Methodological Considerations on the Relationship Between the 1,500-m Rowing Ergometer Performance and Vertical Jump in National-Level Adolescent Rowers : The Journal of Strength & Conditioning Research

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Methodological Considerations on the Relationship Between the 1,500-m Rowing Ergometer Performance and Vertical Jump in National-Level Adolescent Rowers

Maciejewski, Hugo1,2; Rahmani, Abderrahmane3; Chorin, Frédéric3,4; Lardy, Julien3; Samozino, Pierre2; Ratel, Sébastien5

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Journal of Strength and Conditioning Research 33(11):p 3000-3007, November 2019. | DOI: 10.1519/JSC.0000000000002406
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In rowing, the boat velocity results from the rower's ability to produce a high level of power both on the foot stretchers and to the oar's handle (15). In fact, power-generating capacity at each stroke is one of the main factors of performance in rowing events (21). Approximately 47% of the global power produced by competitive rowers results from the contribution of lower limbs (i.e., hip and knee extensors) (19). Therefore, evaluating lower limb power production capacity could be of great interest for rowing coaches seeking to better individualize strength training sessions or potentially identify future talents.

In addition to their “on-water” training, competitive rowers regularly undergo strength training sessions during which they perform squat movements to improve muscle power of their lower limbs. Unfortunately, the technical requirements to perform this type of movement while reducing the risks of injury (particularly at the back level) are high. In France, the young rowers' training is mainly focused on “water.” In fact, most of coaches begin to include squat movements in training sessions only when athletes are aged 17–18 years because their use could be inappropriate in less-experienced athletes or in younger rowers. Therefore, although squat movement is useful in strength training programs, its use does not seem to be the most suitable in young French rowers for assessing the muscle power of their lower limbs and hence their rowing performance.

Another alternative consists in using the vertical jump exercise that is particularly popular in the field of performance evaluation. This is mainly due to its technical simplicity, fast implementation, and high reliability regardless of age. The individual performance during explosive vertical jump movements has been highly used to estimate the lower limb explosive capabilities, notably the maximal power output, in a wide variety of physical activities (13,29). Training using different vertical jump programs (combined or not to heavy strength training sessions) was also found to be useful to produce significant gains in lower limb muscle power (23). In rowing, Battista et al. (4) reported that varsity female collegiate rowers experienced higher vertical jump (+3 cm) and 2,000-m rowing ergometer (−25 seconds) performances than novice rowers. However, they found no correlation between both performances. This lack of relationship between jump height and 2,000-m rowing performance may be ascribed to the fact that body mass (BM), which is a significant parameter for rowing performance (7,26), has not been considered in the assessment of vertical jump height.

In this regard, several mathematical methods including jump height and BM have been proposed to evaluate vertical jump performance (3,9,12,14,20,31,32). Among these methods, Davies et al. (12) established a jump index that takes jump height, BM, and gravity into account in the evaluation of vertical jump performance. This index is very simple to calculate and could be easily used by rowing coaches. However, it could be insufficiently accurate for being significantly related to rowing performance in young rowers. Indeed, 2 individuals having the same BM and jump height values can produce different power output if their pushing time/distance differs during the push-off phase. Therefore, this last parameter should be also considered in the evaluation of vertical jump performance.

Recently, Samozino et al. (30) proposed an original and valid method to estimate the lower limb power output during squat jump (SJ) from mechanical laws. The interest of this method is to consider jump height, BM, and pushing distance (i.e., the vertical displacement of the center of mass before takeoff) in the assessment of power output. Interestingly, lower limb maximal power output was found to be significantly correlated with in situ performance in numerous explosive performances involving the lower limbs (e.g., jump, sprint, and changes of direction), which are involved in many sporting activities (11). Hence, the power output developed by lower limbs during a maximal vertical jump could be considered as a good estimate of the lower limb explosive muscular capabilites (18). Given that lower limbs account a large part for power production capacity in rowing (19), the power output in SJ (30) might be significantly correlated with rowing performance. Also, as BM, jump height, and pushing distance are taken into account, power output in SJ could be more closely correlated with rowing performance than the jump index and jump height. However, these hypotheses require further investigation.

