Abstract and Figures

Objective: To compare the short-term effect of power- and strength-oriented resistance-training programs on the individualized load-velocity profiles obtained during the squat (SQ) and bench-press (BP) exercises. Methods: Thirty physically active men (age = 23.4 [3.5] y; SQ 1-repetition maximum [1RM] = 126.5 [26.7] kg; BP 1RM = 81.6 [16.7] kg) were randomly assigned to a power- (exercises: countermovement jump and BP throw; sets per exercise: 4-6; repetitions per set: 5-6; load: 40% 1RM) or strength-training group (exercises: SQ and BP; sets per exercise: 4-6; repetitions per set: 2-8; load: 70%-90% 1RM). The training program lasted 4 wk (2 sessions/wk). The individualized load-velocity profiles (ie, velocity associated with the 30%-60%-90% 1RM) were assessed before and after training through an incremental loading test during the SQ and BP exercises. Results: The power-training group moderately increased the velocity associated with the full spectrum of % 1RM for the SQ (effect size [ES] range: 0.70 to 0.93) and with the 30% 1RM for the BP (ES: 0.67), while the strength-training group reported trivial/small changes across the load-velocity spectrum for both the SQ (ES range: 0.00 to 0.35) and BP (ES range: -0.06 to -0.33). The power-training group showed a higher increase in the mean velocity associated with all % 1RM compared with the strength-training group for both the SQ (ES range: 0.54 to 0.63) and BP (ES range: 0.25 to 0.53). Conclusions: The individualized load-velocity profile (ie, velocity associated with different % 1RM) of lower-body and upper-body exercises can be modified after a 4-wk resistance-training program.
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Changes in the load-velocity profile following power- and
strength-oriented resistance training programs
Journal:
International Journal of Sports Physiology and Performance
Manuscript ID
IJSPP.2019-0840.R2
Manuscript Type:
Original Investigation
Date Submitted by the
Author:
n/a
Complete List of Authors:
Pérez-Castilla, Alejandro; University of Granada, Department of Physical
Education and Sport
García Ramos, Amador; University of Granada, Department of Physical
Education and Sport; Universidad Católica de la Santísima Concepción,
Facultad de Educación
Keywords:
velocity-based resistance training, linear position transducer, training
prescription, back squat, bench press
Human Kinetics, 1607 N Market St, Champaign, IL 61825
International Journal of Sports Physiology and Performance
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1Changes in the load-velocity profile following power- and strength-oriented resistance
2training programs
3
4ABTRACT
5Objective: To compare the short-term effect of power- and strength-oriented resistance training
6 programs on the individualized load-velocity profiles obtained during the squat (SQ) and bench
7 press (BP) exercises. Methods: Thirty physically active men (age: 23.4 3.5 years; SQ 1-
8 repetition maximum [1RM]: 126.5 26.7 kg; BP 1RM: 81.6 16.7 kg) were randomly
9 assigned to a power (PTG; exercises: countermovement jump and bench press throw; sets per
10 exercise: 4-6; repetitions per set: 5-6; load: 40%1RM) or strength training group (STG;
11 exercises: SQ and BP; sets per exercise: 4-6; repetitions per set: 2-8; load: 70-90%1RM). The
12 training program lasted 4 weeks (2 sessions/week). The individualized load-velocity profiles
13 (i.e., velocity associated with the 30-60-90%1RM) were assessed before and after training
14 through an incremental loading test during the SQ and BP exercises. Results: The PTG
15 moderately increased the velocity associated with the full spectrum of %1RM for the SQ (effect
16 size [ES] range: 0.70 to 0.93) and with the 30%1RM for the BP (ES: 0.67), while the STG
17 reported trivial/small changes across the load-velocity spectrum for both the SQ (ES range:
18 0.00 to 0.35) and BP (ES range: -0.06 to -0.33). The PTG showed a higher increase in the MV
19 associated with all %1RM compared to the STG for both the SQ (ES range: 0.54 to 0.63) and
20 BP (ES range: 0.25 to 0.53). Conclusions: The individualized load-velocity profile (i.e.,
21 velocity associated with different %1RM) of lower- and upper-body exercises can be modified
22 after a 4-week resistance training program.
23
24 Keywords: velocity-based resistance training; linear position transducer; training prescription;
25 back squat; bench press.
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26 INTRODUCTION
27 Advances in sports technology have enabled the proliferation of velocity-based resistance
28 training among strength and conditioning professionals.1,2 Findings within the scientific
29 literature support the use of this form of training as a viable method to optimize the prescription
30 and monitoring of resistance training programs.3–5 Furthermore, velocity can be used in a
31 number of ways to support practitioners. First, movement velocity can be used to quickly
32 estimate the 1-repetition maximum (1RM),6,7 which is the main reference to prescribe the loads
33 during resistance training programs.8 Second, the magnitude of velocity loss observed during
34 a set or training session could be a non-invasive and practical indicator of neuromuscular
35 fatigue.9,10 Third, the provision of immediate velocity feedback could increase motivation
36 during training and, consequently, improve training quality.11 Despite these uses, a number of
37 important methodological issues relating to velocity-based training still need to be resolved to
38 support the application of this form of training.
39
40 A large number of studies have reported a strong relationship between movement
41 velocity and the lifted load (%1RM) in a variety of resistance training exercises and
42 populations.6,12–15 One characteristic of the load-velocity (L-V) relationship is that it does not
43 seem to be meaningfully affected by the strength levels or training backgrounds.7,15,16 To date,
44 only 3 studies have explored the changes in the L-V profile following a strength-oriented
45 resistance training program.7,13,17 González-Badillo et al.7 found that the velocity associated
46 with each %1RM during the bench press (BP) exercise showed trivial changes (mean
47 differences: 0.00-0.01 ms-1) after a 6-week resistance training program (3-5 sets 4-12
48 repetitions at 60-85%1RM) despite a noticeable increase in mean 1RM strength being observed
49 (+9.3%). Similarly, Sánchez-Moreno et al.13 showed that the velocity associated with each
50 %1RM during the pull-up exercise was not altered (mean differences: 0.00-0.01 ms-1) despite
51 subjects increasing their 1RM strength on average by 9.8% following a 12-week resistance
52 training program (3-5 sets 50-80% of the maximum number of repetitions to failure). Finally,
53 Balsalobre-Fernández et al.17 reported lower velocities at moderate/heavy loads (i.e., 50-
54 100%1RM) (mean differences: 0.02 ms-1) and an increase in 1RM strength (3.3%) after a 6-
55 week resistance training program (4 sets 6-10 repetitions at 70-80%1RM) conducted with
56 the seated military press exercise. However, it is important to consider that in all the mentioned
57 studies a strength-oriented resistance training program was applied, being possible that the
58 changes in the L-V profile could differ with other types of training (e.g., power-oriented
59 resistance training).
