Sports Exercise Science – Page 13

A Choke-Up Grip Facilitates Faster Swing and Stride Times Without Compromising Bat Velocity and Bat Control

Submitted by: Coop DeRenne, Charles F. Morgan, Department of Kinesiology & Rehabilitation Science, University of Hawaii; Rafael F. Escamilla, Department of Physical Therapy, California State University, Sacramento, CA; Glenn S. Fleisig, American Sports Medicine Institute, Birmingham, AL

Abstract

This study investigated the relationship among hitting components and bat control during the normal and choke-up grip swings. Fourteen intercollegiate and professional baseball players were randomly assigned into five hitting groups. Within each group, the following four hitting components were computed to determine the relationship between bat control in two grip conditions (normal; choke-up): (1) Swing time (bat quickness), (2) stride time, (3) bat velocity, and (4) bat-ball contact accuracy. Results indicated significant differences (p =0.01) between choke-up and normal grips in swing time, stride time, and bat velocity. Players using the choke-up grip swing had significant less swing time and stride time than the normal grip swing. Results also indicated significant greater bat velocities (p = 0.01) with normal grip swings than the choke-up grip swings. In addition, further results indicated no significant differences (p = .90) between choke-up and normal grips in bat-ball accuracy. These findings suggest that the choke-up grip facilitates faster swing time and stride time without compromising bat velocity or contact accuracy.

Key words: bat control, bat quickness, stride time, accuracy.

Introduction

Historically, since major league baseball established its modern day roots in the early 1900’s, the best hitters in the game have studied hitting mechanics to improve their performances (Cobb, 1961; DeRenne, 2007; Gwynn, 1998; Lau, Glossbbrenner, & LaRussa; 1980; Williams, 1970). Though the early day great hall of fame hitters didn’t have the advantages of present day high-technology and research-generated information, the majority of those hitters and those of present day agree that by studying and applying swing kinematics will create greater bat control during competition (Alston & Weiskopf, 1972; Cobb, 1961; DeRenne, 2007; Gwynn, 1998; Williams, 1970). From the one of games early great hitters TyCobb (1961), to modern era hall of famer hitter Ted Williams (1970), to eight time major league batting champion and hall of fame hitter Tony Gwynn (1998) and finally to major league home run king great Barry Bonds, these great hitters share in the belief that increasing bat control is essential for successful hitting (Cobb, 1961; Gwynn, 1998; Williams, 1970).

As intercollegiate baseball evolved in the 29th Century mainly from major league influences, hall of fame intercollegiate head coaches such as Rod Dedeaux (Division I Coach of the 20th Century), John Scolinos (Division II Coach of the 21st Century), Skip Berkman (Division I NCAA Coach of the 1990 Decade), Ron Polk (1978), Jerry Kindall (2000) and Tony Gwynn (1998); and intercollegiate hitting coaches Dr. Coop DeRenne, (2007), All-American Jerry Kindall (2000), and Tony Gwynn (1998) also recognize the importance of increasing bat control in various offensive situations (e.g., two-strikes on the hitter, hit-and-run play, hit to opposite field) (Delmonico, 1996). Yet, ask any of these great managers or hitters to define bat control, and there would no common answer. Furthmore, when these highly successful intercollegiate hitters and coaches discuss the topic of bat control, the majority agrees that choking up on the bat will increase bat control (Berkow & Kaplan, 1992; Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000; Polk, 1978; Stallings, J. & Bennett, B. [Eds.], 2003). If in the opinion of these intercollegiate coaches that choking up on the bat is a hitting technique used specifically in various game offensive hitting situations to increase bat control, then it is important for all collegiate hitters, coaches, and hitting coaches to understand what is bat control and how is it improved.

Anecdotal opinions from intercollegiate head coaches and hitting coaches, suggest that increased bat control is a result of choking up on the bat that may or may not aid in increasing bat speed, or bat swing time (“bat quickness “) (Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000; Polk, 1978; Stallings, J. & Bennett, B. [Eds.], 2003). In addition, intercollegiate hitters believe that as the bat travels through the swing’s range of motion with a choke-up grip, the bat feels lighter and more controllable (a potential psychological factor beyond the scope of this study) (Adair, 1990; Bahill & Karnavas, 1989; Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000) as compared to the normal grip swing with hands held down at the end of the bat.

Limited hitting research studies has been conducted over the past twenty-five years to determine bat control and associated factors (DeRenne, & Blitzbau, 1990; Escamilla, Fleisig, DeRenne, Taylor, Moorman, Imamura, 2009; Fleisig, Zheng, Stodden, & Andrews, 2002; McIntyre, & Pfautsh, 1982; Messier, & Owen, 1985; Messier, & Owen, 1986; Szymanski, D.J., DeRenne. C., & Spaniol, F.J., 2009). Based on limited research on bat control, the primary purposes of this study were to explore the relationship among hitting components (stride time, swing time, bat quickness, bat velocity, and bat-ball accuracy), and bat control during the normal and choke-up grip swings.

Method

Participants

Fourteen adult baseball players (eight college and six professional players one year removed from college) volunteered to participate and were informed of all risks, hazards and benefits for this study. All participants provided written informed consent as approved by the university’s Office of Research Service’s Committee on Human Studies and the federally mandated Institutional Review Board. All participants were required (1) to be injury-free, (2) have a career batting average of least .300, (3) have choke-up grip hitting experiences at the youth, high school and collegiate levels, (4) and possess good hitting mechanics as determined by the players’ respective hitting coaches (DeRenne, 2007; Race, 1961; Welch, Banks, Cook, & Draovitch, 1995). The subjects had an average age, weight, and height of 22.2±2.3 y, 84.8±6.6 kg, and 180.6±3.7 cm, respectively. The college and professional participants were statistically equivalent to each other with respect to age, body mass, body height, bat characteristics, and temporal and kinematic parameters (Escamilla et al., 2009).

Apparatus

Radar gun

Pitched baseballs were assessed during the batting practice session by an electromagnetic radiation radar (CMI Model JF 100) with a transmission frequency of 10.525 GHz + 25 MHz (DeRenne, Ho, Blitzblau, 1990). Pitched ball velocities were recorded as the ball left the pitching machine. This radar gun has been reported to be a valid and reliable instrument to determine ball exit velocities and is accurate within ± 0.22 m/s (DeRenne et al., 1990).).

High-speed cameras

Two synchronized gen-locked 120 Hz video cameras (Peak Performance Technologies, Inc., Englewood, CO) were optimally positioned to view the hitter. To minimize the effects of digitizing error, the cameras were positioned so that the hitter was as large as possible within the viewing area of the cameras.

Computerized motion analysis system

A 3-D video system (Peak Performance Technologies, Inc., Englewood, CO) was used to manually digitize data for all subjects. A spatial model was created, comprised of the top of the head, centers of the left and right mid-toes (at approximately the head of the third metatarsal), joint centers of the ankles, knees, hips, shoulders, and elbows, mid-point of hands (at approximately the head of the third metacarpal), and proximal and distal end of bat. All points were seen in each camera view. Each of these points was digitized in every video field.

Total body swing kinematics was calculated representing 59 measurements. The three most important swing kinematics that represented the total swing effects to be analyzed for swing grip differences were as follows: stride time, swing time (bat quickness), and estimated linear bat velocity at bat-ball contact.

The swing definition

The swing was defined by four events and three phases. The first event was “lead foot off ground”, which represented the beginning of the stride phase and was defined as the first frame in which the lead foot was no longer in contact with the ground. The next event was “lead foot contact with ground”, which represented the end of the stride phase and was defined as the first frame when the lead foot made contact with the ground. “Lead foot off ground” to “lead foot contact with ground” represented the time duration of the stride phase of the swing. The third event was “hands started to move forward”, which was defined as the first frame that both hands started to move forward towards the pitcher in the positive X direction. “Lead foot contact with ground” to “hands started to move forward” represented the time duration of the transition phase of the swing (transition between the stride phase and acceleration phase). The last event was “bat-ball contact”, which was defined as the first frame immediately before bat-ball contact. “Hands started to move forward to bat-ball contact” represented the time duration of the acceleration phase of the swing. Therefore, the “swing” was defined as from “lead foot off ground” to “bat-ball contact”, and consisted of stride, transition, and acceleration phases.

Procedures

Familiarization session

During the initial familiarization session, all players were given a preliminary choke-up questionnaire to provide background evidence of choking up on the bat during their respective youth, high school and collegiate careers. In order to participate in the choke-up hitting study, all players must have had answered the first three questions with a YES, indicating a substantial history of choking-up during their baseball careers. On question four, the players were asked to list the two top reasons why they decided to choke-up on the bat in competitive games. Bat control was listed by all 14 players (100%) as the number one reason for choking up in the games. The second highest reason was a tie: 50% indicated bat-ball contact accuracy was their second choice; and 50% indicated increased bat speed and bat quickness.

In addition, all players received batting practice instructions. At no time did the investigators reveal the purpose of the study to the players. They were told only that the study was a biomechanical hitting study to determine the mechanical commonalities of the 14 adult swing mechanics.

Batting practice sessions

Bats were self-selected, and average bat weight was 8.5±0.3 N (30.6±1.1 oz) and average bat length was 84.8±1.3 cm (33.4±0.5 inch). The same bat was used for both grip swings. The players were randomly assigned into five hitting groups (4-groups of n =3; 1-group of n=2). Each group was randomized as to the order of group hitting and which bat grip to use. During warm-up, each player had two to-three rounds of hitting with each grip swing to become familiar with the speed and locations of the pitched balls, and the timing of the pitches from the pitching machine. Once the warm-up session was completed, the batting practice sessions commenced.

The first batting session was to determine the kinematic and temporal effects of the normal grip and choke -up grip swings. Each hitter rotated within his respective group until he completed 10 hard, full effort swings with a normal grip (hands as far down as possible on the bat); and 10 hard, full effort swings with a choke-up grip (hands 6.35 cm above the normal grip) as a pitching machine “pitched” baseballs to them. In each group, half the group were randomly assigned to hit with a normal grip first and the other half a choke-up grip first, to eliminate a potential timing confounder. The first three normal grip swings and three choke-up swings that met the following pitch and swing criteria were digitized for each hitter.

