Predicting V̇O2 Change from a Single Unidirectional Trial of Cadence Manipulation in Recreational Runners
Thomas Ruediger* and Ryan Dombkowski
Rinker-Ross School of Health Sciences, Trine University, USA
*Corresponding author: Thomas Ruediger, Associate Professor, Rinker-Ross School of Health Sciences, Trine University, 1819 Carew Street, Fort Wayne, IN46805, USA, Tel: +1-810-618-1897, E-mail: firstname.lastname@example.org
Int J Sports Exerc Med, IJSEM-3-049, (Volume 3, Issue 1), Original Article; ISSN: 2469-5718
Received: September 30, 2016 | Accepted: February 07, 2017 | Published: February 10, 2017
Citation: Ruediger T, Dombkowski R (2017) Predicting V̇O2 Change from a Single Unidirectional Trial of Cadence Manipulation in Recreational Runners. Int J Sports Exerc Med 3:049. 10.23937/2469-5718/1510049
Copyright: © 2017 Ruediger t, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The purposes of the study were to describe V̇O2 among recreational runners under self-selected cadence (SS) and manipulated cadence 10 steps per minute faster (SS + 10) during a single treadmill running assessment, examine predictive ability of several variables on V̇O2, and compare estimates of energy expenditure from treadmill software with direct measure of V̇O2.
Airflow and percent of expired O2 were used to calculate V̇O2 from thirteen (7 females and 6 males) runners at self-selected speed at both SS and SS + 10. Respiratory rates and mean tidal volumes were calculated from the collected data. Among all subjects there was no significant difference in V̇O2 between SS and SS + 10. There was a significant increase in V̇O2 from SS to SS + 10 in males, but not females. We observed several patterns of changes in respiratory variables at SS + 10.One pattern (N = 3, all female) was characterized by a modest increase in respiratory rate and large decrease in tidal volume, which resulted in a decrease in total ventilation and accompanied by increases in V̇O2 efficiency at SS + 10. Changes in tidal volume and ventilation were positively correlated with changes in V̇O2 between SS and SS + 10 conditions. None of the other potential predictor variables were significantly correlated with changes in V̇O2. Correlation of treadmill predicted energy expenditure of the Wood way Pro 27 with direct measure of V̇O2 was 0.81. It appears that a quarter of recreational runners may have large improvements in their running efficiency through an increase in cadence.
Efficiency, Step rate, Respiratory pattern, Treadmill estimates
Physiologically, oxygen consumption rate (V̇O2) increases as work increases. Changes in V̇O2 that occur at a fixed work load indicate relative levels of V̇O2 efficiency. V̇O2 may change under a fixed work load due to novelty of tasking, such as changes in gait [1-3]. It has been proposed that changes in V̇O2 efficiency under same load but different gait conditions may be partly explained by altered muscle activity . It has also been proposed that these changes in efficiency may be due anxiety related changes in heart rate and respiration . V̇O2 can be directly measured using commercially available physiological and metabolic equipment. Metabolic equivalents (METS) are often calculated by an algorithm and displayed on monitors on commercial aerobic fitness equipment such as treadmills, bicycles, rowing machines, and steppers.
Submaximal V̇O2 tests without direct measurement of V̇O2 are used to estimate V̇O2max. Although the use of submaximal tests is more efficient and less expensive than direct measure of V̇O2 in assessing V̇O2max and provide a measure of aerobic capability, this estimate of V̇O2max is not appropriate for a comparison of V̇O2 under different conditions . Determining V̇O2 efficiency through a comparison of V̇O2 under different conditions such as cadence for running or gear selection for cycling, becomes increasingly more useful as the event becomes longer. V̇O2 efficiency is of interest to both competitive and recreational runners. Competitive runners are aiming to improve their finish times. Recreational athletes may be aiming to improve their time, but also in some cases to be able to finish.
Running more efficiently without direct measure of V̇O2 is done through repeated trial runs at different running paces and cadences. Direct measure on V̇O2 is available commercially in some markets for about $150-$300. Though relatively expensive compared to indirect estimates of V̇O2, these direct measures may be less costly than the time used to by iterative trial and error, and therefore a more efficient way to improve a completion time or to complete an endurance event. Competitive runners with access to direct assessment of V̇O2 are able to use this method to evaluate and modify training to improve performance.
