KnE Life Sciences | The Fifth International Luria Memorial Congress «Lurian Approach in International Psychological Science» | pages: 462–471

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1. Introduction

Impulsivity is a multidimensional concept that encompasses physiological, behavioral, cognitive, and personality aspects and constitutes a key feature in many psychiatric disorders [25,7]. Impulsivity can be categorized into three factors – impulsive choices, impulsive actions, and impulsive personality traits [24] and further operationalized through such constructs as poor inhibitory control [1,21], difficulties delaying gratification and increased discounting of delays [34,27], poor planning and altered sensitivity to rewards and punishment [11,40,39], emotion dysregulation in the form of positive and negative urgency [32,5,39], the lack of perseverance and proneness to sensation seeking and risk taking [1,29,39], etc. Due to the complex nature of the concept of impulsivity, multiple attempts to develop models of impulsivity have been made (for example, Whiteside et al., 2005, [37]); at the same time, some authors [35,6,23] observed that measures of impulsivity do not necessarily correlate with each other and, thus, their exact contribution to the development and maintenance of psychopathology is still unclear.

Despite the lack of the shared agreement on what defines impulsivity, multiple studies established connections between impulsivity features and substance use and abuse. In non-clinical populations, greater risk-taking and non-planning characteristics [8,4,23], increased discounting of delays [12,8], diminished inhibitory control [8,22], increased urgency and sensation seeking [31], and reduced cognitive control [13] were associated with greater alcohol and other substances use. In clinical populations, the cross sectional comparisons of patients with substance use disorders (SUD) with healthy control groups observed higher levels of impulsivity in patients with SUD; in addition, the higher incidence of SUD was observed in individuals with impulse control disorders [25]. In addition, higher levels of impulsivity, including impaired decision-making, poor planning, reduced inhibitory control, etc., were deemed both the predictors and consequences of alcohol and other substance use disorders ([33], Jentsch et al., 2015, [19]). It was also observed that the relationship between the measures of impulsivity and the symptoms of SUD is not straightforward. Thus, MacKillop et al., (2007) and Field et al. (2007) reported that the levels of impulsivity is associated with symptoms of alcohol use disorder, levels of alcohol consumption, and craving, while Robles et al (2011) found that the impulsivity features don't covary with the severity of substance use disorder.

The goal of the current study is to explore how multiple measures of impulsivity, more specifically, response inhibition, impulsive decision-making, delay discounting, difficulties maintaining sustained attention, and results of self-report on inattention and hyperactivity and might be related to the clinical characteristics of patients with alcohol use disorders (AUD) (i.e., duration of AUD, average duration of past remissions, maximum duration of past remissions, type of alcohol consumption, and the level of craving).

2. Methodology


In the present study, we recruited patients (n = 88), who were undergoing treatment for AUD (F10.2) at the Department of Addictions at V.M. Bekhterev National Research Medical Center for Psychiatry and Neurology (St. Petersburg, Russia). The patients were assessed prior to their discharge from the hospital. Psychosocial and clinical characteristics of the study participants are presented in Table 1.

Table 1

Psychosocial and clinical characteristics of the study participants.

Patients' characteristics (М ± SD)
Age 42 ± 8.6 y.o.
Age of onset of AUD 31 ± 7.6 y.o.
Duration of AUD 11 ± 7.6 years
Average number of remissions 1.1 ± 1.4
Average duration of remissions 8 ± 15 months
Maximum duration of remissions 10 ± 17 months
Type of alcohol consumption (%(n)):
Daily 58 (51)
Binge-drinking 28 (25)
Mixed 14 (12)


For the purpose of this study we used the following measures – biographical questionnaire, experimental tasks (Tower of London (ToL), Continuous Performance Test-Identical Pairs (CPT-IP), Delay Discounting task with monetary rewards (DDT), Stroop task with neutral, emotional, and alcohol stimuli), Penn Alcohol Craving Scale (PACS), and Adult ADHD self-report scale (ASRS-v.1.1).

Biographical questionnaire, which was developed by the authors of this study, contained questions inquiring about patients' demographic characteristics (i.e., age, sex, etc.) as well as clinical characteristics of AUD (i.e., diagnosis, age of onset of AUD, duration of AUD, average number of remissions, average duration of remissions, maximum duration of remissions, etc.).

ToL is a subtest of Brief Assessment of Cognitions – Affective Disorders [14,16]. The ToL is aimed at assessing spatial problem-solving abilities [30]. The participants are presented with two images of three pegs of unequal length with colored balls on them; the aim of the task is to identify the number of balls that should be moved in order to make two images look alike. For the purpose of this study, we used the characteristic of impulsive decision-making, which was calculated as a logarithm of the ratio of correct to erroneous choices made within 20 sec.