60
61 The velocity specificity principle, which states that training-induced adaptations in
62 strength and power are maximized at or near the velocity used during training, is an accepted
63 principle within the scientific community.18,19 For example, McBride et al.20 observed that
64 heavy-load training only improved performance at moderate/low velocities, while light-load
65 training resulted in significant improvements in power and velocity across high, moderate and
66 low velocities. García-Ramos et al.21 reported an increase in the maximal force capacity after
67 heavy-load sprint training, while light-load sprint training improved the maximal velocity
68 capacity. However, no study has examined whether the individualized L-V profiles can be
69 selectively affected by the type of training performed. Based on the available evicence,7,13 it is
70 plausible that high-force low-velocity resistance training will not affect the L-V profiles
71 provided that the subjects perform the repetitions at a fast velocity, while low-force high-
72 velocity resistance training could promote higher velocities for light-medium relative loads
73 (%1RM).
74
75
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76 To address the gaps raised above, the individualized L-V profile was assessed in the
77 present study before and after 2 resistance programs aiming at increasing force production
78 against light (i.e., power-oriented resistance training) and heavy (i.e., strength-oriented
79 resistance training) loading conditions. Specifically, the main aim of this study was to compare
80 the short-term effect of power- and strength-oriented resistance training programs on the L-V
81 profiles obtained during the squat (SQ) and BP exercises. As a secondary aim, we explored the
82 between-session reliability of the individualized L-V profiles during the SQ and BP exercises.
83 We hypothesized that changes in the individualized L-V profiles would differ between both
84 training groups: the power training group (PTG) would show higher velocities associated with
85 each %1RM after training, while the strength training group (STG) would not show a
86 significant change in their L-V profiles.
87
88 METHOD
89 Subjects
90 Thirty male physical education students volunteered to participate in this study. All subjects
91 had resistance training experience (2.8 3.1 years) and were accustomed to performing the SQ
92 and BP exercises as a part of their academic curriculum. None of them suffered from physical
93 limitations, health problems or musculoskeletal injuries that could compromise tested
94 performance. They were forbidden to perform additional strength training over the course of
95 the study. All subjects completed the experimental protocol without missing any session.
96 Subjects were informed of the purpose, procedures, benefits and risks of the study prior to
97 signing a written informed consent form. The study protocol adhered to the tenets of the
98 Declaration of Helsinki and was approved by the Institutional Review Board.
99
100 Design
101 A longitudinal pre-post design was used to compare the short-term effect of 2 resistance
102 training programs (power-oriented vs. strength-oriented) on the individualized L-V profiles
103 obtained during the SQ and BP exercises. The whole study protocol consisted of 11 sessions
104 that were performed during a 6-week period: 2 pretests (week 1), 8 training sessions (weeks 2-
105 5), and 1 posttest (week 6). Following the pretest sessions, subjects were randomly assigned to
106 a power training group (PTG; n = 15, age = 22.6 3.4 years, body mass = 80.3 12.6 kg, body
107 height = 1.77 0.07 m, SQ 1RM = 129.6 25.8 kg, BP 1RM = 85.5 13.0 kg) or a strength
108 training group (STG; n = 15, age = 24.3 3.5 years, body mass = 81.1 14.0 kg, body height
109 1.76 0.05 m, SQ 1RM = 123.3 28.1 kg, BP 1RM = 82.4 20.1 kg). All testing and training
110 sessions were separated by at least 48 hours of rest and were performed at a consistent time of
111 the day for individual subjects (±1 hour).
112
113 Testing procedures
114 Each testing session began with a standardized warm-up consisting of 5 minutes of jogging
115 and dynamic stretching exercises, followed by 2 sets of 10 unloaded SQ, 5 countermovement
116 jumps, and 10 push-ups. After warming up, subjects rested for 3 minutes before undertaking a
117 standard incremental loading test during the SQ and BP exercises performed in a Smith
118 machine.6,12 The initial external load was set at 20 kg for both exercises (mass of the unloaded
119 Smith machine bar). Three repetitions were executed with light loads (mean velocity [MV] >
120 1.00 ms-1), 2 repetitions with medium loads (0.50 ms-1 MV 1.00 ms-1) and 1 repetition
121 with heavy loads (MV < 0.50 ms-1). 10 seconds of rest were implemented between repetitions
122 with the same load and 5 minutes between repetitions with different loads. Subjects received
123 velocity feedback after each repetition and they were encouraged to perform all repetitions at
124 the maximal intended velocity. The specific characteristics of the SQ and BP testing procedures
125 are described below.
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126 Squat (SQ) testing procedure. The load was increased in steps of 20 kg when the MV
127 of the bar was higher than 0.75 ms-1 and in 10 kg when the MV ranged from 0.75 to 0.50 ms-1.
128 The test was finished when subjects performed a repetition at a MV lower than 0.50 ms-1. The
129 average number of loads tested was 6.9 1.1. The individual L-V relationship was obtained
130 from the MV collected under the different loading conditions, and the SQ 1RM was estimated
131 from the individualized L-V relationship as the load associated with a MV of 0.33 ms-1.12 We
132 decided not to evaluate the 1RM by the direct method because some subjects had never
133 performed a 1RM test with the SQ exercise and this could compromise the accuracy of the
134 measurement and increase the risk of injury.22 Therefore, we decided to estimate the SQ 1RM
135 from the L-V relationship as it has been shown to be a time-efficient, accurate, and reliable
136 method of quantifying maximal strength.23,24 Subjects initiated the movement in a fully
137 extended position, with the feet approximately shoulder-width apart, and the bar held across
138 the back at the level of the acromion. From this position, they were required to descend in a
139 continuous motion until their thighs were parallel to the floor (parallel back squat), and
140 immediately after ascending back to an upright position as fast as possible. Subjects were not
141 allowed to jump off the ground.
142
143 Bench press (BP) testing procedure. The load was increased in increments of 10 kg
144 until the MV was lower than 0.50 ms-1. From that moment, the load was increased in steps of
145 5 to 1 kg until the 1RM load was reached. The average number of loads tested was 6.0 1.0.
146 Subjects performed the BP using the 5-point body contact position technique (head, upper
147 back, and buttocks firmly on the bench with both feet flat on the floor) and with a self-selected
148 grip width that was kept constant on every lift. Subjects initiated the task holding the bar with
149 their elbows fully extended. From this position, they were instructed to perform the downward
150 phase until contacting with their chest at the lower portion of the sternum, and immediately
151 after contact they performed the upward phase of the lift as fast as possible (touch-and-go
152 technique). Subjects were not allowed to bounce the bar off their chests nor raise the trunk off
153 the bench.
154
155 Resistance training program
156 The same general warm-up procedure described for the testing sessions was performed at the
157 beginning of each training session. In addition, before the SQ and BP exercises, subjects
158 performed as part of the specific warm-up 1 set of 10 repetitions at 40%1RM, 1 set of 5
159 repetitions at 60%1RM, and 1 set of 2 repetitions at 80%1RM. The PTG and STG completed
160 8 training sessions (twice per week) separated by at least 48 hours during 4 consecutive weeks.
