Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study
- PMID: 32193048
- PMCID: PMC7197459
- DOI: 10.1016/j.drugalcdep.2020.107929
Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study
Abstract
Aims: To determine the accuracy of a wearable sensor to detect and differentiate episodes of self-reported craving and stress in individuals with substance use disorders, and to assess acceptability, barriers, and facilitators to sensor-based monitoring in this population.
Methods: This was an observational mixed methods pilot study. Adults enrolled in an outpatient treatment program for a substance use disorder wore a non-invasive wrist-mounted sensor for four days and self-reported episodes of stress and craving. Continuous physiologic data (accelerometry, skin conductance, skin temperature, and heart rate) were extracted from the sensors and analyzed via various machine learning algorithms. Semi-structured interviews were conducted upon study completion, and thematic analysis was conducted on qualitative data from semi-structured interviews.
Results: Thirty individuals completed the protocol, and 43 % (N = 13) were female. A total of 41 craving and 104 stress events were analyzed. The differentiation accuracies of the top performing models were as follows: stress vs. non-stress states 74.5 % (AUC 0.82), craving vs. no-craving 75.7 % (AUC 0.82), and craving vs. stress 76.8 % (AUC 0.8). Overall participant perception was positive, and acceptability was high. Emergent themes from the exit interviews included a perception of connectedness and increased mindfulness related to wearing the sensor, both of which were reported as helpful to recovery. Barriers to engagement included interference with other daily wear items, and perceived stigma.
Conclusions: Wearable sensors can be used to objectively differentiate episodes of craving and stress, and individuals in recovery from substance use disorder are accepting of continuous monitoring with these devices.
Keywords: Craving; Sensor; Stress; Substance use disorder; Wearable; mHealth.
Copyright © 2020 Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest The authors have no conflicts of interest to disclose.
Figures





Similar articles
-
Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.J Med Internet Res. 2018 Jun 8;20(6):e210. doi: 10.2196/jmir.9410. J Med Internet Res. 2018. PMID: 29884610 Free PMC article.
-
Substance use outcomes for mindfulness based relapse prevention are partially mediated by reductions in stress: Results from a randomized trial.J Subst Abuse Treat. 2018 Aug;91:37-48. doi: 10.1016/j.jsat.2018.05.002. Epub 2018 May 20. J Subst Abuse Treat. 2018. PMID: 29910013 Clinical Trial.
-
Cognitive-behavioral stress management for individuals with substance use disorders: a pilot study.J Nerv Ment Dis. 2007 Aug;195(8):662-8. doi: 10.1097/NMD.0b013e31811f3ffd. J Nerv Ment Dis. 2007. PMID: 17700298 Clinical Trial.
-
Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms.Am J Addict. 2022 Nov;31(6):535-545. doi: 10.1111/ajad.13341. Epub 2022 Sep 5. Am J Addict. 2022. PMID: 36062888
-
Current reporting of usability and impact of mHealth interventions for substance use disorder: A systematic review.Drug Alcohol Depend. 2020 Oct 1;215:108201. doi: 10.1016/j.drugalcdep.2020.108201. Epub 2020 Aug 2. Drug Alcohol Depend. 2020. PMID: 32777691 Free PMC article.
Cited by
-
Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study.JMIR Res Protoc. 2024 Feb 7;13:e46493. doi: 10.2196/46493. JMIR Res Protoc. 2024. PMID: 38324375 Free PMC article.
-
Using a Smartwatch App to Understand Young Adult Substance Use: Mixed Methods Feasibility Study.JMIR Hum Factors. 2024 Jun 20;11:e50795. doi: 10.2196/50795. JMIR Hum Factors. 2024. PMID: 38901024 Free PMC article.
-
Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing.AI (Basel). 2024 Dec;5(4):2725-2738. doi: 10.3390/ai5040131. Epub 2024 Dec 3. AI (Basel). 2024. PMID: 40351335 Free PMC article.
-
Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.Anesth Analg. 2025 Aug 1;141(2):393-402. doi: 10.1213/ANE.0000000000007244. Epub 2024 Oct 16. Anesth Analg. 2025. PMID: 39413034
-
Application of Digital Medicine in Addiction.J Shanghai Jiaotong Univ Sci. 2022;27(2):144-152. doi: 10.1007/s12204-021-2391-4. Epub 2021 Nov 28. J Shanghai Jiaotong Univ Sci. 2022. PMID: 34866856 Free PMC article.
References
-
- Benitez D, Gaydecki PA, Zaidi A, Fitzpatrick AP, 2001. The use of the Hilbert transform in ECG signal analysis. Comput Biol Med 31(5), 399–406. - PubMed