Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: J Abnorm Child Psychol. 2019 Sep;47(9):1509–1520. doi: 10.1007/s10802-019-00524-7

Pubertal Timing and Substance Use in Adolescence: An Investigation of Two Cognitive Moderators

Allison Stumper 1, Thomas M Olino 2, Lyn Y Abramson 3, Lauren B Alloy 4
PMCID: PMC6650315  NIHMSID: NIHMS1525060  PMID: 30848416

Abstract

Off-time pubertal development is a predictor of substance use among adolescents. Early-maturing girls and early- and late-maturing boys appear to be at greater risk for substance use, although findings are more consistent for girls. Although cognitive factors are also important in the etiology of adolescent substance use, few studies have investigated potential cognitive risk and protective factors in these associations. The current study tested whether future orientation or cognitive style (e.g., attributions youth make about the causes and consequences of negative life events) moderated the association between pubertal timing and substance use two years later and whether this effect was stronger for females. Multiple linear regressions revealed cognitive style and future orientation significantly moderated the association between pubertal timing and substance use, and these effects did not differ by sex. Importantly, the pattern of these interactions differed, such that early pubertal timing predicted more substance use in the context of more negative and moderate cognitive styles and greater and moderate future orientation. Follow-up analyses revealed that an adolescent’s attributions about the consequences, globality, and self-worth implications of negative life events significantly moderated the pubertal timing - future substance use association. Furthermore, the pattern of these interactions predicted each of the four types of substances assessed in the context of cognitive style, but only predicted nicotine and marijuana use in the context of future orientation. These results highlight which cognitive factors may influence risk for substance use for early-maturing youth.

Keywords: adolescence, pubertal timing, substance use, cognitive style, future orientation

Adolescence is a crucial time of development during which many youth begin to engage in risky behaviors like substance use (Steinberg, 2008). Adolescence is also the time during which youth undergo pubertal development. It appears that substance use tends to increase with pubertal development, above and beyond the effects of age or grade in school (Patton et al., 2004). In addition, the timing at which youth enter the pubertal transition, often referred to as pubertal timing, also is associated with their propensity to use substances (Patton et al., 2004; Wilson et al., 1994). Indeed, a large body of work has investigated the effect of off-time pubertal development, or going through puberty earlier or later than one’s peers, as a predictor of substance use (Copeland, Shanahan, Miller, Costello, Angold, & Maughan, 2010; Graber, Seeley, Brooks-Gunn, & Lewinsohn, 2004; Mendle, Turkheimer, & Emery, 2007). Most research offers support for early pubertal timing as a risk factor for substance use, but findings are somewhat less consistent when also considering the moderating effect of sex.

For example, early pubertal timing in girls has been consistently linked to an increased risk of engaging in substance use (Mendle et al., 2007; Stice, Presnell, & Bearman, 2001; Westling, Andrews, Hampson, & Peterson, 2008; Wiesner & Ittel, 2002; Wilson et al., 1994). In fact, some research suggests that late pubertal timing is a protective factor for girls, and is associated with a decreased likelihood of engaging in substance use in adolescent girls (Dick, Rose, Viken, & Kaprio, 2000; Ge et al., 2001). There is also evidence that early-maturing boys are at increased risk for engagement in substance use (Kaltiala-Heino, Koivisto, Marttunen, & Fröjd, 2011; Tschann et al., 1994; Wiesner & Ittel, 2002). Conversely, other studies have shown that boys who go through puberty later than their peers are at heightened risk for substance use (Graber et al., 1997; 2004).

Going through puberty earlier than one’s peers may put boys or girls at heightened risk for using substances via a number of processes. Youth who develop earlier than their same-aged peers may be perceived as older or more mature, which may increase their risk for being approached by older, or more deviant, peer groups (Ge et al., 1996; Magnusson, Stattin, & Allen, 1985; Westling et al., 2008). Although these early-maturing adolescents appear older and more mature as a result of their advanced physical devleopment, they lack the emotional or cognitive resources to cope with expectations of older adolescents (Ge & Natasuki, 2009; Susman & Dorn, 2009). This mismatch between their physical and emotional/cognitive development may make them more susceptible to peer-pressure or poor decision making in the context of these older or deviant peer groups. In fact, puberty induces changes in the brain’s socioemotional network, which leads to heightened emotional reactivity and reward-seeking (Dahl & Gunnar, 2009; Hankin, Badanes, Abela, & Watamura, 2010; Nelson, Leibenluft, McClure, & Pine, 2005; Steinberg, 2008). Therefore, early-maturing adolescents may be more emotionally reactive and reward sensitive than their on-time or late-maturing counterparts, thus heightening their risk for engaging in substance use, either to cope with negative emotions or to satisfy the desire for reward.

The trajectory of substance use mirrors brain development across adolescence, increasing as emotional reactivity and reward-seeking increase, and decreasing as brain development becomes complete (Chen & Jacobson, 2012). Overall, rates of substance use increase from early adolescence to the mid-20s, at which point they begin to decline. These patterns are slightly more nuanced when considering sex differences. Females report higher rates of substance use in early adolescence, but after age 14–15, boys report higher rates of substance use, and these differences persist through adulthood (Chen & Jacobson, 2012). Thus, by mid-adolescence, we expect youth to begin initiating substance use, but early-maturing youth may initiate substance use even earlier.

Although experimentation with substances is considered a normative behavior among adolescents, adolescent drug use has been shown to predict later drug abuse, as well as other negative outcomes during adolescence, like risky sexual behavior and injury (Anthony & Petronis, 1995), highlighting the importance of understanding factors that may influence the propensity of early-maturing youth to initiate substance use. Not all early-maturing adolescents go on to use substances or engage in other risky behaviors, which suggests that there are individual differences that moderate the association between pubertal timing and the initiation of substance use. Two unexplored possibilities are future orientation and cognitive style.

During adolescence, individuals undergo significant changes to their cognitive abilities (Steinberg, 2005). For example, future orientation, or the ability to think about the future, anticipate consequences, and plan ahead, develops across adolescence with age (Steinberg, 2008). Greater future orientation has been shown to be negatively associated with problem behaviors in general, including alcohol use (Chen & Vazsonyi, 2013), and individuals with greater future orientation have been found to be less likely to use drugs (Robbins & Bryan, 2004). Similarly, two studies found that present orientation was positively associated with substance use, whereas future orientation was negatively associated with substance use (Keough, Zimbardo, & Boyd, 1999; Wills, Sandy, & Yaeger, 2001). Early-maturing youth who have greater future orientation may be protected from this risk by virtue of their ability to better anticipate consequences of their risky behavior; correspondingly, early-maturing youth who have poorer future orientation may be more likely to engage in substance use. Notably, Steinberg et al. (2009) found that girls exhibited greater future orientation than boys on the Future Orientation Scale. Consequently, early-maturing boys may be especially susceptible to initiating substance use by virtue of poorer future orientation.

