Summary:
A longitudinal study utilizing gamified smartphone tasks has demonstrated that shifts in risky decision-making precede increases in alcohol consumption in people with mild to moderate alcohol use disorder (AUD), rather than occurring as a consequence of drinking. By tracking monthly cognitive-behavioral fluctuations, researchers identified that changes in risk tolerance and information gathering forecast subsequent surges in alcohol intake, paving the way for real-time digital warning systems.
Key Facts:
- Temporal Direction Established: Escalations in risk-taking behavior in mixed-gambling contexts and drops in information sampling directly predicted higher alcohol intake in the following month, confirming risk-taking is a precursor rather than a consequence.
- Non-Reciprocal Pattern: Increased alcohol consumption did not predict future spikes in risky decision-making, ruling out reverse causality even after controlling for baseline drinking volumes.
- Specific Cognitive Biomarker: While general self-control metrics (working memory and response inhibition) did not correlate with monthly drinking changes, subtle shifts in risk sensitivity and information sampling proved highly predictive.
Source: Elsevier / University Hospital Würzburg
Alcohol use disorder (AUD) represents a leading contributor to global disability, chronic illness, and preventable mortality. While decades of cross-sectional clinical research have established that individuals with AUD often display impaired cognitive control and elevated risk-taking, scientists have faced a fundamental “chicken-or-egg” question: do neurocognitive impairments precede and trigger hazardous drinking, or does chronic alcohol exposure degrade executive decision-making?
Now, a pioneering one-year longitudinal study published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging has resolved this question of temporal order.
By tracking individuals with mild to moderate AUD via gamified smartphone tasks, researchers from University Hospital Würzburg and Technische Universität Dresden demonstrated that subtle escalations in risky decision-making precede surges in alcohol consumption, not vice versa.
Tracking Real-Life Fluctuations via Gamified Mobile Tools
Evaluating natural variations in cognitive control and decision-making has historically presented significant methodological hurdles. Traditional clinic-based self-reports and retrospective questionnaires fail to capture dynamic shifts in executive function as they occur in daily life.
To capture these real-world fluctuations, the investigators developed gamified, smartphone-based decision-making tasks administered monthly over the course of a full year. Participants with mild to moderate AUD—a population characterized by meaningful fluctuations in drinking volume rather than fixed dependence—completed mobile tasks assessing cognitive control, risk tolerance across different reward/loss scenarios, and information sampling behavior.
The longitudinal data uncovered a clear predictive timeline:
- Elevated Mixed Risk: Participants who exhibited heightened risk-taking during mixed gambling tasks (scenarios involving potential gains and losses) significantly escalated their alcohol consumption the following month.
- Altered Loss Sensitivity: Lower risk-taking in purely loss-avoidance contexts similarly heralded an upcoming increase in drinking.
- Reduced Information Sampling: When participants showed decreased information sampling biases—making choices impulsively before gathering adequate evidence—their alcohol intake climbed in the subsequent month.
“We found that monthly fluctuations in aspects of decision making predicted subsequent alcohol consumption,” explained co-lead investigator Hilmar Zech, PhD, postdoctoral research fellow at University Hospital Würzburg and Technische Universität Dresden. “Specifically, participants shifted to higher monthly alcohol consumption when their risk-taking was higher in a mixed gambling context and lower in a loss context in the preceding month. Further, when information sampling biases decreased, participants consumed more alcohol in the subsequent month.”
Specificity and Non-Reciprocity
Importantly, the relationship was entirely unidirectional. Prior alcohol intake did not forecast subsequent deterioration in cognitive control or decision-making on subsequent monthly tests.
Furthermore, general measures of impulse control, such as working memory capacity and motor response inhibition, remained stable and did not track drinking shifts. This indicates that relapse vulnerability is driven by specific computations regarding risk evaluation and evidence accumulation rather than generalized executive failure.
