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. Author manuscript; available in PMC: 2023 Nov 1.
Published in final edited form as: Psychol Addict Behav. 2022 Mar 3;36(7):762–774. doi: 10.1037/adb0000825

Longitudinal Associations between Impulsivity and Alcohol and Cannabis Use Frequency, Quantity, and Problems among Military Veterans

Nathan T Kearns 1, Rachel L Gunn 1, Angela K Stevens 1, Benjamin L Berey 1, Jane Metrik 2,1
PMCID: PMC9440156  NIHMSID: NIHMS1777622  PMID: 35238593

Abstract

Objective:

Impulsivity is an established etiological risk factor for alcohol- and cannabis-related outcomes. However, limited work has focused on longitudinal associations between multiple trait impulsivity facets and indices of alcohol and cannabis use among military veterans – a contextually-distinct population that evidences unique impulsive personality traits and substance use patterns.

Method:

A structural equation model (SEM) examined longitudinal associations between five UPPS-P impulsivity facets measured at baseline and six indices of alcohol and cannabis use (i.e., frequency, quantity, and problems) measured at one-year follow-up among 361 returning OEF/OIF/OND veterans.

Results:

Findings indicated baseline sensation seeking was significantly positively associated with one-year alcohol use frequency (β = .18); baseline negative urgency was positively association with alcohol use problems (β = .31); and baseline lack of perseverance (β = .25) and sensation seeking (β = .21) were positively associated with one-year cannabis use problems. None of the baseline impulsivity facets were associated with one-year alcohol use quantity, cannabis use frequency, or cannabis use quantity.

Conclusions:

Results provide preliminary support that impulsivity may play a unique role in understanding alcohol- and cannabis-related problems over time among veterans. Further, results suggest that specific impulsivity facets are prospectively associated with cannabis problems (i.e., lack of perseverance and sensation seeking) and alcohol problems (i.e., negative urgency). Findings reinforce the importance of differentially evaluating impulsivity-substance use associations within contextually-distinct populations (e.g., adolescent, veteran), and highlight potentially meaningful intervention targets among veterans. However, replication is needed with stronger temporal controls and more diverse veteran subsamples.

Keywords: impulsivity, alcohol, cannabis, UPPS, veterans

Introduction

Alcohol and cannabis are among the most frequently used psychoactive substances in the United States (Grant et al., 2017; Han et al., 2017). An expansive literature has linked consumption of alcohol and cannabis to a host of problematic outcomes (e.g., driving under the influence [DUI]; Hasin, 2018; Kearns et al., 2021; Schwartz & Beltz, 2018) and psychological comorbidities (e.g., depression; Bassir Nia et al., 2016; Castillo-Carniglia et al., 2019). Military veterans may be particularly vulnerable to negative outcomes of alcohol and cannabis use, with extant work indicating higher levels of problematic use than in the general population (Bonn-Miller et al., 2012; Larson et al., 2012; Metrik et al., 2018). Further, veterans evidence higher rates of psychophysiological comorbidities that are strongly associated with the initiation and maintenance of alcohol and cannabis use (e.g., posttraumatic stress, pain; Boden et al., 2013; Johnson et al., 2016; Martín-Sánchez et al., 2009) and poorer substance use treatment outcomes (Bonn-Miller et al., 2011; Harris et al., 2012; Kaysen et al., 2014). As such, the identification of prominent risk markers associated with alcohol and cannabis consumption and related negative outcomes among veterans is imperative for the development of targeted, population-specific prevention and intervention efforts.

Extant work implicates the broad construct of impulsivity as a robust etiological risk factor for alcohol and cannabis consumption, as well as the development of substance-related problematic behaviors (Berey et al., 2017; Coskunpinar et al., 2013; Stautz & Cooper, 2013; Wrege et al., 2014). One generally well-accepted framework for evaluation of impulsivity is the UPPS(-P) model – which was developed to integrate commonalities between competing conceptualization of ‘impulsivity’ as either observable behavior (e.g., Lee et al., 2019; Wrege et al., 2014) or, alternatively, as a personality trait(s), which, in turn, influences behavior (e.g., Stautz & Cooper, 2013). The UPPS Impulsive Behavior Scale (UPPS; Whiteside & Lynam, 2001, 2009) and subsequent revised forms/models (e.g., UPPS-P; Lynam et al., 2006) ultimately identified distinct personality facets associated with impulsive behaviors. Briefly, negative and positive urgency describes the tendency to act rashly or without thought in response to intense negative or positive emotional states, respectively; lack of perseverance is characterized by a reduction in the amount of attention and effort exerted on a given task; lack of premeditation is generally defined by swift action without consideration for potential negative consequences of a decision before acting; and sensation seeking is defined by both a tendency to pursue and a general openness to trying new and/or exciting experiences (Cyders et al., 2007; Lynam, Hoyle, et al., 2006)

From this model of impulsivity, an extensive literature has identified both general and population-specific (e.g., adolescents; see Stautz & Cooper, 2013 for review) relationships between varying impulsivity-like facets and indices of alcohol and cannabis use. For example, among adults, alcohol consumption (e.g., quantity) was most strongly associated with lack of perseverance, whereas alcohol-related problems were most strongly related to negative and positive urgency (see Coskunpinar et al., 2013 for review). Conversely, although a much smaller literature has evaluated the influences of UPPS-P impulsivity traits on cannabis-related indices among adults, negative urgency seems to play a stronger direct and indirect role in understanding cannabis-related problems (Kaiser et al., 2012; Um et al., 2019). However, despite a burgeoning literature evaluating associations between facets of impulsivity and indices of alcohol and cannabis use (Amlung et al., 2013; Coskunpinar et al., 2013; McCarty et al., 2017; Stevens et al., 2016, 2018), minimal longitudinal research has focused on understanding the differential role of multiple impulsivity facets on alcohol and cannabis use among military veterans.

