The Dysregulation Profile and Substance Use: Investigating Their Co-Variation From Adolescence to Emerging Adulthood

Abstract

Children and youth with the Dysregulation Profile (DP) experience marked difficulties regulating cognition, affect, and behavior. Although these impairments are known to have lasting effects into adulthood, less is understood about how dysregulation changes during the transition from adolescence to emerging adulthood. Dysregulation has also been linked to increased risk for substance use disorder, yet its relationship with substance use during the adolescence-to-emerging-adulthood transition remains unclear. This study examined how dysregulation covaries with substance use from adolescence to emerging adulthood in 662 Canadian youth (Mage = 15.52) followed over 10 years. Multilevel modeling showed that dysregulation increased during adolescence and declined in emerging adulthood. Both between- and within-person substance use predicted dysregulation: higher average substance use was associated with higher overall dysregulation, and above-average substance use corresponded to higher dysregulation at the same time point. These findings clarify developmental changes in dysregulation and highlight its close association with substance use.

Keywords

dysregulation trajectories adolescence emerging adulthood substance use

According to principles of the Life Course Health Development Framework (Halfon & Forrest, 2018), self-regulation develops across the lifespan and life transitions mark important, sensitive periods at which trajectories can orient towards positive or negative developmental pathways. In Western contexts, the period between adolescence and emerging adulthood is considered a transitional phase given the marked changes in youths’ social, educational, and occupational contexts that offer more opportunities to explore their identity and develop independence with relatively fewer obligations and responsibilities compared to adulthood (Arnett, 2000). During this time, individuals enter new contexts with less supervision from parents or guardians, while navigating and negotiating essential developmental tasks that set their course into adulthood (Mehta et al., 2020). As such, this life transition may be a sensitive period for the positive or negative development of self-regulation.

Positive developmental trajectories of self-regulation assist in the acquisition of skills such as planning, self-monitoring, persisting on challenging tasks, and managing emotions (Robson et al., 2020), which are critical for accomplishing major developmental tasks such as completing education, securing employment, forming intimate relationships, and establishing independence (Arnett, 2000; Masten et al., 1999). Individuals who successfully navigate these tasks are generally better adjusted in adulthood than those who struggle to accomplish them (Pinquart & Pfeiffer, 2020). In this way, positive developmental pathways of self-regulation are characterized by the ability to meet the demands of an environment and thrive.

Conversely, negative developmental pathways related to self-regulation, referred to as dysregulation, are characterized by a persistent pattern of cognitive, emotional and behavioural difficulties that compromise an individual’s ability to adjust to their environments and thrive within them. Dysregulation characterized by extreme scores on the anxious/depressed, attention problems, and aggressive behaviour syndrome scales of the Child Behavioural Checklist (CBCL; Achenbach, 2011; Althoff, Verhulst, et al., 2010; Biederman et al., 2012) predict an increased risk for various mental disorders and functional impairment in late adolescence and adulthood (Althoff, Verhulst, et al., 2010; Biederman et al., 2012; Deutz et al., 2020; Haltigan et al., 2018; Holtmann, Buchmann, et al., 2011; Pettersson et al., 2018).

The cluster of syndromes associated with the Dysregulation Profile (DP) is similar to the general psychopathology factor (P-factor; Deutz et al., 2020; Haltigan et al., 2018). Both constructs predict psychopathological vulnerability, including earlier age of onset, disorder persistence and severity, and risk of comorbid mental disorders (Caspi et al., 2020; Deutz et al., 2020), and adverse functional outcomes, such as suicidal ideation, self-harm, risk taking, psychosocial functioning, and academic underachievement (Deutz et al., 2020; Haltigan et al., 2018). Despite their similarities, the DP comprises of fewer mental health symptoms compared to the P-factor, and is a practical clinical tool for assessment, early detection, and intervention (Deutz et al., 2020). Moreover, the inclusion of attentional problems highlights the importance of behavioural inhibition in psychopathology which is fundamental for affective, cognitive, and behavioural regulation (Barkley, 1997; Beauchaine & Cicchetti, 2019; Hofmann et al., 2012; Robson et al., 2020). The transition from adolescence to emerging adulthood introduces youth to new in occupational, academic, and social contexts that can amplify attentional difficulties; attentional problems associated with the DP may be more salient than the P-factor in predicting adjustment during this transitional developmental period. For example, Ayer et al. (2009) demonstrated that even after accounting for the P-factor, the DP explained variance remaining in high-risk behaviour, such as suicidality and self-harm, and adaptive functioning in social, academic, and extra-curricular activities. Given the heightened demands for self-regulation these activities place on adolescents in increasingly unsupervised environments, the inclusion of attentional problems may be especially sensitive to these functional outcomes.

Although the risk for psychopathology in adolescence and adulthood associated with childhood DP is well-documented (Bellani et al., 2012), more research is needed to describe how dysregulation changes from adolescence into emerging adulthood. Describing the trajectory of DP from adolescence into emerging adulthood is critical for understanding its underlying developmental process and observing periods that are sensitive to change as these may be opportunities to reorient high-risk youth towards positive developmental pathways for self-regulation through interventions. To address this gap in the literature, the current study examines the trajectory of the DP from early adolescence into emerging adulthood. As young adults with the DP have been shown to be more at risk for substance use disorder (Holtmann, Buchmann, et al., 2011), we also examine the longitudinal co-varying between substance use and DP across the transition from adolescence to emerging adulthood.

The Developmental Course of the Dysregulation Profile

The developmental course of the DP across childhood into late adolescence suggests that it follows a quadratic trajectory, increasing from childhood into early adolescence and then declining (Deutz, Vossen, et al., 2018). This trajectory likely corresponds to the maturation of the brain, and its impact on behavioural, emotional, and cognitive development during adolescence. For example, enhanced white matter connectivity between the prefrontal and limbic cortex, and corresponding improvements of inhibitory control on subcortical activity, can contribute to the development of internal self-regulation (Beauchaine & Cicchetti, 2019). Although normative neuromaturational trends may underlie increases in behavioural, emotional, and cognitive regulation from childhood to adolescence, relatively less is known about how patterns of dysregulation, particularly among adolescents with the DP, unfold into emerging adulthood.

