### 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

### References

1. Caiozzo, V.J., Davis, J.A., Ellis, J.F., Azus, J.L., Vandagriff, R., Prietto, C.A., & McMaster, W.C. (1982). A comparison of gas exchange indices used to detect the anaerobic threshold. J Appl Physiol. 53, 1184–1189.
2. Cosgrove, M., Wilson, J., Watt, D., & Grant, S. (1999). The relationship between selected physiological variables of rowers and rowing performance as determined by a 2000m ergometer test. J. Sports Sci. 17, 845-852.
3. Hahn, A.G., Tumilty, D.M., Shakespear, P., Rowe, P., & Telford, R.D. (1988). Physiological testing of oarswomen on Gjessing and Concept II rowing ergometers. Excel. 5, 19-22.
4. Ingham, SA., Whyte, GP., Jones, K., & Nevill, A.M. (2002). Determinants of 2000m rowing ergometer performance in elite rowers. Eur. J. Appl. Physiol. 88, 243-246.
5. Jurimae, J., Maestu J. Jurimae T. & Pihl, E. (2000) Prediction of rowing performance on single sculls from metabolic and anthropometric variables, J. Hum. Mov. Stud. 38, 123-36.
6. Lormes, W., Buckwitz, R., Rehbein, H., & Steinacker, J.M.(1993). Performance and blood lactate on Gjessing and Concept II rowing ergometers. Int J Sports Med. 14(Suppl 1), S29-S31.
7. Mahony, N., Donne, B., & O’Brien, M. (1999). A comparison of physiological responses to rowing on friction-loaded and air-braked ergometers. J Sports Sci. 17, 143-149.
8. Mikulic, P., Smoljanovic, T., Bojanic, I., Hannafin, JA., & Pedisic, Z, (2009). Does 2000m rowing ergometer performance correlate with final rankings at the World Junior Rowing Championship? A case study of 398 elite junior rowers. Journal of Sports Sciences. 27, 361-366.
9. Mikulic, P., Smoljanovic, T., Bojanic, I., Hannafin, JA., & Matkovic, B, (2009). Relationship between 2000m rowing ergometer performance times and World Rowing Championships rankings in elite standard rowers. Journal of Sports Sciences. 27, 907-913.
10. Reichman, S., Zoeller, R., Balasekaran, G., Goss, F., & Robertson, R. (2002). Prediction of 2000m indoor rowing performance using a 30s sprint and maximal oxygen uptake. J. Sport Sci. 20, 681-687.
11. Russell, A., Le Rossignol, P., & Sparrow, W. (1998). Prediction of elite schoolboy 2000m rowing ergometer performance from metabolic, anthropometric and strength variables. J. Sport Sci. 16, 749-754.
12. Soper, C., & Hume, P.A.(2004). Reliability of power output during rowing changes with ergometer type and race distance. Sports Biomech. 3, 237-224.
13. Tachinaba, K., Yashiro, K., Miyazaki, J., Ikegami, Y., & Higuchi, M. (2007). Muscle cross sectional area and performance power of limbs and trunk in the rowing motion. Sports Biomechanics. 6, 44-58.
14. Urhausen, A., Weiler, B., & Kinderman, W. (1993). Heart rate, blood lactate, and catecholamines during ergometer and on-water rowing. Int. J. Sports Med. 14, Suppl 1, 20-23.
15. Vogler, A., Rice, A., & Withers, R. (2007). Physiological responses to exercise on different models of the Concept II rowing ergometer. Int. J. Sports Physiol and Perf., 2, 360-370.
16. Womack CJ. Davis SE. Wood CM. Sauer, K., Alvarez, J., Weltman, A., & Gaesser, G. (1996). Effects of training on physiological correlates of rowing ergometry performance. J. Strength Cond. Res. 10, 234-238.

