a Addictology Division, Mental Health and Psychiatry Department, Geneva University Hospitals, Geneva, Switzerland; b Research Center for Statistics, University of Geneva, Geneva, Switzerland; c Faculty of Medicine, Geneva University, Geneva, Switzerland
Opioid Maintenance Treatment (OMT) is the most effective treatment for opioid use disorder (OUD)  allowing the reduction or disappearance of withdrawal and craving symptoms . On the one hand, OMT is effective in reducing illicit opioid use [3, 4] and improving general health , on the other hand many patients receiving OMT are polysubstance users [6, 7]. Specifically, many heroin users also report using cocaine . Concurrent use of cocaine and heroin is known to increase the risk of death from overdose, as this combination leads, among others, to an increase in the amount and frequency of opioids use . Persistence of cocaine use during OMT is therefore of considerable clinical concern [1, 10, 11].
Addictions, regardless of the substances used, are conceptualized as a unitary phenomenon because of their many clinical and biological similarities. However, stimulants such as cocaine and opiates may be differently associated to impulsivity . While impulsivity is a common feature of all addictions , it seems that different levels of impulsivity lead to different types of risky behaviours and use. Notably, it is recognized that high levels of impulsivity are associated with the use of multiple substances [14, 15]. In heroin and cocaine users, high levels of impulsivity and risky behavior have been extensively reported [6, 16]. The literature is, though, not unequivocal, as some studies found individuals with cocaine use more impulsive and with a higher propensity to take risks than heroin users , while others did not find such differences [7, 18]. These inconsistencies may be due to significant methodological differences in the way impulsivity is assessed, e.g. by psychometric tests or by laboratory tasks [6, 17, 18]. Impulsivity has several components, for each of which an assessment tool has been developed .
Impulsivity reflects a tendency to act prematurely without foresight [20, 21]. Psychometric measures evaluate impulsivity as a personality trait, whereas laboratory tasks are considered to measure state impulsivity [21, 22]. The UPPS-P is a self-report questionnaire which assesses the impulsivity-facets Positive Urgency (PU), Negative Urgency (NU), Lack of Premeditation (LPr), Lack of Perseverance (LPe) and Sensation Seeking (SS) [23, 24]. Laboratory tasks address such behavioral aspects as the inability to suppress inappropriate behavior (motor impulsivity), risk-taking (risky behaviors), and the inability to defer a gratification (impulsive choice) . Motor impulsivity, evaluated by the Stop Signal Reaction Time (SSRT), refers to the ability to inhibit a prepotent motor response . The Delay Discounting Task is designed to assess impulsive decision-making. Accepting a smaller reward in order to obtain the reward immediately is associated with high-impulsive subjects . The Balloon Analogue Risk Task (BART) is an assessment of risk-taking behaviors. Through this task, as in real-world situations, risk-taking is rewarded up until a certain point, beyond which excessive risk-taking leads to greater negative consequences [28, 29]. Addictions have been found to correlate with all these impulsivity components, but cocaine users were reported to present more significant deficits in response inhibition compared to heroin users .
The OUD population is known to engage in polysubstance abuse . Indeed, many people who use heroin also report using cocaine . Concurrent use of cocaine and heroin, such as “speedball,” is known to increase the risk of death from overdose, as this combination leads to an increase in the amount and frequency of opioids used . In addition, polysubstance abuse such as heroin and cocaine, is associated with poor clinical outcomes [1, 10]. Thus, additional cocaine use appears to be a risky behavior in itself. In addition, if cocaine use is frequently associated to heroin use, its consumption persists after people have engaged in OMT. Indeed, persistence of cocaine use in the OUD population receiving OMT during treatment initiation and after is a long-known fact and still a clinical concern . The pursuit of studies, about what distinguishes people with a cocaine use disorder (CUD) and those without among the OUD population, is necessary.
Regarding these different findings, we emitted the hypothesis that in patients with OUD receiving OMT, those with an additional cocaine use would show a higher impulsivity level and risk taking. Answering to this question in naturalistic conditions can lead to a better comprehension of their addictive trajectory and also help to define a therapeutic strategy. To determine whether cocaine users in the OUD population receiving OMT are more impulsive, we included a group of patients with OUD receiving the same OMT, the “Slow Release Oral Morphine” (SROM). SROM is a recently developed OMT available in Switzerland. It has been suggested as an alternative treatment in OUD, especially for individuals that do not tolerate other OMT . The choice of SROM over other OMTs was made in view of its lesser side effects [32, 33], and its wide use in our department. In addition, limiting our inclusions to one type of OMT helps to limit bias. We assessed impulsivity state (BART, Stop Signal Task, and Delay Discounting Task) and trait (UPPS-P), and compared them according to their cocaine status.