In addition to future orientation, cognitive style may serve as a moderator of the association between pubertal timing and substance use. Negative cognitive style involves one’s tendency to attribute negative events to stable and global causes, infer negative self-characteristics, and infer negative consequences from those events, whereas a positive cognitive style involves the tendency to attribute negative events to unstable, specific causes, and to not infer negative consequences or negative self-characteristics following negative events (Abramson, Metalsky, & Alloy, 1989). Although most of the work on negative and positive cognitive styles has been in the context of depression, negative cognitive styles also have been shown to predict externalizing disorders (Alloy et al., 2012) and substance use (Nolen-Hoeksema, Stice, Wade, & Bohon, 2007). Indeed, one study found that early-maturing youth with more negative cognitive styles are more likely to report depressive symptoms during adolescence (Hamilton et al., 2014). Substance use and depression are highly comorbid (Armstrong & Costello, 2002), and the trajectories of both depression and substance use are positively associated across adolescence (Fleming et al., 2008), especially for girls. They also share some of the same etiological factors, like rumination, and depression is even a risk factor for later substance use during adolescence (Nolan-Hoeksema et al., 2007; Diego, Field, & Sanders, 2003). Thus, it is plausible that a more positive cognitive style also could protect early-maturing youth from substance use and a more negative cognitive style could put early-maturing youth at even greater risk of engaging in substance use. Importantly, there is evidence that adolescent girls have more negative cognitive styles than boys (Hankin & Abramson, 2002; Nolen-Hoeksema & Girgus, 1995). This difference, albeit small, is significant, and may mean that pubertal timing will predict substance use more strongly among girls with poorer cognitive styles.

Hypotheses

No study to date has investigated potential cognitive factors as moderators of the association between pubertal timing and the initiation of substance use. Thus, the present study tested cognitive style and future orientation as moderators of the association between pubertal timing and the initiation of substance use in a diverse community sample of adolescents. We hypothesized that early pubertal timing would be more strongly predictive of substance use in adolescents with more negative cognitive styles and with poorer future orientation than in adolescents with more positive cognitive styles and higher future orientation. In light of the evidence that the effects of pubertal timing on substance use are stronger for females, and that cognitive style and future orientation differ by sex, we also explored sex differences in the moderating role of these cognitive factors in the pubertal timing – substance use association. We expected to find significant three-way interactions between pubertal timing, cognitive style or future orientation, and sex predicting to substance use.

Methods

Participants

The study sample was drawn from the Adolescent Cognition and Emotion (ACE) Project, an ongoing prospective, longitudinal study of risk factors for the development of depression during adolescence. Caucasian and African American early adolescents (aged 12 to 13 at baseline) and their mothers or primary female caregivers were recruited from Philadelphia-area public and private middle schools. Recruitment was conducted either through school mailings and follow-up phone calls inviting the mother/primary caregiver and her child to participate (68% of the sample) or through advertisements in Philadelphia-area newspapers (32% of the sample). Eligibility required that the adolescent was 12 or 13 years old, identified as Caucasian/White, African American/Black, or Biracial, and the mother/primary caregiver also agreed to participate. Exclusion criteria were the absence of a mother/primary caregiver, either the child or the mother/primary caregiver’s English reading or speaking level was insufficient to complete study assessments, and either the adolescent or mother/primary caregiver was psychotic, intellectually disabled, or had a pervasive developmental disorder or learning disability. Informed consent was obtained from all individual participants included in the study (N = 636). Of the 636 who completed the baseline assessment, 469 families returned for at least one follow-up assessment. All study procedures were approved by the Temple University Institutional Review Board (IRB protocol #6844) and were in compliance with ethical standards. For further details, please see Alloy et al. (2012).

The sample for the current study consisted of 308 early adolescents with complete data on all measures at baseline (Time 1; Mage =12.94, SD=.76) and follow-up (Time 2; Mage =15.20, SD=1.09) about two years later. In the current sample, 51.0% of the adolescents identified as female (N = 157), 53.2% identified as African American (N = 164), and 42.5% were eligible for subsidized school lunch (N = 131), which takes into account the number of dependents supported on the family’s income and was a proxy for socioeconomic status (SES).

Participants included in the present analyses did not differ from those excluded because of missing data by sex, t(636) = 1.01, p = .31), race, t(636) = −.40, p = .69), or pubertal timing at baseline, t(463) = −.31, p = .76). However, the analytic sample was less likely to be eligible for subsidized school lunch (t(610) = 3.26, p = .001). There were no significant differences between those included in the analytic sample and those excluded on measures of cognitive style (t(630) = −1.90, p = .058) and future orientation (t(467) = 1.92; p = .056).

Procedures

Participants in the present study were assessed at two time points. The goal of the ongoing ACE Project was to interview participants every six months. However, because of the young age of the sample, we did not expect much substance use at the start of the study, and the substance use measure included in the present study was not introduced to the battery of assessments until approximately two years after the start of data collection. Further, the substance use measure was collected at a follow-up that consisted of two sessions, and participants often completed one session but failed to return for the second. As a result, a larger proportion of the sample is missing data on this measure. The current analyses include data from the first time each adolescent completed the substance use measure. Data from the follow up assessments between baseline and the first assessment at which the adolescent completed the substance use measure are not included in the present analyses. Additionally, there was a high degree of variability in when participants returned for their follow ups. Therefore, the first time this measure was completed by each participant varied considerably. To account for this, we controlled for time to follow up in all analyses. Times 1 and 2 were, on average, two years apart (M=26.30 months, SD=12.35, Range = 4.07–68.14 months). At Time 1, participants completed measures of pubertal development, cognitive style, and future orientation. At Time 2, participants completed a measure of substance use.

Measures

Cognitive Style.

The Adolescent Cognitive Style Questionnaire-Modified (ACSQ-M; Alloy et al., 2012) is a modified version of the original ACSQ (Hankin & Abramson, 2002) that assesses inferential styles regarding the internality (“was it caused by something about you or something else?”), stability (“will it cause [the same event] to happen in the future?”), and globality (“will it cause problems in other parts of your life?”) of causes, as well as the consequences (“will other bad things happen to you in the future because of [the event]?”) and self-worth implications (“Is there something wrong with you because of [the event]?”), of negative life events. Adolescents are presented with 12 hypothetical negative events in the achievement, interpersonal, or appearance domains (4 events per domain) and are asked to make inferences about the causes (internal-external, stable-unstable, and global-specific), consequences, and self-worth implications of each event. Each item is rated from 1 to 7, with higher scores indicating a more negative inferential style, and dimension scores range from 12 to 60. Dimension scores also are averaged to compute an overall total composite score. The current study used the overall composite score obtained from this measure; higher scores on this measure indicate a more negative cognitive style, whereas lower scores indicate a more positive cognitive style. The ACSQ has demonstrated excellent internal consistency, and good retest reliability (Alloy et al., 2012; Hankin & Abramson, 2002). Internal consistency for the total score was α = .94 at Time 1.

Future Orientation.