### 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

### Abstract

The purpose of this study was to examine the effectiveness of two different training methods on the performance improvement of dribbling and kicking technical skills of young football players (8-11 years old). The sample consisted of 90 boys aged 8 to 11 years old. Participants were randomly assigned to three practice groups of 30 participants each group, that is, two experiment groups (experiment group A, experiment group B) and one control group. Practice according to training method A included a 20 min warm-up period, 20 min of practicing technical football skills, 20 min game and a 5min cool-down period. Practice according to training method B included a 20min warm-up period followed by 45 min of technical skills’ training with no time given for a cool-down period. Control group participants followed the physical education program according to school’s curriculum. Three measurements were conducted to evaluate technical skills. Initial measurement took place prior the onset of the program, whereas the second measurement conducted after the eighteen (18) weeks of training followed by the final measurement four (4) weeks after the end of the program to assess maintenance of skills attained. Results showed improvement in dribbling and kicking skills of the two experiment groups, whereas no statistically significant differences were noted in the control group for the specific skills measured. Furthermore, it was noted that benefits of training method B maintained for a longer period of time compared to training method A. Future studies using larger samples, in different age ranges, and sports are needed in order to further verify probable advantage of training method B compared to other methods.

**Keywords:** training methods, comparison, children’s football, dribble, kick.

### Introduction

In European football, two training models dominate the field during the last decades on which all training choices that are available to coaches are based. Training model A (15) follows a specific training implementation procedure in football. That is, a training unit, that includes a warm-up period consisted of exercises with or without the use of a ball, the main part aiming to improve performance of technical football skills with no previous fatigue present as no physical condition program precedes followed by a football game adapted to the objectives of the main part. Last, a cool-down period concludes the training unit and signals the end of practice.

In training model B (16) the procedure followed during a training unit in football includes a warm-up period that is adapted to the objectives of the main part with ball use, followed by the main part where only technical training takes place. This pattern is in effect until the onset of the microcycle involving technique application, as from this point and beyond more football game is used. In this training model no cool-down period exists on the contrary to training model A. According to Lehnertz (14), technical training should constitute the final piece of a training unit as dynamic registrations that are created definitely require a consequent phase of consolidation (13).

In summary, differences that are observed between the two training methods during the six microcycles could be described as follows: Training method A includes a 20 min warm-up period, 20 min performance improvement of technical football skills, 20 min football game and 5 min cool-down period. In training method B, a 20 min warm-up period takes place, followed by 45 min for improving performance of technical football skills. According to training method B, no cool-down period is needed and no particular importance and time is given for such purpose.

Planning, guidance, and application of training according to these two models have led previously to successes as well as to failures. Up to now, it is not possible only through the examination of results (success or failure) to say with certainty which is the most advisable method in football training.

Although it is extremely difficult to locate relative studies concerning the comparison of complete and different training methods emphasizing football technique, many researches use football ability tests to measure pass accuracy, ball control and dribbling (6,20,5). Van Rossum and Wijbenga (21) reconstructed Kuhn’s (12) technique tests for football players and applied them in Dutch children teams. Overall, six technique tests are considered by the researches as reliable and valid for football practice: 1. Kick accuracy, 2. Ground pass with accuracy, 3. High pass with accuracy, 4. Dribble, 5. Controlling ball on air using one leg, and 6. Controlling ball on air using two legs.

French and Thomas (10) research conducted to young basketball players aged 8-10 and 11-12 years (high level athletes, beginners, and no-athletes) to examine the importance of knowledge element on the development of basketball skills showed that high level athletes of both age ranges exhibited a better performance in dribbling and shooting skills. Correlation analysis showed that knowledge relevant to sport was related to choice of answer, whereas kicking and dribbling skills were in relation to the motor elements of control and execution. Furthermore, it was noted that the development of relative to sport knowledge plays an important role in the high performance of 8-12 years old children, with cognitive development occurring faster compared to motor development (10).

Das and Benerjee (7) investigated effectiveness of periodicity according to the duration of a football training program applied to young football players. Participants (10-12 years old children) were examined with the use of motor and technical skills’ tests in the beginning of the training process and after four (4), six (6), and eight (8) weeks of training. Motor skill tests included evaluation of 30m speed, explosive force, 4 times of 40m agility, flexibility, and 8 min continuous running. Technique tests included measurement of successive goal shots, controlling ball on air using legs, distant pass accuracy, throw-in distance, and dribbling in specific time-period. Regarding motor skill tests results showed that four weeks were enough to improve agility and aerobic capacity and six weeks of training were necessary to improve muscle power and speed, whereas no improvement was noticed concerning flexibility in eight weeks time. In technical skills testing, after four weeks of football practice an improvement in shooting towards target, controlling ball on air and distant pass was noticed, whereas in throw-in and dribbling skills improvement was evident after six weeks. All technical skills were significantly improved after eight weeks of training (7). According to the results of (7) study, a six weeks training program could be considered sufficient to produce evident training results.

