Putting the screen in screening: technology-based alcohol screening and brief interventions in medical settings.
Despite the push for using SBIs in medical settings, they remain underused. In a recent national survey of U.S. adults, only one in six (15.7 percent) respondents reported discussing alcohol use with a health professional in the past year, with State-specific estimates ranging from 8.7 percent to 25.5 percent (McKnight-Eily et al. 2014). The percentage was higher (34.9 percent), but still inadequate, among those with 10 or more binge-drinking episodes in the past month. An often-cited barrier to SBI implementation is lack of time (Van Hook et al. 2007; Wilson et al. 2011). Computer-facilitated SBI delivery may offer a solution for busy medical settings, allowing more widespread implementation. This article focuses on current- and emerging technology--facilitated SBI tools that have been evaluated in primary care, pediatric, and emergency department (ED) settings. We review studies of technology-based SBI as they relate to adults (18 years or older), pregnant women, and adolescents (17 years or younger), the primary patient populations in which alcohol SBIs have been implemented. The studies reviewed here come from a systematic electronic literature search conducted between February 2014 and December 2014 using PubMed and PsycINFO, as well as the reference lists of published studies and review articles. We summarize the characteristics of the studies, including population, design, and results, in the table.
Value Added With Electronic SBIs
Technology-based SBIs could help increase the frequency and quality of SBI use in medical settings by enhancing efficiency and standardizing implementation. In terms of screening, touchscreen devices or standalone computers with Internet connections can allow patients to enter information in the waiting room prior to an appointment. Programs automatically score the screening results that staff can print or electronically transmit to practitioners. This reduces clinician time needed for administering and scoring a questionnaire during the visit. In addition, programs can be loaded with validated measures that improve the quality of screening and can automatically select appropriate questions according to the patient's age and previous responses. Patients also may be more willing to disclose sensitive information to a computer than to a person (Butler et al. 2009; Turner et al. 1998), and integration of computerized screening results with electronic health records may boost screening and documentation rates (Anand et al. 2012).
Similarly, computer-facilitated brief intervention delivery has the potential advantages of greater standardization, lower cost, and greater ease of implementation compared with face-to-face delivery. As with screening, programs can automatically tailor intervention content to individual patients. Interventions vary based on the program, but, as with face-to-face SBIs, computer-based SBI tools often follow screening with personalized feedback that includes a summary of patients' consumption patterns and risk status, a comparison of their consumption with recommended limits, estimated blood alcohol concentrations for their heaviest drinking occasion in the reported time frame, and a comparison between their consumption and consumption reported by others in their peer group. More extensive programs may incorporate intervention strategies based on principles of evidence-based face-to-face treatments, such as motivational interviewing (Miller and Rollnick 2012) and cognitive-behavioral therapy (Kadden et al. 1995).
Using technology for SBIs in medical settings may be especially valuable for reaching young people who are highly engaged with technology and nearly universal access to computers, cell phones, and the Internet (Madden et al. 2013; Marsch et al. 2007; Pew Research Center's Internet and American Life Project 2014). Indeed, using technology-facilitated alcohol SBIs in medical settings to reach adolescents may be a powerful mechanism to reduce medical costs and gain productive years of life, since alcohol use disorders are strongly linked to the leading causes of adolescent and adult mortality, including motor-vehicle crashes and suicide.
This high level of online engagement has fueled a surge of interest in the potential of standalone Web-based SBI programs to address problematic alcohol use, particularly among college students. These programs provide a means to inexpensively reach people less likely to access traditional health services. Detailed reviews of research on these standalone online alcohol SBIs are provided in articles by Carroll and Cronce in this issue and suggest that, at least among college students and adults, these programs tend to yield small to moderate effects, which are greatest at followups less than 3 months, gradually declining to little or no effect by 12 months (Donoghue et al. 2014). The lack of interpersonal contact with these programs may contribute to lower participation rates and adherence over time (Murray et al. 2013; Naimi and Cole 2014; Postel et al. 2011). In addition, alcohol use is strongly linked to many physical and mental health problems, such as cancer, cirrhosis, and depression (National Center on Addiction and Substance Abuse 2011). Therefore, standalone programs are unlikely to obviate the need for SBIs in medical settings, which is the focus of this review.
Medical Setting SBI for Adults
Twelve studies of varying design and stages of research (reported in 13 published papers) have examined computerized SBIs for adults in medical settings that include four studies in primary care (Bendtsen et al. 2011; Butler et al. 2003; Cucciare et al. 2013; Kypri et al. 2008), seven in EDs (Blow et al. 2006; Karlsson and Bendtsen 2005; Murphy et al. 2013; Neumann et al. 2006; Nilsen et al. 2009; Suffoletto et al. 2012; Trinks et al. 2010; Vaca et al. 2011), and one in a hospital outpatient department (Johnson et al. 2013) (see the table for study details). Half of the studies used a randomized design (Blow et al. 2006; Cucciare et al. 2013; Kypri et al. 2008; Neumann et al. 2006; Suffoletto et al. 2012; Trinks et al. 2010); one used a before-and-after design, with each clinic serving as its own control (Butler et al. 2003); and five are earlier-stage observational studies with small sample sizes (Bendtsen et al. 2011; Johnson et al. 2013; Karlsson and Bendtsen 2005; Murphy et al. 2013; Vaca et al. 2011). Generally, followup, where it existed, was short, with two studies following participants for 3 months, four for 6 months, and three for 12 months. The studies shared some common components.
SBI Delivery Method
All but one study by Suffoletto and colleagues (2012), tested screening and/or brief intervention delivery on a tablet or desktop computer located in the medical setting. Suffoletto and colleagues (2012) delivered their intervention through weekly mobile text messages following patient discharge from the ED.
