Muscular power, the product of load and velocity, has long been recognized as an important factor affecting success in sports (15), older adults' independence and fall probability (3,4,10,11,21,23), and rehabilitation (28,29). Reduced quadriceps power, for example, has been shown to correlate with an increased risk of falls in older adults (1,27), and it has also been associated with acute, combined, anterior cruciate and grade-III medial collateral ligament injuries (12).
The load-velocity curve provides a neuromuscular model on which power training is built and typically includes a power curve (Figure 1). Two central tenets are evident when examining the load-velocity curve. First, the speed at which an object can be moved is inversely related to its mass. This explains why elite powerlifters, for example, often take 5 seconds or more to complete a single repetition when attempting a maximal or near-maximal load (17). Second, power is the product of the force applied to the external load and the velocity at which it is moved. Therefore, during a lift, the highest power output will occur at neither extremely heavy nor extremely light loads.
The load at which an individual is able to achieve their highest peak power is known as the “optimal load” (24). Optimal loading has been studied across a wide range of resistance exercises, modalities, and populations. Soriano et al. (24,25), for example, performed 2 meta-analyses on optimal loading for upper- and lower-body free weight lifts. A total of 11 studies with 434 subjects were included in the former, whereas 27 studies with 468 subjects were in the later analysis. Exercises included bench press, bench press throw, squat, jump squat, power clean, and hang power clean performed predominantly by younger subjects. In both studies, investigators reported that optimal loads varied by exercise.
Similar to Soriano et al. (24,25), Potiaumpai et al. (20) examined optimal loads in 70 community-dwelling older adults on pneumatic resistance training machines. Exercises included chest, seated row, lat pull-down, leg curl, calf raise, and leg press. Once again, optimal loads differed among exercises. Recently, we completed a study examining optimal loading in 34 older (14 men and 20 women) and 46 younger (25 men and 21 women) subjects using plate-loaded machines. The exercises tested included the chest press, leg press, seated row, and calf raise (Dr. Joseph F. Signorile, personal communication, February 2018). Once again, optimal loads differed by exercise. Additionally, younger individuals and men showed their highest power at the velocity end of their load-velocity curves, whereas women and older persons produced their highest power at the load end of their curves.
Although optimal loads for maximizing power gains have been established in pneumatic (20) and plate-loaded machines (13,14,26) for the elbow flexors, no previous study, to our knowledge, has established the loads for one of the most commonly used rehabilitation resistance devices: rubber tubing. Similarly, although extensive research has been conducted using elastic bands and tubes as strength training modalities in populations, such as athletes (2) and the elderly (9), we could find only a single study using elastic tubing to improve power (31). This is problematic because these devices are regularly used in wellness centers, physical therapy clinics, and athletic training rooms and at home by older adults, recreational lifters, and athletes. Additionally, bands and tubes have become dominant tools in functional training programs and are often used as supplemental loading devices during free weight lifts (30). A drawback to determining optimal loads for tubes and bands is that the load increases throughout the range of motion of the exercise; therefore, both load and velocity changes must be monitored throughout an exercise to determine the power produced by each band or tube tested (19).
The purpose of this study was to develop a method to determine peak power in an elastic resistance exercise, the arm curl. Designed as a proof-of-concept investigation, we sought to determine if our novel assessment system could determine the optimal elastic tube individuals should use for power training based on their arm curl 1-repetition maximum (1RM). Because of the largely exploratory nature of this investigation, a convenience sample of exercise-science undergraduate students was used, and a simple, single joint exercise (standing bicep-curl) was selected. Once the assessment method has been established using this exercise, it is our intention to apply it to more complex, functional exercises, such as the chest press and squat.
Experimental Approach to the Problem
Assessing peak power during elastic resistance exercise requires simultaneous quantification of the 2 dynamic variables, load and velocity. Therefore, our testing procedure included measures of dynamic load exerted throughout the concentric portion of the standing arm curl exercise using a strain gauge to which the elastic tube was anchored. Dynamic velocity was also measured throughout the concentric phase using a reflective marker placed on the peak of the second metacarpal phalangeal joint as the exercise was performed in a 3-dimensional (3D) motion capture environment. By synchronizing the collected strain-gauge data with data provided from our motion analysis system, we were able to compute instantaneous power throughout the range of motion. Participants first performed a standing cable biceps curl 1RM test. They were then asked to perform 3 maximal velocity bicep curls using elastic resistance tubes (Power Systems, Knoxville, TN, USA) ranging from very light to ultraheavy resistance, in random order. The highest instantaneous (peak) power obtained using each band was then recorded, and the “optimal tube” for that individual was subsequently determined as the tube with which they achieved their highest peak power. After completion of all data collection sessions, a regression equation was generated to predict the tube (or tubes) with which an individual was most likely to obtain their peak power based on their cable curl 1RM.