Venturelli, Trentin and Bucci (22), evaluated three training methods for improving muscle power of young football players after eight weeks of practice. Twenty one high level football players formed three groups (A1, B1, C1) of 7 individuals each group and followed different training programs. Α1 group followed a free weight-lifting program, B1 practiced according to a specific exercise program and C1 group followed a combined training program. Results showed that the combined training program (weight lifting for thorasic muscles along with jump and speed exercises) produced better results compared to the other two training programs. Fontes et al. (2007), evaluated maximum cardiac frequency and anaerobic threshold of 10 professional A Division category Brazilian football players on three measurements taking place during training that included “technique”, “tactics”, “game with terms” and “regular game”. According to the results, technique had a lower intensity compared to other analyzed types of training, whereas no differences were noted during training regarding “tactics”, “game with terms” and “regular game”. Furthermore, “game with terms” seemed to offer higher intensity compared to other training conditions. In conclusion, although decrease of field dimensions increases, in turn, ball contact, intensity seems to lessen due to the reduction of high in energy actions of football players.

Taxildaris (19), using Heidelberger Basketball Test (4) in adolescent age girls (11-14 years old), examined improvement of football skills’ technique level between a control group of female athletes that did not follow any exercise program and two experiment groups following a “general” exercise program and a “specialized” exercise program. Results showed that group of “specialized” practicing exhibited a statistically significant difference compared to group following the “general” exercise program in terms of pass and shooting technical skills measured. Furthermore, although not statistically significant a tendency in favor of the specialized training group was noticed in dribbling and penetration & shooting skill, whereas control group did not present any progress.

As it is shown, motor and technical football tests have been widely applied by many researches, with special attention given to kicking and dribbling abilities as the most important skills that each football player should possess to the highest degree. Dribbling virtuosity of the footballer who has the ability to control the ball with his legs and surpasses his opponents, is the most effective and fascinating technique that is always admired by every athlete and spectator (3). Dribble use allows possession of ball throughout the football court until a player is free of guard in a favorable position to receive the ball and score.

According to (11), dribble as well as ball guidance presumes the covering of ball with the body, eye-contact with the team-mates, peripheral sight, avoidance of the opponents’ attack and surpassing of opponents using manoeuvres. Furthermore, although dribbling style of each football player is unique, one can recognize the short dribble, the dribble of ball between opponent’s legs, dribble of pretending going left and then heading to the opposite (right) direction, ball transport to the left and then dribbling to the right, speed dribble, dribble with turn and “scissors” dribbling (1,2,8,11,18).

Dribble technique requires high concentration and energy and a relaxed body in perfect balance, whereas it also demands continuous control of the game, placement of the opponents in the football court and transition of team-mates. In real game situations in modern football, defenders are always more that the offending players, taking systematically position in front of their goalkeeper’s post. Therefore, in order to penetrate this defensive “wall” and apart from other technical or tactical inventions, dribbling ability of players is a very important skill for the outcome of the game that is added to the overall strategy of the team (2).

The objective goal in football is scoring, thus, scoring ability with legs, head or with the ball stopped is of vital importance for every football team that requires the best possible technique aiming to send the ball to the opponent goal-net. All other skills in football are of little importance in case players do not take advantage of their opportunity to kick the ball and score. Kicking the ball constitutes the final expression of the game since every time a player attempts to score that doesn’t mean he will succeed, however, without performing this skill is very difficult to accomplish anything. A good scoring ability requires the player to be capable to kick the ball in narrow spaces under the pressure of the opponent, plus, team-mates’ contribution is needed to provide the opportunity for attempting to score under the best possible conditions (11).

