AIDS and Behavior

, Volume 21, Issue 11, pp 3100–3110 | Cite as

Examining the Acceptability of mHealth Technology in HIV Prevention Among High-Risk Drug Users in Treatment

  • Roman Shrestha
  • Tania B. Huedo-Medina
  • Frederick L. Altice
  • Archana Krishnan
  • Michael Copenhaver
Original Paper


Despite promising trends of the efficacy of mobile health (mHealth) based strategies to a broad range of health conditions, very few if any studies have been done in terms of the examining the use of mHealth in HIV prevention efforts among people who use drugs in treatment. Thus, the goal of this study was to gain insight into the real-world acceptance of mHealth approaches among high-risk people who use drugs in treatment. A convenience sample of 400 HIV-negative drug users, who reported drug- and/or sex-related risk behaviors, were recruited from a methadone clinic in New Haven, Connecticut. Participants completed standardized assessments of drug- and sex-related risk behaviors, neurocognitive impairment (NCI), and measures of communication technology access and utilization, and mHealth acceptance. We found a high prevalence of current ownership and use of mobile technologies, such as cell phone (91.5%) including smartphone (63.5%). Participants used mobile technologies to communicate mostly through phone calls (M = 4.25, SD = 1.24), followed by text messages (M = 4.21, SD = 1.29). Participants expressed interest in using mHealth for medication reminders (72.3%), receive information about HIV (65.8%), and to assess drug-related (72.3%) and sex-related behaviors (64.8%). Furthermore, participants who were neurocognitively impaired were more likely to use cell phone without internet and show considerable interest in using mHealth as compared to those without NCI. The findings from this study provide empirical evidence that mHealth-based programs, specifically cell phone text messaging-based health programs, may be acceptable to this high-risk population.


mHealth Text messaging Substance abuse People who use drugs Neurocognitive impairment 



This work was supported by Grants from the National Institute on Drug Abuse for research (R01 DA025943 to FLA) and for career development (K24 DA017072 to FLA; K02 DA033139 to MMC).

Compliance with Ethical Standards

Conflicts of interest

The authors have no conflicts of interest to disclose.

Ethical Approval

The study protocol was approved by the Investigational Review Board (IRB) at the University of Connecticut and received board approval from APT Foundation Inc. 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.


