Kul, Murat; Aydemir, Burakhan

The aim of this study is to examine the effects of different plyometric training methods on the biomotor and taekwondo technical performance of 14-16-year-old taekwondo athletes. The study utilized two different training methods: plyometric exercises on sand and plyometric exercises on taekwondo mats, with a control group performing only regular taekwondo training. The study included 66 athletes, all with at least 5 years of taekwondo experience, from Trabzon, Turkey, in 2024. Participants were divided into two experimental groups: Group 1, performing plyometric exercises on sand, and Group 2, performing plyometric exercises on taekwondo mats. After an 8-week training program, biomotor and technical performance tests were applied to all groups for comparison. The data were tested for normal distribution using the Kolmogorov-Smirnov test, and since the data did not show normal distribution, non-parametric tests were used for analysis. Wilcoxon tests were applied for within-group comparisons, while Kruskal-Wallis tests were used for between-group comparisons. In cases where significant differences were found, Post hoc multiple comparison tests with Bonferroni correction were used to identify which groups showed the differences. The results showed that all groups demonstrated significant improvements in biomotor and technical performance parameters after the training programs. Group 1, the sand-based plyometric training group, showed more pronounced improvements in speed, agility, jump, balance, and technical performance parameters. The findings suggest that sand-based plyometric training has a significant impact on sports requiring speed, power, endurance, and technical skills, such as taekwondo.

AtıfScopus2·WoS2

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Aydemir, Burakhan; Aydoğan, Muhammed Talha; Boz, Emre; Kul, Murat; Kırkbir, Fatih; Özkara, Abdullah Bora

This study aimed to examine the validity and reliability of the AI-based DeepSport application by comparing its outcomes with those from the reference device, OptoJump. The primary dependent variables measured were jump height and anaerobic power during vertical jump assessments. Twelve elite male basketball players voluntarily participated in the study (age = 21.53 ± 1.14 years; sports experience = 6.47 ± 1.01 years). DeepSport uses AI-based image processing from standard cameras, while OptoJump uses optical sensor technology. Both DeepSport and OptoJump systems were utilized to assess participants’ Countermovement Jump (CMJ) and Squat Jump (SJ) performances. A G*Power (version 3.1.9.7) analysis determined the required sample size, adopting a 95% confidence level, 90% test power, and an effect size of 0.25. Validity assessments were conducted using Bland-Altman plots and ordinary least products (OLP) regression analysis, while reliability was evaluated through intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), and smallest detectable change (SDC) analyses. DeepSport showed excellent reliability in CMJ and SJ tests with ICC values > 0.90, and CV ranged between 2.12% and 4.95%. Results were consistent with OptoJump, showing no significant differences according to t-test results (p > 0.05). Bland–Altman analyses indicated no systematic bias and random distribution. These findings confirm that both DeepSport and OptoJump devices demonstrate high reliability and consistency, suggesting their validity and reliability for use in athlete performance assessments by coaches and athletes.

AtıfScopus4·WoS3·OpenAlex5

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