Pitches and swings were standardized according to the following criteria: 1) all pitches were between 32.6-33.5 m/s (73-75 mi/h); 2) the pitch had to be a strike on the inner half of the plate from waist to chest high on the hitter; and 3) all swings digitized and used as trials had to be a line drive hit to left-center outfield that carried in flight beyond a 68.6 m (225 feet) marker positioned in left-center field. From pilot data, hitting kinematic and temporal parameters from multiple swings by a hitter that met the above pitch and swing criteria were found to be remarkably similar between swings, typically varying less than 5-10% for each kinematic or temporal parameter.

The second batting practice session was conducted 48-hours after the first batting practice session in order to control fatigue. The purpose of this second batting practice session was to determine bat-ball contact accuracy performances of each player as the hitters executed normal and choke-up grip swings during a live practice simulated game. Along with the first batting practice session, this accuracy batting practice session represented a bat control measure. Specifically, each hitter was instructed to swing at ten acceptable strikes over the entire plate, (inside, down the middle and outside part of the plate; from knee to chest high) with each swing grip as to execute successful hits. Two rounds of five swings were performed by each hitter to control fatigue. A successful hit was defined as “putting the ball in play” with at least a normal or routine (1) groundball, (2) fly ball or (3) line drive. Unacceptable hits were as follows: (1) A swing and miss, (2) foul ball, (3) a weak pop-up, and (4) a weak groundball. As each hitter attempted to “put the ball in play”, they were instructed to swing accordingly with a “full” count of two strikes and three balls as live base runners were moving. In addition, if the pitch was a ball, they were instructed to take the pitch and swing only at acceptable strikes. Only swinging strikes counted against acceptable pitch strikes until each player accumulated ten acceptable swings from each grip. Again, the accuracy goal for each swing was to successfully put the ball in play. The four-man investigative team determined the following: (1) the first investigator determined if the pitch was acceptable within the 73-75 mph range and if the pitch was a strike or ball; and (2) the remaining three investigators determined the degree of accuracy and degree of hardness (e.g., no hit, weak hit, routine hit, hard hit) of each swing. The mean accuracy average of the three investigators was determined for each player’s swing. The mean average was then converted into percentages, which were described as either a YES, successful; or a NO, unsuccessful swing/hit. Therefore, the resultant percentage describes the percentage out of ten swings that each player put the ball in play (see Table 1).

Table 1. Paired Sample T Test for Hitting Parameters in the Normal and Choke-up conditions (n = 14).

	Normal Grip	Choke-up Grip	p value
*Stride Phase
(Time from Lead Foot Off Ground to Lead Foot Contact with Ground) (s) (% of Swing)	0.375±0.075 64.0±10.5	0.336±0.072 62.8±10.2	p = .01
*Swing
(Time from Lead Foot Off Ground to Bat-Ball contact) (m/s)	0.586±0.078*	0.535±0.083*	p = .01
Bat (distal end) Linear Velocity at Bat-Ball Contact (m/s)	31±4	28±5	p =.01
Contact Accuracy Acceptable Hits Out Of Ten Pitched Strikes (%)	.63±.15	.64±.18	p = .90

Statistical Analysis

All data analyses were performed using SPSS, version 14.0 (Statistical Package for the Social Sciences, 2005). Kinematic and temporal data were averaged for the three normal swings and for the three choke-up swings and used in statistical analyses (Escamilla et al., 2009). Dependent (paired) t-tests were employed to test for differences in kinematic and temporal parameters and bat-ball accuracy between the normal grip and choke-up grip swings. In order to minimize the probability of making a type I error without increases the probability of making a type II error, the level of significance used was set at p < 0.01, resulting in an experiment-wise error of 0.31.

Results

Temporal stride and swing parameters are shown in Table 1. The results of the dependent (paired) sample T-tests revealed no significant differences in bat-ball contact accuracy t(- .12) between the normal and choke-up conditions. Significant differences were found between stride time t(2.88) with the mean choke-up grip swing .039 seconds (10%) faster than the normal grip swing. Similar significant differences where found between swing time t(2.56) with the mean choke-up grip swing .051 (9%) seconds faster to the bat-ball contact. In addition, similar significant differences where found between bat velocity t(.289) with mean normal grip swing 3.0 m/s (10%) faster than the choke-up grip swing.

Discussion

This study investigated the relationship among hitting components and bat control during the normal and choke-up grip swings. We found the choke-up grip facilitates faster swing and stride times without compromising bat velocity and bat control.

Swing time (bat quickness) and stride time

Two important findings in the present study were the significant reduced swing time (increased bat quickness) and stride phase time when using a choke-up grip swing. These results support the belief of many intercollegiate hitting coaches and players (Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000; Polk, 1978; Stallings, J. & Bennett, B. [Eds.], 2003) that using a choke-up grip results in a “quicker” bat during the swing (DeRenne & Blitzbau, 1990). When choking up, the hitters adjusted their swing mechanics for more bat control resulting in less stride time and increased bat quickness while not sacrificing a significant loss in bat velocity. In addition, the choke-up hitter may have better control the bat due to the smaller moment of inertia of the bat about the hands that choking up on the bat creates (Adair, 1990; Bahill & Karnavas, 1989; Fleisig et al., 2002).

Based on the results, the choke-up grip bat controlled swing may give hitters 0.039 seconds or 10% more time to decide whether or not to swing at a pitch. This may help the hitters to see the ball longer, due to the trunk being in a more open position and a smaller moment of inertia of the bat allow the choke-up hitter to have more time to “wait on the pitch”. Furthermore, the decrease in stride phase time using a choke-up grip may result in less total body movement for greater balance and possibly improved visual clarity (DeRenne, 2007), while maintaining the same stride length compared to the normal grip.

Linear bat velocity

Choking up on the bat for more bat control allowed the hitters to reduce the moment of inertia of the bat about the hands (Adair, 1990; Bahill & Karnavas, 1989). That is, more of the bat mass was closer to the hands, so the summation of mass times distance squared (Σ (m·r2)) was reduced. Similarly, in golf, to increase club control and maximize the accuracy of pitching and chipping shots, professional golfers choke-down the grip-handle toward the shaft to produce a lower grip on the club and a slower/shorter backswing (Hume, Keogh, & Reid, 2005). In addition in assessing putting kinematics of low-handicap golfers versus high-handicap players, Paradisis and Rees (2002) reported that low-handicap players positioned their leading hand ~8cm further down the shaft of the club than the high-handicap players. In the present study, while the smaller moment of inertia in the choke-up group may lead to faster movements and to a diminished force production in accordance with the force-velocity relationship for muscle. This may be an important factor in helping to explain why linear bat velocity at bat-ball contact was less using a more controlled choke-up grip swing compared to a normal grip swing. It also may be that while choking up, the bat is “shorter”, thus, the distal endpoint of the bat is closer to the axis of rotation and traveling slower compared to the a normal grip swing. Therefore, the slower bat linear velocity at bat-ball contact when using the choke-up grip compared to the normal grip could be related to either or both of these factors.

Tennis racquet control

Similar in tennis, (Chow, Carlton, Lim, Chae, Shim, Kuenster, and Kokubun, 2003) compared the pre-and post-ball and racquet kinematics of professional adult men and women (n=8) tennis players’ first and second serves. The results indicated a 24.1% decrease in post-impact ball speed from the first to the second serve. This finding was expected since the second serve is considered to be more accurate than the first serve because players are successful on a higher percentage of the second serves (Chow et al., 2003). More importantly, the second finding revealed that there were no significant differences between the pre-impact racquet head speeds of the first and the second serves. On the typical second serve, most elite players will use a sidespin “slice” serve increasing racquet control as a trade-off between ball speed and accuracy (Chow et al., 2003). In others words, it might have been expected that elite tennis players would have slowed down their racquet speed on the second slice serve to ensure greater accuracy. This did not happen. Therefore, there was a speed-accuracy trade-off between ball speed and accuracy, but not racquet movement speed and accuracy. Elite tennis players do not slow down their racquet swing when transferring from the first serve to the second serve. The authors also found no differences between racquet speed and accuracy as typically observed in motor tasks (Fitts, 1954).

In the current study, the choked-up bat velocities declined by a significant 10%; yet, the players’ swing times decreased by a significant 9%, which produced a quicker bat. Hence, as elite baseball hitters and tennis pros seek greater bat control when the hitters choke up and tennis pros serve the second serve, interestingly, both hitters and tennis pros swing just as hard and fast as with their normal swing respectively, with no trade-off between bat and racquet speeds and accuracy.

Furthermore, the current study is similar to the results of the pilot baseball swing study comparing the kinematic and temporal parameters of normal and choke-up grips swings reported by DeRenne & Blitzbau (1990). The result reported by DeRenne & Blitzbau (1990) of greater linear bat velocity at bat-ball contact using the normal grip may appear surprising to many collegiate head coaches and hitting coaches. Most collegiate coaches believe that a “quicker” and controlled swing using the choke-up grip equates to greater bat speed (Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000; Polk, 1978; Stallings, J. & Bennett, B. [Eds.], 2003). Although linear bat velocity was significantly less in the choke-up grip swing compared to the normal grip swing, and although the mass of the bat is the same between normal and choke-up grips, there are data that imply that choking up on a bat may affect the “effective mass” of the bat, resulting in less momentum (mass x velocity) with the choke-up grip (Fleisig et al., 2002). Therefore, using a choke-up grip for more bat control may result in decreased ball flight distance after bat-ball impact, which should be the focus of subsequent hitting studies.

Major league hall of fame hitters, hitting coaches, and managers (Alston & Weiskopf, 1972; Cobb, 1961; Lau, et al., 1998; Williams, 1970), and intercollegiate head coaches and hitting coaches believed that more bat control would produce greater bat-ball accuracy ((Delmonico, 1996; DeRenne, 2007; Gwynn, 1998; Kindall & Winkin, 2000; Polk, 1978; Stallings, J. & Bennett, B. [Eds.], 2003). ). This belief was supported in theory by Bahill’s and Karnavas’s (1989) baseball bat weights study. These investigators suggest that as hitters choke-up on the bat they will make the bat effectively shorter, move the center of mass closer to the hands thereby reducing the moment of inertia, in essence making the bat act like a lighter bat with greater accuracy. In contrast, the results of this study indicated that choking up on the bat did not increase bat-ball contact accuracy. Yet in essence, the hitters were as accurate choking up as with their normal grip swing.

In summary and most importantly, the results of this study suggest that choking up for greater bat control may increase the hitter’s confidence and execution knowing that is able to wait longer for the incoming pitch because he is quicker to the ball, and he is as accurate as his normal grip swing.

