Volleyball is an intermittent sport characterized by short periods of high-intensity activity, consisting mostly of sprinting, jumping, hitting, blocking, and diving, alternated with low-intensity activity, such as standing, walking, and jogging (
). Jumping is a fundamental movement characteristic in many volleyball skills, such as the serve, set, spike, attack, and block ( 34,37,46 ). Vertical jump performance seems to be related to strength and explosiveness characteristics, including peak isometric force, the isometric rate of force development ( 4 ), and peak power calculated from JH ( 26 ). Stronger athletes are able to produce greater ground reaction forces, which are associated with faster sprint times ( 32 ), change of direction speed ( 24,47,50 ), and vertical JH ( 3 ). 28,50
The magnitude of the trainability of various performance variables in athletic populations is important for talent identification and
long-term athlete development ( ). By measuring an athlete's progress over time, sports scientists and strength and conditioning personnel can evaluate an athlete's development and the effectiveness of a training program. Although several studies on elite male volleyball players lasting 1 or more years suggest a relationship between improved maximal strength and explosiveness and greater vertical JH ( 10 ), less is known about the trainability of these qualities in collegiate women's volleyball athletes. Therefore, the purpose of this investigation was to quantify the degree of change in volleyball physical performance characteristics. Specifically, this study investigated the maximal strength and jumping abilities, over approximately 1, 2, and 3 years of supervised sport practice and resistance training in a National Collegiate Athletic Association (NCAA) division I collegiate women's volleyball team. The authors are unaware of any existing studies that quantify changes in physical performance variables in this population for this length of time. 36,38,39 Methods
Experimental Approach to the Problem
An exploratory study on a cohort of women's volleyball athletes was performed based on athlete assessment data from an ongoing sports performance monitoring program. The monitoring program was designed to give information to provide the athlete's current fitness status to the athletes and coaches and to provide associations between variables that may be important for altering training. As a result, the data generated from the athlete monitoring program are hypothesis generating as opposed to hypothesis testing. Monitoring investigations occurred each year at some or all the following times: the beginning of the preseason (August), the end of the competitive season (November or December), the beginning of the off-season (January), and the end of the off-season that emphasized strength and explosiveness (April). Resistance training over the course of the study followed a block periodization model consisting of sequenced phases (strength endurance, basic strength, and strength and explosiveness) (
). Emphasis was placed on increasing maximal strength before explosiveness development through a combination of traditional resistance training exercises (squat and bench press variations) and weightlifting exercises (clean and snatch pulls and power cleans and power snatches from various positions) with various loads to facilitate heavier and lighter training days ( 43 ). On average, the athletes' resistance trained 4 days per week in the off-season and 2 days per week during the fall and spring competitive seasons. Each testing session occurred after a period of reduced training load. On the test day, the order of the tests was as follows: body composition and anthropometric measurements, vertical jumps, and isometric midthigh clean pull. 8 Subjects
A cohort of 29 NCAA division I collegiate women's volleyball athletes were split into 3 groups based on periods of supervised resistance training at the time when data were retrieved from a monitoring program archive: group 1 (
n = 11), group 2 ( n = 9), and group 3 ( n = 9). The length of supervised resistance training was as follows: Mean +/− SD group 1: 0.7 ± 0.3 years (0.3–1.0 years); group 2: 1.6 ± 0.2 years (1.3–1.7 years); and group 3: 2.4 ± 0.6 years (2.0–3.3 years). All athletes had previous experience of competing in volleyball before their collegiate careers. All monitoring tests took place in the Exercise and Sport Science Laboratory on the campus of East Tennessee State University, and the testing results were archived for longitudinal assessment. The study was conducted retrospectively using archived data, and the proposal was submitted to and approved by the East Tennessee State University Institutional Review Board. The participants were informed of the benefits and risks of the investigation before signing an institutionally approved informed consent document. All subjects were 18 years or older. Anthropometric and Body Composition Measurements
Physical characteristics (height, body mass, and body fat percentage) were measured during each laboratory testing session. Height was measured to the nearest 0.1 centimeters using a stadiometer (Cardinal Scale Manufacturing Co., Webb City, MO, USA), and body mass and percent body fat were measured by air-displacement plethysmography (BOD POD; Life Measurement, Inc., Concord, CA, USA) for 53% of the total testing sessions. Athletes were instructed to follow specific protocols suggested by the manufacturer before and during body composition testing. Population-specific equations for resistance-trained individuals were used for each athlete and residual gas volume was predicted. Body mass was measured using a digital scale (Tanita B.F. 350, Tanita Corp. of America, Inc., Arlington Heights, IL, USA), and percent body fat was estimated using a skinfold caliper (Lange; Beta Technology Inc., Cambridge, MD, USA) for 47% of the total testing sessions. The sites and order of skinfold testing were triceps, subscapular, midaxillary, chest/pectoral, suprailiac, abdominal, and thigh (
). Physical characteristics for each group based on the time spent in the monitoring program are reported in 1 Table 1. Table 1.:
Physical characteristics for each group.*
A standardized warm-up was performed before beginning the vertical jump and isometric midthigh clean pull tests. The warm-up consisted of 25 jumping jacks, 5 dynamic midthigh clean pulls with a 20-kg barbell (Werksan USA, Moorestown, NJ, USA), and 3 sets of 5 dynamic midthigh clean pulls with a 40-kg load. A 3-minute rest was included between sets.