Veterans may be unique in their expression of impulsivity traits – and, subsequently, the impact of those facets of impulsivity on indices of alcohol and cannabis use – for three keys reason. First, meta-analytic reviews (e.g., Coskunpinar et al., 2013) indicate that much of our understanding of impulsivity and alcohol use outcomes has been drawn from substantively younger, developmentally-distinct samples (e.g., undergraduates; Mage = 21.7; Arnett, 2000), which may manifest developmentally-specific expressions of impulsivity-related facets (e.g., lack of premeditation; King et al., 2012; Stamates & Lau-Barraco, 2017) relative to older veteran populations. Indeed, recent calls for research emphasize the need to better understand impulsivity in middle-to-older adulthood (Liu et al., 2020). Second, veterans evidence unique trait impulsivity characteristics relative to the general population. For example, individuals drawn to military service may exhibit higher levels of sensation seeking (Breivik et al., 2019; Montes & Weatherly, 2014), as well as higher trait levels of other personality constructs (e.g., self-discipline, Kämpfe & Mitte, 2009; Mullins-Sweatt et al., 2013; Olsen et al., 2014), that are negatively associated with several impulsivity facets – traits that may be developed or bolstered by intensive military education and training (Buller, 2012; Gucciardi et al., 2021; Mantzios, 2014; Volkov, 2005). Third, epidemiological work indicates that veterans exhibit notably distinct rates of alcohol and cannabis use relative to the general adult population; more specifically, recent data from the National Survey on Drug Use and Health (NSDUH) indicated higher rates of alcohol use and lower rates of cannabis use among veterans relative to the general population (Pemberton et al., 2016; Substance Abuse and Mental Health Services Administration, 2020), with other recent findings also evidencing higher rates of alcohol-cannabis co-use among veterans (Bhalla et al., 2019; Davis et al., 2018; Metrik et al., 2018). As such, factors impacting alcohol- and cannabis-related use indices (e.g., frequency, quantity, problems) in other populations may not generalize to veterans.

The limited literature among veterans has most notably indicated associations between negative urgency and alcohol use (e.g., frequency, problems; Hahn et al., 2015; Hawn et al., 2019) and cannabis use (e.g., frequency, problems; Gunn et al., 2018, 2020). However, interpretation of the direct influences of varying facets of impulsivity on alcohol and cannabis use among veterans may be limited by lack of controlling for pre-existing substance use behaviors (e.g., due to cross-sectional data; Hahn et al., 2015; Hawn et al., 2019), focus on specific facet(s) of impulsivity (e.g., negative urgency; Brown et al., 2021; Hahn et al., 2015; Hawn et al., 2019), isolation of specific substances (e.g., alcohol), substance-specific indices (e.g., only alcohol problems; Brown et al., 2021; Gunn et al., 2020; Hawn et al., 2019), or a focus on alcohol and cannabis co-use (e.g., Waddell et al., 2021). Although evaluation and modeling of associations between specific impulsivity facet(s) and specific alcohol- and cannabis-related outcomes may provide several methodological and translational benefits (e.g., practicality and parsimony in analytic modeling), work isolating one facet of impulsivity or one substance (or multiple indices of a singular substance) may lack ecological validity due to an inability to account for the competing influences of other impulsivity facets and substances, especially given unique rates of both alcohol andcannabis use among veterans (Bhalla et al., 2019; Davis et al., 2018; Metrik et al., 2018). Further, given the extant literature evidencing differential influences of specific impulsivity-related traits on substance use indices (cf. frequency, quantity, problems; Coskunpinar et al., 2013) research focused on a particular substance or a singular aspect of substance use may be particularly problematic for advancing that literature. As such, there is the need for research examining associations between multiple facets of impulsivity and multiple indices of alcohol and cannabis use for the development of targeted, population-specific prevention and intervention efforts among veterans.

In order to address these gaps in the literature, the current study used a structural equation modeling (SEM; Hoyle, 1995; Ullman & Bentler, 2003) approach to comprehensively examine UPPS-P impulsivity facets and their longitudinal associations with six unique alcohol and cannabis indices (i.e., frequency, quantity, and problems) in a sample of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn (OEF/OIF/OND) veterans within a singular model. Drawing largely from the extant adult impulsivity-substance use literatures (Coskunpinar et al., 2013; Kaiser et al., 2012; Um et al., 2019), it was expected that urgency would be most strongly positively associated with one-year alcohol and cannabis use frequency, one-year cannabis use quantity, and one-year alcohol- and cannabis-related problems; further, it was expected that lack of perseverance would be positively associated with one-year alcohol use frequency and quantity.

