Quantifying Opioid Withdrawal Using Wearable Biosensors

Misuse and abuse of opioids and associated overdose, referred to as opioid use disorder, is a serious public health issue and has been declared a public health emergency in United States. A common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly overdose. Even though remote monitoring technologies have shown promise in the management of chronic diseases, application of such technologies in opioid withdrawal monitoring has been very limited. This research seeks to use noninvasive wearable sensors to continuously monitor physiologic changes associated with opioid withdrawal. Machine learning–based pattern detection algorithms will be used to explicitly detect and characterize specific features obtained from wearable sensor configurations and existing contextual information. This can provide real-time feedback to health care providers to facilitate interventions.