The Relationship between Running Power and Running Economy in Well-Trained Distance Runners

A novel running wearable called the Stryd Summit footpod fastens to a runner's shoe and estimates running power. The footpod separates power output into two components, Stryd power and form power. The purpose of this study was to measure the correlations between running economy and power and form power at lactate threshold pace. Seventeen well-trained distance runners, 9 male and 8 female, completed a running protocol. Participants ran two four-minute trials: one with a self-selected cadence, and one with a target cadence lowered by 10%. The mean running economy expressed in terms of oxygen cost at self-selected cadence was 201.6 12.8 mL kg 1 km 1 , and at lowered cadence was 204.5 11.5 mL kg 1 km 1 . Ventilation rate and rating of perceived exertion (RPE) were not signi cantly different between cadence conditions with one-tailed paired t-test analysis (ventilation,p= 0.77, RPE,p= 0.07). Respiratory exchange ratio and caloric unit cost were signi cantly greater with lower cadence condition (respiratory exchange ratio,p= 0.03, caloric unit cost,p= 0.03). Mean power at self-selected cadence was 4.4 0.5 W kg 1 , and at lowered cadence was 4.4 0.5 W kg 1 . Mean form power at self-selected cadence was 1.1 0.1 W kg 1 , and at lowered cadence was 1.1 0.1 W kg 1 . There were positive, linear correlations between running economy and power (self-selected cadence and lower cadence,r= 0.6; the 90% con dence interval was 0.2 to 0.8); running economy and form power (self-selected cadence and lower cadencer= 0.5; the 90% con dence interval was 0.1 to 0.8). The ndings suggest running

and at lowered cadence was 1.1 0.1 W kg 1 . There were positive, linear correlations between running economy and power (self-selected cadence and lower cadence,r= 0.6; the 90% con dence interval was 0.2 to 0.8); running economy and form power (self-selected cadence and lower cadencer= 0.5; the 90% con dence interval was 0.1 to 0.8). The ndings suggest running economy is positively correlated with Stryd's power and form power measures yet the footpod may not be suf ciently accurate to estimate differences in the running economy of competitive runners. Keywords: power; running; running economy; running power meter; Stryd; distance runners; wearables; accelerometry 1. Introduction Use of wearable technologies (wearables) like Global Positioning System (GPS) watches, tness trackers, and pedometers has increased in recent years [1]. Wearables have been cited as an advancement in tness tracking since they provide more precise data than classic self-assessment can [2]. Wearable use in performance settings has increased as well, with sports teams and high-level athletes taking advantage of technological advancements to track data over time to pinpoint areas for improvement [3]. Recent advancements in wearables have introduced novel metrics that may be useful to athletes trying to improve their performance. One of these metrics is power. Cycling power meters have been available for years. Cyclists use these in training and racing and the relationships between cycling power output and oxygen consumption (VO2) [4], cycling economy [5], and cycling ef ciency [6] have been established. Most cycling power meters measure Sports2018,6, 142; doi:10.3390/sports6040142

Sports2018,6, 142 2 of 10 power from the force or torque exerted by the cyclist on the pedals or crank and the angular velocity of the crank. Not all of an athlete's metabolic power (VO2) is transmitted into useful mechanical power (cycling power) [7]. Most of the metabolic power or oxygen cost is used to produce heat, to maintain resting metabolic rate, to produce negative work via eccentric muscular contractions, and to produce unnecessary movements if the athlete has poor technique [7]. A variety of methods for measuring or estimating running power have been explored. In these methods different assumptions were made when calculating power and the power calculations required ground reaction force data or kinematic data for input. Due to these differences in assumptions and input data, the values resulting from the power calculations varied widely [8–12]. A well-accepted de nition of running power does not exist [11]. However, a few recent wearables for runners include estimates of running power. The Stryd Summit (Stryd, Boulder, CO, USA) is one of these. The Stryd Footpod is composed of a triaxial accelerometer, a gyroscope, and a barometer embedded into a small shoe-mounted chip. The Footpod estimates running power as well as measures running pace, distance, vertical oscillation, cadence, leg spring stiffness and ground contact time. The Footpod separates running power output into two components: power and form power. Power appears to represent the power output related to changes in the athlete's horizontal motion, whereas form power appears to represent the power output related to changes in the athlete's vertical motion. The Footpod uses undisclosed algorithms to calculate power and form power from the kinematic data it collects from the movements of the user's foot. Running economy is de ned as the energy demand required to sustain a submaximal velocity [13]. Running economy in terms of cost of transport (ml O2 kg 1 km 1 ) is calculated using measurements of steady-state oxygen consumption, participant body mass, and distance traveled [14,15]. Running economy may also be calculated in terms of caloric unit cost using respiratory exchange ratios [14,16]. Running economy is considered