The Future Orientation Scale (FOS; Steinberg et al., 2008) was used to measure future orientation. This measure assessed the degree to which adolescents tended to perceive, anticipate, and plan for the future. This instrument was structured in a manner to minimize socially desirable responding. Specifically, adolescents were presented with a series of pairs of contrasting statements with the word “BUT” between them, and were asked to select the statement that best described them. After selecting the best self-describing statement, they were asked to indicate whether the selected descriptor was really true or sort of true. Responses for each pair of statements were coded on a 4-point Likert scale, ranging from really true for one descriptor to really true for the opposite descriptor. An example of an item is “Some people like to think about all the possible good and bad things that can happen before making a decision BUT Other people don’t think it’s necessary to think about every little possibility before making a decision”. Items were scored such that higher summary scores indicated greater future orientation. The internal consistency of this measure at Time 1 was adequate (α = .77).

Pubertal Timing.

The Pubertal Development Scale (PDS; Petersen, Crockett, Richards, & Boxer, 1988) is a self-report questionnaire containing six items to assess the current status of pubertal development. The questions ask about growth in height, body hair, skin change, breast change (females) or voice change (males), and facial hair (males) or menstruation (females). All questions aside from menstruation are rated on a 4-point scale (1 = no development, 2 = development has barely begun, 3 = development is definitely underway, 4 = development is complete). Menstruation is scored as 1 = I have not yet begun to menstruate and 4 = I have begun to menstruate. The scores on each item are averaged and the scale yields a total score ranging from 1–4. Higher scores mean a more advanced pubertal status. The PDS has been shown to have good psychometric properties (average alpha of .77 for just five items) and good convergent validity (correlations of .61ȓ.67 with physician ratings) (Petersen et al, 1988). Consistent with past research assessing pubertal timing (Alloy, Hamilton, Hamlat, & Abramson, 2016; Dorn, Dahl, Woodward, & Biro, 2006; Dorn, Susman, & Ponirakis, 2003), timing scores were obtained by regressing PDS total score on age. Timing scores were computed separately for males and females. The residual was used as a continuous measure of pubertal timing. The measure was completed by both mothers and adolescents, but just the adolescent’s report of his/her pubertal development was used in this study. However, the correlation between mothers’ and sons’ or daughters’ PDS scores was high (r = .84, p < .001). The mean PDS score at baseline was 2.69 (SD = 0.70; Table 1). The mean score for males was 2.33 (SD=.59), and the mean score for females was 3.04 (SD=0.61). Bond et al. (2006) reported that the mean PDS score in grade 7 for females was 3.38 (SD=0.66), and the mean score for males was 2.66 (SD=0.71), which is consistent with the development of youth in this sample. Only 3 participants reported having complete pubertal development (score of 4, 1% of the sample); 23.7% of the females in this sample reported that they had not yet begun to menstruate at baseline (N = 37). Internal consistency in this sample was α = .66 for girls and α = .74 for boys.

Table 1.

Descriptive statistics of primary study variables.

Sample Males Females
Mean/% SD/N Mean/% SD/N Mean/% SD/N
Nicotine (Never Used) 91.2 % 281 92.7 % 140 89.8 % 141
Alcohol (Never Used) 65.9 % 203 68.2 % 103 63.7 % 100
Marijuana (Never Used) 84.4 % 260 83.4 % 126 85.4 % 134
Other (Never Used) 96.1 % 296 96.0 % 145 96.2 % 151
Any Drug (at least once) 39.0 % 120 35.8 % 54 42.0 % 66
Future Orientation 2.71 0.44 2.68 0.41 2.73 0.46
Cognitive Style 2.51 0.86 2.55 0.84 2.47 0.88
Pubertal Timing 0.00 1.08 0.00 1.05 0.00 1.11
Pubertal Status 2.69 0.70 2.33 0.59 3.04 0.61
Open in a new tab

Note: Higher scores on the pubertal timing measure indicate earlier pubertal timing; Higher scores on the cognitive style measure indicate more negative cognitive style; Higher scores on the Future Orientation measure indicate greater future orientation.

Substance Use.

The Adolescent Alcohol and Drug Involvement Scale (AADIS; Moberg, 2003) is an adaptation of the Adolescent Drug Involvement Scale (ADIS; Moberg & Hahn, 1991). This is a face-valid measure used to assess how often adolescents use alcohol and a range of twelve other substances (e.g., marijuana, hallucinogens, amphetamines, cocaine, etc.). Participants are asked to choose from “Never Used,” “Tried Once or Twice,” “Several Times a Month,” Weekends Only,” “Several Times a Week,” “Daily,” or “Several Times a Day” for each substance. Weights are given to each option (0 = Never Used to 7 = Several Times a Day), and higher scores indicate more drug and alcohol use. A total score (a sum of each of the 13 items) was originally computed. However, given the composition of this young, community sample, there was very limited endorsement on many of the drug items on the measure. Thus, we recoded the measure and estimated a single factor confirmatory factor analysis to reflect drug use propensity. The first three items were kept with the full range of response options so as to not lose variability in degree of use of each substance across participants (e.g., 0–7). Then, all substances beyond marijuana were collapsed into a dichotomous (0/1, used/never used) “other” item. The re-coded measure had four items (cigarettes, alcohol, marijuana, and any other substance). Per recommendations by Muthen, du Toit, and Spisic (2013) for analyses with categorical items, a robust mean-adjusted weighted least square (WLSMV) estimation method was used. All items loaded significantly onto the latent factor (Nicotine: B = .56, SE = .02, p < .001; Alcohol: B = .48, SE = .03, p < .001; Marijuana: B = .95, SE = .03, p < .001; Other: B = .20, SE = .02, p = .20), and model fit was excellent, X2(2) = 4.65, p = .10, RMSEA = 0.06, 90% CI = 0.000–0.13; CFI = 0.99.

Covariates.

The AADIS was not given at baseline, so analyses could not control for initial levels of alcohol/drug use on the AADIS. However, according to a diagnostic interview (Schedule for Affective Disorders and Schizophrenia for School-Age Children-Epidemiologic Version [K-SADS-E], Fifth Edition; Orvaschel, 1995a) given at baseline (Time 1) to the adolescents and their mothers, 0% of the sample endorsed any substance use at Time 1 (when they were age 12.9 on average). Finally, because of the variability in time to follow-up in this study, time to follow-up was computed and entered as a covariate in all analyses1. Correlations between the AADIS latent variable at follow-up and all demographic variables (race, sex, SES) were tested and revealed no significant associations. Therefore, no demographics were entered as covariates.

Data Analysis

To examine whether either cognitive style or future orientation moderated the association of pubertal timing with later substance use propensity, and whether these effects were stronger for females, two separate three-way interactions (pubertal timing × future orientation × sex and pubertal timing × cognitive style × sex) were tested in Mplus (Muthén & Muthén, 2015). In all analyses, covariates (time to follow-up), main effects, all possible two-way interactions, and the three-way interaction were entered. All predictors (pubertal timing, sex, and cognitive style or future orientation) were mean centered. To probe significant interactions, pubertal timing’s relationship with the substance use dependent variable was plotted at one standard deviation above and below the mean of each cognitive factor (Aiken & West, 1991).

