### Abstract

The purpose of this study was to investigate how listening to music while running affects performance and perceived exertion of college students. Twenty-eight undergraduate kinesiology students (17 males, 11 females; age = 22.9 ± 5.9 yrs) were studied to determine if running performance and rating of perceived exertion were affected by listening to music. Running performance (RP) was measured by a 1.5-mile run. Two trials were performed, the first was a running performance without music listening (RPWOML = 12.94 ± 3.35 min) and the second trial was a running performance while music listening (RPWML = 12.50 ± 2.48 min). The second trial was measured five days post the initial trial. Listening to music (music listening) was defined as the subject’s self selection of music tracks and use of a personal digital audio player (e.g. IPod, MP3) during exercise. Perceived exertion without music listening (PEWOML = 14.7 ± 1.3) and perceived exertion with music listening (PEWML = 15.2 ± 2.4) was measured by the Borg 6 to 20 RPE scale. Data analysis was performed on the raw data by utilizing dependent t-tests to calculate and compare sample means. Statistical analyses determined a significant difference (p < .05) between running performance without music listening (RPWOML = 12.94 ± 3.35 min) and running performance with music listening (RPWML = 12.50 ± 2.48 min). However, no significant difference (p < .05) was determined between perceived exertion without music listening (PEWOML = 14.7 ± 1.3) and perceived exertion with music listening (PEWML = 15.2 ± 2.4) as measured by the Borg 6 to 20 RPE scale. In conclusion, the results of this study indicate that music listening has a significant effect on running performance during a maximal 1.5-mile run. However, music listening had no significant effect on rating of perceived exertion at this distance. Based on the results of this study it is recommended that coaches, athletes, and traditional exercisers consider listening to music during training to enhance performance.

**Key Words:** Music Listening, Aerobic, Performance, Rated Perceived Exertion (RPE)

### Introduction

In the past listening to music was relegated to travelling in automobiles, while in the home, while engaged in recreational activities and occasionally at work. Today, the portable music industry (e.g. cassettes, compact discs, and iPod/MP3 digital audio devices) has popularized music “on the go” and invaded just about every environment including training venues. These devices have made it easier for people to enjoy their music and create their own style of workouts with relative ease, regardless of the setting, and has transcended into a multi-million dollar industry (14). Similarly, the sports arena is an environment where music has flourished. Traditionally, music has been used to motivate and inspire people prior to an important event (e.g. pre-game of a critical contest) as well as when they engage in sports and training for competition. Thus, athletes and traditional exercisers alike have used music as an accompaniment to exercise to sustain motivation, resist mental and emotional fatigue, and potentially enhance their physical and athletic performance (10). Scientific inquiry has revealed three key ways in which music can ‘influence’ preparation and competitive performances through dissociation, arousal regulation, and synchronization (3, 4, 6, 8-10). More specifically, research indicates music to be particularly effective in distracting exercisers away from their perceived exertion.

#### Conceptual Framework

Conceptually the underlying framework of using motivational music in exercise and sport devised by Karageorghis et al. (7) indicated two main hypotheses regarding arousal regulation and fatigue dissociation. First, music can be used to alter emotional and physiological arousal and thus can act either as a stimulant or sedative prior to and during physical activity. Therefore, an athlete can use various music tempos as a ‘psych-up’ strategy in preparation for a competition or perhaps an aid to calming over anxiousness. Second, music diverts a performer’s attention from sensations of fatigue during exercise. This diversionary technique, known as dissociation, lowers perceptions of effort. Effective dissociation can promote a positive mood state, thus turning the attention away from thoughts of physiological sensations of fatigue (7).

#### Rated Perceived Exertion

Noble and Robertson (13) define perceived exertion as the subjective intensity of effort, strain discomfort and/or the fatigue that is experienced during an exercise. Currently, the most consistent findings suggest that perceived exertion will rate in lower values when participants exercise to music (12, 13, 22, & 24). The research data compiled from over the past two decades has found music particularly effective in distracting exercisers away from their perceived exertion during physical activity. A study by Nethery, Harmer, and Taaffe (12) found that perceived exertion while exercising to music was lower than for other attentional distracters and for the no distraction condition. Furthermore, Thornby et al. (22) tested exercising participants in the presence of music, no music and noise. They discovered that participants reported a lower perceived exertion while exercising in the presence of music in comparison to the no music and noise conditions.

