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Validity and Test-Retest Reliability of the Vmaxpro Sensor for Evaluation of Movement Velocity in the Deep Squat
Abstract
We aimed at assessing the validity and test-retest reliability of the inertial measurement unit-based Vmaxpro sensor compared with a Vicon 3D motion capture system and the T-Force sensor during an incremental 1-repetition maximum (1RM) test and at submaximal loads. Nineteen subjects reported to the laboratory for the 1RM test sessions, whereas 15 subjects carried out another 3 sessions consisting of 3 repetitions with 4 different intensities (30, 50, 70, and 90% of 1RM) to determine the intra- and interday reliability. The Vmaxpro sensor showed high validity (Vicon: R2 = 0.935; T-Force: R2 = 0.968) but an overestimation of the mean velocities (MVs) of 0.06 ± 0.08 m·s−1 and 0.06 ± 0.06 m·s−1 compared with Vicon and T-Force, respectively. Regression analysis indicated a systematic bias that is increasing with higher MVs. The intraclass correlation coefficients (ICCs) for Vmaxpro were moderate to high for intraday (ICC: 0.662–0.938; p ≤ 0.05) and for interday (ICC: 0.568–0.837; p ≤ 0.05) reliability, respectively. The Vmaxpro is a valid and reliable measurement device that can be used to monitor movement velocities within a training session. However, practitioners should be cautious when assessing movement velocities on separate days because of the moderate interday reliability.
... Thus, the validity of commercially available IMUs for VBT monitoring is still discussed in the field. Multiple studies have assessed the validity of different IMU-based VBT devices such as the Beast Sensor (Beast Technologies S.r.l., Brescia, Italy), VmaxPro (BM Sports Technology GmbH, Magdeburg, Germany) and Push Band 2 (Whoop, Boston, MA, USA) [11,12,[17][18][19] in a variety of free weight and Smith machine-supported barbell exercises. No definitive conclusion has yet been drawn, thus highlighting the need for further investigation. ...
... We assessed its performance during a VBT protocol on healthy, recreational RT athletes by comparing the resulting velocity data of the free-weighted back squat from two different watch positions (i.e., wrist and barbell), compared with Vicon (Vicon 3D Motion Systems, Oxford, UK) as a criterion [22]. As a secondary objective and for a comprehensive and practically oriented assessment, we further compared the results from the Apple Watch with a commercially available IMUbased VBT device, namely the Enode Pro device (Blaumann and Meyer Sports Technology UG, Magdeburg, Germany), formerly known as Vmaxpro [12,19,23,24]. Our aim was to provide scientifically robust, praxis-oriented insights into the validity of the Apple Watch for monitoring barbell kinematics during VBT, given its widespread popularity as a consumer electronic device and the growing interest in VBT among athletes and recreational exercisers. ...
... To our knowledge, our study is the first to use the IMU sensors of a commercially available smartwatch in a VBT validation study. Furthermore, only a few studies to date have assessed the accuracy of the Enode Pro device across different loads and exercises (including the back squat) compared to the Apple Watch and Vicon as the criterion [12,19,23]. ...
Velocity-based training (VBT) is a method to monitor resistance training based on measured kinematics. Often, measurement devices are too expensive for non-professional use. The purpose of this study was to determine the accuracy and precision of the Apple Watch 7 and the Enode Pro device for measuring mean, peak, and propulsive velocity during the free-weighted back squat (in comparison to Vicon as the criterion). Velocity parameters from Vicon optical motion capture and the Apple Watch were derived by processing the motion data in an automated Python workflow. For the mean velocity, the barbell-mounted Apple Watch (r = 0.971–0.979, SEE = 0.049), wrist-worn Apple Watch (r = 0.952–0.965, SEE = 0.064) and barbell-mounted Enode Pro (r = 0.959–0.971, SEE = 0.059) showed an equal level of validity. The barbell-mounted Apple Watch (Vpeak: r = 0.952–0.965, SEE = 0.092; Vprop: r = 0.973–0.981, SEE = 0.05) was found to be the most valid for assessing propulsive and peak lifting velocity. The present results on the validity of the Apple Watch are very promising, and may pave the way for the inclusion of VBT applications in mainstream consumer wearables.