All 12 studies used a self-administered computerized screener that assessed quantity and frequency of alcohol consumption and heavy episodic drinking (HED) episodes. Ten of the 12 studies (Butler et al. 2003; Cucciare et al. 2013; Johnson et al. 2013; Karlsson and Bendtsen 2005; Kypri et al. 2008; Murphy et al. 2013; Neumann et al. 2006; Suffoletto et al. 2012; Trinks et al. 2010; Vaca et al. 2011) used the Alcohol Use Disorders Identification Test (AUDIT) screening tool (Reinert and Allen 2002) or its shortened form, the AUDIT-C (Bush et al. 1998).
Brief Intervention Delivery
Seven of the studies (Blow et al. 2006; Cucciare et al. 2013; Kypri et al. 2008; Neumann et al. 2006; Suffoletto et al. 2012; Trinks et al. 2010; Vaca et al. 2011) only provided the brief intervention portion of the SBI to patients who screened positive for risky drinking, typically defined as AUDIT-C scores of 4 or higher for men and 3 or higher for women, or AUDIT scores of 8 or higher. The other five studies (Bendtsen et al. 2011; Butler et al. 2003; Karlsson and Bendtsen 2005; Murphy et al. 2013; Johnson et al. 2013) provided a brief intervention regardless of alcohol use level.
Brief Intervention Format
The brief interventions in 4 of the 12 studies (Bendtsen et al. 2011; Blow et al. 2006; Karlsson and Bendtsen 2005; Nilsen et al. 2009) were provided to patients using computer-generated printouts, whereas the rest were offline or Web-based computer programs. All but one computerized brief intervention consisted of a single session that lasted 10 to 20 minutes. The outlier examined both a single-dose Web-based brief intervention and a multi-dose version, where patients repeated the brief intervention at the 1- and 6-month followups (Kypri et al. 2008).
Brief Intervention Content
Nearly all of the brief interventions tested in these studies used at least some components of the FRAMES (Feedback, Responsibility, Advice, Menu of options, Empathy, Self-efficacy) model of brief intervention (Hester and Miller 1995). All the brief interventions in these studies provided feedback about the patient's risk level, drinking pattern relative to recommended limits, advice and information to enhance motivation to avoid use, and suggestions for behavior change strategies, if applicable. Capitalizing on a key feature of computerization, most of the brief interventions automatically tailored feedback and information to patients' screening results and other characteristics. That said, one of the randomized studies specifically examined the effect of tailored messages, compared with generic messages, either with or without clinician brief advice and found no significant effect of tailoring on alcohol consumption or related consequences after 12 months (Blow et al. 2006). Instead, patients who received brief advice from clinicians showed greater reductions in drinking than those who only received feedback from the computer SBI. Only one other study (Butler et al. 2003) included a printed report for the clinician with screening results and suggested brief intervention options. All other studies used technology-based self-guided brief intervention, with no explicit clinician involvement.
Among the seven experimental or quasi-experimental trials (Blow et al. 2006; Butler et al. 2003; Cucciare et al. 2013; Kypri et al. 2008; Neumann et al. 2006; Suffoletto et al. 2012; Trinks et al. 2010), findings were mixed, with several reporting differences between the intervention and comparison conditions in follow-up outcomes and others not. Overall, the 12 studies suggested that using technology-based SBIs in medical settings is feasible and acceptable to patients but were not able to clarify whether they are effective.
One controlled trial in a primary care setting (Kypri et al. 2008) found significant reductions in alcohol consumption scores and alcohol-related problems at both the 6- and 12-month followups among university health service patients in New Zealand who screened positive for alcohol problems and received a Web-based brief intervention, compared with patients who received a brochure. Two other trials (Butler et al. 2003; Cucciare et al. 2013) found reductions in alcohol consumption and related consequences out to 6 months, but the reductions were similar for both the standard care control and the computerized SBI groups. A fourth nonexperimental implementation study (Bendtsen et al. 2011) found that patients given access to a computerized SBI kiosk in a primary care clinic showed declines in heavy episodic drinking frequency at a 3-month followup. Patients referred to the SBI by a clinician, as opposed to those who self-initiated SBI use, showed a decline in weekly alcohol consumption. Without a control group, it is impossible to determine how much the decline is attributable to the SBI or some other confounder. That said, this study is unique in its examination of a computerized SBI system that routinely was offered at a primary care clinic, independent of a research study, showing that patients and clinicians are willing to use the system.
Only two of the ED studies used a nonintervention control group. One study (Neumann et al. 2006), a large German trial of 1,139 sub-critically injured ED patients with at-risk drinking, found significantly reduced prevalence of at-risk drinking and alcohol consumption at both the 6- and 12-month followups for patients receiving computerized SBIs compared with those receiving standard care alone. Another, much smaller study (Suffoletto et al. 2012) conducted in three Pennsylvania EDs sent weekly text messages (TMs) to young-adult risky drinkers discharged from the EDs. The intervention group received TMs asking them to evaluate their drinking and providing them with information about setting alcohol consumption goals. Another group received TMs asking them to assess their drinking. A third group simply received TM notifications about the study's 3-month followup. Participants in the goal-setting intervention significantly reduced hazardous drinking behavior, compared with participants in the control groups (Suffoletto et al. 2012). However, this study found the greatest change among those with the highest baseline drinking levels, suggesting potential regression to the mean, which is a statistical phenomenon where more extreme values in data tend to move spontaneously towards the mean over time as a result of a certain amount of natural variation (Barnett et al. 2005). The other two ED studies did not use nonintervention control groups. Instead, they compared different active interventions. Both found that all the interventions tested reduced weekly alcohol consumption and HED frequency (Blow et al. 2006; Trinks et al. 2010), as well as alcohol-related consequences (Blow et al. 2006). All ED studies excluded patients that were intoxicated, had a high blood alcohol concentration at time of recruitment, were suicidal, or were otherwise being referred to psychiatry, which may have excluded patients with the most severe alcohol problems.
