis del estudio. Además de este hallazgo, se observaron reducciones similares en las conductas de riesgo sexual en ambos brazos durante el período de estudio. Aún hay mucho que aprender acerca de las intervenciones con videos administradas por medio del Internet, específicamente en términos de desarrollo metodológico y modos de implementación, incluyendo la frecuencia y la duración de los componentes de la intervención.

Notes

Funding

This study was supported by a grant from the National Institute of Mental Health (R01 MH100973, PI: Hirshfield). A Certificate of Confidentiality was also obtained from the National Institute of Mental Health to provide additional privacy protections for participants enrolled in this study (ClinicalTrials.gov #NCT02023580).

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Centers for Disease Control and Prevention. HIV surveillance report, 2017 November 2018.Google Scholar
  2. 2.
    Singh S, Mitsch A, Wu B. HIV care outcomes among men who have sex with men with diagnosed HIV infection—United States, 2015. MMWR Morb Mortal Wkly Rep. 2017;66(37):969–74.PubMedPubMedCentralGoogle Scholar
  3. 3.
    Gant Z, Bradley H, Hu X, et al. Hispanics or Latinos living with diagnosed HIV: progress along the continuum of HIV care—United States, 2010. MMWR Morb Mortal Wkly Rep. 2014;63(40):886–90.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Whiteside YO, Cohen SM, Bradley H, et al. Progress along the continuum of HIV care among blacks with diagnosed HIV—United States, 2010. MMWR Morb Mortal Wkly Rep. 2014;63(5):85–9.PubMedPubMedCentralGoogle Scholar
  5. 5.
    Hall HI, Holtgrave DR, Tang T, Rhodes P. HIV transmission in the United States: considerations of viral load, risk behavior, and health disparities. AIDS Behav. 2013;17(5):1632–6.PubMedGoogle Scholar
  6. 6.
    Beymer MR, Weiss RE, Bolan RK, et al. Sex on demand: geosocial networking phone apps and risk of sexually transmitted infections among a cross-sectional sample of men who have sex with men in Los Angeles County. Sex Transm Infect. 2014;90(7):567–72.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Rendina HJ, Jimenez RH, Grov C, Ventuneac A, Parsons JT. Patterns of lifetime and recent HIV testing among men who have sex with men in New York city who use grindr. AIDS Behav. 2014;18(1):41–9.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Rosser BR, Oakes JM, Konstan J, et al. Reducing HIV risk behavior of men who have sex with men through persuasive computing: results of the Men’s INTernet Study-II. AIDS. 2010;24(13):2099–107.PubMedGoogle Scholar
  9. 9.
    Wolitski RJ, Gomez CA, Parsons JT. Effects of a peer-led behavioral intervention to reduce HIV transmission and promote serostatus disclosure among HIV-seropositive gay and bisexual men. AIDS. 2005;19(Suppl 1):S99–109.PubMedGoogle Scholar
  10. 10.
    Grov C, Breslow AS, Newcomb ME, Rosenberger JG, Bauermeister JA. Gay and bisexual men’s use of the Internet: research from the 1990s through 2013. Annu Rev Sex Res. 2014;51(4):390–409.Google Scholar
  11. 11.
    Krishnan A, Ferro EG, Weikum D, et al. Communication technology use and mHealth acceptance among HIV-infected men who have sex with men in Peru: implications for HIV prevention and treatment. AIDS Care. 2015;27(3):273–82.PubMedGoogle Scholar
  12. 12.
    Seidenberg AB, Jo CL, Ribisl KM, et al. A national study of social media, television, radio, and internet usage of adults by sexual orientation and smoking status: implications for campaign design. Int J Environ Res Public Health. 2017;14(4):E450.PubMedGoogle Scholar
  13. 13.
    McKirnan DJ, Tolou-Shams M, Courtenay-Quirk C. The Treatment Advocacy Program: a randomized controlled trial of a peer-led safer sex intervention for HIV-infected men who have sex with men. J Consult Clin Psychol. 2010;78(6):952–63.PubMedGoogle Scholar
  14. 14.
    Centers for Disease Control and Prevention. Complete Listing of Risk Reduction Evidence-based Behavioral Interventions. 2019; http://www.cdc.gov/hiv/research/interventionresearch/compendium/rr/complete.html. Accessed 13 March 2019.
  15. 15.
