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Drug and Alcohol Dependence 223 (2021) 108716

Available online 20 April 2021 0376-8716/Published by Elsevier B.V.

Randomized controlled trial of group motivational interviewing for veterans with substance use disorders

Elizabeth J. Santa Ana a,b,*, Steven D. LaRowe c, Mulugeta Gebregziabher a,d, Antonio A. Morgan-Lopez e, Kayla Lamb a, Katherine A. Beavis a, Kinfe Bishu a,f, Steve Martino g,h

a Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Department of Veterans Affairs Medical Center, 109 Bee St., Charleston, SC, 29401, USA b Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, 67 President St., Charleston, SC, 29425, USA c Mental Health Service Line, James H. Quillen VAMC, 53 Memorial Ave, Johnson City, TN, 37684, USA d Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Charleston, SC, 29425, USA e Behavioral Health Research Division, RTI International, Research Triangle Park, 3040 E Cornwallis Rd, Morrisville, NC, 27709, USA f Department of Medicine, Medical University of South Carolina, 135 Rutledge Avenue, Charleston, SC, 29425, USA g Yale University School of Medicine, Department of Psychiatry, 40 Temple St., New Haven, CT, 06510, USA h VA Connecticut Healthcare System, 950 Campbell Ave, West Haven, CT, 06516, USA

A R T I C L E I N F O

Keywords: Dual diagnosis Group treatment Motivational interviewing Substance use disorders Treatment engagement Veterans

A B S T R A C T

Background: Motivational interviewing delivered in a group format is understudied yet promising as a treatment for substance use disorders (SUD). We evaluated the efficacy of group motivational interviewing (GMI) relative to a treatment-control (TCC) for enhancing treatment and self-help engagement and decreasing alcohol and drug use among veterans with SUD and co-existing psychiatric disorders. Method: Veterans (n = 118) with alcohol use disorder were recruited within an outpatient SUD treatment pro- gram and randomized to GMI or TCC upon program entry. Alcohol use, SUD treatment, and 12-step session attendance were primary outcomes. Drug use days was the secondary outcome. Participants were assessed at baseline and at one-and three-month follow-up. Results: Significant differences were observed between GMI and TCC for binge drinking at both one (RR = .74; 95 % CI [.58, .94]) and three-month follow-up (RR = .74; 95 % CI [.59, .91]). At three-month follow-up, significant differences between treatment conditions were observed for alcohol use days (RR = .79; 95 % CI [.67, .94]), number of SUD treatment sessions (RR = 2.53; 95 % CI [1.99, 3.22]), and 12-step sessions attended (RR = 1.64; 95 % CI [1.35–1.98]). Similarly, we observed significant effects for GMI on reducing alcohol consumption in standard drinks (RR = .49; 95 % CI [.25, .95]). Drug use days declined at each follow-up, with no significant differences between treatment conditions. Conclusions: GMI delivered at SUD treatment program entry enhanced treatment session and 12-step group attendance and lowered alcohol consumption among outpatient Veterans. Future research should study how GMI works and its effectiveness in SUD treatment settings.

1. Introduction

Motivational interviewing (MI; Miller and Rollnick, 2013) is a well-established evidence-based treatment that produces small to moderate improvements in substance use outcomes (Hettema et al., 2005; Lundahl et al., 2013; Smedslund et al., 2011), as demonstrated largely within randomized clinical trials of individually delivered MI. However, group treatment is the modal form of intervention within most addiction treatment programs (Wendt and Gone, 2017; Scheidlinger,

2000). Adapting MI for use in groups (Drake et al., 2004; Goldsmith and Garlapati, 2004; Kaminer, 2005; Wagner and Ingersoll, 2013; Wendt and Gone, 2018) and establishing the efficacy of these group treatments is imperative for MI to be fully utilized in SUD treatment settings (Carroll and Rounsaville, 2007).

Recommendations for how to facilitate MI in groups, herein referred to as GMI, that targets substance use began over two decades ago (Foote et al., 1999; Van Horn and Bux, 2001; Walters et al., 2002). Some of the initial approaches became quite well-known in the addiction field (e.g.,

* Corresponding author at: 109 Bee St., Charleston, SC, 29401, USA. E-mail address: [email protected] (E.J. Santa Ana).

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https://doi.org/10.1016/j.drugalcdep.2021.108716 Received 27 October 2020; Received in revised form 2 March 2021; Accepted 4 March 2021

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Velasquez et al., 2001) and culminated in a landmark textbook discus- sing GMI for use among various populations and settings (Wagner and Ingersoll, 2013). A significant challenge when applying MI in groups is ensuring that the spirit, processes, and core interviewing skills of MI are translated and delivered effectively (Miller and Rollnick, 2013; Wagner and Ingersoll, 2013). When facilitating GMI, therapists generate collaborative, compassionate, and accepting interactions (i.e., consis- tent with the “spirit” of MI) among group members. This is typically established by reinforcing group norms during sessions (e.g., offer advice only when solicited by group members, members are free to decide what to do). GMI therapists use semi-structured activities to strategically evoke and support group members’ change talk, namely speech that favors change, while helping members resolve ambivalence for change. An advantage of GMI is that therapists encourage group members to express how they relate to each other’s change talk and to elaborate on their motivations for change. This process where group members mutually ‘cull for change talk’ is referred to as relatedness and can be reliably measured in GMI process analyses (Martino and Santa Ana, 2013; Shorey et al., 2015).