The development of such important technical skills in football should start from a young age. In case a proper selection of training method during practicing and acquisition of these skills takes place, the young athlete will develop his technique at his full potential as well as his effectiveness to perform successfully during training and official football games. However, reviewing the literature it seems that no researches are conducted examining the effectiveness of training methods A and B on the development of such important skills.

The purpose of this study was to compare the effectiveness of the two training methods according to model A (17, 15) and model B (16) in the performance improvement of basic football skills of young football players (8-11 years old).

### Methodology

#### Sample

Random selection was used to choose children from the 3rd to 6th school classes first by selecting nine (9) out of thirty-three (33) primary schools of Volos city. In each school researchers provided information to children and their legal guardians about the purpose of the study.

The initial sample of children showing interest to participate was 98 children who were also informed that their participation was voluntary. Individuals were also aware that all the information provided was confidential and they were free to withdraw at any point without prejudice. Overall, the legal guardians of two children did not sign the consent form whereas 6 children decided not to participate prior the initiation of the first training session. As a final result, 90 boys, aged 8 to 11 years old, all primary school students constituted the final sample of this study. Participants were separated randomly in three (3) groups (experiment A, experiment B and control) of 30 participants each group (See Table 1). Written informed consent was obtained from all participants and their legal guardians prior participation. All experimental procedures were approved by the Institutional Review Board of Ethics Committee for investigations involving human subjects.

#### Training program

Overall duration of research was 22 weeks. Initial measurements of all participants along with participants’ separation in three groups (experiment group A, experiment group B and control group) were conducted prior the beginning of the training program (at the end of October. See Table 2). Next, the training period lasting 18 weeks followed with the final measurement taking place at the end of the training period (beginning of March). An additional measurement four (4) weeks after the end of the training program was also conducted to assess maintenance of skills attained.

Experiment groups followed different training programs focusing on the development of six technical skills (transition of ball, dribbling, pass, ball control, head and kick) with special attention given to the development and monitoring of dribbling and kicking ability. Experiment group A practiced according to training method A and experiment group B followed the training method B, whereas control group individuals did not participate in any football program and simply followed the physical education program according to their school’s curriculum, that included track and field events, general team sports’ practice (in basketball, volleyball, handball, and football) and dance.

The weekly program of the two experiment groups included two training units of 65 min each one (every Tuesday and Thursday) where technical skills were practiced and one training unit playing a 60 min football game (every Saturday). In each training unit, practice for the experiment group A according to training method A included a warm-up period with or without the ball, the main part of practicing technical football skills, a football game and a cool-down period. Practice according to training method B for the experiment group B included a warm-up period with the ball followed by 45 min of technical skills’ training with no time given for a cool-down period. In particular: Table 2.

Furthermore, training programs A and B for the participants of experiment group A and B respectively were divided in three middle-term cycles of six weeks duration each one, with each middle-term cycle including 6 micro-term cycle training units. In each middle-term cycle emphasis was given to the two out of the six skills practiced, whereas the other four skills were practiced as well but to a less quantity (See Table 3).

In addition, the six micro-cycles within each middle-term cycle were structured in pairs according to the aims pursued to achieve in every two training units. Exercises used in micro-cycles were the same so much in training method A as much as in training method B (See Table 4).

All training units were accomplished by two physical education (PE) teachers who were also qualified football coaches with specialty in football. Prior the beginning of research, the two PE teachers were informed and practiced on the application of each training program followed, that is, training program A applied by the 1st coach and training program B applied by the 2nd coach respectively.

#### Testing procedures

Russell (18) tests regarding dribbling and kicking skills were selected for the purpose of this research, as they are immediate, easy to measure and dynamic with no “static” exercises included such as kicking a still ball or controlling the ball on air. Furthermore, they have been devised and recorded by the English Football Federation and evaluated for a long period of research time by the Social Statistics Department of Southampton University, exhibiting a high level (0.90) of reliability (18). The advantage of Russell’s tests is that they are performed in real game conditions (5).

In each measurement its participant performs two skills during testing. The purpose of the kicking test was to gain the highest number of point by aiming to shoot the ball in to the half of the goal furthest away from the player shooting (18).

In dribbling test each individual was asked to imagine the markers as defenders and to dribble the ball as quickly as possible in front and away from markers, ABCD from the start of the course to the finish (18)

The coding system for the number of successful kicking efforts and dribbling in relation to time of skill execution was as follows (See Table 5).