... p ≤ 0.05) and inter-day reliability (ICC: 0.568-0.837; p ≤ 0.05) for the evaluation of mean velocity (MV) in the deep squat (SQ) [10]. The data, however, were obtained in a Smith machine with a guided barbell pathway. ...
... In line with a previous study [10], we found a slight overestima- Obviously, a justification for this cannot be given but it needs to be noted that the manufacturer's user manual specifies that only MVs > 0.15 m · s −1 are detected by the sensor. In our data, however, this was only the case for 9 of the 36 non-acquired data points. ...
We investigated the ecological validity of an inertial measurement unit (IMU) (Vmaxpro) to assess the movement velocity (MV) during a 1-repetition maximum (1RM) test and for the prediction of load-velocity (L-V) variables, as well as the ecological intra-day and inter-day reliability during free-weight bench press (BP)
and squat (SQ). Furthermore, we provide recommendations for the practical use of the sensor. Twenty-three strength-trained men completed an incremental 1RM test, whereas seventeen men further participated in another 3 sessions consisting of 3 repetitions with 4 different loads (30, 50, 70 and 90% of 1RM) to assess validity and
intra- and inter‑day reliability, respectively. The MV was assessed using the Vmaxpro and a 3D motion capture system (MoCap). L-V variables and the 1RM were calculated based on submaximal velocities. The Vmaxpro showed high validity during the 1RM test for BP (r = 0.935) and SQ (r = 0.900), but with decreasing validity at lower MVs. The L-V variables and the 1RM demonstrated high validity for BP (r = 0.808–0.942) and SQ (r = 0.615–0.741) with a systematic overestimation. Coefficients of variance for intra- and inter-day reliability ranged from 2.4% to 9.7% and from 3.2%to 8.6% for BP and SQ, respectively. The Vmaxpro appears valid at
high and moderately valid at low MVs. Depending on the required degree of accuracy, the sensor may be sufficient for the prediction of L-V variables and the 1RM. Our data indicate the sensor to be suitable for monitoring changes in MVs within and between training sessions.
... More recently, two wearable, wireless, IMU-based velocity monitoring devices PUSH2 (PUSH Inc., Toronto, ON, Canada) and Vmaxpro (alias EnodePro; Blaumann & Meyer-Sports Technology UG, Magdeburg, Germany) have grown in popularity among athletes and recreational trainees due to their versatility and relatively affordable price. Several studies [15][16][17] have examined the test-retest reliability of PUSH2 and Vmaxpro devices with free-weight exercises. However, it is important to highlight that the test-retest reliability assessment inevitably contains errors due to biological variation. ...
... However, it should be noted that the validity of these devices was not evaluated in the current study due to the absence of a gold standard (i.e., the MOCAP system). In addition, the validity of GymAware, PUSH2 and Vmaxpro devices has been examined in previous studies using different approaches with conflicting findings reported, especially for PUSH2 and Vmaxpro devices 16,17 . Therefore, future research is required to comprehensively examine the validity of these devices. ...
This study examined the reproducibility of GymAware, PUSH2 and Vmaxpro velocity monitoring devices during resistance training (RT). The sensitivity of these devices to detect the smallest changes in velocity that correspond to true changes in RT performance was also investigated. Fifty-one resistance-trained men and women performed an incremental loading (1RM) test, and two repetitions to failure tests with different loads, 72 h apart. During all repetitions, mean velocity (MV) and peak velocity (PV) were simultaneously recorded by two devices of each brand. Overall, GymAware was the most reliable and sensitive device for detecting the smallest changes in RT performance, regardless of the velocity metric used. Vmaxpro can be considered as an equivalent, cheaper alternative to GymAware for RT monitoring and prescription, but only if the MV metric is used. Caution should be exercised when using PUSH2 in practice due to their comparatively higher, unacceptable measurement error and generally low sensitivity to detect changes in RT performance. Collectively, these findings support the use of MV and PV from GymAware and MV from Vmaxpro devices for RT monitoring and prescription due to their low magnitudes of error; thus, allowing for the detection of meaningful changes in neuromuscular status and functional performance during RT.
