Acute Effect of Proprioceptive Neuromuscular Facilitation - Based Warm-Up on Isokinetic Strength, Endurance and Balance

Acute Effect of Proprioceptive Neuromuscular Facilitation - Based Warm-Up on Isokinetic Strength, Endurance and Balance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Acute Effect of Proprioceptive Neuromuscular Facilitation - Based Warm-Up on Isokinetic Strength, Endurance and Balance Muhammed YILDIZ, Mehmet ÇEBİ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4678537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated the acute effects of proprioceptive neuromuscular facilitation (PNF)-based warm-up on isokinetic strength, endurance, and body balance in 50 soccer players. Measurements were taken on four separate days, with participants resting completely between sessions. Isokinetic strength tests for hip, knee, and ankle flexion and extension were conducted using an isokinetic dynamometer at angular velocities of 60 o /sec, 180 o /sec and 240 o /sec. Endurance was assessed with 25 repetitions at 240 o /sec by analyzing the change in peak torque values. On the first day, players underwent general warm-up, balance tests, and isokinetic strength measurements. Subsequent sessions included these measurements along with additional testing. Results showed that the PNF-based warm-up significantly improved static and dynamic balance compared to other methods (p<,001). Peak torque values at all angular velocities indicated that the PNF-based warm-up was more effective than other warm-ups. Additionally, PNF-based warm-up had a better acute effect on knee joint isokinetic endurance (p<,05). In conclusion, the PNF-based warm-up significantly enhanced static and dynamic balance, isokinetic strength, and endurance in soccer players. It is recommended to include PNF-based warm-ups in soccer training, especially for the lower extremities, and to consider combining it with active warm-up methods for optimal acute performance benefits. Balance Endurance İsokinetic strength Proprioceptive neuromuscular facilitation Warm-up INTRODUCTION Warm-up exercises are performed to improve athletic performance, prevent possible injuries and optimally prepare athletes physiologically and psychologically for the upcoming activities (Ben Moussa Zouita et al., 2013 ). The intensity, duration and type of warm-up can vary and form different protocols aimed at achieving the best adaptation and improvement in performance (Krutsch et al., 2020 ). These protocols include a warm-up method based on proprioceptive neuromuscular facilitation (PNF) techniques. Exercises utilising PNF techniques are more likely to be used in rehabilitation or long-term training than a standard warm-up method (Akyüz et al., 2020 ; Arumugam et al., 2021 ; Lim et al., 2019 ). PNF is designed to elicit motor responses, improve neuromuscular control and function, enhance coordination, and facilitate the development of muscle strength and endurance through patterns involving multiple muscle groups (Ben Moussa Zouita et al., 2013 ; Lazarou et al., 2018 ). Therefore, a PNF-based warm-up method may offer greater physiological benefits compared to other warm-up methods. In sports, even small changes in parameters such as strength, endurance and balance can have a significant impact on performance. Evaluations performed with isokinetic dynamometers provide valuable data that can guide athletes to a better level of performance. A review of the literature shows that there are few studies that focus on the structures around the hip, knee and ankle joints - the major components of the lower limb and the results of repetitive force after balance and isokinetic measurements. Most studies have focussed on the effects of flexion and extension of the knee joint. These studies have shown that static stretching can decrease isokinetic strength of the long muscles, dynamic stretching can increase isokinetic strength, and PNF-type stretching has minimal effects on isokinetic muscle strength (Coelho et al., 2021 ; Krutsch et al., 2020 ). This study was aimed at football players and used combined isotonic contraction and holding-relax movement methods from PNF methods for lower limb patterns (hip flexion abduction and internal rotation, knee flexion and extension, ankle dorsiflexion and eversion, finger extension/hip flexion abduction and external rotation, knee flexion and extension, (ankle dorsiflexion and inversion, finger extension/ hip extension adduction and external rotation, knee flexion and extension, plantar flexion and eversion of the ankle, finger flexion/ hip extension adduction and external rotation, knee flexion and extension, plantar flexion and inversion of the ankle, finger flexion) were performed for 10 repetitions and 3 sets. The acute effects of this warm-up method on isokinetic strength, endurance and body balance were analysed. The study was conducted on football players. Football is one of the sports considered suitable for the study of hip, knee and ankle joints, as the parameters of the lower limb region are the structures in which performance should be focussed. Therefore, the effects of football players on isokinetic strength, endurance and body balance are studied acutely after warm-up (Alt et al., 2022 ; Ithurburn et al., 2019 ). Strength tests are usually performed at low speeds, while power and endurance exercises are performed at high speeds. Angular velocities ≤ 180°/sec are used for strength tests, while velocities > 240°/sec are used for endurance tests (Kurdiova et al., 2014 ; Wilson et al., 1991 ). Therefore, angular velocities of 60°/sec are usually used to obtain information on strength and muscle-specific performance. In training that requires explosive power and fast body movements, power gains are greater at high velocities (180°/s, 240°/s, 300°/s) than at low velocities (30°/s, 60°/s, 90°/s). The increase in strength is also greater at lower speeds (Kyselovičová et al., 2022 ; Müller et al., 2022 ; Opar & Serpell, 2014 ; Palmer et al., 2020 ). During the isokinetic endurance test, the endurance measurement of the flexor and extensor muscles, peak torque values and fatigue indices are recorded with the Cybex device. The fatigue index is calculated by reducing the percentage values of the peak torque during the endurance test. It expresses the percentage change between the first peak torque and the last peak torque. A negative fatigue index indicates that a higher torque is produced at the beginning, i.e. the muscles fatigue in a progressive process and torque production decreases (Claiborne et al., 2009 ; Herman et al., 2012 ; Ikeda & Ryushi, 2021 ; Lee et al., 2022 ). Does Proprioceptive Neuromuscular Facilitation (PNF)-based warm-up have a more acute effect on isokinetic strength, endurance, and balance compared to active and passive warm-up conditions? METHODS This study was designed for repeated measurements. The study analyses acute changes in isokinetic strength, endurance and balance scores of football players by measurements after warm-up protocols. Of the 56 participants, 6 were excluded from the study due to injury during tests and 50 participants completed the study. The participants were informed that they should not consume alcohol or caffeine 24 hours before the test and should not do any strenuous exercise. It was ensured that there was a complete rest period (72 hours) between the days of the measurements. Participants were called for 4 different warm-up protocols and measurements on 4 different days. At the 1st measurement, information about the study, demographic information and a general warm-up protocol were given. Measurements were then taken with an isokinetic dynamometer (Humac Norm, CSMI, USA) and a CSMI brand Prokin TecnoBody device. A passive warm-up protocol (massage) was used for the 2nd measurement. The measurements were performed with an isokinetic dynamometer (Humac Norm, CSMI, USA) and a Prokin TecnoBody device of the CSMI brand. In the 3rd measurement, a protocol with 15 minutes of active warm-up was applied and then isokinetic strength, endurance and balance test measurements were performed. In the 4th measurement, a PNF-based warm-up protocol was applied for 15 minutes and the measurements were repeated (Akdoğan et al., 2013 ; Cramer et al., 2005 ; Lakkadsha et al., 2022 ). Measurements were taken within 30 seconds of the warm-up protocols. Static balance measurements were performed first and then dynamic balance measurements. For the static balance test, participants were positioned in relation to the lines on the x- and y-axis of the balance platform. After the test, the measurement data was recorded. The dynamic balance measurements were carried out on the moving floor. It was set up in the form of a double leg with reference to the x- and y-axis. She was asked to stay on the circular track on the monitor and complete 5 laps within 60 seconds. As balance parameters; Average Balance Error Tracking (ATE), Elliptical Area (EA) (mm 2 ) Area used, Average Lateral Velocity (mm/s) (OMLS), Average COPX (Average Pressure Centre X), Average COPY (Average Pressure Centre Y), Forward - Backward Swing Deviation (FBD), Mean - Lateral Deviation (MLD) (Right - Left Swing Deviation) and Mean Forward - Backward Speed (OFBS) (mm/s) (Castillo et al., 2022 ; Schiltz et al., 2009 ). If the values of these parameters are close to zero, it is assumed that the balance is good, and if the values are away from zero, it is assumed that the balance is poor (S. N. Costa et al., 2021 ; Kannus, 1994 ). Four different methods were used as warm-up protocols. Before the measurement with the isokinetic dynamometer, general short-term warm-up methods for the lower extremities were used. This warm-up was performed to avoid injury during isokinetic tests. It was performed at a level that maintained the general flexibility of the body. The same person (physiotherapist) performed the warm-up protocols and all measurements throughout the study. Throughout the study, all treatments were carried out by a single physiotherapist. In order to eliminate the changes in the manual applications caused by the different practitioners, a single practitioner was designated for this subject. The region in which the PNF techniques are applied is the lower extremity. The movement patterns in the lower extremity are: first pattern (hip flexion abduction and internal rotation, knee flexion and extension, ankle Dorsi flexion and eversion, finger extension), 2nd pattern (hip flexion abduction and external rotation, knee flexion and extension, ankle Dorsi flexion and inversion, finger extension), 3rd pattern (hip extension adduction and internal rotation, knee flexion and extension, ankle Dorsi flexion and inversion, finger extension). Pattern (hip extension adduction and external rotation, knee flexion and extension, ankle plantar flexion and eversion, finger flexion) and pattern 4 (hip extension adduction and external rotation, knee flexion and extension, ankle plantar flexion and inversion, finger flexion). These patterns were performed in 3 sets of 10 repetitions. While the physiotherapist applied these patterns, the participant was asked to perform the movement pattern after being taught it. The physiotherapist applies resistance at certain points. This protocol is complemented by physical application techniques, sometimes sudden and sometimes slow, such as stopping the movement and holding the position, relaxation. During the protocol, the physiotherapist gave verbal instructions in addition to physical guidance. After the warm-up was completed, the measurements were started. The hip, knee and ankle were set according to the established protocol for the Ext and Flex forces. The protocol consists of performing 5 repetitions