These findings coupled with the popularity and substantial profits generated between the association of music and training (14) would seem to indicate a correlation between the use of music and performance. However, the effects of listening to music on performance and other physiological measures are less clear. Therefore, the purpose of this study was to investigate the effect listening to music has on running performance and rating of perceived exertion of college students.

### Methods

#### Experimental Approach to the Problem

Listening to music (music listening) was defined as the subject’s self selection of music tracks and use of a personal digital audio player (e.g. IPod, MP3) during exercise. Running performance was determined by a maximal 1.5 mile run to predict VO2 max. Subjects were asked to complete the distance run in the fastest time possible. Results were recorded in minutes and seconds. A common field test equation, V02 max (ml*kg-1*min-1) = 3.5 + 483 / (time in minutes), was selected to access cardio-respiratory fitness of the subjects utilizing their 1.5 mile running performance (1). Perceived exertion was determined by the Borg 6 to 20 RPE scale. Rating of perceived exertion summarizes the exertion levels between rest and maximum effort numerically from 6 to 20 (2).

#### Subjects

Twenty-eight undergraduate kinesiology students (17 males, 11 females; age = 22.9 ± 5.9 yrs) from a south Texas university were studied to determine if running performance and rating of perceived exertion were affected by listening to music. Institutional Review Board approval and subject informed consent were obtained prior to commencement of the research study.

#### Procedures

All participants were required to fill out an informed consent document two days prior to testing. Participants were then instructed to obtain sufficient sleep (6-8 hours) and avoid food, caffeine, tobacco products, or alcohol for 3 hours prior to testing the 1.5-mile run (1). Prior to testing, a 1.5-mile course was measured with a Rolatape® distance measuring wheel. The start/finish line and .75-mile line were marked off with two cones each on the large sidewalk course. Three testers were used to ensure subjects completed the 1.5-mile run, two researchers were stationed at the start/finish line to collect run times and RPE scores for each participant, while another tester was stationed at the .75-mile line or turn around portion of the course. To complete the 1.5-mile run each participant had to begin at the starting line, run to the .75-mile line, and then simply turn around and run back to the start/finish line. Stopwatches were used to measure 1.5-mile run times. Following the course explanation; the participants were encouraged to warm-up and stretch before starting the 1.5-mile run, as well as verbally read the following instructions for use of the Borg 6 to 20 RPE scale:

> During the exercise test we want you to pay close attention to how hard you feel the exercise work rate is. This feeling should be your total amount of exertion and fatigue, combining all sensations and feelings of physical stress, effort, and fatigue. Don’t concern yourself with any one factor such as leg pain, shortness of breath, or exercise intensity, but try to concentrate on your total, inner feeling of exertion. Try not to underestimate or overestimate your feeling of exertion, be as accurate as you can (20).

The participants completed two separate 1.5-mile runs as a group during their regularly scheduled class time on their campus. The first trial was performed in silence without any form of digital audio device (IPod, MP3) which would enable music listening. Five days post the initial trial, a second 1.5-mile run was administered during the regularly scheduled class meeting. However, in this 1.5-mile run test participants were required to use digital audio devices during the trial to enable music listening. Music selection was not controlled during this experiment; therefore the participants were able to select their favorite musical tracks to accompany them on their second trial run. All run times were recorded as the participants crossed the finish line, and RPE was obtained shortly thereafter when the subjects were asked to pick the number best reflecting their exertion from the Borg 6 to 20 scale poster board on site.

#### Statistical Analysis

An experimental one-group pretest-posttest design was utilized. The subjects completed two 1.5-mile run trials to test the effect of music listening on running performance and rating of perceived exertion. Dependent t-tests were utilized to compare mean data from the experimental conditions: music listening and without music listening. Significance was determined at the probability level of .05.

