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Is There a Way to Predict Opioid Addiction Relapse?

A developing app could potentially predict opioid addiction relapse risk for people recovering from opioid addiction.

Opioids are a group of drugs that are either derived from or replicate the effects of opium.  They have many medical uses, including pain relief, anesthesia, and treatment of diarrhea.  Common opioids include morphine, codeine, heroin, oxycodone (Percocet), and fentanyl, among others.  Opioids produce strong feelings of euphoria by increasing dopamine levels, so they are commonly misused in the United States.  Over ten million Americans over the age of 12 misused opioids in 2018, hinting at a current opioid crisis.

Misusing opioids is dangerous, and it is very easy to overdose because only small amounts of the drugs are needed to produce very strong effects.  Moreover, they are highly addictive, as eight to 12 percent of people who misuse opioids develop an opioid use disorder.  Recovering from opioid addiction is difficult, and there is limited research on predicting opioid reuse in recovering individuals.  To fix this issue, a new app is being developed to predict opioid addiction relapse.

The app consists of a simulated betting game where players can either accept a small reward or gamble for a larger one to test risk-taking behavior.  A high beta-score indicated risk-taking behavior.  Seventy men and women in an opioid addiction treatment program at NYC Health + Hospitals/Bellevue played the game regularly for seven months during their clinic visits.  Fifty patients at the same hospital who were never addicted to opioids also played the game regularly for seven months to serve as a control group.  The findings were published in the Journal of the American Medical Association Psychiatry.

The study found that patients with drastic increases in beta scores were up to 85 percent more likely to reuse opioids.  Conversely, those who did not have drastic increases in beta scores were less likely to reuse opioids.

The findings of this study suggest that this app could potentially help predict opioid addiction relapse.  More research is needed to determine the effectiveness of this app, however, it does provide a basis for further development of these technologies.

Written by Avery Bisbee

References:

Konova, A. B., Lopez-Guzman, S., & Urmanche, A. (2019). Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting. JAMA Psychiatry. doi: 10.1001/jamapsychiatry.2019.4013

Computer game may help to predict reuse of opioids. (2019, December 8). Retrieved December 10, 2019, from https://www.eurekalert.org/pub_releases/2019-12/nlh-cgm120319.php.

National Institute on Drug Abuse. (2019, January 22). Opioid Overdose Crisis. Retrieved December 10, 2019, from https://www.drugabuse.gov/drugs-abuse/opioids/opioid-overdose-crisis.

Opioid Crisis Fast Facts. (2019, December 4). Retrieved December 10, 2019, from https://www.cnn.com/2017/09/18/health/opioid-crisis-fast-facts/index.html.

Image by Niek Verlaan from Pixabay

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