at an angular velocity of 60 o /sec, which was determined for the flexion-extension forces. Before the actual measurements, 5 repetitions were performed, then a 15-second break and 5 repetitions again. At an angular velocity of 180 o /sec, the main measurement data was collected with 10 repetitions after 5 repetitions with 15 seconds of rest. At an angular velocity of 240 o /sec, the main test measurements, consisting of 5 trial repetitions, 15 seconds rest and 25 repetitions, were performed. As the percentage change in the average ratio between the first 5 trials and the last 5 repetitions of the measurement data at an angular velocity of 240 o /sec provides information on both fatigue and repetitive strength (endurance), it was also noted and recorded (Kannus, 1994 ; Palladino et al., 2023 ; van der Horst & Denderen, 2022 ; Zhou et al., 2022 ). RESULTS The number of athletes included in the study was 50. All of the athletes were male football players. The mean age of the participants was 21.54 ± 1.54 years, the mean height was 178.32 ± 5.05 cm and the mean body weight was 67.88 ± 4.68 kilograms. Table 1 Statistical analysis of hip isokinetic muscle strength test data of participants according to warm-up protocols Balance Parameters N Warm-up Protocol Mean SD P COPX (mm) 50 PNF-based warm-up 3,00 2,100 <,001 Active warm-up 5,36 a 2,371 Passive warm-up 7,34 a,b 3,014 Control-General warm-up 8,68 a,b 3,027 COPY (mm) 50 PNF-based warm-up 4,26 3,901 <,001 Active warm-up 6,28 c 3,918 Passive warm-up 10,14 a,d 4,928 Control-General warm-up 14,42 a,b 5,828 FBD 50 PNF-based warm-up 6,54 2,426 <,001 Active warm-up 10,44 a,c 3,308 Passive warm-up 13,18 a,d 3,800 Control-General warm-up 17,26 a,b 4,040 MLD 50 PNF-based warm-up 3,26 1,651 <,001 Active warm-up 5,88 a,c,d 2,463 Passive warm-up 9,68 a,d 3,060 Control-General warm-up 11,18 a 3,218 OFBS 50 PNF-based warm-up 13,16 4,244 <,001 Active warm-up 16,42 a,c,d 4,961 Passive warm-up 20,22 a 5,793 Control-General warm-up 22,84 a 5,829 OMLS 50 PNF-based warm-up 8,54 3,991 <,001 Active warm-up 10,38 c,d 4,580 Passive warm-up 20,22 a,d 5,793 Control-General warm-up 14,68 a 4,697 EA (mm 2 ) 50 PNF-based warm-up 394,12 254,264 <,001 Active warm-up 476,46 d 248,999 Passive warm-up 569,64 a 273,205 Control-General warm-up 661,96 a 275,381 ATE 50 PNF-based warm-up 18,06 3,383 <,001 Active warm-up 20,56 a,c,d 3,649 Passive warm-up 22,58 a 3,990 Control-General warm-up 23,92 a 4,110 p<,05 a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation PNF-based warm-up, statistically significant differences were found in COPX, FBD, MLD, OFBS and ATE parameters compared to active, passive and general warm-up protocols (p < 0.001). Statistically significant differences were found for the parameters Ellipse Area, COPY and OMLS compared to passive and general warm-up protocols (p < 0.001). Active warm-up, a statistically significant difference was found in COPX, COPY, FBD, MLD, OFBS and ATE balance test data compared to passive and general warm-up protocols (p < 0.001). A statistically significant difference was found in the OMLS and Ellipse Area parameters compared to the general warm-up protocol (p < 0.001). Passive warm-up, a statistically significant difference was found in the COPY, FBD, MLD and OMLS parameters compared to the general warm-up protocol (p < 0.001) (Table 1 ). Table 2 Statistical analysis of hip isokinetic muscle strength test data of participants according to warm-up protocols Parameters N Warm-up Protocol Mean SD P 60 O /sec PT ext 50 PNF-based warm-up 337,75 20,185 <,001 Active warm-up 280,60 a 21,617 Passive warm-up 232,40 a,b 22,628 Control-General warm-up 197,40 a,b,c 19,007 180 O /sec PT ext 50 PNF-based warm-up 276,87 37,853 <,001 Active warm-up 196,57 a 28,403 Passive warm-up 162,63 a,b 16,194 Control-General warm-up 141,66 a,b,c 13,800 240 O /sec PT ext 50 PNF-based warm-up 240,01 27,535 <,001 Active warm-up 168,70 a 25,615 Passive warm-up 133,10 a,b 19,019 Control-General warm-up 99,89 a,b,c 16,062 60 O /sec PT flex 50 PNF-based warm-up 238,47 17,340 <,001 Active warm-up 184,25 a,d 14,781 Passive warm-up 207,49 a,d 17,497 Control-General warm-up 192,11 a 18,346 180 O /sec PT flex 50 PNF-based warm-up 154,81 13,508 <,001 Active warm-up 140,24 a 7,869 Passive warm-up 130,22 a,b 14,626 Control-General warm-up 122,79 a,b,c 11,694 240 O /sec PT flex 50 PNF-based warm-up 124,13 14,016 <,001 Active warm-up 106,10 a 8,118 Passive warm-up 102,89 a,d 14,873 Control-General warm-up 112,46 a 11,003 60 O /sec W ext 50 PNF-based warm-up 986,76 21,933 <,001 Active warm-up 870,22 a 16,841 Passive warm-up 753,36 a,b 26,508 Control-General warm-up 560,81 a,b,c 17,036 180 O /sec W ext 50 PNF-based warm-up 1050,51 26,703 <,001 Active warm-up 1028,60 a 22,439 Passive warm-up 962,12 a,b 15,962 Control-General warm-up 918,64 a,b,c 14,269 240 O /sec W ext 50 PNF-based warm-up 1537,64 33,783 <,001 Active warm-up 1444,84 a,c,d 23,785 Passive warm-up 1279,25 a 20,174 Control-General warm-up 1267,89 a 17,826 60 O /sec W flex 50 PNF-based warm-up 576,46 13,074 <,001 Active warm-up 599,92 a,c,d 24,020 Passive warm-up 460,57 a,d 19,106 Control-General warm-up 441,87 a 13,117 180 O /sec W flex 50 PNF-based warm-up 680,94 20,436 <,001 Active warm-up 599,77 a,c,d 14,430 Passive warm-up 558,25 a,d 12,430 Control-General warm-up 522,80 a 12,847 240 O /sec W flex 50 PNF-based warm-up 1163,55 17,583 <,001 Active warm-up 1080,33 a,c,d 14,799 Passive warm-up 988,38 a 21,773 Control-General warm-up 975,58 a 18,995 p<,05 a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation The acute effects of isokinetic muscle strength after warm-up protocols were analyzed. At 60°/sec and 180°/sec PText (Peak torque flexion) angular velocities, hip isokinetic muscle strength values were statistically significantly higher during PNF-based warm-up than during active, passive and general warm-up. At 240°/sec PText (Peak torque extension) angular velocity, the values after the PNF-based warm-up were found to be significantly different from the values of the passive and general warm-up. At 60°/sec and 180°/sec PTflex angular velocity, the muscle strength values after the PNF-based warm-up were significantly higher than for the other three warm-up exercises. At a PTflex angular velocity of 240°/sec, the values after the PNF-based warm-up were statistically significant compared to the active and general warm-up practices. Considering the total extension-flexion work at 60°/sec, 180°/sec and 240°/sec Wext (Work extension) and Wflex (Work flexion) angular velocities, the PNF-based warm-up was found to be significantly higher than the other warm-up protocols. The PText, PTflex, Wext and Wflex values at 60°/sec, 180°/sec and 240°/sec of the active warm-up application were statistically significantly higher than those of the passive and general warm-up application. In the passive warm-up application (massage), the Wflex and Wext values at 60°/sec, 180°/sec and 240°/sec were statistically significantly different from those of the general warm-up application (Table 2 ). Table 3 Statistical analysis of the data from the isokinetic knee muscle test according to the participants' warm-up protocols Parameters N Warm-up Protocol Mean SD P 60 O /sec PT ext 50 PNF-based warm-up 337,75 20,185 <,001 Active warm-up 280,60 a 21,617 Passive warm-up 232,40 a,b 22,628 Control-General warm-up 197,40 a,b,c 19,007 180 O /sec PT ext 50 PNF-based warm-up 276,87 37,853 <,001 Active warm-up 196,57 a 28,403 Passive warm-up 162,63 a,b 16,194 Control-General warm-up 141,66 a,b,c 13,800 240 O /sec PT ext 50 PNF-based warm-up 240,01 27,535 <,001 Active warm-up 168,70 a 25,615 Passive warm-up 133,10 a,b 19,019 Control-General warm-up 99,89 a,b,c 16,062 60 O /sec PT flex 50 PNF-based warm-up 238,47 17,340 <,001 Active warm-up 184,25 a,d 14,781 Passive warm-up 207,49 a,d 17,497 Control-General warm-up 192,11 a 18,346 180 O /sec PT flex 50 PNF-based warm-up 154,81 13,508 <,001 Active warm-up 140,24 a 7,869 Passive warm-up 130,22 a,b 14,626 Control-General warm-up 122,79 a,b,c 11,694 240 O /sec PT flex 50 PNF-based warm-up 124,13 14,016 <,001 Active warm-up 106,10 a 8,118 Passive warm-up 102,89 a,d 14,873 Control-General warm-up 112,46 a 11,003 60 O /sec W ext 50 PNF-based warm-up 986,76 21,933 <,001 Active warm-up 870,22 a 16,841 Passive warm-up 753,36 a,b 26,508 Control-General warm-up 560,81 a,b,c 17,036 180 O /sec W ext 50 PNF-based warm-up 1050,51 26,703 <,001 Active warm-up 1028,60 a 22,439 Passive warm-up 962,12 a,b 15,962 Control-General warm-up 918,64 a,b,c 14,269 240 O /sec W ext 50 PNF-based warm-up 1537,64 33,783 <,001 Active warm-up 1444,84 a,c,d 23,785 Passive warm-up 1279,25 a 20,174 Control-General warm-up 1267,89 a 17,826 60 O /sec W flex 50 PNF-based warm-up 576,46 13,074 <,001 Active warm-up 599,92 a,c,d 24,020 Passive warm-up 460,57 a,d 19,106 Control-General warm-up 441,87 a 13,117 180 O /sec W flex 50 PNF-based warm-up 680,94 20,436 <,001 Active warm-up 599,77 a,c,d 14,430 Passive warm-up 558,25 a,d 12,430 Control-General warm-up 522,80 a 12,847 240 O /sec W flex 50 PNF-based warm-up 1163,55 17,583 <,001 Active warm-up 1080,33 a,c,d 14,799 Passive warm-up 988,38 a 21,773 Control-General warm-up 975,58 a 18,995 p<,05 a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation Peak torque values (60°/sec, 180°/sec, 240°/sec PText): The PNF-based warm-up protocol produced statistically significantly higher peak torque values compared to the other warm-up protocols. The active warm-up protocol also produced higher torque values than the passive and general warm-up protocols, and these differences were statistically significant. The passive warm-up protocol (classic massage) also showed a significant difference to the general warm-up protocol. Peak torque values (60°/sec, 180°/sec, 240°/sec PTflex): The PNF-based warm-up achieved statistically significantly higher values compared to the other three warm-up protocols. With 180°/sec PTflex, the active warm-up was significantly higher compared to the passive and general warm-up. At 60°/sec PTflex, the active warm-up showed a significant difference compared to the general warm-up. Passive warm-up shows a significant difference at all these angular velocities compared to general warm-up. Total work value (60°/sec, 180°/sec, 240°/sec Wext and Wflex): The PNF-based warm-up resulted in statistically significantly higher total work values compared to the other warm-up protocols. The active warm-up protocol also showed significantly higher total work values than the passive and general warm-up protocols. The passive warm-up protocol showed a significant difference in the total work values of 60°/sec Wext, 180°/sec Wext, 60°/sec Wflex and 180°/sec Wflex compared to the general warm-up protocol. To summarise, the PNF-based warm-up protocol stands out as the most effective warm-up method in terms of muscle strength and overall work. Active warm-up also provides higher results compared to other methods. Passive warm-up provides better results than general warm-up (Table 3 ). Table 4 Statistical analysis of the isokinetic muscle test data for the ankle in relation to the participants' warm-up protocols Parameters N Warm-up Protocol Mean SD P 60 O /sec PT ext 50 PNF-based warm-up 133,02 20,705 <,001 Active warm-up 90,67 a,c,d 25,340 Passive warm-up 71,15 a 21,596 Control-General warm-up 73,60 a,b 18,128 180 O /sec PT ext 50 PNF-based warm-up 64,35 15,748 <,001 Active warm-up 49,91 a,c,d 9,430 Passive warm-up 36,45 a 7,317 Control-General warm-up 34,75 a,b 8,656 240 O /sec PT ext 50 PNF-based warm-up 56,89 6,012 <,001 Active warm-up 39,28 a,c,d 7,547 Passive warm-up 32,94 a,d 6,837 Control-General warm-up 28,00 a 4,838 60 O /sec PT flex 50 PNF-based warm-up 54,97 6,071 <,001 