Patients tolerant of risks most often relapse their opioid addictions, Rutgers researchers find
Rutgers researchers have released a study that found those getting treated for opioid addiction were more likely to relapse if they were more tolerant of risky behaviors and situations, according to a University press release.
Approximately 46% of the people studied over their first seven months of treatment relapsed, according to the release. The majority of the relapses occurred among patients who showed a strong tolerance for risk-taking, involving them in situations where the risks associated with their decisions were not certain.
The study’s findings fall within the range that the National Institute on Drug Abuse has found for those who relapse from substance abuse addictions, which is between 40 to 60%, according to the release.
“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,” said Anna Konova, an assistant professor at Rutgers University Behavioral Health Care and Rutgers Robert Wood Johnson Medical School and a faculty member at the Rutgers Brain Health Institute, according to the release.
The 70 patients who participated in the study made 15 study visits over the seven months, where they played a video game that allowed them to make decisions that involved both known and ambiguous risks. A known risk is a risk the patients had complete information on before deciding, and an ambiguous risk is a risk they did not have full information on possible outcomes, according to the release.
The computer game results were then compared with the patient’s clinical assessments of anxiety, craving, withdrawal and nonadherence to treatment, according to the release. A patient’s possible opioid use was measured by random urine tests and self-reporting, according to the release.
“Used in conjunction with clinical assessments, the computer model can be an important risk calculator, allowing clinicians in large, but short-staffed, treatment centers to allocate appropriate attention to those at greater risk for relapse and treatment failure,” Konova said, according to the release. “The goal is to eventually create a mobile application 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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