Materials and Methods
Twenty-three patients were recruited from the Division of Addiction Psychiatry (Service d’Addictologie) of the University Hospital of Geneva (Switzerland), where they receive care for an OUD, more precisely to illicit heroin. They were eligible if they fulfilled all of the following inclusion criteria: Informed consent as documented by signature; able to communicate in French; age over 18 years old; on a stable dose of “Slow Release Oral Morphine (SROM) not modified at least 14 days prior to inclusion. Non-inclusion/exclusion-criteria included unstable psychiatric disorders and acute withdrawal syndrome. Participants received 50CHF-vouchers for their participation. The study has been approved by the ethical committee of the canton of Geneva and was carried out in accordance to the protocol and with principles enunciated in the Declaration of Helsinki and the guidelines of Good Clinical Practice (GCP) issued by ICH.
Whiteside and Lynam  have identified distinct traits of impulsivity. The short UPPS-P Impulsive Behavior Scale  is a 20-item scale (four items per dimension) that assesses five reliable impulsivity facets labeled as: Positive Urgency (Tendency to act rashly in positive emotional contexts); Negative Urgency (Tendency to act rashly in negative emotional contexts); (lack of) Perseverance (Difficulty to remain focused on difficult or boring tasks); (lack of) Premeditation (Difficulty to take into account the consequences of an act before engaging in that act); and Sensation Seeking (Tendency to enjoy and pursue new / exciting activities). All items are scored on a Likert scale ranging from 1 (I agree strongly) to 4 (I disagree strongly). Higher scores designate higher impulsivity.
Alcohol, Smoking and Substance Involvement Screening Test (ASSIST)
The ASSIST is a short screening questionnaire developed by the World Health Organization (WHO) to assess the use of different substances (tobacco products, alcohol, cannabis, cocaine, amphetamine-type stimulants, sedatives and sleeping pills, hallucinogens, inhalants, opiates, and “other drugs”) and the associated consequences [35, 36]. The ASSIST in its current French version (ASSIST V3.0)  is composed of eight questions that determine a risk score for each substance, which allows to conclude the most appropriate intervention for that level of use. The score for each substance is categorized as low risk (occasional or non-harmful use), moderate risk (more regular or harmful use), or high risk (frequent risky use or suggestive of dependence). The ASSIST is therefore a well-validated screening test for substance use disorder in an adult population .
Stop Signal Task
The stop signal paradigm is an impulsivity inhibitory control model. The Stop Task  is the prototypical task used to assess capacity of inhibitory mechanisms, measuring the ability to inhibit a pre-potent response (i.e. a dominant or automatic motor response).
The task consists in responding to a visual signal (go signal) as fast as possible (go task), but to refrain this answer (stop task) when an auditory signal (stop signal) is heard. The frequency of this stop signal is set on one trial out of four (25%), but the delay between the go signal and this stop signal varies, and is successively adjusted to make it tend towards the median reaction time. The latency of the response to the stop signal (stop-signal reaction time) is calculated as a quantitative measure of inhibitory control. Longer stop-signal reaction times are associated with higher impulsivity .
Delay Discounting Task
The Delay Discounting Task is designed to assess impulsive decision-making . This task uses a computerized adjusting-amount procedure to measure how a delay impinging a granted reward decreases the attractiveness of this reward, hence the term “discount”. In a series of choice trials, participants have to decide repeatedly between two options: a smaller amount of money (hypothetically) available immediately or a larger amount of money available after a delay (e.g. $100 immediately or $1000 in one year). The two amounts are presented on a computer screen and the participants are asked to press one of two buttons to indicate their first choice (less money immediately versus more money, time delayed). On successive trials, manipulation of parameters (e.g. the long delay) allows estimation of the rate of discounting, which allows to find the delay at which the large and the smaller amount of reward would be valued equally, namely the ‘equivalence point’. The “equivalence point” is calculated by averaging the ascending and descending values for each time period. The “equivalence point” is the value of the last immediate amount when a participant ceases to prefer the immediate amount and choose the deferred amount, i.e. the point at which the immediate and deferred amounts have the same subjective value for the participant .
Balloon Analogue Risk Task
The Balloon Analogue Risk Task (BART) is a computerized laboratory-based assessment of risk-taking tendencies. Through the task, as in real-world situations, risk-taking is rewarded up until a point at which excessive risk-taking leads to greater negative consequences, which outweigh the positives . In this task, a small, simulated balloon with a balloon pump is displayed on the computer screen. Participants may inflate the balloon by clicking on the pump in exchange for a monetary reward for each pump. With each click, the balloon inflates, and 10 points are added to the participant’s temporary bank. At any point, the participant may decide to stop to inflate the balloon and collect the sum collected on this balloon. The sum is banked in the permanent bank. However, each balloon is set to explode at random in a « pop » sound effect resulting in the loss of all money accumulated for that balloon. Each balloon has a different explosion point and is programmed to pop anywhere between 1 and 64 pumps (maximum number of clicks per balloon), with an average breakpoint at 32 pumps.
