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The ADR Encoder is a reliable and valid device to measure barbell mean velocity during the Smith machine bench press exercise
Abstract and Figures
Velocity-based training is a contemporary resistance training method, which uses lifting velocity to prescribe and assess the effects of training. However, the high cost of velocity monitoring devices can limit their use among strength and conditioning professionals. Therefore, this study aimed to examine the reliability and concurrent validity of an affordable linear position transducer (ADR Encoder) for measuring barbell velocity during the Smith machine bench press exercise. Twenty-eight resistance-trained males performed two blocks of six repetitions in a single session. Each block consisted of two repetitions at 40%, 60%, and 80% of their estimated one-repetition maximum. The mean velocity of the lifting phase was simultaneously recorded with the ADR Encoder and a gold-standard linear velocity transducer (T-Force® System). Both devices demonstrated high reliability for measuring mean velocity (ADR Encoder: CVrange = 2.80-6.40% and ICCrange = 0.78-0.82; T-Force® System: CVrange = 3.27-6.62% and ICCrange = 0.77-0.81). The ADR Encoder provided mean velocity at 40%1RM with a higher reliability than the T-Force® System (CVratio = 1.17), but the reliability did not differ between devices at higher loads (60-80%1RM) (CVratio ≤ 1.08). No fixed or proportional bias was observed for the different loads using least-products regression analysis, while the Bland-Altman plots revealed low systematic bias (0.01 m·s-1) and random errors (0.03 m·s-1). However, heteroscedasticity of the errors was observed between both devices (R2= 0.103). The high reliability and validity place the ADR Encoder as a low-cost device for accurately measuring mean velocity during the Smith machine bench press exercise.
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Background
Monitoring resistance training has a range of unique difficulties due to differences in physical characteristics and capacity between athletes, and the indoor environment in which it often occurs. Traditionally, methods such as volume load have been used, but these have inherent flaws. In recent times, numerous portable and affordable devices have been made available that purport to accurately and reliably measure kinetic and kinematic outputs, potentially offering practitioners a means of measuring resistance training loads with confidence. However, a thorough and systematic review of the literature describing the reliability and validity of these devices has yet to be undertaken, which may lead to uncertainty from practitioners on the utility of these devices.
Objective
A systematic review of studies that investigate the validity and/or reliability of commercially available devices that quantify kinetic and kinematic outputs during resistance training.
Methods
Following PRISMA guidelines, a systematic search of SPORTDiscus, Web of Science, and Medline was performed; studies included were (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of commercially available portable devices that quantify resistance training exercises.
Results
A total of 129 studies were retrieved, of which 47 were duplicates. The titles and abstracts of 82 studies were screened and the full text of 40 manuscripts were assessed. A total of 31 studies met the inclusion criteria. Additional 13 studies, identified via reference list assessment, were included. Therefore, a total of 44 studies were included in this review.
Conclusion
Most of the studies within this review did not utilise a gold-standard criterion measure when assessing validity. This has likely led to under or overreporting of error for certain devices. Furthermore, studies that have quantified intra-device reliability have often failed to distinguish between technological and biological variability which has likely altered the true precision of each device. However, it appears linear transducers which have greater accuracy and reliability compared to other forms of device. Future research should endeavour to utilise gold-standard criterion measures across a broader range of exercises (including weightlifting movements) and relative loads.
This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity profiles (40-100% 1RM) in both exercises on four separate occasions. Mean and peak velocity was measured simultaneously on each device and compared to 3D motion capture for all repetitions. Reliability was assessed via coefficient of variation (CV) and typical error (TE). Least products regression (LPR) (R 2) and limits of agreement (LOA) assessed the validity of the devices. The Gymaware was the most reliable for both exercises (CV < 10%; TE < 0.11 m·s −1 , except 100% 1RM (mean velocity) and 90-100% 1RM (peak velocity)), with MyLift and PUSH following a similar trend. Poorer reliability was observed for Beast Sensor and Bar Sensei (CV = 5.1%-119.9%; TE = 0.08-0.48 m·s -1). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R 2 > 0.42-0.99 LOA = −0.03-0.03 m·s −1). Comparable validity data was observed for MyLift in the back squat. Both PUSH devices produced some fixed and proportional bias, with Beast Sensor and Bar Sensei being the least valid devices across both exercises (R 2 > 0.00-0.96, LOA = −0.36-0.46 m·s −1). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity.
