پرنیان رشیدی

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.  

Locomotive Syndrome (LS) is a prevalent condition among older adults. It significantly impacts the quality of life and increases the risk of falls, fractures, and functional decline. This study aimed to compare the effectiveness of two exercise programs, Vivifrail and Locotra, on improving walking speed in elderly individuals with LS. Thirty elderly women aged 65 and older from the Ranginkamanesepid Day Care Center in Isfahan, Iran, participated in this randomized controlled trial, divided into two groups for an eight-week intervention, conducted three times a week. The primary outcome was the 6-meter walking time test, measured before and after the intervention. Both exercise programs demonstrated improvements in walking speed post-intervention; however, no statistically significant difference was found between the two groups. The Vivifrail group had a mean walking time post-test, while the Locotra group also showed improvement. The effect sizes indicated that both programs were effective but did not significantly outperform each other. Both Vivifrail and Locotra exercise programs are beneficial for enhancing walking speed in elderly individuals with LS, and given the lack of significant differences between the two interventions, healthcare providers may consider either program as a viable option for improving mobility in this population.

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