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    Effects of cooperative learning on students' learning outcomes in physical education: a meta-analysis
    (Frontiers Media Sa, 2025) Boke, Hulusi; Aygun, Yalin; Tufekci, Sakir; Yagin, Fatma Hilal; Canpolat, Burak; Norman, Goktug; Ardigo, Luca Paolo
    This meta-analysis examines the effect of Cooperative Learning (CL) interventions, compared to traditional instructional methods, on students' learning outcomes across affective, cognitive, physical, and social domains in physical education (PE). The review involved a comprehensive search of 12 databases in English, Spanish, and Turkish, with the last search conducted on June 2nd, 2024. Studies included were true experimental or quasi-experimental designs featuring direct CL interventions in PE, covering students of both genders from primary school to university levels. The standardized Cochrane methods were used to identify eligible records, collect and combine data, and assess the risk of bias. Comprehensive Meta-Analysis (CMA) v4 software package was used to yield a summary of quantitative results. Hedges's g was used as the effect size (ES) measure, calculated from pre- and post-tests in both experimental and control groups. Forty-three studies (comprising 60 reports) were initially included, but three studies were excluded as outliers, leaving 40 studies (56 reports) with a total of 3.985 participants for analysis. The random effects model revealed a moderate positive overall effect of CL interventions (ES = 0.459, 95% CI = [0.324, 0.592], p < 0.001), indicating that CL enhances PE students' learning across four domains. Subgroup analyses showed small to moderate ESs for affective (ES = 0.304), physical (ES = 0.471), cognitive (ES = 0.589), and social learning (ES = 0.612). Risk of bias was evaluated using Begg and Mazumdar's rank correlation, the classic fail-safe number, and a funnel plot, all indicating a low risk of bias. Methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). The study was registered on PROSPERO (ID: CRD42024532607). This meta-analysis underscores the effectiveness of CL as a student-centered pedagogical model in PE, demonstrating its positive effect on various learning outcomes in the affective, cognitive, physical, and social domains. The findings provide instructive data and strategies for researchers, practitioners, and policymakers aiming to integrate, implement, or make context-specific adaptations of CL into educational processes, while ESs in the affective, physical, cognitive, and social learning domains provide domain-based implementation guidance for these stakeholders.
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    Is environmental enrichment effective in modulating autophagy markers in the brain exposed to adverse conditions? A systematic review
    (Frontiers Media Sa, 2025) Silva, Clarice Beatriz Goncalves; de Sousa Fernandes, Matheus Santos; Cerqueira, Debora Dantas Nucci; Santos, Gabriela Carvalho Jurema; Yagin, Fatma Hilal; Aygun, Yalin; Tabnjh, Abedelmalek Kalefh
    Autophagy is a key regulator of cellular homeostasis and neuronal survival, particularly under adverse physiological conditions. Environmental enrichment (EE), a non-pharmacological intervention providing enhanced sensory, cognitive, and motor stimulation, may modulate autophagic processes in the brain. This systematic review aimed to synthesize preclinical findings on the effects of EE on autophagy markers in rodent models subjected to diverse adverse conditions. A literature search across PubMed, Scopus, ScienceDirect, and embase yielded eight eligible studies meeting inclusion criteria. EE was found to be generally associated with upregulation of key autophagic markers such as Beclin-1, LC3-II/LC3-I ratio, cathepsins, p62, p-TFEB, and LAMP-1 across brain regions including the cortex, hippocampus, and penumbral area. However, reductions in some markers were also observed, indicating that the modulatory effects of EE are context-dependent and may vary with disease model, brain region, or EE protocol duration. These findings suggest that EE holds promise as an adjunctive strategy to modulate autophagy and mitigate neurodegeneration, though heterogeneity in study design and outcomes warrants caution during interpretation. Further mechanistic and sex-specific studies are needed to clarify the therapeutic relevance of EE-induced autophagic modulation.
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    Machine Learning Classification of Cognitive Status in Community-Dwelling Sarcopenic Women: A SHAP-Based Analysis of Physical Activity and Anthropometric Factors
    (Mdpi, 2025) Gormez, Yasin; Yagin, Fatma Hilal; Aygun, Yalin; Alzakari, Sarah A.; Alhussan, Amel Ali; Aghaei, Mohammadreza
    Background and Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, has increasingly been recognized not only as a physical health concern but also as a potential risk factor for cognitive decline. This study investigates the application of machine learning algorithms to classify cognitive status based on Mini-Mental State Examination (MMSE) scores in community-dwelling sarcopenic women. Materials and Methods: A dataset of 67 participants was analyzed, with MMSE scores categorized into severe (<= 17) and mild (>17) cognitive impairment. Eight classification models-MLP, CatBoost, LightGBM, XGBoost, Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and AdaBoost-were evaluated using a repeated holdout strategy over 100 iterations. Hyperparameter optimization was performed via Bayesian optimization, and model performance was assessed using metrics including weighted F1-score (w_f1), accuracy, precision, recall, PR-AUC, and ROC-AUC. Results: Among the models, CatBoost achieved the highest w_f1 (87.05 +/- 2.85%) and ROC-AUC (90 +/- 5.65%), while AdaBoost and GB showed superior PR-AUC scores (92.49% and 91.88%, respectively), indicating strong performance in handling class imbalance and threshold sensitivity. SHAP (SHapley Additive exPlanations) analysis revealed that moderate physical activity (moderatePA minutes), walking days, and sitting time were among the most influential features, with higher physical activity associated with reduced risk of cognitive impairment. Anthropometric factors such as age, BMI, and weight also contributed significantly. Conclusions: The results highlight the effectiveness of boosting-based models in capturing complex patterns in clinical data and provide interpretable evidence supporting the role of modifiable lifestyle factors in cognitive health. These findings suggest that machine learning, combined with explainable AI, can enhance risk assessment and inform targeted interventions for cognitive decline in older women.
