Neurocognitive predictors of adherence to an online pain self-management program adjunct to long-term opioid therapy
Bibliographic record
Abstract
INTRODUCTION: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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. CLINICALTRIALS.GOV REGISTRATION NUMBER: NCT03309188.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".