CPT-IP is aimed to measure inhibition control [2]. We used the computerized version of the CPT-IP, which was developed using free software PEBL [26]. In this task, the participants are presented with two-, three-, and four-digit numbers and are asked to press the space bar, when two identical numbers are shown consequently. The numbers are shown for 50 ms followed by 950 ms of dark time. The response inhibition index is calculated as the mean of d-prime values for each session.

DDT is aimed at measuring temporal discounting (Kirby & Marakovic, 1996). The participants are presented with a set of hypothetical choices, asking to choose a smaller monetary reward now or greater reward later. The k index was used as a discounting of the delays characteristics in the current study.

Stroop task with neutral, emotional, and alcohol stimuli [36] is aimed at measuring response interference control. The participants are presented with the sheets of paper each containing four columns of names of colors printed in different colored ink (red, green, and blue). The participants are instructed to name the color of the ink of the printed words during the period of 30 sec. In addition, the authors of the current study used Stroop task with emotion stimuli and Stroop task with alcohol stimuli. The former one was derived from the BAC-A and the latter one was developed by the authors of the study.

ASRS v.1.1 [17] is a 6-item self-report instrument, which is developed for using with adults in order to screen for the main symptoms of attention deficits and hyperactivity. Each item is rated on the scale from `never' to `very often'; four or more marks on the shaded area of the instruments' form suggests the presence of adult ADHD symptoms.

PACS [10] is a five item self-report scale that measures the level of craving. The items inquire about the frequency, intensity, duration of craving and the ability to resist it over the previous week; each item is rated from 0 to 6.

Statistical analysis

The mean and the median were calculated for demographic data, clinical characteristics, and self-report measures. Further statistical analysis was done in two steps. The goal of the first step was to identify latent variables. In order to do that, the cluster analysis of variables that are typically associated with impulsiveness, was done using bootstrapping (the null hypothesis was that absence of cluster). Next, the Principal Component Analysis (PCA) with variables' standardization was done for identifying the significant information within the clusters. After PCA, the Bayesian networks with the use of Hill Climbing algorithm (score-based structure learning algorithms) were constructed; in the results, greater negative values refer to stronger effect of the variable. In all calculations, null hypotheses were rejected at the level of p < 0.05.

3. Results

Cluster analysis yielded two distinct groups of impulsivity parameters. The first cluster is comprised of delay discounting and impulsive decision-making indices. The measures of impulsivity in the 1st cluster reflect individual tendency for rush decision-making. The second cluster is comprised of interference control and response inhibition control. The interaction of the parameters within the 2nd cluster suggests that decrease of the first component might result in decrease of the second one and vice versa.

Next, the model of interaction between the parameters of impulsivity and clinical characteristics of the participants was developed (Figure 1).

Figure 1

Relationship between measures of impulsivity and clinical characteristics of the participants.


The model suggests that self-reported symptoms of inattention and hyperactivity had significant effects on the level of craving and the duration of remissions. Maximum duration of remission significantly affected the interference and inhibition control, as measured by Stroop tasks and CPT-IP. The maximum length of remission also affected the level of impulsive decision-making via interference and inhibition control. Surprisingly, no relationships were observed between the temporal discounting as measured by DDT and the reviewed clinical characteristics.

4. Discussion

Heightened impulsivity have long been linked to the development of substance use disorders and associated with the continued use and abuse of the psychoactive substances. Impulsivity is a complex phenomenon and, thus, there is still the lack of unified agreement on the definition, classification, and models of impulsive features. Due to theoretical differences in conceptualizing this construct, the findings on the role of impulsivity in development and maintenance of SUD vary as well.

In the article, we presented the results of the study exploring the relationship between multiple measures of impulsivity, namely temporal discounting (DDT), response inhibition (CPT-IP), interference control (Stroop tasks), impulsive decision-making (ToL), and self-report screening instrument for the symptoms of inattention and hyperactivity (ASRS v1.1). Using cluster analysis, we were able to identify two distinct groups of impulsivity features. One group is comprised of DDT and ToL measures, which generally describe the level of impulsivity in decision-making. Another group is comprised of response inhibition and interference control. Next, we've built a model of relationship between measures of impulsivity and clinical characteristics of participants (namely, duration of AUD, duration and the number of remission, and the type of alcohol consumption). Although the constructed model seemed to differ from the previous reports exploring the multidimensional impulsivity in relation to AUD symptoms (for instance, [4]), the structure of the relationship definitely points out at the effect of impulsivity on the clinical characteristics. Surprisingly, DDT didn't have any significant relationship with any of the clinical parameters. We might hypothesize that some impulsivity features might be the premorbid condition and are not significantly affected by continued substance use, which was observed in our previous studies (Trusova et al., 2018, [18]), as well as reported by other authors (Robles et al., 2012).


This study was supported by Russian Humanitarian Science Foundation grant (No. 16-06-01043).



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