Materials and Methods

Participants and Procedure

The sample was comprised of 361 returning OEF/OIF/OND veterans deployed post 9/11/2001 (Mage = 33.56, 93.4% male) who completed a battery of interviews as part of a larger prospective study on cannabis use and affective disorders (Metrik et al., 2020). Participants who reported using cannabis at least once in their lifetime were recruited from a Veteran’s Health Administration (VHA) facility in the Northeast region of the United States via study advertisements and the VHA OEF/OIF/OND Roster – an accruing database of combat veterans who recently returned from military service in Iraq and Afghanistan and were enrolled in VHA. See Metrik and colleagues (2016) for details of determining samples size, study eligibility, and recruitment procedures. Veterans were screened for eligibility by telephone and were invited for a baseline visit; written informed consent was obtained baseline followed by a battery of interview and self-report assessments. The baseline visit was followed by a one-year (i.e., 12-month) visit (N = 310; 85.9% retention), during which parallel assessments were administered. Data for the current study was derived from this baseline and one-year visit. No data were excluded or manipulated. The study was approved by the university and local VHA Institutional Review Boards. Participants were compensated per visit and a $50 bonus payment for completing all three study visits for a total payment of $200.

Measures

Sociodemographic Information.

Demographic and background information, such as sex, race, age, and marital status was collected at baseline and verified through the VHA Computerized Patient Record System (CPRS).

Impulsivity.

The Short UPPS-P Impulsive Behaviors Scale (S-UPPS-P; Cyders et al., 2014a) was administered during the baseline session to measure impulsivity. The S-UPPS-P is a 20-item self-report inventory that uses a 4-point Likert scale to assesses five subscales of impulsive personality (negative urgency, lack of perseverance, lack of premeditation, sensation seeking, positive urgency), each demonstrating high levels of internal consistency in previous studies (Cyders et al., 2014b). Each subscale demonstrated good internal consistency in the present study (negative urgency α = .77, lack of perseverance α = .69, lack of premeditation α = .82, sensation seeking α = .62, positive urgency α = .83).

Alcohol Use Indices.

The Timeline Followback (TLFB; Dennis et al., 2004; Sobell & Sobell, 1992) was administered at the baseline and one-year visits and used to evaluate past-30-day alcohol use frequency. More specifically, alcohol use frequency was operationalized as the percentage of days in which any “standard” drinks (i.e., defined as the alcohol equivalent of 12 oz. of beer, 5 oz. of wine, or 1.5 oz. of distilled spirits) were consumed. Similarly, the TLFB was also used to calculate alcohol use quantity. More specifically, alcohol use quantity was operationalized as the average number of ‘standard’ drinks consumed on drinking days over the prior 30 days. The Alcohol Use Disorder Identification Test (AUDIT; Allen et al., 1997; Saunders et al., 1993) is a 10-item self-report measure focused on past-year alcohol use symptomatology. The latter seven items comprise the AUDIT-Problems subscale (AUDIT-P; (Sanchez-Roige et al., 2019; Wells & McGee, 2015), which was used to assess alcohol-related problems and consequences. Sample items include, “…have you had feelings of guilt or remorse after drinking?” and “…have you or someone else been injured as a result of your drinking?” AUDIT-P sum scores range from 0 to 28 with higher scores indicating greater alcohol-related problems (α = .84).

Cannabis Use Indices.

The TLFB (Dennis et al., 2004; Sobell & Sobell, 1992) was administered at the one-year follow-up appointment and used to evaluate past-30-day cannabis use frequency. More specifically, for the current study, cannabis use frequency was operationalized as the percentage of days in which any cannabis was consumed. For cannabis use quantity, a single-item, face valid question asked about the amount of cannabis that participants consumed in the past 30 days, “In the past month, on average, how much marijuana per week, do you think you used in ounces?” Responses ranged on a 12-point scale in common 1/16th to 1/8th increments from “Never used regularly” to “More than 1 ounce.” Lastly, the Marijuana Problems Scale (MPS; Stephens et al., 2000) was used to assess cannabis-related problems. The MPS is a self-report 22-item questionnaire that assesses cannabis-related problems over the past 90 days. Sample items include “Has marijuana use caused you to neglect your family?” and “…to miss days at work or miss classes?” A total count of minor and serious problems was used in the present study. The MPS has strong internal consistency (Peters et al., 2011; Stephens et al., 2000) which was also excellent in this sample (α = .91).

Data Analytic Plan

Preliminary Analyses.

A series of preliminary analyses examined zero-order correlations between baseline sociodemographic characteristics (i.e., age, sex, race, ethnicity, marital status, and years since end of last deployment) and each of the six substance use outcomes at the one-year follow-up: alcohol use frequency, alcohol use quantity, alcohol-related problems, cannabis use frequency, cannabis use quantity, cannabis-related problems. Further, a series of preliminary analyses examined zero-order correlations between each facet of baseline impulsivity and each of the six substance use outcomes at the one-year follow-up. Sociodemographic characteristics associated (p < .05) with substance use outcomes were included in the primary model (Kearns et al., 2021).

Primary Analyses.

