1. Introduction
Adolescent substance use is an ongoing concern nationwide. Although it is illegal for individuals to consume alcohol under 21 and cannabis use is either completely illegal or illegal under 21, substance use is common during adolescence. Approximately 30% of 10th graders and 46% of 12th graders have reported using alcohol in the past year, and almost 18% of 10th graders and 29% of 12th graders have reported cannabis use in the past year [1]. Alcohol is the most commonly used illicit substance among adolescents, with 1.19 million adolescents and 11.72 million young adults (18–25-year-olds) reporting binge drinking in the last month [2]. According to the National Center for Drug Abuse Statistics (NCDAS), around 43.7% of youths have tried cannabis in their lifetime and 35.2% of youths have used cannabis in the last year [2]. The range of ages for different survey statistics supports the view that adolescence is an important transitional phase of development. In this study, we focused on middle adolescence, 15–17-year-olds, when a number of health risk behaviors increase.
Early and/or persistent use of substances poses many risks to the adolescents’ biological, psychological, and social development. According to the CDC, adolescents who drink alcohol are more likely to experience symptoms such as school problems, social problems, legal problems, physical problems, physical and sexual violence, increased risk of suicide and homicide, and long-term changes in brain development [3]. Negative consequences of adolescent cannabis use include difficulty in thinking and problem-solving, problems with memory and learning, reduced coordination, difficulty maintaining attention, and problems with school and social life [4].
1.1. Externalizing and Internalizing Behavior Problems
Externalizing problems include mental disorders that are characterized by poor self-regulation and can include behaviors such as rule-breaking, aggression, impulsivity, and inattention [5]. Numerous studies have identified associations between externalizing behavior problems and substance use [6,7,8].
Internalizing problems include behavioral symptoms (e.g., avoidance, worry, withdrawal) and negative emotions (e.g., irritability, sadness) associated with anxiety, depression, and somatization [9]. Findings on the role of internalizing behaviors on substance use are mixed. Some studies indicate internalizing problems may be a risk factor for substance use, hypothesizing that youths may use substances to self-medicate when coping with internalizing problems [10,11]. Other studies found that internalizing symptoms can play a protective role, as social withdrawal and fear of negative consequences can protect adolescents from engaging with deviant peers and substance use [12,13].
Research has focused on both internalizing and externalizing behavior problems and their relationship with substance use [10,13,14]. Most studies found externalizing problems to be the strongest predictor of substance use, especially alcohol use [9,12,13,15]. When controlling for externalizing symptoms, findings suggest that internalizing symptoms serve as a protective factor for substance use [13], such as alcohol [14], cigarette, and cannabis use [10]. However, findings are mixed for cannabis use as some studies found cannabis use to be associated with increased levels of internalizing and externalizing symptoms [16,17].
1.2. Prospective Self
Prospective self [18], is a factor-derived construct proposing that adolescents’ behavioral decisions are influenced by how they identify their current and future selves. Having academic aspirations, exhibiting a resistance to peer influence, and being future-oriented are three concurrent factors of the adolescent self that guide goal-oriented behavior and positive decision-making. Increases in these behaviors promote adolescent identity development, which may serve as a protective factor for internalizing and externalizing problems [18]. Prospective self is negatively associated with externalizing and internalizing problems and confers resilience in youths by moderating the effect of childhood adversity on externalizing problems [18]. In this study, we examine the association of prospective self with substance use specifically.
1.3. Current Study
1.3.1. Alcohol Use Hypotheses
We hypothesized that higher levels of externalizing behavior problems would be associated with more alcohol use. We also hypothesized that prospective self would serve as a protective factor. As adolescents often use alcohol in more social environments with having friends who use substances being the most robust predictor [19], we hypothesized that internalizing behavior problems would not be significantly associated with alcohol use.
We also explored the interaction of externalizing behavior problems and prospective self to evaluate whether prospective self moderated the effect of externalizing problems and alcohol use. We also tested the interaction of internalizing behavior problems and prospective self as a predictor of alcohol use. We are not aware of other studies that have explored these interaction effects, and thus, we do not propose a directional hypothesis.
1.3.2. Cannabis Use Hypotheses
We hypothesized that higher levels of externalizing and internalizing behavior problems would be associated with more cannabis use among adolescents. We also hypothesized that higher levels of prospective self would be associated with fewer cannabis use.
Since we predicted externalizing and internalizing problems to be associated with cannabis use, we explored whether the interactions between internalizing problems and prospective self and externalizing problems and prospective self, would be significant predictors of cannabis use. We are not aware of other studies that have explored these interaction effects as predictors, and thus, we do not propose a directional hypothesis.
1.3.3. Aim
This study aimed to characterize the relationships between internalizing problems, externalizing problems, and prospective self with adolescent alcohol and cannabis use in the past year.
2. Materials and Methods
2.1. Participants
Study characteristics are briefly summarized here with additional details described in Supplementary Section S1. Participants are from the Adolescent Health Risk Behavior (AHRB) study. The AHRB project collected data in schools from 10th to 12th graders (N = 2017, ages 15–17 years) at Wave 1 (March 2015–February 2016) of a longitudinal study designed to characterize behavioral and cognitive correlations of risk behavior trajectories from mid-adolescence to emerging adulthood. The adolescents were recruited from nine public school districts across eight Southeastern Michigan counties. An accelerated cohort design using quota sampling was utilized to approximate the statewide population diversity. To address potential sampling bias, robust analyses using sampling weights derived from the American Community Survey (ACS; year 2015 to match Wave 1 of data collection) are discussed below.
2.2. Measures
We used substance use data collected during Wave 1 to examine participants’ alcohol and cannabis use frequencies in relation to externalizing and externalizing behavior problems and to prospective self. The variables of internalizing and externalizing behavior problems, prospective self, and alcohol and cannabis use were measured through self-report survey questionnaires.
2.2.1. Internalizing and Externalizing Behavior Problems
Internalizing and externalizing behavior problems were measured using the ASEBA Youth Self-Report (YSR) survey [20]. The YSR Survey comprises 112 questions for youths (11–18 years old), aiming to assess the youths’ internalizing and externalizing behaviors [21]. Questions are answered by choosing one of the three options to the statements as follows: (0) Not True, (1) Somewhat or Sometimes True, and (2) Very True or Often True. The total scores are added up, and higher scores indicate behavior problems on that scale. The internalizing and externalizing problem scores were both re-coded on a scale from 0 to 2, where 0 reflects total scores falling within the nonclinical range, 1 indicating total scores falling within the borderline clinical range (T scores of 60 through 63, which indicate approximately the 84th–90th percentile), and 2 indicates total scores falling within the clinical range (T scores ≥ 64 which indicate ≥ 91st percentile) [20]. We also report results using the continuous standardized t-scores in supplementary analyses (see Supplementary Tables S4 and S5). Internalizing behavior problems (∝ = 0.89) and externalizing behavior problems (∝ = 0.91) exhibit high internal reliability.
2.2.2. Prospective Self
A factor-derived score for prospective self comprised scores on the Future Orientation scale [22], the Resistance to Peer Influence (RPI) scale [23], and a scale of academic aspirations. The Future Orientation scale measures future orientation through three subscales as follows: time perspective, anticipation of future consequences, and planning ahead, with higher scores indicating greater future orientation. The RPI scale is a 10-item measure assessing individuals’ ability to resist peer pressure, with higher scores indicating stronger resistance. Academic aspiration was assessed using a single item asking, “suppose you could do just what you’d like and nothing stood in your way. How many of the following things would you WANT to do?”. Higher scores reflect a greater desire to pursue advanced education. Factor analysis indicated a satisfactory fit (CFI = 0.98, RMSEA = 0.03, 90% CI RMSEA = 0.02–0.03, SRMR = 0.02). Prospective self scores were standardized (M = 0, SD = 1).
