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. Author manuscript; available in PMC: 2018 Apr 1.
Published in final edited form as: J Fam Psychol. 2016 Dec 8;31(3):336–346. doi: 10.1037/fam0000266

Decision-Making Style and Response to Parental Involvement in Brief Interventions for Adolescent Substance Use

Timothy F Piehler 1, Ken C Winters 2
PMCID: PMC5378604  NIHMSID: NIHMS825700  PMID: 27929312

Abstract

Adolescent decision making has been previously identified as risk factor for substance abuse as well as a proximal intervention target. The study sought to extend this research by evaluating the role of decision-making style in response to parent involvement in brief substance abuse interventions. Adolescents (aged 12–18 years; n= 259) identified in a school setting as abusing alcohol and marijuana were randomly assigned to complete one of two brief interventions (BIs): either a 2-session adolescent-only program (BI-A) or the 2-session adolescent program with an additional parent session (BI-AP). Interventions were manualized and delivered in a school setting by trained counselors. Adolescent decision-making style was evaluated at intake, and alcohol and marijuana use were evaluated at intake and at a 6-month follow-up assessment. Supporting past research with these interventions, BI-AP demonstrated overall stronger outcomes for marijuana when compared to BI-A. Across both intervention models, an adaptive decision-making style (i.e., constructive, rational) assessed at intake predicted greater reductions in marijuana use. A significant moderation effect emerged for alcohol outcomes. Adolescents with maladaptive decision making tendencies (i.e., impulsive/careless, avoidant) demonstrated the largest benefit from the parental involvement in BI-AP, whereas those with a less impulsive style derived little additional benefit from parental involvement in regard to alcohol use outcomes. Implications for the tailoring of brief interventions for adolescent substance abuse are discussed.

Keywords: Brief Intervention, Substance Use, Decision Making, Adolescence, Parents

Substance use and abuse in adolescence represents one of the most pressing public health concerns in the United States with enormous costs to youth, their families, and to society as a whole (National Drug Intelligence Center, 2011). In order to address this concern, there is a need for further innovations in treating adolescent substance abuse. There is growing evidence that brief interventions (BI) are a cost-effective and efficient approach to effectively addressing substance abuse within this population (Tanner-Smith & Lipsey, 2015; Winters, Fahnhorst, Botzet, Lee, & Lalone, 2012). Interventions that involve parents have also generally been found to be more effective in reducing adolescent substance abuse than those without parental involvement, including in the context of a brief intervention (Tanner-Smith, Wilson, & Lipsey, 2013; Winters et al., 2012). However, there is variability in adolescent response to each of these approaches, and it is less clear which adolescents will benefit most from BI approaches and parental involvement (Piehler & Winters, 2015). The present study investigated the role of adolescent decision making (DM) as a predictor of response to brief intervention approaches as well as a moderator of response to brief interventions with and without parental involvement. We examined DM in this role due its associations with adolescent substance use and parenting, its developmental salience during adolescence, and its previously demonstrated role as an intervention target and mediator of intervention response (Bednar & Fisher, 2003; Casey & Jones, 2010; Winters et al., 2012; Winters, Lee, Botzet, Fahnhorst, & Nicholson, 2014).

DM refers to the complex process involved in selecting an action from among different options with varying outcomes. This process is believed to involve integrating factual knowledge with internal cognitive capacity and is assumed to require skills in controlling, directing and planning behavior (Baumeister, Vohs, & Tice, 2007). In social situations, DM involves social judgment and social problem-solving and interacts with emotional context (e.g., a social situation associated with arousal or high emotionality may elicit different decision making processes than a more neutral social situation; Cauffman & Steinberg, 2000). During adolescence, strong DM skills are predictive of high achievement and low risk for health problems, including substance abuse, whereas weak DM skills are linked to poorer social and health outcomes (Albert & Steinberg, 2011; Fairchild et al., 2009; Ivanov, Schulz, London, & Newcorn, 2008).

The processes underlying DM appear to have developmental features with a particular relevance to adolescence. Logical reasoning, a component of decision-making, develops throughout adolescence (Reyna & Farley, 2006), as does one's management of reasoning biases and one's judgment of reasoning success (Klaczynski, 2001). Also, adolescents have been shown to have a larger gap between procedural knowledge (e.g., memory skills and goal-oriented planning) and factual knowledge than do adults (Steinberg, 2004), suggesting that adolescents may lack strategic options for using existing factual and contextual information and knowledge. These developmental features have been explained as competition between neural systems whereby the “impulsive” system prevails over the “reflective” executive control system (Bickel et al., 2007; Steinberg, 2010). Adolescents typically experience early development of motivational systems responsible for reactivity to emotional stimuli. Appropriate cognitive control to manage those impulses lags behind and does not typically fully develop until later in adolescence. This developmental gap leaves adolescents particularly susceptible to sensation seeking and impulsivity in their DM (Casey & Jones, 2010).

