This analysis of self-reported baseline criminal justice status upon entry into a randomized controlled trial of web-based addiction treatment indicated several notable findings: compared to participants with no reported CJS involvement, those mandated or recommended to treatment by CJS authorities were younger, more likely to be male, and more cannabis-involved. Regardless of CJS-status, all participants experienced the trial or the TES web-based psychosocial intervention in a similar fashion; there were no differences observed in the rate of TES participation, retention in treatment, or end-of-study drug and alcohol abstinence. These findings demonstrate a high level of feasibility and acceptability of web-based interventions among community-based addiction treatment attendees, including those that are involved to varying degrees with the CJS system.

While CJS-involved populations may have more unmet treatment needs (Fazel et al., 2006; Feucht and Gfroerer, 2011), our findings generally were contrary to our hypotheses that criminal justice involved individuals would show greater levels of impairment and less success in web-based treatment; there were no differences in terms of TES initiation, completion, or the trial’s main outcomes of retention and abstinence across the three CJS subgroups. Our hypotheses were based on prior studies characterizing CJS populations as disproportionately poor and unemployed, with low literacy rates and worse mortality and general health outcomes (Binswanger et al., 2007; Greenberg et al., 2007; Hudson, 1987). Additionally, we considered prior studies suggesting that mandated treatment outcomes were often worse compared to other populations (Perron and Bright, 2008; Simpson et al., 1997; Taxman, 1998).

The sample as a whole showed impairment (e.g. relatively low educational attainment, low employment, and relatively higher rates of anxiety and depression), neurocognitive tests below population norms in general, and increasingly lower neurocognitive scores as tests increased in difficulty (e.g. from Numbers Forward to Numbers Reversed). Due to an assumption of potentially lower socioeconomic status and educational achievement, we expected lower rates of Internet use and lower neurocognitive ratings among CJS-involved participants, neither of which were found. If anything, the data suggest that within this relatively impaired overall sample of treatment seekers, those with criminal justice involvement may have less impairment, and be more ready to benefit from a computer-delivered intervention. The younger age of the criminal justice involved patients might explain some of these results, such as a higher overall proportion reporting some recent Internet use. Younger addiction treatment entrants are generally more likely to use the Internet (Cohall et al., 2011), which may have applied to CJS-involved participants in this study. Further, because the CJS-involved participants in this study were able to seek outpatient drug treatment, as opposed to serving jail time, it may be that their criminal histories were shorter and less involved (e.g., infractions related to driving under the influence or possession of cannabis). This would also account for the small number of differences found between the CJS subgroups.

Other studies have found computer-delivered interventions to be successful with CJS involved patients. Inmates completing an attitudinal survey were highly receptive towards receiving computerized adjunct psychotherapy and were “equally divided” between receiving computerized versus clinician-delivered therapy sessions (Ford and Vitelli, 1992). Participants agreed that the computer sessions were “a good use of time”, “enjoyable”, and “interesting” (Ford and Vitelli, 1992). A randomized trial (Alemagno et al., 2009) examined the effectiveness of a brief computerized motivational intervention compared to usual education activities, assessing whether it could decrease HIV risk-behaviors and increase HIV testing among a community-supervised population (i.e. probationers). At 2-months post intervention, the number of those who obtained HIV testing were significantly higher in the experimental vs. control group. Furthermore, those randomized to the computerized intervention appeared to have increased positive attitudes towards changing their risk and awareness of HIV (Alemagno et al., 2009). In a recent clinical trial using TES in 10 state prisons, results showed high rates of TES module completion, improvements in copings skills, and a “more favorable view” of TES than of standard care, which consisted of 2 h, weekly group sessions with a certified Addictions Counselor (Chaple et al., 2013). Although this research is promising, Internet-delivered treatment has been largely unexplored in a community setting, under everyday pressures, although studies are underway (Walters et al., 2014). The current study is a step to begin filling this knowledge gap by exploring computer literacy and web-based treatment among community-based, CJS-involved individuals with substance use disorders.

This analysis has several strengths, including comprehensive assessment of baseline patient characteristics, and a large sample drawn from a wide variety of outpatient clinical treatment programs across the United States. There are also clear limitations. First, since participants were selected from outpatient programs, these findings may not be generalizable to other populations, such as inpatient or incarcerated populations. Second, since eligibility criteria required at least some recent (past 30 days) illicit substance use, participants with alcohol use disorders only and no other drug use were not included. Third, our assessment of medical and psychiatric morbidity was limited, only relying on self-report. A more comprehensive medical and psychiatric assessment with collateral history from participants’ families or their primary care physicians might have yielded different findings. Fourth, our categorization of CJS status was gathered from a single baseline questionnaire item, was not confirmed with administrative CJS data, and may contribute to a loss of information about individual differences (Altman and Royston, 2006). Finally, we do not know if the neurocognitive data reflect premorbid capacity or substance-induced cognitive changes, which could have been further teased out with collateral history.