    Fernandez MI, Hosek SG, Hotton AL, et al. A randomized Controlled trial of POWER: an internet-based HIV prevention intervention for black bisexual men. AIDS Behav. 2016;20(9):1951–60.PubMedGoogle Scholar
  16. 16.
    Bransford J, Sherwood R, Hasselbring T, Kinzer C, Williams S. Anchored instruction: why we need it and how technology can help. In: Nix D, Spiro R, editors. Cognition, education, and multimedia: exploring ideas in high technology. Hillsdale: Lawrence Erlbaum Associates; 1990. p. 115–41.Google Scholar
  17. 17.
    Collins A. Design issues for learning environments. In: Vosniadou S, De Corte E, Glaser R, Mandl H, editors. International perspectives on the design of technology-supported learning environments. Mahwah: Lawrence Erlbaum Associates; 1996.Google Scholar
  18. 18.
    Jonassen D, Howland J, Moore J, Marra R. Learning to solve problems with technology: a constructivist perspective. 2nd ed. Columbus: Merrill/Prentice-Hall; 2003.Google Scholar
  19. 19.
    Rivera AV, DeCuir J, Crawford ND, Amesty S, Harripersaud K, Lewis CF. Factors associated with HIV stigma and the impact of a nonrandomized multi-component video aimed at reducing HIV stigma among a high-risk population in New York City. AIDS Care. 2015;27(6):772–6.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Tuong W, Larsen ER, Armstrong AW. Videos to influence: a systematic review of effectiveness of video-based education in modifying health behaviors. J Behav Med. 2014;37(2):218–33.PubMedGoogle Scholar
  21. 21.
    Calderon Y, Cowan E, Leu CS, et al. A human immunodeficiency virus posttest video to increase condom use among adolescent emergency department patients. J Adolesc Health. 2013;53(1):79–84.PubMedPubMedCentralGoogle Scholar
  22. 22.
    Warner L, Klausner JD, Rietmeijer CA, et al. Effect of a brief video intervention on incident infection among patients attending sexually transmitted disease clinics. PLoS Med. 2008;5(6):e135.PubMedPubMedCentralGoogle Scholar
  23. 23.
    Blas MM, Alva IE, Carcamo CP, et al. Effect of an online video-based intervention to increase HIV testing in men who have sex with men in Peru. PLoS ONE. 2010;5(5):e10448.PubMedPubMedCentralGoogle Scholar
  24. 24.
    Chiasson MA, Shaw FS, Humberstone M, Hirshfield S, Hartel D. Increased HIV disclosure three months after an online video intervention for men who have sex with men (MSM). AIDS Care. 2009;21(9):1081–9.PubMedGoogle Scholar
  25. 25.
    Hirshfield S, Chiasson MA, Joseph H, et al. An online randomized controlled trial evaluating HIV prevention digital media interventions for men who have sex with men. PLoS ONE. 2012;7:e46252.PubMedPubMedCentralGoogle Scholar
  26. 26.
    Schank R, Abelson R. Knowledge and memory: the real story. In: Wyer R, editor. Advances in social cognition, vol. 8. Hillsdale: Lawrence Erlbaum Associates; 1995. p. 1–85.Google Scholar
  27. 27.
    Bandura A. Social learning theory. Englewood Cliffs: Prentice-Hall; 1977.Google Scholar
  28. 28.
    Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs: Prentice Hall; 1986.Google Scholar
  29. 29.
    Brookfield S. Developing critical thinkers: challenging adults to explore alternative ways of thinking and acting. San Francisco: Jossey-Bass; 1987.Google Scholar
  30. 30.
    Freijy T, Kothe EJ. Dissonance-based interventions for health behaviour change: a systematic review. Br J Health Psychol. 2013;18(2):310–37.PubMedGoogle Scholar
  31. 31.
    Schank R, Berman T. The pervasive role of stories in knowledge and action. In: Green M, Strange JJ, Brock TC, editors. Narrative impact: social and cognitive foundations. Mahway: Lawrence Erlbaum Associates; 2002. p. 287–313.Google Scholar
  32. 32.
    Schank R. Dynamic memory: a theory of reminding and learning in computers and people. New York: Cambridge University Press; 1982.Google Scholar
  33. 33.
    Bandura A. Psychological modeling: conflicting theories. New York: Lieber-Atherton; 1974.Google Scholar
  34. 34.
    Bandura A. Analysis of modeling processes, vol. 1-62. New York: Lieber-Atherton; 1974.Google Scholar
  35. 35.