2.2.3. Substance Use
Substance use items for self-reported 12-month alcohol use and 12-month cannabis use are drawn from and are identical to those used in annual, national Monitoring the Future surveys [24]. For alcohol use, participants responded to “On how many occasions (if any) have you had any alcoholic beverage to drink—more than just a few sips during the last 12 months?”, using a seven-point Likert scale, 1 = “0 occasions” to 7 = “40 or more occasions”. For cannabis use, participants responded to “On how many occasions (if any) have you used cannabis or hashish during the last 12 months?”, using a seven-point Likert scale, 1 = “0 occasions” to 7 = “40 or more occasions”.
2.2.4. Sociodemographic Covariates
Sociodemographic covariates, reported by each participant, include the participant’s age in years, sex at birth, race, and grade in school. Participants also reported the level of education completed by each of their parents, which was used as a proxy for socioeconomic status (SES). Response options included the following: (1) grade school or less, (2) some high school, (3) completed high school, (4) some college, (5) completed college, (6) graduate or professional school, and (7) do not know or does not apply. Scores of both parents, if available, are averaged together, and for participants with a single parent, that parent’s educational attainment is used with higher scores indicating more education [25,26,27].
2.3. Analyses
2.3.1. Descriptive Statistics and Correlations
Analyses were performed using STATA [28]. The descriptive statistics are reported to provide insight into the sociodemographic variables in these analyses. Data are reported as the mean (standard deviation) or n (%). Bivariate associations are reported to describe the relationships in the sample.
2.3.2. Main Analyses
Nested linear regression analysis was utilized to evaluate the relationship between the hypothesized independent variables—internalizing problems, externalizing problems, and prospective self—and the substance use dependent variables, alcohol or cannabis use. Three nested models were run for each outcome. The first model included covariates that past research has suggested are related to alcohol and cannabis use [24,25] as follows: (1) race (White non-Hispanic, Black or African non-Hispanic, Hispanic all races, more than one race non-Hispanic, and Other non-Hispanic), with white non-Hispanic as the reference category; (2) sex, with female as the reference category; (3) grade, with 10th grade as the reference category; (4) average parent education (completed grade school or less, some high school, completed high school, some college, completed college and graduate or professional school), with graduate or professional school being the reference category. The second model added our predictors—prospective self, internalizing behavior problems, and externalizing behavior problems with the nonclinical range as the reference category. The final model included interaction terms to explore the potential for prospective self to moderate the effects of internalizing and externalizing problems.
2.3.3. Population Weights
Weights calibrated to the ACS were created to adjust for potential bias resulting from the non-probability sampling design in the AHRB sample [29]. Unlike model-based methods that address the missingness of specific variables, weighting is commonly employed because it is independent of the survey variables of interest [25,26]. The weighted ACS data for individuals aged 15–19 are used to estimate the population distribution and post-stratification weights are then calculated for the AHRB sample [30]. This process involves repeatedly matching the ACS and AHRB data based on the following demographic characteristics: sex, race and ethnicity, and parental education status. Additional details are provided in Supplementary Section S1.
3. Results
3.1. Descriptive Statistics
Descriptive statistics for the weighted sample are presented in Table 1. Participants had a mean age of 16.8 years of age and were predominantly male (50.5%), White non-Hispanic (55.1%), and with parents who had completed at least some college (70.2%) (see Supplementary Table S1 for unweighted parameters). Zero-order correlations among all the variables are reported in Table 2. Externalizing and internalizing problems were positively associated with both alcohol and cannabis use, while prospective self was negatively associated with both alcohol and cannabis use. Prospective self was negatively associated with both externalizing and internalizing problems. Although the variables are highly correlated, diagnoses indicate a low probability of multicollinearity, with the Variance Inflation Factor (VIF) estimates among the predictors ranging from 1.02 to 1.11., According to the rule of thumb, VIF values above 5.0 suggest caution in interpretation.
3.2. Main Analyses
3.2.1. Alcohol
An increase in externalizing behavior problems is significantly associated with an increase in alcohol use at the borderline clinical (b =1.33, p < 0.001) and clinical (b = 1.90, p < 0.001) ranges. An increase in prospective self was associated with a decrease in alcohol use (b = −0.71, p = 0.001). For internalizing behavior problems, findings differ between the borderline clinical and clinical ranges. At the borderline clinical range, there was no significant association between alcohol use and internalizing behavior problems (b = −0.12, p > 0.05). However, at the clinical range, there is a significant negative association where a decrease in internalizing behavior problems is associated with an increase in alcohol use (b = −0.31, p = 0.02). These results remained consistent, even when adjusting for covariates (Model 2, Table 3).
In an exploratory analysis, we examined the interactions of externalizing problems with prospective self and internalizing problems with prospective self to evaluate whether higher levels of prospective self may have served as a protective factor in lower alcohol use for adolescents with higher scores on externalizing and internalizing behavior problems. None of the interactions were significant (Model 3, Table 3).
3.2.2. Cannabis
Our results partially support our hypotheses, whereby higher levels of externalizing behavior problems at the borderline clinical (b = 1.57, p < 0.001) and clinical ranges (b = 2.64, p = 000) were significantly associated with an increase in cannabis use. Findings were not significant for internalizing behavior problems at both the borderline clinical (b = −0.15, p > 0.05) and clinical (b = −0.28, p > 0.05) ranges. Consistent with our hypothesis, an increase in prospective self is significantly associated with a decrease in cannabis use (b = −1.02, p = 0.001). These results remained consistent, even when adjusting for covariates (Model 2, Table 4).
The interaction of externalizing problems with prospective self did not contribute significantly to the prediction of cannabis use (Model 3, Table 4). However, the interaction between internalizing behavior with prospective self was significant and positive (b = 2.04, p = 0.04), suggesting that adolescents with both high internalizing problems and high prospective self engaged in higher cannabis use. On the other hand, the R2 change in Model 3 before and after this interaction term is entered is trivially small (0.1723 vs. 0.1766) and is significant only due to the large sample size. Thus, we did not explore the nature of this interaction further.
3.3. Sensitivity and Exploratory Analyses
3.3.1. Comparison Between Weighted and Unweighted Analyses
All analyses were originally run without using population weights. Though our weights increased variance, thus reducing power, we are presenting them as our main results as they increase the generalizability of our findings. Except for the association between internalizing behavior problems (at the clinical range) with prospective self on alcohol use changing in magnitude and significance threshold, from unweighted (b = −0.19, p = 0.11) to weighted (b = −0.31, p = 0.02), the remaining interpretations are largely unchanged (see Supplementary Tables S2 and S3).
3.3.2. Categorized vs. T-Scores for Internalizing and Externalizing Problems
We re-coded internalizing and externalizing scores in our main analyses to focus on the effects of risk and protective factors on more severe levels of alcohol and cannabis use compared to nonclinical levels. We also conducted the same linear regression analyses using t-score internalizing and externalizing behavior problem scores.
Most of the interpretations remain the same. A notable difference is observed for prospective self, however. Specifically, in the categorical models, prospective self is consistently negatively associated with alcohol and cannabis use (see Supplementary Tables S2 and S3). In contrast, in the continuous t-score models, prospective self is no longer significant for alcohol use (see Supplementary Tables S4 and S5). The exception is Model 3 for cannabis use, where a higher level of prospective self is associated with increased cannabis use (b = 3.05, p = 0.004; see Supplementary Table S5, Model 3). Additionally, while internalizing behavior problems are not a significant predictor of cannabis use in the categorical models, a higher level of internalizing behavior problems is negatively associated with a decrease in cannabis use in the t-score model (b = 0.08, p < 0.001; see Supplementary Tables S3 and S5). Finally, unlike in the categorical model, the interaction between externalizing problems and prospective self contributes significantly to the prediction of cannabis use (b = −0.09, p < 0.001).
