Stable vs. variable eccentric load. Do they induce different training and physical performance outcomes?

1 Stable vs. Variable eccentric load. Do they induce different training and physical 1 performance outcomes? 2 3 Abstract. 4 Since most movement on the field require athletes to produce forces in variable and 5 unpredictable context, the use of training programs based on identical repetitions of an 6 exercise may not be optimal for movement transference. Therefore, the aim of this study 7 was to analyze the effects of unexpected eccentric load variability during resistance 8 training in team sport players. Sixty-three men randomly allocated in two experimental 9 groups (Variable (VTG) and stable (STG) training group) and control group: (CG) 10 volunteered to participate in this study. Experimental groups trained with the same 11 average load of half-squat exercise twice a week for six weeks using rotary inertial 12 devices (RIDs) with (VTG) and without (STG) an unexpected variability of the load. 13 Squat force was measured for every session with force plates. Counter-movement jump 14 (CMJ), sprint, and change of direction performances were measured pre and post-test. 15 CMJ performance improved for VTG (p= 0.014; ES= 0.7) and STG (p=0.005; ES= 16 0.79) but not for CG. Exposure to high eccentric forces with RIDs lead athletes to 17 improve physical performance in the trained force-vector but, since RIDs induce in high 18 levels of variability per se, increasing the level of variability of the exercise will not add 19 benefits to physical performance and training outcomes. 20 21 Keywords: Eccentric; Rotary inertial devices; Flywheel paradigm; Variable load; 22 Strength training. 23 24 Highlights. 25 26 • The relevance of the study is to analyze if increasing the variability of the load 27 will improve the athlete output to different physical performance tests. 28 • The exposure to eccentric overload with RIDs during the squat exercise allows 29 to greater CMJ height improvements than those seen in the literature with RIDs 30 without overloading the eccentric contraction. 31 • RIDs induce in high level of variability per se. Therefore, increasing the level of 32 variability of the exercise will not add benefits to physical performance and 33 training outcomes. 34 35 Introduction. 36 37 In team sports, the ability to perform high intensity actions such as sprints, changes of 38 direction and jumps can lead to better athletic performance (Sattler, Hadzic, Dervisevic, 39 & Markovic, 2015; Tous-Fajardo, Gonzalo-Skok, Arjol-Serrano, & Tesch, 2016). In 40 order to improve physical performance, strength training (ST) has been widely 41 2 employed by physical trainers to achieve enhancements in muscular force, hypertrophy, 42 sprint and coordination (Kraemer & Ratamess, 2004). Training stimuli may vary 43 depending on muscular action, exercise employed, intensity, volume, rest intervals, 44 frequency and variation (Kraemer & Ratamess, 2004). In this regard, most movements 45 in the field require players to produce forces in variable and unpredictable contexts, 46 with an emphasis on the eccentric (ECC) component (Tous-Fajardo et al., 2016). In 47 addition, biomechanical analyses of the production of athletic movements have been 48 conducted focusing on a variety of sport disciplines (Schöllhorn & Bauer, 1998) and the 49 results have indicated that even after thousands of repetitions, it is possible to see 50 significantly different levels of variability in the movement response. Therefore, if 51 movement repetition is never identical, it would make sense to conduct training 52 programs that prepare athletes for new movements that they will undergo in the future 53 (Schollhorn, Beckmann, & Davids, 2010).. In this regard, studies have showed that 54 using more frequent stimuli variation during ST is paramount in order to achieve 55 concomitants increases in strength (Hernandez-Davo & Sabido, 2022; Hernandez-Davo, 56 Sabido, Behm, & Blazevich, 2018). Indeed, load variability has showed greater 57 increases in muscular strength since it avoids accommodation for the neuromuscular 58 system to the stress produced by consecutive stimulus of similar magnitudes (Harries, 59 Lubans, & Callister, 2015; Hernandez-Davo & Sabido, 2022). However, Moreno & 60 Ordoño (Moreno & Ordoño, 2009) suggested that very high levels of variability may 61 cause undesired adaptationsdue to failure in the capacity of the system to elicit a load- 62 adjusted functional response. For instance, excessively long latency periods while 63 waiting for adaptations that may not come or, in the worst case, may even have harmful 64 results. 