Velocity-based training (VBT) is a contemporary method of resistance training that enables accurate and objective prescription of resistance training intensities and volumes. This review provides an applied framework for the theory and application of VBT. Specifically, this review gives detail on how to: use velocity to provide objective feedback, estimate strength, develop load-velocity profiles for accurate load prescription, and how to use statistics to monitor velocity. Furthermore, a discussion on the use of velocity loss thresholds, different methods of VBT prescription, and how VBT can be implemented within traditional programming models and microcycles is provided.
The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s⁻¹. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L⁻¹), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%<20%<10%. Standard deviations representing four-week variability of post-set responses to each protocol were: dRPE, 8–11; B[La], 0.8–1.0; CMJ height, 1.6–2.0; CMJ PPO, 1.0–1.8; CMJ PCV, 0.04–0.06; CMJ 100ms-Impulse, 5.7–11.9. Velocity loss thresholds control the magnitude of perceptual, metabolic, and neuromuscular responses to resistance training. For practitioners wanting to reliably prescribe training that can induce a given perceptual, metabolic, or neuromuscular response, it is strongly advised that velocity-based thresholds are implemented.
This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
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.
The objective of this study was to explore the reliability and concurrent validity of the Velowin optoelectronic system to measure movement velocity during the free-weight back squat exercise. Thirty-one men (age = 27.5 ± 3.2 years; body height = 1.76 ± 0.15 m; body mass: 78.3 ± 7.6 kg) were evaluated in a single session against five different loads (20, 40, 50, 60 and 70 kg) and three velocity variables (mean velocity [MV], mean propulsive velocity [MPV] and maximum velocity [Vmax]) were recorded simultaneously by a linear velocity transducer (T-Force; gold-standard) and a camera-based optoelectronic system (Velowin). The main findings revealed that (1) the three velocity variables were determined with a high and comparable reliability by both the T-Force and Velowin systems (median coefficient of variation of the five loads: T-Force: MV = 4.25%, MPV = 4.49% and Vmax = 3.45%; Velowin: MV = 4.29%, MPV = 4.60% and Vmax = 4.44%), (2) the Vmax was the most reliable variable when obtained by the T-force (p < 0.05), but no significant differences in the reliability of the variables were observed for the Velowin (p > 0.05), and (3) high correlations were observed for the values of MV (r = 0.976), MPV (r = 0.965) and Vmax (r = 0.977) between the T-Force and Velowin systems. Collectively, these results support the Velowin as a reliable and valid system for the measurement of movement velocity during the free-weight back squat exercise.
Purpose:
This study aimed to compare the load-velocity relationship between four variants of the bench press (BP) exercise.
Methods:
The full load-velocity relationship of 30 men were evaluated by means of an incremental loading test starting at 17 kg progressing to the individual one-repetition maximum (1RM) in four BP variants: concentric-only BP, concentric-only bench press throw (BPT), eccentric-concentric BP, and eccentric-concentric BPT.
Results:
A strong and fairly linear relationship between mean velocity (MV) and %1RM was observed for the four BP variants (r(2) > 0.96 for pooled data and r(2) > 0.98 for individual data). The MV associated with each %1RM was significantly higher in the eccentric-concentric technique compared to the concentric-only technique. The only significant difference between the BP and BPT variants was the higher MV with the light-moderate loads (20%1RM-70%1RM) in the BPT using the concentric-only technique. Mean test velocity was significantly and positively correlated between the four BP variants (r = 0.44-0.76), which suggests that the subjects with higher velocities for each %1RM in one BP variant also tend to have higher velocities for each %1RM in the three remaining BP variants.
Conclusions:
these results highlight (1) the need for obtaining specific equations for each BP variant, and (2) the existence of individual load-velocity profiles.
