Authors: Omid Nabavizadeh1 and Ashley A. Herda, PhD2

1Geriatric Medicine Department, University of Colorado-School of Medicine, Aurora, Colorado, United States;
2Department of Health, Sport, and Exercise Sciences, University of Kansas-Edwards Campus, Overland Park, Kansas, United States;

Corresponding Author:
Ashley A. Herda, Ph.D., CSCS*D
Assistant Professor
University of Kansas-Edwards Campus
Department of Health, Sport, and Exercise Sciences
12604 Quivira Road, Overland Park, KS 66213
Phone: (913) 897-8618

Omid Nabavizadeh is a professional research assistant at the University of Colorado.

Ashley A. Herda, Ph.D. is an assistant professor for the exercise science program at the University of Kansas Edwards Campus in Overland Park. Dr. Herda completed her Bachelor of Science in Exercise Science and Health Promotion (2006) from Florida Atlantic University in Boca Raton, Florida. She continued her education at the University of Oklahoma in Norman, Oklahoma where she earned her Master of Science in Exercise Physiology (2008) under the mentorship of Jeff Stout and Doctor of Philosophy in Exercise Physiology (2011) under the mentorship of Dr. Joel Cramer. Dr. Herda’s research interests include the investigation of the effects of nutritional supplements and/or exercise interventions on performance and body composition in men and women across the lifespan.

Relationships Among Muscle Characteristics and Rowing Performance in Collegiate Crew Members


Purpose: The purpose of this study was to explore the relationships among measurements of muscle quality and rowing performance in college-aged club rowers. Methods: Ten men and women (mean ± SD: age: 22.1 ± 4.0 years; ht: 180.5 ± 8.3 cm; wt: 79.0 ± 13.5 kg) volunteered to participate in this study. Ultrasound images were collected at 50% thigh length in a transverse plane to quantify muscle size. The sum cross-sectional area (mCSA) of these muscles was reported. Bioelectrical impedance analysis (BIA) was conducted to predict fat-free mass (FFM) and estimate total leg lean mass. One-repetition maximum leg press (LPMAX) was recorded as well as vertical jump (VJHT; cm). Lastly, participants completed a 2,000m time trial on the rowing ergometer, where the 500m average split was used in analyses. Pearson’s product moment correlations were calculated across all variables and backwards stepwise linear regression was completed using VJHT, LPMAX, FFM, and mCSA as possible predictors of 500m performance. Results: The correlations coefficients among recorded variables were all very high and significant (r = 0.867-0.950; p = 0.001-0.04). The regression analysis indicated VJHT was a significant predictor of 500m time trial performance (R2=0.903; p<0.05). Conclusions: Although rowing may often be considered an endurance sport, the single best predictor of and the strongest correlation to time trial performance is vertical jump height as an index of power. Applications in Sport: Emphasis on plyometric training may serve as one of the most important aspects of athlete development beyond rowing form and mechanics, more so than strength or hypertrophy in collegiate rowers.

Keywords: power, muscle quality, plyometric training, ultrasound


Rowing is a whole-body movement that utilizes nearly 85% of one’s musculature (2, 26). It is both anaerobic and aerobic in nature potentially taxing short- and long-duration energy systems, depending on the distanced event (21, 26). The anaerobic systems utilizing phosphagen and anaerobic glycolysis, are most active for quick, high-intensity bursts lasting up to 15-20 seconds and about 2-minutes, respectively. Confirming the anaerobic nature of the sport, recreational yet competitive rowers tend to compete in the shorter races with the average 500-meter time trial completed in roughly a minute and a half, (21). Despite the necessity of anerobic energy, without lower body power or explosiveness, an individual or boat team may not be successful (18).

The most specific means of training to enhance power is through plyometric exercise (5, 16, 24). Rowing is an extremely demanding and technical sport. A report by Baudouin and Hawkins (2) provides in-depth insight to the specifications of the biomechanical demands. The sport requires individual balance as well as a team-oriented balance among all athletes in the boat. According to Bourdin et al. (3), rowers do not typically need more muscle mass or less fat. If the athlete can maneuver their body, and in proper form, they may obtain peak power regardless of body composition (3). Rowers start in a “catch” position, which is the initial starting point where the oar blade enters the water perpendicular to the plane of the water (2, 3, 11). Subsequently, peak power and ultimately boat propulsion is generated from leg drive (hip and knee extension). This movement supports the athlete’s mass to shift from front to back, gaining acceleration as the athlete’s feet drive in the fixed foot pads (11, 12). Training the drive movement in concentric and eccentric loaded positions (depth jumps and squat jumps) can assist in mimicking the leg drive movement while training the target muscle groups’ metabolic efficiency.

