Quantifying and prescribing training intensity in an objective way is a common problem when designing resistance training sessions (14,27,29). Training intensity, generally understood as a percentage of the maximal effort that the athlete can perform, is considered a fundamental variable for the design of resistance training programs (14,27); in fact, the specific adaptations to resistance training are highly dependent on the intensity of the training stimulus (14,15,27,35). Thus, several methodologies have been used to quantify training intensity for resistance training programs; the 1 repetition maximum (1RM; i.e., the load that can be lifted just once) has been the most widely used (19,28,31). However, prescribing training intensity as a percentage of the 1RM (i.e., 75% 1RM), has a major drawback: it requires performing a maximal lift (direct estimation of the 1RM) (27) or a number of repetitions to failure with submaximal loads (indirect estimation of the 1RM) (12,26). Conducting a 1RM test involves a highly intense effort that might be risky for some populations such as elder people (30). Also, performing repetitions to failure have shown to impair neuromuscular performance even in trained athletes because of the high degree of fatigue it produces (13,20,23). Finally, 1RM values can increase over the course of a few weeks after the beginning of a new training program, especially for untrained populations (14,29,36). Therefore, if coaches desire accurate training load prescriptions, 1RM tests should be administered frequently (19).
Over the past few years, a new body of research has emerged, proposing the use of velocity feedback to quantify training loads for resistance training exercises (3,4,18,21,33). These studies are based on the well-known force-velocity relationship (22,32), for which higher loads are moved at slower velocities, whereas lighter loads are moved at faster velocities. In fact, barbell velocity during the bench press, back squat, and bench pull have shown to be very highly correlated with training intensity in terms of %1RM, with the use of a wide range of loads (2,19,25,33). Therefore, 1RM and each associated relative percentage can be predicted without conducting an actual 1RM test but rather by measuring barbell velocity. Thus, controlling barbell velocity seems to be appropriate to monitor resistance training intensities and to optimize adaptations (17,19).
Although, velocity based training has been proposed as a promising methodology to design resistance training programs without the potential drawbacks of the 1RM measurement (19,25), it also has an important drawback: the technology used to track barbell velocity, such as linear transducers (LTs), professional accelerometers or video-systems (8,10,11,34) are not affordable or practical for many strength and conditioning coaches. Among those devices, the most widely used technology to track barbell velocity are LT because of their accuracy and relative ease of use (10,16,19). Linear Transducers consist of a sensor with a cable that is attached to a barbell, and measure barbell velocity by differentiating cable displacement with respect to time (i.e., linear position transducers) (10) or, more recently, newer devices provide direct measurements by recording electrical signals proportional to cable velocity (i.e., linear velocity transducers) (33). As mentioned above, LTs have one 1 important limitation: they are expensive (more than 2,000 US dollars for the most popular models), which limits their use outside laboratory or professional sport settings. For this, it is necessary to find alternatives to accurately track barbell velocity in the field of sport science, both for simplicity and affordability.
In recent years, several smartphone and smartphone-based wearable technologies (i.e., devices that just need a smartphone app to work, not a PC software) have been validated to measure different parameters related to physical activity (1,5). Indeed, these user-friendly technologies, mostly consisting of accelerometers and gyroscopes, allow the measurement of different variables (such as steps, distance, or calories) by actually wearing its sensors as wristbands, watches, or even t-shirts (7). Specifically, accelerometers measure movement velocity in resistance exercises by integrating the acceleration data with respect to time (6). Although this approach is very different from the method used by LT, it has been demonstrated to be valid for the measurement of barbell velocity in previous research (8). Also, smartphone-based wearable devices don't need PC software to work; they are paired with a smartphone application to transfer data through Bluetooth or Wi-Fi connections in a simple way, which makes easier its setup and use in the field. However, no studies have analyzed a smartphone-based wearable device to track movement velocity during the back squat exercise.
For this, the purpose of this study is to analyze the validity and reliability of a smartphone-based wearable device to measure movement velocity during the barbell back squat exercise.
