Quantifying Opioid Withdrawal through Cardio-mechanical Variability using Multi-modal Wearable Sensors

Michael John Cho, Vikram Abbaraju, Farhan N. Rahman, Jeffrey C. Liu, Afra Nawar, Cali E. Murray, Yi Yang, Joshua Chiok, Jaiyoun Choi, Rachel Bull, Lucy H. Shallenberger, Viola Vaccarino, Amit Shah, J. Douglas Bremner, Omer T. Inan

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Keywords: Wearable Sensors, Seismocardiography, Dynamic Time Warping, Opioid Withdrawal, Cardiovascular Variability, Biomedical Signal Processing, Remote Health Monitoring

TL;DR: By analyzing the heart's mechanical signals with a wearable sensor, this study found that the beat-to-beat shape becomes less variable as opioid withdrawal intensifies, providing an objective way to measure its severity.

Abstract: Opioid use disorder (OUD) is a significant global health issue, leading to severe physiological and psychological impacts and substantial societal costs. Current methods for assessing opioid withdrawal, primarily relying on subjective scales, suffer from limitations such as incomplete symptom capture, recall bias, and imprecision. Wearable sensor technologies offer a promising alternative for objective assessment, with previous studies demonstrating their ability to detect opioid use and measure related physiological changes. In this study we investigated the correlation between local cardio-mechanical variability quantified using dynamic time warping (DTW) distances of seismocardiogram (SCG) signals and subjective opioid withdrawal severity (SOWS) scores. In a 7-day in-patient protocol for individuals with OUD (N = 13), we found a statistically significant inverse correlation: shorter median DTW distances and reduced variance in SCG signals were associated with higher subjective withdrawal scores with statistically significant differences between the highest withdrawal bin and the two lowest bins (p=0.038 and p=0.044, respectively). Our results suggests that local cardio-mechanical variability, as captured by wearable sensors and analyzed with DTW, can serve as a valuable indicator for quantifying opioid withdrawal severity, potentially enabling more timely and effective preventive care.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Jeffrey Liu

Submission Number: 101