Know your audience: predictors of success for a patient-centered texting app to augment linkage to HIV care in rural Uganda

Mark J Siedner, Data Santorino, Jessica E Haberer, David R Bangsberg, Mark J Siedner, Data Santorino, Jessica E Haberer, David R Bangsberg

Abstract

Background: Despite investments in infrastructure and evidence for high acceptability, few mHealth interventions have been implemented in sub-Saharan Africa.

Objective: We sought to (1) identify predictors of uptake of an mHealth application for a low-literacy population of people living with HIV (PLWH) in rural Uganda and (2) evaluate the efficacy of various short message service (SMS) text message formats to optimize the balance between confidentiality and accessibility.

Methods: The trial evaluated the efficacy of a SMS text messaging app to notify PLWH of their laboratory results and request return to care for those with abnormal test results. Participants with a normal laboratory result received a single SMS text message indicating results were normal. Participants with an abnormal test result were randomized to 1 of 3 message formats designed to evaluate trade-offs between clarity and privacy: (1) an SMS text message that stated results were abnormal and requested return to clinic ("direct"), (2) the same message protected by a 4-digit PIN code ("PIN"), and (3) the message "ABCDEFG" explained at enrollment to indicate abnormal results ("coded"). Outcomes of interest were (1) self-reported receipt of the SMS text message, (2) accurate identification of the message, and (3) return to care within 7 days (for abnormal results) or on the date of the scheduled appointment (for normal results). We fit regression models for each outcome with the following explanatory variables: sociodemographic characteristics, CD4 count result, ability to read a complete sentence, ability to access a test message on enrollment, and format of SMS text message.

Results: Seventy-two percent (234/385) of participants successfully receiving a message, 87.6% (219/250) correctly identified the message format, and 60.8% (234/385) returned to clinic at the requested time. Among participants with abnormal tests results (138/385, 35.8%), the strongest predictors of reported message receipt were the ability to read a complete sentence and a demonstrated ability to access a test message on enrollment. Participants with an abnormal result who could read a complete sentence were also more likely to accurately identify the message format (AOR 4.54, 95% CI 1.42-14.47, P=.01) and return to clinic appropriately (AOR 3.81, 95% CI 1.61-9.03, P=.002). Those who were sent a PIN-protected message were less likely to identify the message (AOR 0.11, 95% CI 0.03-0.44, P=.002) or return within 7 days (AOR 0.26, 95% CI 0.10-0.66, P=.005). Gender, age, and socioeconomic characteristics did not predict any outcomes and there were no differences in outcomes between those receiving direct or coded messages.

Conclusions: Confirmed literacy at the time of enrollment was a robust predictor of SMS text message receipt, identification, and appropriate response for PLWH in rural Uganda. PIN-protected messages reduced odds of clinic return, but coded messages were as effective as direct messages and might augment privacy.

Trial registration: Clinicaltrials.gov Keywords: HIV; Uganda; randomized controlled trial; telemedicine; text messaging.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Study schema.
Figure 2
Figure 2
Outcomes by SMS text message format and literacy.