Results

Descriptive Analyses

Descriptive statistics and bivariate correlations among primary study variables are presented in Tables 1 and 2. Drug use at follow-up was significantly positively associated with time to follow-up and cognitive style, and significantly negatively associated with future orientation. Race was significantly positively associated with SES. When analyzed separately by sex, pubertal timing was significantly positively associated with drug use for girls (b = .06, SE = .01, p < .001), but not for boys (b = .07, SE = .05, p = .20). Notably, drug use was not significantly associated with any demographic variables (sex, race, SES). Thus, only time to follow-up was included as a covariate in the prospective analyses. When we considered sex as a moderator, we included the main effect of sex to permit appropriate analytic implementation.

Table 2.

Bivariate correlations of primary study variables.

Drug Use Sex TTFU PT Cog Style FO Race SES
Drug Use 1.00
Sex −0.03 1.00
TTFU 0.15* 0.03 1.00
PT 0.11 0.02 0.00 1.00
Cog Style 0.17** −0.09 −0.04 −0.02 1.00
FO −0.08* 0.04 −0.01 −0.01 0.03 1.00
Race −0.12 −0.03 0.16+ 0.14+ −0.11+ 0.15+ 1.00
SES 0.05 −0.02 0.17+ 0.09 −0.07 −0.02 0.54*** 1.00
Open in a new tab
***

p<.001,

**

p<.01,

*

p<.05,

+

p<.10.

Note. TTFU = Time to follow-up; PT = pubertal timing; Cog Style = cognitive style; FO = future orientation; SES = socioeconomic status (based on free lunch eligibility)

Prospective Analyses

Pubertal Timing and Substance Use

We first tested the 3-way interaction between pubertal timing, cognitive style, and sex predicting substance use propensity at follow-up. Model fit was excellent (X2(26) = 23.23, p =.61; RMSEA = 0.000, 90% CI = .000–.04; CFI = 1.000). There were significant main effects of cognitive style (b = .09, SE = .04, p = .02), and time to follow-up (b = .009, SE = .003, p < .001) predicting substance use at follow-up. The three-way interaction between cognitive style, pubertal timing, and sex was not significant (b = .11, SE = .09, p = .21). However, there was a significant two-way interaction between pubertal timing and cognitive style predicting drug use. We ran the two-way interaction models independently to ensure accurate interpretation of the coefficients. The interaction between pubertal timing and cognitive style remained significant (Table 3 (top); Figure 1; b = .12, SE = .04, p < .001). Decomposition of this interaction revealed that pubertal timing was significantly positively associated with drug use at follow-up (such that drug use was higher among those who matured earlier) among youth with more negative cognitive styles (b = .16, SE = .03, p<.001) and moderate cognitive styles (b = .07, SE = .03, p=.02). Pubertal timing was not associated with drug use among youth with more positive cognitive styles (b = −.10, SE = .11, p=.34).

Table 3.

Results from 2-way interaction between cognitive style and pubertal timing at time 1 predicting substance use at time 2 (top) and results from 2-way interaction between future orientation and pubertal timing, predicting substance use (bottom).

b SE B
Pubertal Timing 0.08 0.07 0.08
Cognitive Style 0.26 0.09 0.25**
Time to Follow Up 0.02 0.01 0.22**
Pubertal Timing × Cognitive Style 0.21 0.10 0.21*
Pubertal Timing 0.07 0.02 0.16+
Future Orientation −0.15 .08 −0.15**
Time to Follow Up 0.01 0.002 0 23***
Pubertal Timing × Future Orientation 0.16 0.06 0.16**
Open in a new tab

Note:

***

p < .001;

**

p < .01;

*

p < .05;

+

p < .10.

Figure 1.

Figure 1.

Open in a new tab

Interaction between pubertal timing and cognitive style predicting substance use at follow up. Higher scores on cognitive style indicate more negative cognitive style. Higher scores on pubertal timing indicate earlier pubertal timing.

We then tested the 3-way interaction between pubertal timing, future orientation, and sex predicting substance use propensity at follow-up. Model fit was excellent X2(26) = 23.96, p = .58; RMSEA = 0.00, 90% CI = 0.00 – 0.04; CFI = 1.00). There was a significant main effect of time to follow-up (b = .009, SE = .002, p <.001). The three-way interaction between future orientation, pubertal timing, and sex was not significant (b = .11, SE = .18, p = .55). The two-way interaction of pubertal timing and future orientation predicting drug use was not significant, but it was marginal, so we ran the pubertal timing by future orientation interaction model independently; it was significant (Table 3 (bottom); Figure 2; b = .16, SE = .06, p = .007). Decomposition of the interaction revealed that pubertal timing was significantly positively associated with drug use at follow-up (such that drug use was higher among those who matured earlier) among youth with greater future orientation (b = .14, SE = .03, p<.001) and moderate future orientation (b = .07, SE = .02, p=.001). Pubertal timing was not associated with drug use among youth with poorer future orientation (b = −.004, SE = .03, p=.91).

Figure 2.

Figure 2.

Open in a new tab

Interaction between pubertal timing and future orientation predicting substance use at follow up. Higher scores on pubertal timing indicate earlier pubertal timing.

Post-Hoc Follow-up Analyses

We conducted two sets of post-hoc follow-up analyses. First, we investigated which subscales of the ACSQ were responsible for the pubertal timing × cognitive style interaction predicting to substance use. Second, we investigated whether the patterns of the pubertal timing × cognitive style or pubertal timing × future orientation interactions were similar when predicting to each individual type of substance (e.g., nicotine, alcohol, marijuana, and other substances).

We first tested the interactions between pubertal timing and internality, stability, globality, consequences, or self-worth implications subscales of the ACSQ. Model fit for all models was excellent (RMSEA < .05 and CFI > .95).

There was a significant interaction between pubertal timing and the (1) globality (b = .01, SE = .03, p < .001), (2) consequences (b = .10, SE = .04, p = .008), and (3) self-worth implications (b = .09, SE = .02, p < .001) subscales. Decomposition of these interactions revealed that pubertal timing was positively associated with drug use among youth with more negative consequences (b = .19, SE = .04, p < .001), globality (b = .16, SE = .02, p < .001), and self-worth implications (b = .16, SE = .03, p<.001) and with moderate consequences (b = .09, SE = .02, p < .001), globality (b = .07, SE = .03, p = .04), and self-worth implications (b = .107 SE = .03, p = .02) attributions, but not more positive attributions (consequences: b = 0.00, SE = .05, p = .996; globality: b = −.03, SE = .05, p = .62; self-worth implications: b = −.02, SE = .04, p = .65). There was not a significant interaction between pubertal timing and the stability (b = .02, SE = .02, p = .46) or internality subscales (b = .02, SE = .03, p = .51).

We then tested the pubertal timing by cognitive style interaction predicting each of the items used to compute the substance use latent variable (e.g., nicotine, alcohol, marijuana, and other drugs). Model fit for all models was excellent (RMSEA < .05 and CFI > .95). We found that the pubertal timing by cognitive style interaction significantly predicted use of nicotine (b = .09, SE = .05, p = .04), alcohol (b = .12, SE = .04, p = .005), and marijuana (b = .20, SE = .06, p <.001), but did not significantly predict use of other substances (b = −.17, SE = .26, p = .051).