  1. 1.
    Degenhardt L, Whiteford HA, Ferrari AJ, Baxter AJ, Charlson FJ, Hall WD, et al. Global burden of disease attributable to illicit drug use and dependence: findings from the global burden of disease study 2010. Lancet. 2013;382(9904):1564–74.PubMedCrossRefGoogle Scholar
  2. 2.
    Centers for Disease Control and Prevention. HIV surveillance report, 2014, vol. 26. Atlanta: Centers for Disease Control and Prevention; 2014.Google Scholar
  3. 3.
    Arasteh K, Jarlais DCD, Perlis TE. Alcohol and HIV sexual risk behaviors among injection drug users. Drug Alcohol Depend. 2008;95(1):54–61.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Marshall BDL, Friedman SR, Monteiro JFG, Paczkowski M, Tempalski B, Pouget ER, et al. prevention and treatment produced large decreases in HIV incidence in a model of people who inject drugs. Health Aff. 2014;33(3):401–9.CrossRefGoogle Scholar
  5. 5.
    Noar SM. Behavioral interventions to reduce HIV-related sexual risk behavior: review and synthesis of meta-analytic evidence. AIDS Behav. 2008;12(3):335–53.PubMedCrossRefGoogle Scholar
  6. 6.
    Strathdee SA, Hallett TB, Bobrova N, Rhodes T, Booth R, Abdool R, et al. HIV and risk environment for injecting drug users: the past, present, and future. Lancet. 2010;376(9737):268–84.PubMedCrossRefGoogle Scholar
  7. 7.
    Volkow ND, Montaner J. The urgency of providing comprehensive and integrated treatment for substance abusers with HIV. Health Aff (Project Hope). 2011;30(8):1411–9.CrossRefGoogle Scholar
  8. 8.
    Goldstein RZ, Leskovjan AC, Hoff AL, Hitzemann R, Bashan F, Khalsa SS, et al. Severity of neuropsychological impairment in cocaine and alcohol addiction: association with metabolism in the prefrontal cortex. Neuropsychologia. 2004;42(11):1447–58.PubMedCrossRefGoogle Scholar
  9. 9.
    Meade CS, Towe SL, Skalski LM, Robertson KR. Independent effects of HIV infection and cocaine dependence on neurocognitive impairment in a community sample living in the southern United States. Drug Alcohol Depend. 2015;149:128–35.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Potvin S, Stavro K, Rizkallah E, Pelletier J. Cocaine and cognition: a systematic quantitative review. J Addict Med. 2014;8(5):368–76.PubMedCrossRefGoogle Scholar
  11. 11.
    Ezeabogu I, Copenhaver MM, Potrepka J. The influence of neurocognitive impairment on HIV treatment outcomes among drug-involved people living with HIV/AIDS. AIDS Care. 2012;24(3):386–93.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Anderson AM, Higgins MK, Ownby RL, Waldrop-Valverde D. Changes in neurocognition and adherence over six months in HIV-infected individuals with cocaine or heroin dependence. AIDS Care. 2015;27(3):333–7.PubMedCrossRefGoogle Scholar
  13. 13.
    Attonito JM, Devieux JG, Lerner BD, Hospital MM, Rosenberg R. Exploring substance use and HIV treatment factors associated with neurocognitive impairment among people living with HIV/AIDS. Front Public Health. 2014;2:105.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Becker BW, Thames AD, Woo E, Castellon SA, Hinkin CH. Longitudinal change in cognitive function and medication adherence in HIV-infected adults. AIDS Behav. 2011;15(8):1888–94.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Anand P, Springer SA, Copenhaver MM, Altice FL. Neurocognitive impairment and HIV risk factors: a reciprocal relationship. AIDS Behav. 2010;14(6):1213–26.PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Shrestha R, Huedo-Medina TB, Copenhaver MM. Sex-related differences in self-reported neurocognitive impairment among high-risk cocaine users in methadone maintenance treatment program. Subst Abus. 2015;9:17–24.Google Scholar
  17. 17.
    Bates ME, Pawlak AP, Tonigan JS, Buckman JF. Cognitive impairment influences drinking outcome by altering therapeutic mechanisms of change. Psychol Addict Behav. 2006;20(3):241–53.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Verdejo-Garcia A, Perez-Garcia M. Profile of executive deficits in cocaine and heroin polysubstance users: common and differential effects on separate executive components. Psychopharmacology. 2007;190(4):517–30.PubMedCrossRefGoogle Scholar
  19. 19.
    Fishbein DH, Krupitsky E, Flannery BA, Langevin DJ, Bobashev G, Verbitskaya E, et al. Neurocognitive characterizations of Russian heroin addicts without a significant history of other drug use. Drug Alcohol Depend. 2007;90(1):25–38.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Vo HT, Schacht R, Mintzer M, Fishman M. Working memory impairment in cannabis- and opioid-dependent adolescents. Subst Abuse. 2014;35(4):387–90.CrossRefGoogle Scholar
  21. 21.
    Shrestha R, Copenhaver M. The influence of neurocognitive impairment on HIV risk behaviors and intervention outcomes among high-risk substance users: a systematic review. Front Public Health. 2016;4:16.PubMedPubMedCentralGoogle Scholar
  22. 22.
    CDC. Effective interventions: HIV prevention that works. Atlanta: CDC; 2016.Google Scholar
  23. 23.