3.3.3. Internalizing and Externalizing Problems Interaction
Internalizing and externalizing behavior problems often co-occur among adolescents with substance-use issues [31]. Therefore, we conducted separate linear regression analyses for alcohol and cannabis use, including our covariates, predictors, and the interaction between internalizing and externalizing behavior problems. For our weighted analyses, the interactions between internalizing and externalizing problems were not significant for both alcohol and cannabis use. In contrast, some levels of the interaction were significant for the alcohol and cannabis use outcomes (see Supplementary Tables S6 and S7). Specifically, having externalizing problems at a clinical range and internalizing problems at a borderline clinical range is associated with an increase in alcohol use (b = 0.99, p = 0.03). Having a borderline clinical range of externalizing problems and a borderline clinical range of internalizing problems was associated with a decrease in cannabis use (b = −1.43; p = 0.007). Finally, having a clinical range of externalizing problems and a borderline clinical range of internalizing problems was associated with an increase in cannabis use (b = 2.10; p < 0.001).
4. Discussion
Our study aimed to examine the effects of internalizing and externalizing behavior problems and prospective self on adolescent alcohol and cannabis use in the past 12 months. To control for potentially confounding factors, we covaried race, ethnicity, sex, school age, and parental education in the first model of all regressions. Finally, we assessed the effects of the interactions between internalizing problems with prospective self and externalizing problems with prospective self to determine whether there were any associations beyond their individual relationships with substance use.
4.1. Alcohol Use
For alcohol use, we hypothesized that externalizing behavior problems would positively correlate with alcohol use, while a higher prospective self would be linked to decreased alcohol use. We also hypothesized that internalizing behavior problems would not be associated with alcohol use. Consistent with our hypothesis, a higher level of externalizing behavior problems was significantly associated with higher alcohol use, and a higher level of prospective self is associated with less alcohol use. The positive relationship between externalizing behavior problems and alcohol use is consistent with other research findings [32,33,34]. An explanation for this relationship could be the positive association between increased externalizing behavior problems and increased socialization with delinquent peers [32].
At the clinical range, lower levels of internalizing behavior problems were significantly associated with higher alcohol use. Though only the more severe range of internalizing behavior problems was associated with increased alcohol use, our results follow the direction of other studies [32,34,35]. A potential explanation for the negative association is that internalizing behavior problems serve as a protective factor against delinquent peer association and, therefore, early alcohol use [32,35]. For instance, internalizing behavior problems could lead to difficulty in interacting with peers early on, which, in turn, is associated with less alcohol use early on as early alcohol use is associated with peer interaction [35].
We also tested whether higher levels of prospective self may have served as a protective factor against alcohol use among adolescents with higher externalizing and internalizing behavior problem scores. None of the interactions were significant, indicating no differential susceptibility to alcohol use among adolescents with higher levels of internalizing or externalizing behavior problems.
4.2. Cannabis Use
For cannabis use, we hypothesized that higher levels of externalizing and internalizing behavior problems would be associated with increased cannabis use, while a higher prospective self would be linked to lower cannabis use. Our results partially supported these hypotheses. Higher levels of externalizing behavior problems were significantly associated with greater cannabis use. However, findings on internalizing behavior problems were not significant. Consistent with our hypothesis, an increase in prospective self was significantly associated with lower cannabis use.
Our findings regarding the positive relationship between a higher level of externalizing behavior problems and a higher level of cannabis use are consistent with some previous research [36,37,38,39]. However, other studies have suggested that while externalizing behavior problems relate to cannabis use, other factors are also powerful predictors of cannabis use, such as early-onset smoking [39].
Our nonsignificant association between internalizing problems and cannabis use contributes to the mixed research findings on this question [10,16,17,36,38,39]. While some findings suggest greater internalizing behavior problems are associated with less cannabis use [8,33], others suggest internalizing behavior problems to be positively associated with cannabis use [16,17,37], and others found no association [39,40]. A potential explanation for not having found a significant association is that we looked at more severe levels of internalizing problems, and at those severe levels, internalizing behaviors are not a significant risk factor. To further this point, in the t-score models, internalizing behavior problems were significantly negative across all three models, meaning that an increase in internalizing problems was associated with decreased cannabis use. The negative association drawn in our t-score models may be explained by the fact we control for externalizing problems. Research suggests that internalizing behavior problems serve as a protective factor when controlling for externalizing problems. When looking at the co-occurrence of internalizing and externalizing problems, there seems to be a weak positive association [10]. Another potential explanation for our findings could be that substance use in adolescence is primarily used in social contexts, and since social withdrawal often accompanies internalizing symptoms, these symptoms can serve as a protective factor [10]. Additionally, fear and worry are also two characteristics of internalizing symptoms that may deter adolescents from taking risks, such as using cannabis [10].
4.3. Prospective Self as a Protective Factor
Prospective self is composed of future orientation, academic aspiration, and resistance to peer influence. Our results indicate that at more severe levels of internalizing and externalizing behavior problems, prospective self serves as a protective factor against alcohol and cannabis use. Our findings are consistent with the only other study examining the relationship between prospective self and externalizing behavior problems [18]. While prospective self is a new construct, research demonstrates that future orientation, academic aspiration, and resistance to peer influence individually work as protective factors against substance use [18]. For example, adolescents with positive future orientation were less likely to engage in risky behaviors, such as alcohol use during sex, and cannabis, drug, and alcohol use problems [41,42,43]. On the contrary, a less positive future orientation was significantly associated with alcohol use during sex, more alcohol problems, and increased substance use [44].
Moreover, research has consistently shown, that compared to adolescent substance users, non-users tend to have higher academic aspirations [43,44]. It is also important to note, however, that there is a bidirectional relationship between academic motivation and aspiration and substance use. On the one hand, the lower one’s academic motivation and aspiration are, the more likely they are to use substances. On the other hand, the more an adolescent uses substances, the more likely their academic motivation and aspiration will decrease [44,45,46].
Peer influence is important to consider when thinking about adolescent substance use because peers have an influence on whether one uses substances [19,47,48,49]. While parents and peers both play influential roles in the decisions that adolescents make, parents tend to have more influence on their child’s plans for the future, while peers have a greater influence on whether the adolescent will use substances or not [19,49,50].
Finally, in our main analyses, we re-coded internalizing and externalizing scores to highlight risk and protective factors on more severe levels of alcohol and cannabis use compared to nonclinical levels. Additionally, we repeated the linear regression analyses using t-scores for internalizing and externalizing behavior problems. In categorical models, prospective self was consistently negatively associated with alcohol and cannabis use. In continuous t-score models, prospective self was no longer significant for alcohol use but was associated with increased cannabis use in one model. These results suggest that prospective self may serve as a protective factor at more severe levels of internalizing and externalizing problems, which may be particularly helpful in intervention programs.
4.4. Interaction Between Internalizing Behaviors and Prospective Self
We also examined whether higher levels of prospective self may have served as a protective factor against cannabis use for adolescents with higher scores on externalizing and internalizing behavior problems. None of the interactions between externalizing problems and prospective self were significant, indicating no differential susceptibility to cannabis use among adolescents with higher levels of externalizing behavior problems. However, the interaction between (clinical range) internalizing behavior problems and prospective self was significant and positive, suggesting that adolescents with both higher internalizing problems and higher prospective self engage in greater cannabis use, beyond their individual predictions.
Interestingly, we found a significant positive contribution from the interaction between clinical range internalizing behavior and prospective self, which suggests that adolescents with both high internalizing problems and high prospective self engage in higher levels of cannabis use. Our findings might underscore a potentially higher-risk population of adolescents that exhibit future orientation, academic aspiration, and resistance to peer influence, but also have high internalizing symptoms. A potential explanation for our findings could be the changed perceptions of cannabis use that may stem from the changes in legalization. The perception that cannabis is not harmful, in turn, may influence adolescents’ decision to use it as self-medication to relieve internalizing symptoms, such as depression. Though studies have found weak effects on the use of cannabis for self-medication [14,40], our study may unveil a change in cannabis use and perception.