Experimental Approach to the Problem
The aim of this study was to test the validity and reliability of a novel smartphone-based wearable device to measure movement velocity during a back squat exercise. Ten recreationally active sport science students were recruited to perform an incremental test on a Smith machine, consisting of 3 maximal repetitions (i.e., with maximal movement speed during the concentric phase of the exercise) during a back squat exercise with 5 different loads ranging from 25 to 85% of their 1RM, i.e., a great part of the load-velocity spectrum (19). Each repetition was simultaneously measured using a linear velocity transducer attached to the barbell and a wearable device worn on the subject's forearm. Both concentric peak and average velocity data from the 2 instruments were compared and analyzed using several validity and reliability tests. Also, load-velocity relationships derived from the linear transducer and the wearable device data were analyzed for each individual to compare the quality of the linear regression between the 2 instruments. A total of 150 repetitions were measured and compared.
The participants of this study were 10 men, physically active sport science students with at least 1 year of barbell back squat training (age = 23.4 ± 5.2 years; age range = 18–28 years; height = 1.81 ± 0.08 m, body mass = 74.0 ± 10.4 kg; back squat 1RM = 83 ± 8.2 kg). The study was undertaken according to the Helsinki declaration, and the Ethics Committee of the Autonomous University of Madrid approved all procedures. Participation of the subjects was voluntary and anonymous, and they were informed of the benefits and risks of the investigation before signing an institutionally approved informed consent document to participate in the study.
Incremental Back Squat Test
The participants completed a standard warm-up comprising 5 minutes of jogging, 5 minutes of lower-body dynamic stretches (hip flexion-extension and abductions-adductions and knee flexion-extension exercises), and 1 set of 5 preparatory back squats with an unloaded plastic bar. Each subject then performed an incremental back squat test on a Smith machine with 5 different loads: 20, 40, 50, 60, and 70 kg, which, according to the subjects 1RM, corresponded approximately to a range between 25 and 85% of their 1RM. This range of loads was selected to obtain different values from the force-velocity spectrum of the subjects. Measuring 5 different points of the force-velocity spectrum (i.e., from light loads, which can be lifted at high speeds to high loads, which can be lifted at slow speeds) has been probed to be key for the analysis of the force/load-velocity relationships and can provide valuable information about the force production capabilities of the subjects. (19,32). Subjects were instructed to maintain a hip width stance, to squat deep (i.e., hips below knees) and to perform the concentric phase of the movement as fast as possible. Three repetitions were performed with each load. Each repetition was followed by 30 seconds of passive rest to avoid fatigue. Accordingly, each subject performed a total of 15 (5 × 3) repetitions. Three minutes of passive rest was provided between the different loading conditions. Before their participation in this study, the lead investigator instructed the participants to arrive in a rested and hydrated state and to avoid alcohol, caffeine, and vigorous exercise in the 48 hours preceding the testing session.
An experienced strength and conditioning coach supervised the testing session.
Each repetition performed during the back squat incremental test was measured using the T-Force linear velocity transducer (Ergotech, Murcia, Spain) (16), considered the criterion in this study, and the PUSH band, a novel smartphone-based wearable device designed to track movement velocity during a variety of resistance exercises (PUSH Inc., Toronto, Canada). The linear velocity transducer was attached to the left extreme of the barbell on the Smith machine, and the PUSH band was worn on the subject's dominant forearm, with the hand supinated, in top of the ulna, 1–2 cm distal to the elbow, and with the main button located proximally according to manufacturers instructions (Figure 1).
The LT, whose reliability has been reported elsewhere (16), measures instantaneous vertical velocity at a sample rate of 1,000 Hz. The LT obtains vertical velocity data (z axis) directly from the electrical signal produced by the cable movement. To register the concentric velocity data using the LT, the device was connected to a PC with Windows 7 and the T-Force v.2.35 software through a USB port. The PUSH wearable device consists of a 3-axis accelerometer and a gyroscope that provides 6 degrees of freedom in its coordinate system. A Butterworth filter is used to smooth the acceleration data, and vertical velocity is calculated by the integration of the vertical acceleration with respect to time using equations (1) and (2):
, t is time, is the instantaneous velocity for a time i,
is the velocity at the beginning of the concentric phase on the back squat (detected by PUSH's internal algorithms), f is the time at the final of the concentric phase, and a is the instantaneous acceleration. Then, the PUSH band calculates the mean velocity of the movement by averaging all instantaneous velocities registered during the concentric phase:
is the average velocity of the concentric phase on the back squat exercise,
is the ith instantaneous velocity measured with the PUSH band, and n is the total number of instantaneous velocities registered during the concentric phase of the movement. Finally, peak velocity was calculated as the highest velocity registered during the concentric phase. Both the LT and the PUSH band's software detect the start and the end of the concentric phase of each repetition with proprietary algorithms that were not shared with us. No calibration procedure is needed for the PUSH system to work.