There is converging research literature that non-reflective, gratification-biased DM is related to risk for substance abuse (Casey & Jones, 2010). This DM style also characterizes adolescence and may explain the typical onset of substance use and abuse during this developmental period (Windle et al., 2009). A lesser tendency to delay gratification is associated with substance use in adolescents and young adults (Bickel et al., 2007; Perry & Carroll, 2008). These DM tendencies seem to leave adolescents especially vulnerable to the reinforcing and motivational properties of alcohol and drugs (Casey & Jones, 2010).

Given the ongoing maturation of adolescent DM capabilities, there is evidence that parents continue to play a role in the development of effective DM beyond childhood. Parenting style has been found to be related to the extent of peer referencing in DM (Bednar & Fisher, 2003). Adolescents with parents characterized by an authoritative parenting style tended to reference their parents as opposed to their peers in their DM more than adolescents with parents characterized by other parenting styles. Such effects have also been noted in longitudinal studies examining specific parenting practices and substance abuse. Adolescents whose parents engaged them in joint DM surrounding everyday tasks demonstrated greater improvements in DM in affectively-salient situations as well as less binge drinking at a one-year follow-up when compared to adolescents whose parents were less likely to engage them in joint DM (Xiao et al., 2011).

Connection to present study

Because adolescent DM is implicated in vulnerability to substance abuse, it is often a target of interventions with this population. The potential role of DM and adolescent substance involvement was observed in our recent evaluation of a BI for moderate-substance abusing adolescents (Winters et al., 2012; Winters et al., 2014). Based on a self-report measure of DM [Social Problem Solving Inventory (SPSI), D'Zurilla & Nezu, 1990], we found at the 6-month follow-up point that adolescents who reported higher overall scores on the PSI, indicating confidence in making healthy decisions in social situations, reported higher concurrent abstinence rates and lower rates of substance use problems compared to those with a lower SPSI score (Winters et al., 2012). A similar trend was found at a subsequent analysis when the 6-month overall SPSI score was found to be related to a 12-month follow-up point (Winters et al., 2014). These data suggest that DM had a mediating role in the effects of the intervention on substance involvement outcomes and support the potential impact of the program on these skills in the adolescent. Notably, the mediating role of DM was consistent across two formats of the BI, including an adolescent only version (BI-A) and an adolescent and parent version (BI-AP).

Proximal intervention targets that mediate intervention outcomes may hold particular promise to also serve as moderators of intervention response (Howe, Beach, Brody, & Wyman, 2015). Our prior analyses did not explore the possibility that pre-intervention DM scores moderated intervention effects. This issue is relevant in light of the potential significance of adapting intervention strategies based on pre-intervention characteristics in order to optimize outcomes (Murphy, Lynch, Oslin, McKay, & TenHave, 2007). Adaptive intervention strategies provide an opportunity to tailor an intervention based on a pre-intervention variable (e.g., those with significant deficits in DM receive a more intensive version of a given program). Research in this area seeks to go beyond measuring general effects of an intervention or treatment by measuring variation in intervention impact and exploring if response may or may not be improved for a particular individual. Such data can inform the development and application of personalized interventions based on the client's unique characteristics.

DM skills may influence the extent to which youth derive benefits from interventions (Fishbein et al., 2006; Riggs & Greenberg, 2009). A tendency towards impulsive and risky decision making in particular has predicted poorer intervention outcomes for adolescents. In one such study, the tendency to select smaller, more immediate rewards over larger, more distant rewards was associated with poorer substance abuse treatment outcomes (Stanger et al., 2012). Utilizing skills learned in an intervention setting requires a variety of adaptive DM strategies, including the ability to effectively consider new response sets in familiar situations that may have previously led to substance use. BIs in particular lack some of the practice and repetition found in full length interventions and require adolescents to more quickly generalize new skills into daily decision making. While not previously investigated, BI outcomes could be particularly impacted by pre-intervention youth DM skills for this reason.

A primary interest in tailoring BIs for youth is better understanding the role of parents. Previous research has revealed that parental involvement generally improves BI outcomes (Piehler & Winters, 2015; Winters et al., 2012). However, soliciting the involvement of parents in interventions often requires additional resources and may present logistical challenges in some contexts (e.g., school-based interventions, foster care, etc.). A better understanding of when parental involvement in BIs is likely to yield the most benefit would allow for a more careful allocation of limited resources. Previous research comparing BIs for adolescent substance use with and without parental involvement has found that youth with a higher severity of symptoms derive more benefit from parental involvement (Piehler & Winters, 2015). Furthermore, a single session of parental involvement in the context of a BI appears to be insufficient for youth who present with conduct problems in addition to substance abuse (Piehler & Winters, 2015).

However, the role of adolescent DM as it relates to parental involvement in BIs is unclear. As noted above, there are clear links between parenting and DM style in adolescents (Bednar & Fisher, 2003). However, it is not known if substance-abusing adolescents with maladaptive DM styles may benefit more from parental involvement in interventions. Parenting interventions targeting adolescent substance abuse often focus on parenting skills that support effective adolescent DM and help adolescents avoid contextual risks (e.g., unsupervised peer environments) that may facilitate poor DM (Schmidt, Liddle, & Dakof, 1996). We speculate that supporting parents in developing these skills will increase the benefit of an intervention for youth with poor DM skills.