The Dysregulation Profile and Substance Use

A confluence of social, biological, and cultural factors also makes emerging adulthood a period associated with the emergence of risky behaviours including substance use and dependency (Schulenberg & Maggs, 2002; Sussman & Arnett, 2014). While youth entering emerging adulthood are tasked with navigating increasing environmental demands in the context of less external monitoring, dysregulated youth entering this stage of life may be at risk for using substances maladaptively. Given the critical importance of the developmental tasks of emerging adulthood to an individuals’ current and future well-being and health, more research is needed to describe how dysregulation unfolds from adolescence into emerging adulthood and how substance use is associated to its trajectory.

Although not all adolescents who use substances develop a disorder in adulthood (Gray & Squeglia, 2018; Swendsen et al., 2012), adolescents who are clinically dysregulated (i.e., meet the criteria for DP) are most vulnerable to developing substance use disorder (Althoff, Verhulst, et al., 2010; Biederman et al., 2012; Holtmann, Buchmann, et al., 2011; Thompson et al., 2011). The co-occurrence between dysregulation and substance use could be explained by baseline negative affect (Baker et al., 2004; Robson et al., 2020). According to the goal-directed choice under negative affect model (Baker et al., 2004), individuals’ initial value of a drug depends on a variety of factors, including their baseline affect. For those who experience more negative affect, such as adolescents with dysregulation, substances may drive goal-directed substance seeking due to their powerful effect on alleviating negative affective states. The ability of substances to alleviate these states raises the anticipated value of the substances, thereby motivating the individual towards subsequent substance use (Baker et al., 2004; Hogarth, 2020) and reinforcing the reliance on substance use behaviours as a form of emotion-regulation strategy. Therefore, dysregulated adolescents may be at risk of developing a reliance on substances to alleviate their baseline negative affect.

Ultimately, substances may be effective at alleviating negative affect in the short-term, but the long-term effects could lead to more dysregulation as negative affect prevents cognitive resources from selecting other self-regulation strategies. Considering the socially normative nature of certain substance use behaviours in early adulthood (e.g., binge drinking at parties or celebrations), motivations such as enhancing positive emotions, bonding with others, or conforming with peers can also become salient reinforcers of substance use (Robillard et al., 2022), escalating the risk of developing substance-use-related disorders.

The current study examines relationship between dysregulation and substance use by first describing how the DP changes across adolescence to emerging adulthood, followed by examining its association with youths’ pattern of substance use during this transitional period. To extend and confirm previous research among adolescent samples (e.g., Deutz, Vossen, et al., 2018), it was predicted that the trajectory of dysregulation from adolescence into emerging adulthood would increase, followed by a decline into emerging adulthood. Given previous research on the relationship between self-regulation and substance use in adolescent samples (e.g., Wills & Aineet, 2008; Wills & Stoolmiller, 2002), we explored whether the developmental trajectory of DP covaried with patterns of substance use, such that higher-than-average levels of substance use would predict higher dysregulation.

Methods

Sample

The Victoria Healthy Youth Survey (V-HYS) is a longitudinal dataset that collected data six times, biennially, between 2003 and 2013 in the city of Victoria, British Columbia, Canada. Using a random sample of 9,500 private telephone listings, 1,036 adolescents and their parents or guardians were eligible to participate in 2003 (Leadbeater et al., 2012). A total of 662 adolescents (320 males) consented to participate in the study at baseline. Adolescents at baseline were between 12 and 19 (M = 15.52, SD = 1.93).

The demographics of the sample at baseline consisted of 85% Caucasians, 4% Asians, 4% multiracial, and 3% Aboriginals. The remaining 4% belonged to other ethnic groups (e.g., Hispanic, Black, or other). Ethnicity, parental levels of education, and living arrangements of the sample were representative of the population from which the sample was drawn (Albrecht et al., 2007).At the final measurement occasion in 2013, 71.6% of participants remained in the study and were between 22 and 29 years old (M = 26.22, SD = 1.93).

Procedure

The V-HYS was administered at each time point by a trained interviewer at the participant’s home or another location that afforded privacy. Informed consent was obtained from participants and parents or guardians at each collection time. The survey at each collection point consisted of a 2-part questionnaire; in part 1, trained interviewed recorded participants answers to questions on their socio-demographics, family, peers and school environment, and mental health which included responses to questions used to derive their DP scores. In part 2, participants responded to survey questions on sensitive issues, which included responses to questions on substance use, on their own to ensure privacy. Participants were compensated with a $35 honorarium at each collection time.

Heavy Episodic Drinking (HED)

HED was assessed in part 2 of the questionnaire at each collection time. Participants were asked how often they have five or more drinks on one occasion in the past year: 0 = never, 1 = a few times a year, 2 = a few times a month, 3 = once a week, and 4 = more than once a week. All participants were provided with the definition of a standard drink to standardized responses (see Evans-Polce et al., 2015).

Cannabis Use

Cannabis use was also assessed in part 2 of the questionnaire at each collection time. Participants were asked, “How often did you use marijuana in the past 12 months?” Responses were coded on a five-point scale: 0 = never, 1 = a few times a year, 2 = a few times a month, 3 = once a week, 4 = more than once a week.