161 The characteristics of the 4-week resistance programs are presented in Table 1. The relative
162 volume load (number of sets number of repetitions %1RM) of the whole training program
163 differed between PTG (8720 AU) and STG (12820 AU).25 The PTG used ballistic exercises
164 (countermovement jump and bench press throw) and the STG their traditional variants (SQ and
165 BP). The 2 exercises (lower-body and upper-body) were performed in a Smith machine
166 separated by 5 min. Lower-body exercises were always performed first. All training sessions
167 were supervised by a skilled experimenter who verbally encouraged the subjects to perform all
168 repetitions at the maximum possible velocity and velocity feedback was provided after each
169 repetition. The bar velocity was measured in all sessions and the load was modified to match
170 the desired %1RM.26
171
172 [Table 1]
173
174
175
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176 Measurement equipment and data analysis
177 Body height and body mass were measured at the beginning of the first testing session using a
178 wall-mounted stadiometer (Seca 202, Seca Ltd., Hamburg, Germany) and a contact electrode
179 foot-to-foot body fat analyzer system (TBF-171 300A, Tanita Corporation of America Inc.,
180 Arlington Heights, IL, USA), respectively. All sessions were performed in a Smith machine
181 (FFittech, Taiwan, China). A linear velocity transducer (T-Force System; Ergotech, Murcia,
182 Spain) was fixed to the bar with a tether and sampled the velocity data at a frequency of 1,000
183 Hz. Validity and reliability of the T-Force system for the recording of MV has been reported
184 elsewhere.27 The repetition with the highest MV (i.e., average velocity from the start of the
185 concentric phase until the bar reached the maximum height) of each loading condition was
186 used to determine the individualized L-V profiles.28 The absolute loads (kg) lifted in each
187 testing session were first expressed as relative loads (%1RM). Thereafter, the individualized
188 MV-%1RM relationships were determined by linear regression models and the MV attained at
189 3 relative loads (30-60%-90%1RM) were the dependent variables considered for the present
190 study.
191
192 Statistical analyses
193 Descriptive data are presented as means and standard deviations (SD). The normal distribution
194 of the data was confirmed by the Shapiro-Wilk’s test (P > .05). The L-V relationships were
195 established by means of linear regression models.29 The goodness of fit of the L-V relationships
196 was assessed through the Pearson’s coefficient of determination (r2). Reliability was assessed
197 from the 2 pretest sessions through the coefficient of variation (CV) by means of a custom
198 spreadsheet.30 Acceptable reliability was determined as a CV < 10%.31 A number of 2-factors
199 mixed analysis of variance (ANOVA) were conducted to evaluate the effects of “time” (within-
200 subject factor: pretest 2 vs. posttest) and “training group” (between-subject factor: PTG vs.
201 STG) on the L-V profile variables (i.e. MV associated with the 30-60-90%1RM). Eta-squared
202 (p2) was calculated for the ANOVA where the values of the effect sizes 0.01, 0.06 and above
203 0.14 were considered small, medium, and large, respectively.32 The magnitude of the changes
204 was also assessed through the Cohen’s d effect size [ES]) with the corresponding 95%
205 confidence intervals. Standardized differences (ES) were calculated using the pretest SD for
206 within-group comparisons and the pretest pooled SD for between-groups comparisons. The
207 criteria for interpreting the magnitude of the ES were: trivial (< 0.20), small (0.20–0.59),
208 moderate (0.60–1.19), large (1.20–2.00), and extremely large (> 2.00).33 All statistical analyses
209 were performed using SPSS software version 22.0 (SPSS Inc., Chicago, IL, USA) and
210 statistical significance was accepted at an alpha level of .05.
211
212 RESULTS
213 The strength of the individualized L-V relationships was very strong for both the SQ
214 (r2 = 0.996 [range: 0.986-1.000]) and BP (r2 = 0.997 [range: 0.985-1.000]) exercises. The MV
215 associated with each %1RM revealed an acceptable reliability for both the SQ (CV 6.16%)
216 and BP (CV 6.30%) exercises. No significant differences in the L-V profiles were observed
217 between the training groups at pretest (P .428). Note also that no significant differences in
218 the velocity of the 1RM (V1RM) were observed before (0.16 0.03 ms-1[0.10-0.20]) and after
219 training (0.16 0.03 ms-1 [0.07-0.19]) for the BP exercise (P = .443).
220
221 The main effect of “time” was significant for the MV attained at 60%1RM and
222 90%1RM for the SQ (F 4.61, P .041, p2 0.14; Table 2), while the main effect of “time”
223 was not significant at any %1RM for the BP (F 1.48, P .235, p2 0.09; Table 3). After
224 training, the PTG moderately increased the MV with the full spectrum of %1RM for the SQ
225 (ES: 0.70 to 0.93), but only with the 30%1RM for the BP (ES: 0.67; Figure 1). However, the
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226 STG reported trivial/small changes in their L-V profile for both the SQ (ES range: 0.00 to 0.35)
227 and BP (ES range: -0.06 to -0.33). No significant “group time” interactions were observed
228 for the SQ (F 3.51, P .071, p2 0.11) or the BP (F 2.65, P .115, p2 0.09). However,
229 the PTG showed a greater increase in the MV associated with all %1RM compared to the STG
230 for both the SQ (ES range: 0.54 to 0.63) and BP (ES range: 0.25 to 0.53) (Figure 2).
231
232 [Table 2]
233 [Table 3]
234 [Figure 1]
235 [Figure 2]
236
237 DISCUSSION
238 The present study explored the short-term effect of power- and strength-oriented resistance
239 training programs on the individualized L-V profiles measured during the SQ and BP exercises.
240 The main finding revealed that the changes in the individualized L-V profiles tended to be
241 training-specific. The PTG reported moderate increases after training in the MV associated
242 with light/medium loads for both the SQ and BP exercises, while no meaningful changes in the
243 L-V profile were observed for the STG. Specifically, the PTG revealed a greater increase in
244 the MV across the full spectrum of %1RM compared to the STG for both the SQ and BP
245 exercises. Therefore, because the individualized L-V profile can be modified after a short-term
246 (8 sessions) resistance training program, we recommend the periodic assessment of the
247 individualized L-V relationship for more accurate prescription of loads during velocity-based
248 resistance training programs.