... The MVs were recorded consistently using the Vmaxpro sensor (Blaumann & Meyer -Sports Technology UG, Magdeburg, Germany) (ecological reliability tested in our laboratory, ICC: 0.708-0.911) (13). Subjects performed 3 trials each, of which only the fastest was used for statistical analyses. ...
The purpose of this study was to determine the acute effects of handball-specific high-intensity interval training (HIIT) on explosive strength and throwing velocity, after varying periods of recovery. Fourteen highly trained male handball players (age: 26.2±4.2 years) performed HIIT consisting of repeated 15-seconds shuttle runs at 90% of final running speed (VIFT) to exhaustion. Upper and lower body explosive strength and throwing velocities were measured before and immediately after HIIT, as well as after 6 hours. These tests included 3 repetitions of both bench press and squat exercise at 60% of the one repetition maximum (1RM) as well as three repetitions of the set shot without run up (SS) and jump shot (JS), respectively. Explosive squat performance was significantly reduced at post (-5.48%, p = 0.026) but not at 6h (-0.24%, p = 1.000). Explosive bench press performance remained statistically unaltered at post (0.32%, p = 1.000) and at 6h (1.96%, p = 1.000). This was also observed in the subsequent throws both immediately post (-0.60%, p = 1.000) (-0.31%, p = 1.000) and at 6h (-1.58%, p = 1.000) (1.51%, p = 0.647). Our data show a reduction in explosive strength of the lower but not upper extremities when preceded by running HIIT. Since throwing velocity was not affected by intense lower body exercise, combining lower body HIIT and throwing practice may be of no concern in highly trained handball players.
The purpose of this investigation was to determine the validity and reliability of the Humac360 linear position transducer (LPT) as compared to Tendo Weightlifting Analyzer. Seventeen recreationally active men and women completed three visits. Visit one included maximal strength assessments via one-repetition maximum (1RM) for the barbell back squat. On visits two and three, participants completed two sets of three repetitions at 30-, 50-, 60-, and 70% 1RM. Mean Concentric Velocity (MCV), Peak Velocity (PV), Displacement (D), and Duration (T) were collected. Repetition data agreement was assessed with Intraclass Correlation Coefficients (ICCs) and were categorized as poor (<0.50), moderate (0.50-0.75), good (0.75-0.90), and excellent (>0.90). Significance was accepted at an alpha (p) value < 0.05. Repetition-to-repetition comparisons between devices demonstrate varying degrees of agreement, with significant differences between devices across all intensities and all measurements (p < 0.001). Inter-set reliability was excellent for MCV, PV, D, and T with the exceptions of MCV and PV at 70% 1RM (ICC 2,k = 0.548 and 0.816). Inter-session reliability data demonstrated reduced agreeableness in an intensity-dependent manner, with ICCs decreasing and SEMs increasing with increases in intensity. The Humac360 LPT does not appear to be valid when compared to the criterion method, though we contend it maintains construct validity. Coaches may use the Humac360 LPT as a tool to monitor fatigue, and the associated changes in trainee movement velocity on an inter-set and inter-session basis.
Velocity-based training (VBT) is an increasingly popular programming strategy used by strength and conditioning professionals to develop their athlete's ability to express force rapidly. To implement the varying forms of VBT effectively within their training regimes, strength and conditioning professionals need to understand the strengths and weaknesses of strategies, such as predicting 1 repetition maximum using the load-velocity profile, modulating training loads using the load-velocity profile, and controlling training volume using the magnitude of velocity-loss. The aim of this review was to highlight these strengths and weaknesses and then provide practical examples of when each programming strategy may be most effectively implemented.