energy demand required to sustain a submaximal velocity [13]. Running economy in terms of cost of transport (ml O2 kg 1 km 1 ) is calculated using measurements of steady-state oxygen consumption, participant body mass, and distance traveled [14,15]. Running economy may also be calculated in terms of caloric unit cost using respiratory exchange ratios [14,16]. Running economy is considered an essential component of distance running performance and is modulated by both biomechanical and physiological factors [17,18]. It is generally considered a better predictor of performance than VO2maxamong similarly capable distance runners [17]. Since running economy is related to oxygen consumption at submaximal velocities [16], improving running economy means lowering oxygen consumption at a given velocity, which may lead to improved performance[13,19–21] . Therefore, it is desirable for competitive distance runners to improve their economy by lowering the energy cost of running. Running economy is related to biomechanical factors including vertical oscillation, ground contact time, stride length, and stride frequency [22,23]. It has been shown that manipulation of stride frequency or stride length can lead to a change in running economy [24–26]. Most notably, De Ruiter et al. [25] measured a signi cant difference in running economy between self-selected cadence and altered cadences at 6%, 12%, and 18%. Measurement of running economy requires expensive equipment (metabolic analyzer) that most runners do not have free access to. As wearable devices have become more sophisticated, athletes and coaches have had access to more data [3]. Wearables present runners with data from daily training that is typically collected in a lab (e.g., vertical oscillation, ground contact time, cadence). While wearable technology use has increased, there have been few studies examining the use of new proprietary measures as an alternative to lab-collected data. Aubry, Power, and Burr [27] assessed power measured by a Stryd Pioneer chest strap as a surrogate for metabolic cost (VO2). The researchers found that power and VO2had a weak positive relationship (r= 0.29). Since running economy is considered central to running performance [17], an alternative metric (power and form power) measured by a wearable sensor (footpod) could be useful to

Power, and Burr [27] assessed power measured by a Stryd Pioneer chest strap as a surrogate for metabolic cost (VO2). The researchers found that power and VO2had a weak positive relationship (r= 0.29). Since running economy is considered central to running performance [17], an alternative metric (power and form power) measured by a wearable sensor (footpod) could be useful to competitive runners. It is unknown if power as measured by the Footpod has a stronger relationship with running economy than power as measured by the Stryd Pioneer chest strap. Furthermore, it is unknown if changes in cadence will cause signi cant change in power or form power, as they can with running economy [24–26]. It was hypothesized that with a decrease in cadence from a self-selected value, power and form power would increase signi cantly along with running economy. Furthermore,

Sports2018,6, 142 3 of 10 it was hypothesized that power and form power would have a positive relationship with running economy. It was predicted that physiological measures of ventilation, respiratory exchange ratio, and psychological measure of rating of perceived exercitation (RPE) would be signi cantly greater with a lower running cadence at the same treadmill speed. The purpose of this study was to measure the correlation between the Footpod power metrics (power, form power) and running economy in well-trained collegiate distance runners at self-selected and lowered cadences. 2. Materials and Methods 2.1. Participants A total of 17 participants were recruited and took part in the study (nine males, eight females). Participants were either current or alumni members of the university's cross country or track and eld team. The study was reviewed and approved by the university's Institutional Review Board. Informed consent was obtained, then running history and physical activity readiness questionnaires were completed before the running trials. The focus of the investigation was on testing the ef cacy of the Footpod power metrics as measures related to running economy. Participant VO2maxvalues were estimated using recent (within 12 months) race performance times (distances from 800 m to marathon) [28,29]. Estimated VO2maxvalues were then utilized to prescribe a workload estimated to be at lactate threshold pace corresponding to 85–89% of velocity at VO2max(vVO2max), which was a training intensity all subjects were familiar with [28,29]. vVO2maxhas been established as a measure that more closely correlates with running performance than either running economy or VO2maxindividually [30,31]. All participants self-reported running at least 25 miles per week. Participants were excluded if a minimum estimated VO2maxvalue that correlated with an estimated VO2maxwas not exceeded (44 mL kg 1 min 1 for women; 50 mL kg 1 min 1 for men). Participant characteristics are reported in Table. Table 1.Participant characteristics separated by gender. Total Sample (n= 17) Males (n= 9) Females (n= 8) Age (years) 20.6 2.3 19.8 1.9 21.5 2.5 Mass (kg) 62.4 6.9 66.4 6.9 57.9 3.3 Height (cm) 175.0 8.2 180.6 6.4 168.6 4.5 Experience level (years) 7.9 3.2 6.1 2.7 10.0 2.4 Estimated