Vertical Jump Measurements
Each athlete performed vertical SJ and countermovement jump (CMJ) with a nearly weightless polyvinyl chloride pipe, a 11-kg barbell, and a 20-kg barbell with a rest period of approximately 1 minute between jumps. Countermovement jumps were performed to assess the athlete's ability to use a stretch-shortening cycle, including the muscle-tendon unit's ability to use stored elastic energy and generate a stretch reflex. Static jumps were performed to test the neuromuscular system's ability to produce force concentrically without the aid of a stretch-shortening cycle. Weighted jumps tested dynamic explosive strength within a limited timeframe (
). Jumps were performed on a 0.91 6 × 0.91-m force plate (Rice Lake Weighing Systems, Rice Lake, WI, USA), and sampling frequency was set at 1,000 Hz. Approximately half of the testing sessions were unfiltered during data analysis, whereas the other half used a fourth order, low pass, Butterworth filter with a cutoff frequency of 100 Hz. Although the implementation of the filter removed noise in the force plate data, filtered and nonfiltered data did not differ practically for the variables reported in this study.
Throughout each jump condition, the barbell was placed behind the neck just below the seventh cervical vertebra. Athletes were instructed during the SJ test to squat to approximately a 90° knee angle position previously established using a manual goniometer. Athletes held this position for 3 seconds to remove the effects of a stretch-shortening cycle. Two practice trials at self-selected 50 and 75% of perceived maximal effort were performed before the maximal effort jumps. Once in the starting position, the subject received a countdown of “3, 2, 1, jump,” and jumped from their preferred depth. The same procedures and instructions used during the SJ condition were applied during the CMJ condition, except that the CMJ began in an upright position and was performed in 1 down-and-up fluid movement.
No countermovement was allowed for the SJs. If a downward deflection of the force-time curve in relation to the athlete's system weight (body mass plus load) as displayed on the computer screen was noted, then the jump was discarded and another jump was performed. In addition, if the athlete or investigator did not believe that the SJ or CMJ was of sufficient quality (e.g., submaximal effort or horizontal displacement occurred), then the jump was repeated. At least 2 trials of each jump per condition were measured. Verbal encouragement was given to each athlete.