65 66 Traditionally, in order to enhance athletic performance, ST programs for team-sport 67 athletes have been based on those designed for individual sports, were loads are 68 constant and programmed according to the maximum weight mobilized inthe concentric 69 (CON) phase of the movement (Zatsiorsky & Kramer, 2006). However, many 70 movements performed in a range of athletic disciplines (i.e. jumps, landings, 71 accelerations, decelerations or changes of direction) require ECC contractions and, 72 therefore, the integration of this modality of muscle contraction will lead to additional 73 increases in physical performance (Gonzalo-Skok et al., 2017). In this regard, it has 74 been seen that activating muscles while lengthening (ECC) will lead to higher 75 performance during the subsequent CON action (Cormie, McGuigan, & Newton, 2010; 76 Van Hooren & Zolotarjova, 2017), so the introduction of exercises that allow for an 77 emphasis on the ECC component may be recommended for the optimization of 78 strength-related adaptations (Cormie et al., 2010; Gonzalo-Skok et al., 2017). Therefore, 79 due to the problems of conventional ST methods, in order to accentuate the ECC action 80 (Norrbrand, Pozzo, & Tesch, 2010), physical trainers have introduced “recent 81 methodologies” like accentuated ECC load (Suchomel et al., 2019) or rotary inertial 82 devices (RIDs) for their training regimes (Tous-Fajardo et al., 2016). RIDs have been 83 designed to use the moment of inertia of a rotating flywheel to provide a maximal 84 resistance load during all phases of movement (Nunez, Galiano, Munoz-Lopez, & 85 Floria, 2020). During the CON phase of the action, the athlete applies force though a 86 strap winded to a rotating flywheel, which initially is in a static position, in order to 87 make it spin. Upon completion of the CON action, the energy transferred to the 88 mechanism makes the strap rewinds; hence, the athlete has to decelerate the flywheel in 89 the subsequent ECC action. ST with flywheel devices have showed positive adaptation 90 3 on muscle mass (Maroto-Izquierdo et al., 2019; Tesch, Ekberg, Lindquist, & 91 Trieschmann, 2004), maximal strength (Maroto-Izquierdo et al., 2019; Tesch et al., 92 2004), sprint (M. de Hoyo et al., 2015; Gonzalo-Skok et al., 2017) , and power output 93 (Maroto-Izquierdo et al., 2019; Suchomel et al., 2019). In addition, it has been 94 suggested that RIDs offer higher ECC muscular activation and force production than 95 conventional ST (Norrbrand et al., 2010; Petre, Wernstal, & Mattsson, 2018) and allows 96 a high variability on force and velocity during the exercise (Nunez, Galiano, Munoz- 97 Lopez, & Floria, 2021). As RIDs guarantee an increase in the variability of the stimulus, 98 it is unknown if increasing the level of variability during ST with RIDs will lead to the 99 aforementioned undesired adaptations. 100 101 Since force application in RIDs varies depending on the application of force exerted in 102 the previous repetition, variable loads will increase the variability of the force applied 103 within the training set. This enhancement in the variability of the force exerted during 104 training may transfer to the variable response of the athletes in competition. However, 105 we do not know if increasing the level of variability, in a device that allows high force 106 variability per se, will bring more benefits to the ST outcomes. Therefore, the aim of 107 this study was to analyze the effects of unexpected ECC load variability for ST with 108 RIDs in team sport players. We hypothesize that the increase in physical performance of 109 team sport players will be greater in an ECC ST program with an increment of the 110 variability of the load. 111 112 Material and Methods. 