Despite suggestions for more investigations evaluating GMI in the scientific literature (Brown et al., 2007; Wagner and Ingersoll, 2013), the efficacy of GMI remains under-investigated. A few studies have shown that GMI demonstrated promising outcomes for reducing sub- stance use and associated consequences, including reducing sexual risk behavior and episodes of intoxication among adolescents and young adults (D’Amico et al., 2013; Michael et al., 2006; LaBrie et al., 2007; Tucker et al., 2017). Despite the high prevalence rate of psychiatric disorders among those with severe substance use disorders (e.g., 53 % average estimate, 74 % among Veterans; SAMHSA, 2017), relatively few studies have tested GMI among adults with severe substance use and co-existing psychiatric disorders, though preliminary findings suggest that GMI reduces psychiatric symptoms and drug craving, increases time to substance use relapse, reduces days of polysubstance use (alcohol, cannabis, and amphetamines), and enhances treatment attendance, 12-step engagement, and treatment retention (James et al., 2004; Santa Ana et al., 2007; Suvanchot et al., 2012; Navidian et al., 2016; Lincourt et al., 2002).

While these extant studies provide initial support for the efficacy of GMI for treating substance use disorders, methodological shortcomings from prior studies need to be addressed to further advance the scientific field involving GMI. First, control conditions in the aforementioned studies involving adolescents and young adults were relatively modest, consisting of either an alcoholics anonymous approach (D’Amico et al., 2013), access to basic services (e.g., food, hygiene), case management, regularly available programs (Tucker et al., 2017), assessment only (Michael et al., 2006), or no control group (LaBrie et al., 2007). Second, only a few studies have objectively evaluated therapist integrity in the delivery of GMI or evaluated discriminability between GMI and control conditions (D’Amico et al., 2013; Santa Ana et al., 2007; Brown et al., 2007; Tucker et al., 2017). Given that delivering GMI proficiently is challenging for many therapists (Wendt and Gone, 2018), clinical trials should include evidence that GMI is implemented as intended (Onken et al., 2014). Third, few GMI studies randomized participants to treat- ment conditions (Martino and Santa Ana, 2013; Wagner and Ingersoll, 2013), possibly due to inherent logistical and analytical challenges when randomizing participants in groups. For example, amassing sufficient numbers of participants prior to randomization allowing for allocations resulting in groups of sufficient size and analyzing data derived from group treatment studies has been historically challenging (Morgan-Lo- pez and Fals-Stewart, 2006, 2007).

The current investigation aimed to address these shortcomings in a randomized, between groups, repeated measures, controlled trial that evaluated the efficacy of GMI, compared to a treatment control condi- tion (TCC) designed as a highly challenging time and therapist- attention- equivalent control, upon outpatient SUD treatment program entry among adult U.S. Veterans with alcohol use and co-existing

psychiatric disorders. A proportion of participants also had illicit drug use disorder. We hypothesized that participants in GMI, relative to participants in TCC, would exhibit a significantly higher treatment session and 12-step self-help group attendance and lower levels of alcohol use with regard to alcohol use days, binge drinking days, and standard drinks consumed1 at one-and three-month follow-up. Second- arily, we hypothesized that GMI, relative to TCC, would lead to fewer days of illicit drug use. Finally, the study evaluated the integrity of treatment delivery as well as the discriminability between GMI and TCC.

2. Methods

2.1. Participants

Veterans seeking outpatient substance use services within the Ralph H. Johnson VA Medical Center (VAMC) were enrolled into the study. Inclusion criteria required alcohol use disorder (abuse or dependence) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association [APA], 2000) and self-reported alcohol use in the past 30 days. Individuals with illicit drug use and co-existing DSM-IV psychiatric disorders were included in the study.

2.2. Procedures

All procedures were approved by the Medical University of South Carolina Institutional Review Board and the Ralph H. Johnson VAMC Research and Development Committee. This study was registered with ClinicalTrials.gov [NCT00706901] prior to study implementation. Par- ticipants were recruited between May 2010 and November 2012. Recruitment occurred through referrals from health professionals (e.g., primary care triage, patients newly enrolled in SUD treatment but had not yet begun formal SUD treatment), word-of-mouth, study flyers, and direct recruitment in outpatient care areas. Upon referral, trained research staff blind to treatment conditions screened potential partici- pants and those individuals were invited for a baseline interview. All participants provided informed consent prior to study participation. Average wait time between study consent and first treatment session was approximately one week. Participants completed follow-up assess- ments in person or by phone with a research assistant blind to treatment assignment at one and three-month follow-up. One-month follow-up data included data collected between the day of consent and 30 days later; while 3-month follow-up data included data collected for 60 days starting the day after the 1-month follow-up so that these follow-up periods do not overlap. Compensation in the form of VA canteen vouchers were provided to participants for completing baseline, after treatment sessions, and follow-up assessments. Yearly reports were submitted to the Data Safety and Monitoring Board.

