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    Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits
    (Mdpi, 2023) Gozukara Bag, Harika Gozde; Yagin, Fatma Hilal; Gormez, Yasin; Gonzalez, Pablo Prieto; Colak, Cemil; Gulu, Mehmet; Badicu, Georgian
    Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision-recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost's performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.
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    Relationships between training load, peak height velocity, muscle soreness and fatigue status in elite-level young soccer players: a competition season study
    (Bmc, 2023) Nobari, Hadi; Eken, Ozgur; Kamis, Okan; Oliveira, Rafael; Gonzalez, Pablo Prieto; Aquino, Rodrigo
    BackgroundThis study aimed to compare training load parameters, delayed onset muscle soreness (DOMS), and fatigue status between season periods (1(st) and 2(nd) halves) in U14 soccer players and to analyze the relationships between training load parameters based on season periods (1(st) and 2(nd) halves) with peak height velocity (PHV), DOMS, and fatigue status in under-14 (U14) young elite soccer players. Additionally, it was intended to analyze if fatigue, DOMS and PHV could explain training load parameters across the season.MethodsTwenty U14 players that competed in the national league participated in this study. The players were monitored during the whole season (26 weeks), and evaluations were carried out at the end of the in-season. Anthropometric and body composition parameters and the maturity offset of each player were utilized to compute each player's age at PHV. Players reported their levels of DOMS and fatigue status using Hooper index questionnaires. The internal load was monitored using the rating of perceived exertion (RPE). Acute weekly internal load (AW), chronic weekly internal load (CW), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) were also obtained.ResultsThe main results showed that TM was higher in the 2(nd) half, while CW, AW and DOMS were higher in the 1(st) half of the season. Moreover, the main correlations showed a positive correlation between PHV and TS (2(nd) half of the season) and between fatigue and TM (1(st) half of the season).ConclusionIn conclusion, variations in well-being status and PHV cannot explain the variations in internal training loads in elite U14 soccer players. In addition, internal training load indices during the first half of the competitive season can promote a fundamental base for progression loads during the second period of the competitive season.