Volitional Dysfunction in Self-control Failures and Addictive Behaviors

The aim of this project is to elucidate whether impairments of cognitive control, performance-monitoring, and value-based decision-making and dysfunctional interactions between underlying brain systems are mediating mechanisms and vulnerability factors for daily self-control failures and addictive disorders.

Study Overview

• Status: Active, not recruiting
• Conditions: Tobacco Use Disorder; Executive Dysfunction; Addictive Behavior; Alcohol Use Disorder (AUD); Self-Control
• Intervention / Treatment: Other: Observational study without interventions

Detailed Description

Failures of self-control during conflicts between long-term goals and immediate desires are a key characteristic of many harmful behaviors, including unhealthy eating habits, lack of exercise and problematic substance use, which often have adverse personal consequences and incur great societal costs. The project aims to elucidate neurocognitive mechanisms mediating deficient self-control, both in daily self-control failures and in substance use disorders and behavioral addictions, which are characterized by a loss of control despite awareness of adverse consequences. A prospective cohort study was launched using a multi-level approach that combines (i) a comprehensive clinical assessment, (ii) behavioral task batteries assessing cognitive control and decision-making functions, (iii) task-related and resting state fMRI, and (iv) Smartphone-based ecological momentary assessment of daily self-control failures. From a representative community sample, three groups of participants were recruited (each n = 100; age 20 - 26) with (a) symptoms of non-substance related and (b) substance-related addictive disorders and (c) syndrome-free controls. Participants are invited to yearly clinical follow-up assessments and further multi-level assessments 3 and 6 years after initial recruitment. Results obtained so far (until 06/2020) provide converging evidence that task performance as well as brain activity in monitoring, control, and valuation networks is reliably associated with the propensity to commit real-life self-control failures. Results support a process model, according to which deficient performance-monitoring leads to an insufficient recruitment of control networks, which attenuates the impact of long-term goals on neural value signals and increases the likelihood of self-control failures. In the final funding period (until 06/2024), the clinical follow-up period will be extended to 7 years. In addition, stress markers will be assessed as possible moderators of self-control. With the cross-lagged panel design it is expected to make a substantial contribution to the central unresolved question whether dysfunctions of cognitive control are causally involved in the development and trajectories of self-control failures and addictive behaviors, as well as to the disputed question of communalities and differences between different addictive disorders. Thereby, the project will to contribute to mechanism-based models of self-control impairments as a foundation for improved prevention and therapy.

• Study Type: Observational
• Enrollment (Actual): 338

Contacts and Locations

Study Locations

• Germany
  • Dresden, Germany, 01062
    • Technische Universität Dresden, Faculty of Psychology

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 19 years to 27 years (Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

The age range was 19 to 27 years at baseline. As expected for our sampling procedure, the addictive disorder severity was mainly mild (baseline 62%) and moderate (baseline 28%).

Description

Inclusion Criteria (at baseline):

1. age 19-27
2. fulfill the criteria for one of three groups (SUD, ND, controls)
3. written informed consent

Exclusion Criteria (at baseline):

1. no written informed consent or limited ability to understand the questionnaires and tasks
2. disorders that might influence cognition or motor performance (e.g. craniocerebral injury)
3. magnetic resonance contraindications
4. current treatment for mental disorders
5. current use of psychotropic medication or substances
6. lifetime psychotic symptoms, bipolar disorder, or other SUD or ND not under study
7. major depression, somatoform, anxiety, obsessive compulsive, or eating disorders within the last 4 weeks

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Substance use disorder (SUD) group; In the substance use disorder (SUD) group, participants had a diagnosis of alcohol and/or tobacco use disorder according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) but no lifetime non-substance-related addictive disorder (ND).	Other: Observational study without interventions
Non-substance-related addictive disorder (ND) group; In the non-substance-related addictive disorder (ND) group, participants were included who fulfilled two or more criteria for a DSM-5 gambling disorder or for an addictive behavior related to Internet use (not for gambling, gaming, or shopping), gaming, or shopping assessed with adapted criteria from DSM-5 substance use disorder (SUD). Participants in the ND group had no lifetime SUD.	Other: Observational study without interventions
Control group; The control participants had no current or lifetime substance use disorder (SUD) or non-substance-related addictive disorder (ND).	Other: Observational study without interventions

