Agreement Between the Iron Path App and a Linear Position Transducer for Measuring Average Concentric Velocity and Range of Motion of Barbell Exercises

Abstract

Kasovic, J, Martin, B, Carzoli, JP, Zourdos, MC, and Fahs, CA. Agreement between the Iron Path app and a linear position transducer for measuring average concentric velocity and range of motion of barbell exercises. J Strength Cond Res 35(2S): S95–S101, 2021—The purpose of this study was to compare average concentric velocity (ACV) and range of motion (ROM) values measured by the Iron Path (IP) app to the previously validated Open Barbell System (OBS) linear position transducer during the front and back squat and conventional and sumo deadlift. Twenty-seven men and women (21 ± 3 years old; 76.7 ± 14.5 kg; 1.72 ± 0.09 m) with squat and deadlift training experience completed a modified one repetition maximum protocol on 4 separate occasions in a randomized order. The IP app and OBS device recorded ACV and ROM during each protocol. The level of statistical significant was set at p ≤ 0.05. Bland–Altman plots showed fairly large limits of agreement for both ACV and ROM. Furthermore, 95% confidence intervals for the intraclass correlation coefficients indicated the agreement in ACV between the devices for each of the 4 lifts to range from 0.648–0.876 to 0.849–0.930 and for agreement in ROM between devices to range from −0.053–0.480 to 0.545–0.770. Compared with the OBS, the IP app recorded significantly (p < 0.05) lower ACV values for the front squat and back squat and greater ROM values for the sumo deadlift. We suggest the IP app should not be used in place of a validated linear position transducer for measuring ACV or ROM for barbell lifts.

Introduction

The average concentric velocity (ACV) during a resistance exercise repetition is inversely related to the load lifted (¹⁸). Because of the strong relationship between ACV and load, measuring ACV during resistance training has potential benefits. Specifically, ACV at submaximal loads can be used to predict the one repetition maximum (1RM) (^13,14). However, this method is not perfect because it has been shown to overestimate the actual 1RM in exercises such as the free-weight back squat (⁴). Furthermore, ACV can be used to prescribe training load (^8,19) such as 4 sets of 8 repetitions between 0.40 and 0.60 m·s⁻¹. This programming of a velocity zone is known as velocity-based training and can be used in lieu of percentage of 1RM (e.g., 4 sets of 8 repetitions at 70% of 1RM). Velocity-based training can allow subjects to complete repetitions at faster velocities (as load is adjusted during a training session) with less mechanical stress than percentage-based training (when training at a fixed percentage of 1RM) (⁶). Despite the potential utility of velocity-based training, its utility has practical limitations such as taking time to set up as well as additional equipment required (⁶). Currently, only study has shown velocity-based to elicit greater strength and power adaptations compared with percentage-based training (⁹).

The utility of the measurement depends upon the accuracy and accessibility of device used. To determine if a device which measures ACV is valid, it must be compared with a criterion device (e.g., three-dimensional motion capture or force plate) using multiple plots of agreement. Because the criterion methods can cost tens of thousands of dollars, it is necessary to examine the validity of cost-effective options for practical usage. Common linear position transducers such as the Tendo Weightlifting Analyzer (∼$1,500) and GymAware Power Tool (∼$2,000) are still quite expensive. Furthermore, the Tendo Weightlifting Analyzer compared less favorably to a three-dimension motion capture system than the more moderately priced Open Barbell System (OBS) (∼$250), which was shown to be valid for both ACV and peak concentric velocity (¹²). In addition, the OBS is an “open source” product, and thus, it can be manufactured by the user for an even lower cost (∼$100).