The participants are only informed that the balloon can explode anywhere from the first pump all the way to the point where it fills the whole screen. After each balloon explosion or money collection, a next balloon appears until a total of thirty balloons have spawned. The main dependent measure on the BART is quantified by the average number of pumps delivered in balloons that did not explode, which is referred to as the mean adjusted pumps (i.e. the average number of pumps on each balloon prior to earning money) . Higher scores imply a higher risk-taking predilection.
This study is observational. Participants had to complete questionnaires about their socio-demographic status and to inform about other substances used with the ASSIST rating questionnaire. All participants had OUD, and experiments were conducted before administration of their regular dose of SROM. Each participant was attributed to one of two groups depending on his/her risk of cocaine use, as evaluated by the ASSIST rating questionnaire, i.e. to the Moderate-High risk cocaine use group (MHR-cocaine) when they had an ASSIST-cocaine score >3 or the Low Risk cocaine use group (LR-cocaine) when they had an ASSIST-cocaine score £ 3. The experimental procedure consisted in the administration of three experimental tasks (Stop Signal, Delay discounting and BART).
Statistical analyses were conducted using R (R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/). Variables were described as frequencies or mean values and standard deviations. A Fisher test was used to test potential differences in categoric variables (e.g., gender, employment, partnership, psychiatric disease and medication) between the two groups. Scores of UPPS, delay discounting, stop signal, BART, and number of substances used obtained for both groups (MHR and LR-cocaine) were compared by using the t-test for independent samples with the FDR correction. All statistical tests were considered significant if p <0.05.
Twenty-three patients were recruited. Their mean age was 43.8 years (SD: 8.7 years). Twenty were males (87%) and 3 females (13%). They met a mean of 9.9 DSM‐5 diagnostic criteria for opioid dependence out of maximum 13 (SD: 3.9). Twelve of the twenty-three participant were attributed to the LR cocaine use group and eleven to the MHR cocaine use group. Table 1 shows participant’s sociodemographic data.
Statistical analyzes applied to our two groups failed to identify any statistical difference either on socio-demographic data nor on clinical characteristics (DSM-5 criteria, doses and duration of SROM, psychiatric disease, psychiatric medication) as shown in Table 1.
Substances consumption habits and values are presented in the table 2. T-test with FDR correction showed that the MHR cocaine group used more different substances (t = -2.71, p-value = 0.012) and also presented more so-called risky consumptions as evaluated by the ASSIST score (t = -2.86, p-value <0.01). This significant proportion of substance abuse in the MHR cocaine group is also observed on the ASSIST total score (t = -2.0995, p-value = 0.045). However, when cocaine-use is left out from the analysis, this effect is lost, showing that apart from cocaine, this group does not use more substances than the LR group. Fisher’s test showed a significantly higher proportion of hallucinogen use (p-value = 0.04) and a tendency to use psychostimulants (p-value = 0.09) for the MHR cocaine group. Performing a t-test with a Bonferroni correction on the ASSIST score of each substance showed a significantly higher proportion of hallucinogen use (t = -2.1926, p-value = 0.032) and a higher tendency to use psychostimulants without being significant (t = - 1.9365, p-value = 0.056).
Table 3 shows mean and standard deviation for all task performances (delay discounting, BART and SSRT) and for the UPPS-P subscale scores, comparing the groups. The LR cocaine group showed a significantly prolonged SSRT (t = 2.29, p-value = 0.033) compared to the MHR cocaine group, but did not differ in other outcomes. Considering the UPPS scores, we found no statistical differences between both groups.
This study aimed to explore several dimensions of impulsivity in patients receiving OMT, distinguishing between those with and without concomitant cocaine abuse. We expected the former to show higher levels of impulsivity. Drug use, and in particular cocaine use, had previously been identified as a factor that can induce, both through its acute and long-term effects, an increase in impulsivity and, consequently, an increase in risky behaviours such as unprotected and unsafe sex [39, 40]. Surprisingly, in the present study, while both groups showed a similar impulsivity profile, one impulsivity component, motor impulsivity, resulted to be significantly higher in the group without cocaine use. Although this result may appear counterintuitive at first sight, we found many evidences in literature that could explained it.
