Far from being safe, cannabis use is associated with multiple health issues and criminal behavior. The drug is also linked to poor school performance due to impaired response times – with users more likely to be injured or killed in a car accident than their sober peers. And despite the sturdy evidence indicating the highly addictive nature of this drug and its associated severe effects, existing detection measures are sorely lacking. Moreover, existing lab tests are unable to discern marijuana intoxication or cannabis-related impairment in daily life accurately.
Therefore, a tracking system alerting the drug user or law enforcement to repetitive substance-related behaviors could allow intervention to stop further self-harm or harm to other persons. However, no system is sensitive enough to record the patterned addictive behavior associated with this drug.
Now, a study from researchers at Rutgers University uses smartphone data, such as GPS, to determine whether someone is high on cannabis and when they are most likely to become intoxicated again. The team states that by using the sensors in the person’s phone, there may be a way to discern when the phone’s owner might be experiencing marijuana intoxication – and to intervene with help at the optimal time of impact to reduce cannabis-related harm. The study is published in the journal Drug and Alcohol Dependence.
Previous studies indicate that marijuana is by far the most commonly used illicit substance among adolescents and young adults – even surpassing tobacco use. Consequently, adolescent marijuana use is associated with impaired memory, difficulty in learning, poorer life outcomes, and changes in the structure and function of specific brain regions. Likewise, side effects of cannabis in young adults have also been recorded, including poor academic and work performance, injuries, and fatalities due to driving while high – along with cardiovascular and cerebrovascular events.
In spite of this, it’s still widely believed that marijuana has fewer side effects than other drugs and is non-addictive despite the fact it is the most widely used illicit drug globally. This misinformation means little qualitative work has been achieved relating to how young people engage with cannabis in the context of street entrenchment and how this lifestyle may lead to other more addictive drugs such as crack or heroin.
In a step towards this goal, the current study uses a combination of native smartphone time features and sensors to identify periods of cannabis intoxication in young people with 90 percent accuracy. Researchers analyzed daily data from fifty-seven young adults aged 18-25 who use cannabis twice a week or more while they go about their life. Participants completed three surveys every day for 30 days that asked when they had last used cannabis, how intoxicated they were at set times during the day, and how much cannabis they consumed. The volunteers also downloaded a smartphone app to analyze information, including GPS, usage statistics, phone logs, and data from accelerometers and other smartphone sensors.
Results show that using time of day and day of week data provides 60 percent accuracy in the self-reporting of cannabis intoxication. When using the smartphone sensor data alone, researchers observed an accuracy rate of 67 percent. In contrast, a combination of time features and data from smartphone sensors produced a 90 percent accuracy when detecting chronic cannabis usage. Specifically, these outcomes determined that travel patterns from GPS data when the subjects reported feeling high and movement data from a smartphone’s accelerometer were the most critical smartphone sensor features to verify self-reported cannabis intoxication.
“This proof-of-concept study indicates the feasibility of using phone sensors to detect subjective cannabis intoxication in the natural environment, with potential implications for triggering just-in-time interventions,” the study’s authors concluded. Moreover, the applications do not use a lot of the phone’s data, allowing the researchers to detect intoxication in everyday life while proving the practicability of using phone sensors to detect cannabis intoxication.
In the future, the researchers state they now plan to use their algorithm to monitor young adults who use cannabis less frequently than the current study’s subjects.
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Michelle is a health industry veteran who taught and worked in the field before training as a science journalist.
Featured by numerous prestigious brands and publishers, she specializes in clinical trial innovation--expertise she gained while working in multiple positions within the private sector, the NHS, and Oxford University.