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Journal of Urban Health

, Volume 87, Issue 1, pp 122–128 | Cite as

Digital Divide: Variation in Internet and Cellular Phone Use among Women Attending an Urban Sexually Transmitted Infections Clinic

  • Lipika SamalEmail author
  • Heidi E. Hutton
  • Emily J. Erbelding
  • Elizabeth S. Brandon
  • Joseph Finkelstein
  • Geetanjali Chander
Article

Abstract

We sought to describe: (1) the prevalence of internet, cellular phone, and text message use among women attending an urban sexually transmitted infections (STI) clinic, (2) the acceptability of health advice by each mode of information and communication technology (ICT), and (3) demographic characteristics associated with ICT use. This study is a cross-sectional survey of 200 English-speaking women presenting to a Baltimore City STI clinic with STI complaints. Participants completed a self-administered survey querying ICT use and demographic characteristics. Three separate questions asked about interest in receiving health advice delivered by the three modalities: internet, cellular phone, and text message. We performed logistic regression to examine how demographic factors (age, race, and education) are associated with likelihood of using each modality. The median age of respondents was 27 years; 87% were African American, and 71% had a high school diploma. The rate of any internet use was 80%; 31% reported daily use; 16% reported weekly use; and 32% reported less frequent use. Almost all respondents (93%) reported cellular phone use, and 79% used text messaging. Acceptability of health advice by each of the three modalities was about 60%. In multivariate analysis, higher education and younger age were associated with internet use, text messaging, and cellular phone use. Overall rate of internet use was high, but there was an educational disparity in internet use. Cellular phone use was almost universal in this sample. All three modalities were equally acceptable forms of health communication. Describing baseline ICT access and the acceptability of health advice via ICT, as we have done, is one step toward determining the feasibility of ICT-delivered health interventions in urban populations.

Keywords

Women’s Health Internet use Cellular phone SMS Text message Sexually transmitted infections 

Notes

Acknowledgements

We would like to acknowledge the assistance of clinical informationist Blair Anton, MLIS, MS, at the Welch Medical Library of Johns Hopkins University School of Medicine.

A poster based on this study was presented at the 32nd Annual Meeting of the Society of General Internal Medicine, May 13–16, 2009, Miami Beach, FL.