“This temporal direction was specific and was not observed vice versa, even after accounting for the amount of previous drinking,” noted co-lead investigator Lorenz Deserno, MD, PhD, Principal Investigator of the Computational and Developmental Psychiatry Lab at University Hospital Würzburg. “The findings also showed that fluctuations in self-control and reward-driven behavior were not associated with fluctuations in drinking.”
Toward Real-Time, Just-In-Time Interventions
By validating smartphone-based behavioral assessments as objective predictors of real-life drinking surges, the study lays the groundwork for digital phenotyping in addiction medicine.
“Decision making and cognitive control cannot be accurately examined through interviews or self-reports and instead require experimental tasks,” said Cameron S. Carter, MD, Editor-in-Chief of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging and Professor at the University of California, Irvine School of Medicine. “The findings of this longitudinal assessment suggest that smartphone-based gamified tasks show promise in identifying periods of heightened risk in people with AUD, paving the way for mechanism-based, real-time interventions.”
While clinical applications require further validation, passive or micro-gamified mobile monitoring could eventually power “just-in-time” adaptive interventions, alerting users or their clinical care teams when cognitive risk markers signal an impending binge before the first drink is consumed.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this AUD and Neuroscience Research:
- Media Contact: Eileen Leahy
- Source: Elsevier
- Image Credit: Image credited to Neuroscience News
- Original Research is Open Access: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (Oct 1, 2026). “Fluctuations in Real-Life Drinking in Alcohol Use Disorder: A 1-Year Longitudinal Study Using Gamified Tasks of Decision Making and Cognitive Control.” Authors: Hilmar Zech, Maria Waltmann, Dominic Reichert, Fabian Arntz, Ying Lee, Markus Reichert, Rachel L. Bedder, Robb B. Rutledge, Thomas Goschke, Bernd Lenz, Patrick Bach, Falk Kiefer, Michael Marxen, Rainer Spanagel, Heike Tost, Ulrich Ebner-Priemer, Michael A. Rapp, Andreas Heinz, Ray Dolan, Michael N. Smolka, and Lorenz Deserno.
- DOI: 10.1016/j.bpsc.2026.07.019
Abstract
Fluctuations in Real-Life Drinking in Alcohol Use Disorder: A 1-Year Longitudinal Study Using Gamified Tasks of Decision Making and Cognitive Control
Background
Alcohol use disorder (AUD) is a major contributor to global disability and mortality. Cross-sectional studies have linked AUD to reduced cognitive control and heightened risky decision making. However, the temporal direction of these effects remains unknown: are cognitive-behavioral alterations a consequence or a precursor of changes in drinking?
Methods
We deployed a battery of smartphone-based, gamified tasks in a 1-year longitudinal ecological momentary assessment study of 603 participants diagnosed with mostly mild-to-moderate AUD. Tasks measured cognitive control (working memory, response inhibition) and decision making (risk-taking, information sampling). Participants completed tasks monthly and reported alcohol consumption every 2 days.
Results
We found that monthly fluctuations in aspects of decision making predicted subsequent consumption. Specifically, participants shifted to higher monthly alcohol consumption when their risk-taking was higher in a mixed gambling context and lower in a loss context in the preceding month. Furthermore, when information sampling biases decreased, participants consumed more alcohol in the subsequent month. This temporal direction—that within-subject fluctuations in task outcomes preceded changes in monthly drinking—was specific, was not observed vice versa, and survived correction for autocorrelation in drinking, indicating risky decision making as a precursor to subsequent drinking. Fluctuations in cognitive control and risk-taking in a win context were not associated with fluctuations in drinking.
Conclusions
Our findings offer novel insights into the cognitive-behavioral forces driving changes in alcohol consumption in AUD: specific alterations in decision making precede changes in consumption. These findings suggest that smartphone-based gamified tasks show promise in identifying periods of heightened risk, paving the way for mechanism-based, real-time interventions in AUD.