The present study involved a re-analysis of our brief intervention outcomes by examining intervention impact as function of variability on pre-intervention DM styles. Specifically, we examined the association of different DM styles on the 6-month alcohol and marijuana outcomes of two brief intervention formats with and without parental involvement. The key research questions and hypotheses for the study included: 1) Will DM style predict overall response to BIs (including both BI-A and BI-AP)? We hypothesized that youth with more adaptive DM styles (e.g., rational) may derive more benefit from a BI than youth with maladaptive DM styles (e.g., impulsive). 2) Will DM style moderate the impact of parental involvement in BIs on substance abuse outcomes? We hypothesized that youth with maladaptive DM styles may benefit more from a BI parenting component than youth with adaptive DM styles.

Methods

Participants

A sample of 259 youth between the ages of 13 and 18 were identified by school counselors due to concerns about drug and alcohol use and recruited from schools enrolled in the study. An additional control group of 56 youth was also recruited but was not a focus of the present analyses and was not included in the current analyses. Detailed analyses comparing the intervention groups versus the control group may be found in previous analyses with this dataset (Winters et al., 2012; Winters et al., 2014). Of the 259 participants in the intervention conditions, 242 met DSM-IV TR (American Psychiatric Association, 2000) diagnostic criteria for a substance use disorder in the prior year. All of the 17 youth who did not meet criteria for any substance use disorder reported either 1 or 2 dependence criteria for at least one substance. All research procedures were approved by the University of Minnesota Institutional Review Board (“Brief Intervention for Drug Abusing Students;” Protocol 0308S51681).

Three participants were lost at the 6-month follow-up assessment (See Participant Retention below) and one participant provided an incomplete baseline assessment. Data from all intervention participants were included in the present analyses. Participants in the present study included 53% males and had a mean age of 16. Participants were 70% European American, 12% African American, 8% Native American, 4% Hispanic, 3% Asian American, and 3% other ethnicities. See Table 1 for descriptive statistics of the full sample and each intervention group.

Table 1.

Baseline characteristics of the full sample by intervention group.

Participant Category Total Sample (n=259) BI-Adolescent Only (n= 136) BI-Adolescent and Parent (n =123)
Gender – % (n)
    Male 52.9% (137) 48.5% (66) 57.7% (71)
    Female 47.1% (122) 51.5% (70) 42.3% (52)
Mean Age – Mean (SD) 16.1 (1.4) 16.2 (1.5) 15.9 (1.2)
Ethnicity – % (n)
    European American 69.5% (180) 66.2% (90) 73.2% (90)
    African American 12.0% (31) 13.2% (18) 10.6% (13)
    Native American 8.1% (21) 10.3% (14) 5.7% (7)
    Hispanic/Latino 4.2% (11) 5.1% (7) 3.3% (4)
    Asian American 3.1% (8) 2.9% (4) 3.3% (4)
    Other ethnicities 3.1% (8) 2.2% (3) 4.1% (5)
Days of Alcohol Use over 90 days – Mean (SD) 4.5 (6.8) 4.8 (7.5) 4.2 (5.8)
Days of Marijuana Use over 90 days - –Mean (SD) 23.9 (31.0) 23.5 (33.3) 24.3 (28.4)
Symptom Count – Mean (SD)
    Alcohol Abuse 3.2 (3.1) 3.0 (3.0) 3.4 (3.2)
    Alcohol Dependence 3.5 (3.2) 3.6 (3.1) 3.4 (3.3)
    Marijuana Abuse 4.4 (3.7) 4.5 (4.0) 4.3 (3.4)
    Marijuana Dependence 3.9 (3.6) 3.8 (3.8) 4.0 (3.3)
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Procedure

Participant recruitment

Over a 26-month period, participants were recruited from participating public school systems in the Twin Cities metro area. Youth were identified by school counselors if caught using drugs, with drugs in their possession, or due to being referred to the counselor by a teacher due to drug-related concerns. Upon meeting these initial criteria, counselors made a recommendation to parents that their son or daughter receive a referral for an assessment by the research staff to determine study eligibility. Youth were not referred to the study if they demonstrated a) a serious, acute mental health problem (e.g., active suicidality, signs of psychosis or delusional thinking), or b) if there was need for the counselor to report the family to social services because of concerns regarding physical or sexual abuse. Youth who met either condition for non-referral were extremely rare. Figure 1 depicts a participant flowchart through the study following Consolidated Standards of Reporting Trials (CONSORT) guidelines (Schulz, Altman, & Moher, 2010). When assessed by research staff, study eligibility required that the participant (a) be between 13 and 18 years of age, (b) receive a score indicating at least a mild substance abuse problem on a substance abuse screening questionnaire (Personal Experience Screening Questionnaire [PESQ]; Winters, 1992), (c) not be currently receiving treatment in another drug treatment program, (d) not report an acute severe psychiatric problem or medical condition such as active suicidal ideation or intellectual disability (no cases were screened out for this reason), and (e) agree to participate along with the parent. None of the participating schools had existing in-school intervention or treatment services; thus, our intervention was unique to the school system. For those youth who met study inclusion criteria, consent (parent) and assent (youth) forms were signed, and participants were subsequently enrolled in the study. Participants were assigned by a project manager to one of two different brief intervention conditions via an urn randomization procedure (Teen Intervene- Adolescent Only [BI-A, n= 136] or Teen Intervene – Adolescent and Parent [BI-AP; n= 123]).

