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.
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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 | 0.000 | 0.001 |
| 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 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".