It is commonly accepted that all addictions, SUDs and behavioral addictions, are associated with motor inhibitory deficits. Often, these observations are made by comparing populations without distinguishing causal factors, or chronic/acute effects of substances . Studies which focused on the acute effect of substance on motor impulsivity showed different impacts depending on the substance. For example, alcohol  or cannabis  impairs inhibitory control, whereas psychostimulants improve it . This improvement of the inhibitory control induced by psychostimulants is observed on subjects whose initial SSRT were slow, and/ or subjects with Attention Deficit Hyperactivity Disorder (ADHD) [40, 43]. Also, in a study on patients with CUD where the SSRT was administered during a period of abstinence from cocaine, an alteration in performance was found . These impaired performances in motor inhibition have been commonly observed in users of stimulants such as amphetamine . On the other hand, injection of methylphenidate compared to saline improves the performance of SSRT in CUD, showing a better inhibitory control induced by the psychostimulant . These studies provide information on the acute effect of drugs and not on the chronic effect. In the light of these data, the significantly lower motor impulsivity in the MHR cocaine group and their tendency to have a greater appetite for psychostimulants led us to hypothesize that the participants in this group were attempting to self-medicate their deficit. Nevertheless, this hypothesis cannot be verified due to our methodological limitations. Indeed, we ignore participants’ last cocaine use, although due to their difficulty in controlling their cocaine use, it can be assumed that their consumption was closer than for the LR cocaine use group. In order to conclude on the self-therapeutic dimension of cocaine use and its acute effect on neurocognitive performance, it would be necessary at least to know if participants were under cocaine influence during task performances, and at best to know the performance without cocaine and just after cocaine use, a maneuver which may be difficult to realize for ethical reasons. The higher tendency to consume psychostimulants in the MHR cocaine use group could be another argument in favor of the self-medication hypothesis. But, this aspect must be balanced by the observation of a significantly higher tendency of the MHR cocaine group to use hallucinogens. In line with this hypothesis, there are many arguments in the literature around the self-medication hypothesis in the CUD, with the notion that ADHD is a risk factor for cocaine use and CUD . Moreover, if the acute administration of psychostimulants is associated with behavioral inhibition, it is important to highlight that chronic exposure may lead to long-term sequelae that result in a defect of motor inhibition . Furthermore, this hypothesis is put forward without being able to make a hypothesis on the origin of this deficit, i.e. to answer the question of whether the deficit is prior to cocaine intake, as is the case in ADHD, or consecutive to chronic cocaine intake. Despite this, it is well-known that patients with ADHD and comorbid CUD have a higher motor impulsivity than those with ADHD only , which implies that the two causes could be intertwined. Nevertheless, this only partially explains why the LR cocaine group showed higher impulsivity. Another reason that could explain why the LR cocaine group showed higher impulsivity is the influence of several substances they use, which we have seen could negatively affect impulsivity, such as alcohol or cannabis [41, 42]. However, LR cocaine group did not consume more alcohol, cannabis or others substances than MHR cocaine group. Another explaination could be an undiagnosed comorbid ADHD. Thus, presence of ADHD in a proportion of our sample could explained our results, since ADHD has been associated with altered motor inhibition . It is important to remember that the psychiatric disease control provided information of only one participant with a diagnosis of ADHD in the MHR cocaine group. But this information does not mean that other participants could not have ADHD, it only means that one participant has already been diagnosed with ADHD. Indeed, if the ADHD is particularly associated in the literature at the CUD, this disease is also too associated to the others substance use disorder [50, 51]. Specifically, it has been reported that in the OUD population the proportion of ADHD could be estimated to be between 11% and 33%, but this disease remains underestimated with currently a low proportion of patients having received medication for ADHD .
Both heroin and cocaine users are known to show poorer performance in decision-making and higher risk-taking than controls [6, 53]. In our study, risk-taking as assessed by the BART was similar in both groups, with and without cocaine use. This result is in agreement with a previous study that found no difference in risk-taking propensity between subjects receiving OMT with cocaine use and abstainers . Yet, it has previously been suggested that different risk profiles may mediate the orientation of substance choice, with higher risk-taking at BART for cocaine rather than for heroin users [17, 54]. Thus, heroin dependent individuals have been characterized in a study published by Ahn et al.  to be lesser risk-taking individuals than stimulant users. However, in contrast to our study, the subjects included in the Ahn et al. study were abstinent, and described as having only a problematic use whereas our participants were mainly polydrug users [12, 17, 54]. Risky decisions and behaviors could be explained by the inability to differ rewards. Delayed discounting was used to assess a behavioral economic index of impulsivity, that is, the extent to which a reward is devalued by its delayed receipt in the future. The use of stimulants such as cocaine is associated in the literature with delay aversion, leading authors to identify it as a stimulant use endophenotype [12, 55, 56]. Primary cocaine users were previously found to have a higher preference for small, immediate rewards compared to primary heroin users . In the present study, we did not find differences in delay discounting between our both groups. Cocaine use did not seem to affect delay discounting here. Most studies that found cocaine users to exhibit greater impulsivity than heroin users examined single use situations. In addition to being associated with substance choice, delay discounting may also be associated with polysubstance use. Thus, individuals using two or more substances use exhibited higher impulsive decision-making than individuals with single substance use, but there was no further cumulative effect with the number of substances used beyond two drugs . In addition, recent meta-analyzes have shown that the delay discounting appears to be affected by SUDs and severity of their addiction its, but is not affected by substance type [58, 59]. The presence of comorbid psychiatric illness could also affect the discounting of delays . However, our two groups are equivalent whether be for the severity of addiction assessed by the number of DSM-5 criteria, or for psychiatric diseases. One could thus tentatively conclude, based on the results of our present study, that the impulsivity components measured by the BART and the Delay Discounting Task are more related to the polysubstance use than the substance choice. In the present sample of polysubstance users receiving OMT a general high level of impulsivity may have masked possible differences between cocaine users and non-users.
