Utilizing mHealth methods to identify patterns of high risk illicit drug use.
BACKGROUND
We assessed patterns of illicit drug use using mobile health (mHealth) methods and subsequent health care indicators among drug users in Baltimore, MD.


METHODS
Participants of the EXposure Assessment in Current Time (EXACT) study were provided a mobile device for assessment of their daily drug use (heroin, cocaine or both), mood and social context for 30 days from November 2008 through May 2013. Real-time, self-reported drug use events were summed for individuals by day. Drug use risk was assessed through growth mixture modeling. Latent class regression examined the association of mHealth-defined risk groups with indicators of healthcare access and utilization.


RESULTS
109 participants were a median of 48.5 years old, 90% African American, 52% male and 59% HIV-infected. Growth mixture modeling identified three distinct classes: low intensity drug use (25%), moderate intensity drug use (65%) and high intensity drug use (10%). Compared to low intensity drug users, high intensity users were younger, injected greater than once per day, and shared needles. At the subsequent study visit, high intensity drug users were nine times less likely to be medically insured (adjusted OR: 0.10, 95%CI: 0.01-0.88) and at greater risk for failing to attend any outpatient appointments (aOR: 0.13, 95%CI: 0.02-0.85) relative to low intensity drug users.


CONCLUSIONS
Real-time assessment of drug use and novel methods of describing sub-classes of drug users uncovered individuals with higher-risk behavior who were poorly utilizing healthcare services. mHealth holds promise for identifying individuals engaging in high-risk behaviors and delivering real-time interventions to improve care outcomes.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States. Electronic address: blinas@jhu.edu.
Beth S
BS
Linas
Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Carl
C
Latkin
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Andrew
A
Genz
Department of Medicine, University of Wisconsin, Madison, WI, United States.
Ryan P
RP
Westergaard
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.
Larry W
LW
Chang
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Robert C
RC
Bollinger
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.
Gregory D
GD
Kirk
D000328:Adult; D001741:Black or African American; D015142:Baltimore / Q000453:epidemiology; D001519:Behavior; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D055030:Drug Users / Q000145:classification* / Q000523:psychology; D005260:Female; D015658:HIV Infections / Q000150:complications; D006297:Health Services Accessibility; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D012959:Socioeconomic Factors; D019966:Substance-Related Disorders / Q000175:diagnosis* / Q000453:epidemiology / Q000523:psychology
Ecological Momentary Assessment; Growth mixture models; HIV; Illicit drug use; mHealth