References

    1. Qiang C, Yamamichi M, Hausman V, Altman D. Mobile applications for the health sector. Washington, DC: World Bank; 2011. Dec, [2015-03-11]. .
    1. Blaya JA, Fraser HS, Holt B. E-health technologies show promise in developing countries. Health Aff (Millwood) 2010 Feb;29(2):244–51. doi: 10.1377/hlthaff.2009.0894.
    1. The World in 2013: ICT Facts and Figures. Geneva: International Telecommunication Union; 2013. [2014-08-10]. .
    1. Tomlinson M, Rotheram-Borus MJ, Swartz L, Tsai AC. Scaling up mHealth: where is the evidence? PLoS Med. 2013;10(2):e1001382. doi: 10.1371/journal.pmed.1001382.
    1. Gurman TA, Rubin SE, Roess AA. Effectiveness of mHealth behavior change communication interventions in developing countries: a systematic review of the literature. J Health Commun. 2012;17 Suppl 1:82–104. doi: 10.1080/10810730.2011.649160.
    1. Aranda-Jan CB, Mohutsiwa-Dibe N, Loukanova S. Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Public Health. 2014;14:188. doi: 10.1186/1471-2458-14-188.
    1. Andreatta P, Debpuur D, Danquah A, Perosky J. Using cell phones to collect postpartum hemorrhage outcome data in rural Ghana. Int J Gynaecol Obstet. 2011 May;113(2):148–51. doi: 10.1016/j.ijgo.2010.11.020.
    1. Haberer JE, Kiwanuka J, Nansera D, Wilson IB, Bangsberg DR. Challenges in using mobile phones for collection of antiretroviral therapy adherence data in a resource-limited setting. AIDS Behav. 2010 Dec;14(6):1294–301. doi: 10.1007/s10461-010-9720-1.
    1. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010 Feb;43(1):159–72. doi: 10.1016/j.jbi.2009.07.002.
    1. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly. 2003;27(3):425–477.
    1. Jennings L, Ong'ech J, Simiyu R, Sirengo M, Kassaye S. Exploring the use of mobile phone technology for the enhancement of the prevention of mother-to-child transmission of HIV program in Nyanza, Kenya: a qualitative study. BMC Public Health. 2013;13:1131. doi: 10.1186/1471-2458-13-1131.
    1. Siedner MJ, Haberer JE, Bwana MB, Ware NC, Bangsberg DR. High acceptability for cell phone text messages to improve communication of laboratory results with HIV-infected patients in rural Uganda: a cross-sectional survey study. BMC Med Inform Decis Mak. 2012;12:56. doi: 10.1186/1472-6947-12-56.
    1. Tomlinson M, Solomon W, Singh Y, Doherty T, Chopra M, Ijumba P, Tsai AC, Jackson D. The use of mobile phones as a data collection tool: a report from a household survey in South Africa. BMC Med Inform Decis Mak. 2009;9:51. doi: 10.1186/1472-6947-9-51.
    1. 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. doi: 10.1080/09540121.2013.774315.
    1. van HA, Norris S, Tollman S, Richter L, Rotheram-Borus MJ. Collecting maternal health information from HIV-positive pregnant women using mobile phone-assisted face-to-face interviews in Southern Africa. J Med Internet Res. 2013;15(6):e116. doi: 10.2196/jmir.2207.
    1. Leon N, Schneider H, Daviaud E. Applying a framework for assessing the health system challenges to scaling up mHealth in South Africa. BMC Med Inform Decis Mak. 2012;12:123. doi: 10.1186/1472-6947-12-123.
    1. Tsai AC, Bangsberg DR, Kegeles SM, Katz IT, Haberer JE, Muzoora C, Kumbakumba E, Hunt PW, Martin JN, Weiser SD. Internalized stigma, social distance, and disclosure of HIV seropositivity in rural Uganda. Ann Behav Med. 2013 Dec;46(3):285–94. doi: 10.1007/s12160-013-9514-6.
    1. Siedner M, Santorino D, Lankowski A, Kanyesigye M, Bwana M, Haberer J, Bangsberg D. An SMS intervention to improve HIV linkage to care: a randomized, comparative effectiveness trial. 21st Conference on Retrovirusesand Opportunistic Infections; March 2014; Boston, MA. 2014.
    1. Ware NC, Idoko J, Kaaya S, Biraro IA, Wyatt MA, Agbaji O, Chalamilla G, Bangsberg DR. Explaining adherence success in sub-Saharan Africa: an ethnographic study. PLoS Med. 2009 Jan 27;6(1):e11. doi: 10.1371/journal.pmed.1000011.
    1. Siedner MJ, Lankowski A, Tsai AC, Muzoora Co, Martin JN, Hunt PW, Haberer JE, Bangsberg DR. GPS-measured distance to clinic, but not self-reported transportation factors, are associated with missed HIV clinic visits in rural Uganda. AIDS. 2013 Jun 1;27(9):1503–8. doi: 10.1097/QAD.0b013e32835fd873.
    1. Lankowski AJ, Siedner MJ, Bangsberg DR, Tsai AC. Impact of geographic and transportation-related barriers on HIV outcomes in sub-Saharan Africa: a systematic review. AIDS Behav. 2014 Jul;18(7):1199–223. doi: 10.1007/s10461-014-0729-8.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377–81. doi: 10.1016/j.jbi.2008.08.010.
    1. Venkatesh V, Thong J, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly. 2012;36(1):157–178.
    1. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science. 2000 Feb;46(2):186–204. doi: 10.1287/mnsc.46.2.186.11926.
    1. Nakamura A, Osonoi T, Terauchi Y. Relationship between urinary sodium excretion and pioglitazone-induced edema. J Diabetes Investig. 2010 Oct 19;1(5):208–11. doi: 10.1111/j.2040-1124.2010.00046.x.
    1. Mitchell KJ, Bull S, Kiwanuka J, Ybarra ML. Cell phone usage among adolescents in Uganda: acceptability for relaying health information. Health Educ Res. 2011 Oct;26(5):770–81. doi: 10.1093/her/cyr022.
    1. Crankshaw T, Corless IB, Giddy J, Nicholas PK, Eichbaum Q, Butler LM. Exploring the patterns of use and the feasibility of using cellular phones for clinic appointment reminders and adherence messages in an antiretroviral treatment clinic, Durban, South Africa. AIDS Patient Care STDS. 2010 Nov;24(11):729–34. doi: 10.1089/apc.2010.0146.
    1. Cohen JF, Sergay SD. An empirical study of health consumer beliefs, attitude and intentions toward the use of self-service kiosks. American Conference on Information Systems (AMCIS); August 4-8, 2011; Detroit, MI. 2011.
    1. van der Kop ML, Karanja S, Thabane L, Marra C, Chung MH, Gelmon L, Kimani J, Lester RT. In-depth analysis of patient-clinician cell phone communication during the WelTel Kenya1 antiretroviral adherence trial. PLoS One. 2012;7(9):e46033. doi: 10.1371/journal.pone.0046033.
    1. Pop-Eleches C, Thirumurthy H, Habyarimana JP, Zivin JG, Goldstein MP, de Walque D, MacKeen L, Haberer J, Kimaiyo S, Sidle J, Ngare D, Bangsberg DR. Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: a randomized controlled trial of text message reminders. AIDS. 2011 Mar 27;25(6):825–34. doi: 10.1097/QAD.0b013e32834380c1.
    1. Lester RT, Ritvo P, Mills EJ, Kariri A, Karanja S, Chung MH, Jack W, Habyarimana J, Sadatsafavi M, Najafzadeh M, Marra CA, Estambale B, Ngugi E, Ball TB, Thabane L, Gelmon LJ, Kimani J, Ackers M, Plummer FA. Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. 2010 Nov 27;376(9755):1838–45. doi: 10.1016/S0140-6736(10)61997-6.
    1. Mbuagbaw L, Thabane L, Ongolo-Zogo P, Lester RT, Mills EJ, Smieja M, Dolovich L, Kouanfack C. The Cameroon Mobile Phone SMS (CAMPS) trial: a randomized trial of text messaging versus usual care for adherence to antiretroviral therapy. PLoS One. 2012;7(12):e46909. doi: 10.1371/journal.pone.0046909.

Source: PubMed

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