These tests could also be used in the form of football exercises during training or combined in one more complex activity. In this way, the coach could assess technical skills of his players not only through testing but also through practice.

Technical skills’ evaluation of the participating children was conducted by four (4) physical education teachers who were also football coaches trained by an experienced instructor. Video tape recording to assess reliability of each coach as well as between coaches judging the results was used through the application of tests on 20 children other than those participating in the program. In particular, 20 children performed the two skills in question prior the onset of the program, their performance was videotaped and PE teachers were then asked to watch the video and evaluate each test two times. Intra-class k correlation factor to assess reliability between coaches was equal to 0.71 (k = 0.71) whereas for each coach, k intra-class correlation factor was 0.80 (k = 0.80).

Final measurement was limited in students who followed training programs consistently, three times per week for eighteen (18) weeks without absences. All students in each group loosing three or more training units were automatically excluded from the study.

#### Statistical analysis

Planning application included a 3×3 measurement (initial, final, maintenance) x Group (experiment group A, experiment group B and control group C) with repeated measurements (ANOVA repeated measures) used to locate possible differences existing among the training methods in terms of performance in dribbling and kicking skills. The importance of differences between the means of cells was examined with the application post hoc test of multiple comparisons Scheffe. The importance of difference between the medium averages of performance was examined using Scheffe post hoc test. Level of statistical significance was set at p0.05.

### Results

Cronbach a analysis, the most widely accepted technique of indicating reliability, was used to examine reliability of skills’ measurements in each group (See Table 6).

Pre-test measures showed that all 3 groups started from the same point of reference in term of base line performance prior the beginning of the intervention (dribbling F(2,87)=2.526, p=.086, and kicking F(2,87)=2.769, p=.068 skills).

Observing means of dribbling technical skill for the experiment groups A and B (See Table 7), an increased performance is noticed from the first (October) up to the third measurement (April). In the control group, an ascendant course of mean is observed in the second measurement (March) as well as a mean stabilization at the third measurement.

Performance difference between the three training methods in dribbling skill was checked with the application of two-way ANOVA, from which one was with repeated measurements. Mauchly’s test showed that the three groups exhibited homogeneity in their variance (p >.050).

The interaction between the two factors (skills and training methods) in relation to time was not found to be statistically significant (See Table 8).

Statistically significant differences were observed between the groups (See Table 9) in dribbling performance [F(2.87) =5.159, (p<.001)], that is, the effect of training methods was different concerning the development of dribbling skill in total measurements.

Application of Scheffe post hoc test showed the following statistically significant differences: a) between the second and third measurement (Scheffe value = 1.80 (p<.050) of training method B and control group program (See Table 7), b) on the third measurement between training method B and control group program (Scheffe value = 2.20 (p<.001) and c) on the third measurement between training method Α and control group program (Scheffe value = 1.70 (p<.050).

No statistically significant differences were noticed between training method A and training method B in dribbling performance (See Table 9), with a Scheffe value on the second measurement equal to.60 (p>.050), whereas on the third measurement Scheffe value was .50 (p>.050). Finally, no statistically significant differences were observed between training method A and control group program on the second measurement (Scheffe value= 1.20, p>.050).

Observing means of kicking skill for the experiment group A (Table 7), an increased performance is noticed from the first up to the second measurement followed by a decrease on the third measurement. In the experiment group B an increased performance from the first up to the second measurement is also observed, followed by a stabilization of performance on the third measurement. Control group exhibited a slight increased course of means from the first up to the third measurement.

As in dribbling skill, performance difference between the three training methods in kicking skill was checked with the application of two-way ANOVA, from which one was with repeated measurements, with Mauchly’s test indicating a homogeneity in groups’ variance (p >.050).

The interaction between the two factors (skills and training methods) in relation to time was not statistically significant (See Table 10).

Statistically significant differences were observed between the groups in kicking performance [F(2.87)=14.533, p<.001], that is, the effect of training methods was different regarding the development of kicking skill in total measurements (See Table 9).