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This study examined the reproducibility of GymAware, PUSH2 and Vmaxpro velocity monitoring devices during resistance training (RT). The sensitivity of these devices to detect the smallest changes in velocity that correspond to true changes in RT performance was also investigated. Fifty-one resistance-trained people performed an incremental loading (1RM) test, and two repetitions to failure (RTF) tests with different loads, 72 hours apart. During all repetitions, mean velocity (MV) and peak velocity (PV) were simultaneously recorded by two devices of each brand. Overall, GymAware was the most reliable and sensitive device for detecting the smallest changes in RT performance, regardless of the velocity metric used. Vmaxpro can be considered as an equivalent, cheaper alternative to GymAware for RT monitoring and prescription, but only if the MV metric is used. Caution should be exercised when using PUSH2 in practice due to their comparatively higher, unacceptable measurement error and generally low sensitivity to detect changes in RT performance. Collectively, these findings support the use of MV and PV from GymAware and MV from Vmaxpro devices for RT monitoring and prescription due to their low magnitudes of error; thus, allowing for sensible detection of meaningful changes in neuromuscular status and functional performance during RT.
To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams.
In the linear regression analyses the smallest coefficient of determination (R ² .) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.
This study aimed to compare the effects of three resistance training (RT) programs differing in the magnitude of velocity loss (VL) allowed in each exercise set: 10%, 30% or 45% on changes in strength, vertical jump, sprint performance and EMG variables. Thirty‐three young men were randomly assigned into three experimental groups (VL10%, VL30% and VL45%; n=11 each) that performed a velocity‐based RT program for 8 weeks using only the full‐squat exercise (SQ). Training load (55‐70% 1RM), frequency (2 sessions/week), number of sets (3) and inter‐set recovery (4 min) were identical for all groups. Running sprint (20 m), countermovement jump (CMJ), 1RM, muscle endurance and EMG during SQ were assessed pre‐ and post‐training. All groups showed significantly (VL10%: 6.4‐58.6%; VL30%: 4.5‐66.2%; VL45%: 1.8‐52.1%; p<0.05‐0.001) improvements in muscle strength and muscle endurance. However, a significant group×time interaction (p<0.05) was observed in CMJ, with VL10% showing greater increments (11.9%) than VL30% and VL45%. In addition, VL10% resulted in greater percent change in sprint performance than the other two groups (VL10%: ‐2.4%; VL30%: ‐1.8%; VL45%: ‐0.5%). No significant changes in EMG variables were observed for any group. RT with loads of 55‐70% 1RM characterized by a low velocity loss (VL10%) provides a very effective and efficient training stimulus since it yields similar strength gains and greater improvements in sports‐related neuromuscular performance (jump and sprint) compared to training with higher velocity losses (VL30%, VL45%). These findings indicate that the magnitude of VL reached in each exercise set considerably influences the observed training adaptations.
The use of inertial measurement unit (IMU) has become popular in sports assessment. In the case of velocity-based training (VBT), there is a need to measure barbell velocity in each repetition. The use of IMUs may make the monitoring process easier; however, its validity and reliability should be established. Thus, this systematic review aimed to (1) identify and summarize studies that have examined the validity of wearable wireless IMUs for measuring barbell velocity and (2) identify and summarize studies that have examined the reliability of IMUs for measuring barbell velocity. A systematic review of Cochrane Library, EBSCO, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 161 studies initially identified, 22 were fully reviewed, and their outcome measures were extracted and analyzed. Among the eight different IMU models, seven can be considered valid and reliable for measuring barbell velocity. The great majority of IMUs used for measuring barbell velocity in linear trajectories are valid and reliable, and thus can be used by coaches for external load monitoring.