Figure 1.

Figure 1

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Participant flowchart following Consolidated Standards of Reporting Trials (CONSORT) guidelines.

Assessments

Youth assessments were conducted in the school at the end of the school day. An experienced research assistant, who was blind to intervention condition, completed each assessment. Assessments were completed during face-to-face interactions with the research assistant. The measures included in the present analyses were completed as a part of a larger assessment battery at baseline and at a 6-month follow up. Youth were compensated with $20 gift cards after the baseline assessment and $40 after completing the 6-month follow up.

Brief Interventions

Intervention sessions with youth were typically conducted in the school at the end of the school day. Each brief intervention consists of 60-minute individual sessions delivered by a counselor using a motivational interviewing (MI) style. Sessions 1 and 2, separated by 7–10 days, are identical for the BI-A and BI-AP conditions. For those participants who were assigned to BI-AP, a third parent-only session was completed. The parent session was typically conducted in the home, but occasionally other settings were utilized such as the school or another location such as a public library (in a confidential setting). For both conditions, session 1 focuses on eliciting information about the youth's alcohol and other drug use and related consequences, assessing willingness to change (Prochaska, DiClemente, & Norcross, 1992), examining the pros and cons of use via a decisional balance exercise (Miller & Rollnick, 2012), exploring triggers of drug use (psychological, social and environmental), identifying drug use goals the youth would like to pursue, and strategizing how to avoid or minimize the negative impact of triggers. Reflecting a MI approach, youth negotiate goals with the counselor, which may include risk or harm reduction or abstinence. Session 2 focuses on the youth's progress in achieving the goals, trouble-shooting why progress may not have not have occurred, adjusting goals if necessary, assessing again willingness to change, and engaging in three exercises intended to increase drug-resistance skills (i.e., dealing with peer pressures, thoughtful decision-making, and utilizing social supports). Finally, long-term goals related to drug use are discussed.

The third session (for BI-AP only) involves delivering the same interactive MI interviewing style to the primary parent or guardian. The focus of this session was informed by an integrative behavioral and family therapy approach (Liddle & Hogue, 2001; Waldron, 1997) and involves these topics and activities: 1) increasing insight of the potential seriousness of their child's substance use problem; 2) improving skills in family communication; 3) parent monitoring and supervision; 4) how the parent can support their child's intervention goals.

Two counselors each administered both the BI-A and BI-AP conditions, with approximately equal distributions of condition assignments between each counselor. This type of “crossed” design (i.e., with counselors administering both treatments) is recommended by Crits-Christoph and Mintz (1991). Both counselors had master's degrees in the behavioral sciences and had experience in delivering structured treatment to substance-abusing adolescents in a school setting. Treatment integrity was monitored through regular supervision meetings between the counselors and the program developer (KCW). Furthermore, research assistants reviewed audiotapes of all sessions in order to complete session adherence checklists. The adherence data indicated that the counselors covered 98% of the key components of the intervention sessions.

Both BI formats have demonstrated effectiveness in reducing adolescent substance use when compared to a control condition (Winters et al., 2012, 2014). See Winters et al. (2012, 2014) for additional information about the brief interventions and their development.

Participant Retention

Three participants in the BI-AP group completed just one of their adolescent sessions (although the parent session was completed in all of these cases), and two parents in the BI-AP group did not complete their single session (although the adolescent completed his/her sessions). These 5 cases were retained in data analyses. All other participants completed their intervention sessions. At the 6-month follow-up assessment, there were three attrition cases, including two from the BI-A and one from BI-AP. See Figure 1 for more details.

Measures

Measures were self-reported by youth and administered as a part of baseline and 6-month follow-up assessment batteries.

Demographics

Participants completed demographic questionnaires at the baseline assessment indicating their sex, ethnicity, and current grade.