249
250 The relationship between movement velocity and the relative load (%1RM) has been
251 proposed to be stable across time even when the individuals’ maximal strength levels are
252 modified.7,13 However, no previous study had examined whether the changes in the L-V
253 profiles could be training-specific. This is the first study that has examined whether the
254 individualized L-V profiles can be selectively affected by a power-oriented (i.e., low-force
255 high-velocity, low volume load approach) or strength-oriented (i.e., high-force low-velocity,
256 low volume load approach) resistance training program. Supporting our hypothesis, the PTG
257 showed higher velocities after training against light-medium loads for both exercises, while the
258 STG did not show any relevant change in their L-V profiles. The lack of changes in the STG
259 is in agreement with the findings of González-Badillo et al.7 and Sánchez-Moreno et al.13 who
260 did not find changes in the L-V profiles during the BP and pull-up exercises after a strength-
261 oriented resistance training program. Conversely, Balsalobre-Fernández et al.17 observed lower
262 velocities at moderate/heavy loads during the seated military press exercise following a
263 strength-oriented resistance training program. The divergent findings reported by Balsalobre-
264 Fernández et al.17 could be explained by the lack of intention to move the load explosively
265 during training18 or the lower V1RM recorded at posttest compared to the pretest.7 Moreover,
266 in accordance with the velocity specificity principle,18,19 an interesting finding was that the
267 PTG reported higher increments in the velocities associated with the full spectrum of %1RM
268 after training compared to the STG during both exercises. The present study evidenced that the
269 individualized L-V profiles during the SQ and BP exercises can be modified after a short-term
270 power-oriented resistance training program, highlighting the importance of assessing the
271 individualized L-V relationship periodically during a long-term training program. The periodic
272 assessment of the individualized L-V relationship is now feasible thanks to a validated
273 procedure called “2-point method” that enables the estimation of the 1RM from the recording
274 of MV against only 2 external loads.23,24
275
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276 Few studies have examined the between-session reliability of the individualized L-V
277 profiles during basic resistance training exercises.13,28,31 García-Ramos et al.28 found a high
278 absolute reliability for the MV associated with relative loads ranging between the 20%1RM
279 and 95%1RM during the concentric-only and eccentric-concentric BP throw variants (CV
280 9.6%). Similarly, a good reliability was reported across the L-V spectrum during the free-
281 weight prone bench pull exercise (CV 7.6%).31 Finally, Sánchez-Moreno et al.13 also
282 observed an average CV of 6.2% for relative loads ranging between the 65%1RM and 95%
283 during the pull-up exercise despite the evaluations were separated by a 12-week resistance
284 training program. In line with these aforementioned studies,13,28,31 an acceptable reliability was
285 observed in the present study for both the SQ and BP. Therefore, our results reinforce the high
286 reliability of the individualized L-V profiles being possible to predict or adjust the exercise
287 intensity during velocity-based resistance training programs.
288
289 Several limitations need to be considered when interpreting the results of the present
290 study. The study sample consisted of recreationally trained individuals, being possible that the
291 changes observed in the L-V profile would be attenuated when training high-level athletes
292 because longer training periods are needed to induce significant changes in performance in
293 more trained populations. In addition, the SQ 1RM was estimated to minimize muscle pain or
294 the risk of injury,22 being plausible that the use of a standard V1RM for all subjects has
295 confounded our findings (i.e., the MV attained with each %1RM would slightly deviate from
296 their true values in subjects with an individual V1RM different than 0.33 ms-1). Therefore, it
297 is important that future studies explore the effects of different resistance training strategies on
298 the stability of the L-V profiles in athlete populations and in other basic resistance training
299 exercises (e.g., prone bench pull, deadlift, etc.).
300
301 PRACTICAL APPLICATIONS
302 The changes in the L-V profiles identified in this study after 8 training sessions support the
303 periodic assessment of the individualized L-V relationship for a more accurate prescription of
304 the exercise intensity (%1RM). Specifically, an individualized L-V profile can be safely and
305 quickly determined by the “2-point method” following 3 simple steps: (i) setting of the
306 exercise-specific V1RM, (ii) recording of the MV against 2 external loads (iii) modeling the
307 individualized L-V relationship and determining the 1RM as the load associated with the
308 V1RM.1
309
310 CONCLUSIONS
311 A 4-week power-oriented resistance training program increases the velocity associated with
312 light-medium submaximal loads, while a strength-oriented resistance training program has
313 trivial effects on the L-V profiles. These results suggest that the changes in the individualized
314 L-V profile (i.e., velocity associated with different %1RM) of the SQ and BP exercises tend to
315 be training-specific.
316
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418 FIGURE CAPTIONS
419
420 Figure 1. Absolute changes in the mean velocity (ms-1) attained at each relative load (%1RM)
421 observed for the power training group (PTG) and strength training group (STG) in the squat
422 (upper-panel) and bench press (lower-panel) exercises (data averaged across the subjects with
423 the SD error bars). ES, Cohen’s d effect size ([posttestmean – pretestmean]/pretestSD) with 95%
424 confidence intervals. %, percent differences ([posttestmean pretestmean]/pretestmean 100).
425 1RM indicates 1-repetition maximum.
426
427 Figure 2. Standardized differences (95% confidence intervals) in the mean velocity attained at
428 each relative load (%1RM) between the power training group (PTG) and strength training
429 group (STG) for the squat (filled circles) and bench press (open circles) exercises. 1RM
430 indicates 1-repetition maximum.
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Table 1. Characteristics of the 4-week training program performed by the power training group (PTG) and strength training group (STG).
Week 1
Week 2
Week 4
Group
Resistance training
variables
S1
S2
S3
S4
S5
S6
S7
S8
Sets repetitions
5 5
5 5
6 5
6 5
6 5
6 5
4 6
4 6
Load (%1RM)
40%
40%
40%
40%
40%
40%
40%
40%
Relative volume load (AU)
1000
1000
1200
1200
1200
1200
960
960
PTG
Inter-set rest
4 min
4 min
4 min
4 min
4 min
4 min
4 min
4 min
Sets repetitions
4 8
4 8
5 4
5 4
5 4
6 2
6 2
6 2
Load (%1RM)
70%
70%
85%
85%
85%
90%
90%
90%
Relative volume load (AU)
2240
2240
1700
1700
1700
1080
1080
1080
STG
Inter-set rest
4 min
4 min
4 min
4 min
4 min
4 min
4 min
4 min
S, session; 1RM, 1-repetition maximum; AU, arbitrary units. The relative volume load was calculated as number of sets number of repetitions
%1RM. The PTG used ballistic exercises (countermovement jump and bench press throw) and the STG the traditional variants (squat and bench
press).
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Table 2. Changes in the mean velocity (ms-1) attained at each relative load (%1RM) from pretest to posttest for the power training group (PTG)
and strength training group (STG) during the squat exercise.
Time
Interaction
Load
(%1RM)
Group
Pretest
(mean SD)
Posttest
(mean SD)
F
P
p2
F
P
p2
PTG
0.98 0.07
1.03 0.10
30
STG
1.00 0.08
1.00 0.07
3.46
0.073
0.11
3.47
0.073
0.11
PTG
0.70 0.04
0.74 0.05
60
STG
0.72 0.05
0.72 0.04
4.61
0.041
0.14
3.51
0.071
0.11
PTG
0.43 0.01
0.44 0.02
90
STG
0.43 0.01
0.43 0.01
6.93
0.014
0.20
1.35
0.255
0.05
1RM, 1-repetition maximum; SD, standard deviation; F, Snedecor’s F; P, P-value; p2, partial eta squared. No significant differences were observed
between PTG and STG (P .05; Independent samples Student’s t-tests).
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Table 3. Changes in the mean velocity (ms-1) attained at each relative load (%1RM) from pretest to posttest for the power training group (PTG)
and strength training group (STG) during the bench press exercise.
Time
Interaction
Load
(%1RM)
Group
Pretest
(mean SD)
Posttest
(mean SD)
F
P
p2
F
P
p2
PTG
1.23 0.08
1.29 0.09
30
STG
1.26 0.13
1.25 0.11
1.48
0.235
0.09
2.65
0.115
0.09
PTG
0.78 0.05
0.81 0.05
60
STG
0.79 0.08
0.78 0.07
0.69
0.414
0.02
2.64
0.116
0.09
PTG
0.33 0.03
0.33 0.02
90
STG
0.33 0.03
0.32 0.04
1.32
0.260
0.05
0.46
0.505
0.02
1RM, 1-repetition maximum; SD, standard deviation; F, Snedecor’s F; P, P-value; p2, partial eta squared. No significant differences were observed
between PTG and STG (P .05; Independent samples Student’s t-tests).