Active warm-up 42,55 a,d 6,488 Passive warm-up 39,98 a,d 8,589 Control-General warm-up 35,12 a,b 5,914 180 O /sec PT flex 50 PNF-based warm-up 34,14 7,208 <,001 Active warm-up 29,74 a,c,d 5,743 Passive warm-up 21,13 a 3,783 Control-General warm-up 21,81 a 6,381 240 O /sec PT flex 50 PNF-based warm-up 28,19 4,001 <,001 Active warm-up 23,00 a 5,730 Passive warm-up 26,17 b 3,642 Control-General warm-up 22,74 a,c 5,253 60 O /sec W ext 50 PNF-based warm-up 468,40 11,143 <,001 Active warm-up 376,36 a 7,013 Passive warm-up 363,43 a,b 14,198 Control-General warm-up 332,69 a,b,c 9,371 180 O /sec W ext 50 PNF-based warm-up 311,01 5,569 <,001 Active warm-up 288,29 a 7,019 Passive warm-up 262,25 a,b 7,742 Control-General warm-up 239,73 a,b,c 7,391 240 O /sec W ext 50 PNF-based warm-up 180,43 11,528 <,001 Active warm-up 138,06 a 6,730 Passive warm-up 109,48 a,b 5,223 Control-General warm-up 118,43 a,b,c 8,344 60 O /sec W flex 50 PNF-based warm-up 175,32 5,890 <,001 Active warm-up 155,07 a 8,149 Passive warm-up 143,25 a,b 4,718 Control-General warm-up 149,22 a,b,c 8,110 180 O /sec W flex 50 PNF-based warm-up 190,07 5,891 <,001 Active warm-up 167,04 a 12,877 Passive warm-up 106,94 a,b 4,727 Control-General warm-up 98,24 a,b,c 5,145 240 O /sec W flex 50 PNF-based warm-up 96,35 4,955 <,001 Active warm-up 78,42 a 6,887 Passive warm-up 69,09 a,b 7,223 Control-General warm-up 60,23 a,b,c 5,569 p<,05 a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: Standard Deviation Isokinetic strength values of the ankles and statistical analysis according to the warm-up protocols. The value of peak torque obtained at angular velocities 60 O /sec PText, 180 O /sec PText and 240 O /sec PText was found to be statistically significant in the PNF-based warm-up protocol compared to other warm-up protocols. In addition, the values after active warm-up at these angular velocities showed a higher torque than the values for the passive and general warm-up protocols and were statistically significant. The PNF-based warm-up protocol values at the 60 O /sec PTflex and 180 O /sec PTflex angular velocities were statistically significant compared to other warm-up values. Active warm-up was statistically significant compared to passive and general warm-up at these angular velocities. In addition, the values in the passive warm-up protocols at the angular velocities 60 O /sec PTflex and 180 O /sec PTflex were statistically significant compared to the general warm-up result values (Table 4 ). Table 5 Statistical analysis of the data from the isokinetic endurance test according to the participants warm-up protocols Measurement Site Parameters N Warm-up Protocol Mean SD P Hip 240 O /sec (ext) 50 PNF-based warm-up 89,91 2,641 <,001 Active warm-up 86,55 a 3,418 Passive warm-up 72,18 a,b 5,292 Control-General warm-up 74,49 a,b 6,454 240 O /sec (flex) 50 PNF-based warm-up 85,03 6,753 <,001 Active warm-up 72,67 a 8,446 Passive warm-up 80,23 a,b 4,538 Control-General warm-up 81,68 b 6,090 Knee 240 O /sec (ext) 50 PNF-based warm-up 76,73 4,419 <,001 Active warm-up 79,87 a 3,725 Passive warm-up 68,19 a,b 5,437 Control-General warm-up 78,41 c 7,011 240 O /sec (flex) 50 PNF-based warm-up 81,38 6,256 <,001 Active warm-up 80,38 7,151 Passive warm-up 73,60 a,b 6,454 Control-General warm-up 67,76 a,b,c 9,669 Ankle 240 O /sec (ext) 50 PNF-based warm-up 70,49 4,556 <,001 Active warm-up 65,16 a 7,451 Passive warm-up 60,75 a,b 4,649 Control-General warm-up 70,33 b,c 6,679 240 O /sec (flex) 50 PNF-based warm-up 67,27 6,515 <,001 Active warm-up 62,74 a 4,667 Passive warm-up 58,99 a,b 5,608 Control-General warm-up 58,77 a,b 5,096 p<,05 a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled general warm-up SD: standard deviation It shows the statistical analysis considering the change in torque values at an angular velocity of 240 O /sec in isokinetic hip strength test measurements performed after the warm-up protocols. When analyzing hip extension endurance as a result of the PNF-based warm-up protocol, it was statistically significant (p<,001) compared to the active, passive and general warm-up protocols. The percentage of hip extension endurance as a result of the active warm-up was statistically significant compared to the passive warm-up. Hip flexion as a result of the PNF-based warm-up was statistically significant compared to the values of the active and passive warm-up. The percentage of endurance in the active warm-up was statistically significant compared to the passive and general warm-up. There was no statistically significant difference between the general warm-up and the PNF-based warm-up. When analyzing the percentage of isokinetic strength of the knee for extension, PNF-based warm-up proved to be statistically significant compared to active and passive warm-up. The endurance level during the active warm-up was found to be statistically significant compared to the passive warm-up. The passive warm-up was found to be statistically significant compared to the general warm-up percentage. The endurance level of knee flexion was statistically significant in the PNF-based warm-up compared to the passive and general warm-up. In addition, the percentage of endurance as a result of the active warm-up was statistically significant compared to the passive and general warm-up percentages. There was no statistically significant difference between the percentages of active warm-up and PNF-based warm-up. When examining the endurance values in the ankle muscles, the PNF-based warm-up for extension (plantar flexion) was statistically significant compared to the active and passive warm-up. In addition, the active warm-up was statistically significant compared to the passive and general warm-up (Table 5 ). DISCUSSION AND IMPLICATIONS The age range (18–25 years) of the 50 soccer players who participated in the study was determined and included taking into account the possible changes in the effects of the physiological process. In the study, which investigated the acute effect of warming up with different stretching times on balance, male and female participants with an average age of about 25 years waited 26 minutes between tests of the exercises performed on the bicycle ergometer. A significant difference was found between balance scores when waiting 15 seconds between exercises instead of 45 seconds, and it was observed that waiting 45 seconds had no effect on balance( Costa et al., 2009 ). In our study, the waiting time after the warm-up exercises should not exceed 15 seconds. The waiting time and any changes that may occur are minimized and attention is drawn to the effects of the desired warm-up. Similar results were obtained as in the study. In a study investigating the acute effect of stretching exercises on balance and jumping, it was found that the balance performance of athletes was positively affected after 10 minutes of stretching exercises on the lower extremities(Handrakis et al., 2010 ). The reason for the better results with PNF-based warm-up is that the Golgi tendon organ and muscle spindle structures are affected and the reaction process to the difference, such as altered load transfer, increases, so we can see similar results with PNF-based warm-up and active warm-up applications. The reason for these similar results reduces the margin of error at the balance level (Jiang et al., 2023 ; Mesut Çelebi & Murat Zergeroğlu, 2017 ). The average margin of error of the dynamic balance data is taken into account for the balance parameters. These values play an important role in sporting activities (Castillo et al., 2022 ; Chen et al., 2022 ). Static and dynamic warm-up exercises on balance In studies investigating the effect of dynamic warm-up, the data obtained as a result of dynamic warm-up were statistically significant in the right-to-left and front-to-back deviation group compared to the static warm-up group Although there was no significance, the margin of error was found to be smaller. This situation shows that the balance is better. A similar situation can be observed with the values for active wam-up. The values of passive and general warm-up protocols were also observed in our study. It was found that the values for right and left deviation as well as anterior-posterior deviation were lower during active warm-up. This suggests that balance is better during active warm-up. In a study of 5-minute warm-up and static stretching exercises in university students, balance scores were found to be worse as a result of static stretching than in the control group. When only warm-up exercises were performed, positive differences were found in static balance scores. However, this positive difference was not significant(Behm et al., 2015 ). The lack of functional movement and the high total work effort measured after 5 minutes of jogging may suggest that a protocol with less than 15 minutes of warm-up time could have a greater immediate effect. However, as would be expected at other angular velocities, a longer warm-up is also shown to have a more effective acute effect. Studies have shown that dynamic stretching exercises have a positive effect on maximal strength performance (Fujisawa et al., 2014 ; Kafkas et al., 2018 ). It has been found that the warm-up protocol with neurophysiological effects such as PNF leads to higher force production. Yamaguchi et al. investigated the effect of dynamic stretching exercises on the muscular performance of muscle movements under different weights against a concentric dynamic constant external resistance in 18 men without health problems. It was found that dynamic stretching exercises performed before the isokinetic strength test increased the gain (Yamaguchi & Ishii, 2005 ). In our study, dynamic warm-up was performed under active warm-up muscle movements including parameters. A similar result was obtained during active warm-up as in the study. It should be noted that the differences in our study are that there is a warm-up protocol beyond the 30-second stretches and only knee extension, i.e., quadriceps strength knee extension and flexion, hip and ankle flexion were included in the study (Fujisawa et al., 2014 ; Yamaguchi & Ishii, 2005 ). Kafkas et al. investigated the effect of different warm-up protocols on 1-max squat repetition performance in 9 male athletes who had been exercising regularly for at least 3 years. The warm-up protocols applied on different days were only 5 minutes of easy tempo running, static warm-up after 5 minutes of easy tempo running, dynamic warm-up after 5 minutes of easy tempo running, PNF warm-up after 5 minutes of easy tempo running. As a result, it was found that the dynamic warm-up led to higher results than the other warm-up protocols. When looking at the average values, it was found that the values achieved by the dynamic warm-up were the highest, followed by PNF and finally the static warm-up (Kafkas et al., 2018 ). The measurements obtained are similar to those in our study as they cover the structures of the lower extremities. Since the angular velocities of the hip, knee and ankle structures in the lower extremities were measured with isokinetic forces of 60 o /sec, 180 o /sec and 240 o /sec, it is assumed that the peak torque values produced with maximal effort are related to the maximal repetition performed. However, in our study, PNF-based warm-up was found to have more positive effects than other warm-up protocols. Another difference chosen for PNF is that the patterns were selected according to the closed kinetic chain principle. It is assumed that these patterns cover complicated behaviors such as running, stopping, kicking or ball holding in sports, which is a different result than in this study. In addition, isometric contraction is less prevalent in the warm-up