2.3. Randomization

A computer generated-randomization list consisting of randomized weeks was created by an independent statistician. Participants were next assigned to GMI or TCC using a variant of block randomization where a

1 A methods paper for the purpose of demonstrating use of Weibull mixture regression as a finite mixture approach for dealing with a preponderance of zero values was previously published utilizing ‘real world data’ from the current study consisting of a smaller subset of the standard drink outcome only (Gebregziabher et al., 2017). This study showed a significantly lower level of standard drinks consumed among participants in GMI compared to participants in TCC, indicating promising effects among participants in GMI using the subset of standard drink data. The current manuscript presents the full range of data on this outcome which includes the one and three-month follow-up data not presented in the aforementioned methods paper.

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set of consecutively admitted participants were assigned to the same treatment condition (either GMI or TCC) with “recruitment week” serving as the unit of randomization. This method of randomization reduces participant drop out and is a time-efficient method for recruiting

sufficient members to form a group.

2.4. Measures

2.4.1. Diagnostic assessment The Structured Clinical Interview for DSM-IV (SCID; First et al.,

2002) was used to screen for substance use disorders. Electronic health record (EHR) review was the primary source for determining partici- pants’ psychiatric disorders. If absent in the EHR, the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) was used for psychiatric disorder determinations.

2.4.2. Demographics Participant gender, age, ethnicity, education, income, and marital

status was collected at baseline through surveys.

2.4.3. Treatment attendance assessment The Treatment Attendance Calendar (TAC; adapted from the TLFB;

Sobell and Sobell, 1992) was used to record the number of objective treatment sessions documented in each participant’s EHR. Treatment sessions included attendance at the outpatient Substance Treatment and Recovery (STAR) program with social workers, psychiatrists, and psy- chologists. Self-reported number of 12-step (Alcoholics Anonymous [AA] and Narcotics Anonymous [NA]) sessions attended were also

Table 1 GMI Session Descriptions.

Session 1

• Introduce guidelines to promote MI-consistent group behavior among the participants (e.g., avoid use of labels, ask permission before giving advice, be non-judgmental)

• Explore common emotions associated with change (e.g., anxiety, fear, shame, uncertainty)

• Discuss pros and cons of substance use • Evaluate importance and confidence to change

Session 2

• Enhance discrepancy between substance use and preferred goals and values using a personalized feedback report derived from baseline study assessments

Session 3

• Explore and clarify values • Examine patients’ personal strengths and ability to use strengths to

avoid substance use • Engage members in a discussion about the interrelationship between

substance use and co-existing psychiatric disorders Session

4 • Enhance motivation to attend outpatient treatment and self-help

participation (e.g., 12-step) wherein group members altruistically assist each other to problem solve solutions to barriers for continuing treatment

Fig. 1. Flowchart of participants through the study.

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recorded. Only treatment sessions attended voluntarily were counted. Treatment attendance was assessed using self-reported estimates for the three months prior to the day of consent to provide a stable estimate of baseline treatment.

2.4.4. Substance use assessment The Timeline Follow-Back (TLFB; Fals-Stewart et al., 2000; Sobell

and Sobell, 1992) was used to assess retrospective self-report of alcohol and illicit drug use (e.g., cocaine, marijuana, stimulants, sedatives, opioids) in the 30 days prior to baseline. Amount of alcohol consump- tion was converted to standard ethanol content units (SECs; or standard drinks) equivalent to 0.5 oz. of ethanol (Miller et al., 1991). Total SECs consumed per day, number of alcohol drinking days, binge drinking days (defined as ≥ 4 consecutive alcohol beverages in a drinking episode for women and men over the age of 65 and ≥ 5 beverages for men under the age of 65(Wechsler et al., 1994), and illicit drug use days were calculated. Urine samples were collected during one-and three-month follow-up and analyzed using Alere iCup Dx Drug Screen for drugs (amphetamines, methamphetamines, benzodiazepines, cocaine, opiates, oxycodone, and cannabinoids) and a breathalyzer (Alco-Sensor III Intoximeters) for alcohol was used to corroborate participant self-reported substance use.

2.4.5. Therapist training and supervision A licensed psychologist and member of the Motivational Interview-

ing Network of Trainers (MINT) trained study therapists. A 20 -h GMI training consisted of intensive instruction on the GMI treatment manual (Santa Ana and Martino, 2009). Therapists conducted practice sessions with volunteer patients in STAR until they demonstrated adequate use of GMI skills. Training in TCC involved a six-hour workshop followed by practice sessions. GMI and TCC practice sessions were reviewed by the PI, who provided coaching and supervisory feedback to therapists and weekly therapy supervision throughout participant recruitment.