Another important aspect of athletic performance is the strength of specific muscle groups. Specifically, muscle quality can be described as the ratio of muscle mass to the amount of force it can produce. Alternatively, muscle quality can be considered the relative strength per muscle group. Previously, muscle quality has been primarily evaluated in older adults (8, 19, 23). Muscle quality assessment is useful when examining changes in functional capacity of individuals, such as significant muscle loss due to age or injury or increased muscular development attributed to training (13, 28). The quantity of muscle mass and volume can easily and non-invasively be assessed using bioelectrical impedance analysis (BIA) to predict fat mass, fat-free mass (FFM), and total body water in various populations (29). Ultrasound (US) is another non-invasive means to quantify muscle mass from a cross-sectional perspective. The relationship among muscle quality and specific rowing performance metrics has yet to be associated, however, it can be elucidated that they are linearly correlated (22, 25).

The primary purpose of this study was to determine the relationship among explosive power via vertical jump and muscle quality as predictors of performance in college-aged rowers. The secondary aim of this study was to explore which variables may best predict rowing performance so coaches can emphasize greater specificity into programming. The researchers hypothesize that although muscle quantity and strength are critical to success in the sport, muscle quality and power may be the best determinants of rowing performance.



Ten collegiate rowers volunteered to participate in this study assessing physiological effects of rowing (men n=8; women n=2; mean ± SD; age: 22.1 ± 4.0 years; ht: 180.5 ± 8.3 cm; wt: 79.0 ± 13.5 kg). Rowing experience levels ranged from 2 months (novice) to 7+ years (varsity) and the pool of participants represented the club crew team. All participants signed a form of written informed consent approved by the University Human Research Protection Program set by the Declaration of Helsinki (IRB# STUDY00141361). Participants also completed a health and exercise status questionnaire to document health, injury, exercise, and supplementation history. None of the participants had taken any nutritional supplements within nine weeks prior to their initial testing date and were free of neuromuscular, cardiovascular, or metabolic conditions.

Study Design

This study was performed as an observational investigation using a single visit to the research laboratory and one additional session before team practice at the University boathouse. During the laboratory visit, participants were asked to visit the laboratory rested (>24 hours since last moderate or vigorous training bout), decaffeinated (no coffee, energy drink, or tea >12 hours), and hydrated. Anthropometrics of height using a wall-mounted stadiometer and body weight using a platform digital scale (Global Industrial, Inc., DeSoto, TX, USA) were measured and recorded. Body composition and muscle imaging were conducted followed by vertical jump and one-repetition maximum (1-RM) leg press. On a separate day, a 2,000m time trial was completed on a rowing ergometer (Concept2, Concept2 Ltd., Morrisville, VT) and participant’s 500m average split was recorded.


Bioelectrical impedance analysis (BIA) was completed using the ImpediMed SFB7 (ImpediMed U.S., Carlsbad, CA) after manufacturer-directed daily calibration. Participants were asked to remove any jewelry, metal, socks, and shoes to reduce any artifact and lay on a padded examination table with their arms and legs extended and not touch any other part of the body for 10 minutes, as suggested by manufacturer protocol. Contact electrodes were placed on the wrist at the level of the ulnar head, between the medial and lateral malleoli of the anterior aspect of the ankle, and a second electrode 5cm distal on the hand and foot, respectively. Participant’s age, height, weight, and sex were entered into the device. Data was recorded and stored on an external drive for subsequent analysis. Segmental leg FFM was estimated using methods by Kaysen et al. (15). Test-retest reliability of the SFB7-estimated FFM was reported with mean coefficient of variation of 6.6±2.3% and an ICC(2,1) (95%CIs) of 0.990 (0.959-0.997).

B-mode imaging of the vastus lateralis (VL) and rectus femoris (RF) of the right leg were performed during the laboratory visit using a Logiq e ultrasound (GE Healthcare, Wauwatosa, WI, USA; FDA 510[K] Number K133533). Ultrasound is a valid, reliable, and non-invasive assessment for the quantification of muscle size (cross sectional area; mCSA) and composition (skeletal muscle infiltration of fat and fibrous tissue). For the measurement, a generous amount of water-soluble transmission gel was applied to the skin to reduce possible near-field artifacts and enhance image clarity. Participants were instructed to lay supine in a relaxed position with the right-leg extended over a foam pad, neutralizing the position of the leg. The scan depth was set to 7 cm, gain was 55 dB, and transducer frequency was 10 mHz. Each of the images were subsequently analyzed by a single investigator using ImageJ (NIH, Bethesda, MD) to measure mCSA and subcutaneous fat.