The pattern of the interaction also was similar across all four substances. For nicotine and alcohol, pubertal timing was significantly positively associated with substance use among youth with more negative cognitive styles (nicotine: b = .13, SE = .05, p = .02; alcohol: b = .15, SE = .05, p = .003), but not moderate cognitive styles (nicotine: b = .05, SE = .04, p = .23; alcohol: b = .05, SE = .04, p = .17), or positive cognitive styles (nicotine: b = −.03, SE = .06, p = .56; alcohol: b = −.05, SE = .05, p = .33). Thus, among youth with negative cognitive styles, those with earlier pubertal timing were more likely to use nicotine or alcohol.

For marijuana, pubertal timing was significantly positively associated with marijuana use among youth with more negative and moderate cognitive styles, although the strength of the association was stronger among youth with negative cognitive styles (negative: b = .34, SE = .07, p <.001; moderate: b = .17, SE = .05, p = .001). Thus, among youth with negative or moderate cognitive styles, those with earlier pubertal timing were more likely to use marijuana. Pubertal timing was not associated with nicotine use among youth with more positive cognitive styles (b = −.01, SE = .07, p = .89).

Finally, we tested the pubertal timing by future orientation interaction predicting each of the four substances that comprised the substance use latent variable. There was not a significant interaction predicting alcohol (b = .08, SE = .09, p = .27) or other substances (b = −.57, SE = .57, p = .32). There was a significant interaction predicting marijuana (b = .25, SE = .11, p = .02) and nicotine use (b = .19, SE = .09, p = .03). Decomposition of the interactions revealed that, for nicotine, pubertal timing significantly positively predicted nicotine use among youth with greater future orientation (b = .12, SE = .05, p = .02), but not moderate (b = .03, SE = .04, p = .39) or poorer (b = −.05, SE = .06, p = .39) future orientation. For marijuana, pubertal timing significantly positively predicted marijuana use among youth with greater (b = .26, SE = .06, p = <.001) and moderate (b = .15, SE = .05, p = .002) future orientation, but not poorer future orientation (b = .04, SE = .07, p = .57).

Discussion

There has been extensive work investigating the impact of pubertal timing on risky behaviors, like substance use, during adolescence. However, few studies have investigated cognitive factors that moderate this association. Results from the current study demonstrate that cognitive style and future orientation moderate the association between pubertal timing and substance use initiation, but the patterns of these interactions differ. Specifically, cognitive style, as defined by the inferences one makes about the internality, stability, globality, consequences, and self-worth implications of negative life events, moderated the association between pubertal timing and substance use, such that early pubertal timing predicted more substance use among youth with more negative or moderate cognitive styles. Future orientation also moderated the association between pubertal timing and substance use; however, the pattern of this interaction was contrary to our expectations. Early pubertal timing predicted more substance use among youth with greater or moderate future orientation.

Post-hoc analyses revealed that an early-maturing adolescent’s inferences about the consequences, globality, and self-worth implications of negative life events are associated with future substance use. In other words, early-maturing youth who believe that other negative outcomes will happen to them, that they will experience problems in other parts of their lives, or that there are negative implications for their own self-worth after negative life events are more likely to use drugs or alcohol. The attributions early-maturing youth make about the stability or internality of the causes of negative life events were not associated with substance use at follow-up. Further, the pattern of interaction was consistent when predicting to all four categories of drugs that comprised the drug use latent variable (e.g., nicotine, marijuana, alcohol, and other substances) in the context of negative cognitive styles, suggesting that early maturing youth with more negative cognitive styles are at equal risk for using each of these four substances. However, the pubertal timing by future orientation interaction only predicted nicotine and marijuana use, such that early-maturing youth with greater future orientation were more likely to use nicotine, and early-maturing youth with moderate and greater future orientation were more likely to use marijuana.

These results are consistent with prior work demonstrating that early-maturing boys and girls are at high risk for substance use during adolescence (Kaltiala-Heino et al., 2003), and that cognitive style is predictive of these same outcomes (Alloy et al., 2000; 2012). We extended past work by demonstrating that early pubertal timing predicts substance use in the context of negative cognitive styles and greater future orientation during adolescence. Not all early maturing adolescents go on to use substances, and these findings highlight a subset of early-maturing youth who are at relatively higher risk by virtue of their more negative cognitive styles or greater future orientation.

Although some of our hypotheses received support, some findings were counter to our expectations. Although there was a main effect of future orientation, such that greater future orientation predicted less substance use, the future orientation by pubertal timing interaction revealed a different pattern of results. Earlier maturing youth were more likely to use substances in the context of greater or moderate future orientation. Pubertal timing was not associated with drug use among youth with poorer future orientation. Thus, our results suggest that future orientation does not mitigate the effect of early pubertal timing on propensity to use substances during adolescence; in fact, greater future orientation seemed to increase the likelihood that youth will initiate substance use. One possible reason for this pattern of results may be related to brain development during adolescence. Puberty induces changes in the brain’s socioemotional network, which leads to heightened emotional reactivity and reward-seeking (Dahl & Gunnar, 2009; Hankin et al., 2010; Nelson et al., 2005; Steinberg, 2008). Therefore, early-maturing adolescents may be more emotionally reactive and reward sensitive than their on-time or late-maturing counterparts. Thus, these youth may find drugs to be particularly rewarding or useful for managing negative emotions. If these youth also have greater future orientation, they may have a greater capacity for understanding the consequences of getting caught using substances, but also a greater ability to plan for avoiding these negative consequences. This hypothesis may be further supported by the finding that the future orientation × pubertal timing interaction predicting drug use was driven by marijuana and nicotine use. Arguably, these two substances are more difficult to obtain than alcohol and may require more coordination and planning to successfully procure and use without detection by parents or other authority figures. Parents also may be less likely to condone experimentation with nicotine and marijuana than alcohol during adolescence, further incentivizing youth to use these substances discreetly. Future work should test this hypothesis and verify whether greater future orientation is indeed a risk factor for substance use among early-maturing youth.

In addition, there was not a consistent main effect of pubertal timing on drug use in any of the models tested or when examining correlations among the study variables. When bivariate correlations between pubertal timing and drug use were run separately for boys and girls, pubertal timing did significantly predict drug use for girls, but not boys (although the difference between these effects was not significant, as evidenced by the lack of a significant pubertal timing × sex interaction). The relation between pubertal timing and drug use is, in fact, more consistent for females in the literature (Graber et al., 1997; 2004; Mendle et al., 2007; Stice et al., 2001; Westling et al., 2008; Wiesner & Ittel, 2002; Wilson et al., 1994;). Also, reported drug use was slightly higher among the females than the males in this sample (Table 1). This gendered difference in drug use in the current study is consistent with research showing that girls report more substance use in early adolescence, but boys report more substance use after age 14–15 (Chen & Jacobson, 2012), the approximate age of the study sample at T2. Thus, the age of the sample, the relatively lower reported use of drug use among males, and the relatively weaker association between pubertal timing and drug use among males could explain the lack of a significant pubertal timing – drug use association in the overall sample. Perhaps a longer follow-up period would yield stronger effects. Future studies should aim to clarify this question.

