Electronic Interventions for Alcohol Misuse and Alcohol Use Disorders

Alcohol misuse is a broad term that incorporates a spectrum of severity, ranging from hazardous use that exceeds guideline limits to misuse severe enough to meet criteria for an alcohol use disorder (AUD). Table 1 provides a glossary of terms along this spectrum. To address the impairment related to alcohol misuse (1) from a public health perspective, the U.S. Preventive Services Task Force recommends screening and brief intervention (2, 3), an approach that reduces alcohol consumption by 3 to 4 drinks per week for up to 12 months after the intervention (4). Screening and brief intervention sessions typically consist of brief assessment, followed by personalized normative feedback and advice to adhere to recommended drinking limits, which are typically defined for men as consuming 4 standard drinks or fewer (1 drink equals 14 g of alcohol) on any day and 14 drinks or fewer per week and for women as 3 drinks or fewer on any day and 7 drinks or fewer per week (5). Table 1. Glossary of Terms on the Spectrum of Alcohol Misuse* Alcohol misuse counseling, including screening and brief intervention, is constrained by barriers, such as inadequate funding, time, and trained personnel (68). In addition, the efficacy of screening and brief intervention in settings other than primary care is not established (9). Electronic interventions (e-interventions) may address some barriers and extend the reach of treatment by reducing demands for clinician time and clinic space while increasing the number of persons who can access treatment and their frequency of accessing treatment. With 87% of the U.S. population using the Internet (10), e-interventions can potentially reach persons with drinking problems who wish to remain anonymous, lack the time or resources for traditional therapy, need to access therapy during nonstandard business hours, or live in rural areas (11, 12). Previous systematic reviews that evaluated e-interventions for alcohol misuse have generally found short-term benefits (1317), but examination of maintenance of intervention effects is needed. Two recent systematic reviews have reported follow-up outcomes at 6 months or longer. However, they did not analyze college student and noncollege adult trials separately (13, 17), despite distinctions between these groups in patterns of alcohol consumption and associated impairment (18, 19). In addition, previous systematic reviews have generally not reported on the efficacy of e-interventions for AUDs (1317) or provided detailed descriptions of treatment intensity, including amount and type of human support (13, 14, 17). To characterize treatment intensity for alcohol misuse and evaluate evidence for their efficacy, we did a systematic review of randomized, controlled trials (RCTs). We compared e-interventions for alcohol misuse with inactive or minimal intervention controls for reducing alcohol consumption and alcohol-related impairment in adults and college students for 6 months or longer. Methods We followed a standard protocol in which key steps, such as eligibility assessment, data abstraction, and risk of bias, were piloted and discussed by team members. A technical report that fully details our methods and results is available (20). We addressed 3 research questions. First, for e-interventions that targeted adults who misused alcohol or had an AUD, what level, type, and method of user support were provided; by whom; and in what clinical context? Second, for adults who misused alcohol but did not meet diagnostic criteria for an AUD, what were the effects of e-interventions compared with inactive controls? Third, for adults who were at high risk for or who had an AUD, what were the effects of e-interventions compared with inactive controls? Appendix Table 1 provides individual trial characteristics. Appendix Table 1. Study Characteristics Data Sources and Searches We searched MEDLINE (via PubMed), the Cochrane Library, EMBASE, and PsycINFO from 1 January 2000 to 18 August 2014 for peer-reviewed, English-language RCTs. We used Medical Subject Heading terms and selected free-text terms for alcohol misuse, therapy types of interest, and electronic delivery. The MEDLINE search was updated on 25 March 2015. The search strategies are shown in Appendix Table 2. We reviewed bibliographies of included trials and applicable systematic reviews for missed publications (14, 15, 2125). To assess for publication bias, we searched ClinicalTrials.gov for trials that met our eligibility criteria (26) and found 2 trials that were completed at least 1 year before our literature search but were unpublished. Appendix Table 2. Search Strategies Trial Selection Two reviewers used prespecified eligibility criteria to assess all titles and abstracts. The full text of potentially eligible trials was retrieved for further evaluation. We included RCTs that compared e-interventions with inactive or active controls in patients with alcohol misuse or an