SBIs for Pregnant Women
Previous studies have shown the benefits of SBIs for addressing alcohol and drug use in pregnant women (Chang 2002; Ondersma et al. 2011). However, only one published randomized-controlled trial (Tzilos et al. 2011) has examined a computerized SBI for alcohol use during pregnancy. This early-stage randomized controlled trial in an urban prenatal care clinic included a convenience sample of 50 pregnant women that either screened positive on the T-ACE alcohol screening tool (Elliot and Hickam 1990; Sokol et al. 1989) or had drinking patterns before pregnancy that exceeded NIAAA drinking limits for women (NIAAA 2010). Participants randomly completed either the computerized SBI or an unrelated questionnaire. Those receiving the intervention gave it high marks for ease of use, likability, and respectfulness. Both intervention and control groups showed significant and equivalent reductions in drinking at the 1-month followup, although babies born to women in the intervention group had higher newborn birth weights.
More recently, Pollick and colleagues (2013) found high acceptability of, and user satisfaction with, a computerized brief intervention for alcohol use in pregnancy (C-BIAP) in a qualitative pilot study among 18 pregnant African-American women. Given the paucity of studies in this population, and that alcohol use in pregnant and parenting women additionally can cause secondary lifelong harm to the fetus or infant, more studies are critically needed to elucidate the utility of computerized strategies to enhance the efficient and effective implementation of alcohol SBIs in prenatal and antenatal clinics.
Numerous studies suggest that computerized screening of adolescent patients for alcohol use problems is acceptable, feasible, and effective in medical settings (Chisolm et al. 2008; Harris et al. 2012; Olson et al. 2009; Ozer et al. 2005; Stevens et al. 2008). Using computerized alcohol screening can increase adolescent satisfaction with the medical encounter (Gadomski et al. 2014; Harris et al. 2012) and efficiently boost physician recognition of substance use issues and patient-physician dialogue around substance-use topics (Harris et al. 2012; Olson et al. 2009; Stevens et al. 2008). These findings may help to bolster the case for increased adolescent screening for alcohol in medical settings, where screening rates remain suboptimal (Hingson et al. 2013).
Few studies have tested integrated computerized alcohol SBIs in adolescents. In fact, only four trials, yielding eight published papers (Cunningham et al. 2009, 2012; Gregor et al. 2003; Harris et al. 2012; Louis-Jacques et al. 2014; Maio et al. 2005; Walton et al. 2010, 2014), support computerized alcohol SBIs as feasible, acceptable, and, in some cases, effective for reducing drinking or alcohol-related problems among adolescents seen in medical settings.
Three of the four studies (Cunningham et al. 2012; Maio et al. 2005; Walton et al. 2014) were randomized controlled trials conducted among adolescent ED patients in the United States. These studies compared adolescents receiving standard care with adolescents receiving an integrated computerized SBI that screened patients and then delivered an approximately 30-minute single-session, highly interactive, tailored brief intervention that reflected principles of motivational interviewing (MI) and the social cognitive theory of behavior change (Bandura 1977). One trial (Maio et al. 2005) implemented a universal brief intervention aimed at both preventing and reducing use in adolescents with minor injuries. The other two only provided the brief intervention for adolescents who reported drinking in the past 12 months (Cunningham et al. 2012) or that screened positive for risky drinking on the AUDIT-C (Walton et al. 2014). The latter two trials additionally compared a single-session, computer-delivered brief intervention with a therapist-delivered version that was similar in content (Cunningham et al. 2012; Walton et al. 2014).
Overall, these ED-based studies found no significant differences in alcohol consumption outcomes between the intervention and standard-care control groups during followup, but some did find that the computer-based SBIs influence other alcohol-related behaviors in certain populations:
* Maio and colleagues (2005) found in post hoc subgroup analysis a significant intervention effect on frequency of alcohol misuse and HED behaviors among adolescents admitting to having driven while impaired before entering the study. It may be that computerized brief interventions based on motivational enhancement approaches, like their face-to-face counterparts, tend to be more effective for individuals that have at least a certain level of substance use, or experience of negative consequences (Blow et al. 2009; Palfai et al. 2011; Spirito et al. 2004). Alternatively, those with greater use may be more subject to regression to the mean (Finney 2008).
* At a 6-month followup, Cunningham and colleagues (2009, 2012) found that their computerized and therapist-delivered brief interventions, which addressed peer violence and alcohol use (Walton et al. 2010) were associated with greater reductions in alcohol-related consequences, such as missing school because of alcohol use, compared with patients receiving the standard-care control. By the 12-month followup, patients receiving the therapist-delivered brief intervention maintained reductions in peer violence, but neither intervention continued to influence alcohol-related outcomes. The authors postulate that it may be difficult to address effectively more than one risk area with a brief intervention.
* Walton and colleagues (2014) examined the intermediate effects of a single-session, computerized or therapist-delivered brief intervention on psychological constructs hypothesized to be key moderators of behavior change. They were looking for the "active ingredients" that bring about change in adolescent risky drinkers. They found that, among 836 urban adolescent ED patients with risky drinking, those receiving either brief intervention significantly increased their perception that it was important to stop drinking, compared with adolescents receiving standard care. In addition, those receiving the therapist-delivered intervention increased their readiness to stop drinking. The analysis teased out two brief intervention components that had the strongest effect on these psychological outcomes, regardless of delivery mode: a review of personal strengths and suggested tools patients could use to reduce or stop drinking. Within the computer-delivered brief intervention, the components that most influenced outcomes were those that helped patients identify more benefits of behavior change, imagine sports activities that could be alternatives to alcohol use, and choose a goal to reduce or stop drinking. In contrast, the component of the therapist-delivered brief intervention that provided normative statistics/personalized feedback about current level of use was associated with negative effects on these cognitive outcomes. This study is ongoing and has yet to determine how these intermediate changes and brief intervention components connect to actual alcohol use and related consequences. However, it represents an important direction for future research into computerized SBI systems, such as the determination of the most effective ingredients, thus promoting the development of the most efficient and effective interventions possible.