Previous studies have revealed that the velocity of the bar can be used to determine the intensity of different resistance training exercises. However, the load-velocity relationship seems to be exercise dependent. This study aimed to compare the load- velocity relationship obtained from two variations of the half-squat exercise (traditional vs. ballistic) using two execution techniques (eccentric-concentric vs. concentric-only). Twenty men performed a submaximal progressive loading test in four half-squat exercises: eccentric-concentric traditional-squat, concentric-only traditional-squat, countermovement jump (i.e. ballistic squat using the eccentric-concentric technique), and squat jump (i.e. ballistic squat using the concentric-only technique). Individual linear regressions were used to estimate the one-repetition maximum (1RM) for each half-squat exercise. Thereafter, another linear regression was applied to establish the relationship between relative load (%RM) and mean propulsive velocity (MPV). For all exercises, a strong relationship was observed between %RM and MPV: eccentric- concentric traditional-squat (R2 = 0.949), concentric-only traditional-squat (R2 = 0.920), countermovement jump (R2 = 0.957), and squat jump (R2 = 0.879). The velocities associated with each %RM were higher for the ballistic variation and the eccentric-concentric technique than for the traditional variation and concentric-only technique, respectively. Differences in velocity among the half-squat exercises decreased with the increment in the relative load. These results demonstrate that the MPV can be used to predict exercise intensity in the four half-squat exercises. However, independent regressions are required for each half-squat exercise since the load-velocity relationship proved to be task specific.
Keywords: velocity-based training; traditional; ballistic; jump squat; eccentric-concentric technique; concentric-only technique.
The purpose of this study was to analyse the validity and reliability of a novel iPhone app (named: PowerLift) for the measurement of mean velocity on the bench-press exercise. Additionally, the accuracy of the estimation of the 1-Repetition maximum (1RM) using the load–velocity relationship was tested. To do this, 10 powerlifters (Mean (SD): age = 26.5 ± 6.5 years; bench press 1RM · kg−1 = 1.34 ± 0.25) completed an incremental test on the bench-press exercise with 5 different loads (75–100% 1RM), while the mean velocity of the barbell was registered using a linear transducer (LT) and Powerlift. Results showed a very high correlation between the LT and the app (r = 0.94, SEE = 0.028 m · s−1) for the measurement of mean velocity. Bland–Altman plots (R2 = 0.011) and intraclass correlation coefficient (ICC = 0.965) revealed a very high agreement between both devices. A systematic bias by which the app registered slightly higher values than the LT (P < 0.05; mean difference (SD) between instruments = 0.008 ± 0.03 m · s−1). Finally, actual and estimated 1RM using the app were highly correlated (r = 0.98, mean difference (SD) = 5.5 ± 9.6 kg, P < 0.05). The app was found to be highly valid and reliable in comparison with a LT. These findings could have valuable practical applications for strength and conditioning coaches who wish to measure barbell velocity in the bench-press exercise.
The purpose of this study was to analyze the validity and reliability of a wearable device to measure movement velocity during the back squat exercise. To do this, 10 recreationally active healthy males (age=23.4±5.2yrs.; back squat 1-repetition maximum =83±8.2kg) performed 3 repetitions of the back squat exercise with 5 different loads ranging from 25-85% 1-RM on a Smith Machine. Movement velocity for each of the total 150 repetitions was simultaneously recorded using the T-Force linear transducer (LT) and the PUSHTM wearable band. Results showed a high correlation between the LT and the wearable device mean (r=0.85; SEE=0.08m/s) and peak velocity (r=0.91, SEE=0.1m/s). Moreover, there was a very high agreement between these two devices for the measurement of mean (ICC=0.907) and peak velocity (ICC=0.944), although a systematic bias between devices was observed (PUSHTM peak velocity being -0.07±0.1m/s lower, p<0.05). When measuring the 3 repetitions with each load, both devices displayed almost equal reliability (Test-retest reliability: LT [r = 0.98], PUSHTM [r = 0.956]; Intraclass correlation coefficient: LT [ICC = 0.989], PUSHTM [ICC = 0.981]; Coefficient of variation: LT [CV = 4.2%, PUSHTM [CV = 5.0%]). Finally, individual load-velocity relationships measured with both the LT (R2 = 0.96) and the PUSHTM wearable device (R2 = 0.94) showed similar, very high coefficients of determination. In conclusion, these results support the use of an affordable wearable device to track velocity during back squat training. Wearable devices, such as the one in this study, could have valuable practical applications for strength & conditioning coaches.
