Factors associated with patterns of mobile technology use among persons who inject drugs

ABSTRACT

Background: New and innovative methods of delivering interventions are needed to further reduce risky behaviors and increase overall health among persons who inject drugs (PWID). Mobile health (mHealth) interventions have potential for reaching PWID; however, little is known about mobile technology use (MTU) in this population. In this study, the authors identify patterns of MTU and identified factors associated with MTU among a cohort of PWID. Methods: Data were collected through a longitudinal cohort study examining drug use, risk behaviors, and health status among PWID in San Diego, California. Latent class analysis (LCA) was used to define patterns of MTU (i.e., making voice calls, text messaging, and mobile Internet access). Multinomial logistic regression was then used to identify demographic characteristics, risk behaviors, and health indicators associated with mobile technology use class. Results: In LCA, a 4-class solution fit the data best. Class 1 was defined by low MTU (22%, n = 100); class 2, by PWID who accessed the Internet using a mobile device but did not use voice or text messaging (20%, n = 95); class 3, by primarily voice, text, and connected Internet use (17%, n = 91); and class 4, by high MTU (41%, n = 175). Compared with low MTU, high MTU class members were more likely to be younger, have higher socioeconomic status, sell drugs, and inject methamphetamine daily. Conclusion: The majority of PWID in San Diego use mobile technology for voice, text, and/or Internet access, indicating that rapid uptake of mHealth interventions may be possible in this population. However, low ownership and use of mobile technology among older and/or homeless individuals will need to be considered when implementing mHealth interventions among PWID.

KEYWORDS:

• Latent class analysis
• methamphetamine
• mHealth
• persons who inject drugs
• risk behaviors
• substance use

Acknowledgments

The authors gratefully acknowledge the STAHR-II staff without whom this analysis would not be possible. The authors would also like to thank the participants, community advisory board, and community collaborators for their invaluable support in conducting this study.

Author contributions

K.M.C. completed the analysis, wrote, and finalized the manuscript. R.F.A. was the data manager for the STAHR-II study. He provided data cleaning code and critical edits to the manuscript. J.C.-M. was the project coordinator for the STAHR-II study. She provided critical edits to the Methods section of the manuscript. L.L. is the statistician for the STAHR-II study. She provided analysis feedback and critical edits to the Statistical Analysis section of the manuscript. S.A.S. is a co-investigator on the STAHR-II study and provided critical edits and analysis feedback for the manuscript. R.S.G. is the principal investigator for the STAHR-II study. He guided and assisted with variable selection for this analysis and provided critical feedback and final approval for the manuscript. All authors contributed to and approved the final manuscript.

Funding

Funding for this study was provided by the National Institutes of Drug Abuse (NIDA), grants R01-DA031074 and 2T32-DA023356. The funding source had no input on the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors declare that they have no conflicts of interest.