Dysregulation

Previously, the DP has been measured using three syndrome scales from the parent-rated Child Behaviour Checklist (CBCL; Achenbach, 2011): aggression, anxious/depressed and attention problems (Althoff, Verhulst, et al., 2010; Ayer et al., 2009). Since these seminal operationalizations, other validated tools have measured the DP such as the Youth Self-Report (Deutz et al., 2020; Haltigan et al., 2018; Rescorla et al., 2019), the Teacher’s Report Form (Rescorla et al., 2019), and the Strengths and Difficulties questionnaire (Deutz, Shi, et al., 2018; Holtmann, Becker, et al., 2011). In the current study, the Brief Child and Family Phone Interview (BCFPI; Cunningham et al., 2009) was administered to each participant in part 1 of the questionnaire and used to assess mental health symptoms at each collection point.

Unlike the CBCL, the BCFPI was developed as a telephone screening tool and outcome measure to improve the surveillance of mental health symptoms in children and youth, and improve accessibility to mental health diagnosis (Cunningham et al., 2009). Items in the BCFPI were constructed to assess six subscales of mental health symptoms: attention deficit/hyperactivity disorder, oppositional defiance disorder, conduct disorder, separation anxiety, generalized anxiety, and major depression. Validity of the BCFPI comes from findings by Boyle et al. (2009) who demonstrated that the BCFPI accurately identified these psychiatric disorders in outpatient children and youth, and by Cunningham et al. (2009) who found comparable sensitivity and specificity in community samples. When compared to the comprehensive assessment delivered by the CBCL, the BCFPI performed equally well according to findings from Andersson et al. (2020). Therefore, given the correspondence between the subscales of the BCFPI and the syndrome scales of the DP, the comparable validity of the BCFPI to the CBCL, and its ability to accurately identify symptoms on the three dimensions of the DP, we constructed our measure of DP using subscales from the BCFPI.

The aggression syndrome scale was created by adding together sum scores on conduct problems and oppositional defiance (α₂₀₀₃ = .76). Conduct problem symptoms were assessed with 7 items and the conduct problems score was created by adding together the score on each item and then dividing the total score by 7. Oppositional defiance (ODD) was assessed with 6 items and similarly, the total score was averaged for each participant. The depressive and anxious syndrome scale was created using 18 items on depressive and anxious symptoms from the BCFPI and were combined into a total score (α₂₀₀₃ = .81). Finally, attention problems were assessed using 6 items and a composite score was created (α₂₀₀₃ = .66; see Table 1 for alpha values at each wave).

Table 1.

Demographics, Dysregulation, and Substance Use of Participants From Adolescence to Emerging Adulthood

Characteristics	T1 (n = 662)	T2 (n = 573)	T3 (n = 538)	T4 (n = 456)	T5 (n = 460)	T6 (n = 478)
Age, mean (SD)	15.52 (1.93)	17.59 (1.93)	19.53 (1.98)	22.39 (2.00)	24.19 (1.96)	26.22 (1.94)
Age, range	12-19	14-21	16-23	19-26	20-27	22-29
DP score, mean (SD)	150.00 (23.90)	152.30 (22.46)	155.03 (24.99)	149.53 (24.05)	144.07 (24.51)	142.23 (24.45)
AD T-score, mean (SD)	50.00 (10.00)	51.96 (10.56)	53.00 (10.63)	51.09 (10.80)	49.76 (11.14)	49.35 (11.53)
AD alpha	.81	.84	.85	.85	.87	.89
AGG T-score, mean (SD)	50.00 (10.00)	50.31 (7.46)	52.09 (9.98)	49.99 (8.80)	47.30 (8.75)	46.43 (8.27)
AGG alpha	.76	.75	.43	.44	.48	.55
AP T-score, mean (SD)	50.00 (10.00)	50.09 (10.27)	49.94 (10.23)	48.47 (9.90)	47.01 (9.99)	46.41 (9.87)
AP alpha	.66	.71	.71	.71	.74	.73
Cannabis use, mdn (IQR)	1.00 (1.00)	2.00 (2.00)	2.00 (3.00)	2.00 (4.00)	2.00 (3.00)	2.00 (3.00)
Heavy episodic drinking, mdn (IQR)	0.00 (1.00)	1.00 (2.00)	1.00 (2.00)	1.00 (2.00)	1.00 (1.00)	1.00 (1.00)
Time in study (centered), mean (sd)	−4.00 (0.00)	−1.93 (.10)	0.04 (.21)	2.83 (.24)	4.65 (.22)	6.72 (.23)

Note. AD = anxious/depressed syndrome scale T-score, AGG = aggression problems syndrome scale T-score, AP = attention problems syndrome scale T-score, DP = Dysregulation profile, T1 = time 1 or wave 1 in 2003, T2 = time 2 or wave 2 in 2005, T3 = time 3 or wave 3 in 2007, T4 = time 4 or wave 4 in 2009, T5 = time 5 or wave 5 in 2011, T6 = time 6 or wave 6 in 2013. The values of each syndrome scale after T1 are relative to the average and SD of the respective syndrome scale at T1. Cannabis use and heavy episodic drinking were assessed on a five-point scale: 0 = never, 1 = a few times a year, 2 = a few times a month, 3 = once a week, 4 = more than once a week; Time in study was centered 4 years into the study.

The raw scores for each of the syndrome scales were then T-scored. T-score for each participant on each syndrome scale was created relative to the mean score at T1 to ensure that scores across the waves represented change from baseline, rather than change relative to the mean at each wave. T-scores on each syndrome scale were then added together to represent the DP at each wave.

Analytic Approach

A series of multilevel models were tested to describe the developmental trajectory of the DP across adolescence to emerging adulthood, and assess its association with substance use. The analysis was performed in RStudio R version 4.3.1. Prior to analyses, multivariate analyses revealed no significant multivariate outliers, although DP scores at each wave were positively skewed and the variance of DP scores across time points was not homogeneous. Nonetheless, since multilevel models are robust to violations of homogeneity and normality and as full information maximum likelihood procedures were used to estimate the parameters, the analysis proceeded as planned (Hoffman, 2015).

