Effects of Savoring Meditation on Positive Emotions and Pain-Related Brain Function: A Mechanistic Randomized Controlled Trial in People With Rheumatoid Arthritis
Bibliographic record
Abstract
Abstract Positive emotions are a promising target for intervention in chronic pain, but mixed findings across trials to date suggest that existing interventions may not be optimized to efficiently engage the target. The aim of the current mechanistic randomized controlled trial was to test the effects of a single skill positive emotion-enhancing intervention called Savoring Meditation on pain-related neural and behavioral targets in patients with rheumatoid arthritis (RA). Participants included 44 patients with a physician-confirmed diagnosis of RA (n=29 included in fMRI analyses), who were randomized to either Savoring Meditation or a Slow Breathing control. Both meditation interventions were brief (four 20-minute sessions). Self-report measures were collected pre- and post-intervention. An fMRI task was conducted at post-intervention, during which participants practiced the meditation technique on which they had been trained while exposed to non-painful and painful thermal stimuli. Relative to Slow Breathing, Savoring significantly reduced experimental pain intensity ratings relative to rest (p<.001), increased cerebral blood flow in the ventromedial prefrontal cortex (vmPFC) and increased connectivity between the vmPFC and caudate during noxious thermal stimulation (z=2.3 voxelwise, FDR cluster corrected p=0.05). Participants in the Savoring condition also reported significantly increased positive emotions ( p s<.05) and reduced anhedonic symptoms ( p <.01) from pre-to post-intervention. These findings suggest that that Savoring recruits reward-enhancing corticostriatal circuits in the face of pain, and future work should extend these findings to evaluate if these mechanisms of Savoring are associated with improved clinical pain outcomes in diverse patient populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".