The one adolescent trial of a computer-facilitated SBI conducted in a primary care setting involved several primary care clinics in the United States and the Czech Republic (Harris et al. 2012). The study utilized a before-and-after comparison design. Each clinic enrolled participants while providing standard care; then the clinic enrolled a comparison group of participants after implementing a computer-facilitated SBI system. The system consisted of three components:
* A pre-visit computerized screening using the CRAFFT behavioral health screening tool designed for children under age 21 (Knight et al. 2002);
* Immediate computer-delivered feedback to patients about their risk level, followed by several interactive pages of science-based and true-life information about substance-related health-risks and other harms; and
* Brief advice from a clinician during the primary care visit based on a printed provider report that suggested discussion points about substance use and related driving/riding risks tailored to each patient according to the screening results.
This multisite study found that U.S. adolescents, but not Czechs, had significantly reduced their alcohol use at the 3- and 6-month followups, although reductions at 12 months were less robust. In addition, the computer-facilitated SBI reduced both drinking initiation and cessation in the U.S. sample (Harris et al. 2012), and the short-term cessation effect actually was largest among drinking youth with friends who drink or approve of drinking (Louis-Jacques et al. 2014). This study also found a significant intervention effect in both countries at the 3-month followup on prevalence of driving after drinking or riding with a driver who had been drinking (Harris et al. 2011).
Because the computer system used in this study was designed to be integrated into a face-to-face primary care visit, these findings cannot disentangle the relative effects of the computerized versus the face-to-face components of the brief intervention. To this end, studies in adolescents are needed that use a factorial design (such as the study by Blow et al. 2006) to test the relative efficacy of clinician advice versus the computerized component.
With only four trials (Cunningham et al. 2009, 2012; Gregor et al. 2003; Harris et al. 2012; Louis-Jacques et al. 2014; Maio et al. 2005; Walton et al. 2010, 2014), the evidence currently is insufficient to recommend computerized alcohol SBIs among adolescents in either EDs or primary care settings. More high-quality studies with randomized controlled designs and large sample sizes are needed, particularly in the primary care setting, which represents a key touch point with the health care system for adolescents where alcohol use can be detected early and where brief interventions are most likely to be effective. Alcohol and drug dependence are chronic, relapsing disorders with high treatment costs that most often begin during childhood. Given the relatively low risks and costs, and potential for benefit, of computerized prevention and early intervention, clinicians may wish to implement them as they become available.
Discussion and Future Directions
Research on technology-facilitated SBIs in medical settings is in its infancy. As such, there remain many questions and methodological issues that researchers should address when evaluating these interventions.
Although there is some evidence that the effectiveness of alcohol SBIs may be greater for people who have already experienced problems or negative consequences of drinking, it is unclear whether such programs are useful for patients with alcohol dependence (Saitz 2010). In addition, more studies should be conducted among pregnant women and adolescents, as well as in diverse racial and ethnic groups. Finally, studies should evaluate the effectiveness of Web-based alcohol SBI in high-risk, underserved, and remote populations, such as military personnel, American Indians, and Eskimo/Inuit, as such systems are particularly suited to access such hard-to-reach groups.
Evidence to date suggests that responses to computerized screening are reliable and comparable to other screening modes (McNeely et al. 2014; Thomas and McCambridge 2008; Williams et al. 2000). However, other studies suggest differences between the two modalities that researchers may want to consider as they design their programs. For example, some studies find that people are more likely to report more sensitive or stigmatized behaviors, such as illicit drug use or higher levels of alcohol consumption, on computer self-administered questionnaires compared with face-to-face interview (e.g., Beck et al. 2014; Butler et al. 2009; Perlis et al. 2004) or even self-administered paper-and-pencil questionnaires (Wright et al. 1998). Additionally, adolescents seem to be particularly sensitive to mode and context effects when reporting sensitive behaviors (Gfroerer et al. 1997; Turner et al. 1998; Wright et al. 1998). In fact, a study of adolescent primary care patients found that their reactions to computerized screening was highly associated with their level of trust in the data being kept secure and private and used only for health care (Chisolm et al. 2008). Other studies suggest that factors such as language (Butler et al. 2003) and gender (Neumann et al. 2004) also may affect computerized screening performance.
There is little evidence to date that the length of the intervention influences its effectiveness. No study in this review directly compared the effects of low-intensity to longer interventions, but there seemed to be no consistent pattern across trials indicating greater efficacy of longer interventions over shorter. A recent meta-analysis (Carey et al. 2012) of a computerized brief intervention targeting college students found that the effectiveness of the intervention was not affected by duration.
As for single-session versus multi-session interventions, the primary care study by Kypri and colleagues (2008) was the only trial reviewed here to compare the two directly. It found no increased benefit of additional brief intervention doses given at 1 and 6 months. This finding corroborates the conclusions of other reviews (Rooke et al. 2010; Donoghue et al. 2014; Kaner et al. 2007) that found no significant effect of the number of treatment sessions on the average effect size of computer-delivered and face-to-face SBIs (Kaner et al. 2007). A more recent 2012 review of face-to-face SBI studies did find larger effect sizes for brief (less than 15 minutes each) multi-contact interventions, compared with very brief (up to 5 minutes) or brief (5 to 15 minutes) single-contact interventions (Jonas et al. 2012). Compared with face-to-face delivery, technology-based delivery modes, including via the Internet or cell phones, offer the advantage of relative ease and low cost of delivering multiple doses. Therefore, further exploration of the question of optimal number of doses is clearly warranted.