Despite the validity and relative affordability of the OBS, smart phone applications such as the Iron Path (IP) app are widely available at minimal cost (<$5). Previous studies have stated validity low-cost options such as the PowerLift app (³) and Beast Wearable device (²) for measuring ACV. However, both studies only compared the measurement devices to the nonvalidated SmartCoach Power Encoder linear position transducer and not a true criterion device. Additional research that compared the PowerLift app among 7 different commercially available devices found it to be the second most valid device, with greater validity than wearable devices such as the PUSH band and Beast sensor (¹⁷). Despite the PowerLift app having a similar cost to the IP app (<$5), the PowerLift app only provides information related to barbell velocity, while the IP app provides more information, such as qualitative information on bar path (i.e., a tracing of the bar path during each repetition) as well as acceleration and range of motion (ROM) in addition to barbell velocity. Given the limited cost and lack of equipment needed to use the IP app, this could potentially be a useful method for measuring ACV. However, currently, it is unknown if the IP app provides measures of ACV and ROM that could be used interchangeably with measures from a linear position transducer. If the IP app provides similar measures of ACV and ROM as a linear position transducer, it could provide an opportunity for individuals to monitor ACV during training without the need for additional equipment and cost of a linear position transducer. Both devices (IP app and OBS device) calculate measures of ACV and ROM rather than measure them directly which could affect the validity and agreement of these 2 devices. Although the OBS device is not considered a true criterion for measuring ACV, linear position transducers are the most commonly used devices in this area of research (¹⁴). Furthermore, the OBS has been validated recently against a criterion three-dimensional motion capture system with small limits of agreement of −0.03 to 0.04 m·s⁻¹ for measures of ACV between the 2 devices (¹²). Therefore, the purpose of this study was to compare ACV and ROM values measured by the IP app and OBS device during free-weight barbell exercises. We chose to perform this study using the squat and deadlift exercises, since the former requires that the eccentric portion precedes the concentric portion of the lift, whereas the latter exercise begins with a motionless barbell and the concentric phase precedes the eccentric phase of the lift. We hypothesize that the IP app will show agreement with the OBS in measuring ACV and ROM such that if the devices could be used interchangeable for measuring ACV.

Methods

Experimental Approach to the Problem

In a randomized order, subjects completed a modified 1RM protocol for the front squat, back squat, conventional deadlift, and sumo deadlift during 4 visits to the laboratory. During the 1RM protocol, a linear position transducer, the OBS, was attached to the barbell while the IP app was simultaneously used to record barbell movement during each repetition. Barbell ACV (m·s⁻¹) and ROM (m) were recorded during the concentric portion of each repetition from each device.

Subjects

Twenty-seven men and women (21 ± 3 years old, age range: 19-35 years old; 76.7 ± 14.5 kg; 1.72 ± 0.09 m; mean ± SD) who had training experience with both the squat (front squat or back squat) and deadlift (conventional or sumo deadlift) participated in this study. All subjects were currently training with at least one form of the squat (FS or BS) and one form of the deadlift (SD or CD) and were familiar with both styles of each lift. This study was approved by Lindenwood University Belleville's Institutional Review Board (approval #00065), and all subjects provided written informed consent to participate.

Procedures

Anthropometrics

Standing height was recorded to the nearest 0.01 m with standard stadiometer (Tanita HR-200; Tanita Corporation, Arlington Heights, IL), and body mass was recorded with a digital scale (Tanita BWB-800S Doctors Scale; Tanita Corporation) to the nearest 0.1 kg.

One Repetition Maximum Protocol

Subjects performed a standardized warm-up on a Monark cycle ergometer (Monark Ergomedic 828 E) at a self-selected light intensity (i.e., rating of perceived exertion 9–11) for 5 minutes. Using the subject's estimated 1RM (e1RM), the loads for the warm-up sets were determined. Following the protocol recommended by Jovanovic and Flanagan (¹⁴), warm-up sets consisted of 2–3 repetitions with 30–40% of the e1RM, 2 repetitions with 40–50% of the e1RM, 1–2 repetitions with 60–70% of the e1RM, and 1 repetition with 70–80% of the e1RM, and 1 repetition with 80–85% of the e1RM. A minimum of 3 minutes was allotted between warm-up sets. Subjects were instructed to lift with maximal effort on every repetition regardless of the load being lifted, and they were encouraged to maintain consistent technique for each attempt. Following the last warm-up attempt, the 1RM was determined as the heaviest load (kilogram) lifted through a full ROM. Up to 5 attempts were used to determine the 1RM and a minimum of 3-minute rest was allotted between each attempt.

Open Barbell System

The OBS device (Squats & Science Labs LLC, Seattle, WA), a linear position transducer, was placed on the floor with the cable aligned perpendicular to the ground during the execution of each repetition. For the deadlift exercises (conventional deadlift and sumo deadlift), the device was placed in the center of the barbell, and for the squat exercises (front squat and back squat), the device was placed on the barbell sleeve on the lifter's right hand side. Average concentric velocity and ROM were recorded directly from the unit's electronic display. The OBS device provides a valid measurement of ACV compared with a three-dimensional motion capture system (¹²).