Transition-linked models of development suggest that the psychosocial context of emerging adulthood could abruptly change the course of DP and so a piecewise model was compared to a quadratic model to first determine the best fitting non-linear trajectory of DP. As opposed to testing two independent slopes in the piecewise model, we tested a slope-deviation slope model so that we could estimate whether the rate of change after controlling for the first slope was significant (Hoffman, 2015).

Time was parameterized as time in study (in years) and centered relative to the mid-point in the study (4 years since T1 in 2003; T3 in 2007) when the majority of participants were bridging the transition from adolescence to emerging adulthood (i.e., 16 to 23 years). Therefore, at T1 in 2003, the average time in study across participants was −4.00 years (sd = 0.00 years), whereas at T3, the average time in study across participants was .04 years (sd = .21 years; see Table 1). In all models, time was standardized prior to model estimation. Random effects were tested for each of the level-one fixed effect and the Nelder Mead optimizer was used to estimate random effects. After finding the best fitting shape for the DP trajectory, the fixed effects were adjusted for age at baseline to adjust for sample age heterogeneity.

A covariation model between substance use measured at each collection point and DP scores was then added to the model that best described the DP trajectory. Between person differences in substance use were separated from within-person effects by adding person-average substance use as predictors of each level one fixed effect (Hoffman, 2015). Unlike average HED, zero average cannabis use was not observed in this data, therefore, between person cannabis use was centered at one instead of zero. Separate models were tested for cannabis use and HED. Between person predictors were standardized and within person predictors were centered within person prior to standardizing. Materials related to this study can be found at: https://osf.io/rswyx/overview.

Results

Descriptive statistics are presented in Table 1. At baseline, the mean DP score was 150 (SD = 23.90). From baseline to T3, mean DP scores increased (mean_T3 = 155.03, SD = 24.99) and subsequently, declined from T3 to T6 (mean_T6 = 142.23, SD = 24.25). Little’s MCAR test found that the data in Table 1 were not missing completely at random (χ²(36) = 461.12, p < .001); compared to participants who never went missing, participants whose DP scores went missing at any wave were significantly more likely to be male, χ²(1) = 29.53, p < .001. Furthermore, participants whose DP scores went missing at any wave also scored significantly higher baseline scores on the attention problems scale, t(1241.5) = 2.13, p = .03, and aggressive behaviours scale, t(1176.8) = 2.04, p = .04. Missing data were handled using Full Information Maximum Likelihood (FIML) estimation in all subsequent analyses.

Scores on the anxious/depressed and aggressive behaviors syndrome scales increased from baseline to T3, and subsequently declined until T6, whereas attention problem scores remained stable until T3 and then declined until T6 (see Table 1). Cannabis use also increased from baseline (mdn_T1 = 1.00, IQR = 1.00) to T3 (mdn_T3 = 2.00, IQR = 3.00). Scores remained stable from T3 to T6 (mdn_T6 = 2.00, IQR = 3.00). Conversely, HED maintained a low and stable median across time points (see Table 1).

Trajectories of Dysregulation Across Adolescence to Emerging Adulthood

Table 2 provides the standardized parameter estimates for the models of change in DP scores across the transition from adolescence to emerging adulthood. As shown, the grand-mean of DP scores was significant in the unconditional model, β₀₀ = 149.59, p < .001, and there was significant variance around it, τ₀₀ = 326.6, 95% CI (16.94, 19.30), p < .001. This baseline model explained 54.3% of the total variance in DP. In the quadratic model, random effects for the quadratic parameter were removed due to model non-convergence whereas random effects were estimated for each fixed effect in the piecewise model. According to the AIC and BIC values, the piecewise model (i.e., model 5) better represented the change in DP across the transition from adolescence to emerging adulthood than the quadratic model (model 7; see Table 2). Furthermore, whereas the quadratic model explained an additional 11.9% compared to the empty means, random intercept model (i.e., model 1), the piecewise model explained an additional 16.2% of variance. The linear parameters of time in the piecewise model (i.e., model 5) were significant and indicated an increasing linear trajectory from baseline to T3 (i.e., the inflection point; β₁₀ = 4.01, SE = .83, p < .001), followed by a significant, declining trajectory from T4 to T6 (β₂₀ = −13.09, SE = 1.34, p < .001; see Table 2).

Table 2.

Characterizing the Shape of Dysregulation Trajectories From Adolescence to Emerging Adulthood

