POS1039 SELF-EFFICACY IN IDIOPATHIC INFLAMMATORY MYOPATHIES: A CROSS-SECTIONAL STUDY FROM THE COVAD-3 DATASET
Bibliographic record
Abstract
Background: Self-efficacy (SE) is fundamental to chronic disease management, emerging as a complex interplay of disease parameters, psychological resilience, social support, comorbidity burden, and financial resources [1]. This demonstrates the need for comprehensive assessment and intervention strategies that address these interconnected domains of wellness. This study investigates SE amongst individuals with idiopathic inflammatory myopathies (IIMs), examining its relationship with disease parameters and health behaviours using data from the Collating the Voice of People in Autoimmune Diseases (COVAD)- 3 study. Objectives: To determine SE levels in IIMs compared with other rheumatic diseases (RMDs) and healthy controls (HC), identify its key predictors, assess associated health outcomes, and evaluate health behaviours linked to higher SE. Methods: We analysed data from the COVAD-3 study cohort, including patient-reported outcomes and the Self-Efficacy in Managing Chronic Disease (SEMCD) scale [2]. Participants were stratified into high (>67th percentile, SEMCD 6.1) and low SE (<33rd percentile, SEMCD 3.6, Figure 1A) groups. Statistical analyses included descriptive statistics, correlation analyses, and multivariate logistic regression to identify SE predictors. Comparative analyses between groups utilised appropriate parametric and non-parametric tests. Based on identified predictors and outcomes, we propose a structured intervention model incorporating peer support, targeted education, and behavioural modification strategies. Thematic analysis was performed to provide qualitative insights into patient experiences and the factors influencing SE (Figure 1B). Results: This analysis of 3,374 participants (439 IIMs, 2,135 RMDs, 800 HCs) from the COVAD-3 registry revealed lower SE scores amongst IIM patients (median SEMCD 5.33) compared to RMDs (5.66) [range 1-10]. IIM patients were characterised by older age at diagnosis, Caucasian predominance, and residence in high HDI regions. They demonstrated shorter disease duration, greater immunosuppressant use, increased autoimmune multimorbidity, and higher functional comorbidity scores compared to RMDs and HCs, while reporting smaller household sizes. Based on SEMCD scores, IIM patients were stratified: 84 showed low SE scores (SEMCD < 3.6) and 164 showed high SE in managing IIMs (SEMCD > 6.1) [Figure 1C, Table 1]. IIM patients with high SE reported significantly better outcomes, including greater satisfaction with life, better physical function (PROMIS Physical SF4a), improved mental health (PROMIS Mental), and better overall physical health (PROMIS Physical). They also had lower fatigue (VAS fatigue), less pain (VAS pain), lower disease activity, and reduced disease damage perception (all p values <.001). Patient trust in health insurance (p = 0.022) and global damage assessment (p = 0.022) were also positively correlated with SE. No significant difference in education level was found with SE in IIMs [Figure 1D]. The logistic regression analysis, adjusted for age, age at diagnosis, gender, and ethnicity identified predictors of high SE using the best-fit model. Factors such as resilience (BRS, OR: 2.582, 95% CI: 0.310–1.588, p=0.004), regular exercise (OR: 3.563, 95% CI: 0.214–2.327, p=0.018), and polytherapy (OR: 3.674, 95% CI: 0.184–2.419, p=0.022) were the positive predictors of high SE. Individuals engaging in regular exercise were more likely to exhibit high SE. Alternatively, the need for increased doses of medication (OR: 0.242, 95% CI: -2.467– -0.369, p=0.008), and increased levels of loneliness (OR: 0.501, 95% CI: -0.995 – -0.385, p < 0.001) were identified as barriers to high SE, thereby reducing the probability of achieving the confidence in managing IIMs effectively. The predictive model demonstrated robust performance (accuracy 84.2%, sensitivity 91.8%, specificity 70.6%) [Figure 