10 Dec Risk-Assessing Computer Games Predictive of Opioid Addiction Relapse

MedicalResearch.com Interview with:

Dr. Anna Konova

Anna Konova, PhD
Assistant Professor, Dept. of Psychiatry & UBHC
Core Faculty, Brain Health Institute
Rutgers University – New Brunswick

MedicalResearch.com: What is the background for this study?

Response: Opioid reuse and relapse are common outcomes even when a person is seeking treatment for their addiction. These reuse events pose many health risks, as well as risk for treatment failure. We currently lack the much needed tools to understand and predict this reuse vulnerability.

In this study, we used computer games that assess a person’s decision making process, to get at psychological processes related to how people make decisions involving risks, when they transitioned between lower and higher reuse vulnerability states during the first few months of opioid treatment.

MedicalResearch.com: What are the main findings?

Response: We found that spikes in their tolerance to a specific type of risk (one associated with partially unknown probabilities of an outcome) was telling of their current likelihood for reuse.  

MedicalResearch.com: What should readers take away from your report?

Response: Computer games might be able to provide new, relatively low-cost information to caregivers about how a patient is doing. This suggests these tools, largely developed by basic scientists, hold promise for future development work in being combined with traditional measures to inform clinical care.

MedicalResearch.com: What recommendations do you have for future research as a result of this work?

Response: There are still many unanswered questions. One important direction for future research is to examine what are effective ways to mitigate risk in real-time. Currently, existing treatment settings do not have the capacity to address this type of risk. Another direction is to translate these tools to mobile platforms for wider reach and to assess predictive utility in diverse treatment settings.

Disclosures: No disclosures directly tied to this study. One of our co-authors is a stakeholder in a mobile technology company, DataCubed, which provides individualized data capture solutions (including smartphone apps) in the field of healthcare and life sciences.

 Citation:

Konova AB, Lopez-Guzman S, Urmanche A, et al. Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting. JAMA Psychiatry. Published online December 08, 2019. doi:https://doi.org/10.1001/jamapsychiatry.2019.4013

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Last Updated on December 10, 2019 by Marie Benz MD FAAD