	Estimate (β)	SE	p	Lower 95% CI	Upper 95% CI	Parameters	Deviance	AIC	BIC	Conditional R²	Marginal R²
Model 1 – Empty means, random intercept						3	27946.1	27952.1	27970.3	54.3%
Intercept ( $γ_{00})$	149.59	.77	<.001	148.08	151.11
Intercept RE ( $τ_{00}$ )	326.6			16.94	19.30
Residual ( $σ_{e}^{2})$	274.6			16.12	17.04
Model 2 – Fixed linear slope, random intercept						4	27318.3	27326.3	27350.4	56.6%	1.5%
Intercept ( $γ_{00})$	149.32	.77	<.001	147.80	150.84
TIS ( $γ_{10})$	−3.01	.30	<.001	−3.60	−2.41
Intercept RE ( $τ_{00}$ )	329.5			17.02	19.38
Residual ( $σ_{e}^{2})$	259.3			15.66	16.56
Model 3 – Random linear slope						5	27192.6	27204.6	27240.8	64.7%	1.3%
Intercept ( $γ_{00})$	149.47	.78	<.001	147.94	151.00
TIS ( $γ_{10})$	−2.81	.39	<.001	−3.58	−2.03
TIS RE ( $τ_{11})$	42.75			−.01	.24
Intercept RE ( $τ_{00}$ )	336.65			17.21	19.58
Residual ( $σ_{e}^{2})$	211.69			14.94	15.02
Model 4 – Deviation slope						6	27072.7	27086.7	27128.9	66.7%	2.9%
Intercept ( $γ_{00})$	155.12	.94	<.001	153.28	156.95
TIS ( $γ_{10})$	3.85	.73	<.001	2.41	5.28
TIS-4 ( $γ_{20})$	−12.72	1.18	<.001	−15.03	−10.40
TIS RE ( $τ_{11})$	44.87			−.02	.22
Intercept RE ( $τ_{00}$ )	337.77			17.24	19.59
Residual ( $σ_{e}^{2})$	200.12			13.70	14.60
Model 5 – Random deviation slope						7	27023.7	27043.7	27104.1	70.5%	3.0%
Intercept ( $γ_{00})$	155.30	1.06	<.001	153.22	157.38
TIS ( $γ_{10})$	4.01	.83	<.001	2.39	5.64
TIS-4 ( $γ_{20})$	−13.09	1.34	<.001	−15.72	−10.47
TIS-4 RE ( $τ_{22}$ )	308.3			.-.73	−.53
TIS RE ( $τ_{11})$	163.7			.44	.63
Intercept RE ( $τ_{00}$ )	502.5			20.69	24.22
Residual ( $σ_{e}^{2})$	176.6			12.81	13.80
Model 6 – Random deviation slope, adjusted for age at baseline						10	26992.7	27018.7	27097.2	70.6%	3.7%
Intercept ( $γ_{00})$	155.29	1.06	<.001	153.21	157.37
FAGE ( $γ_{01})$	−.58	1.06	.58	−2.65	1.49
TIS ( $γ_{10})$	4.01	.82	<.001	2.41	5.61
FAGE ( $γ_{11})$	−3.66	.81	<.001	−5.25	−2.07
TIS-4 ( $γ_{20})$	−13.07	1.32	<.001	−15.67	−10.48
FAGE ( $γ_{21})$	4.52	1.32	<.001	1.94	7.10
TIS-4 RE ( $τ_{22}$ )	288.4			−.75	−.54
TIS RE ( $τ_{11})$	150.3			.45	.65
Intercept RE ( $τ_{00}$ )	503.6			20.68	24.21
Residual ( $σ_{e}^{2})$	176.7			12.81	13.80
Model 7 – Quadratic, random linear slope						6	27100.7	27114.7	27157.0	66.2%	2.6%
Intercept ( $γ_{00})$	149.46	.78	<.001	147.94	150.99
TIS-4 ( $γ_{10})$	−.97	.44	<.001	−1.82	−.11
(TIS-4)² ( $γ_{20})$	−3.27	.34	.03	−3.93	−2.61
TIS-4 RE ( $τ_{11})$	44.39			−.02	.23
Intercept RE ( $τ_{00}$ )	336.75			17.22	19.58
Residual ( $σ_{e}^{2})$	202.19			13.78	14.68

Note. Predictors were standardized to estimate standardized coefficients. FAGE = age at baseline, centered at 12; (TIS-4) = time in study centered 4 years into the study; (TIS-4)² = time in study squared, centered 4 years into the study. Conditional R² represents the proportion of variance explained by the fixed and random effects of the corresponding model, whereas marginal R² represents the proportion of variance explained by the fixed effects only (Nakagawa et al., 2017). A model with a random quadratic effect did not converge. AIC and BIC values suggest that the spline model with random effects fit the data better than the quadratic model. The spline model with FAGE as a time invariant predictor of intercept and slopes significantly improved model fit, χ²(7) = 953.4, p < .001, compared to model 1. Age at baseline significantly modified the spline trajectory such that older participants were characterized by slower increasing DP scores from baseline to T3, and slower decreasing slopes from T3 to T6.

Fit statistics show that the piecewise model adjusted for age at baseline (model 6) fit the data better than the piecewise, random deviation slope model (model 5; see Table 2). Furthermore, the model adjusted for baseline age explained 16.3% more variance in DP scores compared to the baseline model. DP scores in older participants increased and decreased at a slower rate compared to younger participants, whose rates of change were steeper before and after T3 (see Figure 1). This final piecewise model fit the data significantly better than the empty means, random intercept model, χ²(7) = 953.4, p < .001 (see Table 2).

Figure 1.

Dysregulation Profile Trajectories Between the Youngest and Oldest Participants. Note. Plotted trajectories represent conditional means. Dysregulation Profile scores followed a discontinuous trajectory such that scores increased then decreased. Age at baseline significantly modified the DP trajectories such that older participants started with higher DP scores at baseline than younger participants; however, younger participants have significantly steeper rates of change prior to T3 and steeper decreasing rates of change after T3, compared to older participants

Dysregulation and Covariation With Substance Use

Cannabis Use

Given the significant effect of age in spline model previously tested, age was included in the covariation models. Age was also entered as a cross-level interaction with substance use to test whether the relationship between DP scores and substance use depended on age. Table 3 presents the results from the covariation model with cannabis use and HED. In the cannabis use model, between person differences in average cannabis use predicted significant differences in DP scores at intercept, β₀₂ = 5.31, SE = .75, p < .001, such that participants who reported higher mean-level use were predicted to have higher baseline DP scores (see Table 3 and Figure 2). The parameter estimate for within-person cannabis use was also significant, β₃₀ = 2.12, SE = .72 p < .001, such that, on occasions on which participant reported higher cannabis use than their average, their DP scores were higher by 2.12 points (see Figure 2). Between-person differences in cannabis use did not explain random effects around either slope. Therefore, the trajectories of individuals varied systematically based on their average level of cannabis use (see Figure 2). Furthermore, the cross-level interaction between age and within-person cannabis use was insignificant, indicating that the relationship between cannabis use and DP did not depend on age. The cannabis spline model fit the data significantly better than the piecewise, random deviation slope model (model 6), χ²(2) = 2240.6, p < .001, and accounted for 15.7% more variance in DP scores compared to the baseline model (i.e., model 1, see Tables 2 and 3).

