The primary aim of WAIT-AYA is to evaluate a newly developed app-based version of alcohol-specific inhibition training. The project WAIT-AYA administers this alcohol-specific inhibition training for the first time as an internet-based intervention (iAlc-IT), thereby increasing its availability and flexible implementation. This double-blind, multicentric clinical pilot RCT I) tests the acceptability, usability, and feasibility of iAlc-IT, II) gathers preliminary insight about its effects on drinking behavior, and III) examines its neurophysiological effects. To this end, 210 young patients with AUD are recruited in online and face-to-face settings in 5 specialized treatment centers and randomly assigned to receive either iAlc-IT or an internet-based active control condition (iCON) in addition to treatment at usual (TAU). The project thus not only informs a future implementation of iAlc-IT as a blended treatment component into routine care for adolescents and young adults with AUD as well as guides the development of future full-scale RCTs on that subject. Furthermore, the neurophysiological knowledge gathered provides important insights into working mechanisms of iAlc-IT, which adds to the scientific anchorage of this intervention and delineates its neuronal working mechanisms.
- Link: WAIT-AYA on Frutiger Webpage
INTRA investigates the effects of a computerized inhibition training in currently abstinent patients with alcohol use disorder. It examines effects on subjective, behavioral, experimental and neurophysiological level.
More about INTRA: INTRA on SNF database
Predicting post-treatment symptom trajectories is crucial in order to inform decisions concerning type, intensity, and duration of treatment. A large body of research shows associations between predictors and post-treatment outcomes in samples with alcohol use disorder (AUD), but these models do not provide adequate predictions for an individual patient. Recently, machine learning algorithms have been used to establish predictive models in substance use disorder research. MLAUD aims to expand this research and to investigate how machine learning algorithms can be used to improve individual, post-treatment outcome predictions for patients with AUD.
EMOPRO traces the neurophysiological correlates of emotional processing before and after a psychotherapeutic intervention targeting the processing of interpersonal pain.
NECAAD investigates neurophysiological correlates of inhibition and cue reactivity with multi-channel EEG and fMRI in patients with alcohol addiction.
The concept of motivational incongruence, as incorporated in Grawes consistency theory, refers to the fact that the experiences we make do not always match our needs and motives. The amount of motivational inconsistency is highly linked to psychological wellbeing and psychopathological symptoms. MINK traces the neurophysiological correlates of this important transdiagnostical concept with multi-channel EEG.