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Changes in addictive disorder severity	Changes in number of fulfilled criteria according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5)	At baseline and 1, 2, 3, 4, 5, 6, 7 years after baseline
Changes in quantity and frequency of addictive behaviors	Changes in quantity and frequency of addictive behaviours, which are combined into a quantity-frequency index.	At baseline and 1, 2, 3, 4, 5, 6, 7 years after baseline
Changes in cognitive control abilities	The Cognitive Control Task Battery of the Collaborative Research Center (CRC) 940 with nine executive function tasks (Stroop, AX continuous performance, color-shape, stop signal, letter memory, number-letter, go-nogo, 2-back, category switch) is used to derive a latent variable representing individual differences in general executive functioning (GEF). For the latent variable modelling error rates and reaction times from the tasks were combined, were appropriate, into inverse efficiency scores (IESs).	At baseline and 3 and 6 years after baseline
Changes in impulsive decision-making	The Value-Based Decision-Making (VBDM) battery of the Collaborative Research Center (CRC) 940 including four decision-making tasks with a Bayesian adaptive algorithm was used to adaptively assess impulsive decision-making. For the delay and probability discounting tasks, a hyperbolic value function was used describing that the subjective values of delayed (or probabilistic) reward decline hyperbolically according to the discounting rate k. For the mixed gambles task, a simple linear function was used in which loss aversion (λ) is the relative weighting of losses to gains in the participant's. Individuals with higher impulsive decision-making are assumed to display higher k values in the delay discounting task, lower k values in probability discounting tasks, and lower λ values in the mixed gambles task.	At baseline and 3 and 6 years after baseline
Changes in neural correlates of response inhibition	Blood oxygenation level dependent (BOLD) responses in tasks measuring response inhibition (Go/Nogo, Stroop) using 3 Tesla functional magnetic resonance imaging (fMRI).	At baseline and 3 and 6 years after baseline
Changes in neural correlates of error monitoring	BOLD responses in a task measuring error monitoring (Stroop) using 3 Tesla fMRI.	At baseline and 3 and 6 years after baseline
Changes in neural correlates of value-based decision-making	BOLD responses in a task measuring value-based decision-making using 3 Tesla fMRI.	At baseline and 3 and 6 years after baseline
Changes in structural brain characteristics	Gray matter volume, cortical thickness and white matter properties in theoretically motivated regions of interest (e.g., right inferior frontal gyrus (rIFG), ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), anterior insula (aINS)) using 3 Tesla structural MRI.	At baseline and 3 and 6 years after baseline
Changes in real-life self-control	Everyday self-control was assessed using an Ecological Momentary Assessment (EMA) protocol adapted from Hofmann, Baumeister, Förster, and Vohs (2012). Self-control was defined as enactment of desires in conflict-laden situations.	At baseline and 3 and 6 years after baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Intelligence	As control variable we assessed the intelligence quotient (IQ) using the Wechsler Intelligence Test for Adults (WIE).	At baseline
Personality	As moderator variable we assessed the NEO Five Factor Inventory (NEO-FFI; outcomes are the sum scores).	At baseline
Positive and negative affect	As moderator variable we assessed the Positive and Negative Affect Schedule (PANAS; outcomes are the sum scores).	At baseline
Changes in the action and state orientation	As moderator variable we assessed the Action-State Orientation Scale (ACS-90; outcomes are the sum scores).	At baseline and 3 and 6 years after baseline
Changes in impulsivity	As moderator variable we assessed the Barratt Impulsiveness Scale (BIS-11; outcome is the sum score).	At baseline and 3 and 6 years after baseline
Changes in self control	As moderator variable we assessed the Brief Self-Control Scale (BSCS; outcome is the sum score).	At baseline and 3 and 6 years after baseline
Changes in chronic stress	As moderator variable we assessed the Trier Inventory for Chronic Stress (TICS; outcome is the sum score).	At baseline and 3 and 6 years after baseline

Collaborators and Investigators

• Sponsor: Technische Universität Dresden
• Collaborators: German Research Foundation
• Investigators: Principal Investigator: Thomas Goschke, Prof. Dr., Technische Universitat Dresden; Principal Investigator: Michael N. Smolka, Prof. Dr., Technische Universitat Dresden; Principal Investigator: Gerhard Bühringer, Prof. Dr., Technische Universitat Dresden