Furthermore, it is important to keep in mind that BM strongly and positively influences power production capacity in different physical activities, such as rowing (7) (25,26) and vertical jump (10). Indeed, Vaverka et al. (33) have reported in university students that vertical jump performance could differ as a function of the extraload that is added to BM. Thus, whether such a relationship between rowing performance and vertical jump performance exists, it could be confounded by the effect of BM. Moreover, numerous equations proposed in the literature to estimate lower limb power output during vertical jump are known to be influenced by the characteristics of population investigated (e.g., physical activity, performance level, sex, age, and BM). The Samozino et al. (30). approach greatly differs from these previous equations because it is exclusively based on physics and mathematical issues; if there is a BM effect on jump performance, then this seems to be independent on the population studied. To obtain BM-independent performance data, a normalization method based on an allometric approach has been proposed (24). Despite this approach having a certain number of drawbacks (27), the allometric modeling has been the most applied method for normalizing physiological variables to BM. Consequently, such approach should be used for investigating the effects of BM on rowing and vertical jump performances and on their relationship.

Therefore, the purposes of this study were to investigate whether (a) 3 different approaches for evaluating performance in SJ [i.e., vertical jump height, jump index (12), and maximal power produced by lower limbs (30)] are significantly correlated with rowing performance in national-level adolescent rowers and whether (b) such relationships are influenced by BM. Considering the fundamental influence of lower limb power and body mass on both squat jump and rowing performances, we hypothesized that (a) the 1,500-m rowing performance is more closely correlated with the lower limb maximal power than vertical jump height and jump index, and (b) BM acts strongly on the relevance of these relationships. The allometric scaling will be used to check these assumptions.


Experimental Approach of the Problem

The experiments were conducted on 2 sessions performed at 3 days apart at the same time of day. During the first session, the rowers performed a 1,500-m all-out exercise on a rowing ergometer (Model D; Concept2, Morrisville, VT, USA) during an official French indoor rowing competition. Then, during the second session, the participants performed a squat jump test in a controlled laboratory environment. All the participants were fully familiarized with the rowing ergometer and the squat jump movement. Furthermore, participants were instructed not to undertake any strenuous activity during the 24 hours preceding each session.

The experiments were performed on a rowing ergometer over a 1,500-m all-out exercise for many reasons. First of all, the effects of environmental factors (wind, waves, water temperature, etc.), which are likely to influence rowing performance, are limited in this simulated condition. Furthermore, rowing ergometer is often used by coaches for training and fitness/performance testing. Finally, rowing ergometer performance was evaluated during a 1,500-m race since we studied 15- to 16-year-old elite rowers and that the French Rowing Federation sets this competition distance for this age category.


Fourteen young male competitive rowers (mean ± SD 15.3 ± 0.6 years, 178.6 ± 7.4 cm, and 67.9 ± 10.8 kg) volunteered to participate in this study. All of them took part in the French Rowing National Championships the year preceding the experimentation. Nine of them were medalists. The present rowers trained between 3 and 5 times per week during the last 2 years preceding the experimentation. The experiment was conducted in accordance with the Declaration of Helsinki. The study was approved by the local Institutional Review Board (National Institute of Sport, Expertise, and Performance - INSEP). Before giving their written consent, each subject, their parents (or legal guardians), and their coaches were fully informed on the aim, the risks, possible discomfort, and potential benefits of the experiment.


Session 1: 1,500-m All-out Rowing Exercise

A standardized 30-minute warm-up on the rowing ergometer was performed at about 140 b·min−1 by the participants. Then, all of them were asked to cover the distance as fast as possible. The duration over 1,500 m (T1500 in second) was recorded by the investigators using an electronic timer included in the rowing ergometer monitor (PM5, Concept2). Then, T1500 was used to calculate the mean power output (P1500 in Watt) according to the following equation:

P1500 was expressed in absolute and normalized values (see section Allometric modeling procedures). P1500 was considered as the main performance criterion for this first test.