A convenience sample of 38 undergraduate exercise-science students was recruited to participate in the study. All subjects were older than 18 years and signed an informed consent form before participation. All aspects of the study, including experimental protocol and intake documentation, were approved before commencement of the study by the Subcommittee for the Use and Protection of Human Subjects of the University of Miami. Participants were made aware of the risks related to participation, were assured of their right to cease participation for any reason without consequence, and were offered no compensation of any kind. Demographic information for all participants completing the study is presented on Table 1.
A Consort chart showing the flow of participants through the study is presented in Figure 2. Upon arrival at the laboratory, participants completed their consent forms. Height and body mass measurements were then recorded along with participants' sex and age. Each participant's 1RM was established using a single-arm standing cable bicep curl. After determination of the 1RM, participants were given 5 minutes of recovery before the commencement of optimal load testing using the resistance tubes. All data were collected in a 3D movement analysis environment, allowing the quantification of velocity throughout the range of motion. Subjects performed concentric contractions at maximal intended velocity. The force and velocity components of power were synchronized by matching peak vertical displacement and peak force. All data were transferred to an Excel spreadsheet (Microsoft, Redmond, WA, USA) where instantaneous power for each 0.01-second interval was computed by multiplying the synchronized velocity and force readings for each frame during the movement.
Although the standing arm curl may not be a premier exercise chosen for power development, it provided an easily controlled model for testing our procedures because it is a simple single joint exercise, easily administered, and is not as affected by differences in form, experience, coordination, and timing as the more complex, multijoint exercises often used during power training.
Participants' 1RM was determined using the method described by McDonagh and Davies (18). All testing was performed on a selectorized cable machine (Cybex, Medway, MA, USA). Briefly, participants stood with their feet shoulder-width apart with the lower pulley of the machine aligned directly in front of them at ground level. Participants performed 8 warm-up repetitions with a light load (females: 4.5 kg; males: 6.8–9.1 kg), followed by a 60-second recovery and 3 repetitions with a moderate load (females: 6.8–9.1 kg; males: 9.1–13.6 kg). After a 2-minute rest period, the load was increased by 2.3–4.5 kg depending on the ease with which the participant completed the final warm-up set. If successful, the load was increased by an additional 2.3 kg, and the process repeated until the participant felt close to their maximum. At this point, the load increases were reduced to 1 kg and the process repeated until failure. Two-minute recoveries were provided between efforts, and all participants reached their 1RM within 5 attempts.
Optimal Load Setup
During peak power assessments using free weights or selectorized exercise machines, the load remains constant throughout the range of motion of the exercise, meaning only the velocity component of the movement need be recorded. In contrast, during elastic-based power training, load and velocity are dynamic components throughout the range of motion. Therefore, our new assessment tool needed to monitor both variables continually throughout the exercise.
The resistance produced by the rubber tubing throughout the range of motion of the biceps curl exercise was continuously sampled at 100 Hz with a 500 N strain gauge (Chatillon, Corp., Largo, FL, USA) located on the side of a wooden platform (102 × 47 × 15 cm) built specifically for this study. The strain gauge was oriented so that the attachment point of the tube was at ground level relative to the participant (Figure 3).
A commercially available clamping device was used to anchor the tube to the top eyelet of the strain gauge. Slight adjustment of the clamping device allowed all slack to be removed from the tubing before the initiation of each recorded movement.
Velocity data were collected using a 3D motion capture system (BTS Bioengineering, Milan, Italy) at a speed of 100 Hz. A reflective marker was placed on the peak of the second metacarpal phalangeal joint from which vertical displacement during the movement was subsequently recorded. Instantaneous velocity (m·s−1) was computed for each 0.01 interval during the movement by dividing the difference in vertical displacement between adjacent data points by the sample rate.