parameters selected for PNF. This contributes to balance, but its main contribution is to maintain muscle contraction and make the performance of muscle contraction with neuromuscular facilitation effective (Denerel et al., 2019 ; Kafkas et al., 2018 ). There are also studies showing that static and dynamic activities have no effect on the strength performance of individuals (Papadopoulos et al., 2006 ; Torres-Banduc et al., 2021 ). Dynamic stretching exercises have been used in studies of skills requiring strength. When we examined the research, we found that the results obtained in this work supported the data. When examining the data, it was found that the parameters of maximal strength were negatively affected by PNF and static stretching exercises, while this effect was more positive with dynamic stretching. The reason for this is that an elastic force is required for maximal strength performance. It is the rapid application of a high level of force in which the muscle or muscle group performs a concentric contraction immediately after the eccentric contraction. However, PNF and static stretching exercises reduce long-term myotatic reflex sensitivity, which has a negative effect on strength (El-Ashker et al., 2022 ). There are several studies that conclude that static exercise has no specific effect on an individual's balance performance (Bugnet, 2011 ; Chatzopoulos et al., 2015 ; P. B. Costa et al., 2009 ). There are studies that show that PNF-inclusive activities have a positive effect on an individual's balance performance (Arcanjo et al., 2022 ; J. Kim et al., n.d.; Leblebici et al., 2017 ; Pereira et al., 2012; K. C. Seo et al., 2015 ). Pereira et al. had 14 people over the age of 60 perform PNF exercises three days a week for 10 weeks and found a statistically significant improvement in balance test scores (Pereira et al., 2012). Kim et al. reported that continuous PNF exercises positively improved balance ability (K. Kim et al., 2015 ). Seo et al. found that the type of chronic PNF exercises had a positive effect on balance performance (K. Seo et al., 2015 ). Jeon found in his study that PNF exercise types improve people's balance (Jeon, 2013 ). Madak investigated the effect of 8 weeks of PNF exercises on the performance of the Star Balance Test in elite taekwondo athletes in the form of a pre-test and a post-test and found that the athletes' balance performance improved significantly (Madak, 2020 ). Techniques covering muscle-specific movements were used. Hamstring, medial hip adductors, quadriceps muscles and targeted isometric contraction. the application of force and resistance applied in the opposite direction resulted in a situation where there was no movement and contraction. One of the main differences in our study is that no PNF protocol was performed on any muscle. According to the isokinetic endurance values in Table 5 , the warm-up values for the hip 240 O /sec (ext) were found as follows: PNF-based warm-up (89.91 ± 2.641), active warm-up (86.55 ± 3.418), passive warm-up (72.18 ± 5.292) and general warm-up (74.49 ± 6.454). When the values were analyzed, the isokinetic endurance values were higher for the PNF-based warm-up. In the study, participants were trained for 8 weeks and their isokinetic endurance was assessed before and after training. Participants who performed only isokinetic strength training showed an increase in fatigue indices (Lazarou et al., 2018 ). The results of this study are consistent with the findings from our study. In the other study, an increase in strength was found in modern dancers before and after the dance season. The fatigue index also increased (Martyn-Stevens et al., 2012). In soccer players, the effect of a short-term training interruption on fatigue was found to be a 5% reduction (Joo, 2016 ). CONCLUSİON The study found that PNF-based warm-up achieved more effective results. The main reason for this could be that the PNF-based warm-up was performed on the last day of measurement, which is due to the measurement sequence and the presence of test learning. To remedy this situation, changing the order of application of the warm-up protocols in the next studies could eliminate this situation. Apart from the acute effects of the study, further development can be achieved by investigating the combined forms of the warm-up protocols for their applicability. In addition to the acute effects, the long-term effects can also be investigated for the dissemination of the PNF-based warm-up. According to the results obtained, the warm-up protocol can also be used in sports other than soccer that require strength, endurance and balance. Our aim in the study was to see and determine the acute effects of the PNF-based warm-up. Our aim was to illuminate the field for future studies and to find appropriate warm-up methods for all sports, especially for soccer players, to achieve maximum performance and minimize the risk of injury. Finally, PNF-based warm-up was found to have better acute effects on isokinetic strength, endurance and balance in soccer players than other warm-up methods. Declarations CONFLİCT OF INTEREST There are no conflicts of interest among the authors. FUNDİNG This research received no external funding. ACKNOWLEDGMENTS Approval was obtained from Artvin Çoruh University ethics committee. This thesis study dated 19.11.2020, numbered E-18457941-050.01.04-12716 / 34958711-020-12358. It was created from the Doctoral Thesis (Acute effect of Proprioceptive neuromuscular facilitation- based warm-up on isokinetic strength, endurance and balance) at Ondokuz Mayıs University Yaşar Doğu Faculty of Sports Sciences. 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Journal of Physical Therapy Science , 27 (5), 1459–1462. https://doi.org/10.1589/JPTS.27.1459 Seo, K., Park, S. H., & Park, K. (2015). The effects of stair gait training using proprioceptive neuromuscular facilitation on stroke patients’ dynamic balance ability. Journal of Physical Therapy Science , 27 (5), 1459–1462. https://doi.org/10.1589/jpts.27.1459 Torres-Banduc, M. A., Jerez-Mayorga, D., Moran, J., Keogh, J. W. L., & Ramírez-Campillo, R. (2021). Isokinetic force-power profile of the shoulder joint in males participating in CrossFit training and competing at different levels. PeerJ , 9 , e11643. https://doi.org/10.7717/peerj.11643 van der Horst, N., & Denderen, R. van. (2022). Isokinetic hamstring and quadriceps strength interpretation guideline for football (soccer) players with ACL reconstruction: a Delphi consensus study in the Netherlands. Science & Medicine in Football , 1–12. https://doi.org/10.1080/24733938.2021.2024592 Wilson, G. J., Wood, G. A., & Elliott, B. C. (1991). Optical stiffness of series elastic component in a stretch-shorten cycle activity. Journal of Applied Physiology , 70 (2), 825–833. https://doi.org/10.1152/jappl.1991.70.2.825 Yamaguchi, T., & Ishii, K. (2005). Effects of static stretching for 30 seconds and dynamic stretching on leg extension power. Journal of Strength and Conditioning Research , 19 (3), 677–683. https://doi.org/10.1519/15044.1 Zhou, Z., Chen, C., Chen, X., Yi, W., Cui, W., Wu, R., & Wang, D. (2022). Lower extremity isokinetic strength characteristics of amateur boxers. Frontiers in Physiology , 13 , 898126. https://doi.org/10.3389/fphys.2022.898126 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4678537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325627517,"identity":"cf87e20a-284b-4c23-85b7-6bbdcacb61bf","order_by":0,"name":"Muhammed YILDIZ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBAC9gYwJSHDcJiB8QGIyQYiePBo4TnADNbCA9TCbMDAYADXIkFAC1DNAQY2CZAWBoJa+M8fYLrZZsHDd5z9WcXPPX8S+6QbGB+8bWOoM2/AoQXkoNw2CR7JwwxpN3ueGSS2yRxgNpzbBvTeAexa7BmbIVoMDjMcu8FzwMCYTSKBTZoXqAWXy3iYmWFaGNsK/0C0sP/Gq4UNroWZjRloixzIFma8WniYDQ7nnAP5hY1ZWuaAMVBLYrPknHMSkjNwhtjBh49zyurk+M4ff/jxzQE5HvkZyQc/vCmz4ccZykBwgJENhc/YwIAnWqDgDwH5UTAKRsEoGNkAAFKSSQovl4yEAAAAAElFTkSuQmCC","orcid":"","institution":"Artvin Çoruh University","correspondingAuthor":true,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"YILDIZ","suffix":""},{"id":325627518,"identity":"792d55f8-ce19-4b52-8b41-cdd0b41b6cc7","order_by":1,"name":"Mehmet ÇEBİ","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"ÇEBİ","suffix":""}],"badges":[],"createdAt":"2024-07-03 07:47:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4678537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4678537/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62359359,"identity":"fa971fc7-e953-4770-8235-d3b06dfa2e1c","added_by":"auto","created_at":"2024-08-13 09:52:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1234793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4678537/v1/d9456b15-6d3c-4839-9a5c-3839aabaacdb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAcute Effect of Proprioceptive Neuromuscular Facilitation - Based Warm-Up on Isokinetic Strength, Endurance and Balance\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWarm-up exercises are performed to improve athletic performance, prevent possible injuries and optimally prepare athletes physiologically and psychologically for the upcoming activities (Ben Moussa Zouita et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The intensity, duration and type of warm-up can vary and form different protocols aimed at achieving the best adaptation and improvement in performance (Krutsch et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These protocols include a warm-up method based on proprioceptive neuromuscular facilitation (PNF) techniques. Exercises utilising PNF techniques are more likely to be used in rehabilitation or long-term training than a standard warm-up method (Aky\u0026uuml;z et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Arumugam et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PNF is designed to elicit motor responses, improve neuromuscular control and function, enhance coordination, and facilitate the development of muscle strength and endurance through patterns involving multiple muscle groups (Ben Moussa Zouita et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lazarou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, a PNF-based warm-up method may offer greater physiological benefits compared to other warm-up methods. In sports, even small changes in parameters such as strength, endurance and balance can have a significant impact on performance. Evaluations performed with isokinetic dynamometers provide valuable data that can guide athletes to a better level of performance. A review of the literature shows that there are few studies that focus on the structures around the hip, knee and ankle joints - the major components of the lower limb and the results of repetitive force after balance and isokinetic measurements. Most studies have focussed on the effects of flexion and extension of the knee joint. These studies have shown that static stretching can decrease isokinetic strength of the long muscles, dynamic stretching can increase isokinetic strength, and PNF-type stretching has minimal effects on isokinetic muscle strength (Coelho et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Krutsch et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study was aimed at football players and used combined isotonic contraction and holding-relax movement methods from PNF methods for lower limb patterns (hip flexion abduction and internal rotation, knee flexion and extension, ankle dorsiflexion and eversion, finger extension/hip flexion abduction and external rotation, knee flexion and extension, (ankle dorsiflexion and inversion, finger extension/ hip extension adduction and external rotation, knee flexion and extension, plantar flexion and eversion of the ankle, finger flexion/ hip