AUD. We reported effects on alcohol consumption or another eligible outcome at 6 months or longer (Appendix Table 3). E-interventions could be delivered by CD-ROM, online, mobile applications, or interactive voice response (a technology that allows a computer to interact with humans using voice and signaling over analog telephone lines). Two investigators assessed for eligibility, and disagreements were resolved by team discussion or a third reviewer. Appendix Table 3. Inclusion and Exclusion Criteria Data Extraction and Quality Assessment Data abstractions were done by 1 reviewer and confirmed by a second. We assessed each trial's risk of bias using criteria specific for RCTs and summarized overall risk of bias as low, moderate, or high using the approach described by the Agency for Healthcare Research and Quality (26). The Appendix shows questions and the rationale for quality ratings criteria, and detailed quality ratings for each included trial are displayed in Appendix Table 4. Appendix Table 4. Quality of Included Studies Data Synthesis and Analysis We evaluated the overall strength of evidence for selected outcomes as high, moderate, low, or insufficient using the domains of directness, risk of bias, consistency and precision of treatment effects, and risk of publication bias (27). Table 2 shows strength-of-evidence domain and overall ratings. Table 2. SOE, by Outcome Domains* While synthesizing abstracted data, we classified the e-interventions by the level of supplementary human support. Level 1 included e-interventions with no human support; level 2 included e-interventions supplemented by noncounseling interactions with study staff, such as technical support; and level 3 included e-interventions supplemented by counseling with trained staff. The key outcomes were alcohol consumption, meeting recommended alcohol consumption limits, rates of binge drinking, alcohol-related health, social or legal problems, health-related quality of life, and adverse effects. When at least 3 trials reported a given outcome, we did a meta-analysis. We combined continuous outcomes by using mean differences (MDs) or standardized MDs when instruments varied and combined dichotomous outcomes by using risk ratios in random-effects models. Alcohol consumption was converted to a common unit (grams per week) across trials. We used metafor package in R (R Foundation for Statistical Computing) (28) to calculate summary estimates of effect, stratified by condition and sample (college students vs. adults), at 6 and 12 months, with KnappHartung adjustment of SEs of the estimated coefficients (29, 30). When at least 3 trials were rated as low or moderate risk of bias, we excluded trials rated as high risk of bias and did sensitivity analyses to compute summary estimates. We evaluated statistical heterogeneity in treatment effects by using the Cochran Q and I 2 statistics. We planned subgroup analyses, specifying a priori, to explore the following potential sources of heterogeneity: follow-up rates, treatment dose, and level of human support given with the intervention. However, these analyses could not be done because subgroups did not meet the prespecified minimum of 4 trials per subgroup (31). When there were too few trials for quantitative synthesis, we analyzed data qualitatively, focusing on identifying novel aspects of the e-intervention and patterns of efficacy. Role of the Funding Source This review was funded by the U.S. Department of Veterans Affairs. The funding source had no role in the study design, data collection, analysis, preparation of the manuscript, or the decision to submit the manuscript for publication. Results We reviewed 100 full-text articles of the 856 citations that were screened and identified 28 trials that met eligibility criteria (Appendix Figure 1). The populations were divided between college students (n=14) and noncollege adults (n=14). Only 3 trials specifically recruited participants who were at high risk for or who had an AUD. The other 25 trials recruited participants who misused alcohol. A single trial used a mobile device as the delivery platform (32). Strength of evidence for each outcome is summarized in Table 2. Appendix Figure 1. Summary of evidence search and selection. AUD = alcohol use disorder; IVR = interactive voice response; RCT = randomized, controlled trial. * Manuscript reference list includes additional references cited for background and methods. All 28 trials and 3 trials of AUD were qualitatively described, and quantitative meta-analysis was done for 25 trials. E-Intervention Characteristics and Support Seventeen trials were minimal support (level 1) interventions that used no human support, 8 used low noncounseling support (level 2), and 3 included moderate or high (level 3) counseling support. Summary characteristics and support for e-interventions are listed in Table 3. Most trials examined a 1-time intervention (n=19), delivered online or at a desktop computer (n=24), that compared a person's alcohol consu