The PUSH band's sampling rate is 200 Hz. To record the measured data with the PUSH band, the system was linked to an iPhone PUSH app v.1.10.4 using a Bluetooth 4.0 LE connection. Before each set, the load used was selected in the app.
Several statistical analyses were used to test the validity and reliability of the PUSH band compared with the LT with the back squat movement velocity measurement. First, the PUSH band's concurrent validity was tested using Pearson's product-moment correlation coefficient (r). Second, to analyze the reliability of the PUSH band to measure both peak and average velocity in comparison with the LT, the intraclass correlation coefficient (ICC) (2,1) was used. Also, independent t-test and Bland–Altman plots were used to identify potential systematic bias, which were reported through mean-bias and standard deviations. Furthermore, the standard error of estimate (SEE) was also used to inform about the typical error in the measurements. Third, to assess the reliability of the 3 repetitions of each set with both the LT and the PUSH band, the ICC (2,1), the coefficient of variation (CV), and test–retest correlations (through r) were used. Finally, linear regressions were used to analyze the load-velocity relationship for each subject. The level of statistical significance was set at P ≤ 0.05. All calculations were performed using IBM SPSS Statistics 22 for Mac (IBM Corp., Armonk, NY, USA).
Validity and Reliability of the Velocity Measures
When analyzing the whole data set (150 repetitions measured with both the LT and the PUSH band), Pearson's product-moment correlation coefficient revealed a very high association between the LT's and the PUSH band's measured peak velocity (r = 0.91, p < 0.001, SEE = 0.1 m·s−1; Figure 2).
Moreover, there was a very high agreement between the LT and the PUSH band for peak velocity (ICC = 0.944, confidence interval [CI] = 0.923–0.959), as revealed by the mentioned ICC and the Bland–Altman plots (Figure 3).
Furthermore, an independent-measures t-test showed a systematic bias between the LT and the PUSH band for peak velocity (LT: 1.55 ± 0.27 m·s−1, PUSH: 1.47 ± 0.33 m·s−1, CI = 0.01–0.14, p ≤ 0.05); the values obtained with the PUSH band being lower (mean difference: −0.07 ± 0.1 m·s−1).
Finally, when comparing the 3 repetitions of each set, both the LT and the PUSH band were seen to be highly reliable on the measurement of peak velocity (LT: [CV = 4.2 ± 2.5%; ICC = 0.988; CI = 0.98–0.993; test–retest reliability: r = 0.975]; PUSH: [CV = 6.0 ± 3.9%; ICC = 0.981; CI = 0.969–0.988; test–retest reliability: r = 0.952]).
First, Pearson's product-moment correlation coefficient revealed a very high association between the LT's and the PUSH band's measured mean velocity (r = 0.86, p < 0.001, SEE = 0.08 m·s−1). There was a very high agreement between the LT and the PUSH band for mean velocity measurements as well (ICC = 0.907; CI = 0.872–0.933). The independent measures t-test showed a systematic bias between the LT and the PUSH band for mean velocity (LT: 0.77 ± 0.17 m·s−1, PUSH: 0.88 ± 0.22 m·s−1, −0.1, CI = −0.15, −0.06, p < 0.001); the values obtained with the PUSH band being higher (mean difference: 0.11 ± 0.1 m·s−1).
Finally, when comparing the 3 repetitions of each set, both the LT and the PUSH band were seen to be highly reliable on the measurement of mean velocity (LT: [CV = 3.9 ± 2.4%; ICC = 0.989; CI = 0.982–0.993; test–retest reliability: r = 0.98]; PUSH: [CV = 5.0 ± 4.1%; ICC = 0.978; CI = 0.964–0.986; test–retest reliability: r = 0.956]).