extension adduction and external rotation, knee flexion and extension, plantar flexion and inversion of the ankle, finger flexion) were performed for 10 repetitions and 3 sets. The acute effects of this warm-up method on isokinetic strength, endurance and body balance were analysed. The study was conducted on football players. Football is one of the sports considered suitable for the study of hip, knee and ankle joints, as the parameters of the lower limb region are the structures in which performance should be focussed. Therefore, the effects of football players on isokinetic strength, endurance and body balance are studied acutely after warm-up (Alt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ithurburn et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrength tests are usually performed at low speeds, while power and endurance exercises are performed at high speeds. Angular velocities\u0026thinsp;\u0026le;\u0026thinsp;180\u0026deg;/sec are used for strength tests, while velocities\u0026thinsp;\u0026gt;\u0026thinsp;240\u0026deg;/sec are used for endurance tests (Kurdiova et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Therefore, angular velocities of 60\u0026deg;/sec are usually used to obtain information on strength and muscle-specific performance. In training that requires explosive power and fast body movements, power gains are greater at high velocities (180\u0026deg;/s, 240\u0026deg;/s, 300\u0026deg;/s) than at low velocities (30\u0026deg;/s, 60\u0026deg;/s, 90\u0026deg;/s). The increase in strength is also greater at lower speeds (Kyselovičov\u0026aacute; et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Opar \u0026amp; Serpell, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Palmer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the isokinetic endurance test, the endurance measurement of the flexor and extensor muscles, peak torque values and fatigue indices are recorded with the Cybex device. The fatigue index is calculated by reducing the percentage values of the peak torque during the endurance test. It expresses the percentage change between the first peak torque and the last peak torque. A negative fatigue index indicates that a higher torque is produced at the beginning, i.e. the muscles fatigue in a progressive process and torque production decreases (Claiborne et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Herman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ikeda \u0026amp; Ryushi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Does Proprioceptive Neuromuscular Facilitation (PNF)-based warm-up have a more acute effect on isokinetic strength, endurance, and balance compared to active and passive warm-up conditions?\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study was designed for repeated measurements. The study analyses acute changes in isokinetic strength, endurance and balance scores of football players by measurements after warm-up protocols. Of the 56 participants, 6 were excluded from the study due to injury during tests and 50 participants completed the study. The participants were informed that they should not consume alcohol or caffeine 24 hours before the test and should not do any strenuous exercise. It was ensured that there was a complete rest period (72 hours) between the days of the measurements.\u003c/p\u003e \u003cp\u003eParticipants were called for 4 different warm-up protocols and measurements on 4 different days. At the 1st measurement, information about the study, demographic information and a general warm-up protocol were given. Measurements were then taken with an isokinetic dynamometer (Humac Norm, CSMI, USA) and a CSMI brand Prokin TecnoBody device. A passive warm-up protocol (massage) was used for the 2nd measurement. The measurements were performed with an isokinetic dynamometer (Humac Norm, CSMI, USA) and a Prokin TecnoBody device of the CSMI brand. In the 3rd measurement, a protocol with 15 minutes of active warm-up was applied and then isokinetic strength, endurance and balance test measurements were performed. In the 4th measurement, a PNF-based warm-up protocol was applied for 15 minutes and the measurements were repeated (Akdoğan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cramer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lakkadsha et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeasurements were taken within 30 seconds of the warm-up protocols. Static balance measurements were performed first and then dynamic balance measurements. For the static balance test, participants were positioned in relation to the lines on the x- and y-axis of the balance platform. After the test, the measurement data was recorded. The dynamic balance measurements were carried out on the moving floor. It was set up in the form of a double leg with reference to the x- and y-axis. She was asked to stay on the circular track on the monitor and complete 5 laps within 60 seconds. As balance parameters; Average Balance Error Tracking (ATE), Elliptical Area (EA) (mm\u003csup\u003e2\u003c/sup\u003e) Area used, Average Lateral Velocity (mm/s) (OMLS), Average COPX (Average Pressure Centre X), Average COPY (Average Pressure Centre Y), Forward - Backward Swing Deviation (FBD), Mean - Lateral Deviation (MLD) (Right - Left Swing Deviation) and Mean Forward - Backward Speed (OFBS) (mm/s) (Castillo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schiltz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). If the values of these parameters are close to zero, it is assumed that the balance is good, and if the values are away from zero, it is assumed that the balance is poor (S. N. Costa et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kannus, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFour different methods were used as warm-up protocols. Before the measurement with the isokinetic dynamometer, general short-term warm-up methods for the lower extremities were used. This warm-up was performed to avoid injury during isokinetic tests. It was performed at a level that maintained the general flexibility of the body. The same person (physiotherapist) performed the warm-up protocols and all measurements throughout the study. Throughout the study, all treatments were carried out by a single physiotherapist. In order to eliminate the changes in the manual applications caused by the different practitioners, a single practitioner was designated for this subject. The region in which the PNF techniques are applied is the lower extremity. The movement patterns in the lower extremity are: first pattern (hip flexion abduction and internal rotation, knee flexion and extension, ankle Dorsi flexion and eversion, finger extension), 2nd pattern (hip flexion abduction and external rotation, knee flexion and extension, ankle Dorsi flexion and inversion, finger extension), 3rd pattern (hip extension adduction and internal rotation, knee flexion and extension, ankle Dorsi flexion and inversion, finger extension). Pattern (hip extension adduction and external rotation, knee flexion and extension, ankle plantar flexion and eversion, finger flexion) and pattern 4 (hip extension adduction and external rotation, knee flexion and extension, ankle plantar flexion and inversion, finger flexion). These patterns were performed in 3 sets of 10 repetitions. While the physiotherapist applied these patterns, the participant was asked to perform the movement pattern after being taught it. The physiotherapist applies resistance at certain points. This protocol is complemented by physical application techniques, sometimes sudden and sometimes slow, such as stopping the movement and holding the position, relaxation. During the protocol, the physiotherapist gave verbal instructions in addition to physical guidance. After the warm-up was completed, the measurements were started. The hip, knee and ankle were set according to the established protocol for the Ext and Flex forces. The protocol consists of performing 5 repetitions at an angular velocity of 60\u003csup\u003eo\u003c/sup\u003e/sec, which was determined for the flexion-extension forces. Before the actual measurements, 5 repetitions were performed, then a 15-second break and 5 repetitions again. At an angular velocity of 180\u003csup\u003eo\u003c/sup\u003e/sec, the main measurement data was collected with 10 repetitions after 5 repetitions with 15 seconds of rest. At an angular velocity of 240\u003csup\u003eo\u003c/sup\u003e/sec, the main test measurements, consisting of 5 trial repetitions, 15 seconds rest and 25 repetitions, were performed. As the percentage change in the average ratio between the first 5 trials and the last 5 repetitions of the measurement data at an angular velocity of 240\u003csup\u003eo\u003c/sup\u003e/sec provides information on both fatigue and repetitive strength (endurance), it was also noted and recorded (Kannus, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Palladino et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; van der Horst \u0026amp; Denderen, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe number of athletes included in the study was 50. All of the athletes were male football players. The mean age of the participants was 21.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 years, the mean height was 178.32\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05 cm and the mean body weight was 67.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68 kilograms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of hip isokinetic muscle strength test data of participants according to warm-up protocols\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWarm-up Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCOPX (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,34\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,68\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCOPY (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,28\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,14\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,42\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFBD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,44\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,18\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,26\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMLD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,88 \u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,68 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eOFBS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,42 \u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eOMLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,38\u003csup\u003ec,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,22 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,68\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEA (mm\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e394,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e254,264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e476,46\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e248,999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e569,64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273,205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661,96\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275,381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eATE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,56 \u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003ep\u0026lt;,05\u003c/b\u003e a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePNF-based warm-up, statistically significant differences were found in COPX, FBD, MLD, OFBS and ATE parameters compared to active, passive