We explored the test-retest reliability of velocity and power assessed by the GymAware PowerTool system (GYM) in the deadlift and squat by simulating a context with and without a familiarization session. Sixteen resistance-trained individuals completed three testing sessions. In all sessions, velocity and power were assessed by the GYM system in the deadlift and squat exercises with loads of 30, 45, 60, 75, and 90% of one-repetition maximum. The consistency of test results between the first session and the second session was considered to represent the reliability with no familiarization session. The consistency of test results between the second session and the third session was considered to represent the reliability with one familiarization session because the first session simulates a familiarization session. Intraclass correlation coefficients (ICCs) ranged 0.63-0.99 in the deadlift, and 0.78-0.99 in the squat. ICCs were higher than 0.75 for 93 and 100% of all deadlift and squat tests, respectively. For velocity and power, standard error of measurement ranged 0.03-0.08 m/s and 20-176 W, respectively. The coefficient of variation ranged 2.2-10.6% for the deadlift and 2.6-6.9% for the squat tests. Except for peak and mean velocity at 30% of 1RM in the squat, we found no significant improvements in reliability with a familiarization session. The test-retest reliability of velocity and power assessed by the GYM system was moderate-to-excellent for the deadlift and good-to-excellent for the squat. Reliability of velocity and power did not seem to improve with a familiarization session.
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 study aims were to analyze the reliability and validity of the GymAware ™ linear position transducer’s velocity and power measures during the sit-to-stand, compared with the Dartfish 2D videography analysis, and to assess the relationship of age and handgrip strength with velocity and power in 48 older men and women (77.6 ± 11.1 years). The results showed excellent agreement between GymAware- and Dartfish-derived sit-to-stand velocity (intraclass correlation coefficient 2-1 = .94 and power intraclass correlation coefficient 2-1 = .98) measures. A moderate and negative relationship was found between age and velocity ( r = −.62; p < .001) and age and power ( r = −.63; p < .001). A moderate and positive relationship was found between handgrip strength and velocity ( r = .43; p = .002) and handgrip strength and power ( r = .54; p < .001). The results show the GymAware velocity and power measures during the sit-to-stand in older adults to be reliable and valid.
Background
Movement velocity has been proposed as an effective tool to prescribe the load during resistance training in young healthy adults. This study aimed to elucidate whether movement velocity could also be used to estimate the relative load (i.e., % of the one-repetition maximum (1RM)) in older women.
Methods
A total of 22 older women (age = 68.2 ± 3.6 years, bench press 1RM = 22.3 ± 4.7 kg, leg press 1RM = 114.6 ± 15.9 kg) performed an incremental loading test during the free-weight bench press and the leg press exercises on two separate sessions. The mean velocity (MV) was collected with a linear position transducer.
Results
A strong linear relationship between MV and the relative load was observed for the bench press (%1RM = −130.4 MV + 119.3; r² = 0.827, standard error of the estimate (SEE) = 6.10%1RM, p < 0.001) and leg press exercises (%1RM = −158.3 MV + 131.4; r² = 0.913, SEE = 5.63%1RM, p < 0.001). No significant differences were observed between the bench press and leg press exercises for the MV attained against light-medium relative loads (≤70%1RM), while the MV associated with heavy loads (≥80%1RM) was significantly higher for the leg press.
Conclusions
These results suggest that the monitoring of MV could be useful to prescribe the loads during resistance training in older women. However, it should be noted that the MV associated with a given %1RM is significantly lower in older women compared to young healthy individuals.
This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows how different sensors are influential in delivering a variety of readings that are useful in many ways regarding sport attributes. Wearables are increasing in function, and through integrating technology, users are gathering more data about themselves. The amount of wearable technology available is broad, each having its own role to play in different industries. Inertial measuring unit (IMU) and Global Positioning System (GPS) sensors are predominantly present in sport wearables but can be programmed for different needs. In this review, the differences are displayed to show which sensors are compatible and which ones can evolve sensor technology for sport applications.
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.