Comparison of the Load-Velocity Relationships Measured With the 2 Instruments
Finally, we plotted the peak and mean velocities of each subject measured with both the LT and the PUSH band against the load lifted in the incremental test (in kilograms), and fitted a first-order regression line to study the load-velocity relationship obtained with these instruments. The results showed that strong load-velocity relationship exists in the back squat exercise for each individual using both peak and mean velocity values, no matter which instrument was used. Specifically, similar R 2 values were obtained with the LT and the PUSH band for load-peak velocity (LT: R 2 = 0.96 ± 0.07; PUSH: R 2 = 0.94 ± 0.08; Figure 4) and load-mean velocity (LT: R 2 = 0.92 ± 0.05; PUSH: R 2 = 0.94 ± 0.05) relationships.
Results from this study demonstrate a high validity and reliability of the PUSH band, compared with a validated LT, for measuring movement velocity during the back squat exercise. It was observed that the velocity values obtained with the PUSH band were highly correlated (peak velocity: r = 0.91; mean velocity: r = 0.86), with a high level of agreement (peak velocity: ICC = 0.944; mean velocity: ICC = 0.907), with those measured with the LT, despite the presence of a systematic bias by which the values obtained with the PUSH band were significantly different than those obtained with the LT (Peak velocity: 0.7 m·s−1 lower; Mean velocity: 0.11 m·s−1 higher). Further analysis of the data revealed a very high reliability of the PUSH band for measuring both peak (CV = 6.0%; ICC = 0.981; r = 0.952) and mean (CV = 5.0%; ICC = 0.978; r = 0.956) velocity. In fact, PUSH ’s reliability values were very close to those obtained with the LT; thus, if the PUSH band is used on a regular basis, consistency of the data obtained is expected to be very high. Moreover, individual load-velocity relationships, which allow to assess force production capabilities within a wide range of the force-velocity spectrum (24,32) were calculated to be equally strong, irrespective of the system (PUSH or LT) used.
The LT used in this study contains a sensor that directly measures the vertical displacement velocity of its cable (which is attached to the barbell) by transducing electrical signals, and not differentiating cable position with respect to time as compared with other LTs (9,19); consequently accuracy of this LT has been proposed to be very high (16,19,33). In fact, LTs are considered the gold standard for the measurement of barbell velocity by many authors (9,19,25). Although force platforms are considered the criterion for the evaluation of force production capabilities (10,11), when it comes to the measurement of barbell velocity, they seem less appropriate, because what they measure is the velocity of the system's center of mass using forward dynamics (10).
Meanwhile, the PUSH band, which is intended to be worn on the forearm of the subject (similar to a bracelet), measures vertical velocity by integrating the vertical acceleration data with respect to time. Previous accelerometer-based device was validated for lower-limb strength measurements (8); however, its high price point, above 2,000 US dollars for some models, prevents its use for many strength and conditioning coaches. Thus, despite the different calculation methods each of these systems uses (one measuring directly vertical velocity at 1 kHz with a cable attached to the barbell, the other integrating vertical acceleration data at 200 Hz from a sensor placed in the forearm of the subject), which lead to a systematic bias, the PUSH wearable device has shown to be highly valid and reliable for the measurement of movement velocity on the back squat exercise. However, although its use can be recommended for the estimation of back squat barbell velocity, the PUSH band should not be used interchangeably with a LT (i.e., the PUSH band one day, a LT the other day) because of the aforementioned systematic bias between these devices.
The importance of measuring movement velocity has been highlighted in many studies, because movement velocity is very highly correlated with relative intensity in terms of its association with % 1RM (19,33). However, technologies used to measure movement velocity, such as LTs, force platforms, or professional accelerometers, while validated, are still quite expensive and technical in nature, limiting their use outside laboratory settings or high-performance sports centers. Our results demonstrate that a much more affordable device (PUSH band, with a price about 15 times lower than the LT used as a criterion in this study) can be used to measure movement velocity during the back squat exercise. This could have great practical applications for strength and conditioning coaches, because PUSH, like many other smartphone-based wearable devices to track physical activity (7), are much more affordable than professional, laboratory-based instruments and are integrated with user-friendly smartphone apps, instead of using advanced PC software. To the best of our knowledge, this is the first study that demonstrates that a wearable device is a valid means to measure movement velocity during the back squat exercise.
