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A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

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Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological

Shizhe Li¹, Chunzhi Fan², Ali Kargarandehkordi³

¹Department of Statistics, Stanford University, Stanford, CA 94305, USA.

AI (Basel, Switzerland)

|May 12, 2025

Summary

This summary is machine-generated.

Personalized machine learning using Fitbit data shows promise for detecting substance use. Self-supervised learning (SSL) models improved individualized feature extraction, enhancing early detection capabilities for digital interventions.

Keywords:

Fitbit personalized models remote monitoring self-supervised learning substance use wearables

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Area of Science:

Digital health
Machine learning
Wearable biosensors

Background:

Substance use disorders impact millions, necessitating innovative detection methods.
Wearable biosensor data offers potential for real-time substance use monitoring.
Data heterogeneity in machine learning models hinders accurate substance use detection.

Purpose of the Study:

To evaluate personalized machine learning models for detecting drug use from wearable biosignals.
To compare traditional supervised learning with self-supervised learning (SSL) enhanced models.
To assess the feasibility of using Fitbit data for substance use detection.

Main Methods:

Collected Fitbit Charge 5 data and ecological momentary assessments from 9 participants.
Implemented a baseline 1D-CNN supervised learning model.
Developed an experimental SSL-enhanced CNN model for improved individualized feature extraction.

Main Results:

The SSL-enhanced models achieved a higher average area under the receiver operating characteristic curve (0.729) compared to supervised CNNs (0.695).
Optimal threshold selection allowed for balancing sensitivity and specificity.
Findings indicate Fitbit data's potential for substance use monitoring.

Conclusions:

Personalized machine learning, particularly with SSL, shows potential for detecting substance use from wearable data.
Further large-scale research is needed to validate these findings in diverse populations.
This approach could inform the development of real-time digital interventions for substance use disorders.