Wearable Biosensors to Detect Physiologic Change During Opioid Use

Abstract

Introduction Opioid analgesic use is a major cause of morbidity and mortality in the US, yet effective treatment programs have a limited ability to detect relapse. The utility of current drug detection methods is often restricted due to their retrospective and subjective nature. Wearable biosensors have the potential to improve detection of relapse by providing objective, real time physiologic data on opioid use that can be used by treating clinicians to augment behavioral interventions. Methods Thirty emergency department (ED) patients who were prescribed intravenous opioid medication for acute pain were recruited to wear a wristband biosensor. The biosensor measured electrodermal activity, skin temperature and locomotion data, which was recorded before and after intravenous opioid administration. Hilbert transform analyses combined with paired t-tests were used to compare the biosensor data A) within subjects, before and after administration of opioids; B) between subjects, based on hand dominance, gender, and opioid use history. Results Within subjects, a significant decrease in locomotion and increase in skin temperature were consistently detected by the biosensors after opioid administration. A significant change in electrodermal activity was not consistently detected. Between subjects, biometric changes varied with level of opioid use history (heavy vs. nonheavy users), but did not vary with gender or type of opioid. Specifically, heavy users demonstrated a greater decrease in short amplitude movements (i.e. fidgeting movements) compared to non-heavy users. Conclusion A wearable biosensor showed a consistent physiologic pattern after ED opioid administration and differences between patterns of heavy and non-heavy opioid users were noted. Potential applications of biosensors to drug addiction treatment and pain management should be studied further.

Wearable Biosensors to Evaluate Recurrent Opioid Toxicity After Naloxone Administration: A Hilbert Transform Approach

“…The profile change is physiologically similar to that seen with de novo opioid use in prior literature[7]. This makes sense from a physiologic standpoint since the original opioid agonist that caused the overdose in these patients would resume their physiologic effect once naloxone (the antagonist) wears off.…”

“…As described previously [7], to understand the rapid fluctuations and heterogeneity inherent in the measurement of locomotion at three different axes, we estimated the amplitude of the fluctuations using a Hilbert transform method[10]. We applied Hilbert transform to the locomotion data, d ( t ) at each of the axes, XYZ, to obtain the analytic signal $$A\left(t\right)=d\left(t\right)+i\stackrel{\sim }{d}\left(t\right)$$Where i represents the complex variable and $$\stackrel{\sim }{d}\left(t\right)=\frac{1}{\pi t}\ast d\left(t\right)\text{with}\ast \text{representing}\text{convolution}.$$…”

“…Using changes in continuously monitored physiologic parameters, they have already been shown to identify drug use (including opioid use) as it occurs in real time [7]–[9]. In the context of overdose and naloxone treatment, continuous biosensing may provide a way to decrease hospital observation time and possibly allow for monitoring outside of the hospital altogether.…”

Music as an Adjunct to Opioid-Based Analgesia

“…Medical toxicologists have a growing experience using innovative mobile technologies in substance abuse investigations [42][43][44][45][46]. Understanding the ability of music to modulate pain may provide alternatives to opioids.…”

A new mining method to detect real time substance use events from wearable biosensor data stream

“…Although we collected up-down, left-right, and back-forth locomotions, not all are necessary in detecting cocaine use events based on our two separate empirical studies [6], [21]. We first use the correlation analysis to find out how much locomotion is correlated with cocaine use, and which locomotion parameter is the most sensitive.…”