References

  1. 1.
    Hutton HE, McCaul ME, Santora PB, Erbelding EJ. The relationship between recent alcohol use and sexual behaviors: gender differences among sexually transmitted disease clinic patients. Alcohol Clin Exp Res. 2008; 32(11): 2008-2015. Epub 6 Sep 2008.PubMedGoogle Scholar
  2. 2.
    Linke S, Murray E, Butler C, Wallace P. Internet-based interactive health intervention for the promotion of sensible drinking: patterns of use and potential impact on members of the general public. J Med Internet Res. 2007; 9(2): e10.CrossRefPubMedGoogle Scholar
  3. 3.
    Riper H, Kramer J, Smit F, Conijn B, Schippers G, Cuijpers P. Web-based self-help for problem drinkers: a pragmatic randomized trial. Addiction. 2008; 103(2): 218-227.CrossRefPubMedGoogle Scholar
  4. 4.
    Vidrine DJ, Arduino RC, Lazev AB, Gritz ER. A randomized trial of a proactive cellular telephone intervention for smokers living with HIV/AIDS. AIDS. 2006; 20(2): 253-260.CrossRefPubMedGoogle Scholar
  5. 5.
    Rodgers A, Corbett T, Bramley D, Riddell T, Wills M, Lin RB. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tob Control. 2005; 14(4): 255-261.CrossRefPubMedGoogle Scholar
  6. 6.
    Levine D, McCright J, Dobkin L, Woodruff AJ, Klausner JD. SEXINFO: a sexual health text messaging service for San Francisco youth. Am J Public Health. 2008; 98(3): 393-395.CrossRefPubMedGoogle Scholar
  7. 7.
    Rietmeijer CA, Bull SS, McFarlane M, Patnaik JL, Douglas JM Jr. Risks and benefits of the internet for populations at risk for sexually transmitted infections (STIs): results of an STI clinic survey. Sex Transm Dis. 2003; 30(1): 15-19.CrossRefPubMedGoogle Scholar
  8. 8.
    Kalichman SC, Weinhardt L, Benotsch E, DiFonzo K, Luke W, Austin J. Internet access and internet use for health information among people living with HIV–AIDS. Patient Educ Couns. 2002; 46(2): 109-116.CrossRefPubMedGoogle Scholar
  9. 9.
    Mayben JK, Giordano TP. Internet use among low-income persons recently diagnosed with HIV infection. AIDS Care. 2007; 19(9): 1182-1187.CrossRefPubMedGoogle Scholar
  10. 10.
    U.S. Census Bureau, Statistical Abstract of the United States: 2009 (128th Edition) Washington, DC, 2008. Available at: http://www.census.gov/compendia/statab/cats/information_communications/internet_publishing_and_broadcasting_and_internet_usage.html. Accessed on: August 20, 2009.
  11. 11.
    Peterson NB, Dwyer KA, Mulvaney SA. Computer and internet use in a community health clinic population. Med Decis Making. 2009; 29(2): 202-206.CrossRefPubMedGoogle Scholar
  12. 12.
    Harris Interactive. The Harris Poll #36, April 4, 2008. Available at: http://www.harrisinteractive.com/harris_poll/index.asp?PID=890. Accessed on: February 22, 2009.
  13. 13.
    Pew Internet & American Life Project. Pew Internet Project Data Memo. Available at: http://www.pewinternet.org/∼/media//Files/Reports/2006/PIP_Cell_phone_study.pdf. Accessed on: July 29, 2009.
  14. 14.
    Blumberg SJ, Luke JV. Reevaluating the need for concern regarding noncoverage bias in landline surveys. Am J Public Health. 2009; 99(10): 1806-1810.CrossRefPubMedGoogle Scholar
  15. 15.
    Cunningham JA, Humphreys K, Koski-Jannes A, Cordingley J. Internet and paper self-help materials for problem drinking: is there an additive effect? Addict Behav. 2005; 30(8): 1517-1523.CrossRefPubMedGoogle Scholar
  16. 16.
    Hester RK, Squires DD, Delaney HD. The Drinker’s Check-Up: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse Treat. 2005; 28(2): 159-169.CrossRefPubMedGoogle Scholar
  17. 17.
    Kypri K, Saunders JB, Williams SM, et al. Web-based screening and brief intervention for hazardous drinking: a double-blind randomized controlled trial. Addiction. 2004; 99(11): 1410-1417.CrossRefPubMedGoogle Scholar
  18. 18.
    Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered personalized normative feedback intervention. J Consult Clin Psychol. 2004; 72(3): 434-447.CrossRefPubMedGoogle Scholar
  19. 19.
    Weitzel JA, Bernhardt JM, Usdan S, Mays D, Glanz K. Using wireless handheld computers and tailored text messaging to reduce negative consequences of drinking alcohol. J Stud Alcohol Drugs. 2007; 68(4): 534-537.PubMedGoogle Scholar
  20. 20.
    Joo NS, Kim BT. Mobile phone short message service messaging for behaviour modification in a community-based weight control programme in Korea. J Telemed Telecare. 2007; 13(8): 416-420.CrossRefPubMedGoogle Scholar

Copyright information

© The New York Academy of Medicine 2009

Authors and Affiliations

  • Lipika Samal
    • 1
    Email author
  • Heidi E. Hutton
    • 1
  • Emily J. Erbelding
    • 1
  • Elizabeth S. Brandon
    • 1
  • Joseph Finkelstein
    • 1
  • Geetanjali Chander
    • 1
  1. 1.Division of General Internal MedicineJohns Hopkins University School of MedicineBaltimoreUSA

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