Substance use disorder (SUD), or in simple terms, overconsumption of illegal drugs (cocaine, marijuana, etc.), is a significant public health concern. In 2019 alone, drug use disorders accounted for 47% of global deaths. Another study found around 40-60% of people relapse within a year of SUD treatment.
While relapse may sound alarming, it is an essential part of the treatment process. Relapse helps understand triggers and reevaluate and adjust the treatment. However, there isn’t any solid method to accurately identify the time and cause of relapse.
Recent research using AI-powered wearables to monitor drug use holds the potential to counter this problem. Let’s understand how.
The role of AI-powered wearables in monitoring illegal drug abuse
Wearables have sensors to measure different body functions, including heart rate, body temperature, blood pressure and blood oxygen saturation. Several wearable biosensors also measure sweat. As a result, there’s a treasure trove of health data that can be analysed to get a fair idea of potential drug use and abuse.
In a research paper titled “Precision Polysubstance Use Episode Detection in Wearable Biosensor Data Streams,” researchers analysed the data gathered by the wearable device using AI. They developed an algorithm called RP-STREAM to identify substance-use episodes accurately.
Researchers used a device called Affective Q, which measured electrodermal activity, physical activity, body temperature and physiological changes in the patient’s body. Then, they evaluated patient notes, urine tests and data gathered from the device.
Using RP-STREAM’s intelligent mechanism, they were able to observe abnormal activities in the patient, which helped them distinguish substance use episodes from other anomalies in the biosensor data.
“We want to keep working on this method to make it better at recognizing substance use. It can help us understand what leads to substance use and maybe even predict when someone might relapse.
We can also study how people’s tolerance to drugs changes over time in real-life situations, which will help us understand why people use drugs again and how to help them stop.”
– Dr Honggang Wang, chair of the Katz School’s Computer Science and Engineering Department, and one of the authors of the study.
Envisioning a future scenario, AI-powered wearables could serve as a vigilant ally for individuals battling substance abuse.
Imagine an instance where a chronic SUD patient faces an urge to relapse; the wearable device promptly interprets health metrics and alerts caregivers, enabling timely intervention to avert a potential setback.
The growth of AI-powered wearables in healthcare
The advancing role of AI-powered wearables extends beyond fitness goals. It is now finding application in diverse healthcare domains, from remote patient monitoring to drug use surveillance and advanced diagnostics. This technology holds promise for enhancing the effectiveness of treatment strategies and opens up new avenues for preventative measures.
As scientists continue to refine these algorithms and broaden the application of AI in healthcare, we can see the rise of a new era where prevention, monitoring and treatment are increasingly personalised, timely and effective.