2.5. Treatment delivery

GMI and TCC sessions averaged 75 min in length. Therapists comprised five clinical psychologists, three nurse practitioners, and two social workers. In order to feasibly facilitate GMI and TCC within the natural setting of the STAR outpatient program, therapists rotated treatment sessions so that each session was conducted by a different therapist. As in prior studies (Santa Ana et al., 2007), sessions occurred over four consecutive days.

2.6. Treatment integrity assessment

The Motivational Interviewing Treatment Integrity (MITI) protocol, version 3.1.1 (Moyers et al., 2010) is an empirically validated instru- ment that measures therapists’ MI adherence and competence in indi- vidual and group settings (D’Amico et al., 2012). The MITI consists of two components: ‘global dimensions’ (empathy, direction, and MI spirit assessed on a Likert-scale (1 = low adherence; 5 = high adherence), and ‘MI behavioral frequency counts’ (giving information, MI adherence, MI non-adherence, open/closed questions, simple/complex reflections). Summary scores were used to determine MI therapist competence in four categories: percent MI adherence, open questions, complex re- flections; and ratio of reflections to questions. Two undergraduate coders received approximately 40 h of MITI training from an expert coder and supervision meetings were held weekly until coding was complete. Training consisted of in-depth review of the coding manual. The coders rated a total of 40 treatment sessions (29 %; 20 per treatment condition) randomly selected across therapists and sessions, 11 sessions of which were double-coded (28 %; GMI = 5; TCC = 6) for determining inter-rater reliability using intra-class correlations (ICCs; Shrout and Fleiss, 1979).

2.7. Group motivational interviewing (GMI)

Participants randomized to GMI received four sessions consistent with the tenets of MI (Miller and Rollnick, 2013) and based on a man- ualized GMI protocol (Santa Ana and Martino, 2009; Martino and Santa Ana, 2013). GMI aims to engage group members in an empathic and collaborative conversation, focus on behavior change targets, evoke change talk, resolve ambivalence, and depending on readiness for change, initiate change planning. GMI was specifically designed for dually diagnosed members such that it examines the relationship be- tween the members’ substance use and their co-existing psychiatric disorder and explores proactively treating both conditions. Group members are introduced to the ‘GMI culture’ including MI-consistent group norms for maintaining MI Spirit (e.g., avoid giving unsolicited advice). Unlike individual MI, therapists in GMI promote group thera- peutic factors (Yalom, 1995) including group cohesiveness, instillation of hope, universality, and identification. Table 1 contains a description of the four GMI sessions.

2.8. Treatment Control Condition (TCC)

TCC was a four-session, psychoeducational group on substance use and addiction equivalent in time to GMI and designed as a highly

Table 2 Participant Demographics and Clinical Characteristics at Baseline.

Characteristic GMI (n = 59) TCC (n=59) p

Participants per cohort: M (SD) 3.5 (1.1) 2.7 (.84) .017 Gender: n (%) 1.00

Male 54 (92 %) 54 (92 %) Female 5 (8%) 5 (8%)

Age in years: M (SD) 53.0 (8.8) 51.1 (10.6) .31 Race: n (%) .42

African American 32 (54 %) 36 (61 %) Caucasian 27 (46 %) 22 (37 %) Other 0 (0%) 1 (2%)

Education: n (%) .12 ≤ High School 28 (47 %) 24 (41 %) Some College 18 (30 %) 28 (47 %) ≥ College Graduate 13 (22 %) 7 (12 %)

Income: n (%) .72 ≤ $19,999 39 (66 %) 38 (64 %) $20,000 - $49,999 13 (22 %) 16 (27 %) ≥ $50,000 7 (12 %) 5 (8%)

Marital status: n (%) .11 Never married 10 (17 %) 13 (22 %) Separated 8 (14 %) 9 (15 %) Divorced 23 (39 %) 10 (17 %) Married 15 (25 %) 21 (36 %) Widowed 3 (5%) 6 (10 %)

Alcohol diagnosis: n (%) 59 (100 %) 59 (100 %) .75 Abuse 6 (10 %) 5 (8%) Dependence 53 (90 %) 54 (92 %)

Drug diagnosis: n (%) 21 (36 %) 27 (46 %) .26 None 38 (64 %) 32 (54 %) Opioid Use Disorder 1 (1%) 3 (5%) Cocaine Use Disorder 16 (27 %) 18 (30 %) Marijuana Use Disorder 9 (15 %) 9 (15 %) Other 1 (2%) 0 (0%)

Co-existing psychiatric diagnoses: n (%) 53 (90 %) 51 (86 %) .57 None 6 (10 %) 8 (14 %) Depressive Disorders 30 (51 %) 30 (51 %) Anxiety Disorder 27 (46 %) 29 (49 %) Bipolar Disorders 4 (7%) 2 (3%) Mood Disorders 9 (15 %) 3 (5%) Schizophrenia 0 (0%) 1 (2%) Schizoaffective Disorder 1 (2%) 1 (2%) Psychotic Disorder NOS 1 (2%) 1 (2%) Personality Disorders 1 (2%) 1 (2%) Other 2 (3%) 0 (0%)

Note. Totals for drug disorders and co-existing psychiatric diagnoses exceed 100 % because some participants have more than one substance use or co-existing psychiatric disorder.