### Results
The results are divided into two sections: running performance and rating of perceived exertion. Data analysis was performed on the raw data by utilizing dependent t-tests to calculate and compare paired sample means. The mean and standard deviation values for these two measures, according to experimental conditions, are summarized in Table (1).

#### Running Performance

Dependent t-tests were conducted on the subjects running performance times in conditions without music listening and with music listening. Two trials were performed, the first was a running performance without music listening (RPWOML = 12.94 ± 3.35 min) and the second trial was a running performance while music listening (RPWML = 12.50 ± 2.48 min). Statistical analyses found music listening had a significant t (26) = 1.75, p = .0478 impact on running performance as shown in Figure 1. In addition, music listening was found to have a significant t (16) = 2.07, p = .0445 effect on running performance for male subjects, whereas female subject t (10) = 1.23, p = .12 indicated non significance.

#### Rating of perceived exertion
A paired two sample dependent t-test was conducted on the subjects rating of perceived exertion after completing a 1.5-mile running performance in conditions without music listening and with music listening. The result of the two trials found the subjects rated perceived exertion without music listening (PEWOML = 14.7 ± 1.3) to be lower than ratings of perceived exertion with music listening (PEWML = 15.2 ± 2.4). Statistical analysis found the effect of music listening on the groups rated perceived exertion to be non significant t (26) = -1.22, p = .11 as shown in Figure 2. However, music listening was found to have a significant t (10) = -2.96, p = .01 directional effect on reported female rating of perceived exertion scores while non significance t (16) = -.18, p = .4263 was found among male rating of perceived exertion scores.

### Discussion

The effects of listening to music on running performance and the rating of perceived exertion during maximal 1.5-mile runs were investigated. By comparing the recorded ratings of perceived exertion and running times of the two situations, it became clear when the subjects exercised to music their running performance improved collectively. Previous research by Thornby et al. (22) also found that the time spent exercising, the amount of work done, and heart rate were all significantly higher in the presence of music than in the other conditions. Similarly, Edworthy and Waring (4) make the suggestion, in regards to music’s effect on running performance, that the pace of music will influence the pace of exercise. Therefore, the assumption can be made that exercising to fast tempo music should produce faster running performance. However in this study’s case, music selection was not controlled; therefore some participant’s personal preferences might not have met the tempo or vigorous nature of the exercise conducted. Even so, the results of the two trials found the subjects running performance while listening to music (RPWML = 12.50 ± 2.48 min) to be substantially faster than running performance without music listening (RPWOML = 12.94 ± 3.35 min).

These results indicate that music listening has a significant effect (p < .0478) on running performance during a maximal 1.5-mile run. Therefore, the research null hypothesis in regards to music’s effect on running performance has been rejected. Furthermore, male subjects in particular were found to perform better while listening to music.

Additionally, music listening was found to have no significant effect on rating of perceived exertion during a maximal 1.5-mile run. The findings of the most recent research reported the effectiveness of music on the subjects’ perceived exertion rate during submaximal exercise, Copland and Franks (3), Szmedra and Bacharach (20), and Potteiger, et al. (15). These authors suggested that in the absence of external stimulation (e.g. music) participants may focus more strongly on their own efforts and perceive them to be higher. This reasoning provides an explanation as to why traditionally subjects experience decreased RPE, particularly in submaxial exercise where music has been shown to effectively dissociate sensations of fatigue and promote a more enjoyable exercise experience. However, this study evaluated music’s effectiveness on a maximal 1.5-mile run. The result of the two trials found the subjects rated perceived exertion without music listening (PEWOML = 14.7 ± 1.3) to be lower than ratings of perceived exertion with music listening (PEWML = 15.2 ± 2.4). Previous research by Yamishita and Iwai (22) suggest that music’s effect on RPE is limited by the intensity of the exercise. Schwartz et al. (17) experienced similar findings stating that at 75% V02max RPE values did not significantly differ for participants between music and control conditions. Accordingly, these findings share the similar reasoning of Rejeski (16) which suggest that when subjects work at maximal intensities beyond anaerobic threshold, physiological cues dominate the attentional processes leading to external cues, such as music, to become less effective on RPE. Additionally, the results indicate listening to music has no significant effect (p < .05) on rating of perceived exertion during a maximal 1.5-mile run. Therefore, the research null hypothesis regarding music’s effect on rating of perceived exertion has been accepted. Furthermore, female subjects were found to rate RPE more difficult while listening to music. This further supports that music’s dissociative properties exhibited in sub max exercise are not transferred into maximal exercise over 75% VO2 max.