The study's purpose was to compare the response of performing 1, 3 and 5-sets on measures of performance and muscle hypertrophy. Forty eight men, with no weight training experience, were randomly assigned to one of three training groups, 1-SET, 3-SETS, 5-SETS, or control group (CG). All training groups performed three resistance training sessions per week for six months. The 5RM for all training groups increased in the bench press (BP), front lat pull down (LPD), shoulder press (SP) and leg press (LP) (p≤0.05), with the 5RM increases in the BP and LPD being significantly greater for 5-SETS compared to the other training groups (p ≤ 0.05). BP 20RM in the 3- and 5-SETS groups significantly increased with the increase being significantly greater than the 1-SET group and the 5-SETS group increase being significantly greater than the 3-SETS group (p≤0.05). LP 20RM increased in all training groups (p≤0.05), with the 5-SETS group showing a significantly greater increase than the 1-SET group (p≤0.05). The 3- and 5-SETS groups significantly increased elbow flexor muscle thickness (MT) with the 5-SETS increase being significantly greater than the other two training groups (p≤0.05). The 5-SETS group significantly increased elbow extensor MT with the increase being significantly greater than the other training groups (p≤0.05). All training groups decreased percent body fat, increased fat free mass and vertical jump ability (p≤0.05), with no differences between groups. The results demonstrate a dose response for the number of sets per exercise and a superiority of multiple sets compared to a single set per exercise for strength gains, muscle endurance and upper arm muscle hypertrophy.
Analysis of variability and progression in performance of top athletes between competitions provides information about performance targets that is useful for athletes, practitioners, and researchers. In this study, 724 official finals times were analysed for 120 male and 122 female Paralympic swimmers in the 100-m freestyle event at 15 national and international competitions between 2004 and 2006. Separate analyses were performed for males and females in each of four Paralympic subgroups: S2-S4, S5-S7, S8-S10 (most through least physically impaired), and S11-S13 (most through least visually impaired). Mixed modelling of log-transformed times, with adjustment for mean competition times, was used to estimate variability and progression. Within-swimmer race-to-race variability, expressed as a coefficient of variation, ranged from 1.2% (male S5-S7) to 3.7% (male S2-S4). Swimming performance progressed by approximately 0.5% per year for males and females. Typical variation in mean performance time between competitions was approximately 1% after adjustment for the ability of the athletes in each competition, and the Paralympic Games was the fastest competition. Thus, taking into account variability, progression, and level of competition, Paralympic swimmers who want to increase substantially their medal prospects should aim for an annual improvement of at least 1-2%.
Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
BACKGROUND: More practical and less fatiguing strategies have been developed to accurately predict the one-repetition maximum (1RM). OBJECTIVE: To compare the accuracy of the estimation of the free-weight bench press 1RM between six velocity-based 1RM prediction methods. METHODS: Sixteen men performed an incremental loading test until 1RM on two separate occasions. The first session served to determine the minimal velocity threshold (MVT). The second session was used to determine the validity of the six 1RM prediction methods based on 2 repetition criteria (fastest or average velocity) and 3 MVTs (general MVT of 0.17 m×s-1 , individual MVT of the preliminary session, and individual MVT of the validity session). Five loads (≈25-40-55-70-85% of 1RM) were used to assess the individualized load-velocity relationships. RESULTS: The absolute difference between the actual and predicted 1RM were low (range=2.7-3.7%) and did not reveal a significant main effect for repetition criterion (P=0.402), MVT (P=0.173) or their two-way interaction (P=0.354). Furthermore, all 1RM prediction methods accurately estimated bench press 1RM (P³0.556; ES£0.02; r³0.99). CONCLUSIONS: The individualized load-velocity relationship provides an accurate prediction of the 1RM during the free-weight bench press exercise, while the repetition criteria and MVT do not appear to meaningfully affect the prediction accuracy.