All jump trials were recorded and analyzed using a custom program (LabView 8.5.1, 8.6, and 2010; National Instruments Co., Austin, TX, USA). Jump height was calculated for each trial. Jump height was estimated from flight time using the following formula:
g·flight time 2·8 −1, where g is the acceleration due to gravity or 9.81 m·s −2 ( ). All jumps were analyzed using previously established methods ( 27 ). 26 Measurements of Maximal Strength and Strength Scaled to Body Mass
Maximum strength was measured as peak force using an isometric midthigh clean pull, which was performed on a 0.91
× 0.91-m force plate sampling at 1,000 Hz (Rice Lake Weighing Systems) in a custom-designed power rack. The apparatus and standard pull position were established based on previously published information ( ) at a knee angle of 125 ± 5°. The athlete's hands were fixed to the bar using weightlifting straps and standard athletic tape to prevent the hands from moving, as well as to ensure a maximum effort occurred without being artificially limited by handgrip strength ( 12 ). Once established in the proper pulling position, each athlete performed 2 practice trials at self-determined 50 and 75% of perceived maximal efforts. Athletes were instructed to “pull as fast and hard as possible” ( 12 ) during the maximal effort trials. 17
A custom program was used to analyze the trials (LabView 8.5.1, 8.6, and 2010; National Instruments Co.). Analysis consisted of absolute isometric peak force (IPF). In addition, force was allometrically scaled to control for body mass differences through the following equation: allometrically scaled force = force·bodymass
−0.67 ( ). At least 2 maximum effort trials were performed with approximately 1-minute rest between trials. The observed score was the average of the 2 best trials with a peak force difference of ≤200 N. The values from the 2 trials were averaged to reduce random error and indicated the athlete's typical performance level ( 21 ). 15 Statistical Analyses
Performance data from each athlete's initial and final testing sessions were used to examine the differences in physical performances for statistical analyses. Coefficients of variation (CV) and intraclass correlation coefficients (ICCs) were calculated from the 2 trials within each testing session to determine intrasession reliability. Intraclass correlation coefficients were also calculated for the initial and last sessions to examine the relative stability of data (i.e., rank-order relationship between sessions) (
Table 2). Eight 1-way between-subject analyses of variance (ANOVA) tests were performed to compare the groups on static JH (SJH) 0, SJH 11, SJH 20, countermovement JH (CMJH) 0, CMJH 11, CMJH 20, IPF, and allometrically scaled IPF (IPFa). If statistical significance was found, a Tukey's honest significant difference post hoc test was performed to determine where statistical differences occurred. All statistical calculations were performed with IBM SPSS Statistics (version 19; SPSS, Inc., Chicago, IL, USA) and Microsoft Excel 2010 (version 14.0.6129.5000; Microsoft, Redmond, WA, USA). In addition, effect sizes were calculated and presented using partial η 2 values and Cohen's d to determine the magnitude of differences ( Tables 3 and 4). Practical importance of differences were determined by the following rating scale: d < 0.2 (trivial); d = 0.2–0.6 (small); d = 0.6–1.2 (moderate); d = 1.2–2.0 (large); and d = 2.0–4.0 (very large) ( ). The critical alpha level was set at 18 p ≤ 0.05. Because of the exploratory nature of this study, the alpha level was not adjusted for the multiple 1-way ANOVAs. Table 2.:
Relative stability for each performance variable.*†
Results of 1-way between-subject analyses of variance using absolute differences.*†
Group effect size measurements for each performance variable.*
Intrasession test-retest reliability statistics for all testing sessions and groups were as follows: SJH 0, ICC ≥ 0.89, CV ≤ 4.8%; SJH 11, ICC ≥ 0.80, CV ≤ 7.7%; SJH 20, ICC ≥ 0.84, CV ≤ 5.8%; CMJH 0, ICC ≥ 0.88, CV ≤ 5.5%; CMJH 11, ICC ≥ 0.86, CV ≤ 6.6%; CMJH 20, ICC ≥ 0.92, CV ≤ 4.8%; and IPF, ICC ≥ 0.94, CV ≤ 5.0%. Intersession test-retest reliability and CV for SJH and CMJH (
n = 58) were ICC ≥ 0.80 and CV ≤ 6.6%, respectively. Statistical significance ( p ≤ 0.05) was found between 2 time points for SJH 0 for the initial session of group 3 (ICC = 0.98, CV = 2.2%) and CMJH 11 for the last session of group 3 (ICC = 0.99, CV = 2.5%), but the differences were trivial based on the CV. Intersession test-retest reliability and CV for peak force were ICC ≥ 0.94 and CV ≤ 5.0%, respectively. Reliability (ICC) for the BOD POD in our laboratory has been consistently acceptable ( n > 200, ICC ≥ 0.85).