Further, the associations between cognitive factors combined with pubertal timing and substance use did not differ by sex. Although pubertal timing is more consistently linked to substance use among females and sex differences have been found in the cognitive attributes studied, findings in the literature are mixed, and there is evidence that both early-maturing boys and girls are more likely to use substances (Graber et al., 2004; Wiesner & Ittel, 2002). In addition, the follow-up period in the present study is still fairly early in the substance use risk period, particularly for boys (Chen & Jacobson, 2012), as youth were 15.2 years old on average at T2. Thus, results should be interpreted in terms of risk for initiating substance use during adolescence. Perhaps examining a longer window of follow-up would yield stronger effects and could illustrate pubertal timing’s effect on more chronic, problematic substance use in the context of negative cognotive style or greater future orientation. Future work should aim to investigate this question over a longer period of development.

It is interesting to consider that pubertal timing has been shown to be the consequence of a number of factors in childhood, like early adversity, family structure, and family relationships (Ellis et al., 1999; Ellis & Garber, 2000; Mendle, Leve, Ryzin, Natsuaki, & Ge, 2011). Early adversity and family relationships also predict adolescent substance use (Enoch, 2011; Flewelling & Bauman, 1990; Johnson & Pandina, 2009). The current study did not consider the effects of these early childhood experiences, but future work should investigate how early childhood experiences further influence the pubertal timing – substance use link.

It is also important to note that the construct of cognitive style may overlap in some ways with self-esteem or locus of control. However, cognitive style captures the attributions that an individual makes about the causes, consequences, and implications of negative life events, and has been validated as a construct independent from other similar constructs, like neuroticism and self-esteem (Hankin, Lakdawalla, Carter, Abela, & Adams, 2007). Further, it has been found to be predictive of a variety of outcomes, including depression, anxiety, and pessimism/hopelessness (Haeffel, et al., 2008). It is possible that self-esteem and locus of control also moderate the association between pubertal timing and substance use, but the current study did not have the data to examine this question.

This study has important implications for early-maturing adolescents at risk for substance use initiation. Helping these youth to make more positive inferences about negative life events (particularly the globality, consequences, and self-worth implications of those events) could help reduce the likelihood that they use substances. Intervention studies utilizing an experimental design are needed to conclude whether bolstering the cognitive styles of early-maturing youth is an effective strategy for preventing substance use during adolescence. Conversely, although there was a significant negative main effect of future orientation on drug use, improving future orientation may not be particularly effective in mitigating substance use in the context of early pubertal timing. In fact, in this sample, better future orientation emerged as a risk factor among earlier maturing youth.

Pubertal timing also has been investigated as a risk factor for a wide range of psychopathological outcomes, like depression, anxiety, and other externalizing problems (Graber, Lewinsohn, Seeley, & Brooks-Gunn, 1997), all of which have been linked to substance use (Armstrong & Costello, 2002). One other study has demonstrated that cognitive style moderates the relation between pubertal timing and depression (Hamilton, Hamlat, Stange, Abramson, & Alloy, 2014), but an important direction for future research is to test whether cognitive style and other cognitive factors also moderate the effect of pubertal timing on the development of other psychopathological outcomes during adolescence.

The present study has a number of strengths. It is the first study to our knowledge to investigate cognitive attributes as moderators of the pubertal timing – substance use relationship. Additionally, the prospective design of this study allowed us to test how the interaction between pubertal timing and cognitive factors predicted initiation (from no substance use at baseline) of substance use over time. Finally, we studied a community sample diverse in race, sex, and socioeconomic status, increasing these findings’ generalizability.

However, these findings should be interpreted in light of some limitations. First, pubertal timing was assessed relatively late in development – at age 12 to 13. Although there was sufficient statistical variability in our measure of pubertal development to observe meaningful individual differences and very few participants had completed pubertal development at the time of assessment, it is possible that measuring pubertal timing at this age inadequately captured the earliest developing youth or latest developing youth. It would be useful to test the moderating role of cognitive factors on the effects of pubertal timing in a younger sample of youth. However, to address concerns that these findings may be driven by the advanced pubertal status of the sample, we ran the three-way interactions using PDS total score instead of the pubertal timing score. None of the two or three-way interactions in the models were significant, suggesting that it is pubertal timing, not status, that is associated with substance use initiation in the context of negative cognitive styles and greater future orientation. Additionally, these findings can be interpreted in the context of the sample’s distribution of pubertal development (sample M=2.69, SD=0.70; Males M=2.33, SD=.59; Females M=3.04, SD=0.61; Table 1). These statistics are in line with previously published reports of average rates of pubertal development in boys and girls (Bond et al., 2006; Marceau, Ram, Houts, Grimm, & Susman, 2011).

Second, the self-report measure of substance use was not given at baseline, so we could not control for initial levels of substance use on this measure at baseline. However, reports of substance use in the K-SADS diagnostic interview administered at baseline indicated that no individual in the sample reported any substance use. Therefore, it can be reasonably assumed that our findings demonstrate increases in (initiation of) substance use over time. Third, we only measured cognitive style for negative events. In future research, it would be useful to determine whether cognitive style for interpreting positive events also would moderate early-maturing adolescents’ likelihood of substance use. Finally, the sample used in the current study differed from the larger sample on socioeconomic status, somewhat limiting the generalizability of the findings. Although our analytic sample did not have as high a proportion of lower SES adolescents as the larger sample, it still included many low SES youth.

Third, we included only participants with complete data at Times 1 and 2 in the present analyses, which resulted in a study sample that represented 48% of the larger study sample. This introduces the possibility that the sample used in the present analyses was biased on some measure that we did not account for. We were able to empirically evaluate whether the study sample differed from the larger sample on demographic measures or measures included in the present analyses, but we cannot know if there are other relevant factors that differ significantly between those who were included in the present study and those who were not.

Finally, the aim of the current study was to test whether pubertal timing in early adolescence could prospectively predict substance use later in adolescence in the context of cognitive style and future orientation. However, there are individual differences not only in the timing of pubertal development, but also in the tempo of development (e.g., the speed at which one advances through the stages of puberty; Mendle, 2014). In other words, one’s development relevant to his/her peers likely changes throughout adolescence. Therefore, it is also possible that pubertal timing better predicts substance use among youth concurrently. However, the current sample is not suitable to test this hypothesis. Given that the substance use measure was not introduced to the battery of assessments until two years after the start of data collection, youth were 15 years old on average when they first completed it. At that age, many youth, especially females, were very advanced in their development. Thus, there was limited variability in the measure of pubertal timing at the time that substance use was assessed, which would limit the interpretability of our results. Future work should evaluate this question.

It is also worth noting that time to follow up was consistently related to drug use in the present study. One could argue that time to follow up is also a proxy for age and that controlling for age at T2 may be more appropriate. Given some evidence that age does not uniquely predict substance use above and beyond other risk factors (Kaltiala-Heino, Koivisto, Marttunen, & Fröjd, 2011; Patton et al., 2004), we chose to keep time to follow up in the models presented. However, age and time to follow-up were highly correlated (r = .73, p = <.001). Thus, we did test the models controlling for age instead of time to follow up, and the results were nearly identical to those presented here (data available upon request).