It is important to note that although none of the trials were conducted in wet conditions, wind speed and wind direction could not be standardized between trials and this may have been an additional error source. Both performance trials were conducted outdoors at 75 degrees Fahrenheit. However, wind speeds differed between trials; trial one experienced wind speeds of 8 mph with gusts of 14 mph while trial two experienced wind speeds of 18 mph with gusts of 25 mph. Due to these confounding factors conducting the research indoors would have addressed this problem. Unfortunately, an indoor track was not yet available at the university where the research was conducted. Secondly, the participants completed the two running trials together as a group. A natural tendency to compete may have compromised the internal validity of the study. However, the threat to internal validity was preferred to the potential lack of motivation had participants been required to complete the task individually (18).

### Applications In Sport

Music has been found to be an ideal accompaniment for exercise. It has the ability to alter emotional and physiological arousal as well as dissociate a performer’s attention from sensations of fatigue during exercise (19). The tempo of the music can also be used to influence exercise performance as their arousal level will be heightened by the fast tempo (7). If music is applied to these types of situations, music’s impact may have the ability to change the context in which physical work or exercise is performed and become a viable way of positively influencing an individual’s disposition as well as performance (10).

Due to the aforementioned training benefits of listening to music coaches, trainers, as well as performers should be cognizant of this revelation when planning their training regimens. Obviously, this would be especially relevant when engaging in a training session that the athlete and/or coach/trainer identify as being particularly taxing on the performer’s physiological systems. This extra-musical association could very well promote thoughts that inspire physical activity or relaxation within the athlete. For example, an athlete may associate vigorous exercise with the theme from the popular “Rocky” movie series, or possibly dreams of Olympic glory from Vangelis’ “Chariots of Fire.” The resultant association can be attributed not only to the inherent musical characteristics, such as tempo or rhythm, but to the influence of elements of popular culture, such as cinema, television, and radio (6).

In general, the results of the research indicate that exercising to music makes training a more exciting and pleasant experience leading to improved performance. Accordingly, music used as a motivational aid can provide individuals an alternative to address the repetitiveness and mundane nature of many physical activities associated with aerobic performance training.

### Acknowledgements

The authors would like to acknowledge the efforts of Ms. Elizabeth Perez, administrative assistant, in the author’s department for her tireless efforts in support of this study. Her editorial prowess and knowledge of APA style was tremendously helpful in creating a quality manuscript.

