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Concussion is a common brain injury that has significant effects on multiple functional domains in children. However, limited research exists on the relationship between concussion severity and functional performance in this population. This study aimed to examine the relationship between the severity of concussion symptoms and children's balance and functional performance. This cross-sectional study recruited 23 children (9 males and 14 females; mean age 13.9 ± 2.2 years) with clinically diagnosed concussions from a tertiary balance center in 2016. Participants underwent clinical and functional evaluations by specialized physical therapists. Symptom severity was assessed using the Post-Concussion Symptom Scale (PCSS), while functional performance was measured using the Functional Gait Assessment (FGA). There was a trend suggesting a negative correlation between symptom severity (PCSS) and functional performance (FGA), indicating potentially better performance in individuals with milder symptoms. However, this trend was not significant (rs (21) = –.072, p = 0.744). Furthermore, no significant correlation was found between FGA scores and the severity of individual symptoms. The findings suggest that concussion symptom severity may not be directly related to functional performance in children. Therefore, it is crucial to incorporate functional performance measures alongside symptom assessment for comprehensive concussion management. [ABSTRACT FROM AUTHOR]

Context: Research in the area of dual-task paradigms to assess sport-related concussion (SRC) status is growing, but additional assessment of this paradigm in adolescents is warranted. Design: This case-control study compared 49 adolescent athletes aged 12–20 years with diagnosed SRC to 49 age- and sex-matched controls on visual–spatial discrimination and perceptual inhibition (PIT) reaction time tasks performed while balancing on floor/foam pad conditions. Methods: The SRC group completed measures at a single time point between 1 and 10 days postinjury. Primary outcomes were dual-task reaction time, accuracy, and sway. General linear models evaluated differences between groups (P <.05). Logistic regression identified predictors of concussion from outcomes. Area under the curve evaluated discriminative ability of identifying SRC. Results: Results supported significantly higher anterior–posterior (AP) sway values in concussed participants for visual–spatial discrimination and PIT when balancing on the floor (P =.03) and foam pad (P =.03), as well as mediolateral sway values on the floor during visual–spatial discrimination (P =.01). Logistic regression analysis (R2 =.15; P =.001) of all dual-task outcomes identified AP postural sway during the PIT foam dual task as the only significant predictor of concussed status (ß = −2.4; P =.004). Total symptoms (area under the curve = 0.87; P <.001) and AP postural sway on foam (area under the curve = 0.70; P =.001) differentiated concussed from controls. Conclusion: The AP postural sway on foam during a postural stability/PIT dual task can identify concussion in adolescents between 1 and 10 days from injury. [ABSTRACT FROM AUTHOR]

Objective: To find the short-term psychological and hormonal effects of virtual reality training on chronic low back pain in American soccer players. Design, Setting, Participants: The 3-block random sampling method was used on 54 university American soccer players with chronic low back pain, and they were allocated into 3 groups: virtual reality training (VRT; n = 18), combined physical rehabilitation (n = 18), and control (n = 18) groups at University Hospital. They underwent different balance training exercises for 4 weeks. The participants and the therapist who is assessing the outcomes were blinded. Psychological (pain intensity and kinesiophobia) and hormonal (glucose, insulin, Homeostatic Model Assessment of Insulin Resistance, growth hormone, prolactin, adrenocorticotropic hormone, and cortisol) values were measured at baseline, after 4 weeks, and after 6 months. Results: The baseline demographic, psychological, and hormonal data between the VRT, combined physical rehabilitation, and control groups show no statistical difference (P ≥.05). Four weeks following training, the VRT group shows more significant changes in pain intensity and kinesiophobia than the combined physical rehabilitation and control groups (P <.001), and the improvement was noted in the 6-month follow-up. All the hormonal variables (glucose, insulin, growth hormone, prolactin, adrenocorticotropic hormone, and cortisol) show significant changes at 4-week training (P <.001), except for the Homeostatic Model Assessment of Insulin Resistance (P =.075) between the 3 groups. At 6-month follow-up glucose, prolactin, adrenocorticotropic hormone, and cortisol show more significant difference in the VRT group than the other 2 groups (P <.001). At the same time, insulin (P =.694), Homeostatic Model Assessment of Insulin Resistance (P =.272), and growth hormone (P =.145) failed to show significant changes between the groups. Conclusion: Training through virtual reality is an effective treatment program when compared with conventional exercise training programs from a psychological and hormonal analysis perspective in American soccer players with chronic low back pain. [ABSTRACT FROM AUTHOR]

Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification of KOA images based on unsupervised local center of mass (LCM) segmentation method and deep Siamese Convolutional Neural Network (CNN) is presented. First-order statistics and the GLCM matrix are used to extract KOA anatomical Features from segmented images. The network is trained on our clinical data with 75 iterations with automatic weight updates to improve its validation accuracy. The assessment performed on the LCM segmented KOA images shows that our network can efficiently detect knee osteoarthritis, achieving about 93.2% accuracy along with multi-class classification accuracy of 72.01% and quadratic weighted Kappa of 0.86. [ABSTRACT FROM AUTHOR]