This study aimed to determine whether the verbal provision of velocity performance feedback during the free-weight bench press (BP) exercise influences (I) the within-session reliability and magnitude of mean concentric velocity (MCV) values recorded against a range of submaximal loads, and (II) the accuracy of the individualized load-velocity profile to estimate the BP 1-repetition maximum (1RM). Fifteen males (BP 1RM relative to body mass = 1.08 ± 0.22) performed an incremental loading test until reaching the 1RM on 2 separate sessions. Subjects received verbal velocity performance feedback in one session (KR) and no KR was provided in another session (Control). A linear velocity transducer was used to collect the MCV against 4 loads (40-55-70-85%1RM), and the BP 1RM was estimated from the individualized load-velocity relationship modeled through the multiple-point (40-55-70-85%1RM) and 2-point methods (40-85%1RM). The KR condition provided a higher reliability (CV: KR = 2.41%, Control = 3.54%; CV ratio = 1.47) and magnitude (P = 0.001; ES = 0.78) of MCV for the 40%1RM, but no significant differences in reliability (CV ratio 1.15) nor in the magnitude (P 0.058; ES range = 0.00-0.32) were observed for higher loads. The accuracy in the estimation of the 1RM was higher for the KR (absolute errors: multiple-point = 3.1 2.3 kg; 2-point = 3.5 2.1 kg) compared to the Control condition (absolute errors: 4.1 1.9 kg for both multiple-point and 2-point methods). These results encourage the provision of verbal velocity performance feedback during BP testing procedures.
The aim of this study was to assess the reliability and validity of the bar-mounted PUSH BandTM 2.0 to determine peak and mean velocity during the bench press exercise with a moderate (60% one repetition maximum [1RM]) and heavy (90% 1RM) load. We did this by simultaneously recording peak and mean velocity using the PUSH BandTM 2.0 and three-dimensional motion capture from participants bench pressing with 60% and 90% 1RM. We used ordinary least products regression to assess within-session reliability and whether the PUSH BandTM 2.0 could accurately predict motion capture velocity. Results showed that PUSH BandTM 2.0 and motion capture peak and mean velocity reliability was acceptable with both loads. While there was a tendency for the PUSH BandTM 2.0 to slightly overestimate peak and mean velocity, there was no fixed bias. However, mean velocity with 60 and 90% 1RM demonstrated proportional bias (differences between predicted and motion capture values increase with magnitude). Therefore, PUSH BandTM 2.0 peak velocity with 60 and 90% 1RM is valid, but mean velocity is not.
Miller, RM, Freitas, EDS, Heishman, AD, Koziol, KJ, Galletti, BAR, Kaur, J, and Bemben, MG. Test-retest reliability between free weight and machine-based movement velocities. J Strength Cond Res XX(X): 000-000, 2018-Several devices are available to measure muscular power through velocity measurement, including the Tendo FitroDyne. The ability for such devices to produce consistent results is still questioned, and the reproducibility of measurement between free weight and machine exercise has yet to be examined. Therefore, the aim of this investigation was to determine the test-retest reliability for barbell velocity during the bench press (BP) and weight velocity during the 2 leg press (2LP) for loads corresponding to 20-80% of 1 repetition maximum (1RM). Forty recreationally active individuals (22.6 ± 2.5 years; 175.9 ± 10.8 cm; and 76.2 ± 13.2 kg) with a 1RM BP and 2LP of 66.8 ± 32.4 kg and 189.5 ± 49 kg, respectively, volunteered for this study. Subjects completed 1 familiarization visit preceding 3 testing visits, which encompassed 1RM determination and 2 days of velocity testing. Forty-eight hours after 1RM testing, the subjects performed 1 repetition at 20, 30, 40, 50, 60, 70, and 80% of their 1RM for each exercise in randomized order. Subjects returned to the laboratory 1 week later to perform the velocity assessment again in randomized order. Intraclass correlation coefficient (ICC2,1) and relative SEM for the BP and 2LP ranged from 0.56 to 0.98 (3-18.1%) and 0.78 to 0.98 (2.8-7.2%), respectively, and no mean differences were observed between trials. The results suggest high reliability for BP velocity between 30 and 60% 1RM and moderate reliability at 20, 70, and 80% 1RM, while the 2LP displayed high to excellent reliability from 20 to 80% 1RM. Cumulatively, machine-based exercise displayed greater reproducibility; however, additional machine exercises need to be examined to bolster this conclusion.