Face-to-Face vs. Computerized Delivery
Another important question is whether self-guided computerized SBIs are as effective as face-to-face SBIs. Only four of the reviewed trials compared the two modalities. Two trials (Cunningham et al. 2012; Walton et al. 2014) directly compared a 35-minute therapist-delivered SBI and a self-guided computerized SBI provided to adolescent ED patients. Both modalities showed similar reductions in alcohol-related consequences and positive changes in psychological precursors to behavior change compared with a standard-care control (Cunningham et al. 2012; Walton et al. 2014). Other studies and reviews comparing face-to-face and technology-facilitated SBIs outside medical settings find an edge for face-to-face (Carey et al. 2012; Donoghue et al. 2014). It may be that combining face-to-face and technology-based SBI will be the most effective. Such a combination is easily accomplished in a medical setting where patients could complete a computerized portion of the alcohol SBI before a face-to-face encounter. This would screen and "prime" the patient to discuss the topic when meeting with the clinician and could increase clinician fidelity of brief intervention implementation by using "prompts" to guide the clinician. Although computers have certain logistical advantages, they cannot convey empathy, regard, and complex reflections, which represent some of the most important ingredients of brief motivational interventions (Miller and Rollnick 2012). Also, patients may put less attention, thought, and effort into completing a computerized brief intervention compared with a face-to-face intervention (Walters and Neighbors 2011). Future research will benefit from examining a combination of face-to-face and computerized SBI delivery, as it may help to achieve larger and more enduring effects than self-guided computerized SBIs alone (White et al. 2010).
In terms of what intervention studies measure, more need to consider alcohol-related outcomes other than consumption, including negative consequences and problems related to alcohol use such as school problems for adolescents, driving while impaired, traffic violations, and crashes and injuries. Among the studies reviewed here, not all examined these outcomes, yet, in the face-to-face alcohol SBI literature, intervention effects on alcohol-related consequences or risks often have been larger than on alcohol consumption (Newton et al. 2013; Wachtel and Staniford 2010; Yuma-Guerrero et al. 2012). Therefore, failure to measure such outcomes, which have great public health import, may be a missed opportunity to identify some key intervention benefits.
Mediators and Moderators
There is a dearth of studies on mediators and moderators of the effects of computerized SBI in any setting and, in particular, within the small subset of studies examining these interventions within medical settings. Only one study (Walton et al. 2014) reviewed here attempted to elucidate the potential mechanisms and "active ingredients" underlying the effects of the computerized SBIs delivered to adolescents in an ED. Within the broader literature, the meta-analysis by Carey and colleagues (2012) found reduced computerized SBI effectiveness when the intervention included a decisional-balance or values-clarification exercise, identified high-risk situations, or included moderation strategies.
A few studies have found that certain patient characteristics, such as baseline stage-of-change or severity of alcohol involvement also may moderate the effectiveness of computerized SBIs. Among the studies reviewed here, Neumann and colleagues (2006) found greater intervention impact among patients who were contemplating changes in their drinking habits when they entered the study, and Vaca and colleagues (2011) found their SBIs to be more effective among patients reporting recent drinking and driving. The finding that an intervention may be more effective among individuals with more risky drinking behavior matches findings from a recent review of face-to-face alcohol/drug SBIs for adolescents seen in medical settings (Mitchell et al. 2013) and a study of a computerized SBI for college students (Carey et al. 2012).
One of the major methodological issues facing SBI research in general is the degree to which simply being part of a study that assesses alcohol use may affect study results (Elbourne 2014; Finney 2008; McCambridge and Kypri 2011; McCambridge et al. 2014). Indeed, studies find that simply evaluating people's drinking--as would happen in the screening part of an SBI--has a robust effect on drinking behavior over time (Dearing et al. 2013; Epstein et al. 2005). This "assessment reactivity" may underlie the similar changes in both the intervention and control groups seen among many of the studies reviewed here. To reduce the potential for assessment reactivity, future randomized controlled studies could include an additional minimal-assessment control arm that only measures outcomes at the final followup.
There is robust evidence that in-person alcohol SBIs are effective when delivered to patients by staff in medical settings (Moyer 2013; Newton et al. 2013; O'Donnell et al. 2014). However, the implementation rates of these face-to-face SBIs remain suboptimal (Hingson et al. 2013; McKnight-Eily et al. 2014). Technology-based solutions, such as computerized SBI systems, may help to address this problem, but evidence for their effectiveness is less clear. This review found a burgeoning, but still small, research field with only 23 published papers representing 18 different trials evaluating the use of technology-based alcohol SBIs among adults, pregnant women, and adolescents in medical settings. The studies all found that technology-based alcohol SBIs are feasible for delivery in the medical setting and acceptable among patients, but most had methodological limitations. Only 13 of the 18 were controlled trials, and the majority were conducted in adult populations, with just four conducted among adolescents and only two among pregnant women. More than half of the studies took place in EDs, which offers a prime "teachable" moment, particularly for injured patients. However, more studies are needed in primary care and other ambulatory medical care settings, where patients may have periodic and ongoing contact with their health care providers. Such longitudinal patient--clinician relationships would allow for continued support and followup regarding recommended behavior changes. New studies also will benefit from bigger sample sizes to increase the power of their findings, more comprehensive participant recruitment, higher retention rates, and longer follow-up periods.
Finally, a promising new direction for the field would be to evaluate the potential of mobile technologies that can be used in medical settings. Suffoletto and colleagues (2012) demonstrated that mobile devices offer the potential to act as "clinician-extenders," allowing clinicians to support and interact with patients after a visit and potentially boost the effect of a computerized brief intervention delivered in the medical setting. A review by Heron and Smyth (2010) of studies examining the use of ecological momentary interventions delivered through mobile technology, such as cell phones and tablet computers, found them to be feasible and acceptable and show efficacy for addressing a variety of psychosocial and other health behaviors, including alcohol use. Research also may begin to emerge on the use of smartphone apps and social-networking sites like Facebook for underage drinking prevention and intervention.