In a study using direct measure of V̇O2 on ten high mileage (40-110 miles per week) runners, V̇O2 was collected under seven stride length conditions but a constant pace of 7 min/mile . Based on the race results of a local half-marathon  with more than 2000 finishers, this study sample would represent the upper 3% of recreational runners. The self-selected stride length in that study group was 132 cm (143% of leg length) and was never the most efficient . Seven of the runners became more efficient at a shorter stride length and three at a longer stride length . Since running cadence varies inversely with stride length, seven of the runners became more efficient at a higher cadence, three at a lower cadence. When running at ventilatory threshold, self-selected stride frequency varied from optimal stride frequency more among novice recreational runners than among trained runners , with novice runners consistently below their optimal cadence based on V̇O2. Trained runners have also been shown to have better V̇O2 efficiency than untrained runners . It is not known if a more efficient cadence can be determined by direct measure of V̇O2 with a single one directional manipulation of cadence.
Purposes of the study were to report and compare V̇O2 and selected respiratory variables among healthy recreational runners at a self-selected cadence (SS) and single manipulated cadence 10 steps per minute faster than self-selected (SS + 10) during a single treadmill running assessment and to compare estimates of METS from treadmill software with direct measure of V̇O2.
Experimental approach to the problem
V̇O2 was collected from recreational runners running at SS and SS + 10 on a treadmill (Wood way PRO 27, Wood way, Waukesha, WI). Speed was self-selected by each subject but then kept constant for both cadence conditions. Comparisons of V̇O2 and changes in V̇O2 at SS + 10 were made in the entire cohort and dichotomized for gender, self-selected speed, and SS.
The study protocol was approved by the University's Institutional Review Board. Subjects of either gender were recruited by convenience from a local cohort of graduate students or by direct association or relationship with this cohort. Subjects self-identified themselves as recreational runners logging at least 10 miles per week, free of any neuromuscular or neurological disorders, lacking any joint or limb pain or discomfort, and willing to sign the informed consent document. Subjects were excluded with any "YES" answer on the Physical Activity Readiness Questionnaire (PAR-Q). A total of 13 subjects (six male/seven female) volunteered and all 13 completed the study. Subjects were 23-26 years old, had a mean body mass of 78.5 ± 4.8 kg, with a range from 53.5 to 110.5 kg.
Equipment and study familiarization
Each subject ran on two occasions. The first occasion was for familiarization with the treadmill and for fitting and familiarization with mask and tubing (BIOPAC, Goleta, CA). Subjects practiced running at their preferred SS and SS + 10 and also utilizing the Pro Metronome digital-audio metronome app to maintain their cadence (EUMLab, Berlin GE).No data, other than the appropriate mask size, were collected during the familiarization trial.
Experimental procedure and data collection
The gas analysis system was calibrated for airflow and room environmental conditions prior to each subject. Mask was fitted to subject; airflow and percentage of expired O2 were collected throughout the entire experimental procedure. Airflow transducer (SS11LA, BIOPAC, Goleta, CA) and oxygen sensor as part of the expired gas analysis system (GASSYS2-EA, BIOPAC, Goleta, CA) were calibrated appropriately for each trial. Each subject was allowed to warm-up for several minutes with either brisk walking or jogging. When the subject was ready, they were allowed to set the treadmill to their own self-selected running speed and assume their preferred cadence. After V̇O2 reached a stable plateau, each subject was instructed to continue at this metronome assisted SS for three additional minutes. At the end of this period, cadence was increased by 10 steps per min. The subject was then asked to match this new cadence. Once the new cadence was established and V̇O2 again reached a stable plateau, the subject was instructed to continue at this metronome assisted SS + 10 for three additional minutes. At the end of this period the subject was allowed to cool down as desired. V̇O2 for SS and SS + 10 were averaged during the final three minutes of each stage for each subject. Respiratory rate and mean tidal volumes were also calculated from the data for each subject. Typical traces of V̇O2 data plateaus, as well as time periods utilized for data analysis are demonstrated in figure 1.
Figure 1: Typical tracings of oxygen consumption (L O2/min) in two subjects. Arrows indicate the beginning of the two different cadence conditions. Grey boxes demonstrate the period used for data analysis. Panel A denotes a subject with a 29% increase in V̇O2 at SS + 10 while panel B demonstrates a subject with a 39% decrease in V̇O2 at SS + 10. View Figure 1
Data were analyzed with IBM SPSS Statistics 23 (IBM Corp., Armonk, NY).Unless otherwise specified, all data are presented as mean ± standard error (SE); ranges are shown for descriptive data. Significance was assumed at P ≤ 0.05. Subjects were dichotomized by gender (M, F), speed (≥ 7 mph, < 7 mph) and cadence (≥ 165, < 165 steps per min). Unpaired (Student's) t-tests were performed to examine differences between dichotomized groups. Paired t-tests were performed to examine differences in V̇O2 before and after cadence manipulation with each subject serving as their own control. Pearson's (scalar data) and Spearman's (nominal) correlations were calculated for potential predictor variables.