1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error.
2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis.
3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs.
4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate.
5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines.
6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions.
This study aimed to analyze the acute mechanical and metabolic response to resistance exercise protocols (REP) differing in the number of repetitions (R) performed in each set (S) with respect to the maximum predicted number (P).
Over 21 exercise sessions separated by 48-72 h, 18 strength-trained males (10 in bench press (BP) and 8 in squat (SQ)) performed 1) a progressive test for one-repetition maximum (1RM) and load-velocity profile determination, 2) tests of maximal number of repetitions to failure (12RM, 10RM, 8RM, 6RM, and 4RM), and 3) 15 REP (S × R[P]: 3 × 6[12], 3 × 8[12], 3 × 10[12], 3 × 12[12], 3 × 6[10], 3 × 8[10], 3 × 10[10], 3 × 4[8], 3 × 6[8], 3 × 8[8], 3 × 3[6], 3 × 4[6], 3 × 6[6], 3 × 2[4], 3 × 4[4]), with 5-min interset rests. Kinematic data were registered by a linear velocity transducer. Blood lactate and ammonia were measured before and after exercise.
Mean repetition velocity loss after three sets, loss of velocity pre-post exercise against the 1-m·s load, and countermovement jump height loss (SQ group) were significant for all REP and were highly correlated to each other (r = 0.91-0.97). Velocity loss was significantly greater for BP compared with SQ and strongly correlated to peak postexercise lactate (r = 0.93-0.97) for both SQ and BP. Unlike lactate, ammonia showed a curvilinear response to loss of velocity, only increasing above resting levels when R was at least two repetitions higher than 50% of P.
Velocity loss and metabolic stress clearly differs when manipulating the number of repetitions actually performed in each training set. The high correlations found between mechanical (velocity and countermovement jump height losses) and metabolic (lactate, ammonia) measures of fatigue support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training.
The purpose of this investigation was to observe changes in maximal upper-body strength and power and shifts in the load-power curve across a multiyear period in experienced resistance trainers. Twelve professional rugby league players who regularly performed combined maximal strength and power training were observed across a 4-year period with test data reported every 2 years (years 1998, 2000, and 2002). Upper-body strength was assessed by the 1 repetition maximum bench press and maximum power during bench press throws (BT Pmax) with various barbell resistances of 40-80 kg. During the initial testing, players also were identified as elite (n = 6) or subelite (n = 6), depending upon whether they participated in the elite first-division national league or second-division league. This subgrouping allowed for a comparison of the scope of changes dependent upon initial strength and training experience. The subelite group was significantly younger, less strong, and less powerful than the elite group, but no other difference existed in height or body mass in 1998. Across the 4-year period, significant increases in strength occurred for the group as a whole and larger increases were observed for the subelite than the elite group, verifying the limited scope that exists for strength gain in more experienced, elite resistance-trained athletes. A similar trend occurred for changes in BT Pmax. This long-term observation confirms that the rate of progress in strength and power development diminishes with increased strength levels and resistance training experience. Furthermore, it also indicates that strength and power can still be increased despite a high volume of concurrent resistance and endurance training.
Dose-response of 1, 3, and 5 sets of resistance exercise on strength, local muscular endurance, and hypertrophy
- R Radaelli
- Fleck
- Sj
- T Leite
Test-retest reliability between free weight and machine-based movement velocities
- R M Miller
- Freitas
- Eds
- Heishman
- Ad