Conclusions

The present study demonstrated that cognitive factors moderate the relation between pubertal timing and substance use among early-maturing adolescents; specifically, a more negative cognitive style and greater future orientation may serve as risk factors for increased substance use among early-maturing youth. These findings have important implications for cognitive-behavioral interventions targeted at early developing adolescents. Although strategies aimed at decreasing the negativity of early maturing adolescents’ cognitive styles may prove beneficial in reducing risk for substance use, our findings suggest that further enhancing future orientation may not be helpful.

Acknowledgments

This research was supported by National Institute of Mental Health Grants MH079369 and MH101168 to Lauren B. Alloy.

Contributor Information

Allison Stumper, Temple University.

Thomas M. Olino, Temple University

Lyn Y. Abramson, University of Wisconsin-Madison

Lauren B. Alloy, Temple University

References

  1. Aiken LS, West SG, & Reno RR (1991). Multiple regression: Testing and interpreting interactions. Sage. [Google Scholar]
  2. Alloy LB, Abramson LY, Hogan ME, Whitehouse WG, Rose DT, Robinson MS, … & Lapkin JB (2000). The Temple-Wisconsin Cognitive Vulnerability to Depression Project: Lifetime history of Axis I psychopathology in individuals at high and low cognitive risk for depression. Journal of abnormal psychology, 109(3), 403. [PubMed] [Google Scholar]
  3. Alloy LB, Black SK, Young ME, Goldstein KE, Shapero BG, Stange JP, … & Abramson LY (2012). Cognitive vulnerabilities and depression versus other psychopathology symptoms and diagnoses in early adolescence. Journal of Clinical Child & Adolescent Psychology, 41(5), 539–560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Anthony JC, & Petronis KR (1995). Early-onset drug use and risk of later drug problems. Drug and alcohol dependence, 40(1), 9–15. [DOI] [PubMed] [Google Scholar]
  5. Armstrong TD, & Costello EJ (2002). Community studies on adolescent substance use, abuse, or dependence and psychiatric comorbidity. Journal of consulting and clinical psychology, 70(6), 1224. [DOI] [PubMed] [Google Scholar]
  6. Bond L, Clements J, Bertalli N, Evans-Whipp T, McMorris BJ, Patton GC, … & Catalano RF (2006). A comparison of self-reported puberty using the Pubertal Development Scale and the Sexual Maturation Scale in a school-based epidemiologic survey. Journal of adolescence, 29(5), 709–720. [DOI] [PubMed] [Google Scholar]
  7. Chen P, & Vazsonyi AT (2013). Future orientation, school contexts, and problem behaviors: A multilevel study. Journal of Youth and Adolescence, 42(1), 67–81. [DOI] [PubMed] [Google Scholar]
  8. Copeland W, Shanahan L, Miller S, Costello EJ, Angold A, & Maughan B (2010). Outcomes of early pubertal timing in young women: a prospective population-based study. American Journal of Psychiatry, 167(10), 1218–1225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dahl RE, & Gunnar MR (2009). Heightened stress responsiveness and emotional reactivity during pubertal maturation: implications for psychopathology. Development and psychopathology, 21(1), 1–6. [DOI] [PubMed] [Google Scholar]
  10. Dick DM, Rose RJ, Pulkkinen L, & Kaprio J (2001). Measuring puberty and understanding its impact: A longitudinal study of adolescent twins. Journal of Youth and Adolescence, 30(4), 385–399. [Google Scholar]
  11. Diego MA, Field TM, & Sanders CE (2003). Academic performance, popularity, and depression predict adolescent substance use. Adolescence, 38(149), 35. [PubMed] [Google Scholar]
  12. Dorn LD, Dahl RE, Woodward HR, & Biro F (2006). Defining the boundaries of early adolescence: A user’s guide to assessing pubertal status and pubertal timing in research with adolescents. Applied Developmental Science, 10(1), 30–56. [Google Scholar]
  13. Dorn LD, Susman EJ, & Ponirakis A (2003). Pubertal timing and adolescent adjustment and behavior: Conclusions vary by rater. Journal of Youth and Adolescence, 32(3), 157–167. [Google Scholar]
  14. Ellis BJ, & Garber J (2000). Psychosocial antecedents of variation in girls’ pubertal timing: Maternal depression, stepfather presence, and marital and family stress. Child development, 71(2), 485–501. [DOI] [PubMed] [Google Scholar]
  15. Ellis BJ, McFadyen-Ketchum S, Dodge KA, Pettit GS, & Bates JE (1999). Quality of Early Family Relationships and Individual Differences in the Timing of Pubertal Maturation in Girls: A Longitudinal Test of an Evolutionary Model. Journal of Personality and Social Psychology, 77(2), 387–401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Enoch MA (2011). The role of early life stress as a predictor for alcohol and drug dependence. Psychopharmacology, 214(1), 17–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fleming CB, Mason WA, Mazza JJ, Abbott RD, & Catalano RF (2008). Latent growth modeling of the relationship between depressive symptoms and substance use during adolescence. Psychology of Addictive Behaviors, 22(2), 186. [DOI] [PubMed] [Google Scholar]
  18. Flewelling RL, & Bauman KE (1990). Family structure as a predictor of initial substance use and sexual intercourse in early adolescence. Journal of Marriage and the Family, 171–181. [Google Scholar]
  19. Flynn M, & Rudolph KD (2010). The contribution of deficits in emotional clarity to stress responses and depression. Journal of applied developmental psychology, 31(4), 291–297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ge X, Conger RD, & Elder GH (1996). Coming of age too early: Pubertal influences on girls’ vulnerability to psychological distress. Child development, 67(6), 3386–3400. [PubMed] [Google Scholar]
  21. Graber JA, Lewinsohn PM, Seeley JR, & Brooks-Gunn J (1997). Is psychopathology associated with the timing of pubertal development? Journal of the American Academy of Child & Adolescent Psychiatry, 36(12), 1768–1776. [DOI] [PubMed] [Google Scholar]
  22. Graber JA, Seeley JR, Brooks-Gunn J, & Lewinsohn PM (2004). Is pubertal timing associated with psychopathology in young adulthood?. Journal of the American Academy of Child & Adolescent Psychiatry, 43(6), 718–726. [DOI] [PubMed] [Google Scholar]
  23. Hamilton JL, Hamlat EJ, Stange JP, Abramson LY, & Alloy LB (2014). Pubertal timing and vulnerabilities to depression in early adolescence: Differential pathways to depressive symptoms by sex. Journal of adolescence, 37(2), 165–174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hankin BL, & Abramson LY (2002). Measuring cognitive vulnerability to depression in adolescence: Reliability, validity, and gender differences. Journal of clinical child and adolescent psychology, 31(4), 491–504. [DOI] [PubMed] [Google Scholar]
