Effect of a mobile-based resilience training program on resilience and well-being outcomes in individuals with chronic pain
Abstract
Considering the limited resilience demonstrated by individuals suffering from chronic pain, this study aimed to explore how a mobile app designed to enhance resilience against pain affects the resilience, psychological and social well-being of individuals with chronic pain. In this three-arm randomized trial, 108 individuals with chronic pain were randomly allocated to three groups. Due to the nature of the intervention, participant blinding was not achievable. However, both the assessor and the statistician remained blinded. The control group A received standard care, while the control group B participated in a familiarization program addressing chronic pain and its influencing factors. The participants in the intervention group utilized the "resilience against chronic pain" application for eight weeks. Resilience was assessed as the primary outcome, and psychological and social well-being were evaluated as the secondary outcomes, at baseline, immediately after, and 3 months post-intervention. Linear mixed effects models with random intercept showed significant group × time interactions for resilience, psychological well-being, and social well-being. Baseline scores did not differ significantly between groups. The intervention group demonstrated a large improvement in resilience immediately after the intervention (β = 23.345, %95 CI [15.957-30.732], p < 0.001), with significantly higher scores than both control groups; these effects remained significant at 3 months post…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 108 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.