English

CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses

Quantitative Methods 2026-03-10 v2

Abstract

Coping with stress is one of the most frequently cited reasons for chronic cannabis use. Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would be more pronounced during moments of consumption. However, there is a scarcity of publicly available datasets that allow such hypotheses to be tested in real-world environments. This paper introduces a dataset named CAN-STRESS, collected using Empatica E4 wristbands. The dataset includes physiological measurements such as skin conductance, heart rate, and skin temperature from 82 participants (39 cannabis users and 43 non-users) as they went about their daily lives. Additionally, the dataset includes self-reported surveys where participants documented moments of cannabis consumption, exercise, and rated their perceived stress levels during those moments. In this paper, we publicly release the CAN-STRESS dataset, which we believe serves as a highly reliable resource for examining the impact of cannabis on stress and its associated physiological markers. I

Keywords

Cite

@article{arxiv.2503.19935,
  title  = {CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses},
  author = {Reza Rahimi Azghan and Nicholas C. Glodosky and Ramesh Kumar Sah and Carrie Cuttler and Ryan McLaughlin and Michael J. Cleveland and Hassan Ghasemzadeh},
  journal= {arXiv preprint arXiv:2503.19935},
  year   = {2026}
}