Structural equation modeling (Hoyle, 1995; Ullman & Bentler, 2003) was used to examine the longitudinal associations between baseline facets of impulsivity and the six identified substance use outcomes at the one-year follow-up (i.e., alcohol use frequency, alcohol use quantity, alcohol-related problems, cannabis use frequency, cannabis use quantity, cannabis-related problems) in a singular comprehensive model. For the model, latent (endogenous) variables were created for each of the five UPPS-P facets (i.e., negative urgency, lack of perseverance, lack of premeditation, sensation seeking, and positive urgency) based on the existing factor structure of observed (exogenous) impulsivity items (Cyders et al., 2014a; Lynam, Smith, et al., 2006). In order to scale latent variables, the factor loading of one observed variable per latent variable was fixed equal to 1.0. Associations between each of the five baseline impulsivity latent variables and each of the six indices of alcohol and cannabis use were examined. All latent impulsivity facets were allowed to correlate; similarly, all indices of alcohol and cannabis use were allowed to correlate. Dichotomized (i.e., yes/no) baseline past-six-month alcohol use and baseline past-six-month cannabis use, as well as statistically significant sociodemographic variables from the preliminary analyses, were added as covariates to each of the relevant substance use variables (see Figure 1 for schematic of model).

Figure 1.

Figure 1.

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Schematic of Structural Equation Model (SEM) of Baseline Impulsivity Facets and One-Year Substance Use Indices. Exogenous UPPS-P items used to create latent impulsivity variables are not shown. Factor correlations and sociodemographic covariates are not shown. All baseline impulsivity facets and all substance use indices are correlated.

Given that all items comprised four or more response options, data were considered continuous for modeling. Maximum likelihood estimation was used for missing data. Standardized coefficients (β) and p values were interpreted for directionality, statistical significance, and effect size. Model fit was determined by examining the Confirmatory Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) indices. According to Hu and Bentler (1999), models with excellent fit have the following fit statistics: CFI ≥ 0.95 (0.90–0.94) and RMSEA and SRMR ≤ 0.06 (0.07–0.08), with parenthetical values indicating adequate-to-good model fit. All analyses were conducted using the ‘lavaan’ package in R version 4.1.0.

Results

Preliminary Sociodemographic Analyses

Descriptive data are reported in Table 1. Results of the preliminary sociodemographic analyses indicated that baseline age was significantly positively associated with one-year alcohol use frequency (r = .16, p = .005) and negatively associated with one-year cannabis use frequency (r = −.17, p = .002) and one-year cannabis use quantity (r = −.17, p = .003). Male sex was significantly associated with one-year alcohol use quantity (r = −.18, p = .004). Identifying as white was significantly associated with one-year alcohol use frequency (r = −.13, p = .025). Marital status was significantly negatively associated with one-year alcohol use quantity (r = −.15, p = .018). Ethnicity and years since last deployment were not significantly associated with any primary substance use indices. See Table 2 for associations between sociodemographic characteristics, impulsivity facets, and substance-related indices.

Table 1:

Demographics Descriptives for the Full Sample

Variable Full Sample
(N = 361)
Age 33.56 ± 9.43
Sex (Male) 337 (93.4%)
Racea
 Native American 2 (0.6%)
 Asian American 6 (1.7%)
 Black/African American 16 (4.5%)
 Pacific Islander 2 (0.6%)
 White/Caucasian 289 (80.1%)
 Other 29 (8.0%)
 Multiracial 17 (4.7%)
Ethnicity
 Hispanic/Latinx 48 (13.3%)
 Non-Hispanic/Latinx 313 (86.7%)
Marital Status
 Single/Never Married 116 (32.1%)
 Married/Living with Partner 173 (47.9%)
 Separated/Divorced 72 (19.9%)
Years Since Deployment 3.95 ± 2.79
Impulsivity
 Negative Urgency 2.15 ± 0.72
 Lack of Perseverance 1.61 ± 0.49
 Lack of Premeditation 1.76 ± 0.56
 Sensation Seeking 2.88 ± 0.69
 Positive Urgency 1.74 ± 0.67
Substance-Related Indices
 Baseline Alcohol Userb 325 (90.0%)
 Baseline Cannabis Userb 138 (38.2%)
 Baseline Alcohol-Cannabis Co-Userc 128 (35.5%)
 One-Year Alcohol Use Frequency 26.46 ± 31.45
 One-Year Alcohol Use Quantity 4.42 ± 3.08
 One-Year Alcohol-Related Problems 2.70 ± 4.00
 One-Year Cannabis Use Frequency 16.31 ± 33.72
 One-Year Cannabis Use Quantity 0.97 ± 2.14
 One-Year Cannabis-Related Problems 0.93 ± 2.45
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Note. Data are presented as M ± SD, n(%)

a

Data were not available for three participants (0.8%).

b

Data were dichotomized as user or non-user in the past six months for analyses.

c

Co-user defined as use of both alcohol and cannabis in the past six months.

Table 2:

Correlation Matrix for Sociodemographic, Covariate, and Substance-Related Indices