In 2008, medical use of cannabis was legalized in Michigan, and then in 2018, recreational use was legalized. Recent studies examining the impact of legalization on youth cannabis use and perceptions offer mixed results. Two studies found little to no differences in perceptions of cannabis use among adolescents after the legalization of recreational use of cannabis in Washington state [51,52]; however, one study found an increase in problems and use disorder symptoms [51]. Data collected from secondary school students generally suggest decreases in perceived harmlessness and increases in cannabis use, yet not consistently. For example, researchers examining the effects of cannabis legalization on youths in Washington and Colorado found that among 8th and 10th graders in Washington, the perceived harmfulness of cannabis use decreased, and cannabis use increased after the legalization of recreational use [53]. However, no significant difference was found among 12th graders, suggesting that younger adolescents might be more vulnerable to the policy changes [53]. Moreover, they found no significant differences in either perceived harmfulness or cannabis use among Colorado students. Nationally, Monitoring the Future data does indicate a sharp increase in adolescent perception of cannabis as not harmful, with one-fifth of 12th graders perceiving people who use cannabis regularly as posing no risk or harm to their health [54]. Additional research is needed to fully understand the long-term effect of policy changes on adolescent perception and use of cannabis. Our study contributes to the ongoing discourse by highlighting the potential impact of changing cannabis perceptions.
4.5. Implications
4.5.1. Prevention
The findings from the present study have implications for the prevention and treatment of alcohol and cannabis use. Given that there is a significant relationship between externalizing behavior problems and alcohol use, focusing on youths who exhibit greater levels of externalizing behavior problems for the prevention of alcohol use can prove to be the key. For cannabis prevention, offering prevention activities for youths who display either internalizing behavior problems, externalizing behavior problems, or a combination could also be extremely beneficial. Studies suggest that internalizing and externalizing behavior problems play a role in the developmental pathways to substance use [6,7,10,12,14]. Research on family prevention treatments for youths more vulnerable to high-risk behaviors has been effective in preventing antisocial and problem behaviors, but more research should be conducted to better understand the efficacy of incorporating substance use prevention strategies into programs for youth with externalizing and internalizing behavior problems [55,56].
Moreover, prospective self appears to function as a significant protective factor, suggesting that prevention programs that focus on strengthening an adolescent’s future orientation, resistance to peer influence, and academic aspirations may be effective in preventing alcohol and cannabis use. To our knowledge, there are no prevention programs that address all aspects of prospective self; however, a study demonstrates that the incorporation of future orientation and psychological empowerment—confidence, skills, and behavioral strategies to achieve self-identified future goals—into a 5-week summer school prevention program for 6th and 7th graders can support future prevention of substance use [57].
4.5.2. Intervention
Like with prevention programs, our results also suggest avenues for programs intended to reduce adolescent alcohol and cannabis use (i.e., secondary prevention and/or substance use treatment). Given that there is a significant relationship between externalizing behavior problems and alcohol use, addressing externalizing behavior problems may have an effect on decreasing alcohol use. For cannabis, interventions that address internalizing and/or externalizing behavior problems may also have an effect on decreasing youth cannabis use. In considering our results, addressing internalizing and externalizing behaviors as part of substance use interventions may enhance youths’ treatment journey. Finally, incorporating strategies that strengthen one’s prospective self may offer additional benefits for reducing alcohol and cannabis use. Future research should explore the effects of incorporating strategies that foster prospective self into substance use treatment programs.
4.6. Future Research
Our results open a new avenue for research on better understanding the internalizing and externalizing pathways to alcohol and cannabis use and the influence of prospective self in protecting against alcohol and cannabis use. Our study provided insight into the relationship between internalizing and externalizing behavior problems with alcohol and cannabis use, separately, but it did not focus on the co-occurrence of internalizing and externalizing behaviors as a risk factor. Future research should examine the impact of co-occurring conditions on substance use.
Finally, since prospective self is a new latent construct, additional research is needed on its relation to adolescent substance use. It is suggested that prospective self, coined in 2020, plays a role in supporting resilience against externalizing problems [18]. In our study, it seems that prospective self can serve as a protective factor against higher levels of alcohol and cannabis use. Clearly, prospective self seems to be a significant protective factor for adolescents. Therefore, further research could identify what prospective self may be a protective factor for, and how it may be incorporated into prevention and treatment programs.
4.7. Limitations and Strengths
4.7.1. Limitations
As with all studies, there are limitations worth noting. First, alcohol and cannabis use in our sample was relatively low. Around half the participants reported either “0 occasions” or “1–2” occasions; therefore, results may not be as generalizable to youths with higher levels of alcohol and cannabis use. Thus, our results may be more applicable to adolescents who report lower levels of alcohol and cannabis use. Second, our measure of alcohol and cannabis use covered the past 12 months. Since a year is a long time, adolescents may have had difficulty recalling the number of occasions they used either alcohol or cannabis, resulting in an underreporting of their use in our sample. Yet, assessing the past 12-month alcohol and cannabis use is common practice in research with adolescent populations as, overall, adolescents tend to be occasional and less frequent users. Another limitation is the possibility of method covariance between the YSR externalizing scale, which includes a few items regarding substance use, and the prevalence measures of alcohol and cannabis use.
Finally, the results noted that compared to 10th graders, 12th graders seemed to have had fewer incidents of alcohol and cannabis use. According to NIH, a greater proportion of 12th graders use cannabis and alcohol than 10th graders [3]. An explanation for our findings is that the sample may have had fewer high-risk 12th graders than 10th graders. A potential reason for this difference is that when conducting studies with adolescents recruited in school settings, the sample may be missing adolescents at the highest risk, as these are also the adolescents who are more likely to not be in school on the day of the survey. For example, the dropout age for children in Michigan is 16–17 years old, which means that those with the highest risk might have dropped out by the 12th grade, so they were not represented in the survey which was taken at school.
4.7.2. Strengths
Despite our limitations, the present study has a number of strengths. First, we had a large sample size (N = 2017), which is important because it allows for a more precise estimate of the relationships between internalizing and externalizing behavior problems and prospective self with alcohol and cannabis use. Moreover, a larger sample size is important in reducing bias and increasing generalizability. Additionally, our study sampled adolescents from nine public schools across Southeastern Michigan counties. Through the use of a direct quota sampling design, we could approximate the statewide population diversity and, therefore, improve generalizability. Second, since we sample adolescents from across schools instead of sampling a population that falls under a specific category, such as “problematic substance users”, we could generalize the results to the broader U.S. adolescent population. Using weighted analyses adds additional confidence to the generalizability and, thus, population validity of the results [56].
5. Conclusions
Our study investigated how internalizing and externalizing behavior problems, along with prospective self, influenced adolescent alcohol and cannabis use over the past year. The findings indicate that greater externalizing behaviors were associated with increased alcohol and cannabis use. In contrast, a stronger prospective self and higher internalizing behaviors were linked to lower alcohol use, with a stronger prospective self also correlating to reduced cannabis use. Interaction effects were largely non-significant, except for the interaction between prospective self and internalizing behaviors. Specifically, individuals with high internalizing problems and a strong prospective self reported increased cannabis use. These results highlight the importance of prospective self, particularly in the context of severe behavior problems, and suggest that further research is needed to explore the unexpected positive relationship between internalizing behaviors, prospective self, and cannabis use.
Conceptualization, F.G.D.G., S.A.S., E.D.H. and D.P.K.; methodology, F.G.D.G., S.A.S., E.D.H. and D.P.K.; validation, E.D.H., F.G.D.G. and D.P.K.; formal analysis, E.D.H. and F.G.D.G.; investigation, D.P.K. and E.D.H. directed the data collection.; data curation, D.P.K.; writing—original draft preparation, F.G.D.G.; writing—review and editing, F.G.D.G., S.A.S., E.D.H. and D.P.K.; visualization, F.G.D.G.; supervision, D.P.K. All authors have read and agreed to the published version of the manuscript.
