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Identifying biomarkers of drug use recurrence using wearable device technologies and phone applications
James J Mahoney1, Victor S Finomore2, Jennifer L Marton1
1WVU Department of Behavioral Medicine and Psychiatry, West Virginia University School of Medicine, Rockefeller Neuroscience Institute, Morgantown, WV, 26506, USA; WVU Department of Neuroscience, West Virginia University School of Medicine, Rockefeller Neuroscience Institute, Morgantown, WV, 26506, USA.
Background:
Identifying predictors of drug use recurrence (DUR) is critical to combat the addiction epidemic. Wearable devices and phone-based applications for obtaining self-reported assessments in the patient's natural environment (e.g., ecological momentary assessment; EMA) have been used in various healthcare settings. However, the utility of combining these technologies to predict DUR in substance use disorder (SUD) has not yet been explored. This study investigates the combined use of wearable technologies and EMA as a potential mechanism for identifying physiological/behavioral biomarkers of DUR.
Methods:
Participants, recruited from an SUD treatment program, were provided with a commercially available wearable device that continuously monitors biometric signals (e.g., heart rate/variability [HR/HRV], sleep characteristics). They were also prompted daily to complete an EMA via phone-based application (EMA-APP) that included questionnaires regarding mood, pain, and craving.
Results:
Seventy-seven participants are included in this pilot study (34 participants experienced a DUR during enrollment). Wearable technologies revealed that physiological markers were significantly elevated in the week prior to DUR relative to periods of sustained abstinence (p<0.001). Results from the EMA-APP revealed that those who experienced a DUR reported greater difficulty concentrating, exposure to triggers associated with substance use, and increased isolation the day prior to DUR (p<0.001). Compliance with study procedures during the DUR week was lower than any other period of measurement (p<0.001).
Conclusions:
These results suggest that data acquired via wearable technologies and the EMA-APP may serve as a method of predicting near-term DUR, thereby potentially prompting intervention before drug use occurs.
