JMIR Publications

ABSTRACT

Background:

Substance Use Disorder (SUD) involves excessive substance consumption and persistent reward-seeking behaviors, leading to serious physical, cognitive, and social consequences. This disorder is a global health crisis tied to increased mortality, unemployment, and reduced quality of life. Altered brain connectivity, circadian rhythms, and dopaminergic pathways contribute to sleep disorders, anxiety, and stress, which worsen SUD severity and relapse. Factors like trauma and socioeconomic disadvantages heighten risk. Digital health technologies, including wearables and machine learning, show promise for diagnosis, monitoring, and intervention, from relapse prediction to early detection of comorbidities. With high relapse rates and younger patient cases, these innovations could enhance treatment outcomes of SUD.

Objective:

Develop and validate a predictive model with Machine Learning for the duration of a therapy and the rehabilitation or relapse in patients with SUD, using digital physiological measurements, psychological profile, automatic facial emotion recognition and the emotional state during craving.

Methods:

the study will be conducted with adult male patients with SUD at a rehabilitation center and control volunteers. Participants will undergo demographic, psychological and craving assessment, and will also be monitored using a smartwatch for eighteen or six months respectively. All participants will be reassessed at the sixth month of monitoring. The collected data will be then used to train models with a neural network, which will then be validated against other models and compared with other algorithms. Demographic, psychological, digital biomarkers and craving profiles will be created, correlations will be analyzed, and they will be compared with controls, to generate a digital phenotype of SUD. When the model achieves an adequate validity (AUC=≥0.80) a graphic user interface will then be designed for clinical use.

Results:

The integration of accessible wearables, routine recovery data, and emotional state assessments could enhance SUD rehabilitation by enabling personalized treatment and reducing relapse risks. This approach, leveraging affordable technology, addresses global public health challenges and supports social reintegration, particularly for economically vulnerable populations.

Conclusions:

Accessible and affordable wearables, like commercial smartwatches, combined with psychologic, demographic and emotional state data, used with a machine learning predictive model, may be able to be used as tools to enhance SUD rehabilitation and preventing relapse.