### References

1. American College of Sports Medicine. ACSM’s guidelines for exercise testing and prescription (5th ed.). Baltimore, MD: Lippincott Williams & Wilkins, 2000.
2. Borg, E. and Kaijser, L. A comparison between three rating scales for perceived exertion and two different work tests. Scandinavian Journal of Medicine & Science in Sports, 16: 57-69, 2006.
3. Copland, B. and Franks, B. Effects of types and intensities of background music on treadmill endurance. The Journal of Sports Medicine and Physical Fitness, 31(1): 100-103, 1991.
4. Edworthy, J. and Waring, H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics, 49: 1597-1610, 2006.
5. Gfeller, K. Musical components and styles preferred by young adults for aerobic fitness activities. Journal of Music Therapy, 25: 28-43, 1988.
6. Karageorghis, C. and Terry, P. The psychophysical effects of music in sport and exercise: a review. Journal of Sport Behavior, 20(1): 54-68, 1997.
7. Karageorghis, C., Terry, P., and Lane, A. Development and initial validation of an instrument to assess the motivational qualities of music in exercise and sport: The Brunel Music Rating Inventory. Journal of Sport Sciences, 17: 713-724, 1999.
8. Karageorghis, C., Jones, L., and Low, D. Relationship between exercise heart rate and music tempo preference. Research Quarterly for Exercise and Sport, 77(2): 240-251, 2006.
9. Karageorghis, C., and Priest, D. Music in Sport and Exercise: An update on research and application. The Sport Journal, 11(3): Retrieved October 25, 2008, from
<http://www.thesportjournal.org/article/music-sport-and-exercise-update-research-and-application>, 2008.
10. Mohammadzadeh, H., Tartibiyan, B., and Ahmadi, A. The effects of music on the perceived exertion rate and performance of trained and untrained individuals during progressive exercise. Physical Education and Sport, 6(1): 67-74, 2008.
11. Nethery, V. Competition between internal and external sources of information during mental exercise: influence on RPE and the impact of exercise load. Journal of Sports Medicine and Physical Fitness, 17: 172-178, 2002.
12. Nethery, V, Harmer, P, and Taaffe, D. Sensory mediation of perceived exertion during submaximal exercise. Journal of Human Movement Studies, 20: 201-211, 1991.
13. Noble, B. and Robertson, R. Perceived exertion. Champaign, IL: Human Kinetics, 1996.
14. O’Rourke, B.K. Email interview, March 5, 2011.
15. Potteiger, J., Schroeder, J., and Goff, K. Influence of music on rating of perceived exertion during 20 minutes of moderate intensity. Perceptual and Motor Skills, 91: 848-854, 2000.
16. Rejeski, W. Perceived exertion: An active or passive process. Journal of Sports Psychology, 75: 371-378, 1985.
17. Schwartz, S., Fernall, E., and Plowman, S. Effects of music on exercise performance. Journal of Cardiopulmonary Rehabilitation, 10: 312-316, 1990.
18. Simpson, S. and Karageorghis, C. The effects of synchronous music on 400-m sprint performance. Journal of Sport Sciences, 24(10): 1095-1102, 2006.
19. Smoll, F. and Schultz, R. Relationships among measures of preferred tempos and motor rhythm. Perceptual and Motor Skills, 8: 883-894, 1978.
20. Szmedra, L. and Bacharach, D. Effect of music on perceived exertion, plasma lactate, nor epinephrine, and cardiovascular homodynamic during treadmill running. Journal of Sports Medicine and Physical Fitness, 19(1): 32-37, 1998.
21. Thompson, D. and West, K. Ratings of perceived exertion to determine intensity during outdoor running. Canadian Journal of Applied Physiology, 23(1): 56-65, 1998.
22. Thornby, M., Haas, F., and Axen, K. Effect of distractive auditory-stimuli on exercise tolerance in patients with COPD. Chest, 107: 1213-1217, 1995.
23. Yamashita, S. and Iwa, K. Effects of music during exercise on RPE, heart rate and the autonomic nervous system. Journal of Sports Medicine and Physical Fitness, 46: 425-430, 2006.

### Tables

#### Table 1
Effects of Music Listening on Running Performance and RPE

### Figures

#### Figure 1
Running performance mean comparison among groups


#### Figure 2
RPE mean comparison among groups


### Corresponding Author

Randy Bonnette, Ed.D.
Department of Kinesiology, Unit 5820
6300 Ocean Drive
Corpus Christi, TX 78412
<Randy.Bonnette@tamucc.edu>
(361)825-3317

Randy Bonnette is the chair of the Kinesiology Department in the College of Education at Texas A&M University – Corpus Christi.

### Abstract

Many studies have examined the relationship between 2000m rowing ergometer performance and physiological variables, often suggesting that rowing ergometer performance models can be used to predict on-water performance. While studies have examined the kinematic, oxygen consumption, and electromyography similarities between rowing ergometry and on-water rowing, this is the first study to examine the relationship between physiological variables measured on the ergometer and 2000m performance on the water.

Nineteen elite heavyweight male rowers (26.2 ± 3.6 years, 92.2 ± 4.3 kg, 192.2 ± 4.5 cm) participated in the study. All testing was done over a two-week period. A 2000m (2K) on-water pursuit time trial in single scull boats, where the athletes started 30 seconds apart and competed over a 2K course for time, and a 2K ergometer performance test, were conducted on consecutive days in the first week. A progressive continuous incremental ergometer VO2 max and a modified 45s rowing Wingate test, to measure peak (Peak 45) and average power (Ave 45), were performed a week later. Ventilatory threshold (VT) was determined from a plot of VE/VO2. All ergometer testing was done on a Concept II model C rowing ergometer.