Adolescent Diagnostic Interview (ADI)

The Substance Use Disorder module of the ADI was used to assess DSM-IV criteria for abuse and dependence at baseline and at a 6-month follow-up (Winters & Henly, 1993). This highly structured interview covered current symptoms of abuse (e.g., failure to fulfill obligations due to use, legal problems due to use, etc.) and dependence criteria (e.g., tolerance, withdrawal, etc.) for any substance used five or more times during the youth's lifetime. At baseline, lifetime symptoms were evaluated. At the 6-month follow-up, symptoms were evaluated during the prior 3 months. ADI substance-specific symptom counts of abuse (ranging from 0 to 4) and dependence (ranging from 0 to 7) were used as indictors of problematic marijuana and alcohol use. The ADI has strong test-retest reliability and validity (Winters & Henly, 1993; Winters, Stinchfield, Fulkerson, & Henly, 1993).

Timeline Followback (TLFB)

The TLFB procedure was used to assess the number of alcohol and marijuana use days at baseline and the 6-month follow up (Sobell & Sobell, 1996). TLFB asks participants to report the number of days of use in the prior 90 days using a calendar. The TLFB has good evidence for reliability and validity with adolescents (Winters, 2003).

Social Problem Solving Inventory-Revised: Short Form (SPSI-R:SF)

Participants completed the SPSI-R:SF at baseline in order to assess their typical DM style when faced with social problems (D'Zurilla & Nezu, 1990; Hawkins, Sofronoff, & Sheffield, 2009). The SPSIR:SF consists of 25 items, including five subscales each comprised of five items. The version of the SPSI-R:SF utilized in the current study was adapted by Latimer and colleagues to include specific references to drugs and alcohol related problems (Latimer, Winters, D'Zurilla, & Nichols, 2003). Respondents indicate “true” or “false” to each item to indicate whether the item reflected their approach to problem solving most of the time. The subscales reflect a theoretical model of social problem solving, including two adaptive and three maladaptive scales, created by D'Zurilla and Nezu (1990). The Positive Problem Orientation (PPO) subscale evaluates the use of constructive problem solving strategies such as perceiving problems as challenges and optimism about the solving problems (e.g., “If I can’t solve a problem at first, I know if I keep trying, I will eventually be able to solve it”). The Rational Problem Solving (RPS) subscale assess the tendency to apply effective problem solving strategies in a careful, rational, and systematic manner (e.g., “Before I try to solve a problem, I set a goal so that I know exactly what I want to happen”). The Negative Problem Orientation (NPO) subscale evaluates the extent that problems may be perceived as threatening or resulting in negative emotions (e.g., “I feel nervous and unsure of myself when I have to decide whether or not to use drugs/alcohol”). The Impulsive-Careless Style (ICS) subscale examines the tendency to solve problems in an impulsive, reckless, or careless manner (e.g., “When making decisions, I go with my ‘gut feeling’ without thinking too much about what could happen”). Finally, the Avoidance Style (AS) subscale evaluates the tendency to cope with problems through procrastination, passivity, or avoidance (e.g., “I put off trying to solve my problems for as long as I can”). The various versions of the SPSI, including the SPSI-R:SF, have evidence for strong internal consistency as well as structural, discriminant, and convergent validity with adolescents (Hawkins et al., 2009; Sadowski, Moore, & Kelley, 1994; Yetter & Foutch, 2014). For the current study, we formed adaptive and maladaptive problem solving composite measures reflecting the conceptualization proposed by D'Zurilla and Chang (1995). The PPO and RPS subscales were averaged to form a single adaptive DM composite scale. The NPO, ICS, and AS subscales were similarly averaged to form a single maladaptive DM composite scale. Alpha reliabilities for these scales were acceptable (adaptive DM α = .70; maladaptive DM α = .74). The variables were coded such that higher scores on the adaptive DM scale reflected a greater use of adaptive DM strategies, and higher scores on the maladaptive DM scale reflected greater use of maladaptive DM strategies.

Analytic Strategy

The effectiveness of the intervention models in reducing substance use when compared to a control group in this sample has been previously demonstrated (Winters et al., 2012, 2014). The focus of the current paper was to understand how DM style may be related to response to the two different intervention models. An intent-to-treat analysis strategy was used in evaluating differences between the two assigned intervention conditions, meaning that participants were included in their originally assigned condition irrespective of their participation or completion of the assigned intervention. The two composite DM scales from the SPSI-R:SF were examined as moderators of intervention condition on alcohol and marijuana use outcomes. We estimated structural equation models using the structural equation modeling program Mplus version 7 (Muthén & Muthén, 1998-2015). For alcohol and marijuana use, latent variables were created for each substance at baseline and the 6-month follow up using three indicators, including symptoms of abuse, symptoms of dependence, and number of days of reported use over the previous 90 days. Separate models were estimated for alcohol and marijuana. For each model, a stepwise approach was used to examine predictors of 6-month marijuana and alcohol outcomes. A main effects model was initially estimated including demographic factors (participant sex, grade level, and minority status), the corresponding baseline substance use latent variable, intervention condition (BI-A versus BI-AP), and DM style (adaptive and maladaptive DM) as covariates. A subsequent interaction model was estimated by adding two interaction terms (adaptive DM × intervention condition; maladaptive DM × intervention condition) to the main effects model.