113 114 Subjects. 115 116 Sixty-three physically active men involved in different team sports disciplines (i.e. 117 soccer, volleyball, basketball and rugby) volunteered to participate in this study. 118 Subjects did not perform any extra ST session with their respective teams during the 119 development of the study. Subjects had more than two years of experience with ST but 120 none with RIDs. Participants were randomly assigned to a “stable training load” group 121 (STG; n=21; age 23.2 ± 2.8 years; weight 77.6 ± 16.7 kg, height 174.8 ± 5.7 cm), a 122 “variable training load” group (VTG; n=21; age 22.7 ± 2.7 years; weight 76.2 ± 9.8 kg, 123 height 177.3 ± 6.6 cm) and a control group (CG; n=21; age 23.3 ± 3.7 years; weight 124 72.4 ± 5.5 kg, height 175.4 ± 5.71 cm). The study was approved by two hospitals (main 125 hospitals in the city) ethics committee (0398-N-17) and was conducted in accordance 126 with the Declaration of Helsinki. No physical limitations or health problems were found 127 during the training procedure. 128 129 Study Design. 130 131 4 In this study we developed an experimental cohort study design (pre-post-test). The 132 study lasted 8 weeks. During the first week, one familiarization session with the training 133 device and the pre-test were conducted on two different days with a minimum 134 separation of 72 h. During pre-test, counter-movement jump (CMJ), 20m sprint time, 135 foot contact time and flight time during sprint, and change of direction abilities (V-cut) 136 were measured. The sample were first stratified based on their physical abilities and 137 then randomly assigned to each group. Participants in VTG and STG performed two 138 training sessions per week with 48-72 h rest between them, for a total of 12 ST sessions 139 (6 weeks). The training intervention consisted of 4 sets of 7 repetitions of half-squat 140 exercise with an electric-motor RID with 3-min rest between sets, with and without 141 increment of the variability of the load respectively. During the intervention period, CG 142 continued with their daily life and associated physical activity. Post-test values were 143 obtained during the last week in identical conditions as pre-test. 144 145 Counter-movement Jump (CMJ). 146 The warm-up protocol followed in order to measure CMJ ability consisted of 5 minutes 147 of jogging, 10 full squats with body weight and 5 progressive CMJ exercises. CMJ was 148 performed with the participant standing in an upright position with hands on the hips to 149 avoid arm swings. A fast downward movement was immediately followed by a fast 150 upward vertical movement as high as possible, all in one sequence. A 1-minute rest 151 interval was applied between sections of the protocol. After the warm-up protocol, 3 152 maximal CMJs were performed on a force platform (Quattro Jump, Kistler 100 153 Instrument AG, Winterthur, Switzerland) sampling at 500 Hz and the average of the 154 three jumps was saved for future analysis. Subjects rested for 10s between maximal 155 CMJs. The intra class correlation coefficient (ICC) for CMJ was 0.94 (95% confidence 156 interval, CI: 0.90–0.97) and the coefficient of variation (CV) was 4.01%. 157 158 20m Sprint Time. 159 Once the jump ability was measured, subjects were requested to perform two 160 progressive and one maximum 20-meter sprints on an indoor running track. After a 161 warm-up, two maximum 20m sprints were measured. Photocell timing gates (Witty, 162 Microgate, Bolzano, Italy) were placed at 0, 10 and 20m so that two speed variables 163 were obtained for each subject: time to cover 0–10 m (T10) and 0–20 m (T20). To start 164 the test, subjects placed their self-selected foot 1 meter behind the first timing gate. A 1- 165 minute rest was applied between progressive and maximal sprints. The best T10 and 166 T20 were saved for analysis. The ICCs and CV were 0.85 (95% CI: 0.71–0.91) and 167 2.51% for T10, and 0.94 (95% CI: 0.89–0.97) and 1.43% for T20. 168 169 Foot Contact time and flight time during sprint. 