Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity ha...

Full description

Saved in:
Bibliographic Details
Published in:AI (Basel) 2024-12, Vol.5 (4), p.2725-2738
Main Authors: Li, Shizhe, Fan, Chunzhi, Kargarandehkordi, Ali, Sun, Yinan, Slade, Christopher, Jaiswal, Aditi, Benzo, Roberto M, Phillips, Kristina T, Washington, Peter
Format: Article
Language:English
Subjects:
Addictive behaviors
Artificial intelligence
Artificial neural networks
Assessments
Data collection
Deep learning
Feasibility studies
Fitbit
Heterogeneity
Labels
Machine learning
Optimization
personalized models
Real time
remote monitoring
Self-supervised learning
Sensors
Sleep
Smartphones
substance use
wearables
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale.
ISSN:2673-2688
DOI:10.3390/ai5040131