Procedures were approved by the University of Michigan Institutional Review Board (IRB ID# HUM00084650).
Informed consent was obtained from all subjects involved in the study. All data collection was approved by the University of Michigan IRB, to ensure protection and confidentiality to all participants. For minors, parental consent and participant assent were obtained, and participant consent was obtained for those who were 18 years old.
The investigators are committed to sharing the data generated through this research. Readers seeking access to these data should contact Daniel Keating (
This research was supported, in part, by a grant from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD; R01HD075806, D.P. Keating, Principal Investigator). The authors thank Peter Batra, Joshua Hatfield, Meredith House, Kyle Kwaiser, Kathleen LaDronka and the U-M Survey Research Operations staff for their support.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Weighted sociodemographic characteristics of participants (N = 2017).
| Demographic Information | |
|---|---|
| Age, M (SD) | 16.8 (1.1) |
| Sex, n (%) female | 985 (49.5) |
| Race/Ethnicity, n (%) | |
| Black or African American non-Hispanic | 291 (14.4) |
| White non-Hispanic | 1110 (55.1) |
| Hispanic all races | 419 (8.0) |
| Other | 123 (6.1) |
| More than one race non-Hispanic | 76 (3.6) |
| Current level of education, n (%) | |
| 10th grade | 887 (44.0) |
| 12th grade | 1130 (56.0) |
| Average Level Parent Education, n (%) | |
| Completed less than grade school | 27 (1.4) |
| Some high school | 174 (9.0) |
| Completed high school | 375 (19.4) |
| Some college | 439 (22.8) |
| Completing college | 600 (31.2) |
| Graduate or professional school after college | 313 (16.2) |
Means, standard deviations, and Pearson correlation matrix for study variables.
| Variable | M | S.E. | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|---|---|
| 0.15 | 0.46 | — | ||||||
| 0.27 | 0.63 | 0.31 *** | — | |||||
| 51.13 | 10.06 | 0.62 *** | 0.38 *** | — | ||||
| 54.37 | 11.46 | 0.32 *** | 0.71 *** | 0.57 *** | — | |||
| −0.001 | 0.20 | −0.27 *** | −0.13 *** | −0.35 *** | −0.12 *** | — | ||
| 1.11 | 1.49 | 0.28 *** | 0.06 * | 0.41 *** | 0.10 *** | −0.12 *** | — | |
| 0.79 | 1.69 | 0.34 *** | 0.07 * | 0.38 *** | 0.10 *** | −0.17 *** | 0.55 *** | — |
*** p < 0.001, * p < 0.05.
Multi-level models: associations between internalizing problems, externalizing problems, and prospective self (IV), and 12-month alcohol use.
| Predictors | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| b | SE | p | b | SE | p | b | SE | p | |
| 12-Month Self-Reported Alcohol Use | |||||||||
| Intercept | 0.95 | 0.12 | <0.001 | 0.86 | 0.11 | <0.001 | 0.87 | 0.11 | <0.001 |
| Sex (ref = female) | |||||||||
| Male | −0.25 | 0.09 | 0.004 | −0.28 | 0.08 | <0.001 | −0.28 | 0.08 | <0.001 |
| Subject Grade (ref = 10th grade) | |||||||||
| 12th grade | 0.74 | 0.08 | <0.001 | 0.73 | 0.08 | <0.001 | 0.72 | 0.08 | <0.001 |
| Race/Ethnicity (ref = White non-Hispanic) | |||||||||
| Black or African American non-Hispanic | −0.57 | 0.09 | 0.001 | −0.58 | 0.08 | <0.001 | −0.58 | 0.09 | <0.001 |
| Hispanic all races | −0.29 | 0.14 | 0.03 | −0.30 | 0.13 | 0.02 | −0.30 | 0.13 | 0.02 |
| Other | −0.69 | 0.17 | <0.001 | −0.58 | 0.16 | <0.001 | −0.58 | 0.16 | <0.001 |
| More than one race non-Hispanic | −0.28 | 0.13 | 0.03 | −0.33 | 0.13 | 0.01 | −0.34 | 0.13 | 0.01 |
| Average Parent Education (ref = graduate school or professional school after college) | |||||||||
| Completed grade school or less | 0.19 | 0.53 | 0.71 | 0.14 | 0.50 | 0.79 | 0.14 | 0.50 | 0.78 |
| Some high school | 0.16 | 0.24 | 0.52 | 0.17 | 0.24 | 0.46 | 0.18 | 0.24 | 0.46 |
| Completed high school | 0.02 | 0.14 | 0.91 | −0.04 | 0.13 | 0.76 | −0.04 | 0.14 | 0.76 |
| Some college | 0.09 | 0.13 | 0.49 | 0.14 | 0.12 | 0.26 | 0.14 | 0.12 | 0.27 |
| Completed college | 0.07 | 0.13 | 0.61 | 0.12 | 0.12 | 0.34 | 0.11 | 0.12 | 0.35 |
| Externalizing Behavior Problems (ref = WNL) | |||||||||
| Borderline clinical range | 1.33 | 0.21 | <0.001 | 1.33 | 0.23 | <0.001 | |||
| Clinical range | 1.90 | 0.21 | <0.001 | 1.90 | 0.30 | <0.001 | |||
| Internalizing Behavior Problems (ref = WNL) | |||||||||
| Borderline clinical range | −0.12 | 0.14 | 0.36 | −0.13 | 0.14 | 0.36 | |||
| Clinical range | −0.30 | 0.13 | 0.02 | −0.28 | 0.13 | 0.03 | |||
| Prospective Self | −0.71 | 0.22 | 0.001 | −0.78 | 0.25 | 0.002 | |||
| Externalizing Behavior × Prospective Self | |||||||||
| Borderline clinical range | −0.02 | 0.99 | 0.98 | ||||||
| Clinical range | −0.18 | 1.08 | 0.87 | ||||||
| Internalizing Behavior × Prospective Self | |||||||||
| Borderline clinical range | 0.40 | 0.83 | 0.63 | ||||||
| Clinical range | 0.59 | 0.70 | 0.40 | ||||||
Model 1 included sociodemographic covariates. Model 2 added risk and protective factors, and Model 3 included interactions of internalizing and externalizing behaviors with prospective self. ASEBA Youth Self-Report Survey. Ref = reference category; WNL = within normal levels.
Multi-level models: associations between internalizing problems, externalizing problems, and prospective self (IV) and 12-month cannabis use.