The authors disclose that they have no competing financial interests.
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Sion Kim Harris, Ph.D., and John R. Knight, M.D.
Sion Kim Harris, Ph.D., is assistant professor in the Department of Pediatrics at Harvard Medical School and a research associate in the Department of Medicine, Boston Children's Hospital, Boston, Massachusetts.
John R. Knight, M.D., is associate professor in the Department of Pediatrics at Harvard Medical School; senior associate in the Department of Medicine; and associate in the Department of Psychiatry at Boston Children's Hospital, Boston, Massachusetts.
Table 1 Characteristics of Computer-Assisted Alcohol Screening and Brief Intervention (SBI) Studies Conducted in Health Care Settings Authors Study Population Setting (Year) Adults (Age 18 or Older): Primary Care Butler et al. English-or Spanish- Primary care (2003) speaking primary practices in care patients (ages 18-99, N = 2,053 Massachusetts, screened, 128 New York, and screened positive Florida and completed followup, 68% female) Kypri et al. University health University (2008) service patients health service screening positive in New Zealand for at-risk drinking (ages 17-29; N = 975 screened, 429 screened positive, 52% female) Bendtsen et Primary care Primary care al. (2011) patients with risky clinics in one drinking (ages 18 or Swedish county older; N = 7,863 screened, 3,169 screened positive, 578 received e-SBI, 347 completed followup, 41% female) Cucciare et Military veterans Veterans al. (2013) screening positive for alcohol misuse Affairs primary (N = 167, 12% care clinics in female) California Adults (Age 18 or Older): Emergency Department (ED) Karlsson -and ED patients (ages ED of Bendtsen 18-70, N = 44, university (2005) % female not hospital in Sweden available) Blow et al. Sub-critically Midwestern (2006) injured ED patients level 1 trauma screening positive center in for at-risk drinking university (ages 19 or older, hospital N = 4,476 screened, 577 screened positive and received BI, 29% female) Neumann et Sub-critically ED in Germany al. (2006) injured ED patients screening positive for at-risk drinking (ages 18 or older, N = 1,139, 79% female) Nilsen et al. ED patients County hospital (2009) screening positive ED in Sweden for risky drinking Trinks et al. (ages 18-69, (2010) N = 1,570 screened, 560 screened positive and received BI, 93 completed followup, 39% female) Vaca et al. English- or Spanish- University (2011) speaking ED patients hospital ED (ages 18-65 or in California older, N = 4,375 screened, 742 screened positive and received BI, 385 consented to follow- up, 35% female) Suffoletto et ED patients (ages Urban EDs in al. (2012) 18-24; N = 109, 52 Pennsylvania screened positive, 45 consented to participate, 64% female) Murphy et al. ED patients (ages ED of urban (2013) 21-85 years, academic N = 517, 63% medical center female) in New York Adults (Age 18 or Older): Hospital Outpatient Clinics Johnson et Hospital outpatients Hospital al. (2013) (ages 18 or older, ambulatory N = 99 completed care center in SBI, 69 invited for Australia followup, 46% female) Pregnant Women Tzilos et al. Pregnant women Urban prenatal (2011) screening positive care clinic in for problem alcohol Michigan use (ages 18-45, N = 50) Pollick et Pregnant African- Urban prenatal al. (2013) American women care clinic in who screened Michigan positive for problem drinking but quit during pregnancy (ages 18-29, N = 18) Adolescents (Age 17 or Younger) Gregor et al. ED patients with ED of academic (2003) minor injuries (ages medical Maio et al. 14-18 years, centers in (2005) N = 655, 33% Michigan female) Cunningham ED patients with Urban ED in et al. (2009, past-year violence Michigan 2012) and alcohol use (ages 14-18, Walton et al. N = 3,338 screened, (2010) 726 screened posi- tive and consented to study, 56% females) Harris et al. Primary care patients Primary care (2012) (ages 12-18, clinics in New N = 2,092 in United England, and Louis-Jacques States [USA], 589 Prague, Czech et al. (2014) in Czech Republic Republic [CZR]; USA/CZR 57%/47% females) Walton et al. ED patients Urban ED in (2014) screening positive Michigan for risky drinking (ages 14-20, N = 4,389 screened, 1,053 screened positive, 836 consented to study, 48% female) Authors Screening and Study Design/ (Year) Other Measures Treatment Conditions Adults (Age 18 or Older): Primary Care Butler et al. 1) Alcohol Use Before-and-after, (2003) Disorders each site own Identification Test control: (AUDIT) 1) Control phase (N 2) Stage-of-change = 66): Standard care measure with AUDIT after visit 2) Treatment phase (N = 62): 20-minute computerized SBI completed in medical office before visit, with tailored feedback and information to reduce risky drinking; clinician can be given printed report with suggested brief interventions Kypri et al. 1) AUDIT Randomized (2008) controlled trial 2) Past-2-weeks (RCT) three groups: alcohol consumption 1) Single-dose 10- minute Web-based SBI 3) Alcohol Problems (N = 138): Scale Assessment, personalized normative feedback, risk status, comparison of consumption with recommended limits 2) Multi-dose Web- based SBI (N = 145): same as above repeated at 1 and 6 months 3) Control (N = 146): Information pamphlet only Bendtsen et 1) Average weekly Observational study al. (2011) use of two cohorts: 2) Heavy episodic 1) "Self-referred" drinking (HED) (N = 139): occasions per computerized SBI in month clinic completed on own initiative 2) "Staff-referred": (N = 208) invited by clinician to complete computerized SBI after visit Behavioral intervention (BI) for both was printout of personalized written feedback Cucciare et 1) AUDIT-C RCT two groups: al. (2013) 2) Timeline Follow- 1) Intervention (N = Back 89): Standard care plus Web-based 10- 3) Alcohol-related minute SBI with consequences assessment, personalized normative feedback, education, summary of alcoholrelated consequences and risk factors, and self-reported motivation to change 2) Control (N = 78): Standard care only (brief