Iron Path App

The IP app (version 1.9; William Bishop, Bothell, WA) was used to record ACV and ROM using an iPhone 8 plus at a quality of 1,080 p at the standard video recording speed of 30 frames per second. This was the default recording speed for IP app used on the iPhone 8 plus. The phone was held motionless on a support stand 1.83 m to the left of the end of the barbell at a height of either 0.21 m (for the deadlifts) or 1.07 m (for the squats) in line with the barbell in the sagittal plane. The camera position was based on pilot testing, which ensured that the full ROM was able to be recorded for all repetitions. The video recorded of each repetition included both the concentric and eccentric portion of each repetition. During the video analysis, the highlighted region of interest to be traced was placed around the plates on the barbell. Once the trace video process was complete, the file was saved and exported to Microsoft Excel (Microsoft Corp, Redmond, WA), which contained an instantaneous velocity (m·s⁻¹), acceleration (m·s⁻²), and distance (m) value for each video frame (values from each frame listed sequentially row by row). From the original video, the approximate time stamps of the beginning and end of the concentric portion of each repetition were noted for reference during the analysis of each Excel file. From the Excel file, using the approximate time stamps as reference, the concentric portion of each repetition was identified as the first frame (data row) with a positive velocity value and ending at the frame (data row) immediately before the frame in which velocity values registered as zero or negative. All velocity values (all data rows) during the concentric portion of the repetition were averaged to determine ACV. Range of motion was determined over the same band of cells as ACV and calculated as the absolute value of the difference between the distance value recorded at the beginning and at the end of the concentric portion of the repetition. Data were not smoothed, and all data were reported in their raw form.

Statistical Analyses

All data are presented as mean ± SD. The agreement of the IP app in relation to the OBS was evaluated using Bland–Altman plots (⁷) and folded empirical cumulative distribution plots (mountain plots), along with Wilcoxon signed-rank paired-samples t-tests, and the intraclass correlation coefficient (ICC) (based on a single-rater, absolute agreement, two-way mixed-effects model) (¹⁵). SPSS version 25 was used for calculation of the ICC and ICC 95% confidence interval (CI) while JASP version 9.2 was used for Wilcoxon signed-rank analyses. Statistical significance was set as p ≤ 0.05. RStudio (version 1.1.456, Boston MA) was used to produce the Mountain plots, and Microsoft Excel was used to produce the Bland–Altman Plots. The SEM was calculated as SEM = $SD\times \sqrt{1\mathrm{-}\text{ICC}}$.

Results

Descriptive Data, t-tests, ICCs, and SEMs

Table 1 presents the descriptive data for all dependent variables as well as the ICCs and SEMs. The front-squat ACV and back-squat ACV and ROM were significantly lower (p < 0.05) for the IP app compared with the OBS device. The sumo deadlift ROM was significantly greater (p < 0.05) for the IP app compared with the OBS device.

Table 1 - Descriptive statistics.^*†

	N		OBS	IP	p	ICC	ICC 95% CI	SEM
Front squat	49	ACV (m·s−1)	0.44 ± 0.18	0.41 ± 0.14	0.041	0.789	0.648 to 0.876	0.07
		ROM (m)	0.515 ± 0.067	0.507 ± 0.064	0.216	0.231	−0.053 to 0.480	0.057
Back squat	65	ACV (m·s−1)	0.44 ± 0.15	0.43 ± 0.23	<0.001	0.825	0.698 to 0.897	0.06
		ROM (m)	0.526 ± 0.073	0.502 ± 0.098	0.004	0.384	0.156 to 0.574	0.061
Conventional deadlift	98	ACV (m·s−1)	0.44 ± 0.18	0.42 ± 0.15	0.153	0.897	0.849 to 0.930	0.05
		ROM (m)	0.548 ± 0.048	0.552 ± 0.071	0.184	0.502	0.337 to 0.636	0.042
Sumo deadlift	93	ACV (m·s−1)	0.39 ± 0.18	0.39 ± 0.17	0.654	0.866	0.805 to 0.909	0.02
		ROM (m)	0.489 ± 0.073	0.498 ± 0.079	0.037	0.643	0.545 to 0.770	0.027

^*ACV = average concentric velocity; ROM = range of motion; OBS = Open Barbell System; IP = Iron Path app; ICC = intraclass correlation coefficient; ICC 95% CI = ICC 95% confidence interval.

^†All data shown as mean ± SD.