and general warm-up protocols (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Statistically significant differences were found for the parameters Ellipse Area, COPY and OMLS compared to passive and general warm-up protocols (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Active warm-up, a statistically significant difference was found in COPX, COPY, FBD, MLD, OFBS and ATE balance test data compared to passive and general warm-up protocols (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A statistically significant difference was found in the OMLS and Ellipse Area parameters compared to the general warm-up protocol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Passive warm-up, a statistically significant difference was found in the COPY, FBD, MLD and OMLS parameters compared to the general warm-up protocol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of hip isokinetic muscle strength test data of participants according to warm-up protocols\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWarm-up Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280,60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232,40\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197,40\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196,57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162,63 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141,66 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168,70 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25,615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133,10 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99,89 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184,25 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207,49 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192,11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140,24 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130,22 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122,79 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106,10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102,89\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112,46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e986,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e870,22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e753,36\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560,81\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1050,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1028,60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e962,12\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e918,64\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1537,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33,783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1444,84\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1279,25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1267,89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599,92\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e460,57\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441,87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e680,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599,77\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558,25\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522,80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1163,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1080,33\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988,38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e975,58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003ep\u0026lt;,05\u003c/b\u003e a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe acute effects of isokinetic muscle strength after warm-up protocols were analyzed. At 60\u0026deg;/sec and 180\u0026deg;/sec PText (Peak torque flexion) angular velocities, hip isokinetic muscle strength values were statistically significantly higher during PNF-based warm-up than during active, passive and general warm-up. At 240\u0026deg;/sec PText (Peak torque extension) angular velocity, the values after the PNF-based warm-up were found to be significantly different from the values of the passive and general warm-up. At 60\u0026deg;/sec and 180\u0026deg;/sec PTflex angular velocity, the muscle strength values after the PNF-based warm-up were significantly higher than for the other three warm-up exercises. At a PTflex angular velocity of 240\u0026deg;/sec, the values after the PNF-based warm-up were statistically significant compared to the active and general warm-up practices. Considering the total extension-flexion work at 60\u0026deg;/sec, 180\u0026deg;/sec and 240\u0026deg;/sec Wext (Work extension) and Wflex (Work flexion) angular velocities, the PNF-based warm-up was found to be significantly higher than the other warm-up protocols. The PText, PTflex, Wext and Wflex values at 60\u0026deg;/sec, 180\u0026deg;/sec and 240\u0026deg;/sec of the active warm-up application were statistically significantly higher than those of the passive and general warm-up application. In the passive warm-up application (massage), the Wflex and Wext values at 60\u0026deg;/sec, 180\u0026deg;/sec and 240\u0026deg;/sec were statistically significantly different from those of the general warm-up application (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of the data from the isokinetic knee muscle test according to the participants' warm-up protocols\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWarm-up Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280,60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232,40\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197,40\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37,853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196,57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162,63 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141,66 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168,70 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25,615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133,10 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99,89 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184,25 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207,49 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192,11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140,24 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130,22 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122,79 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106,10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102,89\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112,46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e986,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e870,22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e753,36\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560,81\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1050,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1028,60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e962,12\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e918,64\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1537,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33,783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1444,84\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1279,25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1267,89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599,92\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e460,57\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441,87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e680,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599,77\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e558,25\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522,80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1163,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1080,33\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988,38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e975,58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003ep\u0026lt;,05\u003c/b\u003e a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePeak torque values (60\u0026deg;/sec, 180\u0026deg;/sec, 240\u0026deg;/sec PText): The PNF-based warm-up protocol produced statistically significantly higher peak torque values compared to the other warm-up protocols. The active warm-up protocol also produced higher torque values than the passive and general warm-up protocols, and these differences were statistically significant. The passive warm-up protocol (classic massage) also showed a significant difference to the general warm-up protocol. Peak torque values (60\u0026deg;/sec, 180\u0026deg;/sec, 240\u0026deg;/sec PTflex): The PNF-based warm-up achieved statistically significantly higher values compared to the other three warm-up protocols. With 180\u0026deg;/sec PTflex, the active warm-up was significantly higher compared to the passive and general warm-up. At 60\u0026deg;/sec PTflex, the active warm-up showed a significant difference compared to the general warm-up. Passive warm-up shows a significant difference at all these angular velocities compared to general warm-up. Total work value (60\u0026deg;/sec, 180\u0026deg;/sec, 240\u0026deg;/sec Wext and Wflex): The PNF-based warm-up resulted in statistically significantly higher total work values compared to the other warm-up protocols. The active warm-up protocol also showed significantly higher total work values than the passive and general warm-up protocols. The passive warm-up protocol showed a significant difference in the total work values of 60\u0026deg;/sec Wext, 180\u0026deg;/sec Wext, 60\u0026deg;/sec Wflex and 180\u0026deg;/sec Wflex compared to the general warm-up protocol. To summarise, the PNF-based warm-up protocol stands out as the most effective warm-up method in terms of muscle strength and overall work. Active warm-up also provides higher results compared to other methods. Passive warm-up provides better results than general warm-up (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of the isokinetic muscle test data for the ankle in relation to the participants' warm-up protocols\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWarm-up Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90,67\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25,340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71,15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73,60\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49,91 \u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36,45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34,75 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39,28\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32,94\u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,55 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39,98 \u003csup\u003ea,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,12 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,74\u003csup\u003ea,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec PT\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26,17\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,74\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376,36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363,43\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14,198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332,69\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e311,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288,29 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262,25 