| Predictors | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| b | SE | p | b | SE | p | b | SE | p | |
| 12-Month Self-Reported Cannabis Use | |||||||||
| Intercept | 0.38 | 0.14 | 0.005 | 0.27 | 0.13 | 0.04 | 0.29 | 0.13 | 0.03 |
| Sex (ref = female) | |||||||||
| Male | 0.07 | 0.11 | 0.55 | 0.06 | 0.11 | 0.58 | 0.04 | 0.11 | 0.72 |
| Subject Grade (ref = 10th grade) | |||||||||
| 12th grade | 0.65 | 0.11 | <0.001 | 0.60 | 0.11 | <0.001 | 0.60 | 0.11 | <0.001 |
| Race/Ethnicity (ref = White non-Hispanic) | |||||||||
| Black or African American non-Hispanic | −0.16 | 0.13 | 0.22 | −0.13 | 0.12 | 0.29 | −0.13 | 0.12 | 0.28 |
| Hispanic all races | 0.29 | 0.24 | 0.22 | 0.30 | 0.23 | 0.19 | 0.33 | 0.22 | 0.14 |
| Other | −0.72 | 0.12 | <0.001 | −0.58 | 0.12 | <0.001 | −0.61 | 0.11 | <0.001 |
| More than one race non-Hispanic | 0.22 | 0.18 | 0.23 | 0.15 | 0.19 | 0.55 | 0.11 | 0.19 | 0.56 |
| Average Parent Education (ref = graduate school or professional school after college) | |||||||||
| Completed grade school or less | −0.61 | 0.32 | 0.06 | −0.69 | 0.31 | 0.02 | −0.78 | 0.32 | 0.01 |
| Some high school | 0.43 | 0.33 | 0.12 | 0.45 | 0.31 | 0.14 | 0.44 | 0.31 | 0.15 |
| Completed high school | 0.28 | 0.21 | 0.17 | 0.18 | 0.19 | 0.33 | 0.17 | 0.18 | 0.36 |
| Some college | 0.04 | 0.16 | 0.79 | 0.04 | 0.09 | 0.15 | 0.08 | 0.15 | 0.61 |
| Completed college | −0.08 | 0.15 | 0.62 | −0.01 | 0.14 | 0.94 | −0.02 | 0.14 | 0.89 |
| Externalizing Behavior Problems (ref = WNL) | |||||||||
| Borderline clinical range | 1.57 | 0.35 | <0.001 | 1.52 | 0.37 | <0.001 | |||
| Clinical range | 2.64 | 0.40 | <0.001 | 2.70 | 0.50 | <0.001 | |||
| Internalizing Behavior Problems (ref = WNL) | |||||||||
| Borderline clinical range | −0.15 | 0.21 | 0.50 | −0.17 | 0.21 | 0.43 | |||
| Clinical range | −0.28 | 0.18 | 0.13 | −0.23 | 0.17 | 0.19 | |||
| Prospective Self | −1.02 | 0.32 | 0.001 | −1.10 | 0.35 | 0.001 | |||
| Externalizing Behavior × Prospective Self | |||||||||
| Borderline clinical range | −1.87 | 1.79 | 0.30 | ||||||
| Clinical range | −0.72 | 1.74 | 0.68 | ||||||
| Internalizing Behavior × Prospective Self | |||||||||
| Borderline clinical range | −0.07 | 1.48 | 0.96 | ||||||
| Clinical range | 2.04 | 0.97 | 0.04 | ||||||
Model 1 included sociodemographic covariates. Model 2 added risk and protective factors, and Model 3 included interactions of internalizing and externalizing behaviors with prospective self. ASEBA Youth Self-Report Survey. Ref = reference category; WNL = within normal levels.
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Miech, R.A.; Johnston, L.D.; Patrick, M.E.; O’Malley, P.M. Monitoring the Future National Survey Results on Drug Use, 1975–2023: Overview and Detailed Results for Secondary School Students; Monitoring the Future Monograph Series; Institute for Social Research, University of Michigan: Ann Arbor, MI, USA, 2024; Available online: https://monitoringthefuture.org/results/annual-reports/ (accessed on 1 October 2024).
2. Teenage Drug Use Statistics [2023]: Data & Trends on Abuse. Available online: https://drugabusestatistics.org/teen-drug-use/ (accessed on 11 April 2023).
3. Underage Drinking|CDC. Available online: https://www.cdc.gov/alcohol/fact-sheets/underage-drinking.htm (accessed on 11 April 2023).
4. CDC Cannabis and Teens. Available online: https://www.cdc.gov/cannabis/health-effects/cannabis-and-teens.html (accessed on 28 July 2024).
5. Samek, D.R.; Hicks, B.M. Externalizing Disorders and Environmental Risk: Mechanisms of Gene-Environment Interplay and Strategies for Intervention. Clin. Pract.; 2014; 11, pp. 537-547. [DOI: https://dx.doi.org/10.2217/cpr.14.47]
6. Couwenbergh, C.; van den Brink, W.; Zwart, K.; Vreugdenhil, C.; van Wijngaarden-Cremers, P.; van der Gaag, R.J. Comorbid Psychopathology in Adolescents and Young Adults Treated for Substance Use Disorders: A Review. Eur. Child Adolesc. Psychiatry; 2006; 15, pp. 319-328. [DOI: https://dx.doi.org/10.1007/s00787-006-0535-6]
7. Miettunen, J.; Murray, G.K.; Jones, P.B.; Mäki, P.; Ebeling, H.; Taanila, A.; Joukamaa, M.; Savolainen, J.; Törmänen, S.; Järvelin, M.-R. et al. Longitudinal Associations between Childhood and Adulthood Externalizing and Internalizing Psychopathology and Adolescent Substance Use. Psychol. Med.; 2014; 44, pp. 1727-1738. [DOI: https://dx.doi.org/10.1017/S0033291713002328]
8. Voisin, D.R.; Kim, D.H.; Bassett, S.M.; Marotta, P.L. Pathways Linking Family Stress to Youth Delinquency and Substance Use: Exploring the Mediating Roles of Self-Efficacy and Future Orientation. J. Health Psychol.; 2020; 25, pp. 139-151. [DOI: https://dx.doi.org/10.1177/1359105318763992]
9. Zahn–Waxler, C.; Klimes–Dougan, B.; Slattery, M.J. Internalizing Problems of Childhood and Adolescence: Prospects, Pitfalls, and Progress in Understanding the Development of Anxiety and Depression. Dev. Psychopathol.; 2000; 12, pp. 443-466. [DOI: https://dx.doi.org/10.1017/S0954579400003102]
10. Colder, C.R.; Scalco, M.; Trucco, E.M.; Read, J.P.; Lengua, L.J.; Wieczorek, W.F.; Hawk, L.W. Prospective Associations of Internalizing and Externalizing Problems and Their Co-Occurrence with Early Adolescent Substance Use. J. Abnorm. Child Psychol.; 2013; 41, pp. 667-677. [DOI: https://dx.doi.org/10.1007/s10802-012-9701-0]
11. King, S.M.; Iacono, W.G.; McGue, M. Childhood Externalizing and Internalizing Psychopathology in the Prediction of Early Substance Use. Addiction; 2004; 99, pp. 1548-1559. [DOI: https://dx.doi.org/10.1111/j.1360-0443.2004.00893.x]
12. Hussong, A.M.; Jones, D.J.; Stein, G.L.; Baucom, D.H.; Boeding, S. An Internalizing Pathway to Alcohol Use and Disorder. Psychol. Addict. Behav.; 2011; 25, pp. 390-404. [DOI: https://dx.doi.org/10.1037/a0024519]
13. Rieselbach, M.M.; Gresko, S.; Corley, R.P.; Hewitt, J.K.; Rhee, S.H. An Examination of the Protective Role of Internalizing Symptoms in Adolescent Substance Use. J. Psychopathol. Behav. Assess; 2024; 46, pp. 12-24. [DOI: https://dx.doi.org/10.1007/s10862-024-10119-2]
14. Colder, C.R.; Frndak, S.; Lengua, L.J.; Read, J.P.; Hawk, L.W.; Wieczorek, W.F. Internalizing and Externalizing Problem Behavior: A Test of a Latent Variable Interaction Predicting a Two-Part Growth Model of Adolescent Substance Use. J. Abnorm. Child Psychol.; 2018; 46, pp. 319-330. [DOI: https://dx.doi.org/10.1007/s10802-017-0277-6]
15. Olson, A.E.; Shenk, C.E.; Noll, J.G.; Allen, B. Child Maltreatment and Substance Use in Emerging Adulthood: Internalizing and Externalizing Behaviors at the Transition to Adolescence as Indirect Pathways. Child Maltreat.; 2022; 27, pp. 490-500. [DOI: https://dx.doi.org/10.1177/10775595211010965]
16. Wasserman, A.M.; Shaw-Meadow, K.J.; Moon, T.J.; Karns-Wright, T.E.; Mathias, C.W.; Hill-Kapturczak, N.; Dougherty, D.M. The Externalizing and Internalizing Pathways to Marijuana Use Initiation: Examining the Synergistic Effects of Impulsiveness and Sensation Seeking. Dev. Psychol.; 2021; 57, pp. 2250-2264. [DOI: https://dx.doi.org/10.1037/dev0001267]
17. Girgis, J.; Pringsheim, T.; Williams, J.; Shafiq, S.; Patten, S. Cannabis Use and Internalizing/Externalizing Symptoms in Youth: A Canadian Population-Based Study. J. Adolesc. Health; 2020; 67, pp. 26-32. [DOI: https://dx.doi.org/10.1016/j.jadohealth.2020.01.015]
18. Zinn, M.E.; Huntley, E.D.; Keating, D.P. Resilience in Adolescence: Prospective Self Moderates the Association of Early Life Adversity with Externalizing Problems. J. Adolesc.; 2020; 81, pp. 61-72. [DOI: https://dx.doi.org/10.1016/j.adolescence.2020.04.004]
19. Kandel, D.B. On Processes of Peer Influences in Adolescent Drug Use: A Developmental Perspective. Adv. Alcohol. Subst. Abus.; 1985; 4, pp. 139-163. [DOI: https://dx.doi.org/10.1300/J251v04n03_07]
20. Bordin, I.A.; Rocha, M.M.; Paula, C.S.; Teixeira, M.C.T.V.; Achenbach, T.M.; Rescorla, L.A.; Silvares, E.F.M. Child Behavior Checklist (CBCL), Youth Self-Report (YSR) and Teacher’s Report Form (TRF): An Overview of the Development of the Original and Brazilian Versions. Cad. Saúde Pública; 2013; 29, pp. 13-28.