While Pearson correlations showed that VO2 max (r = -0.55, p< 0.05), Peak 45 (r = -0.43, p< 0.05), and Power at VO2 max (r= -0.733, p< 0.05) were significantly related to 2K ergometer performance, there was no correlation between any of the measured variables and 2K on-water performance (Height, r=0.273; Weight, r=0.373; VO2 max, r=0.049; VT, r=0.043; Peak 45, r= -0.229; Ave 45, r= -0.200; Power at VO2 max, r= -0.292). Additionally, there was no correlation between 2K ergometer performance scores and 2K on water performance scores (r= 0.120). The data suggest that physiological and performance tests performed on a rowing ergometer are not good indicators of on water performance. While it is a common practice for many, rowing coaches and sport scientists should be cautious when using the rowing ergometer to predict on water performance or select rowing crews.

**Key Words:** Rowing, physiological tests, ergometer, on water

### Introduction

The rowing ergometer (erg) has become an important tool for training and physiological monitoring of rowers. The erg has allowed sport scientists and researchers to overcome environmental factors such as current, water temperature, and wind, that can make physiological monitoring and research on rowers difficult. As a result researchers have been able to study and describe the relationship between a variety of physiological variables and rowing ergometer performance (2,10,11,13,16 ).

The repeatability of results within a brand and between models of a brand of ergometer (12,15) is quite good but there are differences in the physiological responses to rowing on different ergometers (3,6,7). While the ergometer has aided in the advancement of the body of knowledge on rowing, rowers criticize the “feel” of the ergometer compared to rowing on water. During the recovery phase of the rowing stroke on water, the mass of the boat slides underneath the rower (7). On most brands of rowing ergometer the opposite occurs, the mass of the rower must move up and down the slide bar during recovery and leg drive (7).

Other than the single study where Urhausen, Weiler, and Kindermann (14) examined the differences in the heart rate-lactate relationship between on-water and ergometer rowing and found that, for a given level of lactate, heart rate values were significantly higher on the water, the relationship between rowing ergometer physiological data to on-water performance has not been studied extensively. The purpose of this study was to examine the relationship between rowing ergometer physiological and performance data to 2000m on-water rowing performance.

### Methods

#### Participants

Nineteen heavyweight male rowers (26.2  3.6 years, 92.2  4.3 kg, 192.2  4.5 cm) who were part of a Canadian National Team training camp participated in the study. Sixteen of the subjects went on to compete at the World Championships. All testing was done over a two-week period. All athletes agreed to participate in physiological monitoring as part of their training program. All procedures were approved by the Rowing Canada Sports Science and Medicine Committee.

#### Rowing time trial (2K row)

A 2000m time trial in single scull boats was performed on the first day of the investigation. Athletes reported to the lake at 7:00 am and were given 60 minutes to prepare for the time trial. Each athlete performed a self-selected warm-up similar to what they would use before racing. Following the warm-up, athletes reported to the start line and were sent off 30s apart. The time trial was also being used for ranking in the team selection process; increasing the athlete’s motivation to perform well. All athletes were familiar with the technique involved in sculling, having raced or trained in sculling boats in the previous six months.

#### Maximal 45s sprint test

A modified Wingate sprint test on the Concept II Model C rowing ergometer was performed 90 minutes following the maximal oxygen uptake test. Subjects performed a self-selected warm-up for 10 minutes. After the warm-up the ergometer was programmed for a 45s trial and the damper was set to provide a “drag factor” of 200, the maximum that is normally attained on a used ergometer. Because dust, worn parts and other factors can affect the amount of resistance provided by each stop in the resistance control dial, the “drag factor” is the method used on the Concept II Model C ergometer for standardizing the resistance setting between ergometers.