Publications and helpful links

General Publications

• Kraplin A. Conceptualizing behavioural addiction in children and adolescents. Addiction. 2017 Oct;112(10):1721-1723. doi: 10.1111/add.13846. Epub 2017 May 15. No abstract available.
• Kronke KM, Wolff M, Benz A, Goschke T. Successful smoking cessation is associated with prefrontal cortical function during a Stroop task: A preliminary study. Psychiatry Res. 2015 Oct 30;234(1):52-6. doi: 10.1016/j.pscychresns.2015.08.005. Epub 2015 Aug 20.
• Kraplin A, Hofler M, Pooseh S, Wolff M, Kronke KM, Goschke T, Buhringer G, Smolka MN. Impulsive decision-making predicts the course of substance-related and addictive disorders. Psychopharmacology (Berl). 2020 Sep;237(9):2709-2724. doi: 10.1007/s00213-020-05567-z. Epub 2020 Jun 5.
• Kronke KM, Wolff M, Mohr H, Kraplin A, Smolka MN, Buhringer G, Goschke T. Predicting Real-Life Self-Control From Brain Activity Encoding the Value of Anticipated Future Outcomes. Psychol Sci. 2020 Mar;31(3):268-279. doi: 10.1177/0956797619896357. Epub 2020 Feb 5.
• Wolff M, Kronke KM, Venz J, Kraplin A, Buhringer G, Smolka MN, Goschke T. Action versus state orientation moderates the impact of executive functioning on real-life self-control. J Exp Psychol Gen. 2016 Dec;145(12):1635-1653. doi: 10.1037/xge0000229. Epub 2016 Oct 13.
• Kronke KM, Wolff M, Mohr H, Kraplin A, Smolka MN, Buhringer G, Goschke T. Monitor yourself! Deficient error-related brain activity predicts real-life self-control failures. Cogn Affect Behav Neurosci. 2018 Aug;18(4):622-637. doi: 10.3758/s13415-018-0593-5.
• Kronke KM, Wolff M, Shi Y, Kraplin A, Smolka MN, Buhringer G, Goschke T. Functional connectivity in a triple-network saliency model is associated with real-life self-control. Neuropsychologia. 2020 Dec;149:107667. doi: 10.1016/j.neuropsychologia.2020.107667. Epub 2020 Oct 31.
• Wolff M, Enge S, Kraplin A, Kronke KM, Buhringer G, Smolka MN, Goschke T. Chronic stress, executive functioning, and real-life self-control: An experience sampling study. J Pers. 2021 May;89(3):402-421. doi: 10.1111/jopy.12587. Epub 2020 Sep 3.
• Kronke KM, Mohr H, Wolff M, Kraplin A, Smolka MN, Buhringer G, Ruge H, Goschke T. Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis. Cogn Affect Behav Neurosci. 2021 Oct;21(5):936-947. doi: 10.3758/s13415-021-00913-w. Epub 2021 Jun 1.
• Wolff M, Kronke KM, Goschke T. Trait self-control is predicted by how reward associations modulate Stroop interference. Psychol Res. 2016 Nov;80(6):944-951. doi: 10.1007/s00426-015-0707-4. Epub 2015 Sep 24.
• Kraplin A, Joshanloo M, Wolff M, Kronke KM, Goschke T, Buhringer G, Smolka MN. The relationship between executive functioning and addictive behavior: new insights from a longitudinal community study. Psychopharmacology (Berl). 2022 Nov;239(11):3507-3524. doi: 10.1007/s00213-022-06224-3. Epub 2022 Oct 3.
• Kraplin A, Kupka KF, Fröhner JH, Krönke K-M, Wolff M, Smolka MN, Bühringer G, Goschke T. Personality Traits Predict Non-Substance Related and Substance Related Addictive Behaviours. SUCHT. 2022; 68(5), 263-277. doi:10.1024/0939-5911/a000780

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2014
• Primary Completion (Estimated): December 31, 2023
• Study Completion (Estimated): June 30, 2024

Study Registration Dates

• First Submitted: July 27, 2020
• First Submitted That Met QC Criteria: July 30, 2020
• First Posted (Actual): August 5, 2020

Study Record Updates

• Last Update Posted (Actual): September 8, 2023
• Last Update Submitted That Met QC Criteria: September 6, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: Cognitive control; Impulsivity; Decision-making; Risk-seeking
• Additional Relevant MeSH Terms: Mental Disorders; Chemically-Induced Disorders; Alcohol-Related Disorders; Substance-Related Disorders; Compulsive Behavior; Impulsive Behavior; Alcoholism; Tobacco Use Disorder; Behavior, Addictive
• Other Study ID Numbers: SFB940 C01; Project number 178833530 (Other Grant/Funding Number: Deutsche Forschungsgemeinschaft (DFG))

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Data and analytic codes within publications will be shared via the Open Science Framework (OSF). Final archiving of the participant data will be conducted with the Open Access Repository and Archive at the Technische Universität Dresden (OpARA).
• IPD Sharing Time Frame: Data and analytic codes within publications will be shared from the time of publications. Final archived data will be shared after the study is completed in 2024.
• IPD Sharing Access Criteria: Open Access
• IPD Sharing Supporting Information Type: ANALYTIC_CODE

Study Data/Documents

1. Official study group website
2. English description of the Collaborative Research Centre
   Information comments: The study is conducted within the Collaborative Research Centre 940 funded by the Deutsche Forschungsgemeinschaft (DFG).

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No