Sociodemographics Baseline Impulsivity Facets One-Year Substance Use
Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
Sociodemographics
 1. Age -
 2. Sex −.02 -
 3. Racea −.05 .04 -
 4. Ethnicity .14** .07 −.44** -
 5. Marital Status .29** < .01 −.05 .04 -
 6. Deployment .18** .01 .07 −.01 .14** -
Baseline Impulsivity
 7. Negative Urgency −.07 < .01 −.03 .02 .09 .18** -
 8. Lack of Perseverance −.08 .03 .05 < .01 −.03 .08 .21** -
 9. Lack of Premeditation −.13* −.01 −.03 .07 −.01 .02 .43** .41** -
 10. Sensation Seeking −.22** −.17** −.02 −.10 −.03 −.13* −.01 −.11* .07 -
 11. Positive Urgency −.15** −.14** .06 −.11* −.01 .08 .56** .15* .43** .18** -
Baseline Substance Useb
 12. Alcohol User −.09 .02 −.05 .02 −.11* −.04 .03 < .01 .05 −.10 .04 -
 13. Cannabis User −.29** < .01 .06 −.08 −.03 .10 .19** .14** .12* .07 .23** .07 -
One-Year Substance Use
 Alcohol
 14. Frequency .16** −.11 −.13* .07 −.02 .03 .01 .03 .06 .04 −.03 .27** −.06 -
 15. Quantity −.07 −.18** .01 −.02 −.14* < .01 .17** < .01 .17** .09 .25** .05 .11 .11 -
 16. Problems −.09 −.08 −.08 .04 −.09 .02 .32** .03 .23** .10 .30** .05 .12 .28** .37** -
 Cannabis
 17. Frequency −.17** < .01 .03 −.02 −.07 .06 .10 .06 .14* .11 .10 −.01 .58** −.03 .07 .02 -
 18. Quantity −.17** .03 .05 −.08 −.05 .07 .15** .03 .12* .05 .11 −.03 .55** −.08 .12 .07 .83** -
 19. Problems −.05 −.05 −.03 .05 .07 .07 .10 .15** .07 .09 .07 −.11 .32** −.05 .05 .05 .35** .41** -
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Note. Data are presented as bivariate, zero-order Pearson correlation coefficients.

a

Participants were categorized as “White” vs. “Non-White/Multiracial” for correlational analyses.

b

Baseline use variables are based on dichotomous (yes/no) response to past-six-month use.

*

p < .05

**

p < .01

Preliminary Evaluation of Measurement Model

Evaluation of the measurement model for the five-factor model of baseline impulsivity facets indicated adequate-to-good fit across RMSEA = .07 (95% CI: .06-.08) and SRMR = .06, with slightly lower than adequate fit for CFI = .88. Observed loadings in the current study were similar to previously published psychometric factor structure findings for the UPPS-P (Billieux et al., 2012; D’Orta et al., 2015). Factor loadings ranged from .60-.72 for negative urgency, .51-.70 for lack of perseverance, .68-.80 for lack of premeditation, .48-.60 for sensation seeking, and .71-.76 for positive urgency.

Evaluation of Structural Model (Primary Analyses)1

The final SEM model indicated similar overall fit to the measurement model with CFI = .87, RMSEA = .05 (95% CI: .05-.06), and SRMR = .07. After controlling for relevant sociodemographic variables, as well as baseline binary alcohol use (i.e., yes/no past-six-month alcohol use for alcohol indices) and baseline cannabis use (i.e., yes/no past-six-month cannabis use for cannabis indices), results indicated that no baseline facets of impulsivity were significantly associated with one-year alcohol use quantity, cannabis use frequency, or cannabis use quantity. Baseline sensation seeking was significantly positively associated with alcohol use frequency (β = .18, p = .036). Baseline negative urgency was significantly positively associated with one-year alcohol use problems (β = .31, p = .010). Further, baseline lack of perseverance (β = .25, p = .006 and baseline sensation seeking (β = .21, p = .018) were significantly positively associated with one-year cannabis-related problems. See Table 3 for full model estimates.

Table 3:

Beta Coefficients and Significance Values between Impulsivity Facets and Substance Use Indices

Variables β 95% CI p Variables β 95% CI p
Alcohol Use Frequency Cannabis Use Frequency
 Negative Urgency .05 (−.17, .27) .674  Negative Urgency .07 (−.13, .26) .490
 Lack of Perseverance .05 (−.13, .22) .608  Lack of Perseverance −.03 (−.18, .13) .733
 Lack of Premeditation .10 (−.10, .29) .329  Lack of Premeditation .07 (−.10, .23) .432
 Sensation Seeking .18 (.01, .34) .036  Sensation Seeking .14 (−.01, .28) .069
 Positive Urgency −.15 (−.37, .07) .186  Positive Urgency −.17 (−.35, .02) .084
 Baseline Alcohol Use .29 (.19, .39) < .001  Baseline Cannabis Use .59 (.51, .66) < .001
Alcohol Use Quantity Cannabis Use Quantity
 Negative Urgency .10 (−.15, .35) .441  Negative Urgency .13 (−.07, .33) .209
 Lack of Perseverance −.15 (−.36, .06) .155  Lack of Perseverance −.10 (−.26, .05) .197
 Lack of Premeditation .15 (−.07, .36) .176  Lack of Premeditation .08 (−.10, .25) .379
 Sensation Seeking −.02 (−.22, .17) .805  Sensation Seeking .01 (−.14, .16) .871
 Positive Urgency .17 (−.08, .42) .182  Positive Urgency −.14 (−.33, .06) .170
 Baseline Alcohol Use .03 (−.16, .21) .779  Baseline Cannabis Use .55 (.47, .63) < .001
Alcohol-Related Problems Cannabis-Related Problems
 Negative Urgency .31 (.07, .54) .010  Negative Urgency .13 (−.10, .36) .266
 Lack of Perseverance −.10 (−.29, .09) .319  Lack of Perseverance .25 (.07, .44) .006
 Lack of Premeditation .10 (−.11, .30) .370  Lack of Premeditation −.14 (−.34, .06) .177
 Sensation Seeking .07 (−.11, .24) .483  Sensation Seeking .21 (.04, .38) .018
 Positive Urgency .07 (−.16, .30) .569  Positive Urgency −.15 (−.38, .08) .202
 Baseline Alcohol Use .06 (−.09, .20) .463  Baseline Cannabis Use .29 (.19, .39) < .001
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Note: Bolded baseline impulsivity facets are statistically significant. Sociodemographic characteristics identified in the preliminary analyses were included in the modeling.