Participants performed an all-out 45s effort with verbal encouragement. Participants were asked to row full strokes on each stroke of the test rather than use the partial strokes that are often incorporated at the start of a race. Power (W) for every stroke was calculated and displayed on the Concept II computer and recorded by the investigators. Peak power (Peak 45) was the highest power obtained on any individual stroke. Mean power (Mean 45) was the average of the individual stroke power over the 45s trial as calculated by the Concept II computer.

#### Ergometer time trial (2K erg)

On the second day of the investigation all subjects completed a 2000m rowing ergometer time trial. Subjects reported to the ergometer centre at 7:00 am and were given 60 minutes to prepare for the time trial. They followed similar warm-up procedures to those they did for the on-water trial the previous day. All subjects started the time trial at the same time to create a competitive atmosphere. Prior to the start of the time trial each rowing ergometer was calibrated to a “drag factor” of 120, which is the drag factor that was in use for all selection-based testing.

#### Maximal oxygen uptake (VO2 max)

One week after the ergometer time trial, maximal oxygen uptake was measured using a Parvomedics True One metabolic cart (Parvomedics Inc, Park City Utah). Subjects performed a continuous incremental test on the Concept II Model C rowing ergometer. All subjects started at 290 watts, increasing wattage by 30 watts every three minutes throughout the test. The test was stopped when the subjects reached volitional fatigue or were unable to complete a stage within five watts of the intended wattage. Power at VO2 max (VO2 power) was determined as the average power for the final stage of the test as calculated by the Concept II computer.

#### Anaerobic Threshold (AT)

Anaerobic threshold (AT) in L/min of oxygen and power at anaerobic threshold (AT power) were determined from a plot of VE/VO2 using the procedure described by Caiozzo et al (1982).

#### Statistical Analysis

Pearson correlation coefficients (r) were calculated to establish the relationship between rowing ergometer physiological parameters and on-water rowing performance. A Student T-test was used to determine whether a difference existed between on-water rowing 2K time and 2K ergometer time. Statistical significance was determined using a probability level of p<0.05.

### Results

The 2K ergometer times (6:05.4± 5.5s) were significantly faster than the 2K row times (7:35.7 ±11.4s) (p<0.05). There was no correlation between 2K ergometer performance scores and 2K row performance scores (figure 1).

The mean VO2 max score was 5.9 ±0.4 L/min or 63.7±4.1 ml/kg/min. Power at VO2 max averaged 442.5 ±25.5 Watts. Both VO2 power (figure 2) and VO2 max (table 1) were significantly correlated to 2K erg but not to 2K row.

Anaerobic threshold (AT) occurred at 4.9 ± 0.3 L/min or oxygen which was 83.4± 4 percent of VO2 max. AT power ranged from 332-418 watts with a mean of 368.5 ± 21.3. AT power, but not AT, was correlated to 2K erg performance (Table 1). Neither was correlated with 2K water performance.

Peak 45 values ranged from 770-1134 Watts with a mean of 927.6 ± 95.3. There was a significant correlation between Peak 45 and 2K erg times but not with 2K row (table 1). Mean45 values ranged from 737-896 watts with a mean of 796.9 ± 74.2. There was no correlation between Mean45 and either 2K erg or 2K row.

### Discussion

Monitoring training and changes in physiological parameters is a challenge in rowing. Changes in weather and water conditions between tests can make it difficult to compare data and draw valid conclusions. Rowing ergometers were originally designed so that rowers in colder climates could continue to train in a fashion similar to their sport during periods when they could not be on the water. The ability to perform a rowing movement in a controlled environment made the rowing ergometer an attractive tool for monitoring changes in physiological variables. The findings of the present study are consistent with others (10,11) that have found significant correlations between rowing ergometer 2000m performance and VO2 max, AT and maximal power from a modified Wingate test performed on the same type of ergometers. The relationship between VO2 max and 2K erg performance in the current study is lower than that seen by Ingham et al. (4) and Cosgrove et al. (2), who found r = 0.88 and r = 0.85, respectively. Cosgrove (2) studied club level college-aged males of varying ability and while the Ingham study examined World Championship finalists both males and females as well as lightweight and heavyweight rowers were included in the analysis, creating a more heterogeneous group. The subjects in the current study were more homogenous in respect to their 2K erg times compared to other studies; 5:59-6:12 in the current study compared to ranges of 7:32.9- 8:07 in Riechman et al. (10), 6:20-7:26-in Russel et al. (11) and 6:30-7:45 in Cosgrove et al (2). Because a sport performance is multifaceted, with physiological, biomechanical, technical and psychological factors all playing roles in the final outcome, as the group performance becomes more homogenous it is less likely that any single physiological variable will be a strong distinguisher of performance.

