A Randomized Control Trial Comparing Common Errors Made by Women During Three Different Methods of Pelvic Floor...

Background and Objectives: The aim of this study was to compare the effects of pelvic floor muscle contraction training (PFMCT) using verbal education, digital vaginal palpation (DVP), or perineometer on the common errors made during pelvic floor muscle contraction (PFMC) in women. Materials and Methods: A total of 48 women participated, and they were randomly assigned to three groups (Group I: PFMCT with verbal education, n = 16; Group II: PFMCT with DVP, n = 16; and Group III: PFMCT with perineometer, n = 16). Participants who had not previously received PFMCT were evaluated for pelvic floor muscle strength using the Modified Oxford Scale (MOS), and pelvic floor muscle activation was assessed with electromyographic biofeedback (EMG-BF). Possible errors during pelvic floor muscle contraction (gluteal, adductor and/or abdominal muscle contractions, stop breathing (breath holding), enhanced inhaling, and straining) were evaluated through inspection, palpation, or EMG-BF. After pre-training evaluations, all participants received training on pelvic floor. After this general training, each group received PFMCT using the specific training method for their group. After the training, the same evaluations were repeated. The sessions were conducted one-on-one and lasted for an average of one hour. Results: After the training, MOS values increased in Group II and Group III, while EMG-BF values only increased in Group II (p < 0.05). The number of incorrect movements during PFMC decreased after the training in all three groups (p < 0.05). The abdominal muscle contraction value monitored by EMG-BF only decreased in Group II (p < 0.05). Conclusions: Our study demonstrated that the PFMCT applied using the DVP method was more effective in creating more accurate and stronger muscle contractions and reducing common errors when compared to pre- and post-training values. Significant differences were observed between the groups in terms of performance improvements, with Group II showing the most notable progress. These results support the potential for DVP to yield better outcomes when used in PFMT.