Data for mass, SS, and self-selected running speed are summarized in table 1. Males (n = 6) were significantly heavier than females (n = 7). The mean SS and self-selected running speed of all subjects (n = 13) were 166 ± 3 steps per min (spm) and 6.7 ± 0.4 mph, respectively. Dichotomized by gender, there were no significant differences in SS or self-selected running speed. Dichotomized by speed, individuals who elected to run faster (n = 6) did not display any significant differences in either mass or SS than those who elected to run at a slower pace (n = 7). Dichotomized by cadence, there were no significant differences in mass or self-selected running speed in those subjects who elected to run at either higher (n = 7) or lower (n = 6) cadences.
Table 1: Participant demographics and self-selected cadences (SS) and speeds (mean ± SE). * = significantly different. View Table 1
Overall (n = 13), there was no change in V̇O2 between SS (45.1 ± 3.2 L O2/kg/min) and SS + 10 (46.8 ± 3.9 mL O2/kg/min) (Figure 2).There were, however, large differences between individuals in the V̇O2 response to increased cadence. While the mean SS + 10 V̇O2 was 104 ± 5% of the SS V̇O2, the range was a 32% increase to a 39% decrease. Seven out of 13 subjects displayed decreased V̇O2 efficiency (> 5% increase in V̇O2) at SS + 10. A typical trace of this effect in presented in figure 1A. Three subjects exhibited < 2% change in V̇O2. Three subjects displayed increased V̇O2 efficiency (> 5% decrease in V̇O2) at SS + 10. V̇O2 tracing from the most extreme subject, a 39% V̇O2 decrease at SS + 10, is presented in figure 1B.
Figure 2: Mean V̇O2 (mean ± SE) at the two cadence conditions with no significant difference. View Figure 2
There were no significant differences in V̇O2 between individuals with a higher (> 165spm) or lower (≤ 165spm) cadence at SS or SS + 10, nor where there any changes in V̇O2 from SS to SS + 10 within these groups (data not shown). Individuals who ran faster displayed higher initial V̇O2 values (52.7 ± 3.5 mL O2/kg/min) than those who ran slower (38.7 ± 3.7 mL O2/kg/min, P = 0.020), but there were no significant changes in V̇O2 within these groups after transitioning from SS to SS + 10 (data not shown). Males demonstrated a higher V̇O2 than females at both SS and SS + 10 (Figure 3). Males displayed a significant increase in V̇O2 at SS + 10 compared to SS (52.8 ± 3.8 vs 56.9 ± 4.3 mL O2/kg/min, respectively, P = 0.047) (Figure 3).When combined, females displayed no significant differences in V̇O2 at SS + 10 compared to SS (Figure 3).
Figure 3: V̇O2 (mean ± SE) grouped by gender under the two cadence conditions. * indicates a significant difference between groups and † indicates a significant difference within male group. View Figure 3
We examined several potential predictor variables to explain the change in V̇O2. The potential predictor variables were: gender, mass, height, BMI, step length, leg length, self-selected speed, average weekly running distance, step length as a percentage of leg length, step length change from SS to SS + 10, and step length change from SS to SS + 10 as a percentage of leg length (Table 2). There were no significant correlations between any of the selected potential predictor variables with the change in V̇O2.
Table 2: Raw (cm) and relative (% leg length, %LL) characteristics of participants' stride during self-selected cadence (SS) and 10 steps per minute faster than SS (SS + 10) and including Pearson r-correlation values with respect to changes in VO2 are shown with P-values. View Table 2
Changes in measured and calculated respiratory variables from SS to SS + 10 are presented in table 3. Combined subjects mean changes in V̇O2 were small, but did encompass a wide variety in individual respiratory compensation patterns. Mean respiration rate increased 7.0 breaths per minute at SS + 10. Mean pulmonary O2 extraction decreased 0.33% at SS + 10. Mean tidal volume decreased 0.20 liters/breathe at SS + 10. Mean pulmonary ventilation (VE) increased 6.9 liters/min. There were no significant correlations between changes in respiratory rate or pulmonary O2 extraction with changes in V̇O2 from SS to SS + 10. Raw and percentage changes in VE and tidal volume were positively correlated with changes in V̇O2 from SS to SS + 10.