One of the main purposes of determining the relationship between different physiological variables and rowing performance is to identify those variables that need to be trained to maximize performance (10). The current study demonstrates that although there may be a relationship between some physiological variables and rowing ergometer performance there is no relationship between physiological variables measured on a rowing ergometer and on-water performance in a group of elite heavyweight male rowers. This is the first study to directly compare the relationship between physiological variables determined on a rowing ergometer to on-water performance. Juimae et al. (5) examined the relationships among anthropometric variables, ergometer, and on-water performance, finding that only muscle mass correlated to on-water single scull performance while almost all anthropometric variables were related to ergometer performance. The lack of relationship between physiological or anthropometric predictors of ergometer performance and on-water performance is not surprising given that the relationship between on-water performance scores and ergometer performance scores can vary greatly across boat classes and levels of competition.

In two separate studies that examined the relationship between World Championship ranking or Junior World Championship ranking and 2000m ergometer performance Mikulic et al. (8) and Mikulic et al. (9) found significant correlations in 10 of 13 World Junior events and 17 of 23 World Championship events, but the standard errors were too large to establish accurate ranking predictions for any of the events. The highest correlations (r=0.92) were seen in the junior women’s single scull event, followed by the junior men’s single scull (r=0.80) and the junior women’s double scull (r=0.79). In contrast to the r = 0.12 of the current study, the senior men’s single scull had an r = 0.72. Some of the difference in results of the Mikulic studies (8,9) compared to the current study may be due to the nature of the variables correlated. In both Mikulic studies (8,9) the correlations were with final World or Junior World Championship rankings, whereas the present study looked at the relationship between actual times rowing on the water versus rowing the ergometer. In the Mikulic studies (8,9), the highest correlations were seen in sculling boats, particularly the singles. Athletes competing at a World Championship in sculling boats are normally specialists in that discipline. In the current study although all athletes were familiar with sculling and spent some of their time training in single sculls, 14 of the 19 were not sculling specialists. The lowest correlations in Mikulic’s work (8,9) were also seen in sweep rowers competing in larger boats r = 0.47 for the heavyweight men’s eight and r = 0.21 for the lightweight men’s eight. Ergometer rowing is technically more similar to sculling than to sweep rowing, the technical differences between the sculling specialists and non-specialists may explain the difference in correlations seen in the current study. This clearly supports the notion that there are differences between rowing ergometer performance and on-water rowing performance, particularly for sweep rowing athletes, and that physiological variables determined on the rowing ergometer may not be good predictors of performance on the water.

### Applications In Sport

This study reinforces what many coaches already know; there is more to a rowing performance than physiological test results or rowing ergometer performance scores. Ergometer rowing requires less skill than on-water rowing (10). Rowing technique on the water is a complex skill that requires balance, efficiency, and maintaining the boat speed during the recovery phase. These factors cannot be measured on an ergometer. This makes the rowing ergometer a good tool for studying and tracking physiological changes that occur during a rowing movement and can help coaches identify those athletes who have a large discrepancy between ergometer and on-water performances that may be technique related. However, caution needs to be exercised when trying to extrapolate rowing ergometer performance and physiological scores to on-water performance.

### Tables

#### Table 1

* p< 0.05

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### Coresponding Author

Ed McNeely
560 Proudfoot Lane #1012
London, Ontario
N6H 5C9
Canada
613-371-8913
<e.mcneely@rogers.com>

### Author Bio

Ed McNeely is the senior physiologist at the Peak Centre for Human Performance and a partner in StrengthPro Inc. a Las Vegas based sport and fitness consulting company he is also a National Faculty member of the United States Sports Academy