Scheffe post hoc test application revealed the following statistically significant differences in kicking skill performance: a) on the second measurement (Scheffe value = 1.20 (p<.050) between training method B and training method A (See Table 9), b) on the third measurement between training method B and training method A (Scheffe value = 1.67, p<.001), c) on the second measurement between training method B and control group program (Scheffe value = 2.00, p<.001) and d) on the third measurement between training method B and control group program (Scheffe value = 2.40, p<.001).

Finally, no statistically significant differences were noticed between training method A and control group program in kicking performance with a Scheffe value on the second measurement equal to 1.20 (p>.050) whereas on the third measurement Scheffe value was .50 (p>.050).

Overall, comparison of means revealed statistically important differences existing as regards to performance improvement of dribbling and kicking football skills in total measurements resulting from the application of different football training methods.

### Discussion

The improved performance of dribbling skill recorded in this study as a result of 18 weeks of training is in agreement with (7) stating that a training program of at least six weeks duration can be considered as fair enough to produce positive training results in technical skills’ development.

Both training methods seemed to produce positive results related to an improved performance in dribbling skill up to a point where no differences were located. As a result, comparison between training method A and training method B revealed no statistically significant differences in final and maintenance measurement, although a superiority tendency of training method B compared to training method A was also noticed.

In particular, no statistically important differences in performance were noted between training method A and control group program apart from maintenance measurement. However, between training method B and control group program such differences do exist not only in maintenance results but in final measurement also, a finding that indicates the greater influence of training method B to improve dribbling.

As for kicking skill, comparison of means showed also a statistically significant improved performance resulting from training method B application. The improved performance in kicking skill recorded only for the participants of training group B is not in agreement with (7) study that in general a training program of at least six weeks duration is enough to produce positive results in technical skills’ development. In this study, findings suggest that after 18 weeks of training this was the case for dribbling but not for kicking skill improvement. Thus, it seems that not only duration of program according to (7) notion is important, but also the “nature” of skill itself. In addition measurements between groups during, point out an influence of the training methods on kicking performance that is different according to group.

Comparison between training method A and training method B illustrated statistically important differences in final and maintenance measurement. More specifically, individuals following training method B improved their kicking skill performance according to final measurement and sustained this performance until maintenance measurements. On the contrary, participants of training group A improved slightly their performance on final measurement whereas on maintenance measurement returned to their initial performance level. As an overall result, individuals practicing according to training method B exhibited a greater improvement of kicking skill performance compared to A group individuals up to a point that this improvement was statistically significant.

Furthermore, results showed that training method A and control group program did not produce performance differences in kicking ability as no differences were noted between the two methods in final and maintenance measurement. On the contrary, such differences were evident between training group B and control group in the same measurements. The fact that training method B significantly improved kicking skill performance compared to training method A and control program, indicates clearly the superiority of training method B in order to improve the specific skill measured.

Overall, results of this study showed that the use of training method B helped young players to improve performance more according to skill and to maintain training results for a longer period of time. Thus, findings suggest that when it comes to young players, football coaches should use more the training method B suggested by (16) compared to any other method (17, 15).

Future studies should examine whether the application of the two training methods produce different results in all fundamental football skills taught, apart from dribbling and kicking, that is, transition of ball, pass, head, and ball control. In this way, it would be ascertained whether training method B is more proper for teaching basic football skills to young players throughout their whole developmental period. Moreover, future studies should use larger samples, in different age ranges and sports (e.g. basketball, volleyball, handball etc) in order to further verify the probable advantage of training method B.

### Applications In Sport

Improvement of fundamental football technical skills influences to a great extent the progress of young footballers in their later athletic course and future (Van Rossum & Kunst, 1993). The findings of this study suggest that when it comes to technical skills’ improvement, football coaches of young players should use more training method B during the initial microcycles of each training session compared to any other method so as to maximize performance and achieve high dexterity levels of crucial football skills such as dribbling and kicking.

