Computer game identifies people at risk of opioid relapse

A computer game-based study by Rutgers University has found that people undergoing treatment for opioid addiction are more likely to relapse if they are more tolerant of risks.

Researchers said that 46% of the 70 patients involved in the study, published by JAMA Psychiatry, returned to opioid use within the first seven months of their treatment.

With the majority of relapses occurring in patients who showed a willingness to take unknown risks, a correlation they identified based on the subject’s performance in a computer game created for the study.

The game, which patients played for financial rewards, required them to make decisions that involved two types of risk; known risk, in which they had complete information on the likelihood of a decision’s outcome to lead to reward. And ambiguous risk, in which they did not have full information on the possible outcomes.

The researchers then measured the game results against clinical assessments of the patient’s anxiety, craving, withdrawal and nonadherence to treatment, relying on random urine tests and self-reporting to determine opioid use.

According to the National Institute on Drug Abuse, the relapse rate for substance use disorders is estimated to be between 40 and 60%.

Anna Konova, assistant professor at Rutgers University Behavioral Health Care and Rutgers Robert Wood Johnson Medical School, and faculty member in the Brain Health Institute said the findings will help clinicians better predict which patients are most vulnerable to relapse. She said:

‘Although it is well known that people addicted to opioids cycle through periods of abstinence and use, we lack the tools needed to prospectively identify when these transitions are more likely to occur.

‘Here, given that opioid use during treatment is quite risky, we wanted to examine whether a patient’s tolerance for risky decisions is informative about their vulnerability to relapse.

‘Used in conjunction with clinical assessments, the computer model can be an important risk calculator, allowing clinicians in large, but short-staffed, treatment centres to allocate appropriate attention to those at greater risk for relapse and treatment failure.

‘The goal is to eventually create a mobile app based on the game that people can play remotely, which could convey information about relapse risk in real-time to the patient, clinician or caretaker.’

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