Bland–Altman and Mountain Plots

Bland–Altman plots depict the mean bias (solid black line) and limits of agreement (dashed black lines) for AVC (Figure 1) and ROM (Figure 2) for the front squat (A), back squat (B), conventional deadlift (C), and sumo deadlift (D). The limits of agreement were as follows: front-squat ACV −0.17 to 0.23 m·s⁻¹, ROM −0.157 to 0.167 m; back-squat ACV −0.13 to 0.20 m·s⁻¹, ROM −0.132 to 0.195 m; conventional deadlift ACV −0.13 to 0.16 m·s⁻¹, ROM −0.122 to 0.114 m; and sumo deadlift ACV −0.15 to 0.17 m·s⁻¹, ROM −0.129 to 0.110 m. The mountain plots for ACV (Figure 3) for front (A) and back (B) squat depict ACV for IP to not be tightly conformed to the zero difference line and show long tails (0.30–0.40 m·s⁻¹), which suggests lower agreement between the devices. Figure 3C, D shows the IP app to be tightly conformed to the zero difference line for both conventional and sumo deadlift ACV; however, the tails (>0.20 m·s⁻¹) do suggest a small degree of disagreement between devices. Mountain plots for ROM (Figure 4) illustrate that conventional deadlift (C) and sumo deadlift (D) ROM are tightly conformed to the zero difference line (tails ∼0.100 m) suggesting agreement between devices. However, mountain plots for front squat (A) and back squat (B) ROM show the IP app ROM is not tightly conformed to the zero difference line with longer tails (>0.200 m), suggesting lower agreement between the devices.

Figure 1.: Bland–Altman plots showing agreement in ACV between devices for the front squat (A), back squat (B), conventional deadlift (C), and sumo deadlift (D). Solid black line represents mean bias, and dashed lines represent limits of agreement. ACV = average concentric velocity.

Figure 2.: Bland–Altman plots showing agreement in ROM between devices for the front squat (A), back squat (B), conventional deadlift (C), and sumo deadlift (D). Solid black line represents mean bias, and dashed lines represent limits of agreement. ROM = range of motion.

Figure 3.: Mountain plots showing agreement in ACV between devices for the front squat (A), back squat (B), conventional deadlift (C), and sumo deadlift (D). ACV = average concentric velocity.

Figure 4.: Mountain plots showing agreement in ROM between devices for the front squat (A), back squat (B), conventional deadlift (C), and sumo deadlift (D). ROM = range of motion.

Discussion

The main finding of this study is that, compared with a validated linear position transducer, the IP app yielded significantly different measures of AVC and ROM, and there was relatively low agreement between the devices. Specifically, the IP app underestimates ACV and ROM for the squat while overestimating ROM for the sumo deadlift compared with the OBS device. The agreement of the IP app compared with the OBS device seems to be affected by the type of lift it is recording with the IP app demonstrating lower agreement for both ACV and ROM in both squat exercises compared with both deadlift exercises. Although the Bland–Altman plots demonstrate small mean biases between the OBS and IP app in measuring ACV and ROM, the limits of agreement show a relatively large range for ACV (∼±0.20 m·s⁻¹) and ROM (∼±0.200 m) between the 2 devices for all 4 lifts. Consequently, these differences in ACV could translate into large errors if using ACV to predict the 1RM or for training load prescription. Thus, we reject our hypothesis that the 2 devices could be used interchangeably for measuring ACV.

The lower agreement between devices for squat exercises compared with the deadlift exercises is likely due to the fact that calculation of ACV with the IP app requires the region of interest to be traced, and there is greater variability in the bar path tracing during the squat compared with the deadlift. This variability is due to the transition between the eccentric and concentric phase of the squat. It is possible that the recording frame rate (30 frames per second) added to the error in identifying the beginning of the concentric portion of the squat. By contrast, the stationary bar position before the concentric phase of the deadlift may have allowed the IP app to gauge ACV and ROM more accurately and thus be in greater agreement with the OBS. Notably, our sample size was also lower for the comparison between devices with the squat compared with our sample size for the deadlift. The lower sample size for the squat was due to technical difficulty in obtaining accurate bar path tracings with the IP app from the squat videos, which was also due to the greater variability in bar path for the squat. It was especially difficult to obtain accurate bar path tracing if more than one repetition was performed during the video.

The IP app's accuracy in measuring ROM compared with the OBS device was lower compared with the agreement between the 2 devices in measuring ACV. One explanation for the lower agreement in measuring ROM could be due to the frame rate (30 frames per second) at which the video was recorded for the IP app. In the analysis of the excel files with the IP app data, ACV was derived from the average velocity over a range of data points, whereas ROM was derived as the difference between 2 specific data points. Thus, if the limited frame rate was a source of error for the IP app data, this would have a larger effect on the ROM measurement calculated from 2 specific data points in time in comparison with the ACV measurement, which is calculated as an average value over many data points.