### Tables

#### Table 1
Mean age of groups

#### Table 2
Training unit of groups

#### Table 3
Middle-term cycle structure

Training Program (18 weeks)

#### Table 4
Structure of microcycles in pairs in each middle-term cycle

#### Table 5
Skills scoring system

#### Table 6
Cronbach’s α reliability analysis of results

#### Table 7
Arithmetic means, standard deviations and Schefe’s post hoc tests of significant differences between groups on the dribbling and kicking test

* pairs of groups between whom significant differences have been detected

#### Table 8
ANOVA means in dribbling and training methods according to time

#### Table 9
Independent Groups ANOVA Comparing Mean dribbling and kicking

#### Table 10
ANOVA means in kicking and training methods according to time

### Figures

#### Figure 1
Performance diagram of dribbling skill


#### Figure 2
Performance diagram of kicking skill


### Acknowledgments

I would like to express my gratitude to all young footballers and their parents who made this research possible with their willingness to participate. Also, I would like to thank the Ethics Committee of the University of Thessaly for its guidance throughout the whole research procedure.

### References

1. Azhar, A. (1989). Le Football. Paris: Editions Solar.
2. Bauer, G. (1996). Footbal. Paris: HACHETTE LIVRE.
3. Benigni, A., & Kuk, A. (1998). Lecons de football: drible, passe, tir. Paris:Editions De Vecchi S.A.
4. Bos, K. (1988). Der Heidelberger-Basketball-Test (HBT). Leistungssport, 17-24.
5. Byra, M. (1990). Game-like skill tests. Strategies, 3, 9-10.
6. Crew, V. (1968). A skill test battery for use in service program soccer classes at the university level. Master’s thesis University of Oregon.
7. Das, S. S., & Banerjee, A. K. (1992). Variation in Duration of Training Period on the Performance Variables of Young Soccer Players. NIS Scientific Journal, 15 (3), 116-121.
8. Drampis, Κ. Kellis, S. Liapis, D, Mougios, Β. Saltas, P., & Terzidis, I. (1996). Football in childhood and adolescent age. Thessaloniki: Salto Publishing.
9. Fontes, M., Montimer, L., Condessa, L., Garcia, A., Szmuchrowski, L., & Garcia, E. (2007). Intensity of four types of elite soccer training sessions Journal of Sports Science and Medicine Suppl. (10), 82.
10. French, K., & Thomas, J. (1987). The relation of knowledge development to children’s basketball performance. Journal of Sport Psychology, 9, 15-32.
11. Garel, F. (1978). Football Technique – Jeu – Entrainement. Paris: Editions Amphora.
12. Kuhn, W. (1978). Leistungsverfassung im Sportspiel: Entwicklung einer Fussball – Spezifischen Testbatterie. Karl Schorndorf Verlag.
13. Laudin, H. (1977). Physiologie des Gedächtnisses. Heidelberg.
14. Lehnertz, K. (1990). Techniktraining. In Rieder, H. und Lehnertz, K., Bewegungslernen und Techniktraining (pp. 105-195). Schorndorf: Studienbrief 21/Teil II.
15. Letzelter, M. (1978). Coaching (Translation and editing by Kellis, S). Thessaloniki: Salto Publishing.
16. Martin, D., Carl, K., & Lehnertz, K. (1991). Coaching Manual. Editing by Taxildaris, K. Thessaloniki: Alfa beta Publishing, 1995.
17. Matveiev, L. P. (1977). Aspects fondamentaux de l’entrainement. Editions Vigot.
18. Russell, R. (1988). Coca – Cola Football Association Soccer Star. London, English Football Association.
19. Taxildaris, Κ. (1990). Comparison study of methods improving physical condition factors in basketball for adolescent girls. Doctoral thesis. University of Thrace.
20. Thill, E., Thomas, R., & Caja, J. (1985). Manuel de l’éducateur sportif. Edition Vigot.
21. Van Rossum J.H.A., & Wijbenga, D. (1993). Soccer skills technique tests for youth players: construction and implications. In T. Reilly, J. Clarys and A. Stibbe (Eds), Science and Football II, (pp. 313-318). London: Spon.
22. Venturelli, M., Trentin, F., & Bucci, M. (2007). Strength training for young soccer players. Journal of Sports Science and Medicine Supplement (10), 80-81.

### Corresponding Author

Christos Plainos
Leivaditi 5
University of Thrace /Department of Physical Education and Sport Science
Nea Ionia 38446
Volos, Greece
<chplainos@yahoo.gr>
(697) 773-8230