Session 2: Squat Jump Test

For each subject, the vertical distance between the ground and the right lower limb great trochanter was measured in a 90°-knee angle crouch position using a set square. After a 10-minute warm-up, subjects had to perform 3 maximal SJs with the hands on their hips to limit their additional effects on vertical jump performance (6). Each subject was asked to bend their lower limbs and reach the starting height; the latter was carefully checked using a ruler. After having maintained this crouch position for about 2 seconds, they were asked to apply force as fast as possible and to jump as high as possible. Countermovement was strictly forbidden; video analysis (HC-X929; Panasonic, Osaka, Japan) was used during each trial to carefully check any potential downward movement. At landing, subjects were asked to touch down with the same lower limb position as when they took off, i.e., with extended lower limb and maximal foot plantar flexion. If all these requirements were not met, the trial was repeated. Each trial was followed by a 1-minute rest. The highest of the 3 trials was recorded for subsequent analysis.

Performance in Squat Jump

In accordance with the fundamental laws of dynamics (2), the SJ height (HSJ in cm) was determined from aerial time (tSJ in second) using the following equation:where tSJ was measured with an OptoJump (Microgate SRL, Italy) and g corresponds to the acceleration due to gravity (g = 9.81 m·s−2).

The squat jump index (ISJ in J) was calculated according to the formula proposed by Davies et al. (12):where BM is the body mass.

The mean power generated by lower limbs in SJ (PSJ in Watt) was estimated using the methodological procedures and calculations proposed by Samozino et al. (30):where HPO corresponds to the lower limbs' length change between the starting position and the moment of takeoff.

Allometric Modeling Procedures

As the range of body dimensions (especially BM) was large in young rowers and body dimensions are likely to influence rowing and jump performances, allometric modeling procedures were used to consider the importance of BM into the assessment of P1500, HSJ, ISJ, and PSJ. The allometric relationships obtained between the BM and rowing and jump performances were based on the general allometric equation:where y is the rowing or jump performance (i.e., P1500, HSJ, ISJ, or PSJ), a the proportionality coefficient, x the body mass variable, and b a scaling factor. The resultant power function ratio y/xb is allegedly free from the confounding influence of BM. The statistical approach to allometry is to use a logarithmic transformation as follows:where b is the slope of the linear regression line. The slope is calculated by regression analysis, where b in the regression output, and is equal to the scaling factor, and the inverse log of log a is equivalent to the constant (a) in the equation 5.

Statistical Analyses

Analyses were performed using JMP V12.0.1 (SAS Institute, Cary, NC, USA). Descriptive statistics are expressed as mean ± SD. The reliability for the HSJ between the 3 trials was evaluated using the within-subject coefficient of variation (CV, in %) as outlined by Hopkins (16). Data distributions were first checked by the Shapiro-Wilk normality test. Linear regression models between the parameters were fitted by the least squares method. The squared Bravais-Pearson correlation coefficients (r2) of these linear regression models were calculated. Body mass was not correlated with ISJ and PSJ to avoid any effects of collinearity between the parameters. In accordance with Hopkins (16), the magnitude for squared correlation coefficients was considered as trivial (r2 < 0.01), small (0.01 < r2 < 0.09), moderate (0.09 < r2 < 0.25), large (0.25 < r2 < 0.49), very large (0.49 < r2 < 0.81), nearly perfect (r2 > 0.81), and perfect (r2 = 1.0). The statistical significance was set at 5% (i.e., p ≤ 0.05) and tendency between 5 and 10% (i.e., 0.05 < p < 0.10). Using IBM SPSS Sample Power, the sample size was evaluated for 14 subjects for an expected correlation of r > 0.63, α < 0.05 and power >80%. A posteriori power (β) was evaluated for all analyses.


Allometric Modeling

Common exponents b obtained from allometric modeling between BM and P1500, and jump performance expressed by HSJ, ISJ, and PSJ are 1.04, 0.47, 1.47, and 1.15, respectively, displayed in Table 1. These scaling factors were taken into account in the expression of rowing and SJ performances (Table 1).