The PUSH band is an easy to use, affordable, smartphone-based system that has been demonstrated to be highly valid and reliable in comparison with a professional linear velocity transducer for the measurement of movement back squat velocity. Thus, the PUSH band can be used to monitor and control movement velocity accurately. However, the PUSH should not be used interchangeably with LTs because of the presence of a systematic bias between these devices. This could have great practical applications for strength and conditioning coaches, especially for those implementing velocity-based resistance training programs, because movement velocity can be monitored with any iOS or Android smartphone and a nonexpensive wearable device.
Matt Kuzdub (MSc) is a Sport Science advisor at PUSH. To guarantee the independence of the data analysis, the first author of the article, who has no connection with PUSH, analyzed the entire data set and was the sole contributor to the results section. Mr. Kuzdub contributed significantly to the introduction and practical applications sections of the article, but did not collect any data nor had any access to the data set that was analyzed. The results of this study do not constitute endorsement of the product by the authors or the NSCA.
1. Balsalobre-Fernández C, Glaister M, Lockey RA. The validity and reliability of an iPhone app for measuring vertical jump performance. J Sports Sci 33: 1574–1579, 2015.
2. Bazuelo-Ruiz B, Padial P, García-Ramos A, Morales-Artacho AJ, Miranda MT, Feriche B. Predicting maximal dynamic strength
from the load-velocity relationship in squat exercise. J Strength
Cond Res 29: 1999–2005, 2015.
3. Blazevich A. Are training velocity and movement pattern important determinants of muscular rate of force development enhancement? Eur J Appl Physiol 112: 3689–3691, 2012.
4. Blazevich AJ, Jenkins DG. Effect of the movement speed of resistance training exercises on sprint and strength
performance in concurrently training elite junior sprinters. J Sports Sci 20: 981–990, 2002.
5. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology
: A systematic review. Sports Med 44: 671–686, 2014.
6. Casartelli N, Muller R, Maffiuletti NA. Validity and reliability of the myotest accelerometric system for the assessment of vertical jump height. J Strength
Cond Res 24: 3186–3193, 2010.
7. Chambers R, Gabbett TJ, Cole MH, Beard A. The use of wearable microsensors to quantify sport-specific movements. Sport Med 45: 1065–1081, 2015.
8. Comstock BA, Solomon-Hill G, Flanagan SD, Earp JE, Luk HY, Dobbins KA, Kraemer WJ. Validity of the Myotest in measuring force and power production in the squat and bench press. J Strength
Cond Res 25: 2293–2297, 2011.
9. Cormie P, Deane R, McBride JM. Methodological concerns for determining power output in the jump squat. J Strength
Cond Res 21: 424–430, 2007.
10. Cormie P, McBride JM, McCaulley GO. Validation of power measurement techniques in dynamic lower body resistance exercises. J Appl Biomech 23: 103–118, 2007.
11. Crewther BT, Kilduff LP, Cunningham DJ, Cook C, Owen N, Yang GZ. Validating two systems for estimating force and power. Int J Sports Med 32: 254–258, 2011.
12. Dohoney P, Chromiak JA, Lemire D, Abadie BR, Kovacs C. Prediction of one repetition maximum (1-rm) strength
from a 4-6 rm and a 7-10 rm submaximal strength
test in healthy young adult males. J Exerc Physiol Online 5: 54–59, 2002.
13. Drinkwater EJ, Lawton TW, McKenna MJ, Lindsell RP, Hunt PH, Pyne DB. Increased number of forced repetitions does not enhance strength
development with resistance training. J Strength
Cond Res 21: 841–847, 2007.
14. Folland JP, Williams AG. The adaptations to strength
training. Sport Med 37: 145–168, 2007.
15. Fry AC. The role of resistance exercise intensity on muscle fibre adaptations. Sport Med 34: 663–679, 2004.
16. Garnacho-Castaño MV, López-Lastra S, Maté-Muñoz JL. Reliability and validity assessment of a linear position transducer. J Sports Sci Med 14: 128–136, 2015.
17. González-Badillo JJ, Pareja-Blanco F, Rodríguez-Rosell D, Abad-Herencia JL, Del Ojo-López JJ, Sánchez-Medina L. Effects of velocity-based resistance training on young soccer players of different ages. J Strength
Cond Res 29: 1329–1338, 2015.