The different facets of impulsivity, as assessed by the UPPS, have been found to be vulnerability factors for showing risky behavior and developing substance use disorders [60–62]. If stimulant use would be correlated with predominant sensation-seeking behavior, and heroin use would be more associated with a higher propensity to feel a negative urgency, in our case we found no difference in UPPS between the two groups. But, these results are difficult to compare with ours since the subjects were mono-consumers and abstinent without OMT . However, other studies that found a higher impulsivity in subjects receiving OMT than in controls, found no difference regarding their CUD status . Furthermore, it is important to highlight that our LR cocaine use group is not cocaine abstinent and has shown that they use cocaine recreationally, at least 33% of them. It is important to take this aspect into account because a previous study showed that all cocaine users (recreational and addicted) had the same level of impulsive trait. Moreover, it would seem that impulsivity traits are more associated to the depressive symptoms severity or ADHD , diseases which do not differ between the two groups or which are not sufficiently controlled.
Several limitations are to be considered when interpreting the results of the present study, mainly the limited sample size and the lack of information about the last use of cocaine. The polysubstance use status of our sample may also have blurred the results. Most previous studies focused on one substance, considering polysubstance use as an exclusion criterion. If selecting a single substance use disorder can limit some bias, it does not reflect the reality of most heroin users, who are engaged in long-lasting poly-drug use . Duration of the OMT cure/treatment could represent another limitation. Individuals in our sample had been in treatment for more than three months (mean 996 days for LR and 790 for MHR). Furthermore, it is known that impulsivity is related to the duration of treatment. On the one hand, the duration of treatment can have a downward influence on the level of impulsivity [65, 66]. On the other hand, higher impulsivity is associated with problems in the effectiveness of psychotherapy, including poorer outcomes and lower retention in treatment . It is possible that our sample, engaged in treatment for more than three months, was less impulsive than those not in treatment. However, controlling for dosage and duration of treatment provides a good indicator of stability in treatment and allows comparison between the two groups. Another limitation is the lower proportion of females compared to males included in our sample. However, this underrepresentation of women appears to be consistent with epidemiological data that show a lower proportion of women in the OUD population [68, 69]. A final limitation, is represented by the comorbid psychiatric diseases and medication used by participants. If we did not find any differences between our groups, it is important to specify that these information’s are only declarative. Thus, participants may have omitted data or simply did not know it.
In conclusion, if a higher global impulsivity profile in SUD compared to non-users has been established consistently before, the present study led to the hypothesis that cocaine use could be considered as a self-medication attempt. Future studies on impulsivity should not ignore the existence of comorbid ADHD, and should also monitor precisely when cocaine was last used.
The authors thank all volunteers for participating in the study.
Dr Julie Giustiniani
University Hospital of Geneva CAAP Arve
Route des Acacias 3,
1 Carlsen S-EL, Lunde L-H, Torsheim T. Opioid and Polydrug Use Among Patients in Opioid Maintenance Treatment. Subst Abuse Rehabil. 2020;11:9–18.
2 Fareed A, Vayalapalli S, Stout S, Casarella J, Drexler K, Bailey SP. Effect of methadone maintenance treatment on heroin craving, a literature review. J Addict Dis. janv 2011;30(1):27–38.
3 Beck T, Haasen C, Verthein U, Walcher S, Schuler C, Backmund M, et al. Maintenance treatment for opioid dependence with slow-release oral morphine: a randomized cross-over, non-inferiority study versus methadone. Addiction. avr 2014;109(4):617–26.
4 Amato L, Davoli M, Minozzi S, Ferroni E, Ali R, Ferri M. Methadone at tapered doses for the management of opioid withdrawal. Cochrane Database Syst Rev [Internet]. 28 févr 2013 [cité 11 janv 2021];2013(2). Disponible sur: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017622/
5 Monwell B, Gerdner A. Opioid maintenance treatment: trajectories in and out of treatment. Nord J Psychiatry. janv 2019;73(1):24–30.