Discussion

Various impulsivity domains have been implicated as etiological risk factors for alcohol- and cannabis-related indices of use (Coskunpinar et al., 2013; Stautz & Cooper, 2013; Wrege et al., 2014). However, limited work has focused on longitudinal relationships between multiple trait impulsivity facets and multiple indices of alcohol and cannabis use among military veterans – a developmentally- and contextually-distinct population that evidences unique impulsive personality traits and patterns of substance use (Coskunpinar et al., 2013; Mantzios, 2014; Montes & Weatherly, 2014; Pemberton et al., 2016; Wagner et al., 2007). The current study aimed to extend the literature by examining longitudinal associations between the impulsivity facets of the UPPS-P model with three distinct indices (i.e., frequency, quantity, and problems) of both alcohol and cannabis use among military veterans within a single model. Study hypotheses were partially supported with several unique prospective associations identified between trait impulsivity under the UPPS-P framework and indices of alcohol and cannabis use.

Although limited work has directly focused on veterans, findings regarding impulsivity and alcohol use indices are partially inconsistent with the larger adult literature. For example, juxtaposing meta-analytic findings implicating lack of perseverance as the strongest indicator of alcohol use quantity among non-veterans (Coskunpinar et al., 2013), results in the current study indicate that no specific facet of impulsivity was uniquely associated with quantity of alcohol consumption among veterans. This disparate finding may be partially explained by differences between previous samples (i.e., predominantly undergraduate students) and the current veteran sample. More specifically, veterans report higher trait levels of personality constructs (e.g., self-discipline; Kämpfe & Mitte, 2009; Mullins-Sweatt et al., 2013; Olsen et al., 2014) that are negatively associated with a lack of perseverance; indeed, lack of perseverance was the least endorsed UPPS-P impulsivity trait within the current veteran sample (see Table 1). As such, lack of perseverance may not be as meaningful of an indicator of alcohol use quantity within this subpopulation. Meta-analytic findings in previous impulsivity-substance use research among adults also evidenced small-to-medium mean sample-size weighted effect sizes between all UPPS impulsivity facets and alcohol use frequency (Coskunpinar et al., 2013). However, the current findings indicate that only sensation seeking was significantly associated with frequency of alcohol use among veterans. This discrepancy may similarly be partially explained by sociodemographic differences between general adult samples and the current veteran sample. More specifically, research indicates that individuals drawn to military service may exhibit higher levels of sensation seeking than the general adult population (Breivik et al., 2019; Montes & Weatherly, 2014). As such, following military service, higher sensation-seeking veterans may be drinking alcohol more frequently to enhance or immolate more exciting experiences due to increased boredom and loneliness following transition to civilian life (Lyons, 1993; Myrseth et al., 2017).

Conversely, results suggest that impulsive behavior in response to intense negative emotional states (i.e., negative urgency) may play an important role in understanding subsequent experiences of problematic alcohol use outcomes (e.g., alcohol-related memory loss, injury, failure to fulfill obligations). This finding is consistent with other veteran research evidencing associations between negative urgency and alcohol use problems (Hahn et al., 2015), as well as extant trauma-related work among trauma-exposed samples and veterans implicating negative affective responsivity (i.e., negative alternations in mood and cognitions) as the posttraumatic stress symptom cluster most strongly associated with alcohol use problems (Debell et al., 2014; Kearns et al., 2019, 2020). Collectively, results suggest that general patterns of alcohol use quantity may be more stable among veterans, whereas the frequency of alcohol use sessions (i.e., number of drinking days), as well as the negative behavioral consequences associated with more frequent alcohol use, may be influenced by a trait tendency to seek out new and exciting experiences (i.e., sensation seeking), as well as impulsive reaction to changes in negative affective states after alcohol is consumed (i.e., negative urgency).

Regarding cannabis use, study hypotheses were partially confirmed and findings partially aligned with the broader literature examining distinct impulsivity domains and cannabis use indices. For example, extant work utilizing differing conceptualizations and/or assessments of impulsivity, such as the Barratt Impulsiveness Scale (Patton et al., 1995) and delay discounting (Kirby & Maraković, 1996), have found relatively robust associations between both general and facet-specific impulsivity and cannabis-related indices (Berey et al., 2021; Day et al., 2013; Lopez-Vergara et al., 2019; Metrik et al., 2012). However, fewer studies have evaluated trait impulsivity using the UPPS-P model and cannabis-related outcomes among middle-to-older aged adults and/or military veterans, with existing research focusing predominantly on negative urgency (e.g., Gunn et al., 2020; Kaiser et al., 2012) and sensation seeking (e.g., Ames et al., 2002; Trocki et al., 2009).

