On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory Signals

Abstract

Drug addiction is a chronic brain-based disorder that affects a person's behavior and leads to an inability to control drug usage. Ubiquitous physiological sensing technologies to detect illicit drug use have been well studied and understood for different types of drugs. However, we currently lack the ability to continuously and passively measure the user state in ways that might shed light on the complex relationships between cocaine-induced subjective states (e.g., craving and euphoria) and compulsive drug-seeking behavior. More specifically, the applicability of wearable sensors to detect drug-related states is underexplored. In the current work, we take an initial step in the modeling of cocaine craving, euphoria and drug-seeking behavior using electrocardiographic (ECG) and respiratory signals unobtrusively collected from a wearable chest band. Ten experienced cocaine users were studied using a human laboratory paradigm of self-regulated (i.e., "binge") cocaine administration, during which self-reported visual analog scale (VAS) ratings of cocaine-induced subjective effects (i.e., craving and euphoria) and behavioral measures of drug-seeking behavior (i.e., button clicks for drug infusions) are collected. Our results are encouraging and show that self-reported VAS Craving scores are predicted with a normalized root-mean-squared error (NRMSE) of 17.6% and a Pearson correlation coefficient of 0.49. Similarly, for VAS Euphoria prediction, an NRMSE of 16.7% and a Pearson correlation coefficient of 0.73 were achieved. We further analyze the relative importance of different morphology-related ECG and respiratory features for craving and euphoria prediction. Demographic factor analysis reveals how one single factor (i.e., average dollar ($) per cocaine use) can help to further boost the performance of our craving and euphoria models. Lastly, we model drug-seeking behavior using cardiac and respiratory signals. Specifically, we demonstrate that the latter signals can predict participant button clicks with an F1 score of 0.80 and estimate different levels of click density with a correlation coefficient of 0.85 and an NRMSE of 17.9%.

Zoom-Based Mindfulness-Oriented Recovery Enhancement Plus Just-in-Time Mindfulness Practice Triggered by Wearable Sensors for Opioid Craving and Chronic Pain

“…We show that machine learning models using attributes derived from optical heart rate, SpO2, and accelerometer sensors can detect symptom improvement following JITAI-initiated mindfulness practice sessions with some degree of sensitivity and specificity. Previous research has utilized biometric data to create machine learning models that predict stress (Carreiro et al, 2020 ; Chen et al, 2021 ; Sandulescu et al, 2015 ), pain (Chen et al, 2021 ; Pouromran et al, 2021 ), and craving (Carreiro et al, 2020 ; Gullapalli et al, 2019 ). Sensitivity and specificity metrics to detect stress and craving among populations who use substances have been observed to range between 0.65 and 0.76 (Carreiro et al, 2020 ) and are thus comparable to the values we obtained.…”

“…Developing an objective, machine learning metric for mindfulness effectiveness can unlock new opportunities for a personalized mindfulness approach, using an intelligent recommendation system that can identify optimal mindfulness practices based on user-specific characteristics from a range of possible techniques. Wearable computing systems have also been proposed to recognize moments of craving (Gullapalli et al, 2019 ). By integrating such a craving detection system with the objective mindfulness effectiveness estimation algorithm, we envision a complete closed-loop system that will not only identify optimal moments (based on craving inference) to trigger mindfulness intervention but also continuously learn to update the recommendations based on person-specific traits, contexts, and outcomes.…”

“…Continuous ambulatory monitoring of HRV may alert chronic pain patients on LTOT to practice mindfulness during times of stress, and thereby might prevent opioid misuse. In support of this contention, momentary ratings of stress, positive affect, and craving have been associated with ambulatory measures of HRV (Alinia et al, 2021 ; Bertz et al, 2018 ; Gullapalli et al, 2019 ; Määttänen et al, 2021 ; Murray et al, 2022 ).…”

Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing

“…Oftentimes UbiComp technologies are built and evaluated as if they were fully autonomous, while in reality, they operate in a complicated sociotechnical system moderated by institutional structures and human stakeholders (the "framing trap" [115]). For instance, in opioid use tracking [46], and drug-seeking behavior sensing [47] applications -both encountered in the included papers-the consequence of a predictive outcome depends on the assumption of punitive or restorative justice [48]. According to traditional punitive justice, punishment serves as a deterrent for wrongdoing, and a means to alter behavior.…”

“…As an example, if a ubiquitous substance abuse detection technology is adopted by a restorative system, a false negative outcome might derive an individual struggling with drug addiction from crucial access to rehabilitation services. For instance, "if such a device were found to be reliable, it could be used to monitor early treatment response and therefore could allow clinicians to more rapidly optimize patient care" [47]. On the contrary, if the exact same technology is employed as part of a punitive system, then a false negative outcome might lead to a wrongful accusation or conviction.…”