    Chiasson MA. Ask me, tell me. New York: Public Health Solutions; 2011.Google Scholar
  36. 36.
    Fred says. Chicago: Love Lion Studio; 2013.Google Scholar
  37. 37.
    Hirshfield S, Downing MJ Jr, Parsons JT, et al. Developing a video-based eHealth intervention for HIV-positive gay, bisexual, and other men who have sex with men: study protocol for a randomized controlled trial. JMIR Res Protoc. 2016;5(2):e125.PubMedPubMedCentralGoogle Scholar
  38. 38.
    SurveyGizmo [computer program]. Boulder, CO: Widgix LLC dba SurveyGizmo; 2019.Google Scholar
  39. 39.
    Wilson IB, Lee Y, Michaud J, Fowler FJ Jr, Rogers WH. Validation of a new three-item self-report measure for medication adherence. AIDS Behav. 2016;20(11):2700–8.PubMedPubMedCentralGoogle Scholar
  40. 40.
    Smith A. Technology trends among people of color [Report]. Washington: Pew Internet & American Life Project; 2010.Google Scholar
  41. 41.
    PewInternet.org. Spring tracking survey. Pew Internet & American Life Project. 2011; http://pewinternet.org/Trend-Data/Whos-Online.aspx. Accessed 8 November 2011.
  42. 42.
    Smith A. Mobile access report 2010. Washington: Pew Internet & American Life Project; 2010.Google Scholar
  43. 43.
    Sullivan PS, Khosropour CM, Luisi N, et al. Bias in online recruitment and retention of racial and ethnic minority men who have sex with men. J Med Internet Res. 2011;13(2):e38.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Bezabhe WM, Chalmers L, Bereznicki LR, Peterson GM. Adherence to antiretroviral therapy and virologic failure: a meta-analysis. Medicine. 2016;95(15):e3361.PubMedPubMedCentralGoogle Scholar
  45. 45.
    IBM SPSS Statistics for Windows, Version 22.0 [computer program]. Armonk, NY: IBM Corp.; 2013.Google Scholar
  46. 46.
    Kline RB. Beyond significance testing: statistics reform in the behavioral sciences. 2nd ed. Washington: American Psychological Association; 2013.Google Scholar
  47. 47.
    Horvath KJ, Oakes JM, Rosser BS, et al. Feasibility, acceptability and preliminary efficacy of an online peer-to-peer social support ART adherence intervention. AIDS Behav. 2013;17(6):2031–44.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Mustanski B, Garofalo R, Monahan C, Gratzer B, Andrews R. Feasibility, acceptability, and preliminary efficacy of an online HIV prevention program for diverse young men who have sex with men: the keep it up! intervention. AIDS Behav. 2013;17(9):2999–3012.PubMedGoogle Scholar
  49. 49.
    Bauermeister JA, Pingel ES, Jadwin-Cakmak L, et al. Acceptability and preliminary efficacy of a tailored online HIV/STI testing intervention for young men who have sex with men: the Get Connected! program. AIDS Behav. 2015;19(10):1860–74.PubMedPubMedCentralGoogle Scholar
  50. 50.
    Chen LF, Vander Weg MW, Hofmann DA, Reisinger HS. The Hawthorne effect in infection prevention and epidemiology. Infect Control Hosp Epidemiol. 2015;36(12):1444–50.PubMedGoogle Scholar
  51. 51.
    Morton V, Torgerson DJ. Regression to the mean: treatment effect without the intervention. J Eval Clin Pract. 2005;11(1):59–65.PubMedGoogle Scholar
  52. 52.
    Jones R, Hoover DR, Lacroix LJ. A randomized controlled trial of soap opera videos streamed to smartphones to reduce risk of sexually transmitted human immunodeficiency virus (HIV) in young urban African American women. Nurs Outlook. 2013;61(4):205–15.PubMedPubMedCentralGoogle Scholar
  53. 53.
    Koblin BA, Bonner S, Powell B, et al. A randomized trial of a behavioral intervention for black MSM: the DiSH study. AIDS. 2012;26(4):483–8.PubMedPubMedCentralGoogle Scholar
  54. 54.
    Mansergh G, Koblin BA, McKirnan DJ, et al. An intervention to reduce HIV risk behavior of substance-using men who have sex with men: a two-group randomized trial with a nonrandomized third group. PLoS Med. 2010;7(8):e1000329.PubMedPubMedCentralGoogle Scholar
  55. 55.