  25. Hankin BL, Badanes LS, Abela JR, & Watamura SE (2010). Hypothalamic–pituitary–adrenal axis dysregulation in dysphoric children and adolescents: Cortisol reactivity to psychosocial stress from preschool through middle adolescence. Biological psychiatry, 68(5), 484–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hankin BL, Lakdawalla Z, Carter IL, Abela JRZ, & Adams P. (2003). Are neuroticism, cognitive vulnerabilities and self-esteem overlapping or distinct risks for depression? Evidence from exploratory and confirmatory factor analyses. Journal of Social and Clinical Psychology. 26(1), 29–63. [Google Scholar]
  27. Haeffel GJ, Gibb BE, Metalsky GI, Alloy LB, Abramson LY, Hankin BL, … & Swendsen JD (2008). Measuring cognitive vulnerability to depression: Development and validation of the cognitive style questionnaire. Clinical psychology review, 28(5), 824–836. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Johnson V, & Pandina RJ (1991). Effects of the family environment on adolescent substance use, delinquency, and coping styles. The American journal of drug and alcohol abuse, 17(1), 71–88. [DOI] [PubMed] [Google Scholar]
  29. Kaltiala-Heino R, Kosunen E, & Rimpelä M (2003). Pubertal timing, sexual behaviour and self-reported depression in middle adolescence. Journal of adolescence, 26(5), 531–545. [DOI] [PubMed] [Google Scholar]
  30. Kaltiala-Heino R, Koivisto AM, Marttunen M, & Fröjd S (2011). Pubertal timing and substance use in middle adolescence: a 2-year follow-up study. Journal of Youth and Adolescence, 40(10), 1288. [DOI] [PubMed] [Google Scholar]
  31. Keough KA, Zimbardo PG, & Boyd JN (1999). Who’s smoking, drinking, and using drugs? Time perspective as a predictor of substance use. Basic and applied social psychology, 21(2), 149–164. [Google Scholar]
  32. Marceau K, Ram N, Houts RM, Grimm KJ, & Susman EJ (2011). Individual differences in boys’ and girls’ timing and tempo of puberty: modeling development with nonlinear growth models. Developmental psychology, 47(5), 1389–409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mendle J (2014). Beyond pubertal timing: New directions for studying individual differences in development. Current Directions in Psychological Science, 23(3), 215–219. [Google Scholar]
  34. Mendle J, Leve LD, Van Ryzin M, Natsuaki MN, & Ge X (2011). Associations Between Early Life Stress, Child Maltreatment, and Pubertal Development Among Girls in Foster Care. Journal of Research on Adolescence : The Official Journal of the Society for Research on Adolescence, 21(4), 871–880. 10.1111/j.1532-7795.2011.00746.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mendle J, Turkheimer E, & Emery RE (2007). Detrimental psychological outcomes associated with early pubertal timing in adolescent girls. Developmental review, 27(2), 151–171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Moberg DP (2003). Screening for alcohol and other drug problems using the Adolescent Alcohol and Drug Involvement Scale (AADIS). Madison: Center for Health Policy and Program Evaluation, University of Wisconsin-Madison. [Google Scholar]
  37. Moberg DP, & Hahn L (1991). The adolescent drug involvement scale. Journal of Child & Adolescent Substance Abuse, 2(1), 75–88. [Google Scholar]
  38. Muthén LK, & Muthén B (2015). Mplus. The comprehensive modelling program for applied researchers: user’s guide, 5. [Google Scholar]
  39. Natsuaki MN, Biehl MC, & Ge X (2009). Trajectories of depressed mood from early adolescence to young adulthood: The effects of pubertal timing and adolescent dating. Journal of Research on Adolescence, 19(1), 47–74. [Google Scholar]
  40. Nelson EE, Leibenluft E, McClure EB, & Pine DS (2005). The social re-orientation of adolescence: a neuroscience perspective on the process and its relation to psychopathology. Psychological medicine, 35(2), 163–174. [DOI] [PubMed] [Google Scholar]
  41. Nolen-Hoeksema S, & Girgus JS (1995). Explanatory style and achievement, depression, and gender differences in childhood and early adolescence. Explanatory style, 57–70. [Google Scholar]
  42. Nolen-Hoeksema S, Stice E, Wade E, & Bohon C (2007). Reciprocal relations between rumination and bulimic, substance abuse, and depressive symptoms in female adolescents. Journal of abnormal psychology, 116(1), 198. [DOI] [PubMed] [Google Scholar]
  43. Patton GC, McMorris BJ, Toumbourou JW, Hemphill SA, Donath S, & Catalano RF (2004). Puberty and the onset of substance use and abuse. Pediatrics, 114(3), e300–e306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Petersen AC, Crockett L, Richards M, & Boxer A (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17(2), 117–133. [DOI] [PubMed] [Google Scholar]
  45. Robbins RN, & Bryan A (2004). Relationships between future orientation, impulsive sensation seeking, and risk behavior among adjudicated adolescents. Journal of adolescent research, 19(4), 428–445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Steinberg L (2005). Cognitive and affective development in adolescence. Trends in cognitive sciences, 9(2), 69–74. [DOI] [PubMed] [Google Scholar]
  47. Steinberg L (2008). A social neuroscience perspective on adolescent risk-taking. Developmental review, 28(1), 78–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Steinberg L, Graham S, O’Brien L, Woolard J, Cauffman E, & Banich M (2009). Age differences in future orientation and delay discounting. Child development, 80(1), 28–44. [DOI] [PubMed] [Google Scholar]
  49. Stice E, Presnell K, & Bearman SK (2001). Relation of early menarche to depression, eating disorders, substance abuse, and comorbid psychopathology among adolescent girls. Developmental psychology, 37(5), 608. [DOI] [PubMed] [Google Scholar]
  50. Susman EJ, & Dorn LD (2009). Puberty Handbook of adolescent psychology. [Google Scholar]
  51. Tschann JM, Adler NE, Irwin CE, Millstein SG, Turner RA, & Kegeles SM (1994). Initiation of substance use in early adolescence: the roles of pubertal timing and emotional distress. Health Psychology, 13(4), 326. [DOI] [PubMed] [Google Scholar]
  52. Westling E, Andrews JA, Hampson SE, & Peterson M (2008). Pubertal timing and substance use: The effects of gender, parental monitoring and deviant peers. Journal of Adolescent Health, 42(6), 555–563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wiesner M, & Ittel A (2002). Relations of pubertal timing and depressive symptoms to substance use in early adolescence. The Journal of Early Adolescence, 22(1), 5–23. [Google Scholar]
  54. Wills TA, Sandy JM, & Yaeger AM (2001). Time perspective and early-onset substance use: A model based on stress–coping theory. Psychology of addictive behaviors, 15(2), 118. [DOI] [PubMed] [Google Scholar]
  55. Wilson DM, Killen JD, Hayward C, Robinson TN, Hammer LD, Kraemer HC, … & Taylor CB (1994). Timing and rate of sexual maturation and the onset of cigarette and alcohol use among teenage girls. Archives of pediatrics & adolescent medicine, 148(8), 789–795. [DOI] [PubMed] [Google Scholar]

RESOURCES