Table 1.:
Mean values of the parameters obtained during the 1,500-m rowing ergometer and squat jump (SJ) exercises and comparison between the heaviest and lightest rowers using different normalization methods for body mass (BM).*†

Rowing Ergometer and Squat Jump Performances

The within-subject CV for the 3 maximal HSJ was 3.73%. Mean T1500 and P1500 values as well as mean values of HSJ, ISJ, and PSJ are displayed in Table 1. The corresponding individual values obtained in the heaviest and lightest rowers are also displayed in Table 1.

Correlations Between Anthropometric and Performance Variables

Body mass was significantly correlated with P1500 (Figure 1A, r2 = 0.85, p < 0.0001, β = 100%), ISJ (Figure 1C, r2 = 0.79, p < 0.0001, β = 100%), and PSJ (Figure 1D, r2 = 0.96, p < 0.0001, β = 100%). Furthermore, BM and tended to be correlated with HSJ (Figure 1B, r2 = 0.27, p = 0.0516, β = 48%).

Figure 1.:
Relationships between body mass (BM), mean power output sustained during the 1,500-m rowing ergometer exercise (P1500, A), and jump height (HSJ, B).

Furthermore, as shown in Figure 2, P1500 (W) was significantly correlated with HSJ (r2 = 0.29, p ≤ 0.05, β = 51%), ISJ (r2 = 0.72, p < 0.0001, β = 99%), and PSJ (r2 = 0.86, p < 0.0001, β = 100%).

Figure 2.:
Relationships between the mean power output sustained during the 1,500-m rowing ergometer exercise (P1500) and the squat jump (SJ) performance expressed among jump height (HSJ, A), jump index (ISJ, B), and power output (PSJ, C).

By contrast, when the values were normalized to BM, P1500 was not correlated with HSJ (r2 = 0.01, p = 0.486, β = 5%), ISJ (r2 = 0.04, p = 0.446, β = 9%), and PSJ (r2 = 0.08, p = 0.393, β = 15%). Similarly, P1500 (in W·BM-1.04) was not correlated with HSJ (in cm·BM-0.47) (r2 = 0.03, p = 0.459, β = 8%), ISJ (in J·BM-1.47) (r2 = 0.03, p = 0.459, β = 8%), and PSJ (in W·BM-1.15) (r2 = 0.04, p = 0.446, β = 9%). The squared Bravais-Pearson correlation coefficients (r2) of these linear regressions indicate that BM accounted 97, 97, and 96% for the relationships between P1500 and HSJ, ISJ, and PSJ, respectively.


The aim of this study was to investigate whether 3 different approaches for evaluating performance in SJ (jump height [HSJ], jump index [ISJ], and jump power [PSJ]) were correlated with 1,500-m rowing ergometer performance (P1500) in national-level adolescent rowers. The main results confirm our hypotheses because HSJ, ISJ, and PSJ are significantly correlated with P1500, with a greater correlation coefficient for PSJ, and that these correlations are strongly influenced by BM. More specifically, the allometric scaling showed that BM highly accounts for SJ and rowing performances, and it explains at least 96% for their relationships. However, the similarity between both allometric exponents for PSJ and P1500 (1.15 and 1.04, respectively) means that BM could influence PSJ and P1500 at the same rate, and thus PSJ could be the best correlate of P1500 regardless of BM.

In this study, HSJ was moderately correlated with P1500 and only accounted 29% for its variation with a low statistical power (β = 51%). However, the fact to consider BM in SJ performance (equation 3) noticeably improved the correlation with P1500 because ISJ accounted for 72% of P1500 variation with a quasimaximal statistical power (β = 99%). More remarkably, when the pushing time/distance (i.e., HPO in equation 4) was considered into the calculation of SJ performance using the method proposed by Samozino et al. (30), in addition to BM, PSJ accounted for 86% of P1500 variation (with a maximal statistical power, β = 100%) meaning that PSJ is the best correlate of P1500 compared with HSJ and ISJ. Therefore, among the 3 approaches proposed, the measurement of power output is the most accurate to investigate the relationship between SJ and 1,500-m rowing ergometer performances in adolescent competitive rowers despite the fact that (a) the lower limb extension during SJ and rowing exercises occurs differently (i.e., vertical push off vs. mostly horizontal action, respectively) (5), and (b) the amplitude of knee extension in SJ is significantly lower than that is typically observed in rowing (the knee angle being set, respectively, at 90 vs. 55° for SJ and rowing at the start of push off) (17).

