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Enregistrement W4411415043 · doi:10.1016/j.ard.2025.06.393

POS1039 SELF-EFFICACY IN IDIOPATHIC INFLAMMATORY MYOPATHIES: A CROSS-SECTIONAL STUDY FROM THE COVAD-3 DATASET

2025· article· en· W4411415043 sur OpenAlexaboutno aff
Praggya Yaadav, Maria Rosa Pellico, Mili Sarkar, Anne‐Marie Russell, Somnath Saha, E. Nikiphorou, I. Parodis, Aviya Lanis, Kar Keung Cheng, Laura Andréoli, Jasmine Parihar, Vikas Agarwal

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueInflammatory Myopathies and Dermatomyositis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineCross-sectional studyPathology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,336
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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Même revueAnnals of the Rheumatic DiseasesMême sujetInflammatory Myopathies and DermatomyositisTravaux en français237 207