min 1 for men). Participant characteristics are reported in Table. Table 1.Participant characteristics separated by gender. Total Sample (n= 17) Males (n= 9) Females (n= 8) Age (years) 20.6 2.3 19.8 1.9 21.5 2.5 Mass (kg) 62.4 6.9 66.4 6.9 57.9 3.3 Height (cm) 175.0 8.2 180.6 6.4 168.6 4.5 Experience level (years) 7.9 3.2 6.1 2.7 10.0 2.4 Estimated VO 2max(ml kg 1 min 1 ) 56.6 8.2 63.1 4.6 49.3 3.7 Estimated 5 km time (mm:ss) 18:12 2:19 16:23 1:05 20:17 1:19 Note. Data reported as mean standard deviation (SD). Estimated 5 km time corresponds to estimated VO2max values [28]. 2.2. Instruments A Parvo Medics TrueOne 2400 metabolic analyzer (Parvo Medics, Sandy, UT, USA) was used to record gas exchange measures. Gas exchange data were recorded on a Dell desktop computer. Prior to each testing session, the metabolic analyzer was powered on to warm up and then O2and CO2sensors were calibrated based on known gas tank concentrations and room air measurements. Flowmeter calibrations were then completed using a 3.0 L syringe. Stryd metrics were recorded by a Stryd Summit footpod (Stryd, Boulder, CO, USA) paired to a Garmin Fenix 3 watch (Garmin Ltd., Olathe, KS, USA) with the Stryd Run Data Field. Testing was completed on a Trackmaster TMX425c treadmill (Trackmaster Treadmills, Newton, KS, USA). 2.3. Procedures Prior to testing, participants were given a brief overview of the protocol. Participants then put on the metabolic analyzer mouthpiece and nose clip. The Footpod was attached to the runner's left

Sports2018,6, 142 4 of 10 shoelace equidistant from the participant's malleolus and the toe of their shoe. Participant height and weight values were entered in the Stryd Android app and Garmin watch. Participants completed a two-stage discontinuous running protocol. Data collection began during a ve-minute warm-up at a self-selected “easy” pace. Participant self-selected cadence was then determined during a one-minute run at the participant's assigned threshold pace immediately after the ve-minute warm-up. Following the warm-up and self-selected cadence measurement, participants took a three-minute standing rest on the treadmill. During the rest, researchers calculated a cadence value 10% lower than the participants' self-selected cadence for them to replicate during the experimental protocol. A coin was ipped to determine the order of testing protocol stages. For the self-selected cadence stage, participants ran four minutes at assigned threshold pace while attempting to match their cadence with a metronome chirping at their self-selected cadence from the warm-up period. For the lower cadence stage, participants ran four minutes at assigned threshold pace while attempting to match their cadence with the metronome set to a value 10% lower than their self-selected cadence (actual measured cadence was ~4% lower, see results section). After the rest period, participants began the rst stage of the experimental protocol (self-selected or lowered cadence). Upon completion of their rst stage, participants rested for three minutes. After the rst stage rest period, participants began their second stage (self-selected or lowered cadence). Upon completion of the second stage, participants rested for one minute and data collection ceased. Ratings of perceived exertion (RPE) were recorded at the completion of the self-selected and lowered cadence running stages. Running economy (in terms of O2cost and caloric unit cost), caloric unit cost, VO2, respiratory exchange ratio, ventilation, average power, average form power, and average cadence were calculated from the last minute of each running stage. 2.4. Statistical Analysis Statistical analysis was completed using IBM SPSS Statistics Version 23 (IBM Corp, Armonk, NY, USA). For all statistical analyses, signi cance was set atp< 0.05. One-tailed paired t-tests were run to determine if statistically signi cant differences occurred in variables