170 A 10m infrared timing system (Optojump, Microgate, Bolzano, Italy) was installed 171 between the T10 and T20 gates, so when the subjects performed the sprint test, foot 172 contact time (CT) and flight time (FT) during sprinting were also analyzed. The lower 173 average times of both parameters were saved for analysis. The ICCs and CV were 0.87 174 5 (95% CI: 0.75–0.93) for FT and 4.90%, and 0.87 (95% CI: 0.76–0.93) and 4.51% for 175 CT. 176 177 V-cut. 178 Subjects performed two maximal 25 meter sprints, each containing 4 changes of 179 direction of 45º every 5 meters (Tous-Fajardo et al., 2016), separated by 2 minutes rest. 180 Photocell gates were placed at 0 and 25 meters. To start the test, subjects placed their 181 leading foot 1 meter behind the first timing gates. Subjects had to step on a line, marked 182 on the floor, with the entire foot at each turn. The best time was saved for subsequent 183 analysis. Previous to the test, one submaximal and maximal V-cut test separated by 1 184 minute were performed as a warm-up protocol. The ICCs and CV for V-cut were 0.95 185 (95% CI: 0.90–0.97) and 1.64%. These acceptable reliability measures are in agreement 186 with those seen elsewhere (Gonzalo-Skok et al., 2015) 187 Training period. 188 An 8-min general warm-up, including jogging, joint mobility and body-weight squat 189 exercises, was carried out before training session. The training intervention consisted of 190 4 sets of 7 repetitions of the half-squat exercise with 3-min rest between sets. 191 Participants were requested to apply force throughout the course of the full upward 192 movement at maximal velocity and then resist during the final downward movement, 193 aiming to bring the inertia to a stop at ~90º knee angle before initiating the next cycle. It 194 is commonly regarded that the first few reps are used to accelerate the flywheel 195 (Suchomel et al., 2019). Since we used an electric-motor device (Exentrix, 196 SmartCoach™, Stockholm, Sweden) for the study, these first few repetition used for 197 build momentum were not necessary. In addition, due to higher ECC than CON forces 198 are not guaranteed with RIDs in squat exercise (Nunez et al., 2020), we used the 199 electric-motor device to carry out the training sessions of the study . The software for 200 the device (Exentrix PC Interface – V2.4, SmartCoach™) allows its use as a RID and is 201 configured with a maximum load of 37 inertial units (IU). Each IU is equivalent to a 202 moment of inertia of 0.00134 kg·m2 (Maroto-Izquierdo et al., 2019). In this device we 203 can program different inertia for the CON and ECC actions of the same repetition. 204 Therefore, athletes will potentially be capable of generating ECC overload in the entire 205 range of motion (ROM) (Maroto-Izquierdo et al., 2019). For STG, the IUs used for each 206 set were 2 for the CON and 4 for the ECC; hence, there was a resulting average load of 207 2:4. To enhance the variability and unpredictability of the load, we modified the 208 intensity for each set for VTG (see Table 1). In order to increase training load 209 unpredictability, subjects from VTG did not know the way that the intensity was varied 210 (a different intensity for each set). Eventually, the average work of the training session 211 was the same for both groups. From session 5 to 12, we doubled the intensity to an 212 average of 4:8 IUs for both groups (Table 1). 213 214 During the half-squat exercise, the rope was attached to a harness fastened at the 215 participant’s shoulders and waist (Figure 1). Before squatting, in order to measure the 216 exercise range of motion, the tension of the rope was adjusted while maintaining both 217 legs in extension until they reached 90º knee flexion. Vertical ground reaction force data 218 of the half-squat exercise were recorded every repetitions with the participant standing 219 with one leg on different force platforms (SmartCoach Europe AB, Stockholm, 220 Sweden). We obtained four different values of force for each set. Average vertical 221 6 forces calculated as the average of the seven repetitions of the set for CON and ECC 222 actions (AV_CON and AV_ECC). Peak vertical forces calculated as the best peak force 223 of the seven repetitions in the set for CON and ECC actions (Peak_CON and 224 Peak_Ecc). 