Using a Vertec vertical jump measurement system (Jump USA, Inc. Sunnyvale, California, USA), participants were instructed to perform a countermovement jump (VJHT). Participants were asked to swing their arms down and back and jump as explosively and as forcefully as they could. Participants were allowed two practice jumps (to familiarize with the movement), followed by three test jumps, each separated by one minute rest intervals. Peak power output was estimated using equations from Johnson and Bahamonde (14); VJPOWER = (78.6 x VJ; cm) + (60.3 x mass; kg)- (15.3 x height; cm) – 1308.

Participants were asked to perform a 1-repetition maximum test on an inclined leg press (LPMAX) (Force USA, Draper, Utah, USA) according to the NSCA guidelines (10). Using a plate-loaded hip sled at a 45o incline, participants were asked to sit with his/her back against the backrest and grasp the safety handles to prevent the buttocks from rising off the seat. The participants were then instructed to place his/her feet shoulder width apart and extend their legs to lift the platform off the supports, followed by lowering the sled to 90o knee flexion, and subsequent full leg extension. The first three sets consisted of warm-ups at progressively increasing loads, followed by two to five test trials. Between each attempt, participants were given three-minute rest intervals. The maximum was determined when the participant needed assistance to raise the platform and the previous weight that was successfully completed was used in subsequent analyses. Muscle quality (MQ; kg 1RM / kg LegFFM) was calculated as the relative strength of LPMAX divided by estimated leg FFM from BIA.

Time trials were conducted using a Concept2© rowing ergometer (Concept2, Inc., Morrisville, Vermont, USA). Every participant was familiar with the ergometer and rowing technique. Two-thousand-meter time trial sprints were completed after a 10-minute dynamic warmup. Strong verbal encouragement was provided throughout the test for all participants. The time trials were divided into 500-meter average split times which were recorded for subsequent analysis.

Statistical Analysis

Pearson product moment correlations (r) were recorded (FFM, kg; mCSA, cm2; VJHT, cm; LPMAX, kg; 500m, sec.) or calculated (VJPOWER, W; MQ, kg 1RM / kg FFM) to isolate potential predictors of performance. Stepwise linear regression was conducted using VJHT, VJPOWER, LPMAX, FFM, LegFFM, MQ, and mCSA as possible predictors of 500m performance. An a priori power estimation using Fisher’s Z transformation sample size estimation with an 0.05 type I error rate and power of 0.80 requires a minimum sample of 8 participants for a meaningful relationship to be estimated (27). The researchers acknowledge the present sample (n=10) is not sufficient for four variables to be processed in a regression model together, however it is sufficient to identify the correlations and is helpful to identify potential predictors to provide insight and suggestions to future research from other research groups. All statistical analyses were performed using IBM SPSS Statistics software version 25 (SPSS, Inc., Chicago, IL, USA) and an alpha of p ≤ 0.05 was considered statistically significant for all comparisons.


Table 1 presents individual responses for the dependent variables. The correlation coefficients among recorded variables were all very high and significant (r = 0.867-0.950; p = 0.001-0.04). However, mCSA was not a strong correlate, nor predictor of time trial performance (r = 0.571, p = 0.14). VJHT, VJPOWER, LPMAX, FFM, LegFFM, MQ, and mCSA variables were added into a regression model seeking to estimate 500m performance. After stepwise elimination, the regression analysis indicated VJHT was a single, significant predictor of 500m time trial performance (R2 = 0.903; p < 0.05) as indicated in Figure 1. Many of the variables (VJPOWER, LegFFM, and MQ) were eliminated due to multicollinearity with primary (recorded, not calculated) variables despite having very high correlations with 500m performance (r = 0.924, p = 0.001; 0.889, p = 0.003; and 0.891, p = 0.003, respectively). The second strongest predictor for the model being LPMAX (R2= 0.821, p < 0.05); however, it did not add significantly to the prediction.

Table 1. Individual results for each variable and mean ± SD.

SubSexExp (yrs)Age (yrs)Ht (cm)Wt (kg)FFM (kg)LegFFM (kg)mCSA (cm2)LPMAX (kg)VJHT (cm)500m (sec)500Pred (sec)MQ (kg/kg)
2M   7+20.0188.
5M<1 28.0179.093.387.432.365.4308.463.5110.8103.49.5
6M<1 18.0177.865.260.523.336.5163.345.7130.7130.17.0
7M<1 29.0186.697.072.123.348.0226.835.6127.0145.39.7
9F   1+22.0172.566.655.821.534.9163.349.5129.2124.47.6
Mean  22.10180.5479.0166.4925.0847.87222.7250.62121.39122.748.71
SD  4.048.3313.4511.753.9510.4468.009.9013.5914.841.63
*Crew members with greater than 1 year of experience are considered varsity.

Figure 1. Linear relationship and projection depicting an improvement in 500m split performance as vertical jump height improves (▲) indicates actual time and (●) indicates predicted time per height of vertical jump in male and female rowers.