power, average form power, and average cadence were calculated from the last minute of each running stage. 2.4. Statistical Analysis Statistical analysis was completed using IBM SPSS Statistics Version 23 (IBM Corp, Armonk, NY, USA). For all statistical analyses, signi cance was set atp< 0.05. One-tailed paired t-tests were run to determine if statistically signi cant differences occurred in variables of interest between the two cadence conditions. One-tailed bivariate correlation analyses were run on both cadence conditions separately to determine if there was a relationship between running economy, power, and form power. The Pearson correlation coef cient effect size was evaluated using Cohen's scale [32]: <0.10, trivial; 0.10–0.29, small; 0.30–0.49, moderate; 0.50, large. Correlation coef cient con dence intervals (90% con dence level) were calculated using Fisher'sztransformation [33]. 3. Results Mean data ( standard deviation (SD)) for cadence, VO2, respiratory exchange ratio, RPE and ventilation separated by cadence condition are reported in Table. One-tailed paired t-tests were run on these variables to nd if signi cant differences occurred between the two cadence conditions (self-selected or lowered cadence). The experimental design had the lowered cadence value signi cantly lower compared to self-selected, (t(15) = 6.573,p< 0.001). Physiological variables of VO2,t(16) = 1.856,p= 0.041, and respiratory exchange ratio,t(16) = 1.962,p= 0.034 were both signi cantly greater at the lowered cadence condition. There were no statistically signi cant differences between cadence conditions for the following variables: RPE,t(16) = 1.515,p= 0.075, and ventilation, t(16) = 1.495,p= 0.77.

Sports2018,6, 142 5 of 10 Table 2. Cadence, VO 2, respiratory exchange ratio, rating of perceived exercitation (RPE),and ventilation by cadence condition. Condition Cadence (Strides min 1 ) VO2 (mL kg 1 min 1 ) Respiratory Exchange Ratio RPE Ventilation (L min 1 ) Self-selected cadence 179.6 8.4 52.8 8.7 1.0 0.1 11.7 1.7 93.8 19.3 Lowered cadence 172.5 9.5 * 53.4 8.5 * 1.0 0.1 * 12.1 1.7 96.0 18.4 Percent change 3.9 1.2 2.0 3.9 2.3 Note. Data reported as mean SD. * Paired t-test is signi cant at thep< 0.05 level (one-tailed). Mean data ( SD) for running economy, caloric unit cost, power, and form power are reported in Table. One-tailed paired t-tests were conducted to determine if signi cant differences occurred between the two cadence conditions (self-selected or lowered cadence). The lowered cadence condition resulted in statistically signi cant greater running economy (t(16) = 2.017,p= 0.031), caloric unit cost (t(16) = 2.093,p= 0.027), power (t(16) = 6.349,p< 0.001), and form power (t(16) = 5.664,p< 0.001). Table 3.Running economy and power by cadence condition. Condition Running Economy (ml kg 1 km 1 ) Caloric Unit Cost (kcal kg 1 km 1 ) Stryd Power (W kg 1 ) Form Power (W kg 1 ) Self-selected cadence 201.6 12.8 1.0 0.1 4.4 0.5 1.1 0.1 Lowered cadence 204.5 10.7 * 1.1 0.1 * 4.4 0.5 * 1.1 0.1 * Percent change 1.4 1.0 1.1 5.3 Note. Data reported as mean SD. * Paired t-test is signi cant at thep< 0.05 level (one-tailed). The results of the bivariate correlations of cadence, running economy, caloric unit cost, power, and form power are shown in Table run separately on data collected between the two cadence conditions. It should be noted that the target lowered cadence of 10% below self-selected cadence was not realized (self-selected = 179.6 8.4; lowered cadence = 172.5 9.5 strides min 1 ); however, the decrease in cadence was signi cantly less during the lowered cadence condition (Table). Table 4. Bivariate correlation matrix of cadence, running economy, caloric unit cost, power, and form power (self-selected cadence condition).Cadence Running