Optimal Load Testing
After determination of the 1RM, participants were given a minimum of 5 minutes of recovery before commencement of the optimal load trials using the resistance tubes. The tubes used during optimal load testing were classified by the vendor (Power Systems) as follows: extra light (orange), light (green), medium (red), heavy (blue) extra heavy (purple), and ultraheavy (gray). To begin the test, participants stood on the testing platform with their feet shoulder-width apart and their dominant hand hanging directly above the strain gauge. Participants were instructed to stand naturally with their arms hanging at their sides and palms facing forward (Figure 4).
A research assistant placed the handle of the tube in the participant's hand, and the tube was drawn through the clamp until minimal tension was detected, such that any baseline “slack” had been taken out of the system. Participants were then instructed to perform a single bicep curl as quickly as possible when given a verbal cue from the tester. The order of tubes was randomized, and participants were blinded to the tube being tested. This procedure was repeated 3 times for each tube with a recovery to baseline tension between contractions. The functional length of the tube, defined as the distance between the anchor point and the middle of the lateral aspect of the handle, was measured for use as a covariate; however, it was not ultimately used.
All data were transferred to an Excel spreadsheet where power was computed. To determine power for each tube during each contraction, vertical displacement data from the motion capture device were imported into the same spread sheet as the strain gauge data, and both data sets were placed on a single line graph to allow synchronization of the 2 variables. This was achieved by aligning peak vertical displacement to peak force for each repetition. The reason peak vertical displacement and peak force were selected for synchronization is that based on the definition of an elastic material, the point at which it is exerting its greatest force will be when it has been stretched furthest from its anchor point. Velocity was then computed for each data point using the stated sampling rate of 100 Hz to establish time and changes in displacement from the previous data point. Power was calculated as the product of force and velocity at each time-matched data point. Peak power for each tube was defined as the highest power output attained during the 3-repetition set. Figure 5 presents a sample graph of the rising (concentric) portion of a subject's performance produced using a gray (heavy) tube. The tube that generated the highest peak power was considered the optimal tube for power training.
Although participants' 1RM was a continuous variable, to facilitate our analysis, we assigned them to one of 5 ordered groups depending on their strength: group 1 = 1RM < 6.8 kg, group 2 = 6.8 kg ≤ 1RM < 11.3 kg, group 3 = 11.3 kg ≤ 1RM < 15.9 kg, group 4 = 15.9 kg ≤ 1RM < 20.5 kg, and group 5 = 1RM ≥ 20.5 kg. Tubes were assigned a ranking according to which produced the highest peak power for each subject (1 = least resistance; 5 = most resistance). A linear regression analysis was then run between participants' strength groups and tube rankings. Age, sex, height, body mass, and arm length were also considered for inclusion in the regression model.
Participants' 1RM values were found to explain approximately half of the between-subject variance in optimal tube selection (F(1,37) = 40.43; p < 0.0001; R 2 = 0.529). The intercept (B0) for the associated regression equation was 1.333 ± 0.364 (mean ± SE), meaning individuals with a 1RM below 6.8 kg are expected to reach their peak power with a light or medium-resistance tube. The slope of the equation (B1 = 0.698; t (37) = 3.661; p = 0.001) indicates that for every unit increase in strength group membership, the ordinal ranking of the tube (least resistant = 1; most resistant = 5) with which participants are expected to obtain their highest peak power output increases by 0.698. The orange (very easy) tube was not considered for analysis because no subject attained his or her highest power output using that tube. None of the covariates significantly reduced the amount of explained variance or increased the predictive power of the model and were therefore left out of the model. Based on the results derived from our novel assessment device, the suggested tubes to maximize power training for each strength group are presented in Table 2.
This study was conducted to test the viability of a newly developed method for determining peak power and subsequently, optimal elastic tube use during power training with elastic resistance. To reduce the impact of differences in form, experience, coordination, and timing on our results, a simple, easily administered, single-joint exercise (standing arm curl) was chosen over more complex, multijoint exercises.
