A wireless sensor on the wrists of patients, measuring psychophysiological signs in real time by respiration and electrocardiogram (ECG) analyses, accurately detected opioid use with 80 percent accuracy.
In what could prevent opioid users from misusing drugs, experts are working on a project to develop smartwatches with the ability to detect emotional and psychological patterns opioid users show hours before indulging in substance abuse. The project is funded by the National Science Foundation’s Smart and Connected Health program, an independent agency funded by the United States government.
The University of Massachusetts recently announced that “a research team … has received a $1.1 million grant to further develop a smartwatch sensor designed to support the long-term recovery of people with opioid use disorder (OUD).”
“Over 20 million people are struggling with substance use disorders, and the yearly economic impact in the United States is $1.45 trillion in economic loss and societal harm,” says the Innovation to Impact, a Yale University programme, page.
According to the United States’ National Center for Drug Abuse Statistics, “10.1 million or 3.7 percent of Americans misuse opioids at least once over a 12-month period,” with “1.6 million or 15.8 percent qualify as having an opioid use disorder.” The same source notes that in the US, “Almost 50,000 people die every year from opioid overdose,” and that “Opioids are a factor in at least 7 out of every 10 overdose deaths.”
Mobile sensor expert Tauhidur Rahman, assistant professor in the University of Massachusetts' College of Information and Computer Sciences, is collaborating with colleagues at Syracuse University and SUNY Upstate Medical University on the project.
“We have the technology to detect these craving moments and incorporate an intervention to avoid scenarios of drug use,” says Rahman, who specialises in creating mobile, health-related sensors in the MOSAIC Lab he co-directs.
“The tiny, wireless sensor uses machine learning to determine if the psychophysiological signs detected in real time by respiration and electrocardiogram (ECG) are consistent with opioid cravings,” the news release explains. “Such cravings are one of the primary causes of OUD [opioid use disorder] relapse and fatal overdose following a period of abstinence.”
The sensor alerts the user to the craving signs, and the user is steered towards meditation and mindfulness-based interventions that “ultimately could be personalized based on the user’s behaviors and input from their clinician.”
“Nothing like this exists today,” Rahman says, “and we believe that mobile technologies can provide an effective mechanism for people with addiction to monitor their condition and manage their cravings better.”
The opioid craving sensor project came out of research published in 2019 by Rahman, lead author Bhanu Teja Gullapalli – a PhD student working in Rahman’s lab – and others on the use of cardiac and respiratory signals to sense cocaine craving, euphoria, and drug seeking behavior, the news release explains.
In September, the researchers appeared in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies with a paper called “OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks”.
They ask the question “Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor?” and use 36 hospitalised subjects to test whether a smartwatch can sense and defuse opioid cravings.
The patients, who were suffering from an acute pain condition and were prescribed opioid painkillers, wore a wrist sensor that measured their physiological signals around the clock.
The researchers wrote that the patients wore a “non-invasive wrist sensor (between 1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval).” They added that they “collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations.”
The researchers used a series of mathematical processes called convolution for the machine-learning aspect of the research.
“Once we run convolution, we extract features of the raw data and then train neural networks that can automatically learn to see the physical characteristics and physiological trends that indicate opioid use,” Rahman explains. “So, just by looking at a watch and monitoring a few parameters, we can tell when someone has taken an opioid. We have 80 percent accuracy on a high level with our current form of technology.”
One positive aspect of this research is that it can be applied to other substance use disorders, and the NSF-funded [National Science Foundation] project “includes a machine-learning course for clinicians and awareness workshops for middle-school girls.”
Gullapalli says the sensor in the experiment could be used further to ensure the proper use of prescribed opioid pain medications and prevent opioid use disorder (OUD).
“The doctor can ask the patient to wear the smartwatch and the system will track how frequently the patient is using the drug, how the patient’s physiology is changing and determine if the patient is developing a dependence on opioids,” explains Gullapalli, who, according to the news release, was recently accepted into Yale's highly selective Innovation to Impact program for entrepreneurship for substance use disorder, funded by National Institute for Drug Abuse.
The goal of the NSF project is to propel and endorse innovative technology. “Successful execution of the research will begin to test the effectiveness of integrating passive sensing, adaptive artificial intelligence (AI) and mindfulness interventions on regulating drug craving,” the grant summary concludes.
Social media is bold.
Social media is young.
Social media raises questions.
Social media is not satisfied with an answer.
Social media looks at the big picture.
Social media is interested in every detail.
social media is curious.
Social media is free.
Social media is irreplaceable.
But never irrelevant.
Social media is you.
(With input from news agency language)
If you like this story, share it with a friend!
We are a non-profit organization. Help us financially to keep our journalism free from government and corporate pressure
0 Comments