225 226 ***Insert Table 1 abut here*** 227 228 Statistical Analysis. 229 230 Data are shown as mean ± SD. Before any comparison, the normality of the distribution 231 of the data was tested using the Shapiro-Wilk test. Changes in performance markers 232 were tested using a two-way mixed repeated-measures analysis of variance (ANOVA) 233 test (time (pre vs. post) and by group (CG vs. VTG vs. STG) interaction), followed by 234 Bonferroni post hoc test-specific grouping differences. Partial eta-squared (η2p) was 235 used, with the following ranges: <0.01 (trivial), 0.01-0.06 (small), 0.06-0.15 (moderate) 236 and >0.15 (large) (Cohen, 2013). For all the analyses, a significance level of p< 0.05 237 was used. Threshold values for assessing magnitudes of the Cohen’s d (d) effect size 238 (ES,90% CI), with the following ranges: < 0.20 (trivial), > 0.20 (small), > 0.60 239 (moderate), > 1.2 (large) and > 2.0 (very large) (Hopkins, Marshall, Batterham, & 240 Hanin, 2009). All the statistical analysis were performed using JASP software (JASP 241 Team 2019, Version 0.11.1, University of Amsterdam). 242 243 Results. 244 245 Sixteen subjects dropped out during the training program for reasons not related to the 246 study, so the final numbers of participants were 17, 16 and 14 for STG, VTG and CG, 247 respectively. 248 249 Changes in functional test from pre-test to post-test for each group are shown in Table 250 2. A significant time*group interaction (p= 0.005; η²p= 0.16; large) was observed for 251 CMJ (Table 2). Both experimental groups, VTG (p= 0.014; ES= 0.70; moderate) and 252 STG (p= 0.005; ES= 0.79; moderate), showed a significant simple main effect of time 253 on CMJ performance. CG did not show significant changes in any variable studied. No 254 significant time*group interaction were observed for 20m sprint time, foot contact time 255 and flight time during sprint, and V-cut. 256 257 ***Insert Table 2 about here*** 258 259 7 Evolution of the vertical ground reaction forces during the squat are plotted in Figure 2. 260 No significant time*group interaction was observed for the force variables during the 261 training period. There were a main effect of time from session 1 to 4 for every variable 262 studied (p< .001; η2p> 0.313; large) but not for Peak_CON (see Figure 2). Force output 263 did not change from session 5 to 12. During all training period ECC forces were higher 264 than CON for average (p< .001, η²p= 0.811; large) and peak values (p< .001, η²p= 265 0.650; large). 266 267 ***Insert Figure 2 about here *** 268 269 Discussion. 270 271 The aim of this study was to analyze the effects of unexpected ECC load variability for 272 ST with RIDs in team sport players.. The main findings of this study were: a) CMJ 273 performance showed significant improvements for both experimental groups after the 274 training period; b) 20m sprint time, foot contact time and flight time during sprint, and 275 V-cut test remained unchanged after the training period; c) Force variables changed 276 similar in both experimental groups during the training period; d) There were an 277 increased in AV_CON, AV_ECC and Peak_ECC, from the first session to 4th session 278 of the ST in both experimental groups. Thus, in contrast to our hypothesis, the 279 improvements in physical performance of team sports athletes will be similar in a RIDs 280 training program with and without an increment of the variability of the load. 281 282 In our study, both experimental groups showed significant improvements in CMJ 283 performance (ES> 0.7; moderate) after the training period (see Table 2). Since squats 284 offers a resistance in the same force vector that CMJ test (vertical force vector), these 285 results were expected. Several studies have showed improvements in CMJ ability via 286 squat training with RIDs(M De Hoyo et al., 2015; Gonzalo-Skok et al., 2017; Núñez et 287 al., 2018; Petre et al., 2018). We obtained a magnitude of change on CMJ greater than 288 other studies with similar protocols (ES: 0.42-0.48) (Gonzalo-Skok et al., 2017; Núñez 289 et al., 2018). Higher ECC forces during the exercise (ECC