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Figure 1. Absolute changes in the mean velocity (ms-1) attained at each relative load (%1RM) observed
for the power training group (PTG) and strength training group (STG) in the squat (upper-panel) and bench
press (lower-panel) exercises (data averaged across the subjects with the SD error bars). ES, Cohen’s d
effect size ([posttestmean – pretestmean]/pretestSD) with 95% confidence intervals. %, percent
differences ([posttestmean – pretestmean]/pretestmean 100). 1RM indicates 1-repetition maximum.
261x282mm (72 x 72 DPI)
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Figure 2. Standardized differences (95% confidence intervals) in the mean velocity attained at each relative
load (%1RM) between the power training group (PTG) and strength training group (STG) for the squat (filled
circles) and bench press (open circles) exercises. 1RM indicates 1-repetition maximum.
236x226mm (72 x 72 DPI)
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... This variable is more difficult to control concerning the one-arm pull-ups, especially in individuals who perform this exercise in an assisted form. In future studies, it would be worthwhile to compare the PAPE response in subjects performing one-arm pull-ups in unassisted and assisted forms and also to determine the magnitude of relief more precisely, perhaps based on the velocity of the movement known to be related to RM magnitude (González-Badillo and Sánchez-Medina, 2010;Loturco et al., 2021;Pérez-Castilla and García-Ramos, 2020). The one-arm pull-up is slightly different in movement structure compared to the power slap, and despite the involvement of the same muscle groups, muscle activation patterns may slightly vary (Dickie et al., 2017;Kozin et al., 2020;Leslie and Comfort, 2013). ...
Preprint
This study aimed to compare the acute effects of performing two kinds of pull-ups: traditional, pronated grip pull-ups performed with two arms and additional weight with loading intensity of 5RM and one-arm pull-ups, on specific upper body climbing power. Twenty-four advanced climbers participated in the study. The International Rock Climbing Research Association (IRCRA) Power Slap Test was chosen to assess specific upper body climbing power. All athletes performed the test under three conditions: control (without a conditioning activity) and both kinds of pull-ups as conditioning activities. Results revealed significant improvements in the Power Slap's distance, power, velocity, and force in 5RM weighted pull-ups, but not in one-arm pull-ups. In the latter case, participants reached higher power values after the conditioning stimulus, but the effect size was small. Also, the differences with the remaining variables (power, speed, and force) were non-significant. The results suggest that weighted pull-ups with a 5RM intensity and not one arm pull-ups seem to be an effective PAPE stimulus. Therefore, the former can be used as a conditioning activity before an explosive climbing exercise such as the Power Slap on a campus board.
... 2 For example, individualized load-velocity (L-V) relationships are used to regulate the training intensity, 3,4 quantify training-induced fatigue, 5,6 and assess changes in neuromuscular performance after training interventions. 7,8 Note also that individualized L-V relationship has been recommended over the generalized L-V relationship equations because the velocity associated with each relative load is subject-specific. 2 Furthermore, a novel application of the L-V relationship consists of determining the L-V relationship variables (load-axis intercept [L 0 ], velocity-axis intercept [v 0 ], and the area under the L-V relationship line [A line = L 0 ·v 0 /2]), which may be accurate indicators of the maximal capacities of producing force, velocity, and power, respectively. 9 In comparison to the forcevelocity (F-V) relationship parameters (see Jaric 10 for further details), the assessment of the L-V relationship variables may be simpler and more reproducible because i) the force output does not need to be computed for the modeling, and ii) the extrapolation needed from the experimental points to v 0 is reduced because only the external load lifted is considered for the analysis. ...
Article
Purpose: This study aimed to examine the reliability and validity of load-velocity relationship variables obtained through the two-point method using different load combinations and velocity variables. Methods: Twenty men performed two identical sessions consisting of two countermovement jumps against four external loads (20-40-60-80 kg) and a heavy squat against a load linked to a mean velocity of 0.55 m·s-1 (load0.55). The load-velocity relationship variables (load-axis intercept [L0], velocity-axis intercept [v0], and area under the load-velocity relationship line [Aline]) were obtained using three velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and peak velocity [PV]) by the multiple-point method including (20-40-60- 80-load0.55) and excluding (20-40-60-80) the heavy squat, as well as from their respective two-point methods (20-load0.55, and 20-80). Results: The load-velocity relationship variables were obtained with an acceptable reliability (CV≤7.30%; ICC≥0.63). The reliability of L0 and v0 was comparable for both methods (CVratio=1.11-1.12), but the multiple-point method provided Aline with a greater reliability (CVratio=1.26). The use of a heavy squat provided the load-velocity relationship variables with a comparable or higher reliability than the use of a heavy countermovement jump load (CVratio=1.06-1.19). The PV provided the load-velocity relationship variables with the greatest reliability (CVratio=1.15-1.86) followed by MV (CVratio=1.07-1.18), and finally MPV. The two-point methods only revealed an acceptable validity for MV and MPV (ES≤0.19; r≥0.96; CCC≥0.94). Conclusions: The two-point method obtained from a heavy squat load and MV or MPV is a quick, safe, and reliable procedure to evaluate the lower-body maximal neuromuscular capacities through the load-velocity relationship.
... Furthermore, the use of LVPs as a longitudinal tool relies on the stability of velocity at relevant percentages of 1RM, irrespective of physiological adaptations. Whilst scarce, previous literature suggests that mean velocity is stable following bouts of acute strength training (~4-6 weeks) [21,42,43], providing confidence in the predictive models. Future research, however, should seek to further investigate the stability of the LVP across longer time periods (e.g., full macrocycle) as well as predict 1RM over multiple sessions, as often, predictive models can be misleadingly concluded as valid and reliable when only applied to one session's worth of data. ...
Article
Full-text available
The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30–60% 1RM) + back squat (70–100% 1RM)) or back squat only (0 load, 30–100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; = 0.90) and back squat (p = 0.004, = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, = 0.67–0.80), but not quadratic (p = 0.632–0.929, = 0.001–0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.
... A positive training program would change MCV with a fixed absolute load/s or the load that can be lifted at a fixed speed. It has been shown that loadvelocity profiles (i.e., the velocity associated with each %1RM) can be altered after a period of training of 4 to 6 weeks [49,72]. Thus, it is recommended to re-assess the load-velocity profile every ≈4-6 weeks, or after specific cycles of RT, in order to evaluate the effects of the RT. ...
Article
Full-text available
While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon.
... A previous study showed that changes in the velocity performed against a certain %1RM were specific to the relative load used during the training program [28], although only the %1RM values of the pretest were considered. Similarly, specific changes in individual LV relationships obtained from BP and SQ exercises have been recently reported after power and strength training programs [29]. To the best of our knowledge, only one previous study explored the effect of different set configurations on LV for the BP exercise [27], and reported similar changes in power and velocity after eight weeks of training with traditional or cluster set configurations. ...