counseling by PCP) Adults (Age 18 or Older): Emergency Department (ED) Karlsson -and 1) Modified AUDIT-C Single-group Bendtsen acceptability (2005) 2) Patients' study: easy of computerized screening and Computerized personalized screening and feedback printout of personalized feedback and advice given to patient Blow et al. 1) Frequency RCT four groups: (2006) of alcohol Computerized consumption screening plus and HED in past computer generated: 3 months 1) Tailored message 2) Drinker booklet with Inventory of clinician-delivered Consequence- brief advice Short Inventory (N = 129) of Problems 2) Tailored message booklet only (N = 121) 3) Generic message booklet with advice (N = 124) 4) Generic message booklet only (N = 120) Neumann et 1) AUDIT RCT two groups: al. (2006) 2) Readiness- 1) Intervention: to-Change Standard care plus questionnaire computerized SBI (N = 561): with 3) Percent of customized normative patients with feedback, advice, at-risk drinking change strategies, (more than 30 g/d and summary letter men; more than printed for patient 20 g/d women) before ED discharge 2) Control (N = 575): Standard care only Nilsen et al. 1) AUDIT-C RCT two groups: (2009) Computerized screening with Trinks et al. printout given to (2010) patient of: 1) "Long-feedback" (N = 52): Traffic light graphic with risk level (hazardous, elevated, or no risk) and other tailored feedback about drinking pattern, and information to enhance motivation to change behavior 2) "Short-feedback" (N = 41): Traffic light graphic only Vaca et al. 1) AUDIT Single-cohort (2011) 2) Drinks per week observational study: Intervention: Computerized SBI involving brief negotiated interview, and personal alcohol reduction plans Suffoletto et 1) AUDIT RCT three groups: al. (2012) 2) Timeline 1) Intervention Follow-Back (N = 15): Weekly text message (TM) feedback with goal setting 2) Assessment only (N = 15): Weekly TM-based assessments, no feedback 3) Control (N = 15): Weekly TM notifying number of weeks until 3-month followup Murphy et al. 1) AUDIT Single-group (2013) 2) Patient feasibility study: acceptance and comprehension 15-minute Web-based questionnaire SBI with assessment, 3) Research staff tailored risk-level questionnaire education, customized normative feedback, list of local alcohol treatment agencies Adults (Age 18 or Older): Hospital Outpatient Clinics Johnson et 1) AUDIT Single-group al. (2013) feasibility study: 2) Peak blood alcohol Computerized SBI concentration with normative (BAC) feedback on screening results 3) Leeds and peak BAC, Dependence comparison to Questionnaire recommended limits (not shown for low- 4) History of Trauma risk drinkers), scale information about health and behavioral risks of different BACs, estimate of spending on alcohol per month, tips for reducing risk and local treatment options Pregnant Women Tzilos et al. 1) T-ACE RCT two groups: (2011) 2) Timeline Follow- 1) Intervention Back (N = 27): 15- to 20-minute 3) Readiness to computerized SBI Change with educational content tailored to 4) Acceptability pregnant women, and of software to their current drinking status and 5) Birth outcome motivation to variables change 2) Control (N = 23): Questionnaire on television show preferences and shown videos of popular shows Pollick et 1) T-ACE Single-group al. (2013) pretesting study 2) Alcohol use Computerized SBI: 3) Acceptability 20-minute of software interactive tailored program 4) Semistructured with content based interview about on MI techniques user experience with normed feedback, decisional balance exercise, menu of change (or relapse prevention) options, referral to local treatment options Adolescents (Age 17 or Younger) Gregor et al. 1) Alcohol Misuse RCT two groups: (2003) Index of negative consequences of 1) Intervention (N = Maio et al. alcohol use 329): Computerized (2005) screening and 2) Binge-drinking single-session BI episodes in past interactive 3 months educational program (virtual house 3) Driving after party) to increase drinking or riding knowledge about with a driver that risks, enhance had been drinking refusal skills, decrease intention to use 2) Control (N = 326): Baseline survey with standard care only Cunningham 1) AUDIT-C RCT three groups: et al. (2009, 2012) 2) POSIT 1) Computerized BI (N = 237) Walton et al. 3) Conflict Tactic (2010) scale 2) Therapist- delivered BI 4) Violence (N = 254) consequences * Both 35 minutes and based on motivational interviewing, with normative feedback and skills training 3) Control (N = 235): standard care with community resource brochure (also given to BI groups) Harris et al. 1) CRAFFT screener Before-and-after, (2012) each site own 2) Timeline Follow- control: Louis-Jacques Back et al. (2014) 1) Control phase 3) Postvisit (USA/CZR N = questionnaire 1,068/297): Treatment as 4) Personal usual (TAU) Consequences Scale 2) Intervention phase (USA/CZR N = 1,028/292): 10-minute computer- assisted screening and provider brief advice (cSBA) with screening, risk- level feedback, educational pages, and provider report with screen results and prompts for 2 to 3 minutes of counseling Walton et al. 1) AUDIT-C RCT three groups: (2014) 2) Alcohol-related 1) Computerized BI consequences (N = 252): Offline (RAPI) "Facebook"-styled program 3) Psychological constructs related 2) Therapist- to behavior delivered BI change: (N = 256) --Importance of --Both BI had cutting back tailored normative feedback, based on --Likelihood to motivational cut down in interviewing and next 30 days cognitive-behavioral strategies --Readiness to stop 3) Control (N = 281): Standard care with --Desire for help community resource to cut down brochure (also given to BI groups) Authors Follow-up Results (Year) Period (% Completed) Adults (Age 18 or Older): Primary Care Butler et al. 6 months * Spanish version (2003) (85%) had lower AUDIT+ detection rates than English version; no such difference found with traditional AUDIT. * AUDIT-C scores declined for both groups during followup; no intervention effect; no difference between language groups. Kypri et al. 6 