Capacity to Describe Inner Experiences Predicts Lower Pain-Related Mind-Wandering during a Smartphone-Based Mindfulness Task in People with Chronic Pain
Bibliographic record
Abstract
Introduction/Aim: Pain-related mind-wandering (PRMW) may facilitate pain and disability in people with chronic pain. This study explored mindfulness skills that predict lower PRMW during mindfulness meditation (MM). Methods: 133 participants (AgeM = 20.5 years, SD = 3.74; Male = 45) were classified into groups: 1-Chronic pain (CP; n = 35) based on self-reported CP diagnoses, 2-Depressive-anxious (DA; n = 67) based on severe symptoms on the Center for Epidemiological Studies-Depression subscale (≥21) or Beck Anxiety Inventory (≥36), or 3-Controls (n = 31) if neither CP nor DA criteria applied. Participants completed the Five Facet Mindfulness Questionnaire (FFMQ) assessing mindfulness skills: observing experiences, describing internal experiences (Describe), acting with awareness, non-judgement and non-reactivity. Participants practiced breath-focused MM on a smartphone (~12-minutes) and pressed “breath” or “other” buttons at the sound of tones if awareness was on breathing or another experience, respectively. The Mind-Wandering Inventory was completed post-MM using 3 items: awareness of bodily pain, thoughts about pain, and other unpleasant sensations. Pearson correlations were conducted between FFMQ and PRMW. Results: Amongst all correlations, only Describe significantly predicted PRMW for CP (r = −0.46, p < 0.01), DA (r = 0.25, p < 0.05), and controls (r = 0.37, p < 0.05). CP participants had significantly higher % breath responses than the other groups. Discussion/Conclusions: The mindfulness skill of describing inner experiences may be a vital component of mindfulness-based pain treatments.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".