It is also worth noting that BM was significantly correlated with P1500 and accounted for 85% in this performance (r2 = 0.85, p < 0.0001). This confirms that a large BM is required to produce high performance levels in rowing (7,26), particularly in adolescent rowers (8,28). However, although previous investigations reported that the finalists to the World Junior Rowing Championships were significantly heavier than the nonfinalists (8), it is important to keep in mind that BM seems to have significant negative (drag) effects on mechanical efficiency and thus rowing performance on water. This is illustrated by the fact that the mean speed during a 2,000-m all-out exercise performed on water in single scull is significantly slower than on a rowing ergometer (3.66 vs. 4.96 m·s−1) in junior elite male rowers (26), irrespective of technical expertise.

Furthermore, BM tended to be correlated with HSJ (Figure 1B), which could influence the relationships between P1500 and SJ performance variables. Hence, to isolate the influence of BM on rowing and SJ performances and their relationships, P1500, HSJ, ISJ, PSJ, and their correlations were further analyzed by considering BM as a scaling factor. Because it is frequently used in the field of performance testing by coaches, notably in rowing, we first applied the BM exponent to 1. We found that differences in rowing and SJ performances between the heaviest (88.7 kg) and lightest (47.9 kg) rowers were highly reduced except for HSJ (Table 1), and none of SJ performance variables was correlated with P1500. In fact, it is worth underlining that HSJ, ISJ, and PSJ relative to BM only explained 1–8% of P1500 variation (in W·kg−1). Then, when the allometric scaling was used to completely take the effect of BM on SJ and rowing performances into account, individual performance differences between the heaviest and lightest rowers quasidisappeared (Table 1), and the contribution of HSJ, ISJ, and PSJ to the P1500 variation remained very low (from 3 to 4%). Taken together, these results indicate that BM has a large influence on SJ and rowing performances, and that BM might highly account for their relationships (at least 96%).

In addition, the BM exponents used for reducing the effect of BM on SJ, and rowing performances were systematically positive (b > 0), meaning that jump and rowing performances increased with BM. Interestingly, the BM exponents were very different according to the parameter considered because the exponents for HSJ and ISJ were, respectively, low and high (0.47 vs. 1.47). This suggests that jump performance increases with increasing BM at a lower rate with HSJ and at higher rate with ISJ. As illustrated in Figure 3, this very large inconsistency in BM exponents shows that performance could be either underestimated or overestimated in the heaviest rowers according to the performance index used. In this context, it can be reasonably assumed that the relationships between P1500 and HSJ (Figure 2A) and ISJ (Figure 2B) could be artificially and differently oriented by the BM effect. Similar BM exponents should be, therefore, used to correlate 2 performance variables that are highly dependent on BM, particularly when testing both heavy and light rowers. Interestingly, in this study, PSJ displayed the exponent the closest to that obtained for P1500 (1.15 vs. 1.04, respectively). This means that BM (i.e., light or heavy) should influence jump and rowing performances at the same rate and that the evolution of these 2 performance indexes (i.e., PSJ and P1500) vs. BM should remain relatively similar. This similarity between both exponents is a key point for potentially identifying talented young rowers for which there could exist a large range of BM because of maturation- and growth-related differences (1). Furthermore, as shown in Table 2, the common exponents calculated from previous equations available in the literature (3,9,12,14,20,31,32) between BM and jump performance were greatly higher than that obtained for PSJ. This suggests that despite significant correlations were found between SJ performance and P1500, those equations may overestimate power output values during SJ in the heaviest rowers and thus artificially influence the relationships. A possible explanation for this lack of concordance is that the equation proposed by Samozino et al. (30) is exclusively based on physics issues. Consequently, power output estimated from this equation is totally independent on the population investigated, which is not the case for the other equations that are strongly influenced by the subjects' anthropometric characteristics investigated (sex, age, and physical activity level).

Figure 3.:
Influence of common exponents (b) on performance (arbitrary unit, a.u.). Common exponents (b) were obtained from the allometric scaling.
Table 2:
. Mean power output values in squat jump (SJ) estimated from different equations available in the literature and their relationships (r 2) with 1,500-m rowing ergometer performance.*†

This study displays some methodological limitations. First, the outcomes were obtained on a population of 14 young competitive rowers. Even if this sample seems limited, its size has been statistically estimated before the study to reach a statistical power higher than 80%. Second, it is important to keep in mind that the relationships between SJ and rowing ergometer performances, and the effects of BM on these relationships may be influenced by several confounding factors (age, sex, body composition, level of physical activity, or performance). Consequently, to know whether the present conclusions are strictly associated with the population of young competitive rowers or common to all rowers, further studies including rowers of different age, sex, and performance levels should be performed. Finally, the power output data during SJ were not directly measured but estimated from a mathematical approach, which could partly limit the accuracy of the present outcomes. Therefore, future studies using a force platform should be performed to provide further insights into the relationships between SJ and rowing ergometer performances and the BM effects on these relationships.

In conclusion, the results of this study indicate that SJ performance expressed among the jump height, jump index, and power output is significantly correlated with 1,500-m rowing ergometer performance in national-level adolescent rowers. However, the calculation of power ouput proposed by Samozino et al. is the best correlate of the 1,500-m rowing ergometer performance. Furthermore, BM highly accounts for squat jump and rowing performances and explains at least 96% for their relationships. However, the similarity of common exponents (b) between these 2 performance indexes and BM (i.e., 1.15 and 1.04 for PSJ and P1500, respectively) suggests that their evolution vs. BM remains relatively similar and that PSJ could be the best correlate of P1500 regardless of BM.

Practical Applications

On a more practical level, the results of this study indicate that the calculation of power from the Samozino et al.'s (30) method is more relevant than HSJ and ISJ to (a) evaluate jump performance and (b) infer the capacity of adolescent rowers to perform 1,500-m all-out rowing ergometer performance, irrespective of their body mass. Such an approach may be requisite because (a) there exists a large range of body mass in adolescent rowers because of maturation- and growth-related differences, (b) coaches use more and more rowing ergometer in their training program, and (c) competitions on rowing ergometer are developing at national and international levels in young rowers. Furthermore, the present findings demonstrate the interest to develop high power output levels in squat jump to optimize rowing ergometer performance during a 1,500-m race. This suggests that rowing training programs in young competitive adolescents should include more explosive movements involving lower limbs to improve their power output and subsequent rowing performance. In addition to “on-water” training sessions, explosive exercises such as squat jump, drop jump, and countermovement jump could be proposed to adolescent rowers twice or 3 times a week instead of/in complement to traditional strength training using additional loads (squat, lunge, press, etc.), according to their training experience. Taken together, this could help coaches to improve their training program and potentially identify talented young rowers.


The authors have no conflicts of interest to disclose. The authors express their gratitude to Vincent Gazan in charge of the “Pôle France Aviron,” rowing association of Nantes, and to the rowers for their personal investment. Part of the results has been used in a previous article for other purposes (22). This study was supported by grants from the French Minister of Youth Affairs, Sports, and Associative Life, the National Institute of Sport, Expertise, and Performance (INSEP), and the Région Pays de la Loire (project ANOPACy). ANOPACy is cofunded by the European Union. Europe commits itself to Pays de La Loire through Regional development european funds. ANOPACy was also labeled by the Pôle de compétitivité industriel EMC2.


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power output; younger oarmen; talent identification; squat jump; methodology

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