We described and compared the baseline characteristics between patients with and without severe fatigue. Differences in continuous variables were analyzed using the independent samples t-test or the Mann-Whitney U test as appropriate, while that of categorical variables were analyzed using the Chi-squared test. We conducted a principal component analysis (PCA) without rotation to evaluate for clusters of variables that represent clinically distinct concepts. We selected variables that were either theoretically linked to fatigue or described in literature to be associated with fatigue (510, 14, 15, 17, 19, 41). Only variables with pre-defined correlations of <0.6 were included in the model. The study rheumatologists discussed to reach a consensus on the inclusion or exclusion of variables with high collinearity (Spearman's rho > 0.6). Principal components with eigenvalues > 1 were entered into linear regression models for BASDAI-fatigue and SF-36 VT, respectively, to determine component(s) significantly associated with each measure of fatigue.

We performed univariable analysis using Spearman's rank correlation to determine the relationship between the study variables and measures of fatigue. In our study, we considered correlations of magnitude ≥ 0.5 as strong, 0.3–0.49 as moderate and 0.1–0.29 as weak (42). In the multivariable analysis, we evaluated for variables associated with each measure of fatigue using stepwise linear regression. Variables with p < 0.2 in the univariate analysis for association with fatigue were included. We also considered variables that either have a strong theoretical basis for association with fatigue or were found to be predictors of fatigue in previous studies.

All statistical analyses were carried out using IBM SPSS Statistic Package, version 25 (IBM, Armonk) and statistical significance was set at p < 0.05.

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