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challenging control condition containing cognitive behavioral skills, including relapse prevention (Larimer et al., 1999), and 12-step facili- tation (Nowinski et al., 1995). Specific topics included addiction as a chronic disease, consequences of substance use, and developing a relapse prevention plan. Group members were encouraged to discuss the material and ask questions. Therapists maintained an instructional stance and were encouraged to avoid explicit MI-consistent skills.

2.9. Data analytic plan

To detect an increase on treatment attendance and a reduction in alcohol use with a medium effect size (.50; Santa Ana et al., 2007) with a two-sided 5 % significance level and power of 80 %, we determined that a total of 118 participants would be needed. For the 118 randomized participants and projecting a loss-to-follow-up rate of 15 %, we expected 100 participants in the 3-month follow-up sample. Baseline character- istics between GMI and TCC were compared using chi-square test for categorical variables and t-test/Wilcoxon rank sum test for continuous outcomes. Because count outcomes in this study (e.g., number of drug use days) were characterized by an excess of zeros, we used Zero-Inflated Poisson (ZIP) models with random intercept to account for correlation between repeated measures of the responses within subjects (Lambert, 1992; Agresti, 2002). We present estimates and the log-rate ratios for the coefficients of the Poisson regression with their respec- tive 95 % confidence intervals (CI) for count outcomes (binge drinking days, alcohol use days, and illicit drug use days, and treatment sessions attended) at one-and three-month follow up. Outcomes were adjusted for baseline values and comparisons of outcomes were conducted among participants with the same values at baseline (e.g., between group par- ticipants with drug use disorder were compared to between group par- ticipants with drug use disorder). For evaluating the non-count outcome, SECs (standard drinks), we used a Weibull two-component finite mixture model (FMM) adjusting for SECs at baseline to determine if SECs differed significantly at the one-and three-month follow-up (Gebreg- ziabher et al., 2017). Given the average group membership was small and group membership was closed and fully nested within groups, such factors reduce the likelihood of group cohort effects (Roback, 2000). Nevertheless, we evaluated potential variability in outcome due to group cohort effects by running primary analyses with the effect of group nested within treatment condition. Residual analysis was used to assess model fit. All analyses were done using Proc NLMIXED3.

3. Results

3.1. Participant characteristics

Fig. 1 shows the flow of participants through the study. One hundred eighteen Veterans were randomized to GMI (n = 59) or TCC (n = 59). The sample was 92 % male and predominantly African American (58 %). All participants (100 %) had an alcohol use disorder, while 40.7 % had co-occurring illicit drug use disorder. Eighty-seven percent of the sample had a co-existing psychiatric disorder. Characteristics of the participant sample are shown in Table 2 along with test for baseline treatment differences, with t-tests for continuous variables and chi-square or Wilcoxon rank sum test for categorical variables. There were no signif- icant treatment condition differences on any demographic or diagnostic variable. Only one adverse event was documented in the study involving a GMI participant who was hospitalized upon further assessment after verbalizing suicidal ideation during a GMI session.

3.2. Treatment retention

The number of participants in each group cohort ranged from two to six members, with an average group number membership of 3.03 (SD = 1.03). On average, participants attended 3.4 of the 4 treatment sessions (SD = 1.2; range: 0–4), which did not differ by treatment condition (p =.17). Treatment completers were defined as participants attending ≥ 3 out the 4 sessions. Eighty-three percent of the total sample were treat- ment completers and this proportion did not differ by treatment con- dition (GMI = 86.4 % vs. TCC = 79.7 %; p = .33).

3.3. Treatment integrity outcomes

Inter-rater reliability was assessed using a two-way mixed, absolute, single-measures ICC ranging from ‘good’ for MI Adherent Behaviors (.64; 95 % CI [.10; .89]) to ‘excellent’ for Global Spirit (.92; 95 % CI [.74; .98]), and MI Non-adherent Behaviors (.98; 95 % CI [.93, .99]; Cicchetti and Nelson, 1994).

Table 3 shows that overall, both global and behavioral count scores were higher for GMI compared to TCC, indicating greater MI integrity for GMI. Lastly, to assess that GMI participants received session material as intended, participants completed quizzes assessing their under- standing of the GMI therapeutic material. Participants in GMI who

Table 3 Means and ICCs of Therapist Global, Behavior and Summary Scores by Treatment Condition.

