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CompletedNCT04498988Updated Aug 9, 2024

Volitional Dysfunction in Self-control Failures and Addictive Behaviors

An observational study in Addictive Behavior, Alcohol Use Disorder (AUD) and Tobacco Use Disorder, sponsored by Technische Universität Dresden. Completed at 1 site in Germany. Open to participants aged 19 Years to 27 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-08-09.

Sponsored by Technische Universität Dresden · Observational

Study type
Observational
Model
Ecologic or community
Time perspective
Prospective
Enrollment
338
Ages
19 Years to 27 Years
Sex
All
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Study summary

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.

Read the 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.

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Conditions studied

  • Addictive Behavior
  • Alcohol Use Disorder (AUD)
  • Tobacco Use Disorder
  • Self-Control
  • Executive Dysfunction

Keywords

  • Cognitive control
  • Decision-making
  • Impulsivity
  • Risk-seeking
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In context

Alcoholism

1,606 studies on the registry are indexed under Alcoholism; 329 are open to participants now.

This study's enrollment of 338 is above the median of 180 across 173 observational studies indexed under Alcoholism.

Browse Alcoholism studies →

Lead sponsor

Technische Universität Dresden is the lead sponsor of 237 studies on the registry; 45 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
19 Years to 27 Years
Sexes eligible
All
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%).

Inclusion criteria

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

Exclusion criteria

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
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Study design

Observational model
Ecologic or community
Time perspective
Prospective
Enrollment
338 participants (actual)
Patient registry
No

Groups and cohorts

  • 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

Interventions

  • OtherObservational study without interventions
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What researchers measure

Primary outcomes

  1. 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)

    Time frame: At baseline and 1, 2, 3, 4, 5, 6, 7 years after baseline

  2. Changes in quantity and frequency of addictive behaviors

    Changes in quantity and frequency of addictive behaviours, which are combined into a quantity-frequency index.

    Time frame: At baseline and 1, 2, 3, 4, 5, 6, 7 years after baseline

  3. 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).

    Time frame: At baseline and 3 and 6 years after baseline

  4. 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.

    Time frame: At baseline and 3 and 6 years after baseline

  5. 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).

    Time frame: At baseline and 3 and 6 years after baseline

  6. Changes in neural correlates of error monitoring

    BOLD responses in a task measuring error monitoring (Stroop) using 3 Tesla fMRI.

    Time frame: At baseline and 3 and 6 years after baseline

  7. Changes in neural correlates of value-based decision-making

    BOLD responses in a task measuring value-based decision-making using 3 Tesla fMRI.

    Time frame: At baseline and 3 and 6 years after baseline

  8. 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.

    Time frame: At baseline and 3 and 6 years after baseline

  9. 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.

    Time frame: At baseline and 3 and 6 years after baseline

Secondary outcomes

  1. Intelligence

    As control variable we assessed the intelligence quotient (IQ) using the Wechsler Intelligence Test for Adults (WIE).

    Time frame: At baseline

  2. Personality

    As moderator variable we assessed the NEO Five Factor Inventory (NEO-FFI; outcomes are the sum scores).

    Time frame: At baseline

  3. Positive and negative affect

    As moderator variable we assessed the Positive and Negative Affect Schedule (PANAS; outcomes are the sum scores).

    Time frame: At baseline

  4. Changes in the action and state orientation

    As moderator variable we assessed the Action-State Orientation Scale (ACS-90; outcomes are the sum scores).

    Time frame: At baseline and 3 and 6 years after baseline

  5. Changes in impulsivity

    As moderator variable we assessed the Barratt Impulsiveness Scale (BIS-11; outcome is the sum score).

    Time frame: At baseline and 3 and 6 years after baseline

  6. Changes in self control

    As moderator variable we assessed the Brief Self-Control Scale (BSCS; outcome is the sum score).

    Time frame: At baseline and 3 and 6 years after baseline

  7. Changes in chronic stress

    As moderator variable we assessed the Trier Inventory for Chronic Stress (TICS; outcome is the sum score).

    Time frame: At baseline and 3 and 6 years after baseline

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Study locations

1 site
  • Technische Universität Dresden, Faculty of Psychology
    Dresden, 01062, Germany
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References and documents

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. PubMed 28508582 ↗
  • 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. PubMed 26321462 ↗
  • 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. PubMed 26403462 ↗
  • 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. PubMed 32500211 ↗
  • 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. PubMed 32024421 ↗
  • 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. PubMed 27736135 ↗
  • 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. PubMed 29654477 ↗
  • 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. PubMed 33130158 ↗
  • 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. PubMed 32858777 ↗
  • 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. PubMed 34075542 ↗
  • 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. PubMed 36190537 ↗
  • 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
  • Kraplin A, Joshanloo M, Wolff M, Frohner JH, Baeuchl C, Kronke KM, Buhringer G, Smolka MN, Goschke T. No evidence for a reciprocal relationship between daily self-control failures and addictive behavior in a longitudinal study. Front Psychol. 2024 May 1;15:1382483. doi: 10.3389/fpsyg.2024.1382483. eCollection 2024. PubMed 38751764 ↗

Individual participant data

Plan to share: Yes — 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).

Supporting information: Analytic code

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Aug 9, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT04498988
Lead sponsor
Technische Universität Dresden
Collaborators
German Research Foundation
Responsible party
Sponsor
First posted
Aug 5, 2020
Start date
Dec 1, 2014
Primary completion
Mar 31, 2024
Completion
Jun 30, 2024
Last update
Aug 9, 2024

Study contacts

Thomas Goschke, Prof. Dr.
principal investigator · Technische Universität Dresden
Michael N. Smolka, Prof. Dr.
principal investigator · Technische Universität Dresden
Gerhard Bühringer, Prof. Dr.
principal investigator · Technische Universität Dresden

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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