1E]. Thematic analysis identified resilience, exercise, and social support as key enhancers of SE, while loneliness and medication burden emerged as significant barriers in IIM management. Conclusion: Self-efficacy emerges as a crucial determinant of physical and mental well-being in individuals living with IIMs. Our findings illuminate a complex interplay of factors: while resilience, regular exercise, and comprehensive medical management foster confidence in self-management, social isolation and treatment burden pose significant challenges. These insights underscore the importance of holistic care approaches that extend beyond traditional medical management. Tailored interventions integrating physical activity, psychological resilience building, and social support networks could meaningfully enhance self-management capabilities and overall wellness in this challenging chronic condition. REFERENCES: [1] Chan, S.W.-C. (2021). Chronic disease management, self-efficacy and quality of life. The Journal of Nursing Research, 29 (1), Article e129. https://doi.org/10.1097/jnr.0000000000000422. [2] Ritter PL, Lorig K. The English and Spanish Self-Efficacy to Manage Chronic Disease Scale measures were validated using multiple studies. J Clin Epidemiol . 2014;67(11):1265-1273. doi:10.1016/j.jclinepi.2014.06.009. Figure 1A) Characteristics of the Self-Efficacy for Managing Chronic Disease Scale in IIMs, B) Variables Analysed, C) Workflow chart depicting the inclusion of participant responses, D) Correlation of variables with self-efficacy in IIMs, E) Multivariable logistic regression for predictors of self-efficacy in managing IIMs. Acknowledgements: Patient Research Partners: Peter Boyd, Linda Kobert, Paula Jordan, Kirtida Oza, Ingrid De Groot, Allison Foss, Celia Meyer, Karin Blomkvist Sporre, Annika Broberg Lavén, Veronica Fatura, Ailsa Bosworth, Malak Aburas, Silvia Aguilera, Rachel Bromley. Patient Support Groups: Cure JM, JCR, CYPLER, EULAR PARE, Myositis Support and Understanding, Myositis UK, The Myositis Association, ARCH Network, ArLAR, Young GRAPPA, APLAR myositis SIG, Myasthenia Gravis Association, Wolverhampton PSG, Patients Alliance for Rheumatic Diseases (PARD), SSc UK, Conquer Myasthenia Gravis, Myasthenia Gravis Association of Western PA, The MG Holistic Society, Myasthenia Gravis Foundation of Michigan, MIHRA, Myositis Canada, AAAA, NRAS, National Association for SLE, TMA Michigan Support Group Co-leader, TMA Adelante Affinity Group Co-leader, Myasthenia Gravis Foundation of America, MIHRA, EULAR Reproductive Health and Family Planning (ReHFaP), Rodney Jansen (Myositis Canada), Asociacion Miastenia de Espana, Conquer MG, Associazione Italiana Miastenia, Associazione Miastenia, EU-MGA, Hellenic Myasthenia Association, Myasthenia Gravis Holistic Society, Myasthenia Gravis Japan, Myasthenia Gravis Ohio, Myasthenia Gravis Society of Canada, Myasthenia Gravis Association of Western PA, Myasthenia Gravis Foundation of America, Myasthenia Gravis Foundation of Bulgaria, Myasthenia Gravis Foundation of Michigan, MyAware, Netherlands MG Association, Stowarzyszenie Miastenia Gravis Face to Face, Mission Arthritis India (MAI), Ankylosing Spondylitis Welfare Society (ASWS), StandForAS, Scleroderma India. Disclosure of Interests: Praggya Yaadav: None declared, Maria Rosaria Pellico: None declared, Manali Sarkar: None declared, Anne-Marie Russell: None declared, Sreoshy Saha: None declared, Elena Nikiphorou received speaker honoraria/participated in advisory boards for Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, and Lilly, holds research grants from Pfizer, and Lilly, Ioannis Parodis received research funding and/or honoraria from Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis, and F. Hoffmann-La Roche AG, Aviya Lanis: None declared, Karen Cheng employed by Sobi working on projects unrelated to this abstract, Laura Andreoli: None declared, Jasmine Parihar: None declared, Vikas Agarwal: None declared, Latika Gupta: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".