Yazar "Demirarar, Onur" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Cluster Resistance Training Results Higher Improvements on Sprint, Agility, Strength and Vertical Jump in Professional Volleyball Players
    (2021) Cin, Merve; Çabuk, Refik; Demirarar, Onur; Özçaldıran, Bahtiyar
    Objective: The purpose of this study is to compare the effects of six-week traditional and cluster resistance training (CRT) on sprint, agility, maximal strength and vertical jump parameters in professional volleyball players. Material and Methods: Twenty-eight professional male volleyball players participated in the present study. Athletes were randomly assigned into two training groups: traditional resistance training (TRT) (n=14) and CRT (n=14) groups. Both training groups performed the resistance trainings during six-week on three nonconsecutive days (Monday, Wednesday and Friday). Subjects underwent countermovement vertical jump, agility t, 10-m, 20-m sprint, one repetition maximal (1RM) back squat (BS), bench press (BP), pull over (PO), military press (MP) and dead lift (DL) tests prior to and after sixweek resistance training program. Results: Both training groups demonstrated statistically significant improvements in 1RM, sprint times, vertical jump displacement and agility t-test (p<0.05). As compared with the TRT group, the CRT group demonstrated large significant gains in 1RM BS (CRT: 4.76%±2.34% vs. TRT: 2.21%±1.57%; p=0.002, effect size (ES)=0.51), 1RM PO (CRT: 7.59%±4.57% vs. TRT: 3.96%±3.65%; p=0.004, ES=0.91), 1RM DL (CRT: 4.19%±2.76% vs. TRT: 1.79%±2.92%; p=0.024, ES=0.875) and 1RM BP (CRT: 5.82%±4.58 vs. TRT: 2.66%±3.51; p=0.033, ES=0.31). As compared with the TRT group, the CRT group demonstrated significantly higher gains in 20-m sprint (CRT: 6.48%±4.70% vs. TRT: 1.81%±1.04%; p=0.001, ES=1.93), agility t-test performances (CRT: 11.35%±3.89% vs. TRT: 2.28%±1.85%; p=0.000, ES=0.81) and vertical jump displacement (CRT: 6.21%±0.90% vs. TRT: 2.13%±0.61%; p=0.000, ES=5.45). Conclusion: This study suggests that CRT provides more advantages than TRT for professional volleyball players.
  • Küçük Resim Yok
    Öğe
    Mechanical power output is unaffected by starting cadence during Wingate-type sprint interval training
    (Nurtekin ERKMEN, 2026) Çabuk, Refik; Ağgön, Eser; Tatlısu, Bülent; Demirarar, Onur; Ağırbaş, Öztürk
    Wingate-type repeated sprint intervals (SITWingate) rely on the attainment of high peak power output (PPO) and the maintenance of average power output (AvPO) as mechanical acute responses that are critical for eliciting aerobic and anaerobic adaptations. In recreationally active participants, the starting cadence of a single WAnT has been shown to influence mechanical power indices. However, the effect of different starting cadences on mechanical power indices during a SITWingate protocol remains unknown. Therefore, the purpose of this study was to determine whether different starting cadences in SITWingate alter mechanical power outputs. Eight recreationally active men (age: 21.1 ± 2.1 years; height: 179 ± 6.4 cm; body mass: 69.8 ± 7.1 kg) volunteered to participate. Participants completed two SIT protocols with different starting cadences (SIT80rpm and SIT120rpm) in sessions separated by ?72 hours. Each protocol consisted of three 30-s all-out cycling bouts against a standardized load corresponding to 7.5% of body mass, interspersed with 4-min passive recovery. For each bout, PPO and AvPO were obtained. Repeated-measures ANOVA indicated no significant main effect of starting cadence on PPO (F(1,7) = 0.11, p = 0.755, ?p² = 0.015), and AvPO (F(1,7) = 0.11, p = 0.753, ?p² = 0.015). Moreover, when the means of the three sprints were compared, no differences between protocols were detected for PPO (p = 0.755) and AvPO (p = 0.753). In conclusion, starting cadences (SIT80rpm vs. SIT120rpm) had no statistically significant effect on SITWingate performance indices among recreationally active men.
  • Küçük Resim Yok
    Öğe
    The success of critical velocity protocol on predicting 10000 meters running performance
    (Iermakov S S, 2023) Cabuk, Baris; Demirarar, Onur; Cin, Merve; Cabuk, Refik; Ozcaldiran, Bahtiyar
    Background and Study Aim The study aims to evaluate which of the critical velocity (CV) estimates of the three widely used models and the best-fit model successfully predict the running performance of 10000 meters. Material and Methods The group of participants in this study consisted of 11 British endurance athletes. The CV estimations were obtained from the models with the athletes' running velocity and exhaustion times of 1500, 3000, and 5000 meters (m). The information was taken from a website where the results of the British athletes are recorded. In terms of selecting endurance athletes, the data of the athletes who ran 1500 m, 3000 m, 5000 m, and 10000 m in the same two years were included in this study. By fitting the data into mathematical models, the CV estimates of the three mathematical models and the individual best-fit model were compared with the 10000 m running velocity. The CV estimates were obtained by fitting the relevant data on the running velocity, exhaustion time, and running distance of the three running distances of athletes to each of the three mathematical models. Results 10000 m running velocity and times of the athletes corresponded to 19.65 +/- 1.26 km/h and 30.4 +/- 1.94 minutes, respectively. The CV values obtained from the three mathematical models and 10000 m running velocity were similar (p > 0.05). Although the lowest total standard error levels were obtained with the best individual fit method, the 10000 m running velocity was overestimated (p < 0.05). Conclusions Three mathematical models predicted 10000 meters of race velocity when an exhaustion interval between 2-15 minutes was used. Even though the mathematically most valid CV value was obtained with the best individual fit method, it overestimated the 10000 m running velocity. When comparing the values of CV and the velocity of running 10,000 meters, our study suggests using the model. This is because the model has the smallest effect size, and there is no statistically significant difference in the total standard error level between the model and the best-fit model.