Partially inconsistent with extant research among veterans and non-veterans (Ames et al., 2002; Gunn et al., 2020; Kaiser et al., 2012; Trocki et al., 2009), the current study did not demonstrate any associations between baseline impulsivity facets and cannabis use (i.e., frequency or quantity). This may be due to methodological or analytic differences in the current study. More specifically, the relatively weak associations between impulsivity facets and cannabis use frequency indicated in extant cross-sectional research and/or research that did not control for any baseline substance use status (e.g., r = .11-.17; Ames et al., 2002; Gunn et al., 2018; Kaiser et al., 2012) may not have been robust to pre-existing substance use behaviors that were included in the current modeling. Further, similar to alcohol use quantity findings, results suggest that impulsivity simply may play a less substantive role in understanding cannabis use frequency or quantity in this older, contextually-distinct veteran sample.

However, the current findings indicated that higher levels of sensation seeking and lack of perseverance were associated with cannabis involvement in ways that resulted in more negative consequences among veterans. Regarding sensation seeking, findings may be partially explained by discrepancies in alcohol and cannabis use by active duty military personal. More specifically, alcohol use remains common among active duty military, and has been associated with coping-oriented use (e.g., self-medication) and negative outcomes (Bray & Hourani, 2007; Schumm & Chard, 2012), whereas cannabis use is still prohibited during active duty due to current federal legalization status. As such, although sensation seeking may not impact military veterans’ problematic alcohol use behaviors, veterans with higher levels of sensation seeking may be more apt to engage in problematic cannabis use behaviors following discharge, given greater acceptability of regular cannabis use and use for medicinal reasons, relative to alcohol. Indeed, veterans evidence higher rates of psychophysiological comorbidities that are associated with problematic cannabis use (e.g., posttraumatic stress, pain; Boden et al., 2013; Johnson et al., 2016; Martín-Sánchez et al., 2009; Metrik et al., 2020). Regarding lack of perseverance, findings may be partially explained by difficulties with community reintegration and boredom among veterans following military service (Cogan, 2016; Hinojosa & Hinojosa, 2011; Holavanahalli et al., 2017; Lyons, 1993). More specifically, boredom associated with reintegration into the community may result in a higher willingness to engage in problematic cannabis use behaviors, especially given the potential novelty of cannabis use, relative to alcohol use. Importantly, the current study did not examine boredom or active duty substance use behaviors. Future research should consider these factors to better understand why sensation seeking and lack of perseverance, specifically, may be associated with problematic cannabis use outcomes among veterans.

There are several potential implications that may be drawn from the current study. Findings provide the first comprehensive longitudinal examination of the UPPS-P impulsivity model with multiple distinct alcohol and cannabis-related substance use indices that directly accounted for competing influences of other impulsivity facets, baseline binary substance use status (i.e., yes/no to use in the past six months), and other sociodemographic characteristics among veterans. Numerous calls within psychology for addressing reliability and replicability have highlighted the need for removing or controlling for the influences of covariates, assuming appropriate theoretical rationale (Asendorpf et al., 2016; Lee, 2012). The current findings suggest that future research targeting specific impulsivity facets and specific substance use indices among veterans should consider assessing and incorporating all UPPS-P facets in their predictive modeling, as well as other sociodemographic characteristics and baseline substance use indices. Notably, this conclusion is consistent with many of the meta-analytic and systematic reviews of the impulsivity and substance use literatures, which have often pressed for the assessment of multiple impulsivity-related traits as an important consideration for future prevention and intervention work (e.g., VanderVeen et al., 2016).

Findings also impress the importance of evaluating impulsivity-related personality facets and alcohol and cannabis use outcomes within developmentally- and contextually-distinct populations. As evidenced by discrepancies in the literature between adolescent and emerging adult impulsivity-substance use findings (cf. Coskunpinar et al., 2013; Stautz & Cooper, 2013), the current findings indicate that impulsivity-substance use associations, drawn largely from developmentally-distinct college student samples, may not consistently generalize to older, contextually-distinct populations (Liu et al., 2020). Indeed, military veterans not only vary in their sociodemographic composition (cf. college students), but are also distinguished by unique experiential factors (e.g., count/type of trauma exposure; Baker et al., 2009; Dedert et al., 2009) and psychological morbidities (e.g., posttraumatic stress disorder [PTSD]; Dursa et al., 2014) known to be associated with both impulsivity and substance use indices (Armour et al., 2016; James et al., 2014; Metrik et al., 2020). Future research with military veterans is needed to replicate the current findings.

The current study also stresses the importance of differentiating between varying substance use indices. Echoing sentiments described by Coskunpinar and colleagues (2013), findings provide additional support for the need to consider alcohol and cannabis ‘use’ as multidimensional constructs – indicating that specificity in defining and operationalizing varying aspects of substance use (e.g., frequency, quantity, and problems) plays an important role in understanding discrepant behavioral indices among military veterans. Future work aiming to advance our understanding of the influence of impulsivity-related personality facets on subsequent substance use behaviors should be purposeful in the selection and description of their outcomes, particularly given notable differences in the current study and extant literature regarding the influences of varying impulsivity-related traits on specific substance use indices (e.g., frequency, quantity, problems; Coskunpinar et al., 2013).