This study examined the validity and reliability of a wearable inertial sensor to measure velocity and power in the free-weight back squat and bench press. Twenty-nine youth rugby league players (18 ± 1 years) completed 2 test-retest sessions for the back squat followed by 2 test-retest sessions for the bench press. Repetitions were performed at 20, 40, 60, 80, and 90% of 1 repetition maximum (1RM) with mean velocity, peak velocity, mean power (MP), and peak power (PP) simultaneously measured using an inertial sensor (PUSH) and a linear position transducer (GymAware PowerTool). The PUSH demonstrated good validity (Pearson's product-moment correlation coefficient [r]) and reliability (intraclass correlation coefficient [ICC]) only for measurements of MP (r = 0.91; ICC = 0.83) and PP (r = 0.90; ICC = 0.80) at 20% of 1RM in the back squat. However, it may be more appropriate for athletes to jump off the ground with this load to optimize power output. Further research should therefore evaluate the usability of inertial sensors in the jump squat exercise. In the bench press, good validity and reliability were evident only for the measurement of MP at 40% of 1RM (r = 0.89; ICC = 0.83). The PUSH was unable to provide a valid and reliable estimate of any other criterion variable in either exercise. Practitioners must be cognizant of the measurement error when using inertial sensor technology to quantify velocity and power during resistance training, particularly with loads other than 20% of 1RM in the back squat and 40% of 1RM in the bench press.
Background
The purpose of this study was to analyze the relationships between muscular performance consisting of a single repetition on the chair squat exercise (CSQ) and different measures of functional capacity, balance, quality of life and cognitive status in older adults.
Methods
A total of 40 participants (22 women, 18 men; age = 72.2 ± 4.9 years) joined the investigation. Muscular performance was assessed by measuring movement velocity in the CSQ with no external load using a validated smartphone application ( PowerLift for iOS). Functional capacity, balance, quality of life and cognitive status were evaluated using the hand-grip strength (HGS) test, the Berg-scale, the EuroQol 5D (EQ-5D) and the Mini mental state examination questionnaire (MMSE). Finally, participants were divided into two subgroups ( N = 20) according to their velocity in the CSQ exercise.
Results
Positive correlations were obtained between movement velocity in the CSQ and HGS ( r = 0.76, p < 0.001), the Berg-scale ( r = 0.65, p < 0.001), the EQ-5D ( r = 0.34, p = 0.03) and the MMSE ( r = 0.36, p = 0.02). Participants in the fastest subgroup showed very likely higher scores in the Berg-scale (ES = 1.15) and the HGS (ES = 1.79), as well as likely higher scores in the MMSE scale (ES = 0.69).
Discussion
These results could have potential clinical relevance as they support the use of a time-efficient, non-fatiguing test of muscular performance (i.e., the CSQ) to evaluate functional capacity and mental cognition in older adults.
Objectives
The velocity of a barbell can provide important insights on the performance of athletes during strength training. The aim of this work was to assess the validity and reliably of four simple measurement devices that were compared to 3D motion capture measurements during squatting. Nine participants were assessed when performing 2 × 5 traditional squats with a weight of 70% of the 1 repetition maximum and ballistic squats with a weight of 25 kg. Simultaneously, data was recorded from three linear position transducers (T-FORCE, Tendo Power and GymAware), an accelerometer based system (Myotest) and a 3D motion capture system (Vicon) as the Gold Standard. Correlations between the simple measurement devices and 3D motion capture of the mean and the maximal velocity of the barbell, as well as the time to maximal velocity, were calculated.
Results
The correlations during traditional squats were significant and very high (r = 0.932, 0.990, p < 0.01) and significant and moderate to high (r = 0.552, 0.860, p < 0.01). The Myotest could only be used during the ballistic squats and was less accurate. All the linear position transducers were able to assess squat performance, particularly during traditional squats and especially in terms of mean velocity and time to maximal velocity.
