Reduction in Anger in Participants with Chronic Pain after a Mobile-Based Mindfulness Intervention
Bibliographic record
Abstract
Introduction/Aim: To evaluate the effects of a novel 12-minute mobile-based mindfulness intervention on anger in participants with chronic pain, depression/anxiety and condition-free controls. Methods: Four groups of university students: n = 42 with chronic pain (CP+app), n = 39 with symptoms of depression/anxiety (DA+app), and 2 groups of condition-free controls (CF+app; n = 54 and CF-app; n = 26) completed the Anger subscale of the Profile of Mood States at baseline (pre) and after (post) a 12-min intervention, during which participants were instructed to pay attention to the flow of breath and press “breath” or “other” buttons on a smartphone at the sound of a tone. The CF-app group attended to their breath for 12 minutes without use of the smartphone app. Results: We used a 2-way ANOVA with Time (baseline, post-intervention) and Group (CP+app, DA+app, CF+app, CF-app) to evaluate Anger scores. The simple main effect of Group was significant at baseline, F(3,152) = 14.83, p < .001, ηp2 = .22, and post-intervention, F(3,152) = 9.57, p < .001, ηp2 = .15. At baseline, the CP+app and DA+app did not differ in Anger scores, which were significantly higher than CF+app and CF-app (p < .05). Post-intervention, anger levels for CP+app dropped to meet those of both CF+app and CF-app, while DA+app remained significantly higher than the rest (p < .05). Simple main effects of Time were significant for CP+app, F(1,152) = 27.90, p < .001, ηp2 = .15 and DA+app, F(1,152) = 15.06, p < .001, ηp2 = .09, but not for CF+pp or CF-app. Discussion/Conclusions: Research has shown that anger can lead to increased pain sensitivity and intensity; therefore regulating anger using mindfulness may be a desirable goal as part of CP treatment.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".