New Research Shows DRUID App Can Distinguish Alcohol and Cannabis Impairment

Study demonstrates ability to identify the cause, not just the presence, of impairment

Published on Feb. 11, 2026

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A new study by Impairment Science, Inc. has found that the DRUID mobile app can reliably distinguish between alcohol-related impairment and cannabis-related impairment through advanced machine learning techniques. The findings represent a major advance in impairment science, showing that digital cognitive and psychomotor testing can identify not only whether a person is impaired, but also the likely cause of that impairment.

Why it matters

Accurately identifying the cause of impairment has long been one of the most difficult challenges in public safety, occupational health, and clinical assessment. These results suggest that a performance-based, brain-function approach may succeed where substance-detection technologies have struggled, opening the door to distinguishing alcohol from cannabis impairment as well as identifying other sources of impairment such as fatigue, concussion, or medical conditions.

The details

The study analyzed nearly 600 supervised test sessions from participants who consumed alcohol on one occasion and cannabis on another. Using advanced machine-learning techniques, researchers found that alcohol and cannabis produce distinct, measurable impairment signatures in DRUID test data. More than 300 models produced machine learning F1 accuracy scores above 0.85 for both substances, a level of accuracy considered good to excellent. High accuracy for alcohol classification did not come at the expense of cannabis accuracy, indicating robust and well-calibrated models.

  • The study, titled 'Preliminary Research to Determine Whether Data Generated by DRUID Can Distinguish Between Alcohol and Cannabis Impairment', was published on February 11, 2026.

The players

Impairment Science, Inc.

A company that develops technology to measure functional impairment for industry, academia, medicine, and athletics. Its DRUID app, along with DRUID Enterprise, helps organizations and individuals proactively manage risk, improve safety outcomes, and support performance across safety-sensitive industries.

Rob Schiller

The CEO of Impairment Science, Inc.

Max Daniller-Varghese, PhD

The co-author of the study.

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What they’re saying

“Across hundreds of machine-learning models, we observed consistently strong and well-calibrated performance in distinguishing alcohol from cannabis impairment.”

— Max Daniller-Varghese, PhD, Co-author of the study (einpresswire.com)

“Historically, impairment detection has focused on identifying chemicals in the body. But chemical detection - especially for cannabis - does not reliably measure impairment, and it cannot address the many non-chemical causes of impairment. Our results show strong evidence that alcohol and cannabis affect cognition and motor performance in different ways, and that DRUID can detect those differences.”

— Rob Schiller, CEO of Impairment Science, Inc. (einpresswire.com)

What’s next

The researchers plan to continue studying the DRUID app's ability to distinguish between different types of impairment and explore its potential applications in public safety, occupational health, and clinical assessment.

The takeaway

This research represents a significant advancement in impairment science, demonstrating that a performance-based, brain-function approach can reliably distinguish between alcohol and cannabis impairment. This opens the door to more accurate and comprehensive impairment detection, which could have far-reaching implications for public safety, workplace safety, and individual health and well-being.