The Impact of Interoceptive Awareness on Pain Catastrophizing and Illness Perception
Bibliographic record
Abstract
This study aimed to investigate the effectiveness of Interoceptive Awareness Training (IAT) in reducing pain catastrophizing and altering illness perceptions among individuals with chronic pain. It sought to determine whether enhancing interoceptive awareness could lead to improved management and perception of chronic pain. Employing a randomized controlled trial design, 30 participants with chronic pain were assigned to either an intervention group, receiving 10 sessions of IAT, or a control group receiving standard care. Measurements of pain catastrophizing and illness perception were taken at baseline, immediately post-intervention, and at a three-month follow-up, utilizing the Pain Catastrophizing Scale (PCS) and the Illness Perception Questionnaire-Revised (IPQ-R), respectively. Participants in the intervention group demonstrated significant reductions in pain catastrophizing scores from baseline to follow-up, as well as significant improvements in illness perception scores. These changes indicate a substantial shift in how participants understood and reacted to their pain post-intervention compared to the control group, which showed no significant alterations in either pain catastrophizing or illness perception. Interoceptive Awareness Training significantly reduced pain catastrophizing and positively altered illness perceptions in individuals with chronic pain. The findings suggest that IAT can be an effective component of comprehensive pain management strategies, highlighting the importance of addressing the psychological dimensions of chronic pain in conjunction with physical treatment modalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".