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



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.



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.



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.
a Division of Global Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Kelly M
KM
Collins
a Division of Global Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Richard F
RF
Armenta
a Division of Global Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Jazmine
J
Cuevas-Mota
b Division of Family Medicine and Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Lin
L
Liu
a Division of Global Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Steffanie A
SA
Strathdee
a Division of Global Public Health, School of Medicine, University of California San Diego , San Diego , California , USA.
Richard S
RS
Garfein
D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D006304:Health Status; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D012309:Risk-Taking; D015819:Substance Abuse, Intravenous / Q000523:psychology*; D017216:Telemedicine / Q000706:statistics & numerical data*; D060145:Text Messaging / Q000706:statistics & numerical data*; D055815:Young Adult
Latent class analysis; mHealth; methamphetamine; persons who inject drugs; risk behaviors; substance use