Article
This study explored the changes in load-velocity relationship of bench press and parallel squat exercises following two programs differing in the set configuration. A randomized controlled trial was carried out in a sample of 39 physically active individuals. Participants were assigned to rest redistribution set configuration, traditional set configuration, or control groups. Over 5 weeks, the experimental groups completed 10 sessions with the 10 repetitions maximum load of both exercises. Rest redistribution sets consisted in 16 sets of 2 repetitions with 60 s of rest between sets, and 5 min between exercises, whereas traditional sets entailed 4 sets of 8 repetitions with 5 min of rest between sets and exercises. The load-velocity relationships of both exercises were obtained before and after the training period. For bench press, an increase of the velocity axis intercept, and a decrease of the slope at post-test were observed in both rest redistribution (p < 0.001, G = 1.264; p < 0.001; G = 0.997) and traditional set (p = 0.01, G = 0.654; p = 0.001; G = 0.593) groups. For squat, the slope decreased (p < 0.001; G = 0.588) and the velocity axis intercept increased (p < 0.001; G = 0.727) only in the rest redistribution group. These results show that rest redistribution sets were particularly efficient for inducing changes in the load-velocity relationship
... For the bench press exercise, participants held the barbell with a self-selected width and the pronated grip [18] and lowered it to their chest in a controlled manner and then, without bouncing the barbell, pushed it maximally until full elbow extension. For the squat exercise, with the barbell positioned across their shoulders, participants descended until their hips were below the knee joint and then ascended as rapidly as possible until their knees were at full extension. ...
Article
Full-text available
This study examined the accuracy of different velocity-based methods in the prediction of bench press and squat one-repetition maximum (1RM) in female athletes. Seventeen trained females (age 17.8 ± 1.3 years) performed an incremental loading test to 1RM on bench press and squat with the mean velocity being recorded. The 1RM was estimated from the load–velocity relationship using the multiple- (8 loads) and two-point (2 loads) methods and group and individual minimum velocity thresholds (MVT). No significant effect of method, MVT or interaction was observed for the two exercises (p > 0.05). For bench press and squat, all prediction methods demonstrated very large to nearly perfect correlations with respect to the actual 1RM (r range = 0.76 to 0.97). The absolute error (range = 2.1 to 3.8 kg) for bench press demonstrated low errors that were independent of the method and MVT used. For squat, the favorable group MVT errors for the multiple- and two-point methods (absolute error = 7.8 and 9.7 kg, respectively) were greater than the individual MVT errors (absolute error = 4.9 and 6.3 kg, respectively). The 1RM can be accurately predicted from the load–velocity relationship in trained females, with the two-point method offering a quick and less fatiguing alternative to the multiple-point method.
Article
This study aimed to examine the effects of altering the intra-session exercise sequence of a concurrent training program on the load-velocity relationship variables obtained from different compound exercises. Physically active subjects (n=24, age = ~21 years) were assigned to one group that performed sprint interval training (sprints: 4-6; intensity: all-out; duration: 30 seconds; rest: 4 minutes) followed by resistance training (exercises: back squat and bench press; sets per exercise: 4-6; load: 60-80% of one-repetition maximum; repetition in reserve: 6-1; rest: 2 minutes) (SIT+RT) or another group that performed the opposite sequence (RT+SIT). Exercises modes were separated by 10 minutes. Both groups trained three times per week over an eight-week period. The individualized load–velocity relationships were assessed before and after training through an incremental loading test during the back squat and bench press exercises and three variables were subsequently calculated: load-axis intercept (L0), velocity-axis intercept (v0), and area under the line (Aline = L0⋅v0/2). Regardless of exercise sequence, both groups increased L0 (ES range = 0.78 to 0.91) and Aline (ES range = 0.50 to 0.55) but decreased v0 (ES range = -0.36 to -0.46) during the back squat exercise, while all load-velocity relationship variables were increased (ES range = 0.04 to 2.20) during the bench press exercise. The SIT+RT group showed a moderately greater bench press v0 increase compared to the RT+SIT group (ES = -1.07). These results indicate that both intra-session exercise sequences can induce comparable improvements in the load-velocity relationship variables after an eight-week concurrent training program.
Article
This study aimed to compare the reliability and agreement of mean velocity (MV) and maximal velocity (Vmax) between the two velocity monitoring devices (GymAware vs. T-Force) most commonly used in the scientific literature. Twenty resistance-trained males completed two testing sessions. The free-weight barbell back squat one-repetition maximum (1RM) was determined in the first session (125.0 ± 24.2 kg; mean ± standard deviation). The second session consisted of two blocks of 16 repetitions (6 repetitions at 45%1RM and 65%1RM, and 4 repetitions at 85%1RM). Half of the repetitions were performed with the GymAware on the left side of the barbell and the other half on the right side of the barbell (opposite placement for the T-Force). MV and Vmax were recorded simultaneously with the GymAware and T-Force. The overall reliability, which was calculated pooling together the data of three loads, did not differ between the T-Force (coefficient of variation [CV] = 5.28 ± 1.79%) and GymAware (CV = 5.79 ± 2.26%) (CVratio = 1.10), but it was higher for Vmax (5.08 ± 1.79%) compared to MV (5.98 ± 2.73%) (CVratio = 1.18). MV was significantly higher for the T-Force (p < 0.001, Δ = 4.42%), but no significant differences between the devices were detected for Vmax (p = 0.455, Δ = 0.22%). These results support the use of both the GymAware and T-Force as gold-standards in studies designed to validate other velocity monitoring devices. However, systematic bias, albeit rather constant, exists for the magnitude of MV between the two devices.
Article
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Background Maximal strength is a critical determinant of performance in numerous sports. Autoregulation is a resistance training prescription approach to adjust training variables based on the individuals’ daily fluctuations in performance, which are a result of training-induced fitness and fatigue, together with readiness from daily non-training stressors. Objective This review aimed to summarise the effects of different subjective and objective autoregulation methods for intensity and volume on enhancing maximal strength. Materials and Methods A comprehensive literature search was conducted through SPORTDiscus, PubMed and Google Scholar. Studies had to meet the following criteria to be included in the review: (1) estimation of 1-RM or a 1-RM test for both pre-test and post-test to measure progression in strength assessment during the training intervention, (2) a training comparison group, (3) participants were healthy, (4) the article had a detailed description of training intensity, training volume, and training frequency during the training intervention, (5) the training intervention lasted for more than four weeks, (6) studies with objective autoregulation methods utilised a validated measuring tool to monitor velocity, (7) English-language studies. Results Fourteen studies met the inclusion criteria, comprising 30 training groups and 356 participants. Effect size and percentage differences were calculated for 13 out of 14 studies to compare the effects of different training interventions. All autoregulation training protocols resulted in an increase in 1-RM, from small ES to large ES. Conclusion Overall, our findings suggest that using both subjective autoregulation methods for intensity, such as repetitions in reserve rating of perceived exertion and flexible daily undulation periodisation, together with objective autoregulation methods for autoregulation intensity and volume, such as velocity targets and velocity loss, could be effective methods for enhancing maximal strength. It is speculated that this is because the implementation of autoregulation into a periodised plan may take into account the athletes’ daily fluctuations, such as fluctuations in fitness, fatigue, and readiness to train. When training with a validated measuring tool to monitor velocity, this may provide objective augmented intra- and interset feedback during the resistance exercise who could be beneficial for increasing maximal strength. Coaches, practitioners, and athletes are encouraged to implement such autoregulation methods into a periodised plan when the goal is to enhance maximal strength.