21. Achenbach, T.M.; Rescorla, L.A. Manual for the ASEBA School-Age Forms & Profiles; University of Vermont, Research Center for Children, Youth, & Families: Burlington, VT, USA, 2001.
22. Steinberg, L.; Graham, S.; O’Brien, L.; Woolard, J.; Cauffman, E.; Banich, M. Age Differences in Future Orientation and Delay Discounting. Child Dev.; 2009; 80, pp. 28-44. [DOI: https://dx.doi.org/10.1111/j.1467-8624.2008.01244.x]
23. Steinberg, L.; Monahan, K.C. Age Differences in Resistance to Peer Influence. Dev. Psychol.; 2007; 43, pp. 1531-1543. [DOI: https://dx.doi.org/10.1037/0012-1649.43.6.1531]
24. Johnston, L.; Miech, R.; O’Malley, P.; Bachman, J.; Schulenberg, J.; Patrick, M. Monitoring the Future National Survey Results on Drug Use, 1975–2019: Overview, Key Findings on Adolescent Drug Use 2020; Institute for Social Research, University of Michigan: Ann Arbor, MI, USA, 2022.
25. Bachman, J.G.; O’Malley, P.M.; Johnston, L.D.; Schulenberg, J.E.; Wallace, J.M. Racial/Ethnic Differences in the Relationship Between Parental Education and Substance Use Among U.S. 8th-, 10th-, and 12th-Grade Students: Findings from the Monitoring the Future Project. J. Stud. Alcohol Drugs; 2011; 72, pp. 279-285. [DOI: https://dx.doi.org/10.15288/jsad.2011.72.279]
26. Demidenko, M.I.; Huntley, E.D.; Du, L.; Estor, C.; Si, Y.; Wagner, C.; Clarke, P.; Keating, D.P. Individual and Community Level Developmental Adversities: Associations with Cannabis and Alcohol Use in Late-Adolescents and Young Adults. J. Youth Adolesc.; 2024; 53, pp. 799-813. [DOI: https://dx.doi.org/10.1007/s10964-023-01881-9]
27. Lumley, T. Survey: Analysis of Complex Survey Samples. 2021; Available online: http://r-survey.r-forge.r-project.org/survey/ (accessed on 31 July 2024).
28. StataCorp. Stata Statistical Software: Release 18; StataCorp LLC: College Station, TX, USA, 2023.
29. Elliott, M.R.; Valliant, R. Inference for Nonprobability Samples. Stat. Sci.; 2017; 32, pp. 249-264. [DOI: https://dx.doi.org/10.1214/16-STS598]
30. U.S. Census Bureau Releases 2014–2018 ACS 5-Year Estimates. 2019; Available online: https://www.census.gov/programs-surveys/acs/news/updates/2019.html (accessed on 31 July 2024).
31. Chan, Y.-F.; Dennis, M.L.; Funk, R.R. Prevalence and Comorbidity of Major Internalizing and Externalizing Problems among Adolescents and Adults Presenting to Substance Abuse Treatment. J. Subst. Abus. Treat.; 2008; 34, pp. 14-24. [DOI: https://dx.doi.org/10.1016/j.jsat.2006.12.031]
32. Fite, P.J.; Colder, C.R.; O’Connor, R.M. Childhood Behavior Problems and Peer Selection and Socialization: Risk for Adolescent Alcohol Use. Addict. Behav.; 2006; 31, pp. 1454-1459. [DOI: https://dx.doi.org/10.1016/j.addbeh.2005.09.015]
33. Helstrom, A.; Bryan, A.; Hutchison, K.E.; Riggs, P.D.; Blechman, E.A. Tobacco and Alcohol Use as an Explanation for the Association between Externalizing Behavior and Illicit Drug Use among Delinquent Adolescents. Prev. Sci.; 2004; 5, pp. 267-277. [DOI: https://dx.doi.org/10.1023/B:PREV.0000045360.23290.8f]
34. Stice, E.; Barrera, M. A Longitudinal Examination of the Reciprocal Relations between Perceived Parenting and Adolescents’ Substance Use and Externalizing Behaviors. Dev. Psychol.; 1995; 31, pp. 322-334. [DOI: https://dx.doi.org/10.1037/0012-1649.31.2.322]
35. Edwards, A.C.; Latendresse, S.J.; Heron, J.; Cho, S.B.; Hickman, M.; Lewis, G.; Dick, D.M.; Kendler, K.S. Childhood Internalizing Symptoms Are Negatively Associated with Early Adolescent Alcohol Use. Alcohol. Clin. Exp. Res.; 2014; 38, pp. 1680-1688. [DOI: https://dx.doi.org/10.1111/acer.12402]
36. Steele, R.G.; Forehand, R.; Armistead, L.; Brody, G. Predicting Alcohol and Drug Use in Early Adulthood: The Role of Internalizing and Externalizing Behavior Problems in Early Adolescence. Am. J. Orthopsychiatry; 1995; 65, pp. 380-388. [DOI: https://dx.doi.org/10.1037/h0079694]
37. Hayatbakhsh, M.R.; McGee, T.R.; Bor, W.; Najman, J.M.; Jamrozik, K.; Mamun, A.A. Child and Adolescent Externalizing Behavior and Cannabis Use Disorders in Early Adulthood: An Australian Prospective Birth Cohort Study. Addict. Behav.; 2008; 33, pp. 422-438. [DOI: https://dx.doi.org/10.1016/j.addbeh.2007.10.004]
38. Griffith-Lendering, M.F.H.; Huijbregts, S.C.J.; Mooijaart, A.; Vollebergh, W.A.M.; Swaab, H. Cannabis Use and Development of Externalizing and Internalizing Behaviour Problems in Early Adolescence: A TRAILS Study. Drug Alcohol Depend.; 2011; 116, pp. 11-17. [DOI: https://dx.doi.org/10.1016/j.drugalcdep.2010.11.024]
39. Korhonen, T.; van Leeuwen, A.P.; Reijneveld, S.A.; Ormel, J.; Verhulst, F.C.; Huizink, A.C. Externalizing Behavior Problems and Cigarette Smoking as Predictors of Cannabis Use: The TRAILS Study. J. Am. Acad. Child Adolesc. Psychiatry; 2010; 49, pp. 61-69. [DOI: https://dx.doi.org/10.1097/00004583-201001000-00010]
40. Womack, S.R.; Shaw, D.S.; Weaver, C.M.; Forbes, E.E. Bidirectional Associations Between Cannabis Use and Depressive Symptoms from Adolescence Through Early Adulthood Among At-Risk Young Men. J. Stud. Alcohol Drugs; 2016; 77, pp. 287-297. [DOI: https://dx.doi.org/10.15288/jsad.2016.77.287]
41. Robbins, R.N.; Bryan, A. Relationships Between Future Orientation, Impulsive Sensation Seeking, and Risk Behavior Among Adjudicated Adolescents. J. Adolesc. Res.; 2004; 19, pp. 428-445. [DOI: https://dx.doi.org/10.1177/0743558403258860]
42. Jackman, D.M.; MacPhee, D. Self-Esteem and Future Orientation Predict Adolescents’ Risk Engagement. J. Early Adolesc.; 2017; 37, pp. 339-366. [DOI: https://dx.doi.org/10.1177/0272431615602756]
43. Patte, K.A.; Qian, W.; Leatherdale, S.T. Is Binge Drinking Onset Timing Related to Academic Performance, Engagement, and Aspirations Among Youth in the COMPASS Study?. Subst. Use Misuse; 2017; 52, pp. 1795-1800. [DOI: https://dx.doi.org/10.1080/10826084.2017.1306562]
44. Paulson, M.J.; Coombs, R.H.; Richardson, M.A. School Performance, Academic Aspirations, and Drug Use among Children and Adolescents. J. Drug Educ.; 1990; 20, pp. 289-303. [DOI: https://dx.doi.org/10.2190/8J0X-LY6D-PL7W-42FA]
45. Andrews, J.; Smolkowski, K.; Hops, H.; Tildesley, E.; Ary, D.; Harris, J. Adolescent Substance Use and Academic Achievement and Motivation; Department of Education: San Francisco, CA, USA, 1991.