Electronic supplementary material
The online version of this article (10.1186/s13104-017-3012-z) contains supplementary material, which is available to authorized users.
The use of bar velocity to estimate relative load in the back squat exercise was examined. Eighty strength-trained men performed a progressive loading test to determine their one-repetition maximum (1RM) and load-velocity relationship. Mean (MV), mean propulsive (MPV) and peak (PV) velocity measures of the concentric phase were analyzed. Both MV and MPV showed a very close relationship to %1RM (R2 = 0.96), whereas a weaker association (R2 = 0.79) and larger SEE (0.14 vs. 0.06 m•s-1) was found for PV. Prediction equations to estimate load from velocity were obtained. When dividing the sample into three groups of different relative strength (1RM/body mass), no differences were found between groups for the MPV attained against each %1RM. MV attained with the 1RM was 0.32 ± 0.03 m•s-1. The propulsive phase accounted for 82% of concentric duration at 40% 1RM, and progressively increased until reaching 100% at 1RM. Provided that repetitions are performed at maximal intended velocity, a good estimation of load (%1RM) can be obtained from mean velocity as soon as the first repetition is completed. This finding provides an alternative to the often demanding, time-consuming and interfering 1RM or nRM tests and allows to implement a velocity-based resistance training approach.
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 purpose of this study was to investigate the relationship between movement velocity and relative load in three lower limbs exercises commonly used to develop strength: leg press, full squat and half squat. The percentage of one repetition maximum (%1RM) has typically been used as the main parameter to control resistance training; however, more recent research has proposed movement velocity as an alternative. Fifteen participants performed a load progression with a range of loads until they reached their 1RM. Maximum instantaneous velocity (Vmax) and mean propulsive velocity (MPV) of the knee extension phase of each exercise were assessed. For all exercises, a strong relationship between Vmax and the %1RM was found: leg press (r(2)adj = 0.96; 95% CI for slope is [-0.0244, -0.0258], P < 0.0001), full squat (r(2)adj = 0.94; 95% CI for slope is [-0.0144, -0.0139], P < 0.0001) and half squat (r(2)adj = 0.97; 95% CI for slope is [-0.0135, -0.00143], P < 0.0001); for MPV, leg press (r(2)adj = 0.96; 95% CI for slope is [-0.0169, -0.0175], P < 0.0001, full squat (r(2)adj = 0.95; 95% CI for slope is [-0.0136, -0.0128], P < 0.0001) and half squat (r(2)adj = 0.96; 95% CI for slope is [-0.0116, 0.0124], P < 0.0001). The 1RM was attained with a MPV and Vmax of 0.21 ± 0.06 m s(-1) and 0.63 ± 0.15 m s(-1), 0.29 ± 0.05 m s(-1) and 0.89 ± 0.17 m s(-1), 0.33 ± 0.05 m s(-1) and 0.95 ± 0.13 m s(-1) for leg press, full squat and half squat, respectively. Results indicate that it is possible to determine an exercise-specific %1RM by measuring movement velocity for that exercise.
Manipulation of external loads typically provides a range of force, velocity, and power data that allows for modeling muscle mechanical characteristics. While a typical force-velocity relationship obtained from either in vitro muscles or isolated muscle groups can be described by a hyperbolic equation, the present review paper reveals the evidence that the same relationship obtained from maximum-performance multi-joint movements could be approximately linear. As a consequence, this pattern also results in a relatively simple shape of the power-velocity relationship. The parameters of the linear force-velocity relationship reveal the maximum force, velocity and power. Recent studies conducted on various functional movement tasks reveal that these parameters could be reliable, on average moderately valid, and typically sensitive enough to detect differences among populations of different physical abilities. Therefore, the linear force-velocity relationship together with the associated parabolic power-velocity relationship could provide both a new and simplified approach to studies of the design and function of human muscular system and its modeling. Regarding the practical applications, the reviewed findings also suggest that the loaded multi-joint movements could be developed into relatively simple routine tests of the force-, velocity- and power-generating capacity of the neuromuscular system.