Our principal finding is that the assessment tool presented in the current study has the capacity to measure instantaneous power during the performance of a simple elastic resistance exercise and to allow determination of the optimal tube to maximize power during that exercise. Using this tool, we were able to demonstrate that specific tubes can be recommended for individuals during power training based on their 1RM strength. Furthermore, our assessment method can help practitioners to identify the tube that will optimize power training using their patient's 1RM in that exercise. As demonstrated using the biceps curl as a model, individuals with a 1RM below 6.8 kg are expected to produce their greatest power gains using a light or medium-resistance tube, those with a 1RM between 6.8 and 11.4 kg will maximize power gains using a medium-resistance tube, those with a 1RM between 11.4 kg and 15.9 kg should use a heavy-resistance tube to maximize power, and anyone with a 1RM between 15.9 and 20.4 kg should experience their greatest power improvement with a very heavy–resistance tube. Finally, if individuals exhibit a 1RM greater than 20.4 kg, the greatest power improvements should occur with a very heavy– or extremely heavy resistance tube. Finally, for the standing arm curl, very light resistance tubes may have no clinical utility when the goal of the intervention is to maximize improvements in muscular power.
A limited number of studies can be identified that have used elastic resistance to improve musculoskeletal power. This may be explained, in part, by the high complexity of the assessment techniques required to study the topic. Unlike free-weights or selectorized weight-stack machines, where exact loads can be set, prescription of loads using exercise bands and tubes is only possible using color codes (16). This is problematic because of injury risk (20) and the inability for users to readily interchange tubes because of a lack of consistent color-coded loading among manufacturers (5). Additionally, the resistance provided by a given section of elastic tubing increases linearly with the degree of elongation after an initial nonlinear period corresponding to taking the “slack” out of the system (22). This is expected given the behavior of an ideal elastic material (5).
The method outlined in this article will provide a tool to establish specific optimal loads for various elastic resistance tubes and bands offered by different manufacturers. The equations developed using this method, however, are dependent upon establishing individuals' 1RM before elastic resistance training. Fortunately, alternatives to 1RM testing have been offered for use outside formal exercise settings. Colado and Triplett (8) suggested standardizing exercise intensities to perceptions of effort rather than 1RM. These researchers recruited 45 sedentary middle-aged women and randomly assigned them to weight machine training (n = 14; mean age ±SD = 51.07 ± 6.81), training with elastic bands (n = 21; mean age ± SD = 54.14 ± 2.87), or a nonexercising control group (n = 10; mean age ± SD = 53.90 ± 1.85). They demonstrated that they could match training loads between the exercise groups using a rating scale of perceived effort, the OMNI Resistance Exercise Scale (OMNI-RES).
Two recent validation studies (6,7) of the OMNI-RES using elastic-based resistance training lend credibility to the use of perceived exertion, rather than 1RM, as a method to determine strength groups upon which elastic power prescription can be based. To improve the utility of our elastic tube power training methodology, we suggest that this research be repeated using perceived exertion as the criteria for determining optimal loads. Doing so would increase the utility of our method because individuals would no longer need to go to a wellness center or health care facility to determine their 1RMs before being able to power train with elastic resistance at home or in a clinic.
This article outlines a new method to determine the elastic tube that will provide the greatest improvements in power for the elbow flexors during high-speed training. Our method is novel and overcomes the significant technical challenge of matching dynamic velocity information to dynamic force information to determine real-time power outputs. The study was limited by the lack of linear resistance progression among the color-coded tubes used. Given our results, we believe that this methodology should be used to establish predictive equations across other commonly used elastic resistance devices, such as bands, and be applied across different manufacturers. Additionally, it should be used to evaluate optimal elastic tubes and bands for use with more functional exercises, such as the chest press, squat, or rotator cuff exercises. We believe that this study should also be repeated to compare optimal loading equations resulting from 1RM versus perceived exertion because the latter may provide a more feasible method for use in physical therapy and orthopedic clinics, training rooms, or at home. Given the importance of power in daily life, sports performance and rehabilitation, and the widespread use of bands and tubes for resistance training by physical therapists and other health professionals, the development of optimal loads using these devices will open a gateway for power training prescription in environments where other resistance training methods are not feasible.
Ultimately, the practical application of this research is that it will allow practitioners to identify the optimal resistance tube or band that will maximize power based on their clients' 1RM. More significantly, however, researchers and manufacturers can apply our novel technique to determine the optimal tube or band for power training across multiple exercises, population groups, and brands of tubing and bands. Strength and conditioning professionals, athletic trainers, and therapists can then offer athletes, untrained individuals, and persons with specific neuromuscular diseases or musculoskeletal limitations elastic exercises that can maximize improvements in muscular power. Application of these findings to environments where the use of free weights, machines, or other resistance training modalities is not feasible will give individuals who may otherwise have little access to power training the opportunity to experience the benefits associated with this type of exercise.
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