Finite element-based prioritization of pelvic floor muscles for rehabilitation to maintain urinary and fecal control in elderly women

Objective This study aims to utilize finite element analysis (FEA) to explore the effects of different rehabilitation training methods on the ability of elderly women to maintain urinary and fecal control. It also seeks to determine the muscle prioritization during pelvic rehabilitation training, providing a scientific basis for personalized rehabilitation nursing. Methods A 3D pelvic-thigh modeling was constructed based on CT and MRI images from a 70-year-old Chinese elderly female volunteer. Model validity was verified by assessing relative changes in waist circumference, RVA, and ARA against imaging measurements, with geometric deviations controlled within 10%. The material properties of the muscles were altered to simulate the effects of five different physical rehabilitation methods. By comparing changes in the retrovesical angle (RVA) and anorectal angulation (ARA) under different muscle material properties settings, the relationship between rehabilitation training methods and urinary and fecal control was quantified. Results The constructed model demonstrated high geometric consistency with pelvic floor anatomy, showing less than 8.28% deviation from imaging-based measurements. As muscle material properties improved, the RVA gradually decreased, and the ARA gradually increased, approaching normal ranges. The results highlight the critical roles of the levator ani, pelvic floor, rectus abdominis, erector spinae, and hip muscles. Conclusion The findings from this simulation indicate the potential efficacy of rehabilitation training in supporting urinary and fecal control. The study emphasizes the importance of personalized pelvic floor rehabilitation programs based on gender differences, muscle status, and dysfunction types, offering new perspectives and possibilities for using FEA in elderly populations. Nevertheless, the findings are derived from a single-subject model and computational simulations without direct clinical validation, which may limit generalizability. Clinical Trial Registration identifier (ChiCTR2400080749) (20240206).