Articles in Press
Published:
2026-09-07
Original Research
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Effectiveness of adding single leg hop training to wobble board training in improving dynamic balance and lower limb explosive power in 13–14-year-old basketball players: A randomized controlled trial
Lanang Imam Herlambang1, I Made Muliarta2, Wahyuddin Wahyuddin31. Master's Program in Sport Physiology, Faculty of Medicine, Universitas Udayana , ID2. Master's Program in Sport Physiology, Faculty of Medicine, Universitas Udayana , ID3. Faculty of Physiotherapy, Universitas Esa Unggul , ID|63 views | 86 downloads | page 248-263AbstractBackground: Dynamic balance and lower-limb explosive power are important for youth basketball performance. Wobble board training may improve postural control, while single leg hop exercises may provide additional benefits through dynamic unilateral loading. However, evidence regarding their combined effects in adolescent basketball players remains limited.
Objective: To determine whether adding single leg hop training to wobble board training produces greater improvements in dynamic balance and lower-limb explosive power than wobble board training alone in 13–14-year-old basketball players.
Methods: This randomized controlled trial included 40 players randomly allocated (1:1) to wobble board training (control; n = 20) or combined wobble board and single leg hop training (experimental; n = 20). Both groups trained for six weeks, three 30-minute sessions per week. Dynamic balance (primary outcome) and lower-limb explosive power (secondary outcome) were assessed using the Y-Balance Test (YBT) and Vertical Jump Test (VJT), respectively. Within- and between-group changes were analysed using paired- and independent-samples t-tests (α = 0.05).
Results: All participants completed the intervention. Both groups improved significantly in YBT and VJT performance (p < 0.001). The experimental group showed greater improvements in YBT (mean change: 15.32 vs 5.23 cm; mean difference = 10.08 cm; 95% CI [7.73, 12.43]; p < 0.001; d = 2.75) and VJT (5.75 vs 0.82 cm; mean difference = 4.94 cm; 95% CI [4.22, 5.66]; p < 0.001; d = 4.41).
Conclusion: Adding single leg hop training to wobble board training produced greater improvements in dynamic balance and lower-limb explosive power. However, the findings do not establish an isolated single-leg-hop effect or injury-prevention benefit.
APA Citation StyleHerlambang, L. I. , Muliarta, I. M. , & Wahyuddin, W. (2026). Effectiveness of adding single leg hop training to wobble board training in improving dynamic balance and lower limb explosive power in 13–14-year-old basketball players: A randomized controlled trial. Physical Education and Sports: Studies and Research, 248-263. https://journal.rezkimedia.or.id/pes/article/view/760 -
Association between SDQ-TR-assessed hyperactivity symptoms and step count during school activities in school-aged children
Najwa Citamajida1, Adnan Faris Naufal21. Faculty of Health Science, Universitas Muhammadiyah Surakarta , ID2. Faculty of Health Science, Universitas Muhammadiyah Surakarta , ID|46 views | 34 downloads | page 264-275AbstractBackground: Hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD) and may manifest as increased motor activity. Objectively measured step count may provide information about ambulatory movement associated with hyperactivity symptoms, while developmental differences may also influence movement patterns in children.
Objective: To examine the association between hyperactivity symptom severity and step count during school activities and to compare step count and hyperactivity scores between children aged 5 and 12 years.
Methods: This cross-sectional study included 40 children (20 males and 20 females) aged 5 and 12 years from two schools in Surakarta, Indonesia, recruited using total sampling. Hyperactivity symptoms were assessed using the hyperactivity/inattention subscale of the Strengths and Difficulties Questionnaire Teacher Report (SDQ-TR), while step count was objectively measured using a Maxfit pedometer during one school day. Spearman's rank correlation, independent-samples t-tests, and Mann–Whitney U tests were used for statistical analysis.
Results: Hyperactivity scores were strongly and positively correlated with step count (rₛ = 0.918, p < 0.001). Step count differed significantly between the age groups (p = 0.041), with higher values observed among 5-year-old children than among 12-year-old children. In contrast, hyperactivity scores did not differ significantly between the two age groups (p = 0.701).
Conclusion: Higher hyperactivity symptom severity was associated with greater step count during school activities. Step count differed between the two age groups, whereas hyperactivity scores did not. These findings suggest that objectively measured ambulatory movement and hyperactivity symptoms may capture related but developmentally distinct aspects of children's behaviour.
APA Citation StyleCitamajida, N. , & Naufal, A. F. (2026). Association between SDQ-TR-assessed hyperactivity symptoms and step count during school activities in school-aged children. Physical Education and Sports: Studies and Research, 264-275. https://journal.rezkimedia.or.id/pes/article/view/778 -
Exploratory machine-learning classification of student performance in physical education using fitness, engagement, and academic indicators
Princes Lloyda Peralta1, Shekiro Raposas2, Reinalyn Bucsit31. General Education Department, College of Inclusive Education, Lorma Colleges , PH2. College of Computer Studies and Engineering, Lorma Colleges , PH3. General Education Department, College of Inclusive Education, Lorma Colleges , PH|70 views | 0 downloads | page 276-289AbstractBackground: Physical Education (PE) performance is multidimensional and may be associated with fitness, engagement, attendance, motivation, and prior achievement. Machine learning can identify classification patterns across these indicators, but predictive claims require clear temporal ordering and rigorous validation.
Objective: This study compared four machine-learning classifiers for categorizing student PE performance while minimizing target leakage and addressing class imbalance.
Methods: An exploratory, leakage-controlled classification analysis used a public Kaggle dataset of uncertain provenance. The dataset contained 500 records, 19 substantive variables, and one identifier, with Performance categorized as Low, Average, or High Performer. Because the label-construction rule was undocumented, Performance was treated as an inherited categorical outcome. Overall PE Performance Score, Improvement Rate, and Final Grade were excluded because they could contain current, change-based, or outcome-related information. Categorical predictors were one-hot encoded, numeric predictors were standardized, and preprocessing was performed within training folds. Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression were evaluated using stratified five-fold cross-validation with class-weighted learning.
Results: Random Forest achieved the highest accuracy (0.856), whereas Logistic Regression achieved the highest balanced accuracy (0.749) and macro-F1 (0.645). Its out-of-fold recall was 0.759 for Low, 0.720 for Average, and 0.750 for High Performers, indicating that accuracy alone may inadequately reflect performance in an imbalanced multiclass setting.
Conclusion: Leakage-controlled, class-aware evaluation is essential in machine-learning classification of student performance. These findings are exploratory and should not be interpreted as validated prospective predictions. Future studies should use documented data, explicit temporal ordering, larger balanced samples, and independent external validation.
APA Citation StylePeralta, P. L. , Raposas, S. , & Bucsit, R. (2026). Exploratory machine-learning classification of student performance in physical education using fitness, engagement, and academic indicators. Physical Education and Sports: Studies and Research, 276-289. https://journal.rezkimedia.or.id/pes/article/view/790