Additional models were estimated by adding interaction terms between intervention condition and each demographic variable in a stepwise fashion to the interaction models. These interaction terms did not reflect a priori research questions, but were instead estimated to better control for the possibility that any observed DM style by intervention condition interaction effects could instead reflect interactions with demographic variables rather than DM style (Hull, Tedlie, & Lehn, 1992). Because the demographic variables demonstrated modest correlations with the DM scales (See Table 1), it is important to rule out any demographic by intervention condition interactions as potentially driving the hypothesized interactions with DM style. In order to avoid potential problems with multicollinearity (related to multiple intervention condition interaction terms) and improve model parsimony, non-significant demographic variable by intervention condition interaction terms were not retained in the final models.

DM and demographic variables were mean centered when creating interaction terms. Table 2 presents the bivariate correlations, means and standard deviations of the key study variables. All models estimated included the full sample (n = 259). A small number of missing values were present due to a few participants not completing items or measures and loss at longitudinal follow-up. We managed missing data with the full information maximum likelihood (FIML) procedure used by Mplus version 7. This method is very efficient when analyzing data from samples with moderate to low levels of missing values and allows for participants with missing data to contribute to model estimation (Enders & Bandalos, 2001). We evaluated the fit of each estimated model to the data according to Hu and Bentler (1999), who suggest that a CFI greater than or equal to .95 and an SRMR below .09 indicate acceptable model fit.

Table 2.

Correlations, means, and standard deviations of key study variables.

Measure 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
1. BL Alcohol Abuse –
2. BL Alcohol Dependence .79*** –
3. BL TLFB Alcohol .32*** .36*** –
4. BL Marijuana Abuse .44*** .34*** .27*** –
5. BL Marijuana Dependence .36*** .40*** .25*** .74*** –
6. BL TLFB Marijuana .13* .16* .29*** .48*** .53*** –
7. 6M Alcohol Abuse .10 .10 .33*** .13* .13* .14* –
8. 6M Alcohol Dependence .15* .18*** .41*** .18*** .20*** .18*** .82*** –
9. 6M TLFB Alcohol .20*** .19*** .40*** .19*** .11 .12 .70*** .76*** –
10. 6M Marijuana Abuse .11 .11 .23*** .29*** .34*** .37*** .50*** .44*** .48*** –
11. 6M Marijuana Dependence .17*** .19*** .18*** .29*** .41*** .33*** .43*** .47*** .41*** .78*** –
12. 6M TLFB Marijuana .11 .15* −.23*** .23*** .25*** .38*** .33*** .40*** .40*** .64*** .63*** –
13. Adaptive DM .02 −.01 −.11 −.09 −.16* −.14* −.02 −.03 −.01 −.15* −.19*** −.18*** –
14. Maladaptive DM .08 .11 −.02 .07 .11 .00 .01 .01 −.03 .00 .09 −.01 −.49*** –
15. Sex −.06 .13* −.12* −.24*** −.08 −.16* −.01 .04 .07 −.01 .05 .03 −.11 .16* –
16. Grade .17*** .23*** .22*** .14* .19*** .16* .08 .11 .11 .02 −.02 .07 .27*** −.23 −.06 –
17. Minority Status .06 .07 .17*** .07 .06 .01 .06 .10 .06 .02 −.04 −.04 −.14* −.01 −.08 .04 –
18. Intervention Condition .07 −.02 −.05 −.03 .03 .01 −.13* −.10 −.15 −.09 −.14* .14* .01 .04 −.09 −.07 .08 –
Mean 3.22 3.51 4.51 4.43 3.88 23.87 .42 .68 2.59 .79 1.00 7.63 .70 .39 1.47 10.51 .67 −.05
Standard Deviation 3.10 3.15 6.77 3.69 3.56 31.00 1.18 1.72 4.82 1.79 2.18 14.94 .22 .22 .50 1.47 .46 1.00
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Notes. BL = Baseline; TLFB = Timeline Followback; 6M = 6 Month; DM= Decision Making

*

p < .05

***

p < .01

***

p < .001.

Results

No significant differences in participants’ demographic characteristics or baseline substance use were found between intervention groups (see Table 1). Tables 2 includes descriptive statistics and correlations among all study variables. Main effects models were initially estimated for alcohol and marijuana. These models included latent dependent variables indicated by symptoms of abuse, dependence, and days of reported use reflecting the relevant substance. A corresponding latent variable reflecting the same indicators measured at baseline was included as a covariate in each model in order to control for baseline use. The main effects alcohol model was an acceptable fit for the data (χ2(31) = 67.88, p<.001, CFI = .96, SRMR= .04). Baseline alcohol use was a strong predictor of the corresponding 6-month follow-up alcohol use latent variable (b= .45, β = .48; p <.001). Demographic and DM variables did not reliably predict 6-month alcohol use. Intervention condition also did not achieve significance as a predictor, but demonstrated a trend-level effect of the BI-AP intervention producing stronger alcohol use outcomes when compared to BI-A (b= −.11, β = −.10; p =.08; Cohen's d = .10). The marijuana use model also demonstrated an acceptable fit (χ2(40) = 83.61, p<.001, CFI = .95, SRMR= .05). Baseline marijuana use was also a strong predictor of the corresponding 6-month marijuana use latent variable (b= .24, β = .44; p <.001). Intervention condition reliably predicted 6-month marijuana use (b= −.22, β = −.14; p <.05; Cohen's d = .14). Youth who received BI-AP demonstrated improved marijuana use outcomes when compared to youth who received BI-A. An adaptive DM style was negatively associated with 6-month marijuana use (b= −1.05, β = −.15; p <.05). Youth with a more adaptive DM style exhibited improved marijuana use outcomes when compared to youth who did not report adaptive DM tendencies. Maladaptive DM style and demographic variables were not associated with marijuana use outcomes.

