2.1 Participants
The final sample consisted of 258 participants who indicated that they were using at least one substance at least once a month. See Table 1 for sociodemographic and substance use-related characteristics of participants. Further details are available in the supplementary material. Inclusion criteria were (1) age between 18 and 40 years, (2) native German speakers, (3) no reported use of any substance (except for nicotine) on the day of the survey. We excluded these participants to avoid effects of acute intoxication on questionnaire responses. Participants were recruited mainly through advertisements in clubs, webpages associated with the techno music scene, and counseling centers as well as through postings and flyers. Participants gave informed consent prior to participation. Participants could earn course credit (six participants) or take part in a lottery to win one of three 10 euro vouchers. The study protocol was approved by the local ethics committee (EK 146042019) and followed the guidelines stated by the Declaration of Helsinki.
2.2 Procedure
Participants completed an online-survey (approx. 20 minutes) comprising sociodemographic information and the measures described below.
Table 1
Sociodemographic and substance use-related characteristics of participants
Characteristic
|
N
|
%
|
Gender
|
|
|
female
|
148
|
57.4
|
male
|
109
|
42.2
|
not specified
|
1
|
0.4
|
Highest secondary school degree
|
|
|
upper
|
226
|
87.6
|
intermediate
|
28
|
10.9
|
lower
|
2
|
0.8
|
Current use of
|
|
|
alcohol
|
236
|
91.5
|
nicotine
|
159
|
61.6
|
cannabis
|
117
|
45.3
|
stimulants
|
79
|
30.6
|
hallucinogenic substances
|
29
|
11.2
|
opioids
|
14
|
5.4
|
misused medication
|
5
|
1.9
|
inhalants
|
4
|
1.6
|
other substances
|
3
|
1.2
|
|
M
|
MD
|
SD
|
range
|
age
|
26.1
|
25.0
|
5.0
|
18-40
|
currently used substance classes
|
2.5
|
2.0
|
1.3
|
1-6
|
substance classes used lifetime
|
4.7
|
5.0
|
2.0
|
1- 9
|
Note. M and SD represent mean and standard deviation, respectively.
2.3 Material
2.3.1 Substance Use Questionnaire
This questionnaire measured the frequency and quantity of substance use for the following substance classes from the DSM-5: alcohol, tobacco, cannabis, stimulants, opioids, hallucinogenic substances, misused medication, inhalants, other substances. Participants were first asked whether they had ever used any of the substances from the respective substance class. If they answered ‘yes’, participants indicated, among other variables not relevant here, their average frequency of use within the past three months on a 6-point Likert scale ranging from 1 to 6 (almost daily, 3-4 times a week, 1-2 times a week, 1-2 times a month, less than once a month, never) and the average subjective quantity of use on a standard occasion within the past three months on a 5-point Likert scale ranging from 1 (very little) to 5 (very much)[1]. We computed individual degree of substance use scores for each substance class (product of quantity and frequency values, possible maximum: 30) and a total degree of substance use score (sum of the individual degree of substance use scores, possible maximum: 270).
2.3.2 Substance-Related Problems Questionnaire
This questionnaire measured substance-related problems operationalized by the number of DSM-5 SUD symptoms from the A criterion participants reported to have experienced within the past 12 months (range 0 - 11). Questions were based on the German version of the Structured Clinical Interview for DSM-5 Disorders – Clinician Version (29) and adapted to the online application. Participants were instructed to consider all substances when judging whether they had experienced each symptom. Thus, this index does not equal a diagnosis of a specific substance use disorder, but rather an index of problem severity across substances as experienced symptoms may be attributable to different substances.
2.3.3 UPPS Impulsive Behavior Scale
This 45-item self-report questionnaire assesses four subscales representing facets of impulsivity as identified by Whiteside and Lynam (30): (1) negative urgency (e.g., “In the heat of an argument, I will often say things that I later regret.”; possible range: 12 - 48), (2) (lack of ) premeditation (e.g., “I usually make up my mind through careful reasoning.”; possible range: 11 - 44), (3) (lack of) perseverance (e.g., “Once I start a project, I almost always finish it.”; possible range: 10 - 40), and (4) sensation seeking (e.g., “I welcome new and exciting experiences and sensations, even if they are a little frightening and unconventional.”; possible range: 11 - 44). Items are rated on a 4-point Likert scale ranging from 1 (strongly agree) to 4 (strongly disagree). If necessary, items are reverse coded so that higher values on a subscale always indicate higher impulsivity/sensation seeking. The subscales of the German version (31) have good internal consistency (cronbachs α = .81 - .85) and external validity (32).
2.3.4 Monetary Choice Questionnaire (MCQ)
The MCQ (German version; 33) assesses delay discounting. It comprises 27 binary choices between varying amounts of money (8 - 66 Euros) available either now or with a delay (7 - 238 days). Values and delays of the options are predefined to cover a high range of discounting rates for low, medium, and high values of delayed rewards, respectively. The discounting rate k can be modeled assuming a hyperbolic function (34)
[1] For reasons of brevity of the online survey and our decision to combine individual substances to substance classes, we did not assess objective quantities of substance use. The results of this study do not change when using only total frequency rather than the total degree of substance use score as a control variable (see supplementary material for further details).
where V is the subjective value of a delayed reward with the amount A after the delay D.
The parameter k is estimated based on the proportion of choices that are consistent with every possible value of k (for a thorouh description please refer to 35). As k is dependent on value (36), k is estimated for small, medium and large delayed rewards, respectively, and the geometric mean of these values constitutes an individual’s k-value.
2.4 Data analysis
All analyses were conducted in R (37). We log-transformed the k-parameter due to skewedness. Pearson’s correlations were computed to examine bivariate associations. Two-tailed tests of the difference between two dependent correlations with one variable in common were conducted to test whether correlations differed significantly. Subsequently, we computed partial correlations of all predictor variables with substance-related problems controlled for the degree of substance use to explore their respective contribution outside the context of other predictor variables. Last, we conducted two hierarchical linear regressions for the degree of substance use and substance-related problems to identify the unique variance explained by sensation seeking, impulsivity-related traits, and log(k). For both regression models, we entered gender as a covariate in the first step, because it was significantly related to both outcome variables, while age was not (ps > .05). We excluded the group “gender not specified” from these regression analyses (n = 1). For the model predicting the degree of substance use, we added the UPPS subscales in the second and log(k) in the third step. For the model predicting substance-related problems, we added the degree of substance use as a covariate in the second step, the UPPS subscales in the third step and log(k) in the fourth step. To test on an exploratory basis whether a regressor explained more variance in the criterion variable than another regressor, we computed finite-sample F-statistics using the “linearHypothesis” function from the car package (38). We applied a significance level of α = .05 for all analyses.