Table 3.

Characterizing Fluctuations and Changes in Dysregulation Related to Substance Use

	β	SE	Lower 95% CI	Upper 95% CI	Para-meters	Deviance	AIC	BIC	Conditional R²	Marginal R²
Model 1 – Within and between person cannabis use					12	24752.1	24782.1	24871.4	70%	8.4%
Intercept ( $γ_{00})$	154.26**	1.06	152.19	156.34
FAGE ( $γ_{01})$	−1.19	1.05	−3.25	.86
BP cannabis ( $γ_{02})$	5.31**	.75	3.84	6.78
TIS ( $γ_{10})$	2.47**	.84	.82	4.14
FAGE ( $γ_{11})$	−2.99**	2.91	−4.59	−1.38
(TIS-4) ( $γ_{20})$	−18.97**	.33	−24.69	−13.24
FAGE ( $γ_{21})$	2.30**	.82	.89	3.70
WP cannabis ( $γ_{30})$	2.12**	.72	1.48	2.77
(TIS-4) RE ( $τ_{22}$ )	264.1
TIS RE ( $τ_{11}$ )	122.3
Intercept RE ( $τ_{00}$ )	460.0
Residual ( $σ_{e}^{2})$	181.2
Model 2 – Within and between person heavy episodic drinking					12	26900.1	26930.1	27020.6	66.9%	7.6%
Intercept ( $γ_{00})$	154.22**	.94	152.31	155.91
FAGE ( $γ_{01})$	−1.55	.93	−3.46	.26
BP heavy episodic drinking ( $γ_{02})$	5.00**	.77	3.51	6.53
TIS ( $γ_{10})$	2.45*	.80	.75	4.06
FAGE ( $γ_{11})$	−3.17**	.73	−4.63	−1.70
(TIS-4) ( $γ_{20})$	−11.09**	1.25	−13.52	−8.45
FAGE ( $γ_{21})$	4.48**	1.17	2.15	7.01
WP heavy episodic drinking ( $γ_{30})$	1.31**	.33	.65	1.96
(TIS-4) RE ( $τ_{22}$ )	229.52		.23	.30
TIS RE ( $τ_{11}$ )	42.11		.03	.27
Intercept RE ( $τ_{00}$ )	313.20		16.49	18.84
Residual ( $σ_{e}^{2})$	198.10		13.65	14.48

Note. Predictors were standardized to estimate standardized coefficients. FAGE = age at baseline, centered at 12; TIS = time in study, TIS-4 = time in study centered 4 years into the study; BP = between person; WP = person mean centered; Between person cannabis use was centered at 1. Conditional R² represents the proportion of variance explained by the fixed and random effects of the corresponding model, whereas marginal R² represents the proportion of variance explained by the fixed effects only (Nakagawa et al., 2017). Between person cannabis use and heavy episodic drinking modified the intercepts, predicting higher average DP trajectories. Cannabis use and heavy episodic drinking fluctuated with dysregulation. On occasions when substance use was higher than a participant’s average use, dysregulation was also higher. According to AIC and BIC values, the cannabis covariation model significantly improved model fit compared to the piecewise, deviation model (model 6).

*p < .05, **p < .001.

Figure 2.

Predicted Dysregulation Profile Trajectories for High and Low Average Cannabis Users, Adjusted for Within-Person Variation. Note. Plotted trajectories represent conditional means. As demonstrated by the solid lines, higher-average cannabis use predicted higher dysregulation profile trajectories over time. As demonstrated by the dashed lines, on occasions when participants reported higher than their average level of cannabis use, dysregulation profile scores were also higher than the scores predicted by average cannabis use. Given that age at baseline did not modify the effect of within person cannabis use, the figure displays the predicted effect of between and within person cannabis use on dysregulation profile trajectories and scores for 12-year-old participants at baseline. Heavy episodic drinking demonstrated a similar pattern of covariation to cannabis use

Heavy Episodic Drinking

Finally, a model with covariation between DP scores and within-person HED was conducted to test the relationship between HED and DP scores. Like the cannabis model, between person differences in average HED were related to significantly higher DP scores at baseline, γ₀₂ = 5.00, SE = .77, p < .001 (see Figure 2). Between-person differences did not explain random effects around either slope. Therefore, the trajectories of individuals varied systematically based on their average level of HED (see Figure 2). Furthermore, the fixed effect for within-person HED was significant, γ₃₀ = 1.31, SE = .33 p < .001, such that DP scores were predicted to be higher on occasions when participants reported higher heavy episodic drinking than their average. Although the HED model fit the data significantly better than the random deviation slope model adjusted for age at baseline, χ²(2) = 92.6, p < .001, the AIC and BIC values suggested that it did not improved model fit compared to the random deviation slope, adjusted for age at baseline (model 6; see Table 3).

Discussion

The aims of this research were to describe the trajectory of the DP from adolescence to emerging adulthood, and explore how substance use covaried with levels of dysregulation across this transitional period. Findings revealed non-linear pattern of dysregulation characterized by increasing DP scores in adolescence and then decreasing into emerging adulthood. Levels of dysregulation were influenced by both within- and between-person differences in substance use. Specifically, youth characterized by higher average substance use demonstrated higher average dysregulation trajectories over time compared to youth with lower substance use. Within-person associations were also observed, such that higher than their average substance use was associated with higher dysregulation scores at a given wave.

Change in Dysregulation From Adolescence to Emerging Adulthood

Dysregulation followed a non-linear trajectory in which scores increased from early to late adolescence and then declined during emerging adulthood, suggesting that the transition to emerging adulthood might be an important turning point when dysregulation declines. This pattern of dysregulation corroborates previous research among adolescent samples using the CBCL (e.g., Deutz, Vossen, et al., 2018), which showed that dysregulation followed a quadratic trend, although our findings suggest that the inflection point at which dysregulation declines occurs during the transition to emerging adulthood. Compared to the quadratic model, the piecewise model explained a substantial portion of the variance in the outcome (i.e., 70.6% of variance compared to 66.2% of variance explained by the quadratic model), which aligns with our expectation that DP follows a non-linear trajectory across the period from adolescence to emerging adulthood.