Thesis
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Vertical jumping is one of the most commonly used motor skills to train and evaluate lower-body performance in various populations. The squat jump (SJ) and countermovement jump (CMJ) are the two of the jump modalities that have received the most scientific attention. Jump height is considered the main indicator of vertical jump performance. However, there are several procedures that are used interchangeably in the scientific literature to estimate jump height. In this sense, the first topic of this Doctoral Thesis is the “identification of the most reliable procedure to determine the jump height: take-off velocity vs. flight time”. The analysis of the vertical ground reaction force (VGRF) data recorded by a force platform allows obtaining other relevant performance variables such as mean, peak and time to peak values of force, power and velocity, rate of force development, impulse, or duration of the different phases of the jump. However, one of the main methodological aspects that should be considered when determining the different performance variables of the vertical jumps from the force-time data recorded by a force platform is how to select the jump starting threshold. Therefore, the second topic of this Doctoral Thesis aims to examine the “influence of the threshold used to determine the start of the movement on the performance variables during the vertical jump exercise”. Finally, vertical jumps have been used more recently to assess the lower-body muscle function. The force-velocity relationship must be modelled to determine the maximum capacities to produce force, velocity, and power. This testing procedure consists in the application of multiple external loads (between 5 and 9 loads) that allows obtaining a wide range of force and velocity data. Subsequently, the data are modelled through a simple linear regression model to determine the force-velocity relationship parameters. In this context, the last topic of this Doctoral Thesis deals with the “optimization of the procedure used to evaluate the force-velocity relationship in the vertical jump exercise: two-point method”. Based on the three topics presented, the main objective of this Doctoral Thesis was to establish a body of knowledge that improves the procedures for evaluating vertical jumps performed against different overloads on a force platform. To respond to this objective, we have five studies published in journals indexed in the Journal Citation Reports, whose objectives were: (1) to compare the reliability and magnitude of the jump height obtained from two standard procedures (take-off velocity and flight time) of analysing force platform data in the SJ (study 1) and CMJ (study 2) exercises performed with a free-weight bar or with a Smith machine (first topic); (2) to analyse the influence of five thresholds used to determine the onset of movement during the SJ (study 3) and CMJ (study 4) exercises on the reliability and magnitude of different kinetic and kinematic variables reported by a force platform against a range of external loads (second topic); and (3) to compare the reliability and concurrent validity of the force-velocity relationship parameters obtained from the two-point method by varying the distance between the experimental points with respect to the multi-point method in the SJ and CMJ exercises (study 5; third topic). The results of this Doctoral Thesis show that: (1) the use of a Smith machine together with the flight time procedure provides the most reliable measurement of jump height, while the least reliable option is to use the take-off velocity procedure by performing the jump with a Smith machine (studies 1 and 2); (2) the threshold used to detect the start of the jump movement influences both the reliability and the magnitude of the vertical jump performance variables, being recommended to use the threshold that considers the standard deviation (SD) of the weighing phase (i.e., the jump starts 30 ms before the instant in which the VGRF is greater [SJ] or less [CMJ] than the system weight [SW]  5 SD) because it provides reliability comparable to conservative thresholds and consider more force signal for the analysis (studies 3 and 4); and (3) the two-point method based on the most distant loads is a reliable and valid procedure as compared to the multiple-point method so it can be considered as a quick and less prone to fatigue alternative for testing the lower-body muscle function during the vertical jump exercises.
Article
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Objective: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load-velocity relationship modeled through the multiple- and 2-point methods. Methods: Eleven men performed an incremental test on a Smith machine against 5 loads (45-55-65-75-85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45-55-65-75-85%1RM), or only at the 2 most distant loads (45-85%1RM), was considered for the multiple- and 2-point methods, respectively. Results: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70-0.83; r = .93-.94; SEE = 4.45-4.80 kg), and especially the Beast Sensor (effect size = 0.36-0.84; r = .50-.68; SEE = 9.44-11.2 kg), was lower. Conclusions: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.
Article
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This aims of this study were (I) to determine the velocity variable and regression model which best fit the load-velocity relationship during the free-weight prone bench pull exercise, (II) to compare the reliability of the velocity attained at each percentage of the one-repetition maximum (1RM) between different velocity variables and regression models, and (III) to compare the within- and between-subject variability of the velocity attained at each %1RM. Eighteen men (14 rowers and four weightlifters) performed an incremental test during the free-weight prone bench pull exercise in two different sessions. General and individual load-velocity relationships were modelled through three velocity variables (mean velocity [MV], mean propulsive velocity [MPV] and peak velocity [PV]) and two regression models (linear and second-order polynomial). The main findings revealed that (I) the general (Pearson's correlation coefficient [r] range = 0.964-0.973) and individual (median r = 0.986 for MV, 0.989 for MPV, and 0.984 for PV) load-velocity relationships were highly linear, (II) the reliability of the velocity attained at each %1RM did not meaningfully differ between the velocity variables (coefficient of variation [CV] range = 2.55-7.61% for MV, 2.84-7.72% for MPV and 3.50-6.03% for PV) neither between the regression models (CV range = 2.55-7.72% and 2.73-5.25% for the linear and polynomial regressions, respectively), and (III) the within-subject variability of the velocity attained at each %1RM was lower than the between-subject variability for the light-moderate loads. No meaningful differences between the within- and between-subject CVs were observed for the MV of the 1RM trial (6.02% vs. 6.60%; CVratio = 1.10), while the within-subject CV was lower for PV (6.36% vs. 7.56%; CVratio = 1.19). These results suggest that the individual load-MV relationship should be determined with a linear regression model to obtain the most accurate prescription of the relative load during the free-weight prone bench pull exercise.
Article
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This aim of this study was to compare the reliability and validity of seven commercially available devices to measure movement velocity during the bench press exercise. Fourteen men completed two testing sessions. The bench press one-repetition maximum (1RM) was determined in the first session. The second testing session consisted of performing three repetitions against five loads (45-55-65-75-85% of 1RM). The mean velocity was simultaneously measured using an optical motion sensing system (Trio-OptiTrack™; “gold-standard”) and seven commercially available devices: 1 linear velocity transducer (T-Force™), 2 linear position transducers (Chronojump™ and Speed4Lift™), 1 camera-based optoelectronic system (Velowin™), 1 smartphone application (PowerLift™), and 2 inertial measurement units (PUSH™ band and Beast™ sensor). The devices were ranked from the most to the least reliable as follows: (I) Speed4Lift™ (coefficient of variation [CV] = 2.61%), (II) Velowin™ (CV = 3.99%), PowerLift™ (3.97%), Trio-OptiTrack™ (CV = 4.04%), T-Force™ (CV = 4.35%), Chronojump™ (CV = 4.53%), (III) PUSH™ band (CV = 9.34%), and (IV) Beast™ sensor (CV = 35.0%). A practically perfect association between the Trio-OptiTrack™ system and the different devices was observed (Pearson’s product-moment correlation coefficient (r) range = 0.947-0.995; P < 0.001) with the only exception of the Beast sensor (r = 0.765; P < 0.001). These results suggest that linear velocity/position transducers, camera-based optoelectronic systems and the smartphone application could be used to obtain accurate velocity measurements for restricted linear movements, while the inertial measurement units used in this study were less reliable and valid.