Descriptive statistics and percent differences revealed statistically greater improvements in SJH at 0 kg (19.7%), 11 kg (23.8%), and 20 kg (30.6%) loads between groups 1 and 3, with large effect sizes (0 kg,
d = 1.35; 11 kg, d = 1.23; and 20 kg, d = 1.20, p ≤ 0.05). The percent difference of absolute and allometrically scaled IPF showed statistically greater improvements of 44.4 and 41.2%, respectively, with large effect sizes between groups 1 and 3 (IPF, d = 1.22; and IPFa, d = 1.32, p ≤ 0.05) ( Tables 5 and 6). Group percent differences between the initial and final testing sessions for each variable are displayed in Figure 1. Confidence intervals (95%) of the mean values for jump and isometric midthigh clean pull measures are shown in Figures 2–4. Post hoc tests revealed statistically significant differences between groups 1 and 3 for SJH 0, SJH 11, SJH 20, CMJH 11, IPF, and IPFa ( Table 3). Table 5.:
Group initial and final scores for each performance variable.*†
Group absolute and percent differences for each performance variable.*†
Group comparisons of the percent differences between the initial and final testing sessions. *Groups 1 and 3 were statistically different (
p ≤ 0.05). Error bars indicate SD. SJH = static jump height; CMJH = countermovement jump height; IPF = isometric peak force; IPFa = allometrically scaled isometric peak force. Figure 2.:
Confidence intervals (95%) of the mean for the unloaded and loaded static and countermovement jump heights. *Groups 1 and 3 were statistically different (
p ≤ 0.05). Percent differences are given in parentheses. SJH = static jump height; CMJH = countermovement jump height. Figure 3.:
Confidence intervals (95%) of the mean for isometric peak force. *Groups 1 and 3 were statistically different (
p ≤ 0.05). Percent differences are given in parentheses. Figure 4.:
Confidence intervals (95%) of the mean for allometrically scaled isometric peak force. *Groups 1 and 3 were statistically different (
p ≤ 0.05). Percent differences are given in parentheses. Discussion
The purpose of this cohort study was to describe the changes in maximal strength and vertical jump performance over the average of 0.7 ± 0.3 (group 1), 1.6 ± 0.2 (group 2), and 2.4 ± 0.6 (group 3) years in NCAA division I women's volleyball athletes. The primary findings of this study were as follows: (a) Statistically greater improvements were observed in SJH 0, SJH 11, SJH 20, and CMJH 11 from an average resistance training age of 0.7–2.4 years. (b) Statistically greater improvements occurred in IPF, and IPF allometrically scaled to body mass from an average resistance training age of 0.7–2.4 years.
The ability to produce higher maximal power or repeated power than one's opponent is arguably the greatest indicator of competitive success in sports (
). Maximal peak muscular power, the product of force and velocity, has been defined as the “greatest instantaneous power during a single movement performed with the goal of producing maximal velocity at takeoff, release, or impact” ( 25,31,51 ). Several studies have indicated a strong association between peak power and measures of explosive strength (e.g., vertical jumping and dynamic midthigh pulls) ( 7 ), including weighted jumps ( 5,44 ). Based on the impulse-momentum relationship and the laws of constant acceleration, the outcome of a horizontal or vertical jump is dependent on the impulse which determines takeoff velocity ( 41 ). Because vertical jumping involves moving one's body weight against gravity, a certain level of strength is required to produce a great enough impulse to leave the ground. In fact, strong correlations have been found between measures of strength (1 repetition maximum [1RM] squat) and static and countermovement jump performance ( 49 ). Furthermore, training-induced improvements in maximal strength have been shown to increase vertical JH and power output ( 41 ). 44
Results of this study reveal statistically greater improvements in SJH at 0 kg (19.7%), 11 kg (23.8%), and 20 kg (30.6%) loads between groups 1 and 3, with large effect sizes (0 kg,
d = 1.35; 11 kg, d = 1.23; 20 kg d = 1.20, p ≤ 0.05). Although not statistically significant, greater improvements that were small and moderate occurred for all SJ conditions between groups 1 and 2, and 2 and 3. These changes suggest that a greater training age is associated with an improvement in the muscle's ability to produce force concentrically, independent of stretch-shortening cycle function, and possibly because of an emphasis on strength development.