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239,73 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138,06 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109,48 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118,43 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155,07 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143,25 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149,22 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e180\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167,04 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106,94 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98,24 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec W\u003c/b\u003e\u003csub\u003e\u003cb\u003eflex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78,42 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69,09 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60,23 \u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003ep\u0026lt;,05\u003c/b\u003e a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled, general warm-up SD: Standard Deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIsokinetic strength values of the ankles and statistical analysis according to the warm-up protocols. The value of peak torque obtained at angular velocities 60\u003csup\u003eO\u003c/sup\u003e/sec PText, 180\u003csup\u003eO\u003c/sup\u003e/sec PText and 240\u003csup\u003eO\u003c/sup\u003e/sec PText was found to be statistically significant in the PNF-based warm-up protocol compared to other warm-up protocols. In addition, the values after active warm-up at these angular velocities showed a higher torque than the values for the passive and general warm-up protocols and were statistically significant. The PNF-based warm-up protocol values at the 60\u003csup\u003eO\u003c/sup\u003e/sec PTflex and 180\u003csup\u003eO\u003c/sup\u003e/sec PTflex angular velocities were statistically significant compared to other warm-up values. Active warm-up was statistically significant compared to passive and general warm-up at these angular velocities. In addition, the values in the passive warm-up protocols at the angular velocities 60\u003csup\u003eO\u003c/sup\u003e/sec PTflex and 180\u003csup\u003eO\u003c/sup\u003e/sec PTflex were statistically significant compared to the general warm-up result values (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of the data from the isokinetic endurance test according to the participants warm-up protocols\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWarm-up Protocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eHip\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (ext)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86,55\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72,18\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74,49\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (flex)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72,67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80,23\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81,68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eKnee\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (ext)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79,87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68,19\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78,41\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (flex)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73,60\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67,76\u003csup\u003ea,b,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9,669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eAnkle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (ext)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65,16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60,75\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70,33\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e240\u003c/b\u003e\u003csup\u003e\u003cb\u003eO\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/sec (flex)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNF-based warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;,001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62,74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePassive warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58,99\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl-General warm-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58,77\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003ep\u0026lt;,05\u003c/b\u003e a: significant difference with PNF-based warm-up. b: significant difference with active warm-up. c: significant difference with passive warm-up, d: significant difference with controlled general warm-up\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt shows the statistical analysis considering the change in torque values at an angular velocity of 240\u003csup\u003eO\u003c/sup\u003e/sec in isokinetic hip strength test measurements performed after the warm-up protocols. When analyzing hip extension endurance as a result of the PNF-based warm-up protocol, it was statistically significant (p\u0026lt;,001) compared to the active, passive and general warm-up protocols. The percentage of hip extension endurance as a result of the active warm-up was statistically significant compared to the passive warm-up. Hip flexion as a result of the PNF-based warm-up was statistically significant compared to the values of the active and passive warm-up. The percentage of endurance in the active warm-up was statistically significant compared to the passive and general warm-up. There was no statistically significant difference between the general warm-up and the PNF-based warm-up. When analyzing the percentage of isokinetic strength of the knee for extension, PNF-based warm-up proved to be statistically significant compared to active and passive warm-up. The endurance level during the active warm-up was found to be statistically significant compared to the passive warm-up. The passive warm-up was found to be statistically significant compared to the general warm-up percentage. The endurance level of knee flexion was statistically significant in the PNF-based warm-up compared to the passive and general warm-up. In addition, the percentage of endurance as a result of the active warm-up was statistically significant compared to the passive and general warm-up percentages. There was no statistically significant difference between the percentages of active warm-up and PNF-based warm-up. When examining the endurance values in the ankle muscles, the PNF-based warm-up for extension (plantar flexion) was statistically significant compared to the active and passive warm-up. In addition, the active warm-up was statistically significant compared to the passive and general warm-up (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION AND IMPLICATIONS","content":"\u003cp\u003eThe age range (18\u0026ndash;25 years) of the 50 soccer players who participated in the study was determined and included taking into account the possible changes in the effects of the physiological process. In the study, which investigated the acute effect of warming up with different stretching times on balance, male and female participants with an average age of about 25 years waited 26 minutes between tests of the exercises performed on the bicycle ergometer. A significant difference was found between balance scores when waiting 15 seconds between exercises instead of 45 seconds, and it was observed that waiting 45 seconds had no effect on balance( Costa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In our study, the waiting time after the warm-up exercises should not exceed 15 seconds. The waiting time and any changes that may occur are minimized and attention is drawn to the effects of the desired warm-up. Similar results were obtained as in the study. In a study investigating the acute effect of stretching exercises on balance and jumping, it was found that the balance performance of athletes was positively affected after 10 minutes of stretching exercises on the lower extremities(Handrakis et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The reason for the better results with PNF-based warm-up is that the Golgi tendon organ and muscle spindle structures are affected and the reaction process to the difference, such as altered load transfer, increases, so we can see similar results with PNF-based warm-up and active warm-up applications. The reason for these similar results reduces the margin of error at the balance level (Jiang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mesut \u0026Ccedil;elebi \u0026amp; Murat Zergeroğlu, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The average margin of error of the dynamic balance data is taken into account for the balance parameters. These values play an important role in sporting activities (Castillo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Static and dynamic warm-up exercises on balance In studies investigating the effect of dynamic warm-up, the data obtained as a result of dynamic warm-up were statistically significant in the right-to-left and front-to-back deviation group compared to the static warm-up group Although there was no significance, the margin of error was found to be smaller. This situation shows that the balance is better. A similar situation can be observed with the values for active wam-up. The values of passive and general warm-up protocols were also observed in our study. It was found that the values for right and left deviation as well as anterior-posterior deviation were lower during active warm-up. This suggests that balance is better during active warm-up. In a study of 5-minute warm-up and static stretching exercises in university students, balance scores were found to be worse as a result of static stretching than in the control group. When only warm-up exercises were performed, positive differences were found in static balance scores. However, this positive difference was not significant(Behm et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The lack of functional movement and the high total work effort measured after 5 minutes of jogging may suggest that a protocol with less than 15 minutes of warm-up time could have a greater immediate effect. However, as would be expected at other angular velocities, a longer warm-up is also shown to have a more effective acute effect. Studies have shown that dynamic stretching exercises have a positive effect on maximal strength performance (Fujisawa et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kafkas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It has been found that the warm-up protocol with neurophysiological effects such as PNF leads to higher force production.