Percorrer por autor "Amaro, Bruna"
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- A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health PoliciesPublication . Forte, Pedro; Encarnação, Samuel; Monteiro, A.M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Branquinho, Luís; Amaro, Bruna; Sampaio, Tatiana; Flores, Pedro Miguel; Silva-Santos, Sandra; Ribeiro, Joana; Batista, Amanda; Ferraz, Ricardo; Rodrigues, FilipeThe increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.
- Effects of a 10-week detraining period on gross motor skills in young tricking practitionersPublication . Branquinho, Luís; Benítez-Sillero, Juan de Dios; Amaro, Bruna; Moreira, Paula; Moreira, Flávio; Teixeira, José Eduardo; Forte, Pedro; Ferraz, RicardoTricking has emerged as a martial arts sport that combines acrobatics, gymnastics, kicks and jumps to create multiple visually striking movements. The effects of a period of detraining in young tricking practitioners still unclear. The main objective of this study was to verify the effect of a 10week detraining period on different motor skills in young tricking practitioners. A group of 17 children (age: 10.18 +/- 0.98 years) tricking practitioners were analyzed in a predetraining period and a post -detraining period using agility test, vertical impulse test, horizontal impulse test and pushup test, sit-up test. The agility and sit-ups variables show significant differences of large effect ( Delta = 6.82, p = .001, d = 2.80; increment = -13.76, p = .003, d = 1.27) respectively. Vertical impulse and push-ups showed significant differences between training phases a moderate effect ( Delta = -5.13, p = .007, d = .85; Delta = -8.37, p = .006, d = 1, 13). Results showed that agility and abdominal strength test sit ups were those that decreased to a greater extent in these subjects with a large effect, being the vertical jump as well as the push up, the motor tests that decreased moderately, while the horizontal jump did not vary significantly.
- Machine learning prediction of adolescent obesity using physical fitness dataPublication . Sampaio, Tatiana; Encarnação, Samuel; Amaro, Bruna; Ribeiro, Joana; Branquinho, Luís; Monteiro, António M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Leite, Luciano Bernardes; Malheiro, Alexandra; Rodrigues, Pedro M.; Beat Knechtle; Flores, Pedro Miguel; Forte, PedroThe escalating prevalence of obesity among adolescents has emerged as a critical global public health challenge. Machine learning techniques have been used to predict obesity in adolescents. This study aimed to develop and validate a robust obesity prediction model for adolescents using this hybrid approach, leveraging data from a diverse cross-sectional population-based study. The hybrid method combines statistical inference with non-linear machine learning to enhance prediction accuracy. Physical fitness data were collected from the FITescola® tests. Multiple tests were employed to evaluate physical fitness. Multiple Poisson's multiple regression method was applied to identify the most predictive variables set of the adolescent's body mass index (BMI) classification. The model's goodness-of-fit statistics indicate a strong fit, with a log-likelihood of 8068.6 and a Pseudo R-squared value of 0.8853, where the aerobic fitness (AF), upper limb strength (ULS) and lower limb flexibility (LLF) presented an inverse association with the adolescent's BMI. In contrast the adolescent's core strength presented a positive association with their body mass. The random forest regression showed that an average of 35 repetition on the yo-yo test predicted a healthy BMI percentile [predBMIperc = 0.31]. In addition, the model presented good validity [MAE = 0.36, MSE = 0.20, RMSE = 0.45, R2 = 0.54]. The model's strong fit and accurate random forest regression's predictions suggest that physical fitness components, such as aerobic fitness, upper limb strength, lower limb power, and core strength, play a significant role in obesity risk among adolescents.