Interaction models were next estimated by adding interaction terms between intervention condition and the two DM style variables. The alcohol use model (See Figure 2) was again an acceptable fit for the data (χ2(39) = 75.31, p<.001, CFI = .96, SRMR= .03). All main effects were consistent with those observed in the main effects model. The interaction term between maladaptive DM style and intervention condition was a significant predictor of 6-month alcohol use (b= −.68, β = −.14; p <.05). Figure 3 graphically depicts this interaction effect. For those youth who reported low levels of a maladaptive DM style, outcomes were equivalent for the BI-AP and BI-A intervention conditions. For those youth who reported high levels of maladaptive DM, the BI-AP intervention resulted in improved outcomes when compared to the BI-A intervention. The regions of significance of the interaction effect were calculated and are included in Figure 3 (Curran, Bauer, & Willoughby, 2006). Youth with maladaptive DM scores .05 above the mean or higher demonstrated significant benefits of parent involvement in the BI with regard to alcohol use outcomes. Youth with maladaptive DM scores falling in this region of significance included 38% of the BI-A group (n = 52) and 39% of the BI-AP group (n = 48).

Figure 2.

Figure 2

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Alcohol use interaction model.

Figure 3.

Figure 3

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Six-month alcohol use outcomes by intervention condition and maladaptive decision making style.

The marijuana use model interaction model (See Figure 4) was an acceptable fit (χ2(52) = 96.63, p<.001, CFI = .95, SRMR= .05). Neither DM style by intervention condition interaction term predicted 6-month marijuana use. All main effects were consistent with those from the main effects model. Intervention condition (b= −.22, β = −.14; p <.05; Cohen's d = .14) and adaptive DM style (b= −1.05, β = −.15; p <.05) remained significant predictors of marijuana use outcomes.

Figure 4.

Figure 4

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Marijuana use interaction model.

Subsequent models were estimated by adding interaction terms between intervention condition and each demographic variable (participant sex, grade level, and minority status). None of these interaction terms were significant predictors when included in the alcohol and marijuana use models. The maladaptive DM style and intervention condition interaction term retained its significance in the presence of these interaction terms in the alcohol use model. These non-significant interaction terms were not included in the final models.

Discussion

Brief interventions represent a promising approach to addressing adolescent substance abuse due to their cost- and time-effective nature. However, many questions remain about BIs, including which adolescents are most likely to benefit from BIs, when to involve other stakeholders (i.e., parents) in BIs, as well as how these recommendations might vary in addressing the use of different substances. The identification of relevant tailoring variables that could assist in treatment planning with BIs represents a potentially important innovation in BI delivery. This study examined the role of adolescent DM style in response to two different brief interventions for adolescent substance use: an adolescent-only intervention (BI-A) and an adolescent- and parent-intervention (BI-AP). Results demonstrated unique effects for DM style as related to marijuana and alcohol outcomes. For marijuana use, an adaptive problem solving style predicted a stronger response overall to both BI models. For alcohol use, an interaction emerged between a maladaptive DM style and intervention model. Adolescents with more impulsive or careless DM benefited the most from parental involvement in BI.

Consistent with previous investigations, BI-AP demonstrated overall stronger effects for marijuana use when compared to BI-A (Piehler & Winters, 2015; Winters et al., 2012). Parent involvement in BIs may be broadly recommended in addressing marijuana use. Previous research has also supported the BI-AP intervention as resulting in stronger alcohol use outcomes when compared to BI-A (Winters et al., 2012). However, the current findings revealed additional complexity when alcohol outcomes were considered in context of adolescent DM style. While there was a trend towards overall superiority of the BI-AP intervention for alcohol use, examination of the interaction effect revealed that those youth who reported an above average tendency towards maladaptive DM benefited the most from parental involvement.

When recommending a parent component in BIs, it is important to note that involving parents may not always be feasible or practical, particularly for school-identified youth. In these cases, it is helpful to know when parent involvement is most likely to be beneficial or potentially unlikely to improve outcomes. When considering alcohol use as a primary intervention target, the current results implicate maladaptive problem solving as a potential tailoring variable in this regard. Youth who describe themselves as impulsive or careless decision makers appear to benefit the most from parent involvement. Those youth who are less likely to be impulsive in their DM demonstrate equivalent alcohol use outcomes irrespective of parent involvement in their BI. It may be that motivating parents’ involvement with their children, even in the context of a single intervention session, results in them providing additional scaffolding and support of effective DM. These parenting behaviors appear to offset what would otherwise be negative consequences associated with maladaptive DM on alcohol use.