This study was performed to determine the best indicator for peak rowing performance and provide potential training mechanism suggestions in collegiate male and female crew members. The primary finding from this investigation was that vertical jump performance was highly correlated to 500m time trial speed. These results suggest these athletes could employ explosive power to enhance their performance potential by emphasizing plyometric training. However, the correlation relationship is not causal. Additionally, these results also indicate the strong negative relationship of relative strength, referred to as muscle quality to rowing performance. As the lower body strength relative to the amount of leg lean mass (muscle quality) increases, the 500m split time decreases, and subsequently, improves performance.

Strength, power, and muscular endurance exercises have been reported to be strong predictors for peak stroke power in rowers (18). Similarly, the present study reported explosive power, measured by VJHT, as the single best predictor of performance. However, Kramer et al. (16) reported vertical jump to predict power utilizing novice female rowers, but power lacked the relationship with rowing performance (16). This is not the case in the present study. Many coaches select specific exercises and means of assessment that provide useful information about an individual’s strengths and weaknesses in their ability to generate power, which can specifically assist with athlete placement in boats (1, 7, 18). Overall, the incorporation of high-intensity interval training (HIIT) combined with plyometric and resistance exercise may enhance the athlete as a whole, as suggested by Driller et al., (4). Conversely, Arne et al. (1) reported a consistent low-intensity training program was related to success in young world-class athletes. Lawton et al. argues no single weight room exercise can be a dominant determinant of success outcome measures (17). The present results indicate maximal strength or muscle quality (strength relative to mass) were good predictors (R2=0.844), but not the best when compared to vertical jump height. These collectively provide support for a variety of training modalities for a well-rounded athlete (3, 4, 5, 18).

As an additional technical aspect to the sport, the lower body’s early leg drive provides greater water resistance against the oar blade if and when placed correctly in the water, prior to the upper-body late drive (2, 26). Through this description, power is conducted mostly through the lower limbs (when the blade is in the water). Greene et al. (9) explored leg shank length and the potential biomechanical advantage of certain limb lengths, noting technique is the most impactful despite limb length. Based on strength and the quantity of muscle per leg length, the researchers quantified muscle quality index for the lower body and report no additional benefit for 500m predicted time from MQ over VJHT. Similar results presented by Maciejewski et al. (20) compare squat jump to 1,500-m time trial. Maciejewski et al. also suggests improving the lower limb’s explosive power can improve power output as well as rowing performance (20), as confirmed in the present study. Further, Egan-Shutter et al. (6) noted plyometric training enhanced 500m performance and rowing economy in 4-weeks, despite no rowing sprint training in the 4-week period. Collectively, these support our suggestion of emphasizing plyometric training as a modality coaches should include in pre- and in-season training programs.

Although rowing may often be considered an endurance sport (12), and often is at professional and elite distances, the single best predictor of 500m time trial performance in this case is vertical jump height as an index of power. Further confirmation includes the strong significant correlation between performance and vertical jump notwithstanding the face the relationship is not causal, it probably would not hurt if these athletes trained explosively by means of plyometric jumps, for example. Previous research has highlighted the importance of anaerobic, resistance exercise to enhance rowing performance (29). This study provides support for use of plyometric and explosive lower body power lifting to enhance performance of these athletes. These training modalities and the impact on performance should be evaluated further and with larger sample sizes. Of importance to note are the study limitations that may alter the generalizability of these results. This study was limited in sample size and 80% of the participants were male. However, the pattern of response in power vs. performance relationship stood strong despite these limitations. Future research should include an even distribution of males and females as well as experience level to elaborate on these results rather than confounding them as well as investigate the impact of suggested training methodologies to potentially enhance performance metrics.


The data demonstrates the applicability of monitoring vertical jump height as a surrogate of power output in collegiate club crew members. Considering the high correlation and prediction capacity of VJHT and the insignificant correlation of mCSA with rowing performance, these results suggest that lower-body power development through training, such as, plyometric exercise may be more beneficial than hypertrophy-resistance exercise.

Applications in Sport

The further addition of power development with resistance may be a beneficial suggestion for coaches to implement. Aerobic training aside, plyometric training may serve as one of the most important aspects of athlete development beyond rowing form and mechanics, more so than strength or hypertrophy resistance training in collegiate rowers. Coaches and athletes can easily monitor their explosive performance to estimate performance without being exhibited to exhaustive work bouts on the water. These results may even apply to performance readiness during illness or injury recovery.


We would like to thank the crew team coaches for their time and effort commitment to this study. We would also like to thank Yong Koh for his assistance with image processing. The authors have no competing interests to declare, and no funding was obtained nor utilized for completion of this study.


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