flatfoot

Background: flatfoot is a common deformity characterized by the midfoot arch collapses during walking. As the midfoot is responsible for shock absorption, persons with flatfoot experience increased risk of injuries Aim: to the purpose of this study was to compare some motor-physical factors, lower extremity performance and stability index in male adolescents 10-12 having structural flat foot with and without insole. Materials and Methods: Subjects who utilized medical insoles for four to six months were selected in access from technical orthopedic in Karaj. After parents got aware of the purpose and conditions participating in research, their height and weight were measured. In the first day, they were assessed by motor-physical factors test (agility, speed, endurance), lower extremity performance (one-leg jump, cross jump, maximum distance access, walking six meters) and stability index wearing insole and on the next day without wearing insole. To analyze data, paired t-test was utilized. Wilcoxson was used respectively for parametric and non-parametric data. Results : According to the paired t-test a significant difference was observed in the 6-meter timed test. Mean difference was slower in wearing insole compared to not wearing insole. Moreover, stability index results showed more stability when not wearing insole which eyes were closed to significant. Conclusion: According to the results, wearing insole in individuals with flat foot, could slow down the walking speed. However, wearing insole may improve stability index when visual input is not in access.

Over the past decade, there has been a rapid increase in the study of using artificial intelligence (AI) to improve the quality of life of individuals with disabilities. Therefore, the study aims to investigate the effect of a 6-week AI-generated core stability training program on balance and flatfoot in blind female students. This quasi-experimental study selected 30 female students aged 9-12 years with flatfoot in Tehran City, dividing them into two groups: one for experimental (N = 15) and another for control (N = 15). The experimental groups had six weeks of AI-based intervention with three sessions per week. During this period, the control group engaged in the routine activities of the physical education class. The navicular drop index and Y balance test were done as pre-posttest, respectively. The Covariance (ANCOVA) was used for inferential statistics. Data analysis was conducted at a significance level of 95% with an alpha level less than or equal to 0.05. The findings showed that there was a significant difference between the two groups in the scores of the Y balance test (p<0.035) and the navicular drop test (p<0.001), even when the pre-test effect was taken into account (covariate). By leveraging AI to design tailored exercise regimens, practitioners can enhance postural control and musculoskeletal health in visually impaired individuals. These results underscore the potential of AI-assisted rehabilitation strategies in special education settings, highlighting the need for further researc