6 Verdejo-García AJ, Perales JC, Pérez-García M. Cognitive impulsivity in cocaine and heroin polysubstance abusers. Addict Behav. mai 2007;32(5):950–66.
7 Huhn AS, Brooner RK, Sweeney MM, Yip SW, Ayaz H, Dunn KE. Increased neural activity in the right dorsolateral prefrontal cortex during a risky decision-making task is associated with cocaine use in methadone-maintained patients. Drug Alcohol Depend. 1 déc 2019;205:107650.
8 Leri F, Bruneau J, Stewart J. Understanding polydrug use: review of heroin and cocaine co-use. Addiction. janv 2003;98(1):7–22.
9 Barocas JA, Wang J, Marshall BDL, LaRochelle MR, Bettano A, Bernson D, et al. Sociodemographic factors and social determinants associated with toxicology confirmed polysubstance opioid-related deaths. Drug Alcohol Depend. 1 juill 2019;200:59–63.
10 Heikman PK, Muhonen LH, Ojanperä IA. Polydrug abuse among opioid maintenance treatment patients is related to inadequate dose of maintenance treatment medicine. BMC Psychiatry. 6 juill 2017;17(1):245.
11 Maremmani I, Pani PP, Mellini A, Pacini M, Marini G, Lovrecic M, et al. Alcohol and cocaine use and abuse among opioid addicts engaged in a methadone maintenance treatment program. J Addict Dis. 2007;26(1):61–70.
12 Ahn W-Y, Vassileva J. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug Alcohol Depend. 1 avr 2016;161:247–57.
13 Kreek MJ, Nielsen DA, Butelman ER, LaForge KS. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci. nov 2005;8(11):1450–7.
14 Verdejo-García A, Del Mar Sánchez-Fernández M, Alonso-Maroto LM, Fernández-Calderón F, Perales JC, Lozano O, et al. Impulsivity and executive functions in polysubstance-using rave attenders. Psychopharmacology (Berl). juin 2010;210(3):377–92.
15 Bailey AJ, Farmer EJ, Finn PR. Patterns of polysubstance use and simultaneous co-use in high risk young adults. Drug Alcohol Depend. 1 déc 2019;205:107656.
16 Dissabandara LO, Loxton NJ, Dias SR, Dodd PR, Daglish M, Stadlin A. Dependent heroin use and associated risky behaviour: the role of rash impulsiveness and reward sensitivity. Addict Behav. janv 2014;39(1):71–6.
17 Bornovalova MA, Daughters SB, Hernandez GD, Richards JB, Lejuez CW. Differences in impulsivity and risk-taking propensity between primary users of crack cocaine and primary users of heroin in a residential substance-use program. Exp Clin Psychopharmacol. nov 2005;13(4):311–8.
18 Nielsen DA, Ho A, Bahl A, Varma P, Kellogg S, Borg L, et al. Former heroin addicts with or without a history of cocaine dependence are more impulsive than controls. Drug and Alcohol Dependence. 1 juill 2012;124(1):113–20.
19 MacKillop J, Weafer J, C Gray J, Oshri A, Palmer A, de Wit H. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacology (Berl). sept 2016;233(18):3361–70.
20 Xu S, Korczykowski M, Zhu S, Rao H. Assessment of risk-taking and impulsive behaviors: A comparison between three tasks. Soc Behav Pers. 2013;41(3):477–86.
21 Antons S, Brand M. Trait and state impulsivity in males with tendency towards Internet-pornography-use disorder. Addictive Behaviors. 1 avr 2018;79:171–7.
22 Swann AC, Bjork JM, Moeller FG, Dougherty DM. Two models of impulsivity: relationship to personality traits and psychopathology. Biol Psychiatry. 15 juin 2002;51(12):988–94.
23 Whiteside SP, Lynam DR. The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences. 1 mars 2001;30(4):669–89.
24 Rømer Thomsen K, Callesen MB, Hesse M, Kvamme TL, Pedersen MM, Pedersen MU, et al. Impulsivity traits and addiction-related behaviors in youth. J Behav Addict. 1 juin 2018;7(2):317–30.
25 Bari A, Robbins TW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. sept 2013;108:44–79.
26 Logan GD, Schachar RJ, Tannock R. Impulsivity and inhibitory control. Psychological Science. 1997;8(1):60–4.
27 Rachlin H, Raineri A, Cross D. Subjective probability and delay. J Exp Anal Behav. mars 1991;55(2):233–44.
28 Lejuez CW, Read JP, Kahler CW, Richards JB, Ramsey SE, Stuart GL, et al. Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied. 2002;8(2):75–84.
29 White TL, Lejuez CW, de Wit H. Test-retest characteristics of the Balloon Analogue Risk Task (BART). Exp Clin Psychopharmacol. déc 2008;16(6):565–70.