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    Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence
    (Frontiers Media Sa, 2025) Gormez, Yasin; Yagin, Fatma Hilal; Yagin, Burak; Aygun, Yalin; Boke, Hulusi; Badicu, Georgian; Aghaei, Mohammadreza
    Objectives This study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors.Methods Six ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.Results The CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance.Conclusion This research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.
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    ‘Shared Emotions in Shared Weaves’: Perceived Positivity Resonance and the Social–Emotional Benefits of Equine-Assisted Therapy
    (Tech Science Press, 2026) Aygun, Yalin; Tufekci, Sakir; Norman, Goktug; Canpolat, Burak; Yagin, Fatma Hilal; Tufekci, Sacide; Šarinić, Larisa Draščić
    Backgrounds: Children’s mental ill health has risen worldwide in recent years, placing increasing emotional demands not only on autistic children but also on their families. A holistic perspective on supportive therapies besides medical treatment is essential. There is a growing need for research and practice that explore equine-assisted therapy through innovative relational frameworks. This qualitative study had two main aims: first, to understand how parents perceived the social and emotional benefits of their autistic child’s involvement in equine-assisted therapy; and second, to explore how parents experienced positive resonance with their child during simultaneous parent–child involvement in the therapy. Methods: Eighteen parents (twelve mothers and six fathers) were interviewed across two equine-assisted therapy centers in Türkiye and Croatia. The data were analyzed using reflexive thematic analysis. Results: Analysis resulted in a synthesis of how parents experienced equine-assisted therapy as a social and emotional milieu for growth, as moments of shared positive affect (the experiential component of positivity resonance), and as episodes of caring nonverbal synchrony (the behavioral component of positivity resonance). Specifically, parents saw the actual and potential benefits of horse-based therapies for themselves and their autistic children, particularly in the shared moments of positive affect that emerged during the riding sessions and in the caring nonverbal synchrony that unfolded through gentle, coordinated movements, mutual gaze, and quiet bodily attunement. Conclusions: The findings suggest that this unique parent–child connection is rare in everyday life yet deeply meaningful, offering brief but powerful experiences of warmth, ease, and feeling ‘in sync’, which contribute to mental health and emotional well-being. Copyright © 2026 The Authors.
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    The effects of French contrast method on explosive power and speed-related skills in female soccer players: a randomized controlled trial
    (Bmc, 2026) Aslan, Tahir Volkan; Aygun, Yalin; Tufekci, Sakir; Yagin, Fatma Hilal; Buyukcelebi, Hakan; Ahmad, Irshad; Ardigo, Luca Paolo
    Background The purpose of this study was to examine whether the addition of a 6-week French Contrast Method (FCM) to routine soccer training affects agility, vertical jump height and 30-m sprint performance in female soccer players. Methods A pretest-posttest control-group design was used. Twenty-four female soccer players (> 3 years of playing experience; regular training) participated. The experimental group performed a 6-week FCM program in addition to routine soccer training, while the control group continued routine soccer training only. Agility, vertical jump and 30-m sprint tests were administered at baseline and post-intervention. Within the scope of the study analyses, the primary outcomes (agility, vertical jump, and sprint performance) were derived from within-group pretest-posttest comparisons, whereas the secondary outcomes were obtained from findings related to the participants' descriptive characteristics. 2 Result The group x time interaction was found to be significant in the Illinois Agility Test ( F = 16.813 , p = 0.0004 eta rho & sup2; = 0.433). Performance time decreased from 18.44 +/- 0.59 s to 17.09 +/- 0.365 (Delta = - 1.35s) in the experimental group, while the change was limited in the control group (Delta = - 0.72s) ; the mean difference between groups was - 1.13 s (95% CI: -1.54 to-0.72). A significant group x time interaction was also detected in vertical jump performance ( F = 12.415 , p = 0.002 eta rho & sup2; = 0.361), with an increase of +3.31 cm in the experimental group and +0.77 cm in the control group (between-group difference: +2.54 cm; 95% CI:-0.64 to 5.72). In contrast, the group x time interaction was not significant for the 30 m sprint performance ( F = 0.869 , p = 0.361 eta rho & sup2; = 0,038). Conclusion These results indicate that the FCM training program is effective in improving female soccer players' agility and vertical jump performance, but does not create a significant difference between groups in 30 m sprint performance. These findings not only extend the scientific literature but also provide actionable strategies for coaches and practitioners.