the target lowered cadence of 10% below self-selected cadence was not realized (self-selected = 179.6 8.4; lowered cadence = 172.5 9.5 strides min 1 ); however, the decrease in cadence was signi cantly less during the lowered cadence condition (Table). Table 4. Bivariate correlation matrix of cadence, running economy, caloric unit cost, power, and form power (self-selected cadence condition).Cadence Running Economy Caloric Unit Cost Stryd Power Form Power Cadence - Running economy 0.4 ( 0.7 to 0.0) - Caloric unit cost 0.4 * ( 0.7 to 0.0) 1.0 ** (0.98 to 1.0) - Stryd power 0.4 ( 0.7 to 0.0) 0.6 ** (0.2 to 0.8) 0.6 ** (0.2 to 0.8) - Form power 0.8 ** ( 0.9 to 0.6) 0.5 * (0.1 to 0.8) 0.5 * (0.1 to 0.8) 0.8 ** (0.5 to 0.9)- * Correlation is signi cant at thep< 0.05 level (one-tailed). ** Correlation is signi cant at thep< 0.001 level (one-tailed). 90% con dence intervals reported in parentheses. Table Footpod's power metrics (power and form power) for the self-selected cadence condition. Correlation coef cient values were statistically signi cant and negative for the following relationships: cadence and caloric unit cost (r= 0.7 to 0.0); cadence and form power (r= 0.9 to 0.6). Values were statistically signi cant and positive for the following relationships: running economy and caloric unit cost (r= 0.9 to 1.0); running economy and power (r= 0.2 to 0.8); running economy and form power (r= 0.1 to 0.8); caloric unit cost and power (r= 0.2 to 0.8); caloric unit cost and form power (r= 0.1 to 0.8); power and form power (r= 0.5 to 0.9).

Sports2018,6, 142 6 of 10 In general, power was positively related to running economy for both cadence conditions. Correlation scatterplots for running economy and power metrics are shown in Figure. Table shows the relationships between cadence, running economy, caloric unit cost, and the Footpod's power metrics (power and form power) for the lowered cadence condition. Values were statistically signi cant and positive for the following relationships: running economy and caloric unit cost (r= 0.9 to 1.0); running economy and power (r= 0.2 to 0.8); running economy and form power (r= 0.2 to 0.8); caloric unit cost and power (r= 0.2 to 0.8); caloric unit cost and form power (r= 0.2 to 0.8); power and form power (r= 0.4 to 0.9). Correlation coef cient values (90% con dence limits) were statistically signi cant and negative for the following relationships: cadence and running economy (r= 0.8 to 0.1); cadence and caloric unit cost (r= 0.8 to 0.2); cadence and form power (r= 1.0 to 0.7). Figure 1. Correlation scatterplots for power metrics and running economy. Black circles and solid line of best t represent self-selected cadence condition. Grey diamonds and dotted line of best t represent lowered cadence condition. (a) Self-selected cadence power and running economy (r= 0.6); lowered cadence power and running economy (r= 0.6). (b) Self-selected cadence form power and running economy (r= 0.5); lowered cadence form power and running economy (r= 0.5). (c) Self-selected cadence power and caloric unit cost (r= 0.6); lowered cadence power and caloric unit cost (r= 0.5). (d) Self-selected cadence form power and caloric unit cost (r= 0.5); lowered cadence form power and caloric unit cost (r= 0.6). Table 5. Bivariate correlation matrix of cadence, running economy, caloric unit cost, power, and form power (lowered cadence condition). Cadence Running Economy Caloric Unit Cost Stryd Power Form Power Cadence - Running economy 0.5 * ( 0.8 to 0.1) - Caloric unit cost 0.5 * ( 0.8 to 0.2) 1.0 ** (0.9 to 1.0) - Stryd power 0.4 ( 0.7 to 0.0) 0.6 * (0.2 to 0.8) 0.5 * (0.2 to 0.8) - Form power 0.9 **

This study measures correlations between running economy and power in distance runners.