Neurocognitive predictors of adherence to an online pain self-management program adjunct to long-term opioid therapy

While pain self-management programs can significantly improve patient outcomes, poor adherence is common and the need for research on predictors of adherence has been noted. A potential, but commonly overlooked, predictor is cognitive function. Our aim, then, was to examine the relative influence of various cognitive functional domains on engagement with an online pain self-management program.

A secondary analysis of a randomized controlled trial testing the impact of E-health (a 4-month subscription to the online Goalistics Chronic Pain Management Program) plus treatment as usual, relative to treatment as usual alone, on pain and opioid dose outcomes in adults receiving long-term opioid therapy of morphine equivalence dose ≥20 mg; 165 E-health participants who completed an on-line neurocognitive battery were included in this sub-analysis. A variety of demographic, clinical, and symptom rating scales were also examined. We hypothesized that better processing speed and executive functions at baseline would predict engagement with the 4-month E-health subscription.

Ten functional cognitive domains were identified using exploratory factor analysis and the resultant factor scores applied for hypothesis testing. The strongest predictors of E-health engagement were selective attention, and response inhibition and speed domains. An explainable machine learning algorithm improved classification accuracy, sensitivity, and specificity.

The results suggest that cognition, especially selective attention, inhibitory control, and processing speed, is predictive of online chronic pain self-management program engagement. Future research to replicate and extend these findings seems warranted.

NCT03309188.