Browsing by Author "Borba, Diego"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Effect of a school soccer competition with consecutive day games on the recovery status of U-19 playersPublication . Chaves, Suene; Cerqueira, Matheus S.; Tucker, Guilherme; Miloski, Bernardo; Coelho, Daniel; Borba, Diego; Magalhães, Pedro; Ferreira-Júnior, JoãoThe present study aimed to evaluate the effect of a school soccer competition with consecutive day games on the recovery status of U-19 players. Thirty-one school athletes (17.1±1.1 years) who played a U-19 school soccer competition (composed of two groups of four soccer teams each, followed by semifinals and final) were randomly evaluated. Games lasted 70 min (two periods of 35 min with 15 min rest interval), and they were played on consecutive days with 24 h between each game. Delayed onset muscle soreness (DOMS) and Total Quality Recovery (TQR) were measured before group phase games (n= 31) and semifinals games (n= 18). The internal game load was measured by the session rate of perceived exertion (session-RPE) method. TQR was higher before the first game when compared to the other games (p< 0.001). DOMS increased after the first game and did not return to baseline before the fourth game. Both session-RPE and internal load of the fourth game were higher than in the other games (p< 0.001). In addition, there was no correlation between internal game load and TQR (p> 0.05). The monotony observed during the evaluated period was 3.1±2.0 AU. The results indicate that the 24 h rest period seems to be insufficient for complete recovery of U-19 soccer school athletes, suggesting the organization of U-19 school soccer competitions with higher rest interval between games and search for methods to increase the recovery rate.
- Relationship Between Quality of Life, Level of Physical Activity, Physical Fitness, and Body Composition on the Academic Performance of High School Students in an Integrated Educational SystemPublication . Gazolla, Jeann C.; Ferreira-Júnior, João; Encarnação, Samuel; Schneider, André; Monteiro, António M.; Teixeira, José Eduardo; Forte, Pedro; Oliveira, João P.; Borba, Diego; Costa, Carlos Manuel Azevedo; Vieira, Carlos A.Adolescence is a critical period for the development of physical and cognitive health. Understanding how lifestyle and physical health parameters relate to academic performance and quality of life may inform school-based interventions. Purpose: This study aimed to evaluate the relationship between physical activity level (PAL), quality of life (QoL), physical fitness (PF), strength, speed and agility, body composition, and academic performance (AP) in high school students. Research Design: A cross-sectional, correlational study using multiple linear regression models to assess predictive relationships. Study Sample: 365 students (aged 16.93 ± 0.94 years) participated in the study. Data Collection and Analysis: Evaluations included Body Mass Index (BMI); PAL; QoL; PF (handgrip strength, countermovement vertical jump, and agility); and AP. A multiple linear regression was conducted using AP as the dependent variable, with BMI, jump performance, agility, handgrip strength, and PAL scores as predictors. Five additional multiple linear regressions were performed, each with a QoL domain as the dependent variable, and the same set of predictors as in the AP model. Participants’ age and sex were included as covariates in all models. Results: Significant predictive capacity was observed for AP (F = 2.22, p = .028, R = 0.31, R2 = 0.093) and two QoL domains: physical health (F = 2.32, p = .021, R = 0.28, R2 = 0.079) and psychological health (F = 2.32 and p = .021, R = 0.28, R2 = 0.079); however, with weak correlation coefficients (0.2 ≤ R <0.4). Only jump performance and age significantly affected the AP model (β = 0.038, p = .014) and the psychological health domain model (β = 0.48, p = .018). Conclusions: The predictors explained 9.3% of the variance in AP and 7.9% of the variance in physical health and psychological health in QoL domains, suggesting that additional factors (e.g., socioeconomic status, dietary habits) may play a role. The findings highlight the importance of multifactorial approaches in future research.
- Relationship Between Quality of Life, Level of Physical Activity, Physical Fitness, and Body Composition on the Academic Performance of High School Students in an Integrated Educational SystemPublication . Gazolla, Jeann C.; Ferreira-Júnior, João; Encarnação, Samuel; Schneider, André; Monteiro, António M.; Teixeira, José Eduardo; Forte, Pedro; Oliveira, João P.; Borba, Diego; Costa, Carlos M.; Vieira, Carlos A.Adolescence is a critical period for the development of physical and cognitive health. Understanding how lifestyle and physical health parameters relate to academic performance and quality of life may inform school-based interventions. Purpose: This study aimed to evaluate the relationship between physical activity level (PAL), quality of life (QoL), physical fitness (PF), strength, speed and agility, body composition, and academic performance (AP) in high school students. Research Design: A cross-sectional, correlational study using multiple linear regression models to assess predictive relationships. Study Sample: 365 students (aged 16.93 ± 0.94 years) participated in the study. Data Collection and Analysis: Evaluations included Body Mass Index (BMI); PAL; QoL; PF (handgrip strength, countermovement vertical jump, and agility); and AP. A multiple linear regression was conducted using AP as the dependent variable, with BMI, jump performance, agility, handgrip strength, and PAL scores as predictors. Five additional multiple linear regressions were performed, each with a QoL domain as the dependent variable, and the same set of predictors as in the AP model. Participants’ age and sex were included as covariates in all models. Results: Significant predictive capacity was observed for AP (F = 2.22, p = .028, R = 0.31, R2 = 0.093) and two QoL domains: physical health (F = 2.32, p = .021, R = 0.28, R2 = 0.079) and psychological health (F = 2.32 and p = .021, R = 0.28, R2 = 0.079); however, with weak correlation coefficients (0.2 ≤ R <0.4). Only jump performance and age significantly affected the AP model (β = 0.038, p = .014) and the psychological health domain model (β = 0.48, p = .018). Conclusions: The predictors explained 9.3% of the variance in AP and 7.9% of the variance in physical health and psychological health in QoL domains, suggesting that additional factors (e.g., socioeconomic status, dietary habits) may play a role. The findings highlight the importance of multifactorial approaches in future research.