Table 3: Differences in measured respiratory variables under two cadence conditions. bpm = breaths per minute. Pearson r-correlation values are shown P-values. View Table 3
Measured V̇O2 were converted to METS utilizing the common conversion of 1 MET = 3.5 mL O2/kg/min. Under this assumption, measured METS at SS and SS + 10 were both positively correlated with the predicted METS from the treadmill software predicted value. Utilizing a linear best-fit model, measured MET versus predicted MET correlations for SS and SS + 10 were 0.81 and 0.80, respectively (data not shown). These models produce measured MET values which averaged 16% (SS) and 20% (SS + 10) above treadmill software predicted values. Both of these best-fit equations produce regression models which deviate more from treadmill software predicted values at higher METS.
Subjects in the current study were similar with regard to age [8,10,11] and sample size [1,8,11] when compared to previous studies. BMI of subjects in the current study was 25.4 kg/m2 which is higher than in previous studies which ranged from 21-22 kg/m2 [6,10,11]. Mean weekly running mileage [6,11] and subjects' self-selected pace [1,10-12] were lower than in most of previous studies. Subjects in the current study ran a pace similar to Ruiter, et al.  and that is representative of a cross-section of recreational runners .
Mean V̇O2 did not change with a single cadence manipulation (Figure 2). Freely chosen running speed and cadence were not predictive of changes in V̇O2 with cadence manipulation. There was a significant difference in V̇O2 and changes in V̇O2 when dichotomized by gender (Figure 3). As a group, the six males were more efficient at SS. None had more than 5% increase in efficiency at SS + 10; four subjects had more than 5% decrease in efficiency at SS + 10; two showed no change at SS + 10. As a group, the seven females showed no change in running efficiency (Figure 3). Three had more than 5% increase in efficiency at SS + 10, three had more than 5% decrease in efficiency at SS + 10, and one showed no change at SS + 10.
V̇O2 is a product of VE and O2 extraction (FiO2-FeO2) and VE is a product of respiratory rate and tidal volume. Changes in O2 extraction were not correlated with changes in V̇O2 at increased cadence. Changes in VE were correlated with increased V̇O2 at increased cadence. Previous studies have not discussed which component of VE, respiratory rate or tidal volume is responsible for this correlation. Twelve subjects had an increase in respiratory rate at SS + 10 compared to SS. This is expected and is consistent with locomotor-respiratory coupling previously reported (13). At constant work, this would be expected to be accompanied by a decrease in tidal volume, which was observed (Table 3). Overall, increases in respiratory rate outweighed the decrease in tidal volume with a resultant mean increase in VE (Table 3). This increase in VE would be expected to be accompanied by a decrease in O2 extraction at constant work, which was observed.
Changes in V̇O2 may be explained by four observed respiratory pattern changes at SS + 10. The first pattern (N = 7) was a large increase in VE dominated by an increase in respiratory rate. A second pattern (N = 2) produced little change in VE due to the canceling effect of moderate increase in respiratory rate and moderate decrease in tidal volume. A third pattern was demonstrated by only one subject who had no change in respiratory rate and moderate decrease in tidal volume; the resultant decrease in VE was accompanied by an increased O2 extraction. The fourth pattern (N = 3) was characterized by a small increase in respiratory rate and large decrease in tidal volume resulting in decreased VE and increase in efficiency.
The three subjects that were more efficient at SS + 10 were all female, representing 23% of subjects in this study is consistent with Morgan, et al. . A decrease in tidal volume was the single best predictor of increased V̇O2 efficiency at higher cadence in this study (Table 3).
While shallower breathing alone may predict a slight decrease in V̇O2, a more plausible explanation for the large decrease in V̇O2 in this small group is increased running efficiency. Increasing cadence should result in a concomitant decrease in vertical oscillation and has been previously reported . Gender differences in lower extremity running mechanics have been reported , which may partially explain the wider variability in V̇O2 changes in females with cadence manipulation.
It is not known if manipulating respiratory rate would be an independent predictor of change in V̇O2 while running. To our knowledge correlations between respiratory rates, tidal volumes, and total VE with V̇O2 while running have not been reported in the peer-reviewed literature.