Our study is similar to a number of other recent investigations on the validity and reliability of different devices in measuring ACV. The PUSH band wearable device was reported to have good to excellent (ICC = 0.907; 95% CI = 0.872–0.933) agreement in measuring ACV during the back squat compared with a linear position transducer (¹). However, similar to our study, the Bland–Altman plots revealed relatively large limits of agreement (∼±0.20 m·s⁻¹) in ACV between the devices (¹), which would suggest a significant error if using the devices interchangeably for velocity-based training. Similarly, another study reported good to excellent agreement based on ICCs (ICC = 0.923; 95% CI = 0.889–0.946) between the PUSH band and a criterion three-dimensional motion capture system. However, the PUSH band underestimated ACV during the bench press and it was concluded that the PUSH band did not provide a valid measure of ACV (¹⁶). Several studies have also investigated the validity and reliability of the PowerLift app in measuring ACV (^2,3,17). During the free-weight bench press exercise, the PowerLift app was reported to have excellent agreement with a linear position transducer in measuring ACV based on ICCs (ICC = 0.965; 95% CI = 0.952–0.974) with smaller limits of agreement (∼±0.11 m·s⁻¹) than observed in this study with the IP app (³). However, the criterion in that study was a nonvalidated SmartCoach Power Encoder linear position transducer (³). Slightly greater (∼±0.15 m·s⁻¹) limits of agreement were found between the PowerLift app and SmartCoach Power Encoder in ACV during the hip thrust exercise (²). Furthermore, Perez-Castilla et al. (¹⁷) showed larger limits of agreement (∼±0.22 m·s⁻¹) between the PowerLift app and a three-dimensional motion capture for ACV during a Smith machine bench press. Another low-cost video software (Kinovea) was investigated for mean propulsive velocity during the bench press; however, this study reported large mean biases and was only compared with a linear position transducer and not a criterion device (²⁰). Although this study compared the IP app with a linear position transducer, the OBS used presently was previously found to be valid (¹²). Specifically, Goldsmith et al. reported low limits of agreement (−0.04 to 0.03 m·s⁻¹) for the OBS versus a three-dimensional motion capture for ACV. In other words, although this study did not compare the IP app with a true criterion, the IP app was compared with the linear position transducer, which, to the best of our knowledge, has been shown to have the best level of agreement with any criterion device reported in the literature to date (¹²).

Overall, our study suggests that the IP app does not provide similar ACV and ROM measurements to the OBS device. Although our study is not the first to show the lack of agreement between a low-cost app and a linear position transducer, we believe the present findings are unique because the OBS has been truly validated against a criterion, and thus, we can be reasonably confident that the results of this study would be similar versus a criterion device. Importantly, we also demonstrated the type of exercise performed (squat vs. deadlift) may affect the agreement between the IP app and a linear position transducer. Although the viewing angle, camera height, and recording speed used with the IP app in this study were within the recommendations for weightlifting video analysis, the camera distance from the barbell (<2 m) was less than what is recommended (≥10 m) (¹¹). This may have affected the accuracy of the IP app but was also the largest distance feasible within the physical constraints of the laboratory. However, we feel this distance would be comparable with the distance available within most training facilities, and thus, our setup provided a practical representation of how the IP app would be used.

Practical Applications

The agreement in measuring ACV between the IP app and OBS device varies depending on the type of lift performed. Based on the difficulty in obtaining accurate tracings of the bar path with the IP app and the relatively large limits of agreement between the 2 devices, we would not recommend the IP app as a tool for measuring ACV as a means to adjust training loads regardless of the exercise performed (squat or deadlift). Differences in measured ACV values of 0.2 m·s⁻¹could translate into 15–20% differences in training load for free-weight barbell exercises (¹⁰) if using the 2 devices interchangeably. Furthermore, Banyard et al. (⁵) reported that a within-individual velocity difference of 0.06 m·s⁻¹ from session-to-session is a worthwhile change. Thus, the 0.20-m·s⁻¹ difference between devices could erroneously suggest improvement or regression in performance from session-to-session if the devices were used interchangeably. Although the IP app may be inexpensive and reduce the requirement for additional equipment to measure ACV, the data suggest the IP app does not provide similar measures of ACV as a validated linear position transducer. The IP app may provide more useful information to quantitate bar path data under circumstances in which the barbell is motionless before the concentric portion.