The shape of the trajectory is consistent with neurodevelopmental evidence that the maturation of the cortical and limbic regions continues well into early adulthood and therefore, the development of regulatory abilities also extends into this period (Beauchaine & Cicchetti, 2019; Gogtay et al., 2004; Stephanou et al., 2016). Moreover, DP scores among younger youth increased more rapidly compared to older participants. Although non-linear changes were still observed for older participants, the rate of these changes were not as dramatic compared to younger participants due to differences potentially due to neuromaturational differences.

A strength in our investigation was that, although self-regulation is a thoroughly researched human trait (Billore et al., 2023), our measure of dysregulation emphasized the importance of attention, which is a relatively novel conceptualization of psychopathology (Deutz et al., 2020). It has been previously demonstrated that the attentional aspect of DP is uniquely related to educational outcomes and explains more variance in academic achievement than the general psychopathology factor (GP-factor; Deutz et al., 2020). This incremental validity of the DP over the GP-factor suggests that considering attention in the conceptualization of psychopathology is meaningful for contextualizing the effect of the DP on functional outcomes related in adolescence and emerging adulthood, such as educational and occupational attainment.

Dysregulation and Substance Use

Findings also demonstrated that patterns of dysregulation were associated with substance use. Specifically, higher average cannabis use or HED were associated to higher dysregulation trajectories. In the context of this between-person effect, substance use and dysregulation fluctuated together, such that substance use above an adolescents’ personal average use was associated to significantly higher DP scores at the same wave. More research is needed to assess the directionality and temporal sequencing between DP scores and substance use. It is possible that dysregulation predicts subsequent substance use. Given that negative affect is a characteristic of dysregulation, youth with self-regulation difficulties may be more likely to initiate substance use as a means of avoiding aversive emotions, reinforcing this behaviour as a form of emotion regulation strategy. According to Gross’ (2015) Extended Process Model of Emotion Regulation, the selection of emotion regulation strategies relies on one’s awareness and identification of emotions as well as understanding their attributable causes and contexts (Luminet & Zamariola, 2018). Therefore, persistent avoidance or rejection of negative emotional experiences can hinder the development of emotional knowledge, emotion regulation skills, and limit access to effective emotion regulation strategies. This may lead to inappropriate selection of regulation strategies through behaviours such as substance use, which often misalign with situational demands and personal values (Cole et al., 1994).

On the other hand, substance use may have a dysregulating effect. While substance use may offer immediate short-term relief, withdrawal can also paradoxically amplify the salience of negative affect, reinforcing the subsequent selection of substance use for further affect regulation (Baker et al., 2004). Ultimately, the cycle of alleviating negative affect with substances may contribute to fewer available strategies for self-regulation and diminished ability to manage substance use related urges, increasing the risk of substance use maintenance and dependency (Hogarth, 2020).

Beyond affect regulation accounts, the observed association between substance use and dysregulation may also reflect shared underlying vulnerabilities, consistent with common liability models. For example, deficits in executive functioning, shaped by genetic (Capsi et al., 2014), neurodevelopmental (Ford & Courtois, 2014), or early environmental influences (Jucksch et al., 2011; Wang et al., 2018), may contribute to dysregulation, substance use, or both among youth. From this perspective, the co-influential pathways between substance use and dysregulation appears to emerge from overlapping risk processes, underscoring the complexity of developmental pathways linking DP and substance use across adolescence and emerging adulthood.

Implications & Future Research

From a clinical and developmental perspective, the transition from adolescence to emerging adulthood represents a period of critical structural and contextual changes. As youth navigate transitions out of secondary school into postsecondary education or the workforce, many also experience shifts from child/youth to adult mental health services. Although dysregulation appears to decline at the group level in emerging adulthood, above average dysregulation and substance use fluctuate together. It is possible that these transitions may exacerbate dysregulation in a subset of youth due to reduced external structure, increased autonomy, and discontinuities in care. As emerging adults are not identified as a distinct population in the Canadian public health system, policy, funding, and service delivery lacks protocols to support the transition from child/youth to adult mental health systems (Murray & Knudson, 2023). This service gap may leave youths “aging out” of provided care without adequate support precisely when demands increase.

Our findings underscore the importance of a tiered prevention and intervention approach. Universal strategies, such as school-wide screening and social-emotional learning (SEL) interventions that strengthen emotion regulation and executive functioning. This may provide a preventative foundation, while informing selective or indicated interventions for youths exhibiting persistently elevated or worsening DPs, particularly during key developmental transition periods. In addition, greater attention to transition planning, continuity of care, and clinical service delivery for emerging adults as a distinct population within the Canadian context is warranted.

Future research also would benefit from examining shared genetic and socioenvironmental vulnerabilities underlying DP as well as employ intensive measurement designs (e.g., ecological momentary assessment) to capture real-time fluctuations in affect, regulation difficulties, and substance use urges. Such designs would allow for examination of reinforcing mechanisms and individual-level risk processes to identify optimal targets of intervention.

Limitations

Despite the strengths of this investigation, some limitations should be noted. First, the data analyzed was not missing at random; when comparing participants who dropped out of the study to those who remained until the final wave of assessment, males were significantly more likely to drop out compared to females, χ²(1) = 8.22, p = .004. Whereas 57.45% of the participants with missing scores were males, only 42.55% were females. Furthermore, participants with missing score had greater attentional problems compared to participants who remained in the study, t(323.01) = −2.11, p = .04, d = .19, 95% CI [-3.61, −.13]. Given that missing data was strongly related to being male, and having higher scores on attention problems, estimates in our models maybe biased towards females, and less dysregulated participants.