Conclusions

In conclusion, although time was not significantly different in the acceleration phase between normal and choke-up grips, the total time of the swing (from stride initiation to bat-ball contact) was significantly less with the choke-up grip, which supports the belief of many coaches and players that using a bat controlled choke-up grip results in a “quicker” overall swing. This “quicker bat” implies that with the bat controlled choke-up grip, a hitter can wait longer in order to determine how to handle the incoming pitch. In addition, because linear bat velocity was significantly less in the choke-up grip compared to the normal grip, there may be less momentum with the choke-up grip because of the differences in mass distribution of the bat with choking up, which may result in decreased ball flight distance after impact. A decreased flight distance (power) may not be so negative, since the hitter’s main goal is more solid contact accuracies.

References

Adair, R.K. (1990). The physics of baseball. New York, NY.: Harper & Row Publishers.

Alston, W. & Weiskopf, D. (1972). The complete baseball handbook. Boston: Allyn and Bacon, Inc.

Bahill, A.T. & Karnavas, W.J. (1989). Determining ideal baseball bat weights using muscle force-velocity relationships. Biological Cybernetics, 62, 89-97.

Berkow, I., & J. Kaplan (1992). The gospel according to Casey. New York: St. Martin’s Press.

Chow, J.W., Carlton, L.G., Lim, Y.T., Chae, W.S., Shim, J.H., Kuenster, A.F., & Kokubun, K. (2003). Comparing the pre-and post-impact ball and racquet kinematics of elite tennis players’ first and second serves: A preliminary study. Journal of Sport Sciences, 21, 529-537.

Cobb, T. (1961). My life in baseball: The true record. Garden City, NY: Doubleday Co.

Delmonico, R. (1996). Offensive baseball drills. Champaign, IL.: Human Kinetics.

DeRenne, C. (2007). The scientific approach to hitting: Research explores the most difficult skill in sport. San Diego, CA, University Readers.

DeRenne, C., & Blitzbau, A. (1990). Why your hitters should choke up. Scholastic Coach, Jan, 106-107.

Durocher, L. (1975). Nice guys finish last. New York, NY.: Simon & Schuster.

Escamilla, Fleisig, R.F., S., DeRenne, C, Taylor, M.K., Moorman, C.T., Imamura, R., et al. (2009). Effects of bat grip on baseball hitting kinematics. Journal of Applied Biomechanics, 25, 203-209.

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Fleisig, G. S., Zheng, N., Stodden, D. F., & Andrews, J. R. (2002). Relationship between bat mass properties and bat velocity. Sports Engineering, 5, 1-8.

Gwynn, T. (1998). The art of hitting. Vancouver, BC, Canada: GT Publishing.

Hume, P.A., Keogh, J., & Reid, D. (2005). The role of biomechanics in maximizing distance and accuracy of golf shots. Sports Medicine, 35 (5): 429-449.

Kindall, J., & Winkin, J. (2000). The baseball bible. Champaign, IL.: Human Kinetics.

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Messier, S. P., & Owen, M. G. (1986). Mechanics of batting: Effect of stride technique on ground reaction forces and bat velocities. Research Quarterly in Exercise and Sport, 57, 329-333.

Paradisis, G. & Rees, J. (2002). Kinematic analysis of golf putting for expert and novice golfers: In: Hong, Y., editor. Proceedings of XVIII International Symposium on Biomechanics in Sports; 2002 Jul 23-26. Hong Kong. Hong Kong: Department of Sports Science and Physical Education, The Chinese University of Hong Kong, 2002: 325-8.

Polk, R. (1978). Baseball playbook. West Point, Miss.: Sullivan’s Printing.

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Szymanski, D.J., DeRenne. C., & Spaniol, F.J. (2009). 1Contributing factors for increased bat swing velocity: A Brief Review. Journal of Strength and Conditioning Research (in press).

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U.S. Sports Academy2013-11-25T17:59:33-06:00April 8th, 2010|Sports Exercise Science, Sports Management|Comments Off

Optimizing Development of the Pectoralis Major

Miguel Jagessar and Michael Gray, Academy of Sport and Leisure, The University of Trinidad and Tobago, Trinidad, West Indies.

Abstract

Jagessar, M. Optimizing development of the pectoralis major. 2009. This article seeks to determine optimum body/hand position and the best exercises for development of the pectoralis major. Gaps in the field of literature are also addressed. Body/hand position, execution, width of grip, trunk inclination, dumbbells and barbells are all variables that affect the prime movers (pectoralis major, anterior deltoid and triceps brachii) of the bench press. Electromyography is a technique used for recording changes in electrical potential of muscle fibres that are associated with their contractions Payton, C. J., Bartlett, R. M. (Eds.) (2008). Electromyographic (EMG) studies are well known for determining muscle activity. Due to the overwhelming contradictory information and various variations of the bench press, EMG studies have been undertaken. The research has shown that the horizontal barbell bench press done with a grip between 165% to 190% biacromial width produces maximum EMG activity in the pectoralis major. The clavicular (upper) head produces maximum activity in the close grip incline barbell bench press. Dumbbells and barbells can be used interchangeably to overcome training plateaus.
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U.S. Sports Academy2016-10-20T14:29:17-05:00January 8th, 2010|Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

Effects of Three Modified Plyometric Depth Jumps and Periodized Weight Training on Lower Extremity Power

Damon P. S. Andrew, Troy University; John E. Kovaleski, University of South Alabama; Robert J. Heitman, University of South Alabama; Tracey L. Robinson, Adams State College

Abstract

Plyometric exercises increase muscular power and are most effective when designed to complement the specific movements required of the athletic activity. This study compared the effects of modified depth jump plyometric exercises versus a periodized weight training program on the following functional tests: one-legged vertical jump, two-legged vertical jump, 30-meter sprint, standing broad jump, and 1 RM of the seated single leg press. Sixty-four untrained participants (18-28yr) were randomly assigned to one of the following groups: hip depth jump (n = 12), knee depth jump (n = 13), ankle depth jump (n = 13), weight training (n = 13), or a control (n = 13). Experimental groups trained two days a week for 12 weeks. Statistically significant improvements were observed among the plyometric groups for functional tests of power and the weight training group for functional tests of strength and speed. Results indicate that modified plyometric depth jumps offer a greater degree of specificity related to power training in athletes.

Key Words

Hip depth jump, knee depth jump, ankle depth jump, muscle power, resistance training, plyometrics

Introduction

The term “plyometrics” refers to specific exercises which encompass a rapid stretching of muscle that is undergoing eccentric stress followed by a concentric, rapid contraction of that muscle for the purpose of developing a forceful movement over a short period of time (Chu, 1983). One particular plyometric activity, the depth jump, has been shown to improve power in the vertical jump (Batholemew, 1985; Miller, 1982; Parcells, 1977; Verkhoshanski & Tatyan, 1983). Depth jumps are a type of dynamic exercise where an individual steps off a box 20 to 80 centimeters in height, lands, and performs an explosive vertical jump (Wilson, Murphy, & Giorgi, 1996). The depth jump is thought to enhance vertical jump performance through the quickening of the amortization phase, which is the electromechanical delay from the initiation of eccentric to the initiation of concentric muscle actions of the movement (Steben & Steben, 1981).

Plyometric depth jumps have been modified to generate greater stresses at the joints of the hip, knee, and ankle (Holcomb, Lander, Rutland, & Wilson, 1996a). These variations were identified as the hip depth jump (HDJ), knee depth jump (KDJ), and ankle depth jump (ADJ). Each variation included modifications to the range of motion of the joint being emphasized during the eccentric portion of the depth jump. The HDJ, KDJ, and ADJ are thought to increase the workload, and thus power, at the particular joint for which they are named. The need for such a modification stemmed from biomechanical analysis of both the vertical and depth jumps. In biomechanical analysis of the vertical jump, the hip was found to contribute 23-39% of the total work done during the vertical jump (Bobbert, Huijing, & Van Ingen Schenaue, 1987; Bobbert, MacKay, Schinkelshoek, Huijing, & Van Ingen Schenaue, 1986; Hubley & Wells, 1983; Van Soest, Roebroeck, Bobbert, Huijing, & Van Ingen Schenaue, 1985). However, two analyses of the depth jump revealed the hip contribution to be only 19% and 13% respectively (Bobbert et al., 1986, 1987). Consequently, the traditional plyometric depth jump does not stress the hip joint to the extent that it is used during the vertical jump, the functional task it was originally designed to enhance.

Biomechanical analysis of the modified plyometric depth jumps was also performed to analyze joint contribution through total work done at each joint (Holcomb et al., 1996a). Total work at the hip, knee, and ankle joints was 80%, 5%, and 15%, respectively, during the HDJ. Analysis of the KDJ revealed contributions of 37% at the hip joint, 49% at the knee joint, and 14% at the ankle joint. The joint contributions during the ADJ were reported to be 24%, 20%, and 56% at the hip, knee, and ankle joints, respectively. Therefore, each depth jump primarily stressed the particular joint for which it was named.

The effectiveness of training programs is routinely measured via functional test performance. Functional tests usually contain a series of movements that have high correlations with athletic activity and are used for research, evaluation, and rehabilitation purposes. Biomechanical analyses of functional tests can reveal percent joint contributions to the activity. Table 1 contains the percent joint contributions of modified plyometric depth jumps and selected functional tests for this study. Although specific joint contributions have not been calculated for the 30-meter sprint or seated single leg press, some research has examined the power output of these functional tests. Researchers have identified the hip to be a dominant force producer in sprints of short duration (Mero & Komi, 1990; Mero ,Komi, & Gregor, 1992; Mero & Peltola, 1989). Wilk et al. (1996) examined the electromyographic activity of the quadriceps and hamstring muscles during a two-legged seated leg press and found a high degree of quadriceps activity, suggesting significant power contributions from the knee joint. When compared to the squat, the seated leg press allows for smaller compressive forces to the tibiofemoral joint (Escamilla et al., 1998), making the activity an ideal accommodation for untrained participants.