© Georg Thieme Verlag KG Stuttgart · New York.
Strength training is a critical exercise stimulus for inducing changes in muscular strength, size and power (6). Recently, linear position transducers have gained in popularity as a means to monitor velocity in strength training exercises. The measurement error of such devices has been shown to be low and both relative and absolute reliability have been shown to be acceptable (2, 7, 11). The purpose of this article is to provide the overview and benefits of monitoring movement velocity in strength training exercises, along with providing the basis for novel “velocity-based” strength training prescription. We have covered the following practical applications: Guidelines to develop a velocity/load profile for athletes; Using the velocity load/profile to predict and monitor changes to maximal strength; Using velocity monitoring to control fatigue effects of strength training; Using velocity monitoring as an immediate performance feedback to promote the highest level of effort in specific training exercises and stronger adaptive stimuli. Linear position transducers are reliable and valid tools to help strength and conditioning practitioners monitor and optimize their strength training programs.
The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was performed to determine the theoretical load at zero velocity (LD0). Data from each of the 3 studies were analyzed separately and also presented as overall group mean. Thereafter, correlation analysis provided quantification of the relationships between 1RM and LD0. Practically perfect correlations (r = ∼0.95) were observed in our samples, confirming the ability of the load-velocity profile to accurately predict bench press 1RM.
This study aimed to compare the load-velocity and load-power relationships of three common variations of the squat exercise. 52 strength-trained males performed a progressive loading test up to the one-repetition maximum (1RM) in the full (F-SQ), parallel (P-SQ) and half (H-SQ) squat, conducted in random order on separate days. Bar velocity and vertical force were measured by means of a linear velocity transducer time-synchronized with a force platform. The relative load that maximized power output (Pmax) was analyzed using three outcome measures: mean concentric (MP), mean propulsive (MPP) and peak power (PP), while also including or excluding body mass in force calculations. 1RM was significantly different between exercises. Load-velocity and load-power relationships were significantly different between the F-SQ, P-SQ and H-SQ variations. Close relationships (R² = 0.92–0.96) between load (%1RM) and bar velocity were found and they were specific for each squat variation, with faster velocities the greater the squat depth. Unlike the F-SQ and P-SQ, no sticking region was observed for the H-SQ when lifting high loads. The Pmax corresponded to a broad load range and was greatly influenced by how force output is calculated (including or excluding body mass) as well as the exact outcome variable used (MP, MPP, PP).
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.
In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
This study examined the possibility of using movement velocity as an indicator of relative load in the bench press (BP) exercise. One hundred and twenty strength-trained males performed a test (T1) with increasing loads for the individual determination of the one-repetition maximum (1RM) and full load-velocity profile. Fifty-six subjects performed the test on a second occasion (T2) following 6 weeks of training. A very close relationship between mean propulsive velocity (MPV) and load (%1RM) was observed (R (2)=0.98). Mean velocity attained with 1RM was 0.16+/-0.04 m x s(-1) and was found to influence the MPV attained with each %1RM. Despite a mean increase of 9.3% in 1RM from T1 to T2, MPV for each %1RM remained stable. Stability in the load-velocity relationship was also confirmed regardless of individual relative strength. These results confirm an inextricable relationship between relative load and MPV in the BP that makes it possible to: 1) evaluate maximal strength without the need to perform a 1RM test, or test of maximum number of repetitions to failure (XRM); 2) determine the %1RM that is being used as soon as the first repetition with any given load is performed; 3) prescribe and monitor training load according to velocity, instead of percentages of 1RM or XRM.
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
Bar velocities capable of optimising the muscle power in strength-power exercises
- I Loturco
- L A Pereira
- Ccc Abad
Loturco I, Pereira LA, Abad CCC, et al. Bar velocities capable of optimising the muscle power in strength-power exercises. J Sports Sci 35:
734-741, 2017.