months * Both intervention (2008) (84%) groups had lower alcohol consumption, 12 months AUDIT scores, and (84%) alcohol problems at 6 and 12 months compared with the control group. * Single-dose and multi-dose effects similar; provision of up to two additional sessions did not increase efficacy. Bendtsen et 3 months * No significant al. (2011) (60%) between-group differences at baseline and 3 months. * "Staff-referred" had reduction in weekly alcohol use but "self-referred" did not. * Significant reduction in HED for both. * Follow-up responders more likely to be older, have lower weekly alcohol use at baseline than non-responders; no difference in HED. Cucciare et 3 months * Alcohol al. (2013) (86%) consumption and severity of 6 months alcohol-related (84%) problems declined for both groups. * No differences between groups. Adults (Age 18 or Older): Emergency Department (ED) Karlsson -and N/A * 95% rated computer Bendtsen use. ratings (2005) * 67% rated being screened positively. * 76% rated feedback and advice printout positively. * 74% preferred printout over nurse or doctor delivery. * 93% would read advice. Blow et al. 3 months * All groups reduced (2006) (86%) mean drinks per week, HED, and 12 months alcohol-related (86%) consequences by 12 months. * No difference in outcomes between tailored vs. generic message conditions. * Brief advice had greater reductions than no advice, particularly among females and those aged 22 and older. Neumann et 6 months * Significant al. (2006) (63%) intervention effects at 6 and 12 months: 12 months intervention group (58%) had lower percent of patients reporting at-risk drinking, and greater decrease in alcohol intake, compared with control subjects. Nilsen et al. 6 months * 41% of those (2009) (17%) requested to do computer SBI did. Trinks et al. (2010) * Both groups had reduced weekly alcohol consumption and HED frequency at 6 months. * No differences in change over time between groups. * 6-month respondents had lower HED frequency at baseline than non-respondents. Vaca et al. 6 months * 47% of at-risk (2011) (57%) drinkers reduced drinking to below NIAAA-recommended limits. * Decreased frequency of driving while impaired. * Reductions greater among those with AUDIT scores higher than 8. Suffoletto et 3 months * 93% of al. (2012) (86%) intervention and assessment groups replied one or more times to weekly TM queries about drinking; 80% of intervention group replied to all 12 weeks of queries. * Intervention reduced heavy- drinking days and drinks per drinking day more than assessment-only. Murphy et al. N/A * 98% completed CASI (2013) program. * 89% liked program. * 93% found it easy to use. * 90% accurately reported alcohol risk level after program completion. Adults (Age 18 or Older): Hospital Outpatient Clinics Johnson et Within few * 93% of eligible al. (2013) days of visit consenting patients (75%) completed SBI. * 94% found it easy to complete. * 95% reported responding honestly. * 80% found feedback useful. * 96% had no concern about privacy. Pregnant Women Tzilos et al. 1 month * High acceptability (2011) (96%) of computerized screening and BI. * Both groups showed significant decline in reported alcohol consumption during followup; no differ- ences between groups. * Babies born to BI group had significantly higher birth weight compared with control subjects. Pollick et N/A * High ratings for al. (2013) software approval, ease of use, and perceived helpfulness. * Videos and graphs/ charts rated most useful components. Adolescents (Age 17 or Younger) Gregor et al. 3 months Overall sample (2003) (93%) * 94% liked program, Maio et al. 12 months 74% reported it (2005) (89%) made them rethink their alcohol use, 5% needed assistance to use it. * No differences in alcohol outcomes between intervention and control: both decreased from baseline to 3 months, but returned to baseline levels by 12 months. Subgroup with baseline drinking and driving * Alcohol misuse and binge drinking lower at 12 months in intervention group. Cunningham 3 months * 3 months: computer et al. (2009, (86%) and therapist BI 2012) groups showed 6 months similar significant Walton et al. (86%) reductions in (2010) positive alcohol 12 months and violence (84%) attitudes, increases in refusal self- efficacy. * 6 months: Both BI groups less likely to report alcohol- related consequences than control group, but no effect on drinking frequency. * 12 months: significant therapist-BI effect on peer aggression and victimization; no BI effect (computer or therapist) on any alcohol variables. Harris et al. 3 months * 3 months: cSBA (2012) (73%/88%) significantly reduced alcohol Louis-Jacques 12 months use rates compared et al. (2014) (73%/90%) with TAU in USA sample but not in CZR sample. Larger cSBA cessation effect found among drinking youth with peer risk (having friends who drank). * 12 months: cSBA effect attenuated but still significant among New England youth. Walton et al. Immediate * Increased (2014) posttest (99%) importance of change in both BI groups compared with control groups. * Increased readiness to stop in Therapist BI group. * BI components positively related to changes in psychological constructs: Computer BI --Benefits of change --Alternate activities --Choosing goal to reduce or stop Both --Tools for reducing or stopping use --Personal strengths review NOTES: Abbreviations: AUDIT-C: Alcohol Use Disorders Identification Test-Consumption items (items 1-3) CASI: Computerized alcohol screening and intervention CRAFFT: Car, Relax, Alone, Forget, Family/Friends, Trouble PCP: Primary care provider POSIT: Problem Oriented Screening Instrument for Teenagers RAPI: Rutgers Alcohol Problem Index T-ACE: Mnemonic for 4-item screener for problem alcohol use (Tolerance, Annoyed, Cut down, Eye-opener)
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|Title Annotation:||SPECIAL SECTION: Prevention|
|Author:||Harris, Sion Kim; Knight, John R.|
|Publication:||Alcohol Research: Current Reviews|
|Date:||Mar 22, 2014|
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