Jumping from a static position has been associated with isometric midthigh clean pull rate of force development (
). Although not reported in this study, rate of force development is a function of the rate of increase in muscle activation by the nervous system ( 12 ) and is considered a measure of explosive strength. Based on Newton's second law, force is directly responsible for the acceleration of an object. It can be argued that the faster a given force level is attained (applied in the appropriate direction), the greater the resultant acceleration within a limited time frame—making rate of force development crucial for rapid movements ( 23,45 ). Newton's second law also demonstrates the need for high force production to achieve high accelerations and subsequent JH. In this study, the percent difference of absolute and allometrically scaled IPF showed statistically greater improvements of 44.4 and 41.2%, respectively, with large effect sizes between groups 1 and 3 (IPF, 35 d = 1.22; IPFa, d = 1.32, p ≤ 0.05). Furthermore, stronger athletes are able to generate greater maximal and submaximal power throughout a wide loading spectrum than weaker athletes ( ), suggesting that maximum strength is related to unweighted and weighted jumping ability ( 22,30 ). 26
The absolute difference in CMJH with an 11-kg load was the only countermovement condition that showed a statistically greater improvement (22.6%) with a moderate effect size between groups 1 and 3 (
d = 1.18, p ≤ 0.05). Although not statistically significant, CMJH 0 and CMJH 20 had greater improvements of moderate effect sizes (CMJH 0, d = 0.82; and CMJH 20, d = 1.00) between groups 1 and 3. Small-to-moderate effect sizes occurred for CMJH 0, 11, and 20 kg between groups 1 and 2, and 2 and 3. Interestingly, the improvements between the weighted jump conditions, for both SJH and CMJH, were greater than the unloaded condition (e.g., the 20-kg load improved to a greater extent than the 11-kg load, and the 11-kg load improved to a greater extent than the 0-kg load). Maximum strength has been shown to play a major role in both SJH and CMJH even at relatively light weights ( ), the data from this study suggest a greater window of adaptation in the heaviest condition. Because volleyball involves repeated jumping with one's own body weight ( 26,41 ), it is possible that these athletes had a smaller window of adaptation to the unweighted jumps, given their volleyball training age at the beginning of their collegiate careers, whereas a greater window to adapt to the weighted jump conditions existed because of minimal strength training experience. 9
Improvements in maximal power can be achieved by increasing maximal force and velocity of shortening. However, a high level of maximal power (explosiveness) is more likely to be attained by first having a high level of strength (
). In other words, the ability to generate force rapidly is of limited benefit if the resultant impulse is low ( 8 ). Stronger athletes can produce greater ground reaction forces, which are associated with faster sprint times ( 8,48 ), change of direction ( 24,47 ), and vertical JH ( 3 ). Harris ( 28 ) found that a combination of heavy strength training and high-power exercises resulted in a greater improvement in maximum strength and explosiveness performance, as shown in 1RM squat, 1RM ¼ squat, 1RM clean grip midthigh pull, vertical JH, vertical jump average and peak power, and 10-yard shuttle when compared with high force or high-power training alone, in collegiate football athletes with at least 1 year of supervised strength training experience. Furthermore, these improvements occurred after a structured “block” periodized resistance training program divided into phases, emphasizing early maximum strength development and shifting to more specific power and speed exercises later in the training cycle ( 14 ). The results of this study suggest that maximum strength and JH in NCAA division I women's volleyball athletes can be improved through a combination of traditional resistance training exercises (squat and bench press variations) and weightlifting exercises (clean and snatch pulls and power cleans and power snatches from various positions) with various loads in the absence of plyometric training outside normal volleyball practice. 2,42
Longitudinal studies spanning the collegiate careers of other sports, including men's and women's basketball, women's gymnastics, and American football athletes have been conducted (
). A common finding among these studies is that the greatest performance gains occur during the first or second year of strength training at the collegiate level. Several studies on collegiate football athletes report no statistically significant changes in lower-body power ( 11,16,19,20,29,33,40 ), JH ( 20 ), or speed ( 40 ) beyond years 1 or 2. Although large initial increases in fitness are relatively easy to attain in untrained individuals ( 20,29 ), the extent to which performance can be improved beyond 1 or 2 years in collegiate women's volleyball athletes is less clear. This study provides evidence that vertical JH and maximum strength, both absolute and relative to body mass, continue to improve beyond the first 2 years in female division I collegiate volleyball athletes using block type periodized programs. 13 Practical Applications
A combination of both traditional resistance training exercises and weightlifting variations at various loads, in addition to volleyball practice, seem to be effective at increasing maximal strength by 44% and vertical JH by 20–30%, in NCAA division I women's volleyball athletes after about 2.5 years of training. Furthermore, these characteristics can be improved in the absence of additional plyometric training outside normal volleyball-specific practice. There seems to be a greater window of adaptation for improvements in loaded jump performance compared with unloaded jumps, perhaps because of the number of years collegiate athletes have spent playing the sport as opposed to years developing strength. Monitoring the long-term fitness adaptation of athletes can provide meaningful feedback regarding the trainability of athletes. Likewise, coaches can use the percent improvements reported in this study as references to assess the efficacy of their training program.
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