AN EMOTION REGULATION THERAPY FOR LATER-LIFE PAIN: EVIDENCE OF EARLY TREATMENT EFFECTS
Bibliographic record
Abstract
Abstract Chronic pain (CP) is a common, morbid, and costly disorder in older adults. Guidelines encourage clinicians to employ non-pharmacologic therapies for its management, but current psychological interventions (e.g., CBT for pain) have modest treatment benefits and their effects are largely unknown in older cognitively impaired adults. We developed PATH-Pain, an emotion regulation therapy focused on reducing negative emotions and augmenting positive emotions. PATH-Pain is appropriate for use by older adults with CP, negative emotions, and a wide range of cognitive functioning. Treatment consists of 8 weekly individual sessions followed by 4 monthly booster sessions. One hundred older adults (ages 60+) with CP (≥ 3 months) and at least mild-to-moderate levels of negative emotions (per the Positive and Negative Affect Schedule) were randomized to receive PATH-Pain versus Usual Care (UC). Cognitive screening revealed that 44 participants were cognitively intact (Montreal Cognitive Assessment (MoCA) score ≥26), while 56 evidenced mild-to-moderate cognitive impairment (MoCA=16-25). Participants completed follow-ups at 5 (n=89) and 10 weeks (n=84), while 24-week assessments are ongoing. Examination of the treatment × time interaction in a repeated-measures mixed model indicate the presence of treatment effects. PATH-Pain (vs. UC) participants experienced significant reductions in pain intensity (p<0.044) and pain-related disability (p<0.003). Reductions in pain-related disability score were more pronounced among cognitively impaired individuals. The PATH-Pain group also demonstrated significant reductions in emotional suppression (p<0.019) and depression (p<0.009) scores. These results suggest that PATH-Pain is an effective treatment for the management of pain in cognitively intact and cognitively impaired older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".