Article
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This study aimed to compare the accuracy of different velocity-based methods and repetitions-to-failure equations for predicting the one-repetition maximum (i.e., maximum load that can be lifted once; 1RM) during two upper-body pulling exercises. Twenty-three men were tested in two sessions during the lat pulldown and seated cable row exercises. Each session consisted of an incremental loading test until reaching the 1RM followed by a set of repetitions-to-failure against the 80%1RM load. The 1RM was estimated from the individual load-velocity relationships modelled through four (~40, 55, 70, and 85%1RM; multiple-point method) or two loads (~40 and 85%1RM; two-point method). Mean velocity was recorded with a linear position transducer and a smartphone application. Therefore, four velocity-based methods were used as a result of combining the two devices and the two methods. Two repetitions-to-failure equations (Mayhew and Wathan) were also used to predict the 1RM from the load and number of repetitions completed. The absolute differences with respect to the actual 1RM were higher for the repetitions-to-failure equations than velocity-based methods during the seated cable row exercise (P=0.004), but not for the lat pulldown exercise (P=0.200). The repetitions-to-failure equations significantly underestimated the actual 1RM (P<0.05; range: -6.65 to -2.14 kg), while no systematic differences were observed for the velocity-based methods (range: -1.75 to 1.65 kg). All predicted 1RMs were highly correlated with the actual 1RM (r≥0.96). The velocity-based methods provide a more accurate estimate of the 1RM than the Mayhew and Wathan repetitions-to-failure equations during the lat pulldown and seated cable row exercises.
Article
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Purpose: This study examined the relationships between different loading intensities and movement velocities in the bench-press exercise (BP) in Paralympic powerlifters. Methods: Seventeen National Paralympic powerlifters performed maximum dynamic strength tests to determine their BP one-repetition maximum (1RM) in a Smith-machine device. A linear position transducer was used to measure the movement velocity over a comprehensive range of loads. Linear regression analysis was performed to establish the relationships between the different bar-velocities and the distinct percentages of 1RM (%1RM). Results: Overall, the correlations between bar-velocities and %1RM were strong over the entire range of loads (R² values ranged from 0.80 to 0.91), but the precision of the predictive equations (expressed as mean differences [%] between actual and predicted 1RM values) were higher at heavier loading intensities (~20% for loads ≤ 70% 1RM, and ~5% for loads ≥ 70%1RM). In addition, it seems that these very strong athletes (e.g., 1RM relative in the BP = 2.22 ± 0.36 kg.kg-1, for male participants) perform BP 1RM assessments at lower velocities than those previously reported in the literature. Conclusions: The load-velocity relationship was strong and consistent in Paralympic powerlifters, especially at higher loads (≥ 70% 1RM). Therefore, Paralympic coaches can use the predictive equations and the reference values provided here to determine and monitor the BP loading intensity in National Paralympic powerlifters.
Article
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This study aimed to compare the between-session reliability of the load-velocity relationship between (1) linear vs. polynomial regression models, (2) concentric-only vs. eccentric-concentric bench press variants, as well as (3) the within-participants vs. the between-participants variability of the velocity attained at each percentage of the one-repetition maximum (%1RM). The load-velocity relationship of 30 men (age: 21.2±3.8 y; height: 1.78±0.07 m, body mass: 72.3±7.3 kg; bench press 1RM: 78.8±13.2 kg) were evaluated by means of linear and polynomial regression models in the concentric-only and eccentric-concentric bench press variants in a Smith Machine. Two sessions were performed with each bench press variant. The main findings were: (1) first-order-polynomials (CV: 4.39%–4.70%) provided the load-velocity relationship with higher reliability than second-order-polynomials (CV: 4.68%–5.04%); (2) the reliability of the load-velocity relationship did not differ between the concentric-only and eccentric-concentric bench press variants; (3) the within-participants variability of the velocity attained at each %1RM was markedly lower than the between-participants variability. Taken together, these results highlight that, regardless of the bench press variant considered, the individual determination of the load-velocity relationship by a linear regression model could be recommended to monitor and prescribe the relative load in the Smith machine bench press exercise.
Article
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This study aimed (1) to analyze the accuracy of mean propulsive velocity to predict the percentage of the 1-repetition maximum in the seated military press exercise and (2) to test the effect of gender and of a resistance training program on the load–velocity profile. The load–velocity relationships of 26 men and 13 women were evaluated by means of an incremental loading test up to the individual 1-repetition maximum. Additionally, the load–velocity relationships of 24 of those 26 men were measured again after a six-week resistance training program. Individual load–velocity relationships had very high coefficients of determination and low standard errors of the estimate (R2 = 0.987; standard error of the estimate = 0.04 m/s). Differences higher than 10% between the individual and the general load–velocity profiles as well as a high between-participants’ variability for the mean propulsive velocity attained at each 1-repetition maximum (coefficient of variation = 12.9–24.6%) were identified. The load–velocity profiles proved to be affected by both the gender (higher mean propulsive velocity at each %1-repetition maximum for men) and the resistance training program (lower mean propulsive velocity at each %1-repetition maximum after training). Taken together, these results speak in favor of creating individual profiles instead of using general equations when using the load–velocity relationship to estimate relative load.
Article
Traditionally, resistance training intensity has been based upon a percentage of an individual’s 1RM. However, there are numerous shortcomings with this approach, including its failure to consider an athlete’s conditional, day-to-day training readiness. In order to address these limitations, the use of various progressive auto-regulated resistance training protocols has been suggested in the literature. Recent advances in the monitoring of movement velocity offer a unique approach by which to optimise the use of auto regulated resistance training. By matching established acute resistance training variables to specific movement velocities, the strength and conditioning practitioner can optimise resistance training intensity and objectively identify the onset of neuromuscular fatigue.
Article
This study examined the reliability and validity of three methods of estimating the one-repetition maximum (1RM) during the free-weight prone bench pull exercise. Twenty-six men (22 rowers and four weightlifters) performed an incremental loading test until reaching their 1RM, followed by a set of repetitions-to-failure. Eighteen participants were re-tested to conduct the reliability analysis. The 1RM was estimated through the lifts-to-failure equations proposed by Lombardi and O'Connor, general load-velocity (L-V) relationships proposed by Sánchez-Medina and Loturco and the individual L-V relationships modelled using four (multiple-point method) or only two loads (two-point method). The direct method provided the highest reliability (coefficient of variation [CV] = 2.45% and intraclass correlation coefficient [ICC] = 0.97), followed by the Lombardi's equation (CV = 3.44% and ICC = 0.94), and no meaningful differences were observed between the remaining methods (CV range = 4.95-6.89% and ICC range = 0.81-0.91). The lifts-to-failure equations overestimated the 1RM (3.43-4.08%), the general L-V relationship proposed by Sánchez-Medina underestimated the 1RM (-3.77%), and no significant differences were observed for the remaining prediction methods (-0.40-0.86%). The individual L-V relationship could be recommended as the most accurate method for predicting the 1RM during the free-weight prone bench pull exercise.