30 Kenney SR, Anderson BJ, Bailey GL, Stein MD. Expectations about alcohol, cocaine, and benzodiazepine abstinence following inpatient heroin withdrawal management. Am J Addict. janv 2019;28(1):36–42.
31 Socias ME, Wood E, Dong H, Brar R, Bach P, Murphy SM, et al. Slow release oral morphine versus methadone for opioid use disorder in the fentanyl era (pRESTO): Protocol for a non-inferiority randomized clinical trial. Contemp Clin Trials. avr 2020;91:105993.
32 Klimas J, Gorfinkel L, Giacomuzzi SM, Ruckes C, Socías ME, Fairbairn N, et al. Slow release oral morphine versus methadone for the treatment of opioid use disorder. BMJ Open. 2 avr 2019;9(4):e025799.
33 Verthein U, Beck T, Haasen C, Reimer J. Mental Symptoms and Drug Use in Maintenance Treatment with Slow-Release Oral Morphine Compared to Methadone: Results of a Randomized Crossover Study. EAR. 2015;21(2):97–104.
34 Billieux J, Rochat L, Ceschi G, Carré A, Offerlin-Meyer I, Defeldre A-C, et al. Validation of a short French version of the UPPS-P Impulsive Behavior Scale. Compr Psychiatry. juill 2012;53(5):609–15.
35 Humeniuk R, Ali R, Babor TF, Farrell M, Formigoni ML, Jittiwutikarn J, et al. Validation of the Alcohol, Smoking And Substance Involvement Screening Test (ASSIST). Addiction. juin 2008;103(6):1039–47.
36 WHO ASSIST Working Group. The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST): development, reliability and feasibility. Addiction. sept 2002;97(9):1183–94.
37 Khan R, Chatton A, Nallet A, Broers B, Thorens G, Achab-Arigo S, et al. Validation of the French version of the alcohol, smoking and substance involvement screening test (ASSIST). Eur Addict Res. 2011;17(4):190–7.
38 Lynam DR, Miller JD. Personality Pathways to Impulsive Behavior and Their Relations to Deviance: Results from Three Samples. J Quant Criminol. 1 déc 2004;20(4):319–41.
39 Johnson MW, Johnson PS, Herrmann ES, Sweeney MM. Delay and probability discounting of sexual and monetary outcomes in individuals with cocaine use disorders and matched controls. PLoS One. 2015;10(5):e0128641.
40 de Wit H. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol. janv 2009;14(1):22–31.
41 Liu Y, van den Wildenberg WPM, de Graaf Y, Ames SL, Baldacchino A, Bø R, et al. Is (poly-) substance use associated with impaired inhibitory control? A mega-analysis controlling for confounders. Neurosci Biobehav Rev. oct 2019;105:288–304.
42 de Wit H, Crean J, Richards JB. Effects of d-Amphetamine and ethanol on a measure of behavioral inhibition in humans. Behavioral Neuroscience. 2000;114(4):830–7.
43 Tannock R, Schachar RJ, Carr RP, Chajczyk D, Logan GD. Effects of methylphenidate on inhibitory control in hyperactive children. J Abnorm Child Psychol. oct 1989;17(5):473–91.
44 Li CR, Milivojevic V, Kemp K, Hong K, Sinha R. Performance monitoring and stop signal inhibition in abstinent patients with cocaine dependence. Drug Alcohol Depend. 1 déc 2006;85(3):205–12.
45 Li C-SR, Morgan PT, Matuskey D, Abdelghany O, Luo X, Chang JLK, et al. Biological markers of the effects of intravenous methylphenidate on improving inhibitory control in cocaine-dependent patients. Proc Natl Acad Sci U S A. 10 août 2010;107(32):14455–9.
46 Oliva F, Mangiapane C, Nibbio G, Berchialla P, Colombi N, Vigna-Taglianti FD. Prevalence of cocaine use and cocaine use disorder among adult patients with attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. J Psychiatr Res. 9 nov 2020;
47 Smith JL, Mattick RP, Jamadar SD, Iredale JM. Deficits in behavioural inhibition in substance abuse and addiction: A meta-analysis. Drug and Alcohol Dependence. 1 déc 2014;145:1–33.
48 Crunelle CL, Veltman DJ, van Emmerik-van Oortmerssen K, Booij J, van den Brink W. Impulsivity in adult ADHD patients with and without cocaine dependence. Drug Alcohol Depend. 1 avr 2013;129(1–2):18–24.
49 McAlonan GM, Cheung V, Chua SE, Oosterlaan J, Hung S, Tang C, et al. Age-related grey matter volume correlates of response inhibition and shifting in attention-deficit hyperactivity disorder. Br J Psychiatry. févr 2009;194(2):123–9.