GMI (n = 20) TCC (n = 20) p ICC [95 % CI]

M SD M SD

Global Scores Evocation 4.4 .69 3.3 .85 <.001 .78 [.38, .93] Collaboration 4.7 .59 3.5 .97 <.001 .95 [.82, .99] Autonomy/Support 4.2 .78 3.1 .22 <.001 .90 [.68. .97] Empathy 4.6 .54 3.5 1.0 <.001 .75 [.30, .93] Direction 4.7 .50 4.8 .37 .48 .74 [.32, .92]

Behavioral counts Giving information 5.8 5.1 8.3 8.8 0.27 .97 [.88, .99] MI-adherent 9.5 5.2 4.5 4.1 .002 .64 [1.0, .89] MI non-adherent .90 2.5 1.5 2.0 .39 .98 [.93, .99] Closed question 11.3 12.4 10.1 7.1 .70 .91 [.71, .98] Open questions 23.6 20.5 8.9 6.4 .004 .97 [.89, .99] Simple reflections 52.3 37.6 14.5 8.1 <.001 .94 [.79, .98] Complex reflections 16.7 11.2 1.8 1.8 <.001 .91 [.71, .98]

Summary scores MI Global Spirit (mean) 4.4 .69 3.3 .60 <.001 % MI-adherent 95.1 .13 71.9 .31 .004 % Open questions 61.7 .28 45.7 .17 .03 % Complex reflections 27.5 .13 9.0 .09 <.001 Reflections/Questions ratio 2.4 1.7 .97 .60 .001

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completed the quiz (64 %) achieved an average score of 93 %, indicating a high degree of content knowledge and understanding of therapeutic material.

3.4. Treatment outcomes

3.4.1. Treatment and 12-step session attendance Table 4 presents group comparison of treatment engagement means

and standard deviations at baseline and one- and three-month follow-up. Rate ratios and 95 % CI on GMI and TCC on number of STAR and 12-step

sessions attended are presented in Table 5. ZIP models revealed that participants in GMI, compared to TCC, attended a significantly higher number of STAR and 12-step sessions at the three-month follow-up (ps < .001), with a more than 2.5 fold increase in STAR session attendance (Exp [0.9274] = 2.53) and a 64 % increase in 12-step session attendance (Exp [0.4523] = 1.64). Differences were not significant at the one- month follow-up.

3.4.2. Substance use Table 4 presents group comparisons of substance use means and

standard deviations at baseline and one and three-month follow-up. Rate ratios and 95 % CIs for GMI and TCC on binge drinking days, alcohol use days, and illicit drug use days are presented in Table 5. ZIP models revealed that participants in GMI exhibited significantly fewer binge drinking days compared to participants in TCC at both one-and three-month follow-up (p = .01 and <.01 respectively), with GMI associated with a 26 % reduction in binge drinking days (Exp [− .30] = .74) compared to TCC at both follow-ups. Participants in GMI, compared to TCC, exhibited significantly fewer alcohol use days at three-month follow-up (p = <.01; 21 % reduction (Exp [− 0.2315] = 0.79). Two- component FMM Weibull analysis revealed GMI reduced SECs signifi- cantly more relative to TCC at three-month follow-up (p = .04). After adjusting for baseline differences, GMI was associated with a 51 % reduction in SECs at three-month follow-up compared to TCC. Although alcohol use days and SECs declined from baseline in both treatment conditions, there were no significant between group differences at one- month follow-up. Illicit drug use days similarly declined from baseline in both treatment conditions, with no significant between group differ- ences at either follow-up.

3.4.3. Convergence of participant urine toxicology and breath alcohol testing

Self-reported alcohol use was 100 % consistent with breath alcohol testing at one-month follow-up, while only one individual (1%) exhibited a false negative at three-month follow up. For illicit drug use, four participants (3%) exhibited false negative results at one- and three- month follow-up. These data indicate that participant self-reports involving substance use tended to converge with breath alcohol and urine toxicology results.

4. Discussion

The current study adds to the small but growing literature examining the efficacy of GMI on treatment engagement and substance use among adults with substance use and co-existing psychiatric disorders. Study findings demonstrate that GMI was delivered with integrity and par- ticipants received an adequate dose of each intervention. At three- month follow-up, GMI was more effective than TCC in reducing num- ber of alcohol use days, binge drinking days, and standards drinks consumed (SECs). In addition, by three-month follow-up, GMI was more effective than TCC in promoting greater outpatient SUD treatment and

Table 4 Baseline and Follow-up Non-Zero Value Means and Standard Deviations for Substance Use and Treatment and 12-Step Session Attendance.