    Huedo-Medina TB, Shrestha R, Copenhaver M. Modeling a theory-based approach to examine the influence of neurocognitive impairment on HIV risk reduction behaviors among drug users in treatment. AIDS Behav. 2016;20:1646–57.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Thigpen MC, Kebaabetswe PM, Paxton LA, Smith DK, Rose CE, Segolodi TM, et al. Antiretroviral preexposure prophylaxis for heterosexual HIV transmission in Botswana. N Engl J Med. 2012;367(5):423–34.PubMedCrossRefGoogle Scholar
  25. 25.
    Grant RM, Lama JR, Anderson PL, McMahan V, Liu AY, Vargas L, et al. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. N Engl J Med. 2010;363(27):2587–99.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Baeten JM, Donnell D, Ndase P, Mugo NR, Campbell JD, Wangisi J, et al. Antiretroviral prophylaxis for HIV prevention in heterosexual men and women. N Engl J Med. 2012;367(5):399–410.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Choopanya K, Martin M, Suntharasamai P, Sangkum U, Mock PA, Leethochawalit M, et al. Antiretroviral prophylaxis for HIV infection in injecting drug users in Bangkok, Thailand (the Bangkok Tenofovir Study): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet. 2013;381(9883):2083–90.PubMedCrossRefGoogle Scholar
  28. 28.
    Haberer JE, Baeten JM, Campbell J, Wangisi J, Katabira E, Ronald A, et al. Adherence to antiretroviral prophylaxis for HIV prevention: a substudy cohort within a clinical trial of serodiscordant couples in East Africa. PLoS Med. 2013;10(9):e1001511.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    WHO. mHealth: New horizons for health through mobile technologies: second global survey on eHealth. Geneva: WHO; 2011.Google Scholar
  30. 30.
    Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 2013;10(1):e1001362.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Lester RT, Ritvo P, Mills EJ, Kariri A, Karanja S, Chung MH, et al. Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. 2010;376(9755):1838–45.PubMedCrossRefGoogle Scholar
  32. 32.
    Milward J, Lynskey M, Strang J. Solving the problem of non-attendance in substance abuse services. Drug Alcohol Rev. 2014;33(6):625–36.PubMedCrossRefGoogle Scholar
  33. 33.
    Mbuagbaw L, van der Kop ML, Lester RT, Thirumurthy H, Pop-Eleches C, Ye C, et al. Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials. BMJ Open. 2013;3(12):e003950.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Pop-Eleches C, Thirumurthy H, Habyarimana JP, Zivin JG, Goldstein MP, De Walque D, et al. Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: a randomized controlled trial of text message reminders. AIDS (London, England). 2011;25(6):825.CrossRefGoogle Scholar
  35. 35.
    Cole-Lewis H, Kershaw T. Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev. 2010;32(1):56–69.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Militello LK, Kelly SA, Melnyk BM. Systematic review of text-messaging interventions to promote healthy behaviors in pediatric and adolescent populations: implications for clinical practice and research. Worldviews Evid Based Nurs. 2012;9(2):66–77.PubMedCrossRefGoogle Scholar
  37. 37.
    Finitsis DJ, Pellowski JA, Johnson BT. Text message intervention designs to promote adherence to antiretroviral therapy (ART): a meta-analysis of randomized controlled trials. PLoS ONE. 2014;9(2):e88166.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Greenspun H, Coughlin S. mHealth in an mWorld: How mobile technology is transforming health care. Washington: Deloitte Center for Health Solutions; 2012.Google Scholar
  39. 39.
    Fox S, Duggan M. Mobile health 2012. Washington, DC: Pew Internet & American Life Project; 2012.Google Scholar
  40. 40.
    McClure EA, Acquavita SP, Harding E, Stitzer ML. Utilization of communication technology by patients enrolled in substance abuse treatment. Drug Alcohol Depend. 2013;129(1–2):145–50.PubMedCrossRefGoogle Scholar
  41. 41.
    Firth J, Cotter J, Torous J, Bucci S, Firth JA, Yung AR. Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophr Bull. 2015. doi: 10.1093/schbul/sbv132.PubMedPubMedCentralGoogle Scholar
  42. 42.
    Copenhaver MM, Lee IC, Margolin A. Successfully integrating an HIV risk reduction intervention into a community-based substance abuse treatment program. Am J Drug Alcohol Abus. 2007;33(1):109–20.CrossRefGoogle Scholar
  43. 43.
    Copenhaver MM, Lee IC. Optimizing a community-friendly HIV risk reduction intervention for injection drug users in treatment: a structural equation modeling approach. J Urban Health. 2006;83(6):1132–42.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Fisher JD, Cornman DH, Osborn CY, Amico KR, Fisher WA, Friedland GA. Clinician-initiated HIV risk reduction intervention for HIV-positive persons: formative research, acceptability, and fidelity of the options project. J Acquir Immune Defic Syndr. 1999;2004(37 Suppl 2):S78–87.Google Scholar
  45. 45.
    Wilson IB, Lee Y, Michaud J, Fowler FJ, Rogers WH. Validation of a new three-item self-report measure for medication adherence. AIDS Behav. 2016. doi: 10.1007/s10461-016-1406-x.Google Scholar
  46. 46.