50 Simon N, Rolland B, Karila L. Methylphenidate in Adults with Attention Deficit Hyperactivity Disorder and Substance Use Disorders. Curr Pharm Des. 2015;21(23):3359–66.
51 Crunelle CL, van den Brink W, Moggi F, Konstenius M, Franck J, Levin FR, et al. International Consensus Statement on Screening, Diagnosis and Treatment of Substance Use Disorder Patients with Comorbid Attention Deficit/Hyperactivity Disorder. Eur Addict Res. 2018;24(1):43–51.
52 Vold JH, Aas C, Skurtveit S, Odsbu I, Chalabianloo F, Halmøy A, et al. Dispensation of attention deficit hyperactivity disorder (ADHD) medications in patients receiving opioid agonist therapy; a national prospective cohort study in Norway from 2015 to 2017. BMC Psychiatry. 12 mars 2020;20(1):119.
53 Hobkirk AL, Bell RP, Utevsky AV, Huettel S, Meade CS. Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users. Drug Alcohol Depend. 1 janv 2019;194:32–9.
54 Lejuez CW, Bornovalova MA, Daughters SB, Curtin JJ. Differences in impulsivity and sexual risk behavior among inner-city crack/cocaine users and heroin users. Drug Alcohol Depend. 14 févr 2005;77(2):169–75.
55 Bickel WK. Discounting of delayed rewards as an endophenotype. Biol Psychiatry. 15 mai 2015;77(10):846–7.
56 MacKillop J. Integrating behavioral economics and behavioral genetics: delayed reward discounting as an endophenotype for addictive disorders. J Exp Anal Behav. janv 2013;99(1):14–31.
57 Moody L, Franck C, Hatz L, Bickel WK. Impulsivity and polysubstance use: A systematic comparison of delay discounting in mono, dual, and tri-substance use. Exp Clin Psychopharmacol. févr 2016;24(1):30–7.
58 Kluwe-Schiavon B, Viola TW, Sanvicente-Vieira B, Lumertz FS, Salum GA, Grassi-Oliveira R, et al. Substance related disorders are associated with impaired valuation of delayed gratification and feedback processing: A multilevel meta-analysis and meta-regression. Neurosci Biobehav Rev. janv 2020;108:295–307.
59 Amlung M, Vedelago L, Acker J, Balodis I, MacKillop J. Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction. janv 2017;112(1):51–62.
60 Vergés A, Littlefield AK, Arriaza T, Alvarado ME. Impulsivity facets and substance use initiation: A comparison of two models of impulsivity. Addict Behav. janv 2019;88:61–6.
61 Stautz K, Cooper A. Impulsivity-related personality traits and adolescent alcohol use: a meta-analytic review. Clin Psychol Rev. juin 2013;33(4):574–92.
62 Tomko RL, Prisciandaro JJ, Kutty Falls S, Magid V. The Structure of the UPPS-R-Child Impulsivity Scale and its Relations with Substance Use Outcomes Among Treatment-Seeking Adolescents. Drug Alcohol Depend. 1 avr 2016;161:276–83.
63 Vonmoos M, Hulka LM, Preller KH, Jenni D, Schulz C, Baumgartner MR, et al. Differences in self-reported and behavioral measures of impulsivity in recreational and dependent cocaine users. Drug and Alcohol Dependence. 1 nov 2013;133(1):61–70.
64 Crummy EA, O’Neal TJ, Baskin BM, Ferguson SM. One Is Not Enough: Understanding and Modeling Polysubstance Use. Front Neurosci. 2020;14:569.
65 Liao D-L, Huang C-Y, Hu S, Fang S-C, Wu C-S, Chen W-T, et al. Cognitive control in opioid dependence and methadone maintenance treatment. PLoS ONE. 2014;9(4):e94589.
66 Yang L, Xu Q, Li S, Zhao X, Ma L, Zheng Y, et al. The effects of methadone maintenance treatment on heroin addicts with response inhibition function impairments: Evidence from event-related potentials. Journal of Food and Drug Analysis. 1 juin 2015;23(2):260–6.
67 Hershberger AR, Um M, Cyders MA. The relationship between the UPPS-P impulsive personality traits and substance use psychotherapy outcomes: A meta-analysis. Drug Alcohol Depend. 1 sept 2017;178:408–16.
68 Observatoire européen des drogues et des toxicomanies. Rapport européen sur les drogues: tendances et évolutions. Luxembourg, Luxembourg: Office des publications officielles des Communautés européennes; 2013.
69 Pierce M, Millar T, Robertson JR, Bird SM. Ageing opioid users’ increased risk of methadone-specific death in the UK. Int J Drug Policy. mai 2018;55:121‑7.
Published under the copyright license
“Attribution – Non-Commercial – NoDerivatives 4.0”.
No commercial reuse without permission.