Table 1
Percent joint power contribution of modified plyometric depth jumps and functional tests

	Hip Joint	Knee Joint	Ankle Joint
Hip depth jump (22)	80	5	15
Knee depth jump (22)	37	49	15
Ankle depth jump (22)	24	20	56
30-m sprint	N/A	N/A	N/A
One-legged VJ (39)	34.4	23.9	41.7
Two-legged VJ (25)	28	49	23
Two-legged VJ (39)	32.9	37.7	29.4
Two-legged VJ (35)	40	24.2	35.8
Two-legged VJ (22)	57	23	20
Standing broad jump (35)	45.9	3.9	50.2
Seated single leg press	N/A	N/A	N/A

Holcomb Lander, Rutland, and Wilson (1996b) continued their research with a progressive resistance eight week training study comparing the modified plyometric depth jumps to other methods that have shown to significantly increase vertical jump height, including conventional plyometric depth jumps (Adams, O’Shea, O’Shea, & Climstein, M, 1992;, Blattner & Noble, 1979; Brown, Mayhew, & Boleach, 1986; Gehri, Ricard, Kleiner, & Kirkendall, 1998; Hewett, Stroupe, Nance, & Noyes, 1996; Huber, 1987; Polhemus & Burkhardt, 1980; Verkhoshanski & Tatyan, 1983; Wilson et al., 1996), countermovement jumps (Clutch, Wilton, McGown, & Bryce, 1983; Gehri et al., 1998), and weight training (Baker, Wilson, & Carlyon, 1994; Blaket, 1985; Ford et al., 1983; Stowers et al., 1983). The researchers chose to combine all three of the modified depth jumps into the training schedule of one group (Mod. Plyo) and compared that group to a traditional depth jump group (Plyo), a countermovement jump group (CMJ), a weight training group (WT), and a control group (CON). The weight training group performed four lower extremity exercises with progressive resistance including standing plantar flexion, knee extension, knee flexion, and leg press, while the control group did not train. The 51 college age male participants in the study trained three times per week for eight weeks. The exercise volume was controlled so that each group performed an identical number of repetitions, whether it involved lifting weights or jumping.

The results showed non-significant improvement for all groups during the static jump. All training groups improved performance in the countermovement jump (CMJ improved 4.0%; WT improved 4.7%; Plyo improved 6.5%; Mod. Plyo improved 4.5%), but the CON group performance decreased 3.2%. The traditional plyometric group differed significantly from the control group (9.7% difference). The lack of significant improvement of the Mod. Plyo group was attributed to a possible negative impact on the learning of the proper technique required for a successful jump due to altered range of motion of the plyometric depth jumps. We suggested that future research incorporate a longer period of training to assure a higher training effect.

Weight training has been shown to enhance power primarily through gains in peak force of the muscle rather than rate of force development (Hakkinen, Allen, & Komi, 1985a). Plyometric training of the lower extremity has been demonstrated to promote power primarily through increased rate of force development rather than increased peak force of the muscle (Bobbert, 1990; Hakkinen, Komi, & Allen, 1985b, Lundin, 1985). A positive relationship has been established between plyometric training and improvement in several functional tests of the lower extremity in addition to the vertical jump (Lyttle, Wilson, & Ostrowski, 1996; Wilson, Newton, Murphy, & Humphries, 1993). However, recent developments in modified plyometric depth jumps show promise of increased specificity for power training of the lower extremity (Holcomb et al., 1996a, 1996b). According to the principle of specificity (Wilmore & Costill, 1994), one should expect that a training program designed to stress the specific physiological systems required for the output activity would result in optimal performance. Holcomb et al. (1996b) grouped all of the modified plyometric depth jumps into one training program, which eliminated the possibility to determine the specific effects of each modified plyometric depth jump. Therefore, the purpose of this research was to assess the effects of three types of plyometric depth jumps and weight training on the (a) one-legged vertical jump with a countermovement, (b) two-legged vertical jump with a countermovement, (c) 30-meter sprint, (d) standing broad jump with a countermovement, and (e) 1 RM of the seated single leg press following a 12-week training program. The separation of the three modified plyometric depth jumps into distinct groups along with the addition of other functional tests for the lower extremity should show the increased training specificity of the modified plyometric depth jumps.

Hypothesis

Based on the biomechanical data concerning joint contributions in Table 1, the researchers formulated the following hypotheses:

H1: Participants who trained using the hip depth jump will significantly improve their 30-meter sprint times versus the participants who train using the knee and ankle depth jumps, weight training, and the control group.
H2: Participants who trained using the knee depth jump will significantly improve their two-legged vertical jump heights versus the participants who train using the hip and ankle depth jumps, weight training, and the control group.
H3: Participants who trained using the ankle depth jump will significantly improve their one-legged vertical jump heights and standing broad jump distances versus the participants who train using the hip and knew depth jumps, weight training, and the control group.
H4: Participants who weight trained the lower extremity will significantly improve their 1RM of the seated single leg press versus the participants who train using the hip, knee, and ankle depth jumps, and the control group.

Methods

Participants

Sixty-four recreationally active college-aged individuals volunteered for this study (Table 2). The participants did not perform either plyometric or weight training of their lower extremity for a period of at least six months prior to the study. After approval by the University’s IRB, all participants signed an informed consent.

Table 2
Descriptive group data

	HDJ^a	KDJ^a	ADJ^a	WT^a	CON^a
Number	12	13	13	13	13
Sex^b	M=9; F=3	M=11; F=2	M=8; F=5	M=7; F=6	M=9; F=4
Height (cm)	174.8 ± 8.3	177.0 ± 7.5	176.8 ± 9.7	175.3 ± 11.7	173.6 ± 11.4
Mass (kg)	70.6 ± 13.5	75.8 ± 14.3	72.8 ± 12.4	69.6 ± 15.5	76.4 ± 17.9
Age (yr)	22.3 ± 2.6	20.8 ± 1.6	20.8 ± 1.3	21.0 ± 2.4	22.0 ± 1.7

a) HDJ = hip depth jump, KDJ = knee depth jump, ADJ = ankle depth jump, WT = weight training, CON = control;
b) M = male, F = female

Participants were randomly assigned to one of five groups: hip depth jump, knee depth jump, ankle depth jump, weight training, or a control group that did not train.

Depth Jump Protocol

Three plyometric depth jump groups performed only the specific exercise for which their group was named. The exercises were performed as described by Holcomb et al. (1996b). For the hip depth jump, the subject began to flex the trunk during the fall from the box so that the trunk was flexed to 45° upon landing and continued to flex the trunk until the trunk was parallel to the ground. In the knee depth jump, the subject landed fairly erect, and flexed to beyond 90° at the knee, all while keeping the trunk erect. During the ankle depth jump, the subject remained as erect as possible when landing except for slight flexion at the knee. For all three jump groups, the participants jumped vertically with maximum effort as quickly as possible after landing.

All three depth jump groups performed an identical training protocol that included seven sets of 12 repetitions, which resulted in a total of 2016 repetitions for the 24 training sessions. Each jump set was followed by a period of rest from three to four minutes. Training intensity, defined as initial height of the depth jump, began with a 15.24 cm (six inch) drop height and progressed an additional 15.24 cm every three weeks, ending with a 60.96 cm (24 inch) drop height. The modified plyometric training groups were monitored by a researcher for correct jump form to ensure proper joint stress.

Weight Training Protocol

The weight training group’s exercises included the seated single leg press, standing calf raise, and knee extension and flexion for each leg. The weight training program was designed to first develop muscle strength with progression to workouts that emphasized muscle power. This periodized approach consisted of four phases with each phase lasting three weeks. The first phase involved three sets of ten repetitions of the subject’s ten repetition maximum for each exercise. The second phase included three sets of eight repetitions of the subject’s eight repetition maximum for each exercise. The third phase involved three sets of six repetitions of the subject’s six repetition maximum for each exercise. Finally, the fourth phase included three sets of four repetitions of the subject’s four repetition maximum for each exercise. The subject’s one repetition maximum for each exercise was measured prior to each phase, and a chart that estimates weight for designated multiple repetitions based on the one repetition maximum was used as a guide for training weight selection (Fleck & Kraemer, 1987). The weight training group completed a total of 2016 repetitions at the conclusion of the 24 workout sessions. The weight training protocol was more periodized than that of the modified plyometric depth jump groups because both repetitions and intensity were manipulated for the weight training group, whereas only intensity was manipulated for the modified plyometric depth jump groups.

Testing Protocol

Both the two-legged and one-legged vertical jumps were performed with a countermovement, with the subject’s dominant leg used for one-legged jumping. Testing procedures included having the subject standing flat-footed and erect facing a marked wall while extending the dominant arm. The highest height at which the fingers touched the wall was recorded. The subject then jumped vertically with maximum effort. The Vertec jump training system (Sports Imports, Inc., Columbus, Ohio) was used for data collection, and the best of three trials was recorded. The total vertical jump score was calculated in centimeters as the standing height score from the marked wall subtracted from the jumping height score of the Vertec. The vertical jump results along with the subject’s weight were used as variables in an equation to convert the data into Watts, a true measure of power that allows a fair comparison between participants (Sayers, Harackiewicw, Harman, Frykman, & Rosenstein, 1999). The Sayers formula (Sayers et al., 1999) is as follows: Peak Power (W) = 60.7 × [jump height (cm)] + 45.3 × [body mass (kg)] – 2055.

The standing broad jump was performed by jumping horizontally from a starting line with a countermovement. The participants began in a standing position with both feet firmly positioned on the ground. The participants jumped horizontally with maximum effort landing on both feet, and the distance covered from the heel of the foot closest to the back of the starting line was measured. The best of three trials was recorded in centimeters.

The 30m sprint was performed by running a distance of 30 meters from a stationary position as quickly as possible. The participants began in a crouched sprinter’s position without blocks and were timed using a Solo time 450 electronic timing system with a hand pad (Solo Time, Denver, Colorado). The hand pad was placed on the starting line and was contacted by the subject’s hand after an acceptable starting position was obtained. The use of this device allowed the subject to begin the sprint at his or her own command by releasing the hand from the hand pad with the initiation of the sprint. When pressure to the hand pad was released, the electronic timing device was activated until the subject crossed an electric beam at the finish line. The participants performed three sprint trials and were allowed three minutes rest between each trial. The best of three trials for the time (seconds) it took the subject to travel 30 meters was recorded.

The dominant and non-dominant leg press was performed using a Paramount leg press machine (Paramount Fitness Equipment Co., Los Angeles, California). The participants were placed in a seated position with approximately 90° of knee flexion and instructed to lift the maximum amount of weight possible using only a single leg against the weight plate. The one repetition maximum mass for the dominant and non-dominant legs was recorded in kilograms along with the subject’s seat position data to ensure identical seat position from the pre to post test.

