Browsing by Author "Yilmaz, Eda"

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The Relationship Between Smartphone and Game Addiction, Leisure Time Management, and the Enjoyment of Physical Activity: A Comparison of Regression Analysis and Machine Learning Models
(MDPI, 2025) Namli, Sevinc; Car, Bekir; Kurtoglu, Ahmet; Yilmaz, Eda; Demir, Gonul Tekkursun; Guvendi, Burcu; Aldhahi, Monira I.
Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15-19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R2, mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R2 = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R2 = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R2 = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions.
The Role of Mental Toughness, Sport Imagery and Anxiety in Athletic Performance: Structural Equation Modelling Analysis
(Springer Nature, 2025) Demir, Gonul Tekkursun; Namli, Sevinc; Cakir, Ergun; Batu, Batuhan; Ates, Fatih; Yilmaz, Eda; Cagin, Musab
Mental toughness (MT), anxiety, and sport imagery (SI) are characteristics that are effective in the ups and downs of athletes' lives. The fact that these three characteristics, which have a direct effect on the performance of athletes (especially elite athletes), have not been examined by structural equation modeling in the literature to the best of our knowledge has led to the need for this study. The present study investigates the relationship between MT, anxiety levels, and SI skills among elite athletes in the 19-26 age group. A total of 407 elite athletes (143 females and 264 males) actively competing participated in the study, which was conducted within the framework of a correlational research model. Data were collected using the Mental Toughness Scale (MTS), the Sports Imagery Inventory (SII) and Anxiety subscale of the Emotion in Sport Scale (ESS). The theoretical model proposed to examine the effects of MT on SI and anxiety was tested using Structural Equation Modelling (SEM). It was found that the fit indices of the model established in the study gave a good fit, and the coefficients obtained were statistically significant (p <.05). The study revealed that athletes with higher MT had lower levels of anxiety and anxiety had a negative effect on SI skills (p <.05). Moreover, athletes with higher MT show high levels of SI abilities (p <.05). The present study suggests that training programs aimed at improving SI skills may also contribute to the development of MT.