\u003c/p\u003e \u003cp\u003eYamaguchi et al. investigated the effect of dynamic stretching exercises on the muscular performance of muscle movements under different weights against a concentric dynamic constant external resistance in 18 men without health problems. It was found that dynamic stretching exercises performed before the isokinetic strength test increased the gain (Yamaguchi \u0026amp; Ishii, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In our study, dynamic warm-up was performed under active warm-up muscle movements including parameters. A similar result was obtained during active warm-up as in the study. It should be noted that the differences in our study are that there is a warm-up protocol beyond the 30-second stretches and only knee extension, i.e., quadriceps strength knee extension and flexion, hip and ankle flexion were included in the study (Fujisawa et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yamaguchi \u0026amp; Ishii, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Kafkas et al. investigated the effect of different warm-up protocols on 1-max squat repetition performance in 9 male athletes who had been exercising regularly for at least 3 years. The warm-up protocols applied on different days were only 5 minutes of easy tempo running, static warm-up after 5 minutes of easy tempo running, dynamic warm-up after 5 minutes of easy tempo running, PNF warm-up after 5 minutes of easy tempo running. As a result, it was found that the dynamic warm-up led to higher results than the other warm-up protocols. When looking at the average values, it was found that the values achieved by the dynamic warm-up were the highest, followed by PNF and finally the static warm-up (Kafkas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe measurements obtained are similar to those in our study as they cover the structures of the lower extremities. Since the angular velocities of the hip, knee and ankle structures in the lower extremities were measured with isokinetic forces of 60\u003csup\u003eo\u003c/sup\u003e/sec, 180\u003csup\u003eo\u003c/sup\u003e/sec and 240\u003csup\u003eo\u003c/sup\u003e/sec, it is assumed that the peak torque values produced with maximal effort are related to the maximal repetition performed. However, in our study, PNF-based warm-up was found to have more positive effects than other warm-up protocols. Another difference chosen for PNF is that the patterns were selected according to the closed kinetic chain principle. It is assumed that these patterns cover complicated behaviors such as running, stopping, kicking or ball holding in sports, which is a different result than in this study. In addition, isometric contraction is less prevalent in the warm-up parameters selected for PNF. This contributes to balance, but its main contribution is to maintain muscle contraction and make the performance of muscle contraction with neuromuscular facilitation effective (Denerel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kafkas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There are also studies showing that static and dynamic activities have no effect on the strength performance of individuals (Papadopoulos et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Torres-Banduc et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDynamic stretching exercises have been used in studies of skills requiring strength. When we examined the research, we found that the results obtained in this work supported the data. When examining the data, it was found that the parameters of maximal strength were negatively affected by PNF and static stretching exercises, while this effect was more positive with dynamic stretching. The reason for this is that an elastic force is required for maximal strength performance. It is the rapid application of a high level of force in which the muscle or muscle group performs a concentric contraction immediately after the eccentric contraction. However, PNF and static stretching exercises reduce long-term myotatic reflex sensitivity, which has a negative effect on strength (El-Ashker et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are several studies that conclude that static exercise has no specific effect on an individual's balance performance (Bugnet, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chatzopoulos et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; P. B. Costa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). There are studies that show that PNF-inclusive activities have a positive effect on an individual's balance performance (Arcanjo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; J. Kim et al., n.d.; Leblebici et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pereira et al., 2012; K. C. Seo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePereira et al. had 14 people over the age of 60 perform PNF exercises three days a week for 10 weeks and found a statistically significant improvement in balance test scores (Pereira et al., 2012). Kim et al. reported that continuous PNF exercises positively improved balance ability (K. Kim et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Seo et al. found that the type of chronic PNF exercises had a positive effect on balance performance (K. Seo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Jeon found in his study that PNF exercise types improve people's balance (Jeon, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Madak investigated the effect of 8 weeks of PNF exercises on the performance of the Star Balance Test in elite taekwondo athletes in the form of a pre-test and a post-test and found that the athletes' balance performance improved significantly (Madak, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Techniques covering muscle-specific movements were used. Hamstring, medial hip adductors, quadriceps muscles and targeted isometric contraction. the application of force and resistance applied in the opposite direction resulted in a situation where there was no movement and contraction. One of the main differences in our study is that no PNF protocol was performed on any muscle. According to the isokinetic endurance values in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the warm-up values for the hip 240\u003csup\u003eO\u003c/sup\u003e/sec (ext) were found as follows: PNF-based warm-up (89.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.641), active warm-up (86.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.418), passive warm-up (72.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.292) and general warm-up (74.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.454). When the values were analyzed, the isokinetic endurance values were higher for the PNF-based warm-up. In the study, participants were trained for 8 weeks and their isokinetic endurance was assessed before and after training. Participants who performed only isokinetic strength training showed an increase in fatigue indices (Lazarou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The results of this study are consistent with the findings from our study. In the other study, an increase in strength was found in modern dancers before and after the dance season. The fatigue index also increased (Martyn-Stevens et al., 2012). In soccer players, the effect of a short-term training interruption on fatigue was found to be a 5% reduction (Joo, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSİON","content":"\u003cp\u003eThe study found that PNF-based warm-up achieved more effective results. The main reason for this could be that the PNF-based warm-up was performed on the last day of measurement, which is due to the measurement sequence and the presence of test learning. To remedy this situation, changing the order of application of the warm-up protocols in the next studies could eliminate this situation. Apart from the acute effects of the study, further development can be achieved by investigating the combined forms of the warm-up protocols for their applicability. In addition to the acute effects, the long-term effects can also be investigated for the dissemination of the PNF-based warm-up. According to the results obtained, the warm-up protocol can also be used in sports other than soccer that require strength, endurance and balance. Our aim in the study was to see and determine the acute effects of the PNF-based warm-up. Our aim was to illuminate the field for future studies and to find appropriate warm-up methods for all sports, especially for soccer players, to achieve maximum performance and minimize the risk of injury. Finally, PNF-based warm-up was found to have better acute effects on isokinetic strength, endurance and balance in soccer players than other warm-up methods.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLİCT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest among the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDİNG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from Artvin \u0026Ccedil;oruh University ethics committee. This thesis study dated 19.11.2020, numbered\u0026nbsp;E-18457941-050.01.04-12716 / 34958711-020-12358. It was created from the\u0026nbsp;Doctoral Thesis (Acute effect of Proprioceptive neuromuscular facilitation- based warm-up on isokinetic strength, endurance and balance) at Ondokuz Mayıs University Yaşar Doğu Faculty of Sports Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMY: Conceptualization, Data curation, Formal Analysis, Investigation, Visualization, Writing- original draft, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eM\u0026Ccedil;: Investigation, Methodology, Validation, Writing-review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkdoğan, E., Ertan, H., \u0026Uuml;niversitesi, A., Eğitimi, B., \u0026amp; Y\u0026uuml;ksekokulu, S. (2013). 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Lower extremity isokinetic strength characteristics of amateur boxers. \u003cem\u003eFrontiers in Physiology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 898126. https://doi.org/10.3389/fphys.2022.898126\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Balance, Endurance, İsokinetic strength, Proprioceptive neuromuscular facilitation, Warm-up","lastPublishedDoi":"10.21203/rs.3.rs-4678537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4678537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the acute effects of proprioceptive neuromuscular facilitation (PNF)-based warm-up on isokinetic strength, endurance, and body balance in 50 soccer players. Measurements were taken on four separate days, with participants resting completely between sessions. Isokinetic strength tests for hip, knee, and ankle flexion and extension were conducted using an isokinetic dynamometer at angular velocities of 60\u003csup\u003eo\u003c/sup\u003e/sec, 180\u003csup\u003eo\u003c/sup\u003e/sec and 240\u003csup\u003eo\u003c/sup\u003e/sec. Endurance was assessed with 25 repetitions at 240\u003csup\u003eo\u003c/sup\u003e/sec by analyzing the change in peak torque values. On the first day, players underwent general warm-up, balance tests, and isokinetic strength measurements. Subsequent sessions included these measurements along with additional testing. Results showed that the PNF-based warm-up significantly improved static and dynamic balance compared to other methods (p\u0026lt;,001). Peak torque values at all angular velocities indicated that the PNF-based warm-up was more effective than other warm-ups. Additionally, PNF-based warm-up had a better acute effect on knee joint isokinetic endurance (p\u0026lt;,05). In conclusion, the PNF-based warm-up significantly enhanced static and dynamic balance, isokinetic strength, and endurance in soccer players. It is recommended to include PNF-based warm-ups in soccer training, especially for the lower extremities, and to consider combining it with active warm-up methods for optimal acute performance benefits.\u003c/p\u003e","manuscriptTitle":"Acute Effect of Proprioceptive Neuromuscular Facilitation - Based Warm-Up on Isokinetic Strength, Endurance and Balance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 05:03:20","doi":"10.21203/rs.3.rs-4678537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"113f5bf2-7e99-455f-ba31-f772fe5b83e9","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T09:44:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 05:03:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4678537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4678537","identity":"rs-4678537","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}