This study used exploratory factor analysis (EFA) and Rasch analysis to determine the functional factors from multiple trunk function assessments in early-stage stroke patients. Additionally, it aimed to identify the physical characteristics of patients associated with each factor.
The study included 200 stroke patients (75.9±12 years, 86 females, 162 with cerebral infarction) admitted between December 2023 and January 2024. Physical function evaluations included limb function (Fugl-Meyer Assessment-FMA, Medical Research Council-MRC scale, Stroke Impairment Assessment Set-SIAS), along with activities of daily living and gait assessments. Trunk function was measured using the Verheyden and Fujiwara of Trunk Impairment Scale (TIS-V and TIS-F), Functional Assessment for Control of Trunk (FACT), and Trunk Control Test (TCT), conducted 1.2±2.5 days post-stroke. EFA was used to classify the functional factors, followed by Rasch analysis to confirm unidimensionality and item difficulty within each factor. To examine the relationship between functional factors and patient characteristics, ANOVA was performed by grouping the cases according to each factor from the Person Item MAP results of Rasch analysis. R version 4.4-1 and WINSTEPS version 5.7.2 were used for statistical analysis.
EFA identified four functional factors: static sitting, basic movements, dynamic sitting tasks (difficult), and dynamic sitting tasks (easy). Rasch analysis confirmed unidimensionality for all factors, with the exception of easy dynamic sitting tasks, which required the exclusion of four items. Furthermore, Rasch analysis revealed a hierarchical structure in item difficulty, suggesting a layered nature of trunk function. ANOVA revealed significant differences in limb function (measured by FMA) across factors, with higher scores observed in factors 4, 3, 1, and 2, respectively. Sensory and muscle strength measures (MRC scale and SIAS) showed lower significant differences between factors.
The study confirmed that trunk function assessments reflect multidimensional aspects of trunk function. EFA identified four key functional factors, predominantly reflecting static and dynamic sitting tasks, further classified by task difficulty and trunk compensation strategies. Additionally, motor function, rather than sensory function or muscle strength, significantly influenced these factors. The findings suggest the need for developing a comprehensive trunk function assessment that accounts for these identified factors, particularly those linked to motor function.
These results highlight the multidimensional nature of trunk function assessments and suggest that the severity of motor paralysis significantly impacts the classification of trunk function factors. A new, integrative assessment tool, considering these factors, may improve the evaluation of trunk function in stroke patients.
Trunk function
Assessment