18. Gonzalez-Badillo JJ, Rodriguez-Rosell D, Sanchez-Medina L, Gorostiaga EM, Pareja-Blanco F. Maximal intended velocity training induces greater gains in bench press performance than deliberately slower half-velocity training. Eur J Sport Sci 14: 772–781, 2014.
19. González-Badillo JJ, Sánchez-Medina L. Movement velocity as a measure of loading intensity in resistance training. Int J Sports Med 31: 347–352, 2010.
20. Gorostiaga EM, Navarro-Amézqueta I, Calbet JAL, Hellsten Y, Cusso R, Guerrero M, Izquierdo M. Energy metabolism during repeated sets of leg press exercise leading to failure or not. PLoS One 7: e40621, 2012.
21. Hatfield DL, Kraemer WJ, Spiering BA, Häkkinen K, Volek JS, Shimano T, Maresh CM. The impact of velocity of movement on performance factors in resistance exercise. J Strength
Cond Res 20: 760–766, 2006.
22. Hill AV. The heat of shortening and the dynamic constants of muscle. Proc R Soc Lond B Biol Sci 126: 136–195, 1938.
23. Izquierdo-Gabarren M, De Txabarri Expósito RG, García-Pallarés J, Sánchez-Medina L, De Villarreal ESS, Izquierdo M. Concurrent endurance and strength
training not to failure optimizes performance gains. Med Sci Sports Exerc 42: 1191–1199, 2010.
24. Jaric S. Force-velocity relationship of muscles performing multi-joint maximum performance tasks. Int J Sports Med 36: 699–704, 2015.
25. Jidovtseff B, Harris NK, Crielaard JM, Cronin JB. Using the load-velocity relationship for 1RM prediction. J Strength
Cond Res 25: 267–270, 2011.
26. Julio UF, Panissa VLG, Franchini E. Prediction of one repetition maximum from the maximum number of repetitions with submaximal loads in recreationally strength
-trained men. Sci Sports 27: e69–e76, 2012.
27. Kraemer WJ, Ratamess NA. Fundamentals of resistance training: Progression and exercise prescription. Med Sci Sports Exerc 36: 574–688, 2004.
28. Pearson SN, Cronin JB, Hume PA, Slyfield D. Effects of a power-focussed resistance training intervention on backward grinding performance in America's cup sailing. Sport Biomech 8: 334–344, 2009.
29. Peterson MD, Rhea MR, Alvar BA. Applications of the dose-response for muscular strength
development: A review of meta-analytic efficacy and reliability for designing training prescription. J Strength
Cond Res 19: 950–958, 2005.
30. Pollock ML, Carroll JF, Graves JE, Leggett SH, Braith RW, Limacher M, Hagberg JM. Injuries and adherence to walk/jog and resistance training programs in the elderly. Med Sci Sports Exerc 23: 1194–1200, 1991.
31. Robertson RJ, Goss FL, Aaron DJ, Gairola A, Kowallis RA, Ying L, White B. One repetition maximum prediction models for children using the omni rpe scale. J Strength
Cond Res 22: 196–201, 2008.
32. Samozino P, Rejc E, Di Prampero PE, Belli A, Morin JB. Optimal force-velocity profile in ballistic movements–altius: Citius or fortius? Med Sci Sports Exerc 44: 313–322, 2012.
33. Sanchez-Medina L, Gonzalez-Badillo JJ, Perez CE, Pallares JG. Velocity- and power-load relationships of the bench pull vs. bench press exercises. Int J Sports Med 35: 209–216, 2014.
34. Sañudo B, Rueda D, Pozo-Cruz BD, de Hoyo M, Carrasco L. Validation of a video analysis software package for quantifying movement velocity in resistance exercises. J Strength
Cond Res 2014. Epub ahead of print.
35. Schoenfeld BJ. Is there a minimum intensity threshold for resistance training-induced hypertrophic adaptations? Sports Med 43: 1279–1288, 2013.
36. Schoenfeld BJ, Wilson JM, Lowery RP, Krieger JW. Muscular adaptations in low- versus high-load resistance training: A meta-analysis. Eur J Sport Sci 12: 1–10, 2014.