Data Analysis

Paired sample T-tests were used to analyze the difference between pre and post-test scores. A One-Way Analysis of Variance (ANOVA) was performed on the pre-test scores for all groups on all functional tests. Due to significant differences between groups in pre-test dominant leg press scores, Analysis of Co-variance (ANCOVA) was used for subsequent analysis of functional test data. Significant findings from ANCOVA prompted Bonferroni adjusted independent sample T-tests for post hoc analysis. These T-tests compared the group hypothesized to excel in that particular functional test to the other groups. All tests were performed at the 0.05 alpha level of significance.

Results

Percent change from pre- to post-testing for all functional tests are presented in Table 3.

30 Meter Sprint

For the 30m sprint, only the weight training group lowered their times significantly (t = 2.226, df = 1, 12; p = .046) from pre to post-test, but the group’s improvement was not found to be significantly better than any other group (F = 1.181, df = 4, 63; p = .165).

Leg Press

Significant improvements were noted for the HDJ (t = -8.130, df = 1, 11; p < .001), KDJ (t = -8.849, df = 1, 12; p < .001), ADJ (t = -4.054, df = 1, 12; p = .002), and WT (t = -9.142, df = 1, 12; p < .001) groups for the dominant leg press. The WT group recorded the most improvement and was found to be statistically greater than the ADJ (t = 1.917, df = 1, 12; p = .035) and CON (t = 6.073, df = 1, 12; p < .001) groups.

Similar results were obtained for the non-dominant leg press. Significant improvements were gained by the HDJ (t = -6.607, df = 1, 11; p < .001), KDJ (t = -8.973, df = 1, 12; p < .001), ADJ (t = -4.068, df = 1, 12; p = .002), and WT (-8.652, df = 1, 12; p < .001) groups. Even though the WT group improved the most, it was statistically superior to only the CON (t = 3.959, df = 1, 12; p < .001) group.

Standing Broad Jump

Significant improvements for the HDJ (t = -2.687, df = 1, 11; p = .021), KDJ (t = -4.466, df = 1, 12; p < .001), and ADJ (t = -6.287, df = 1, 12; p < .001) groups were observed for the standing broad jump. The ADJ group recorded the greatest improvement but was not found to be statistically greater than any other group (F = 1.386, df = 4, 63; p = .125).

Vertical Jump

For the one-legged vertical jump, significant improvements were recorded for the KDJ (t = -4.335, df = 1, 12; p < .001), ADJ (t = -2.981, df = 1, 12; p = .011), and CON (t = -2.920, df = 1, 12; p = .013) groups. Even though the KDJ group improved the greatest, it was not statistically superior to any other group (F = 1.537, df = 4, 63; p = .102).

In the two-legged vertical jump, the results showed significant improvements for the KDJ (t = -3.721, df = 1, 12; p = .003), ADJ (t = -3.865, df = 1, 12; p = .002), and CON (t = -2.792, df = 1, 12; p = .016) groups. The ADJ group showed the most improvement and was found to be statistically superior only to the WT (t = 2.380, df = 1, 12; p = .014) group.

Discussion

The influence of the principle of specificity of exercise (Wilmore & Costill, 1994) was evident when examining the results of this study. In general, the modified plyometric depth jump groups excelled in functional tests of power, while the periodized WT group performed better in functional tests of speed and strength. However, not all testing outcomes occurred as expected.

The WT group showed the greatest increases in dominant and non-dominant leg press strength. In regards to the principle of specificity of exercise, this outcome was expected since the WT group incorporated dominant and non-dominant leg press exercises in their training protocol. In addition, significant increases in leg strength were also gained by the HDJ, KDJ, and ADJ groups. Previous plyometric training studies (Adams, 1984; 14, 34) have reported gains in leg strength (12.7 to 23.8%), but not to the magnitude shown by the modified plyometric depth jump groups (29.1 to 48.4%) with this study. Chu (NSCA, 1986) describes plyometric depth jumping as an activity that acts to increase the neuromuscular system’s ability to perform concentric contraction more effectively because the forces encountered in plyometric exercises lead to greater synchronous activity of motor units and earlier recruitment of larger motor units via the myotatic reflex. Therefore, the significant increases in leg strength experienced by the modified plyometric depth jump groups may be in response to an enhanced neuromuscular system.

A review of the biomechanical aspects of lower extremity functional tests revealed the contributions of each joint to the performance of a particular functional test. Muscle activation patterns involving EMG analysis of sprint running during its initial phases show maximal power output occurring at the hip joint (Mero & Komi, 1990). Although sprinting primarily measures speed, a short distance was chosen to maximize analysis of acceleration time, thereby increasing the measurement of power. Therefore, those training for power at the hip joint should have a physiological advantage when performing a short sprint. However, only the periodized WT group improved significantly from pre to post-testing. The possible explanations for this finding include the sprinting distance, which may have been too short to emphasize power production, and the use of untrained participants, who may have had low levels of muscle strength before training.

A study concerning the kinetics of broad jumping reported the joint power contributions of the hip, knee, and ankle joints to be 45.9%, 3.9%, and 50.2%, respectively (Robertson & Fleming, 1987). The ADJ group recorded the greatest gains as expected, but the HDJ and KDJ groups also attained significant improvements. Perhaps the general gains in lower extremity power by the modified plyometric depth jump groups enabled significant improvements in broad jumping distances.

Van Soest, Roebroeck, Bobbert, Huijing, and Van Ingen Schenau (1985) reported the joint power contributions of the hip, knee, and ankle joints during the one-legged vertical jump to be 34.4%, 23.9%, and 41.7%, respectively. The greatest gains in the one-legged vertical jump were experienced by the KDJ group, but significant improvements were also recorded for the ADJ and CON groups. The CON group also achieved significance despite showing the lowest percentage of height gain of all groups. The dominance of the KDJ group in this functional test was unexpected due to its reported low involvement in the activity when compared to the other joints of the lower extremity (Van Soest et al., 1985). Perhaps the knee joint is more important to power production during the one-legged vertical jump than previously reported.

Biomechanical analysis of the two-legged vertical jump showed the joint contributions for the hip, knee, and ankle joints to range from 28 to 57%, 23 to 49%, and 20 to 35.8%, respectively (Holcomb et al., 1996a; Hubley & Wells, 1983; Robertson & Fleming, 1987; Van Soest et al., 1985). The ADJ group improved most from pre to post-test, but significant results were also recorded for the KDJ and CON groups. Although the CON group agreed not to undertake any additional training outside of their normal daily activities, perhaps the normal activities of the physical education students selected for the control group influenced their performance on the functional tests. However, this possibility is merely speculation as an exit interview was not conducted due to time constraints.

An equalization of training volume was attempted between groups in this study through equating total training repetitions. Future training studies involving modified plyometric depth jumps should examine variables such as length of training period, participants’ prior training status, and training volume and intensity. Limited research has compared the training stimuli of depth jumping versus weight lifting in regards to the magnitude of stimulus provided by each respective training repetition. Perhaps lifting a particular weight produces a greater stimulus to the muscle than depth jumping from a particular height, or vice versa.

Furthermore, the exercise performed by the WT group emphasized involvement of the entire lower extremity, while the modified plyometric depth jumps primarily stressed one particular joint and muscle group. Perhaps a fairer comparison could be made if the weight training exercises were designed to be joint specific and then compared to the respective modified plyometric depth jump. The inclusion of weight training with the plyometric exercise, which has been reported to produce a synergistic training effect in traditional plyometric activities (Lyttle et al., 1996), could also be examined.

In summary, the effectiveness of four training methods constructed for their potential improvement of strength, speed, and power among untrained participants was examined in this study. Generally, functional tests requiring power were dominated by the modified plyometric training groups while the periodized weight training group prevailed on tests emphasizing strength and speed. The strength and conditioning professional can apply these results to better create training programs for athletes desiring strength, speed, and power of the lower extremity.

About the Authors

Damon P.S. Andrew is the Dean of Health and Human Services at Troy University in Troy, Alabama. John E. Kovaleski and Robert J. Heitman are from the Department of Health, Physical Education and Leisure Studies at the University of South Alabama in Mobile, Alabama. Tracey L. Robinson is from the Department of Human Performance and Physical Education at Adams State College in Alamosa, Colorado.

Corresponding author:

Damon P. S. Andrew, Ph.D.
Dean, College of Health and Human Services
Troy University

153 Collegeview
Troy, AL 36082
Office: 334-670-3712
Fax: 334-670-3743
dandrew@troy.edu

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Wilson, G., Newton, R., Murphy, A., & Humphries, B. (1993). The optimal training load for the development of dynamic athletic performance. Medicine and Science in Sports and Exercise, 25, 1279-1286.

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U.S. Sports Academy2013-11-25T19:27:24-06:00January 8th, 2010|Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

Comparison of 5km Running Performance after 24 and 72 hours of Passive Recovery

By: Andrew Bosak, Philip Bishop, J. Matthew Green, G Hawver, and Mike Iosia

Abstract

Recovery from a hard running effort determines when a runner can run at an intense level again. Overtraining is often caused by insufficient recovery, which ultimately hurts endurance performance. The number of recovery hours needed to sufficiently restore the body back to peak racing condition is unknown. The purpose of this study was to compare 5km running performance after 24 hours and 72 hours of recovery. Twelve well-trained runners (9 males and 3 females) completed two successive 5km performance trials on two separate occasions. Immediately following the baseline 5km trial, runners recovered passively for 24 hrs (R24) and 72 hrs of passive recovery (R72), and then performed a second 5km trial. The 5km time trial sessions were separated by 6-7 days of normal training and performed in a counterbalanced order. R24 (19:59 + 1.9 min) was significantly (p = 0.03) slower than baseline (19:49 + 1.9 min). However, no significant differences (p = 0.21) were found between R72 (19:30 + 1.5 min) and baseline (19:34 + 1.6 min). HRave for R24 (177.3 + 6.3 b/min) was the same as baseline (177.3 + 7.3 b/min), yet R72 HRave (177.9 + 6.3 b/min) was significantly higher (p = 0.04) than baseline (175.4 + 6.5 b/min). RPEend for R24 (19.5 + 0.8) was not significantly different (p = 0.39) than baseline (19.6 + 0.8), but R72 RPEend (19.8 + 0.6) was significantly (p = 0.01) greater than baseline (19.3 + 0.9). For the R24 trials, 9 participants ran a mean 17.4 + 12.1 secs slower and 3 participants ran a mean of 13.3 + 6.8 secs faster than baseline. During R72, three individuals ran a mean 10.3 + 5.7 secs slower, five individuals ran a mean 17.4 + 12.9 secs faster, and four individuals ran within 3.3 + 1.8 secs of their first run. Results indicate that 72 hrs of passive recovery, on average, permits maintenance of successive 5km time trial performance, yet individual variability existed regarding rate of decline of 2nd trial performance. Future research is needed to determine if a longer or shorter recovery time will maintain or improve 5km racing performance.