Recreational runners are not routinely measuring V̇O2 directly, but do have access to estimates of energy expenditure from treadmill software in METS or kilocalories. Sources of error include variance in consumption around accepted conventions of METS and consistency of actual treadmill belt speed with machine settings. Consistency of treadmill belt speeds may be influenced by drive, motor, temperature of belt, environment, subject mass, and selected speed. In our opinion, the current study demonstrates appropriate correlation of energy expenditure between Woodway PRO 27 treadmill software and direct measure of V̇O2 given the inherent variability of these estimates. Based on the results of this study, the treadmill software appears to be accurate with regards to energy expenditure near the lower end of vigorous exercise as defined by the American College of Sports Medicine (> 6 METS) .
An opportunity exists to improve performance in some recreational runners with cadence manipulation with direct measure of V̇O2. Given the short time commitment for direct V̇O2 measurement under different cadences and the ease of data analysis, this should be useful to coaches and runners to monitor performance increases within a given running season. Given the magnitude of improvement in three of our subjects, cadence manipulation may be the difference in finishing or not finishing a long distance running event. Similarly, there may be an opportunity to improve performance in competitive and recreational rowers, bicyclists, swimmers, cross-country skiers, and other extended length events.
There are several limitations to this study. Sample size was appropriate for a pilot study but limited both power and generalizability. We did not collect heart rate and rating of perceived exertion and these measures of effort expenditure would provide support for changes in V̇O2 during cadence manipulation.
Future research will investigate the effects of cadence manipulation on V̇O2 in a larger group of runners with more age, body composition, and running speed diversity. We will also include both increase and decrease manipulation of cadence and assess reliability of V̇O2 measures.
The authors declare no conflicts of interest, and no external sources funded this research.
The study protocol was approved by the Trine University Institutional Research Review Board (approved 12 Feb 2016) and all participants provided their written informed consent.
Research was Conducted
Trine University Health Sciences Education Center, Trine University Rinker-Ross School of Health Sciences, Fort Wayne, IN.
Cavanagh PR, Williams KR (1982) The effect of stride length variation on oxygen uptake during distance running. Med Sci Sports Exerc 14: 30-35.
Flynn TW, Connery SM, Smutok MA, Zeballos RJ, Weisman IM (1994) Comparison of cardiopulmonary responses to forward and backward walking and running. Med Sci Sports Exerc 26: 89-94.
Hooper TL, Dunn DM, Props JE, Bruce BA, Sawyer SF, et al. (2004) The effects of graded forward and backward walking on heart rate and oxygen consumption. J Orthop Sports Phys Ther 34: 65-71.
Flynn TW, Soutas-Little RW (1993) Mechanical power and muscle action during forward and backward running. J Orthop Sports Phys Ther 17: 108-112.
Grossman P (1983) Respiration, stress, and cardiovascular function. Psychophysiology 20: 284-300.
McArdle WD, Katch FI, Katch VL (2015) Exercise Physiology: Nutrition, Energy, and Human Performance. (8th edn), Baltimore, MD: Lippincott, Williams, and Wilkins.
(2015) Fort 4 Fitness. Half-marathon results.
de Ruiter CJ, Verdijk PW, Werker W, Zuidema MJ, de Haan A (2014) Stride frequency in relation to oxygen consumption in experienced and novice runners. Eur J Sport Sci 14: 251-258.
Morgan DW, Bransford DR, Costill DL, Daniels JT, Howley ET, et al. (1995) Variation in the aerobic demand of running among trained and untrained subjects. Med Sci Sports Exerc 27: 404-409.
Högberg P (1952) How do stride length and stride frequency influence energy-output during running? Arbeitsphysiologie 14: 437-441.
Morgan D, Martin P, Craib M, Caruso C, Clifton R, et al. (1994) Effect of step length optimization on the aerobic demand of running. J Appl Physiol (1985) 77: 245-251.
Williams KR, Cavanagh PR (1987) Relationship between distance running mechanics, running economy, and performance. J Appl Physiol (1985) 63: 1236-1245.
Hoffmann CP, Bardy BG (2015) Dynamics of the locomotor-respiratory coupling at different frequencies. Exp Brain Res 233: 1551-1561.
Tseh W, Caputo JL, Morgan DW (2008) Influence of gait manipulation on running economy in female distance runners. J Sports Sci Med 7: 91-95.
Ferber R, Davis IM, Williams DS 3rd (2003) Gender differences in lower extremity mechanics during running. Clin Biomech (Bristol, Avon) 18: 350-357.
Pescatello LS, Arena R, Riebe D, Thompson PD (2014) ACSM's Guidelines for Exercise Testing and Prescription. (9th edn), J Can Chiropr Assoc 58: 328.