Treatment Condition

Variable GMI TCC

n M SD n M SD pa

Binge drink days Baseline 56 13.23 8.13 57 14.58 9.42 .99

1-month 20 8.45 8.32 25 13.12 12.06 .01 3-month 23 11.35 13.91 22 18.27 23.33 <.01

Alcohol drink days Baseline 59 15.80 7.99 59 16.36 8.74 .87 1-month 28 9.78 8.11 30 12.10 11.33 .07 3-month 34 13.18 14.38 27 18.70 22.63 <.01

SEC Baseline 59 178.87 143.27 59 201.54 132.04 .37 1-month 28 86.89 122.99 30 126.12 155.24 .22 3-month 34 99.72 183.71 27 212.05 316.24 .04

Drug use days Baseline 20 7.55 6.15 26 8.27 8.57 .95 1-month 14 2.86 2.03 15 2.67 2.13 .81 3-month 10 4.50 2.12 9 3.78 3.42 .17

Substance use treatment sessionsb

Baseline 10 17.30 17.85 10 25.60 16.71 .35 1-month 48 10.71 8.50 44 11.75 8.51 .96 3-month 44 15.16 13.17 37 12.81 11.48 <.001

12-step sessions Baseline 11 25.82 30.06 10 41.70 63.54 .91 1-month 27 19.67 15.49 28 18.61 16.29 .99 3-month 27 45.37 36.96 29 38.31 40.26 <.001

Note. SEC = standard ethanol content unit. a Based on two component finite mixture Weibull models for SEC and zero-

inflated Poisson regression for binge drink, alcohol drink, and drug use days and substance use treatment and 12-step sessions.

b Substance use treatment sessions refers to sessions at the Substance Treat- ment and Recovery (STAR) at the Charleston VA Medical Center.

Table 5 GMI versus TCC Rate Ratios and Percent Change for Substance Use and Treatment and 12-Step Session Attendance at Follow-Up.

1-month 3-month

Variable Rate Ratio 95 % CI Rate Ratio 95 % CI

Binge drink days 0.74 [0.58, 0.94] 0.74 [0.59, 0.91] Alcohol drink days 0.83 [0.68, 1.01] 0.79 [0.67, 0.94] SECa 0.68 [0.37, 1.25] 0.49 [0.25, 0.95] Drug use days 1.08 [0.60, 1.93] 1.49 [0.84, 2.64] Substance use treatment sessionsb 1.57 [1.24, 2.00] 2.53 [1.99, 3.22] 12-step sessions 1.48 [1.20, 1.82] 1.64 [1.35, 1.98]

Note. SEC = standard ethanol content unit; CI = confidence interval. a Percent Change from Weibull FMM regression. b Substance use treatment sessions refers to sessions at the Substance Treatment and Recovery (STAR) program at the Charleston VA Medical Center.

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12-step session attendance. GMI was not more effective than TCC in reducing number of days of illicit drug use. Although results were pri- marily based on participant self-reports, the convergence of self-reports with alcohol breathalyzer and urine drug samples indicates that these reports were reliable and valid.

The positive impact of GMI on treatment attendance is consistent with literature demonstrating that MI enhances treatment adherence and retention (Hettema et al., 2005; Lawrence et al., 2017). Higher treatment engagement is associated with reductions in substance use among patients with SUD (Hawkins et al., 2012). Similar to MI, GMI may confer beneficial treatment effects by engaging individuals in action-oriented SUD treatment programs (Carroll et al., 2006; Burke et al., 2003; Dunn et al., 2001). Our results are consistent with prior studies evaluating GMI in a civilian sample of dually diagnosed psy- chiatric inpatients (Santa Ana et al., 2007) and homeless young adults (Tucker et al., 2017). The latter study found that GMI delivered in drop-in resource centers significantly lowered frequency of alcohol use in the past three months (p = .01; d = .31) and trended toward lower likelihood of heavy drinking in the past 30 days (p = .05; d = .22) compared to control drop-in resource centers (Tucker et al., 2017). GMI similarly resulted in significantly lower consumption of SECs (OR = .6, 95 % CI [.2, 1.6]) and binge drinking (OR = .2, 95 % CI [.1, .7]) among participants who continued to consume alcohol during the one to three-month follow-up phase in a psychiatric inpatient sample (Santa Ana et al., 2007). With regard to mild to moderate effect sizes, GMI may confer similar alcohol treatment outcomes to individual MI and may be effective in inpatient and outpatient settings for civilian and Veteran samples.

Several potential mechanisms may have influenced results in the current study. GMI is specifically designed to enhance proactive treat- ment attendance for making a change in substance use. Another possible mechanism is whether participant change talk serves as mediator on therapist MI skill and treatment outcome. Higher percentage of change talk relative to sustain talk in MI predicts treatment outcome (Magill et al., 2018). Some prior studies demonstrated that GMI produces high percentages of change talk (Osilla et al., 2015; D’Amico et al., 2013). Moreover, Shorey et al. (2015) demonstrated a novel change talk phe- nomenon referred to as relatedness, or synergistic exchange of change talk occurring between group members (Martino and Santa Ana, 2013). In this study, relatedness occurred more frequently in GMI compared to the control condition (Shorey et al., 2015). However, we cannot draw the conclusion that change talk in this study served as a mediator until we analyze the process by which change talk may or may not have been associated. Our research team is in the process of examining how change talk in GMI may mediate treatment outcomes and future studies should examine mediation effects of relatedness in GMI and treatment outcomes.