Introduction

Coaches and runners constantly strive to identify legal methods to improve runners’ performances. Factors such as tempo runs, hill repeats, long-slow distance days, striders and build-ups, intervals and repeats, dietary intake, and sleep patterns, are continually tested and adjusted to produce better performance. However, one factor often overlooked is recovery. Many runners feel that to race faster, they should have longer daily runs, run more miles per week, or train faster and harder. This often leads to overtraining, which hurts performance. Recovery from hard running efforts plays a vital role in determining when a runner can run at an intense level again (Fitzgerald, 2007).

Previous studies have focused on recovery from long endurance races such as marathons and ultra-marathons (Gomez et al., 2002; Martin & Coe, 1997; Noakes, 2003). Recovery from these endurance efforts revolves around repairing of damaged muscle fibers and replenishing glycogen stores (Fitzgerald, 2007; Gomez et al., 2002; Nicholas et al., 1997). In shorter duration endurance activities, such as a 5km (3.1 miles), 10km (6.2 miles) race, or hard training runs, Foss and Keteyian (1998) indicate that muscle and liver glycogen levels may be normalized 24 hrs after exercise, but muscle function may not be fully recovered and performance measures may be sub-optimal.

Former University of Oregon track coach Bill Bowerman first popularized the concept of hard/easy training, indicating that intense workouts such as an interval session, tempo run, or long run, should be followed the next day by an easy run (Dellinger & Freeman, 1984). Using Bowerman’s method, a runner would have an intense workout every 48 hrs to allow muscle function to be restored to normal (O’Conner & Wilder, 2001). Also, New Zealander Jack Foster indicated a runner should take one recovery day for every mile completed in a race [Brown & Henderson, 2002; Galloway, 1984; Henderson, 2000; Higdon, 1998; Sinclair, Olgesby, & Piepenburg, 2003). However, Henderson indicated that it may be better to take one easy day per kilometer (Brown & Henderson, 2002; Henderson, 2000). Although, Bowerman, Henderson, & Foster’s statements about recovery days after a race or hard effort seem reasonable, the appropriate recovery duration as well as what is considered “easy” has not been previously studied.

Gomez et al. (2002) determined that strength and power capabilities of distance runners after a 10km race normalized after 48 hrs of passive recovery. Thus, it is likely that participants would be fully recovered, which would allow them to maximize performance during another 10km race. Because 5km is half the distance of 10km, it may be logical to presume only 24 hrs of passive recovery may be needed, instead of the required 48 hrs for 10km. However, this hypothesis was not supported when we tested two distance runners of above average abilities in a pilot study as the participants were not able to achieve similar 5km performance after 24 hrs of passive recovery. Twenty-four hours may not be a sufficient amount of time for the dissipation of muscle fatigue or soreness (Brown & Henderson, 2002). Therefore, the purpose of this study was to compare 5km running performance after 24 hrs of passive recovery versus 72 hrs of passive recovery.

Methods

Participants

Participants for the study were 12 well trained male (n = 9) and female (n = 3) runners currently engaged in rigorous training. Runners from the local road running and track club, local triathlon competitors, and former competitive high school and college runners, were recruited by word of mouth. Participant inclusion criteria included: (a) Subjects must have been currently involved in a distance running training program, (b) Had previously run 16-22 min for male runners or 18-24 min for a female runner for 5km, (c) Currently averaging at least 20-30 miles (running) per week, (d) Have previously completed at least five 5km road or track races, (e) Have a VO2max of at least 45 ml/kg/min (females) or 55 ml/kg/min (males), and (f) Provided sufficient data (from running history questionnaires, Physical Activity Readiness Questionnaires, and Health Readiness Questionnaires) that reflected good health.

Participants completed a short questionnaire regarding their running background, racing history, and current training mileage. All participants were volunteers and signed a written informed consent outlining requirements and potential risks and benefits resulting from participating.

Procedures

Participants were assessed for age, height, body weight, and body fat percent using a 3-site skinfold technique (Brozek & Hanschel, 1961; Pollock, Schmidt, & Jackson, 1980). Participants were fitted with a Polar Heart Rate Monitor and then completed a graded exercise test (GXT) to exhaustion lasting approximately 12-18 minutes. VO2max, heart rate (HR), and Ratings of Perceived Exertion (RPE) were collected every minute.

All GXTs were completed on a Quinton 640 motorized treadmill. The test began with a 2 min warm-up at 2.5 mph. Speed was increased to 5 mph for 2 min, followed by 2 min at 6 mph, 2 min at 7 mph, and 2 min at 7.5 mph. At this point, incline was increased two percent every 2 min thereafter until the participant reached volitional exhaustion (ie., the felt like they could no longer continue running at the required speed and grade). Once the participant reached volitional exhaustion, they were instructed to cool down until they felt recovered.

Approximately five days later, participants performed their first 5km race between the hours of 6:30 am and 7:30 am. The time of day for each performance trial was consistent throughout the study. All performance trials were completed on a flat hard-surfaced 0.73 mile loop. Prior to each trial, participants completed visual analog scales pre and post a 1.5 mile warm-up run, regarding their feelings of fatigue and soreness within the quadriceps, hamstrings, gastrocnemius, lower body, and total body muscle groups. Visual analog scales were 15 cm lines where participants placed an “X” on the line indicating their feelings (with 0 = no fatigue or soreness and 15 = extreme fatigue or soreness). The visual analog scales evaluated participants’ status before the start of every time trial. Participants were also required to rate their perceived exertion (RPE) after the warm-up, prior to the start and during each 5km, to see if feelings of effort remained consistent between each trial, as well as during each lap and after each performance trial.

Participants underwent a 1.5 mile warm-up prior to every 5km performance trial (Kaufman & Ware, 1997). Participants completed successive 5km performance trials on two separate occasions. Immediately following a baseline 5km trial, runners recovered passively for 24 hrs (R24) or 72 hrs of passive recovery (R72) and then performed a second 5km trial. The 5km time trial sessions were in a counterbalanced order and were separated by 6-7 days of normal training. All participants were required to have 24 hrs of passive recovery prior to each baseline. Passive recovery was deemed as no exercise or extensive physical activity during the allotted recovery hours. During each time trial, average HR (HRave) and ending RPE (RPEend) were recorded to determine if effort for each 5km trial was consistent. All runners competed with runners of equal ability to simulate race day and hard training conditions with verbal encouragement provided often and equally to each participant. At the end of every performance trial, each runner was instructed to complete a low intensity 1.5 mile cool-down. Each testing session required approximately 60 min.

Statistical Analysis

Basic descriptive statistics were computed along with Repeated Measures of Analysis of Variance (MANOVA) for making comparisons between R72 and R24 performance trials regarding finishing times, HRave, RPEend, and fatigue/soreness responses. All statistical comparisons were made at an a priori p < 0.05 level of significance. Data was expressed as group mean + standard deviation and individual results.

In order to evaluate individual responses, data from each participant’s first 5km trial was compared to their second 5km trial using a paired T-test. The least significance group mean difference (p < 0.05) was determined and group mean finishing time was adjusted to determine the amount of change in seconds, between baseline and treatment trials, needed for significance. The time change between the first trial run and the adjusted baseline run was divided by the first trial run and expressed as a mean number of seconds and as a percent for both the R24 (9.5 secs or 0.8%) and R72 (7.0 secs or 0.6%) trials. The percent values were applied to each individual baseline time in order to determine how many seconds (positive or negative) the second performance trial time had to be over or under the first performance trial, in both R24 and R72 conditions, to quantify as a response. Participants were then labeled as non-responders, positive-responders (faster during successive trial), and negative-responders (slower during successive trial).

Results

Descriptive characteristics are found in Table 1. The participants were between the ages of 18 and 35 (majority of subjects were between ages of 20-28) years. All participants were trained runners or triathletes (where running was their specialty event).

Table 1
Participant (Males = 9 & Females = 3) Descriptive Statistics

Mean Standard Deviation

________________________________________________________________________

Males Females Group Males Females Group


Age (yrs)	25.6	22.0	24.7	5.0	1.0	4.6
Height (cm)	175.3	168.0	173.5	6.2	18.2	10.0
Weight (kg)	78.0	61.7	73.9	10.9	10.0	12.6
Body Fat (%)	10.9	21.9	13.7	1.3	2.0	5.1
VO2max (ml/kg/min)	63.3	59.7	62.4	5.0	7.9	5.6
Pre-study 5km Personal Best (min)	18:57	21:31	20:19	1:54	2:05	2:02
Average Weekly Mileage	31.7	30.1	30.5	7.4	7.7	7.5
Days Per Week	4.9	4.6	4.7	1.5	1.1	1.2

________________________________________________________________________

Mean finishing times, HRave, and RPEend for 1) R24 vs baseline and 2) R72 vs baseline are found in Table 2. R24 was significantly (p = 0.03) slower (10 secs) than baseline, where as R72 was not significantly (p = 0.21) different from baseline. Regarding HRave, no significant differences (p = 1.00) were found between R24 and baseline, yet R72HRave was significantly (p = 0.04) greater than baseline. Significance (p = 0.39) was not found between R24 RPEend and baseline, but R72 RPEend was significantly (p = 0.01) higher than baseline.

Table 2

Comparison of R24 (24 hrs) vs R72 (72 hrs) Trials

________________________________________________________________________

Baseline R24 Baseline R72

________________________________________________________________________

Finish Time (min)	19:49 + 1.9	19:59 + 1.9*	19:34 + 1.6	19:30 + 1.5
Average HR (b/min)	177.3 + 7.3	177.3 + 6.3	175.4 + 6.5	177.9 + 6.3*
Ending RPE	19.6 + 0.8	19.5 + 0.8	19.3 + 0.9	19.8 + 0.6*

________________________________________________________________________

R24 trials = 24 hrs of passive recovery between baseline and R24

R72 trials = 72 hrs of passive recovery between baseline and R72

*indicates significant difference between respective baseline trial.