Missing males and participants with more attention problems from the data has important implications for the strength of the relationships we observed in this study. Although the factor structure of the DP is invariant across genders (Deutz et al., 2016), males seem more likely to meet the threshold for DP than females (Althoff, Rettew, et al., 2010; Jucksch et al., 2011). Furthermore, given that higher attentional problems are definitional of the DP, these characteristics of our missing participants may suggest that participants with higher DP scores went missing from our study. Despite these limitations, findings demonstrate a relationship between the dysregulation profile and substance use which is worthy of further investigation, potentially in clinical samples with a greater proportion of individuals with elevated DP scores.

Moreover, although the study’s hypotheses received support, our methodological approach does not address the directionality between substance use and dysregulation. This limitation reflects the importance of time and time scale to understanding the behavioural processes under investigation. Specifically, we measured substance use by asking participants to reflect on their behaviour over the past year. Given that substance use is a dynamic daily process, future research should consider using intensive measurement design to describe the association between dysregulation and substance use at the day-to-day level of its process.

In terms of generalizability, although the present sample was predominantly made up of White Canadian (85%) in the Pacific Northwest with data collected prior to the legalization of cannabis use (2003-2013), existing evidence suggests that similar developmental patterns have been observed across diverse populations. Studies conducted in diverse racial, ethnic, religious, political, and national contexts have identified similar mean DP scores (Rescorla et al., 2019), suggesting that the DP trends observed in this study may be generalized beyond this specific cohort. However, the legal and social context surrounding cannabis use has changed substantially since the period of data collection, and these shifts warrant caution in extending the findings to more recent cohorts.

Evidence examining youth cannabis use following legalization has yielded mixed results. A review conducted by Kourgiantakis et al. (2024) found an overall increase in cannabis use among 18–24-year-olds post-legalization. However, among youths aged 18 and under, the results were mixed with some studies reporting increases in use, especially among those who have used prior to legalization, and others indicating little to no change overall. As such, developmental trajectories may differ for adolescents growing up in post-legalization contexts. Future research should examine cannabis use in the context of DP in more diverse samples and explicitly assess the pathways through which youths obtain access to cannabis, including social and illicit sources, to better understand how evolving norms and availability shape use patterns.

Finally, our study used the self-report measures for assessing dysregulation and substance use behaviour. Therefore, socially desirable responding may have underestimated the severity of dysregulation and substance use. In efforts to curb this issue, questions on substance use were administered to participants without the presence of a research assistant.

Despite these limitations, our results contribute a description of dysregulation from adolescence to emerging adulthood, and its covariation with substance use. Our conceptualization of psychopathology offers evidence that substance use is associated with cognitive, behavioural, and emotional dimensions of dysregulation, adding a multidimensional perspective on the relationship between dysregulation and substance use.

Providing adolescents with higher-than-average dysregulation other means of self-regulation through intervention may help promote more adaptive means of coping. Although internal resources for cognitive, behavioural, and affect regulation are notably poorer for individuals with higher levels of dysregulation (Robson et al., 2020), fewer internal resources do not preclude the use of external resources to support positive developmental pathways for self-regulation. As such, interventions designed to provide external means for self-regulation may be especially useful. For instance, previous research has demonstrated the effectiveness of curriculum-based interventions, in which teacher’s receive professional training and implement classroom-based activities to foster self-regulation, for improving behavioural problems (Webster-Stratton et al., 2008) and academic achievement (Lemberger et al., 2015; Pears et al., 2014). Likewise, physical activity or exercise-based interventions have proven effective at promoting self-regulation in children and adolescents (Hillman et al., 2014; Lakes & Hoyt, 2004).

Given the common factors associated with dysregulation and substance use, interventions geared towards reducing substance use may be equally beneficial to promoting self-regulation skills in adolescence. Currently, school-based and family-based interventions demonstrate the best evidence for reducing cannabis and alcohol use (Das et al., 2016). Ultimately, multicomponent interventions that include strategies at different levels of the ecological system in which adolescents are embedded (i.e., family, peers, school, community, government) are likely to be most effective at reducing substance use initiation, improve self-regulation, and most importantly, the prevalence of psychopathologies in emerging adulthood.

Supplemental Material

Supplemental Material - The Dysregulation Profile and Substance Use: Investigating their Co-Variation from Adolescence to Emerging Adulthood

Supplemental Material for The Dysregulation Profile and Substance Use: Investigating their Co-Variation from Adolescence to Emerging Adulthood by Tara R. Cooper-Dubé, Alice Shen, and Paweena Sukhawathanakul in Emerging Adulthood

Footnotes

ORCID iD

Tara R. Cooper-Dubé

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Canadian Institutes of Health Research; 79917; 838-20000-075; 93533.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Tara R. Cooper-Dubé is a PhD student at the University of Victoria in the Lifespan Health and Development program in the department of Psychology. Tara’s research interests focus on individual differences in lifespan health related to environmental, psychological, and social life experiences. In her PhD, she is exploring opportunities across the lifespan to extend one’s healthspan and delay cognitive decline.

Alice Shen is a PhD student in the Clinical Psychology program at the University of Victoria. Her research interest involves biopsychosocial risk and protective factors underlying risky behaviours such as substance use and behavioural addictions, disordered eating, and non-suicidal self-injury. She is also interested in exploring how adult ADHD influences risky behaviours through factors such as emotion regulation, impulse control, and distress tolerance.

Dr. Paweena Sukhawathanakul is an assistant professor at the University of Victoria in the department of Psychology. Her research is centered on identifying factors that enhance or undermine the social and emotional development of children and adolescents. Because youth thrive in contexts that affirm their sense of purpose and identity, she is especially interested in understanding how we can best support the synergy between youths’ individual strengths and available opportunities in their environment.

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