Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.