    Ward J, Darke S, Hall W. The HIV risk-taking behaviour scale (HRBS) manual. Sydney: National Drug and Alcohol Research Centre, University of New South Wales; 1990.Google Scholar
  47. 47.
    Krishnan A, Ferro EG, Weikum D, Vagenas P, Lama JR, Sanchez J, 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. 2014. doi: 10.1080/09540121.2014.963014.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Copenhaver M, Shrestha R, Wickersham JA, Weikum D, Altice FL. An exploratory factor analysis of a brief self-report scale to detect neurocognitive impairment among participants enrolled in methadone maintenance therapy. J Subst Abus Treat. 2016;63:61–5.CrossRefGoogle Scholar
  49. 49.
    Spreen O, Strauss E. A compendium of neuropsychological tests: administration, norms, and commentary. Oxford: Oxford University Press; 1998.Google Scholar
  50. 50.
    Dwan TM, Ownsworth T, Chambers S, Walker DG, Shum DHK. Neuropsychological assessment of individuals with brain tumor: comparison of approaches used in the classification of impairment. Front Oncol. 2015;5:56.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    IBM Corp. IBM SPSS statistics for windows, version 23. Armonk: IBM Corp; 2015.Google Scholar
  52. 52.
    Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Res Notes. 2010;3(1):1–7.CrossRefGoogle Scholar
  53. 53.
    Pew Research Center. Technology device ownership: 2015. Washington, DC: Pew Research Center; 2015.Google Scholar
  54. 54.
    Chang LW, Njie-Carr V, Kalenge S, Kelly JF, Bollinger RC, Alamo-Talisuna S. Perceptions and acceptability of mHealth interventions for improving patient care at a community-based HIV/AIDS clinic in Uganda: a mixed methods study. AIDS Care. 2013;25(7):874–80.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Kim J, Zhang W, Nyonyitono M, Lourenco L, Nanfuka M, Okoboi S, et al. Feasibility and acceptability of mobile phone short message service as a support for patients receiving antiretroviral therapy in rural Uganda: a cross-sectional study. J Int AIDS Soc. 2015;18(1):20311.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Horvath T, Azman H, Kennedy GE, Rutherford GW. Mobile phone text messaging for promoting adherence to antiretroviral therapy in patients with HIV infection. Cochrane Database Syst Rev. 2012;3:Cd009756.Google Scholar
  57. 57.
    Huang D, Sangthong R, McNeil E, Chongsuvivatwong V, Zheng W, Yang X. Effects of a phone call intervention to promote adherence to antiretroviral therapy and quality of life of HIV/AIDS patients in Baoshan, China: a randomized controlled trial. AIDS Res Treat. 2013;2013:580974.PubMedPubMedCentralGoogle Scholar
  58. 58.
    Wallace SE, Graham C, Saraceno A. Older adults’ use of technology. SIG 15 Perspect Gerontol. 2013;18(2):50–9.CrossRefGoogle Scholar
  59. 59.
    Charness N, Boot WR. Aging and information technology use: potential and barriers. Curr Dir Psychol Sci. 2009;18(5):253–8.CrossRefGoogle Scholar
  60. 60.
    Yasmin F, Banu B, Zakir SM, Sauerborn R, Ali L, Souares A. Positive influence of short message service and voice call interventions on adherence and health outcomes in case of chronic disease care: a systematic review. BMC Med Inform Decis Mak. 2016;16(1):1–14.CrossRefGoogle Scholar
  61. 61.
    Calderon Y, Cowan E, Nickerson J, Mathew S, Fettig J, Rosenberg M, et al. Educational effectiveness of an HIV pretest video for adolescents: a randomized controlled trial. Pediatrics. 2011;127(5):911–6.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Hirshfield S, Downing JM Jr, Parsons TJ, Grov C, Gordon JR, Houang TS, 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.PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    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.PubMedCrossRefGoogle Scholar
  64. 64.
    Chuck C, Robinson E, Macaraig M, Alexander M, Burzynski J. Enhancing management of tuberculosis treatment with video directly observed therapy in New York City. Int J Tuberc Lung Dis. 2016;20(5):588–93.PubMedCrossRefGoogle Scholar
  65. 65.
    Goggin K, Liston RJ, Adelson Mitty J. Modified directly observed therapy for antiretroviral therapy: a primer from the field. Public Health Rep. 2007;122(4):472–81.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Wald DS, Butt S, Bestwick JP. One-way versus two-way text messaging on improving medication adherence: meta-analysis of randomized trials. Am J Med. 2015;128(10):1139.e1131–5.CrossRefGoogle Scholar
  67. 67.
    Rosenbaum S. The patient protection and affordable care act: implications for public health policy and practice. Public Health Rep. 2011;126(1):130–5.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Community Medicine & Health CareUniversity of Connecticut Health CenterFarmingtonUSA
  2. 2.Institute for Collaboration on Health, Intervention, and PolicyUniversity of ConnecticutStorrsUSA
  3. 3.Department of Allied Health SciencesUniversity of ConnecticutStorrsUSA
  4. 4.Department of Internal Medicine, AIDS ProgramYale UniversityNew HavenUSA
  5. 5.Department of CommunicationUniversity at Albany, State University of New YorkAlbanyUSA

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