Figure 1 displays individual differences between R24 and R72 performance trials. To be considered a non-responder, the individual time change had to fall within 0.8% of baseline performance for R24 and 0.6% of baseline performance for R72.

Figure 1. Changes in Individual Finishing Times (R72 vs R24)

Positive and negative responders (Table 3) were identified when individual time change was greater than 0.8% for R24 trials and 0.6% for R72 trials, with a positive responder being one whose 2nd performance trial time improved (expressed as a negative value) and a negative responder being one whose 2nd performance trial time slowed (expressed as a positive value).

Table 3

Comparison of Individual R24 and R72 Performance Trials
________________________________________________________________________

Participant Baseline R24 Time Baseline R72 Time

(min) (min) Change (min) (min) Change

(secs) (secs)

________________________________________________________________________

1	16:41	17:06	+25*	16:42	16:36	-6*
2	17:38	17:17	-21*	17:25	17:32	+7*
3	17:44	17:50	+6*	17.44	17:37	-7*
4	18:58	19:13	+15*	18:38	18:48	+10*
5	19:00	19:11	+11*	20:05	20:08	+3
6	19:05	19:38	+33*	19:35	19:49	+14*
7	20:17	20:09	-8*	19:49	19:48	-1
8	21:01	21:14	+13*	20:13	20:05	-8*
9	21:05	21:21	+16*	20:49	20:37	-12*
10	21:53	22:24	+31*	21:30	20:36	-54*
11	22:07	21:56	-11*	21:14	21:20	+6
12	22:18	22:25	+7*	21:05	21:02	-3
MEAN	19:49	19:59@	9.8	19:34	19:30	-4.3

________________________________________________________________________

R24 trials = 24 hrs of passive recovery between R24 and baseline

R72 trials = 72 hrs of passive recovery between R72 and baseline

* = responder

– = faster

+ = slower

@ = significance

Three individuals responded negatively to R72 by running a mean 10.3 + 5.7 secs slower during R72. Five individuals responded positively to R72 by running a mean 17.4 + 22.9 secs faster than baseline. Four individuals were considered non-responders to R72 with a mean time change of 3.3 + 1.8 secs.

Nine individuals responded negatively to R24 by running a mean 17.4 + 12.1 secs slower than baseline. Three individuals responded positively to R24 by running a mean 13.3 + 6.8 secs faster. There were no non-responders to the R24 trials. It is important to note that only two (participants 3 and 10) of three individuals who were negative responders to R72 also responded negatively to R24. Also, there were no individuals who positively responded to both R72 and R24.

There were no significant differences between R24 and baseline trials vs R72 and baseline trials for soreness and fatigue regarding pre and post warm-up scores on the fatigue/soreness visual analog scales (Table 4).

Table 4

Soreness and Fatigue Responses: R24 vs R72 Trials

________________________________________________________________________

Pre Warm-up Post Warm-up

________________________________________________________________________


		Soreness	Fatigue	Soreness	Fatigue
R24 Trials	Baseline	6.8 + 1.3	7.0 + 0.6	6.7 + 0.9	6.3 + 0.8
R24 Trials	Day 2	7.1 + 1.0	6.6 + 0.8	6.9 + 1.1	6.5 + 0.6
R72 Trials	Baseline	5.8 + 1.3	5.9 + 0.9	6.2 + 0.6	6.3 + 1.4
R72 Trials	Day 2	6.3 + 0.6	5.8 + 0.5	6.5 + 0.9	5.9 + 0.8

________________________________________________________________________

No significant differences were found between trials
Subjects appeared to be fully recovered before each trial

Discussion

The primary purpose of this study was to compare 5km racing performance after 24 hrs of passive recovery versus 72 hrs of passive recovery. Other than a few somewhat related studies by Bosak et al. (2008 & 2009), the necessary duration of passive recovery from 5km time trials has not previously been studied. Results indicate that 72 hrs of passive recovery, on average, permits maintenance of second 5km time trial performance, yet individual variability existed regarding rate of decline of 2nd trial performance. Individuals must therefore test themselves or coaches must test their athletes to determine optimal recovery time that allows for improved performance during successive 5km efforts.

R24 was significantly (p = 0.03) slower (10 secs) than baseline. However, no significant differences (p = 0.21) occurred between R72 and baseline (Table 2). Due to the catabolic nature of the running process, pain results from microtears and swelling (edema) within the muscle, which require sufficient passive recovery time prior to undergoing another intense running effort (Brown & Henderson, 2002). Increased passive recovery time can also be used to reduce the reflex muscle spasm and spastic conditions that accompany pain. Thus, it is logical to assume longer hours of passive recovery following a 5km race, may attenuate soreness and fatigue prior to the next race or hard running effort, which would potentially allow performance to be maintained or at least minimize impairment (Fitzgerald, 2007). Therefore, in this study, it is hypothesized that 72 hrs of passive recovery facilitated a more effective recovery allowing participants to actually run a few seconds faster than baseline. Since, subjects were required to have 24 hrs of passive recovery before each baseline it is likely that subjects were more fully recovered for R72 than for either baseline performance trial, thereby producing slight improvements during R72 performance trial.

There were no significant differences between R24 and baseline trials versus R72 and baseline trials for soreness and fatigue (Table 4) regarding pre and post warm-up scores on the fatigue/soreness visual analog scales. These results indicated that all runners tended to feel the same prior to each baseline and treatment trial. The assumption, therefore, is that each runner felt a similar level of preparedness before every trial. However, individual variability (Figure 1) existed among runners, which makes it important to focus on the effects of passive recovery (24 hrs and 72 hrs) on each individual.

Four individuals were considered non-responders to R72 with a mean time change of positive or negative 3.3 + 1.8 secs. It is possible that the intensity needed to complete the 5km performance trial was less than what was needed to fatigue these 4 non-responders.

Five individuals responded positively (Table 3) to R72 running a mean of 17.4 + 12.9 secs faster during the second trial. The potential reason for improved performance during R72 may be due to the fact that the 5 participants may have been in a more rested state as compared to their status prior to the first trial. Several of those subjects who did run faster during R72 verbally indicated that they “felt better” (regarding fatigue and muscle soreness) prior to the start of the second 5km as compared to how they were feeling before the baseline trial.

Despite the fact that as a group the participants ran a mean 10 seconds slower during R24 vs baseline, three individuals responded positively to R24 by running a mean 13.3 + 6.8 secs faster than baseline. The improvements during R24 could have been due to the fact that the 5km distance may not have been sufficient enough to fatigue these individuals from baseline, which allowed each runner to be recovered before the start of the second trial.

In terms of participants who ran slower (Table 3) during R24 and R72 performance trials, 9 individuals ran a mean time of 17.4 + 12.1 seconds slower after 24 hrs of passive recovery. Apparently, 24 hrs of passive recovery was not sufficient enough to allow muscle function to return to normal (Brown & Henderson, 2002). However, despite having 72 hrs of passive recovery, 3 participants still ran a mean of 10.3 + 5.7 secs slower than baseline. The decreased performance during R72 may have been a result of the runners having a “feeling of staleness” in their legs from completing no exercise for 72 hrs as explained by Mujika et al. (2001), where he suggested that many collegiate and post-collegiate runners often complain of feeling “stale” if they haven’t run in a few days. A potential loss of “feel” during exercise has been implied to occur in competitive athletes as a result of a reduction in training frequency (Mujika et al., 2001).

Despite R72 HRave being significantly (p = 0.04) greater than baseline and R24 HRave being the same as baseline, there were no consistent patterns of HRave and increased or decreased performance among participants during all R72 and R24 trials. It can be assumed that a lower HRave was associated with less effort since HR and intensity levels are related. However, only participant 7 ran faster and had a higher HRave during R24 and R72. During the R72 trials, only participants 4, 10, and 12 ran slower and had a lower HRave during second trial performance. During the R24 trials, only 1, 3, 5, 6, ran slower and had a lower HRave during second trial performance.

As for RPEend, no significant difference (p = 0.40) occurred between R24 and baseline, yet R72 was significantly (p = 0.01) greater than baseline. Also, scores on the pre and post warm-up fatigue/soreness visual analog scales were not significantly different between R24 and baseline trials vs R72 and baseline trials, indicating that all runners individually tended to feel the same prior to each 5km trial. Therefore, since inconsistencies exist between HRave, RPEend, and performance trials, while no significant differences occurred regarding fatigue/soreness responses, it is assumed that all participants displayed similar efforts during each 5km performance trial.

Conclusion

The results of the study indicate that 72 hrs of passive recovery, on average, permits maintenance of second day 5km performance. The study displays evidence that in most runners, 24 hrs of passive recovery did not provide sufficient recovery time for restoration of proper muscle function in agreement with Foss and Keteyian (1998) and Sinclair, Olgesby, & Pierpenburg (2003). For most runners, performance after 24 hrs of passive recovery may be impaired due to the inability to recruit sufficient muscle fibers in active muscles, as a result of residual muscle fatigue (Noakes, 2003). On average, more than 24 hrs of passive recovery is necessary for most runners to achieve optimal 5km race performance (Bosak et al., 2008). Since it was apparent that individual variability in recovery occurred in our study, individuals and coaches must therefore test themselves and their athletes to determine optimal recovery time, which may vary even within individuals depending upon other factors.

References

Bosak, A., Bishop, P., & Green, M. (2008). Active vs passive recovery in the 72 hours after a 5km race. The Sport Journal, 11 (3).

Bosak, A., Bishop, P., Green, M., & Hawver, G. (2009). Impact of cold water immersion on 5km racing performance. The Sport Journal, 12 (2).

Brown, R. L. & Henderson, J. (2002). Fitness Running (2nd ed.). Champaign, IL: Human Kinetics.

Brozek, J. & Hanschel, A. (1961). Techniques for Measuring Body Composition. Washington, DC: National Academy of Sciences.

Dellinger, B. & Freeman, B. (1984). The Competitive Runners’ Training Book: Techniques and Strategies to Prepare Any Runner for Any Race. New York, NY: Macmillan Publishing Company.

Fitzgerald, M. (2007). Brain Training for Runners. New York, NY: Penguin Group, Inc.