    Castor D, Pilowsky DJ, Hadden B, et al. Sexual risk reduction among non-injection drug users: report of a randomized controlled trial. AIDS Care. 2010;22(1):62–70.PubMedGoogle Scholar
  56. 56.
    Padian NS, McCoy SI, Balkus JE, Wasserheit JN. Weighing the gold in the gold standard: challenges in HIV prevention research. AIDS. 2010;24(5):621–35.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Du Bois SN, Johnson SE, Mustanski B. Examining racial and ethnic minority differences among YMSM during recruitment for an online HIV prevention intervention study. AIDS Behav. 2012;16(6):1430–5.PubMedPubMedCentralGoogle Scholar
  58. 58.
    Huang E, Marlin RW, Young SD, Medline A, Klausner JD. Using grindr, a smartphone social-networking application, to increase HIV self-testing among black and Latino men who have sex with men in Los Angeles, 2014. AIDS Educ Prev. 2016;28(4):341–50.PubMedPubMedCentralGoogle Scholar
  59. 59.
    Young SD, Holloway I, Jaganath D, Rice E, Westmoreland D, Coates T. Project HOPE: online social network changes in an HIV prevention randomized controlled trial for African American and Latino men who have sex with men. Am J Public Health. 2014;104(9):1707–12.PubMedPubMedCentralGoogle Scholar
  60. 60.
    Noar SM, Willoughby JF. eHealth interventions for HIV prevention. AIDS Care. 2012;24(8):945–52.PubMedPubMedCentralGoogle Scholar
  61. 61.
    Bazazi AR, Wickersham JA, Wegman MP, et al. Design and implementation of a factorial randomized controlled trial of methadone maintenance therapy and an evidence-based behavioral intervention for incarcerated people living with HIV and opioid dependence in Malaysia. Contemp Clin Trials. 2017;59:1–12.PubMedPubMedCentralGoogle Scholar
  62. 62.
    De P, Downing MJ Jr, Hirshfield S. Cost analysis of implementing a video-based eHealth intervention for HIV-positive gay, bisexual, and other men who have sex with men. AIDS Educ Prev. 2018;30(4):301–8.PubMedGoogle Scholar
  63. 63.
    Noar SM. Computer technology-based interventions in HIV prevention: state of the evidence and future directions for research. AIDS Care. 2011;23(5):525–33.PubMedPubMedCentralGoogle Scholar
  64. 64.
    Sullivan PS, Jones J, Kishore N, Stephenson R. The roles of technology in primary HIV prevention for men who have sex with men. Curr HIV/AIDS Rep. 2015;12(4):481–8.PubMedGoogle Scholar
  65. 65.
    Simoni JM, Kutner BA, Horvath KJ. Opportunities and challenges of digital technology for hiv treatment and prevention. Curr HIV/AIDS Rep. 2015;12(4):437–40.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Sabina Hirshfield
    • 1
    Email author
  • Martin J. DowningJr.
    • 2
  • Mary Ann Chiasson
    • 3
  • Irene S. Yoon
    • 4
  • Steven T. Houang
    • 5
  • Richard A. Teran
    • 3
  • Christian Grov
    • 6
  • Patrick S. Sullivan
    • 7
  • Rachel J. Gordon
    • 8
  • Donald R. Hoover
    • 9
  • Jeffrey T. Parsons
    • 10
    • 11
  1. 1.Department of MedicineSUNY Downstate Medical CenterBrooklynUSA
  2. 2.Department of Psychology, Lehman CollegeCity University of New York (CUNY)BronxUSA
  3. 3.Department of EpidemiologyColumbia UniversityNew YorkUSA
  4. 4.Gartner L2, Research and AdvisoryNew YorkUSA
  5. 5.Department of Health BehaviorUniversity of North Carolina at Chapel HillChapel HillUSA
  6. 6.Department of Community Health and Social SciencesCUNY Graduate School of Public Health and Health PolicyNew YorkUSA
  7. 7.Department of EpidemiologyEmory UniversityAtlantaUSA
  8. 8.Departments of Medicine and Epidemiology, Division of Infectious DiseasesColumbia UniversityNew YorkUSA
  9. 9.Department of Statistics and Biostatistics and Institute for Health Care Policy and Aging ResearchRutgers UniversityPiscatawayUSA
  10. 10.Department of Psychology, Hunter CollegeCity University of New York (CUNY)New YorkUSA
  11. 11.Graduate CenterCity University of New York (CUNY)New YorkUSA

Personalised recommendations