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    Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction
    (Frontiers Media Sa, 2023) Gulu, Mehmet; Yagin, Fatma Hilal; Gocer, Ishak; Yapici, Hakan; Ayyildiz, Erdem; Clemente, Filipe Manuel; Ardigo, Luca Paolo
    Primary study aim was defining prevalence of obesity, physical activity levels, digital game addiction level in adolescents, to investigate gender differences, relationships between outcomes. Second aim was predicting game addiction based on anthropometric measurements, physical activity levels. Cross-sectional study design was implemented. Participants aged 9-14 living in Kirikkale were part of the study. The sample of the study consists of 405 adolescents, 231 girls (57%) and 174 boys (43%). Self-reported data were collected by questionnaire method from a random sample of 405 adolescent participants. To determine the physical activity levels of children, the Physical Activity Questionnaire for Older Children (PAQ-C). Digital Game addiction was evaluated with the digital game addiction (DGA) scale. Additionally, body mass index (BMI) status was calculated by measuring the height and body mass of the participants. Data analysis were performed using Python 3.9 software and SPSS 28.0 (IBM Corp., Armonk, NY, United States) package program. According to our findings, it was determined that digital game addiction has a negative relationship with physical activity level. It was determined that physical activity level had a negative relationship with BMI. In addition, increased physical activity level was found to reduce obesity and DGA. Game addiction levels of girl participants were significantly higher than boy participants, and game addiction was higher in those with obesity. With the prediction model obtained, it was determined that age, being girls, BMI and total physical activity (TPA) scores were predictors of game addiction. The results revealed that the increase in age and BMI increased the risk of DGA, and we found that women had a 2.59 times greater risk of DGA compared to men. More importantly, the findings of this study showed that physical activity was an important factor reducing DGA 1.51-fold. Our prediction model Logit (P) = 1/(1 + exp(-(-3.384 + Age*0.124 + Gender-boys*(-0.953) + BMI*0.145 + TPA*(-0.410)))). Regular physical activity should be encouraged, digital gaming hours can be limited to maintain ideal weight. Furthermore, adolescents should be encouraged to engage in physical activity to reduce digital game addiction level. As a contribution to the field, the findings of this study presented important results that may help in the prevention of adolescent game addiction.
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    Judo specific fitness test performance variation from morning to evening: specific warm-ups impacts performance and its diurnal amplitude in female judokas
    (Bmc, 2022) Eken, Ozgur; Clemente, Filipe Manuel; Nobari, Hadi
    Background A number of specific tests are used to standardize competition performance. Specific Judo fitness test (SJFT) can be applied by considering the start of the competition qualifiers in the morning and the continuation of the final competitions in the evening. The improvement of test performances can be achieved with warm-up for elevating heart rate (HR) and muscle temperature such as raise, activate, mobilise, potentiate (RAMP) protocols. Purpose The aim of this study is to evaluate the effects of different warm-up protocols on SJFT at different times of the day in female judokas. Methods Ten volunteer women participated in this study, who regularly participated in judo training for more than 5 years and actively competed in international competitions. Judokas completed SJFT, either after no warm-up, or RAMP protocols like specific warm-up (SWU), and dynamic warm-up for two times a day in the morning: 09:00-10:00 and in the evening: 16:00-17:00, with at least 2 days between test sessions. The following variables were recorded: throws performed during series A, B, and C; the total number of throws; HR immediately and 1 min after the test, and test index after different warm-ups. Results When analyzed evening compared to the morning without discriminating three warm-up protocols, evening results statistically significant number of total throws performed during series A, B, and C, the total number of throws; HR immediately and 1 min after the test, and test index than morning results (p < 0.01). Moreover, RAMP protocols interaction with time have demonstrated an impact on SJFT for index [F-(2) = 4.15, p = 0.024, eta(2)(p): 0.19] and changes after 1 min HR [F-(1.370)= 7.16, p = 0.008, eta(2)(p): 0.29]. HR after 1 min and test index results were statistically significant in favor of SWU (p < 0.05). Conclusions In conclusion, SJFT performance showed diurnal variation and judo performances of the judokas can be affected more positively in the evening hours especially after RAMP protocols.
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    Using machine learning to determine the positions of professional soccer players in terms of biomechanical variables
    (Sage Publications Ltd, 2023) Yagin, Fatma Hilal; Hasan, Uday C. H.; Clemente, Filipe Manuel; Eken, Ozgur; Badicu, Georgian; Gulu, Mehmet
    This study aimed to predict professional soccer players' positions with machine learning according to certain locomotor demands. Data from 20 male professional soccer players (five defenders, eight midfielders, and seven attackers) from the same team were tracked daily with a global navigation satellite system. A total of 1910 individual training sessions were recorded. The 10-fold cross-validation method was used. Soccer player positions were predicted using predictive models created with random forest (RF), gradient boosting tree, bagging classification, and regression trees algorithms, and the results were evaluated with comprehensive performance measures. Ratios and an importance plot were used to analyze the importance of the variables according to their contributions to the estimation. The findings show that the RF model achieved 100% accuracy, which means that RF can predict all player positions (100%). Running distance (26.5%), total distance (17.2%), and player load (15.8%) were the three variables that contributed the most to the estimation of the RF model and were the most important factor in distinguishing player positions. Consequently, our proposed machine learning approach (RF model) can reduce false alarms and player mispositioning.