Lastly, the current findings may provide initial guidance to help in bolstering interventions for alcohol-related problems, as well as cannabis-related problems among veterans. More specifically, the current findings indicate that interventions focused on developing skills to manage emotional reactivity to strong negatively or positively valanced experiences (e.g., Mindfulness and Modification Therapy [MTT]; Wupperman, 2019) may be particularly useful for reducing alcohol-related problems. Further, interventions that encourage alternative activities that both maintain the attention of veterans and produce reinforcing, rewarding effects – activating the mesolimbic reward pathways through adaptive/socially-acceptable stimulating behaviors (Bardo et al., 1996; Zuckerman, 1994) – may be beneficial for reducing alcohol use frequency and cannabis-related problems. Further, evidence-based psychotherapies, such as Cognitive Behavioral Therapy for Substance Use Disorders disseminated and implemented within the Veterans Health Administration (VHA), specifically address alternative sources of substance-free reinforcement via recreational counseling as part of veteran’s recovery process (DeMarce et al., 2021). Other community reintegration programs incorporating engagement in stimulating physical activity and alleviating boredom through social interaction have also shown promise in reducing problematic outcomes among veteran following deployment/discharge (Baird et al., 2018; Gorman et al., 2018; Holavanahalli et al., 2017). However, given the dearth of impulsivity-substance use research among military veterans, more work is needed to replicate the current findings before allocation of resources or substantive modifications to prevention and intervention efforts can be recommended.

The findings should be interpreted with consideration of several limitations. First, results should be considered in light of inherent bias (e.g., recall bias) in self-report measures of impulsivity and substance use indices. Results should also be considered in the context of sociodemographic characteristics. More specifically, the current sample was disproportionally comprised of male participants (i.e., 93.4%). Future work is needed to understand if associations between UPPS-P impulsivity facets and alcohol- and cannabis-related indices can be replicated in more demographically diverse samples of military veterans. Second, given the novelty of understanding impulsivity, alcohol, and cannabis use indices among veterans, the current study focused on the broadest UPPS-P structure (i.e., treating each facet as a unique construct). Further, although the current model evidenced similar factor loadings to previous published UPPS-P models, one of the fit indices in the current study was below recommended cutoffs (CFI = .88). Future research may consider alternative UPPS structures (Smith & Cyders, 2016; Watts et al., 2020) to replicate and extend findings among veterans.

Lastly, although the current study design provides important preliminary information on prospective associations between UPPS-P impulsivity facets and substance use outcomes among veterans, interpretation of findings may be limited to use of a binary baseline substance use covariate (i.e., yes/no past-six-month use). Future research on impulsivity and substance use among veterans should consider more intensive longitudinal approaches (e.g., ecological momentary assessment) or longitudinal designs following anticipated change in substance use behaviors (e.g., following deployment, trauma exposure, or intervention); future research may also consider longitudinal designs and data analytic approaches with multiple timepoints (i.e., three or more) that can disaggregate between-person and within-person effects, given notability limitations of two-timepoint analyses.

Conclusion

The current study meaningfully adds to the literature as the first comprehensive longitudinal assessment of all facets in the UPPS-P impulsivity model and three indices (i.e., frequency, quantity, and problems) of alcohol and cannabis use among military veterans. Overall, findings indicated that negative urgency may play an important role in understanding alcohol-related problems, specifically, whereas lack of perseverance and sensation seeking may be more relevant to cannabis-related problems and alcohol use frequency. Alternatively, none of the trait facets of impulsivity may be consistent indicators of alcohol use quantity, cannabis use frequency, or cannabis use quantity among veterans. Finding also impress the importance of considering other facets of impulsivity in modeling of substance-related indices among veterans. Lastly, results reinforce the importance of differentially evaluating impulsivity-substance use associations within contextually-distinct populations (e.g., adolescent, veterans), and highlight potentially meaningful targets for future prevention and intervention among veterans.

Supplementary Material

Supplemental table

Public Health Significance Statements.

  • This longitudinal study indicated that the impulsivity-related personality facet of sensation seeking was associated with alcohol use frequency; negative urgency was associated with alcohol-related problems; and lack of perseverance and sensation seeking were prospectively associated with cannabis-related problems among military veterans.

  • This study suggests that none of the UPPS impulsivity facets were prospectively associated with alcohol use quantity, cannabis use frequency, or cannabis use quantity among military veterans.

  • This study highlights the importance of differentially evaluating multiple impulsivity facets, varying indices of substance use behavior (i.e., frequency, quantity, and problems), and evaluating impulsivity-substance use associations in contextually-distinct populations.

Acknowledgements.

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. This study was not preregistered. Materials and analysis code for this study are available by emailing the corresponding author. The authors gratefully acknowledge Cassandra Delapaix, Rebecca Swagger, Madeline Benz, Hannah Wheeler, Suzanne Sales, and Julie Evon for their contribution to the project.

Support: Research was supported in part by National Institute on Alcohol Abuse and Alcoholism (NIAAA; F31 AA027142) and National Institute on Drug Abuse (NIDA; T32 DA016184) grants to the first author; K08AA027551 to the second author; T32DA016184 to the third author; T32AA007459 to the fourth author; and R01DA033425 to the last author.

Footnotes

1

Subset analyses were conducted including only participants that consumed alcohol or cannabis within the given time points of outcomes (i.e., baseline, one-year assessments). Overall, results remained largely consistent with this more inclusive model, with no statistically significant coefficient shifting more than β =.05. The largest coefficient shift occurred with sensation seeking and cannabis problems from β = .21 to β = .17 with reduced sample size. Notably, overall fit was slightly reduced across all indices (CFI = .86, RMSEA = .06, SRMR = .07) in the subset model. As such, the more inclusive model was retained for interpretation. See Supplementary Table 1 for full results of the subset analyses.

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