Irrespective of the intervention type, adaptive DM styles were associated with superior marijuana use intervention outcomes. Rational decision makers tend to consider and weigh multiple options in their problem solving. This style may have supported adolescents’ efforts to reduce their marijuana use. It is perhaps not surprising that adolescents with a tendency to approach problems carefully and rationally demonstrated more success in generalizing intervention strategies to real world settings. Strategies such as avoiding high risk situations associated with use and responding to social pressures often require youth to consider longer term outcomes over more immediate goals.

When considering the current findings, it is important to note that adolescent DM has now been identified as a moderator of BI outcomes in the present study as well as a mediator of outcomes in previous research (Winters et al., 2014). While perhaps not immediately intuitive, the connection between moderators of intervention response and proximal mediating outcomes is a logical one (Howe et al., 2015). Specific characteristics such as maladaptive DM that are etiologically linked to a distal outcome such as substance use are likely mediators of interventions targeting that outcome. Furthermore, individual variability in that mediator is likely to impact the extent of response to an intervention strategy. In relation to the current findings, youth with initial DM deficits may require a higher dose or more intensive intervention (i.e., parent involvement) to improve their DM skills and accordingly reduce their alcohol use when compared to youth without DM deficits. This type of moderated mediation conceptualization appears to hold promise for future research in identifying moderator/mediator variables that may be useful for tailoring interventions to individual needs (Howe et al., 2015).

Notably, participants in the current study reported generally higher rates of marijuana use when compared to alcohol use. While national trends reflect an overall higher prevalence of adolescents reporting alcohol use, daily use of marijuana is reported more commonly than daily use of alcohol in recent surveys (Miech, Johnston, O'Malley, Bachman, & Schulenberg, 2016). Furthermore, consistent with the current sample, higher usage of marijuana has been reported commonly in adolescent samples identified as displaying early or more patterned substance use behaviors (Brown, D'Amico, McCarthy, & Tapert, 2001; Walton et al., 2010).

The unique effects observed for alcohol and marijuana models support efforts to examine these substances separately. Some of the distinct features of alcohol and marijuana use may account for the observed differences, which include factors such as availability, social influences, perceived harm, and perceived normative use. Consistent with the higher prevalence of marijuana use in the sample when comparted to alcohol, anecdotal reports from counselors suggested that marijuana use tended to receive more discussion during the adolescent-focused intervention sessions. We speculate that strong adaptive DM skills allowed youth to derive greater benefit from the youth-focused goal setting and planning components included in these sessions in both intervention formats. Because session goals may have more often targeted marijuana, youth with strong adaptive DM skills demonstrated greater reductions in their use of marijuana when compared to alcohol. The unique interaction effect observed in relation to alcohol use may to relate to the stronger social component of alcohol use when compared to marijuana. We hypothesize that parental monitoring of peer activities may be particularly important in reducing alcohol use for youth with impulsive DM tendencies. In response to the parent component of BI-AP, parents likely increased their monitoring of peer activities and reduced opportunities to make poor decisions regarding alcohol use among youth with those tendencies. Further research will be important to evaluate these substance-specific findings.

Several limitations are important to consider when interpreting the current results. The study's predominance of white, middle-class participants may affect the generalizability of the results. The use of school-based recruitment may also limit generalizability to other recruitment settings. An important consideration when interpreting study results is that the number of sessions between groups was not equated; youth in both conditions (BI-A and BI-AP) received equal number of adolescent sessions (2), but there was an additional parent session for cases assigned to the BI-AP group. While youth dosage was equated across the two conditions, some effects could have resulted from the overall differing dosage across the two conditions rather than the specific involvement of parents. Also, the use self-reported data may be influenced by response biases, though there is evidence of the validity of adolescent substance use self-report (Maisto, Connors, & Allen, 1995).

Overall, the current results are supportive of continued efforts to identify predictors and moderators of intervention response in the area of adolescent substance abuse. Decision-making style in particular appears to hold promise for further investigation as a tailoring variable. While still in early stages, research into such personalized approaches to intervention seems to have considerable potential for increasing efficiency of service delivery and improving outcomes.

Supplementary Material

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Acknowledgments

This study was supported by grants DA017492, AA14866, DA027841, and DA035882 from the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This clinical trial is registered under NCT00350909 on clinicaltrails.gov. Portions of this research were presented at the 2016 annual meeting of the Society for Prevention Research. Dr. Winters is now affiliated with the Winters Consulting Group.

Contributor Information

Timothy F. Piehler, Department of Family Social Science, University of Minnesota

Ken C. Winters, University of Minnesota

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