Foss, M. L. & Keteyian, S. J. (1998). Fox’s Physiological Basis for Exercise and Sport. Ann Arbor, MI: McGraw-Hill.

Galloway, J. (1984). Galloway’s Book on Running. Bolinas, CA: Shelter Publications, Inc.

Gomez, A. L., Radzwich, R. J., Denegar, C. R., Volek, J. S., Rubin, M. R., Bush, J. A., Doan, B. K., Wickham, R. B., Mazzetti, S. A., Newton, R. U., French, D. N., Hakkinen, K., Ratamess, N. A., & Kramer, W. J. (2002). The effects of a 10-kilometer run on muscle strength and power. Journal of Strength and Conditioning Research, 16, 184-191.

Henderson, J. (2000). Running 101: Essentials for Success. Champaign, IL: Human Kinetics.

Higdon, H. (1998). Smart Running. Emmaus, PA: Rodale Press, Inc.

Kaufmann, D. A. & Ware, W. B. (1977). Effect of warm-up and recovery techniques on repeated running endurance. The Research Quarterly, 2, 328-332.

Martin, D. E. & Coe, P. N. (1997). Better Training for Distance Runners (2nd ed.). Champaign, IL: Human Kinetics.

Mujika, I., Goya, A., Ruiz, E., Grijalba, A., Santisteban, J., & Padilla, S. (2001). Physiological and performance responses to a 6-day taper in middle-distance runners: influence of training frequency. International Journal of Sports Medicine, 23, 367-373.

Nicholas, C. W., Green, P. A., Hawkins, R. D., & Williams, C. (1997). Carbohydrate intake and recovery of intermittent running capacity. International Journal of Sport Nutrition, 7, 251-260.

Noakes, T. (2003). Lore of Running (4th ed.). Champaign, IL: Human Kinetics.

O’Conner, F. G. & Wilder, R. P. (2001). Textbook of Running Medicine. New York, NY: McGraw-Hill.

Pollock, M. L., Schmidt, D. H., & Jackson, A. S. (1980). Measurement of cardiorespiratory fitness and body composition in the clinical setting. Comprehensive Therapy, 6, 12-27.

Sinclair, J., Olgesby, K., & Piepenburg, C. (2003). Training to Achieve Peak Running Performance. Boulder, CO: Road Runner Sports Inc.

Authors’ References:

Dept. of Sport Health Science, Life University, Marietta, GA 30060
Dept. of Kinesiology, University of Alabama, Tuscaloosa, AL 35401
Dept. of Health, PE, and Recreation, University of North Alabama, Florence, AL 35632
Dept. of Health and Human Performance, Georgia Southwestern State University, Americus, GA 31709
Dept. of Health, Exercise Science, and Secondary Education, Lee University, Cleveland, TN 37320

U.S. Sports Academy2016-10-20T13:58:44-05:00October 5th, 2009|Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

Prevention of ACL Injuries in Female Athletes through Early Intervention

Submitted by: Rodney C. Whittle, Ph. D., Associate Professor, HPER, Alabama A&M University and Brett Crow, Physical Education Instructor, Whitesburg Middle School, Huntsville, Alabama

Abstract

With respect to physical education, increased participation in sport equals success. One of the main goals of physical educators is to enable individuals to become proficient in lifelong activities. Hopefully, this proficiency will lead to a healthier and more fulfilling life. Beginning with Title IX and continuing over the last two decades, there has been an explosion of youth sports opportunities. As children have begun to participate in sports programs at earlier ages, parents have started feeling pressure to enroll their children in similar programs in order for them to remain competitive. As a result, children become increasingly proficient at their respective sports at earlier ages. This proficiency, while benefiting the respective sport, is not without its consequences. One of the most notable consequences of increased participation in sports at an earlier age is in the area of sports injuries (Rentrom, 2008).

Introduction

Over the last two decades, female participation in sport has risen dramatically. Moreover, the rate of females acquiring injuries to their anterior cruciate ligament (ACL) has risen at an alarmingly dramatic rate. According to recent studies by Arendt (1995), females are between two to eight times more likely to injure their ACL than their male counterpart in similar sporting events. Typically, these injuries are occurring in sports such as basketball, volleyball and soccer. Participants in these sports are usually involved in a lot of quick cutting motions, jumping motions and rapid slowing or decelerating movements. ACL injuries generally prevent a student from participation throughout the remainder of the season, and some injuries can permanently end a student’s ability to successfully participate (Rentrom, 2008).

The Cost

ACL injuries usually come at a very high cost to the participant and their family. The cost of the medical treatment alone can easily run thousands of dollars. Moreover, this type of injury can greatly reduce an athlete’s self esteem and confidence. Therapy must also be considered, which places a high burden on family members with respect to the time lost and money spent. These losses combined, often make ACL injures catastrophic losses to athletes and their families.

Causes

With approximately 70% of ACL injuries coming from non-contact incidents, many studies have been conducted in order to find causes or preventative measures to counteract the problem. These studies have attempted to narrow the causes and help reduce the occurrence of ACL injuries in female athletes. Presently, research has narrowed its focus to a handful of probable causes. In female athletes, the factors include, but are not limited to: Increased valgus movements during landing, pre-menstrual hormone levels, narrower intercondylar notch width and smaller AC ligaments (Griffin, L. Y., 2000). Research has also noted different firing sequences of leg muscles in male and female athletes. These firing differences help explain some of the different responses that females exhibit to athletic movements and thereby expose themselves to higher risk during those movements. As a result, females find themselves at a biomechanical disadvantage to males when it comes to ACL strength and stability (Ireland, 2002).

Prevention

The good news is that studies have concluded that the incidence of ACL injuries can be reduced through neuromuscular training (Roniger, L. R., 2007). With this type of training, females have been shown to reduce valgus moments when landing (Foster, J. B., 2007). Moreover, as a result of the training, female athletes can incorporate more muscular control and experience less ligament dependence during movements such as cutting, landing, jumping and rapid deceleration. With appropriate training, which can and should be done in the physical education classroom, female athletes can significantly reduce their risk of a catastrophic non-contact ACL injury (Mandelbaum, 2005).

Muscular training to reduce the risk of ACL injuries is not a difficult task. Furthermore, the training falls right into the Physical Education guidelines of helping individuals lead healthier and more satisfying lives. Certainly all of the muscles in the leg would benefit from strength training and stretching, however, this paper will focus on the larger muscles in the Hamstrings and Quadriceps. Most athletes have strong quads because of the amount of work that those muscles do during exercise. A study by Chappell, J., et.al. in 2007 concluded that females landed with less knee flexion, increased quadriceps activation and less hamstring activation. This resulted in increased ACL loading during the landing phase and therefore increased the risk of damage. With this in mind, greater hamstring strength should be a priority in most female athletes. The hamstrings, however, are often overlooked during training. There is much debate, but generally the hamstrings should optimally fall within 60 – 80% of the strength of the quads. The following hamstring strengthening exercises would work well for school Physical Education programs. The first exercise is the squat. A slight bend in the waist and a deep knee bend are necessary to lower your hands to the floor. After your hands have touched the floor and you have counted to three, then return to the starting position. Throughout the exercise, your back must be straight so that the legs and buttocks do the work. The second exercise is the leg curl. This exercise is done from the standing position, preferably facing a table or a stage. While keeping the right leg straight, bring the left foot up toward the buttocks. You should feel the strain in your hamstring as you touch your left heel to your buttocks. Repeat the exercise until the hamstring is fatigued. Repeat with the exercise with the right leg as you keep the left leg straight. The third exercise is the kickback. Stand close to and facing a wall. While keeping the right leg straight, kick the left backwards as far as possible. This will vary from one to three feet depending upon flexibility. Keep the left leg at the furthest position for a count of one. Move the left leg to the initial position. There should be very little bend at the waist and both the legs must be kept straight throughout the exercise. Repeat the procedure for the right leg while keeping the left leg straight. Toe raises will also help stabilize the knee. Simply stand with you feet about shoulder width apart and lift your heals, one at a time, as high as possible before lowering them back to the ground. Start off with sets of 10 and increase as possible.

The final area which can be easily addressed in physical education programs and will help reduce the risk of ACL injures is jump training. These jumping exercises should be conducted with proper form. Proper form includes keeping the legs together, not allowing the knees to come apart, landing softly with bent knees, and finally, forcing the individual to remain balanced at all times. Do not allow anyone to rush through the exercises. These jumps should be over a small cone and should incorporate both legs at the same time. The first set should be done by jumping forward over the cone and then jumping backwards to the initial starting position. The second exercise would be to have the individual jump from side to side over the cone and then jump back to the original position.

These exercises, if done correctly and in conjunction with a proper stretching regimen, could help reduce the incidence of ACL injuries in female athletes. Further tracking of female students participating in a structured physical education setting would substantiate the reduction of this type injury.

References

Arendt, E., Dick, R. (1995). Knee injury patterns among men and women in Collegiate basketball and soccer: NCAA data and review of literature. Am J Sports Med, 23, 694-701.

Griffin, L. Y., et al. (2000). Noncontact anterior cruciate ligament injuries: Risk factors and prevention strategies. J Am Acad Orthop Surg, 8, 141-150.

Roniger, L. R. (2007, October). ACL prevention programs show benefit for teen athletes. J Biomechanics.

Foster, J. B. (2007, November). Soft landing studies find effects beyond sagittal plane of knee. J Biomechanics.

Mandelbaum, B.R., Silvers, H. J., Wantanabee, D.S., et al. (2005). Effectiveness of a neuromuscular and proprioceptive training program in preventing anterior cruciate ligament injuries in female athletes: 2-year follow-up. Am J Sports Med, 33, 1003-10.

Rentrom, P., Ljungqvist, A., Arendt, E., et al. (2008). Non-contact ACL injuries in female athletes: An international Olympic committee current concepts statement. British Journal of Sports Medicine, 42, 394-412.

Ireland, M. L. (2002). The Female ACL: Why is it more prone to injury? Orthopedic Clinics of North America, 33, issue 4.

Chappell, J.D., Creighton, R.A., Giuliani, C., Bing Y., Garrett, W.E., (2007). Kinematics and elecgtromyoghrapy of landing preparation in vertical stopping. Am J Sports Med, 35, 235-241.

U.S. Sports Academy2013-11-25T19:41:46-06:00July 10th, 2009|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Women and Sports|Comments Off