ابراهیم ابراهیمی

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

Intellectual impairment is one of the most common mental disorders associated with impaired balance and impaired motor performance. The aim of this study was to investigate the effectiveness of game-based intervention on postural control and motor performance in children with intellectual impairment. The population of this study was all of the intellectual impaired girls of Tehran and Mashhad City, and sample consisted of 20 educable intellectual impaired girls, who were healthy physically and voluntarily participated in this research. The subjects were randomly divided into two equal experimental and control groups (n=10). The experimental groups had six weeks of game-based intervention with two sessions per week. During this period, the control group control group engaged in the routine activities of the physical education class. The Timed Up and GO and Stork tests were done as pre-posttest, respectively. Data were analyzed through descriptive and analysis of independent T-test. The findings showed that the implementation of the six-week exercise program led to improvements in static balance (P=0.001) and motor performance (P=0.001) of the participants in the game group compared to the control group. The findings of the study underline the importance of early, structured interventions for children with mild intellectual disabilities, particularly those based on game. Such interventions not only improve motor performance and balance but also contribute to overall physical, cognitive, and social development. Given the positive impact of game-based interventions, they should be integrated into rehabilitation and educational programs for children with intellectual disabilities to support their development and improve their quality of life.

Over the past decade, research on artificial intelligence (AI) has expanded significantly, exploring its potential to enhance the quality of life for older adults. Therefore, the study aims to investigate the effect of a 4-week AI-generated Pilates training program on motor performance and fear of falling in older adults. This quasi-experimental study selected 30 female older adults aged 65 years and older, dividing them into two groups: one for experimental (N = 15) and another for control (N = 15).  The experimental groups had four weeks of AI-based intervention with three sessions per week. During this period, the control group engaged in the routine activities. The Timed Up and Go and the Falls Efficacy Scale-International (FES-I) questionnaire were done as pre-posttest, respectively. The independent t-test 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 TUG test (p<0.03) and the FES-I questionnaire (p<0.001). By utilizing AI to develop personalized exercise programs, healthcare practitioners can improve motor performance and reduce the fear of falling in older adults. These findings highlight the potential of AI-driven rehabilitation strategies in geriatric care, emphasizing the need for further research to refine program parameters and extend their benefits to a broader aging population.  

This study aims to examine the validity of prescribed AI-generated exercise interventions for general health and weight loss. This study developed an AI-generated exercise program using the Delphi method. The Delphi process involved administering a structured questionnaire within the relevant domain, whereby a panel of experts evaluated the appropriateness of the program. To assess validity, three established measures were employed: the Content Validity Ratio (CVR), the Content Validity Index (CVI), and the Impact Score (IS). In addition, the Fleiss’ Kappa coefficient (κ) was calculated to determine the degree of agreement (reliability) among the experts. All statistical analyses were conducted using SPSS software version 27 and Microsoft Excel. The IS indicates that all exercises achieved the required level of validity. Also, based on the CVI and CVR, all exercises demonstrated acceptable content validity for general health and weight loss. However, the negative kappa value suggests poor agreement among the experts for the mentioned variables. The findings indicate that while platforms such as ChatGPT-4o can produce broadly appropriate exercise content, expert agreement on the validity of these protocols was very low. Accordingly, AI should be viewed as a supplementary tool that can assist in designing individualized health and weight management programs.