overload) may be responsible 290 for these positive changes on CMJ since the exposition to high ECC loads have showed 291 greater enhancements in strength and power outputs (Suchomel et al., 2019). A better 292 capacity for storing kinetic energy at the end of the ECC phase will allow for higher 293 energy releases in the subsequent CON phase which leads to increases in CON force 294 production (Hernandez-Davo, Sabido, & Blazevich, 2021) However, increasing the 295 variability of the load to the training session did not evoke any additional changes to 296 CMJ height. Recently, it has been reported that RIDs provide high levels of variability 297 on force and velocity during the exertion of the exercise (Moras et al., 2018; Nunez et 298 al., 2021). Therefore, it is possible that half-squat RIDs offers a high levels of 299 variability per se, and increasing this level of variability, will not add benefits to CMJ 300 outcomes, but no undesired adaptations either. 301 302 8 According with the affirmation that training should focus on applying force in the 303 desired direction (Morin, 2013), we did not obtain a significant improvement in 20m 304 sprint time, foot contact time and flight time during sprint, and V-cut after the training 305 period (see Table 2). These results could be explained by the vertical force vector 306 exercise (i.e. Half-squat) trained in our study. However, some research on RIDs training 307 topic found improvements in change of direction and sprint performances after training 308 with vertically oriented exercises (i.e. Half-squat) (M De Hoyo et al., 2015; Gonzalo- 309 Skok et al., 2017; Núñez et al., 2018). Since all studies which found improvements in 310 change of direction and sprint times used inertial loads ranged from 0.10 to 0.27 kg·m2 311 for their training programs, we could think that the inertial load used for our study were 312 too light for improving this ability. Therefore, further research is need to analyze the 313 importance of design ST based on specific muscles, force vector applied and inertial 314 load to improve sport performance with RIDs. 315 316 We acknowledge several limitations in the current study. First, the subjects who 317 volunteered to participate did not have any experience with RIDs. It has been showed 318 that the highest reliability with RIDs is achieved after four sessions (Piqueras-Sanchiz et 319 al., 2020), so transfer of our results to more experienced athletes should be done with 320 caution since experience with RIDs maximizes the benefits of this training (Galiano, 321 Floria, Muñoz-Lopez, & Nuñez, 2021; Tous-Fajardo, Maldonado, Quintana, Pozzo, & 322 Tesch, 2006). We also believe that the loads used in our study were not heavy enough to 323 optimize performance in horizontally-oriented movements, since positive results have 324 been found with similar half-squat training and higher inertial loads (i.e. 0.029 to 0.27 325 kg·m2).In addition, performing strength measurements for pre and post-test for all three 326 groups would have given us more information about strength development with RIDs 327 for the experimental groups in comparison with the control one. Finally, since ST was 328 based on unexpected condition, future studies may consider to measure potential 329 adaptation on test with higher degree of uncertainty. 330 331 Conclusion. 332 333 Most movement on the field require players to produce forces in variable and 334 unpredictable contexts. Since RIDs induce in high levels of variability per se, increasing 335 this level of variability will not add benefits to physical performance and training 336 outcomes. The use of half-squat exercise with eccentric overload on a RID, for team 337 sports ST programs, allows athletes to enhance the physical performance transference to 338 those sports movements with similar force vector was applied. 339 340 Acknowledgments. 341 342 The authors have no conflicts of interest to disclose. 343 344 9 References. 345 346 Cohen, J. (2013). Statistical power analysis for the behavioral sciences: Routledge. 347 Cormie, P., McGuigan, M. R., & Newton, R. U. (2010). 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