Although there were reductions in illicit drug use overall, the null result is consistent with some prior MI studies similarly reporting non- significant differences between treatment groups on drug use out- comes (Donovan et al., 2001; Miller et al., 2003; Carroll et al., 2006). We did not require participants to have drug use disorders or recent drug use at baseline, which likely created a floor effect for the drug use outcome analysis. Indeed, only 40.7 % of our sample had drug use disorders, potentially resulting in insufficient power to detect group differences. Also, although a prior randomized controlled trial (Santa Ana et al., 2007) showed that GMI resulted in significantly fewer drug use days, this finding was found only at the 1-month follow-up. Moreover, given that TCC in the current study was designed as a highly challenging control condition that included CBT, relapse prevention skills, and 12-step facilitation material, this potentially diminished superiority of GMI over TCC for significantly reducing illicit drug use. Thus, future studies designed to specifically test the efficacy of GMI for impacting drug use are needed utilizing treatment-as-usual comparators for eval- uating incremental value of GMI on such outcomes.

This study has several limitations. Our sample consisted of Veterans

who were predominantly African American males. Findings may not generalize to non-Veteran women and adolescents and generalizability is limited to settings with more female patients. Second, as mentioned previously, only 40.7 % of our sample had a drug use disorder, which may have diminished power to detect between group differences in drug use. Third, our follow-up period was only three months - a period not long enough to evaluate sustained patterns of treatment involvement or reductions in alcohol or drug use. Future studies should extend the follow-up period to 6 months or longer so the impacts of study in- terventions on long-term behavioral changes can be evaluated. Fourth, therapists had differing levels of availability depending on their ongoing clinical duties. As such, it was not possible to evenly assign treatment sessions across therapists, which could have reduced opportunities to develop more cohesion and trust. Session therapist assignments were examined and it was concluded that no therapist was overrepresented among sessions across treatment conditions.2 An additional limitation of this current study was that average group member size may not repre- sent standard group sizes in other outpatient treatment programs. It is important to note that although GMI patients are recruited from the larger pool of patients attending treatment services, not all patients will be appropriate for GMI (e.g., patients already sufficiently motivated to change their substance use need not attend GMI), thus, GMI is designed to function as a “branch off treatment.” Sixth, we did not assess whether group climate, cohesion, or other processes were associated with outcome. Lastly, we evaluated psychiatric disorder diagnoses amongst our participants using either EHR when participants had already been evaluated for psychiatric disorders by a STAR psychiatrist during a triage appointment, or using the MINI, when patients had not yet been evaluated for psychiatric disorder during a triage appointment. Thus, participants in this study may have had more serious mental illness than was captured, particularly if EHR data did not contain the full range of psychiatric disorders among the participants. Despite these limitations, the current study benefits from use of an active attention control con- dition, block-randomization, urine and breath toxicology convergence measures, and independent evaluation of outcomes using blinded personnel. In addition, GMI was delivered with integrity as supported by comprehensive training and supervision including a GMI manual.

5. Conclusions and clinical implications

The results of this study support the application of GMI to engage patients in outpatient SUD treatment and reduce alcohol use. Delivery of GMI by clinical rather than research staff, enhances the feasibility of using GMI in clinical programs and demonstrates GMI’s capacity to expand availability of MI in addiction treatment programs where group treatment is dominant. Refining strategies to train therapists in GMI and organizationally support its implementation will be important to sustain its practice in SUD treatment programs. Given its potential for reduced labor intensity, determining its effectiveness across programs and cost- effectiveness in future studies will be essential for more widespread use of GMI for individuals with substance use disorders.

Role of funding source

This work was supported by a Clinical Science Research and Devel- opment (CSR&D) Career Development Award (CDA-2) to Dr. Santa Ana (CDA-2-016-08S) from the United States (U.S.) Department of Veterans Affairs (Clinical Sciences Research and Development). CSR&D had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit for publication. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

2 Data regarding therapist-group assignments is available upon request by the first author.

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Contributors

Elizabeth Santa Ana: Designed the study, wrote the protocol, and co- wrote manuscript.

Steven LaRowe: Co-wrote manuscript. Mulugeta Gebregziabher: Assisted with data analysis and interpre-

tation of data for manuscript. Kinfe Bishu: Assisted with data analysis and interpretation of data for

manuscript. Antonio Morgan-Lopez: Assisted with randomization and data

analysis. Kayla Lamb: Contributed to recruitment, retention, and co-wrote

manuscript. Katherine Beavis: Conducted literature searches and co-wrote

manuscript. Steve Martino: Contributed to project design and co-wrote

manuscript. All authors contributed to and have approved the final manuscript.

Declaration of Competing Interest

All authors declare they have no conflicts of interest.

Acknowledgements

The authors thank Dr. Tara Wright, Dr. Kathryn Bottonari, Dr. James Harbin, Dr. Edward K. Maher, Mr. Edward Burns, Ms. Julie Brown, Ms. Delia R. Chariker, and Ms. Melanie G. Smith at the Ralph H. Johnson VAMC for their assistance with study participants.

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