46. Stoddard, S.A.; Pierce, J. Alcohol and Marijuana Use and Intentions Among Adolescents: The Role of the Reasoned Action Approach and Positive Future Orientation. Youth Soc.; 2018; 50, pp. 758-779. [DOI: https://dx.doi.org/10.1177/0044118X16671610]
47. Bauman, K.E.; Ennett, S.T. On the Importance of Peer Influence for Adolescent Drug Use: Commonly Neglected Considerations. Addiction; 1996; 91, pp. 185-198. [DOI: https://dx.doi.org/10.1046/j.1360-0443.1996.9121852.x]
48. Moore, G.F.; Cox, R.; Evans, R.E.; Hallingberg, B.; Hawkins, J.; Littlecott, H.J.; Long, S.J.; Murphy, S. School, Peer and Family Relationships and Adolescent Substance Use, Subjective Wellbeing and Mental Health Symptoms in Wales: A Cross Sectional Study. Child Ind. Res.; 2018; 11, pp. 1951-1965. [DOI: https://dx.doi.org/10.1007/s12187-017-9524-1]
49. Gray, K.M.; Squeglia, L.M. Research Review: What Have We Learned about Adolescent Substance Use?. J. Child Psychol. Psychiatry; 2018; 59, pp. 618-627. [DOI: https://dx.doi.org/10.1111/jcpp.12783]
50. Schuler, M.S.; Tucker, J.S.; Pedersen, E.R.; D’Amico, E.J. Relative Influence of Perceived Peer and Family Substance Use on Adolescent Alcohol, Cigarette, and Marijuana Use across Middle and High School. Addict. Behav.; 2019; 88, pp. 99-105. [DOI: https://dx.doi.org/10.1016/j.addbeh.2018.08.025]
51. Blevins, C.E.; Marsh, E.; Banes, K.E.; Stephens, R.S.; Walker, D.D.; Roffman, R.A. The Implications of Cannabis Policy Changes in Washington on Adolescent Perception of Risk, Norms, Attitudes, and Substance Use. Subst. Abus.; 2018; 12, 1178221818815491. [DOI: https://dx.doi.org/10.1177/1178221818815491]
52. Mason, W.A.; Hanson, K.; Fleming, C.B.; Ringle, J.L.; Haggerty, K.P. Washington State Recreational Marijuana Legalization: Parent and Adolescent Perceptions, Knowledge, and Discussions in a Sample of Low-Income Families. Subst. Use Misuse; 2015; 50, pp. 541-545. [DOI: https://dx.doi.org/10.3109/10826084.2014.952447]
53. Cerdá, M.; Wall, M.; Feng, T.; Keyes, K.M.; Sarvet, A.; Schulenberg, J.; O’Malley, P.M.; Pacula, R.L.; Galea, S.; Hasin, D.S. Association of State Recreational Marijuana Laws with Adolescent Marijuana Use. JAMA Pediatr.; 2017; 171, pp. 142-149. [DOI: https://dx.doi.org/10.1001/jamapediatrics.2016.3624]
54. Sarvet, A.L.; Wall, M.M.; Keyes, K.M.; Cerdá, M.; Schulenberg, J.E.; O’Malley, P.M.; Johnston, L.D.; Hasin, D.S. Recent Rapid Decrease in Adolescents’ Perception That Marijuana Is Harmful, but No Concurrent Increase in Use. Drug Alcohol Depend.; 2018; 186, pp. 68-74. [DOI: https://dx.doi.org/10.1016/j.drugalcdep.2017.12.041]
55. Dishion, T.J.; Andrews, D.W. Preventing Escalation in Problem Behaviors with High-Risk Young Adolescents: Immediate and 1-Year Outcomes. J. Consult. Clin. Psychol.; 1995; 63, pp. 538-548. [DOI: https://dx.doi.org/10.1037/0022-006X.63.4.538]
56. Stoddard, S.A.; Hughesdon, K.; Khan, A.; Zimmerman, M.A. Feasibility and Acceptability of a Future-Oriented Empowerment Program to Prevent Substance Use and School Dropout among School-Disengaged Youth. Public Health Nurs.; 2020; 37, pp. 251-261. [DOI: https://dx.doi.org/10.1111/phn.12706]
57. Keating, D.P. Adolescent Health Risk Behavior: The Road Ahead. J. Adolesc. Health; 2024; 74, pp. 397-399. [DOI: https://dx.doi.org/10.1016/j.jadohealth.2023.12.008]
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Adolescent alcohol and cannabis use pose significant developmental risks. This study examined internalizing and externalizing problems as risk factors and prospective self as a protective factor in relation to alcohol and cannabis use. Self-report data were collected from a diverse sample (N = 2017) of 15–17-year-olds using the Youth Self Report (YSR) for behavior issues and prospective self, a factor-derived variable reflecting future orientation, resistance to peer influence, and academic aspirations. Participants reported on 15 health risk behaviors, including alcohol and cannabis use. Weighted linear regressions evaluated associations between risk and protective factors and substance use. Results indicated a higher externalizing behavior was linked to increased alcohol and cannabis use, while a higher prospective self and internalizing behavior were linked to reduced alcohol use. Higher prospective self was associated with less cannabis use. Interaction terms were largely non-significant, except for the interaction between prospective self and internalizing behavior, where higher internalizing problems and high prospective self were linked to increased cannabis use. The findings highlight the importance of prospective self, especially at clinically elevated levels of behavior problems, and recommend further exploration of the unexpected positive associations between internalizing behavior and prospective self with cannabis use.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Stoddard, Sarah A 2
; Huntley, Edward D 3
; Keating, Daniel P 4
1 Department of Human Development, Teachers College, Columbia University, New York, NY 10027, USA
2 Department of Systems, Populations, and Leadership, School of Nursing, University of Michigan, Ann Arbor, MI 48109, USA;
3 Institute of Social Research, University of Michigan, Ann Arbor, MI 48104, USA;
4 Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA