ABSTRACT

BURLEIGH, S. L., A. R. CRAWFORD, J. S. EADES, B. J. KING and D.J. MARSHALL.

Predictors of 2000m Ergometer Rowing Performance Based off Specific Rowing Fitness Tests

Purpose: The aim of this study was to predict 2000-m ergometer rowing time from the results of

anaerobic and aerobic fitness tests and to investigate how and why these results impact on rowing

performance. Methods: 81 healthy participants of relatively high fitness levels (mean ± SD age

21.94±3.36 years, weight 73.83±13.12kg, height 174.99±9.79 cm, VO2 max 38.95±8.59 mL.kg.min-

1) completed a VO2 max test in week 1, isometric quadriceps and hamstring strength and power

testing, maximal vertical jump, 30sec Wingate maximal sprint cycle and 20m sprint time tests in

week 2 and 2x2000m rowing ergometer time trails separated by a week. Results: VO2max (l/min)

showed a strong correlation with both actual finishing time (r = -0.699) and mean power (r = 0.755)

in 2000m rowing. Peak power output in the VO2max test also correlated highly with actual

finishing time (r = -0.680) and mean power (r = 0.716) in 2000m rowing. VO2max (l/min)

contributed to 47.6% and 55.7% of the variance in predicted finishing time and predicted mean

power output in 2000m rowing performance, respectively. Conclusion: Exercises and movements

that mimic the explosive and aerobic nature of rowing are shown to be valid indicators of rowing

performance, and can used as a simple and effective way of predicting performance.

Key Words: Correlation, Indicator, VO2max

INTRODUCTION

Paragraph 1. Rowing performance requires the contribution of aerobic and anaerobic

energy systems, as success is reliant on an individual’s or team’s ability to sustain high power

output and levels of endurance to complete events in the shortest possible time (1, 5, 7, 8, 15).

Studies have demonstrated high positive correlations between rowing performance and a rower’s

VO2max, along with the oxygen consumption and power outputs reached at 4 mmol.L-1 of blood

lactate [BLac] (4, 6). These performance determinants provide invaluable information to coaches,

medical staff and fitness professionals for the structuring and implementation of training programs

for development of current and future athletes within the sport.

Using Anaerobic and Aerobic Fitness Tests to Predict 2000-m Ergometer Rowing PerformanceS.L Burleigh, A.R Crawford, J.S Eades, B.J King & D.J Marshall

October 2015

Paragraph 2. Testing rowing performance in race-like conditions involves many logistical

challenges, especially if coaches wish to assess capabilities of individual athletes (4, 6, 7, 18).

Utilising predictive methods, such as outcomes of physical fitness tests, could prove to be an

effective technique to obtain important information related to performance in a controlled

environment. It is widely accepted that a person’s VO2max is a reliable measure of ability to perform

aerobic exercise (1, 4, 6, 8, 15). Around 70-86% of energy production contributing to the overall

energy demands of 2000-m rowing is supplied by oxidative metabolism (7, 8, 20) and studies by

Bourdin et al. and Kendall et al. found VO2max (L/min) to be accountable for ~84% of the variance

in mean power output (MPO) achieved during 2000-m rowing performance (1, 8). Due to the

whole-body nature of rowing technique, a large muscle mass is utilised during performance and, as

a consequence, there is a high demand for oxygen supply to the upper and lower extremities (1).

Psychological factors aside, VO2max test results can assist in identifying athletes with high aerobic

capabilities and those who have the potential to sustain greater power outputs during a race.

Paragraph 3. Another factor that has been identified in previous research is power output

and VO2 achieved at 4 mmol.L-1 of [BLac], which can demonstrate a key difference in performance

between elite and non-elite rowers (4, 6). For those with a higher VO2 than their more skilled

competitors, at this level of [BLac] they may experience fatigue earlier due to an increase in

metabolic acidosis resulting from increased anaerobic glycolysis and H+ concentrations (4). The

sudden rise in [BLac] occurs at a lower relative intensity (%VO2max) in less trained individuals when

compared to highly trained individuals, who experience this rise at ~80-90% of VO2max (2).

According to Izquierdo-Gabarren et. al.’s 2010 study, stroke power achieved at 4 mmol.L-1 [BLac]

has a high correlation with the average power achieved in a 20 minute all out test (7). This may be

another important factor for coaches to consider when training and selecting rowing teams, as the

MPO of athletes with a lower VO2 at 4 mmol.L-1 of [BLac] gives an indication of that athlete’s

resistance to fatigue during long distance rowing events (2).

Paragraph 4. Current literature on variables related to predicting 2000-m rowing

performance lacks information regarding ways in which fitness tests of strength, speed and power

can be used if testing in race-like environments is not possible. One purpose of this study was to

undertake physical fitness tests, which are relevant to rowing, that have not yet been widely studied.

Huang, Nesser and Edwards found a high correlation (r = -0.736) between vertical jump (VJ) and

2000-m rowing performance, which was the largest correlation between any of their tests (5). This

study assessed the validity and reliability of the VJ test in predicting rowing performance, as lower

leg power has been proven to have major implications on overall power produced during a single

stroke (3). Measures of joint kinetics and their impact on rowing performance have not yet been 1

closely analysed in the current literature on rowing performance, and as a result this study examines

the use of hamstring and quadriceps torque in predicting finishing time (FT) and mean power output

(MPO). Lastly, split times from the 20m running test will be utilised to further examine the

predictability of 2000-m rowing performance. Therefore, the overall purpose of this study is to

predict 2000-m rowing performance using a combination of different physical fitness tests aimed at

aerobic and anaerobic components of rowing performance.

METHODS

Subjects

Paragraph 5. 81 university students participated and possessed the following characteristics

(mean ± SD): age 22 ± 3 years, weight 73 ± 13kg and height 175 ± 10cm. All subjects were

assessed and noted to be free of any preventative health issues. Each subject provided written,

informed consent to participate in the research. Prior to commencement of testing, height (cm) and

body mass (kg) were recorded for each subject. Height, measured using a stadiometer (220, Seca,

Hamburg, Germany), was recorded to the nearest 1 cm. Body mass was measured using electronic

scales (UC-321, A&D Medical, San Jose, USA) to the nearest 1 kg.

Physiological testing

Paragraph 6. In week 1, an incremental exercise test was conducted on a cycle ergometer

(828E, Monark Exercise AB, Vansbro, Sweden) to assess VO2max and peak power output (PPO).

The subject was connected via mouthpiece and hoses to a metabolic cart (Powerlab 8/30, A&D

Instruments, San Jose, USA), configured to collect O2 uptake every 30 seconds. After a five-minute

warm up, subjects commenced cycling at a self-nominated rate between 60-80 rpm, which was to

be maintained for the test duration. Exercise commenced at 50W and increased by 25W every three

minutes until volitional fatigue. Heart rate was recorded at the end of each three-minute period with

a HR monitor (FT1, Polar Electro OY, Kempele, Finland). [BLac] levels, which were analysed with

an electronic analyser (Lactate Pro2, Arkray, Shiga, Japan), were used to determine % of VO2max

at an equivalent [BLac] of 4 mmol.L-1.

Strength, Power and Speed Testing

Paragraph 7. In week 2, subjects warmed up on a cycle ergometer for five minutes at a self-

selected RPM rate, with workload set at 1 KP. After warm-up, quadriceps and hamstring strength 2

(60O.s.1) and power (240O.s.1) for both dominant and non-dominant legs were measured using an

Isokinetic dynamometer (Humac Norm, CSMI Solutions, Stoughton, USA). Three maximal

contractions were completed at both speeds.

Paragraph 8. VJ height was measured using a vertec (Yardstick, Swift Performance

Equipment, Wacol, QLD). Subjects were instructed to lower their body to a knee angle of 90O,

extend explosively up and touch the vertec at the highest point possible. The best of three attempts

(jump height minus standing height) was used for analysis.

Paragraph 9. Three 20 metre running sprints were also conducted. Split times were recorded at

5 and 20-m using timing gates (Smart Speed, Fusion Sport, Sumner Park, Queensland).

Performance Testing

Paragraph 10. In week 3, after repeating the week 2 warm up, a 2000-m maximum-effort

rowing test on a rowing ergometer (D, Concept2, Morrisville, USA), set at level 2/10, was

conducted. Subjects were reminded during the bout to maintain correct movement sequences, and

were verbally encouraged to maintain intensity. FT (s) and MPO (W) were recorded. The test was

repeated in week 4, with results from this test being used as the basis for comparative analysis.

Statistical Analysis

Paragraph 11. All statistical analysis was undertaken using SPSS (22, IBM, Armonk, USA) and

results are reported throughout as mean ± SD unless stated. P<0.05 was used for establishing

statistical significance. Pearson correlation coefficients (r) were used to determine strength of

association of each independent variable to the FT and the MPO from the rowing trial. Using

stepwise linear regression analysis, independent variables with a high correlation to FT/MPO were

selected for development of each regression model. FT was excluded from the predictive MPO

model and vice-versa, as they were taken from the same test and are inextricably linked. When

analysing actual versus predicted FT and MPO, only complete records (n=65) were included.

Records were excluded a) when values for actual FT or MPO were missing (n=7), and/or b) when

values for independent variables required for accurate calculation of predicted FT or MPO were

missing (n=9).

RESULTS

Physiological and Performance Variables

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Paragraph 12. Results (mean ± standard deviation) of the physiological and performance

variables measured during the preliminary fitness tests are displayed in Table 1.

Correlations

Paragraph 13. Pearson’s correlation coefficient (r) was used to determine the correlation

between 2000m rowing performance and each physiological or performance variable. A significant

correlation (P<0.01) was found between 2000m rowing trial MPO and height (r=0.672), mass

(r=0.508), PPO for 30 sec Wingate  (W) (r=0.604), MPO (W) (r=0.635) and W/kg (r=0.517) for 30

sec Wingate test, vertical jump (r=0.536) and 20m sprint time (r=0.523). Except for dominant quad

torque at 60°/sec, a significant (P<0.01) large correlation (r2=0.5-0.7) was found between all

dominant and non-dominant quad and hamstring torques and MPO in the rowing trial. Rowing

finishing time had a significant correlation (P<0.01) with height (r=-0.626), absolute VO2 (L/min)

(r=-0.699), PPO for the VO2 test (W) (r=-0.680), PPO for the 30s Wingate test (W) (r=-0.543),

MPO for the 30s Wingate (W) (r=-0.589), vertical jump (r=-0.550) and 20m sprint time (r=0.527).

Paragraph 14. A very large (r=-0-699, P<0.01) correlation was found to exist between

maximal oxygen uptake (L/min) and actual finishing time from the 2,000m rowing performance

test, as represented in Figure 1. Figure 2 demonstrates a very large correlation (r=0.755, P<0.01)

between maximal oxygen uptake (L/min) and MPO in 2,000m rowing performance.

Paragraph 15. Figure 3 demonstrates a large to very large (r=-0/680, P<0.01) correlation

between Peak Power Output (W) during the VO2max test in correlation to actual 2000m-ergometer

rowing finish time. Figure 4 demonstrates Peak Power Output (watts) during VO2max test in

correlation to Mean Power Output in 2000m ergometer rowing trial. A very large correlation

(r=0.720, P<0.01) was found to exist between PPO in VO2max test (W) and MPO (W) in 2000m

rowing performance.

Regression Analysis.

Paragraph 16. From the statistical regression analysis, two regression equations were

produced to predict both finishing time and MPO for 2000m rowing performance respectively.

Regression equations are shown in Table 2. Finishing time can be independently predicted from

maximal oxygen uptake (L/min), dominant quad torque at 240 degrees, percentage of VO2max

corresponding to 4 mmol.L-1of [BLac] and vertical jump. 62.7% of the variance in 2000m rowing

finishing time can be attributed to a change in any of these variables. MPO for a 2000m rowing

performance can be independently predicted from maximal oxygen uptake (L/min), vertical jump,

and maximal oxygen uptake (mL/kg/min. 65.9% of the variance in predicted MPO can be attributed

to any of these variables.4

Paragraph 17. Figure 5 demonstrates P-FT from regression equation in correlation to A-FT

from 2000m ergometer rowing trial. Compared to mean P-FT, the mean A-FT was 3.26 seconds

slower (497.69±40.07secs vs 500.96±52.86secs).

Paragraph 18. Figure 6 demonstrates P-MPO from regression equation in correlation to A-

MPO from 2000m ergometer rowing trial. Compared to P-MPO, A-MPO was 1.98 W greater

(187.34 ± 55.44 vs. 189.32 ± 44.96.

DISCUSSION

Paragraph 19. Many studies have demonstrated high positive correlations between a

rower’s VO2max and rowing performance measures, along with the oxygen consumption and power

outputs reached at 4mmol.L-1 of [BLac] during physiological testing (4, 7). This study aimed to

analyse VO2max, PPO, quadriceps strength, VJ and 20m sprint time (with split at 5m) results and,

using regression equations, to predict FT and MPO for a 2000-m ergometer rowing performance.

Paragraph 20. It has been noted in previous studies that a higher VO2max is desired in

rowing, as it is beneficial for improved performance outcomes and maintaining a cadence of

between 34-38 strokes.min-1 during a race (13, 15, 18), as seen in Figure 1. During a 2000-m race,

anaerobic glycolysis contributes approximately 21-30% of energy demand, while 70-86% is derived

from aerobic metabolism (4, 8). VO2max testing is therefore a useful tool to identify potential rowing

performance (15, 18, 21). According to Izquierdo-Gabarren et al. and others, absolute VO2max

accounts for a substantial 49-81% of variance in rowing performance (4, 7, 8). This variance is in

accordance to this study, and is shown in the large distribution of finishing times correlating to

VO2max in Table 2. The results indicated that absolute VO2max (L/min) showed the strongest

correlation with both actual FT (r= -0.680) and MPO (r=0.716), compared to relative VO2max

(ml/kg/min) (r= -0.480, r=0.509 respectively). Rowers at elite levels spend the majority of a race

between 96-98% of VO2max and therefore need to have a significantly high VO2max (5). Subjects in

the current study were untrained in rowing, and elicited a range of VO2max values. This was one of

the main variables responsible for the distribution of results in rowing performance but is not the

only significant indicator of performance.

Paragraph 21. According to testing data, the strength of an athlete’s dominant quadriceps

was also a significant indicator of potential performance. There is strong emphasis in rowing on the

ability to extend both legs powerfully and to maintain high force production throughout a race (23).

Yoshiga and Higuchi found that elite rowers were able to develop more leg strength bilaterally than

the sum strength of each leg individually, which is beneficial for rowing performance as bilateral

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force summation is a necessity (23). Studies have shown that isokinetic and isometric knee

extension are strongly correlated to rowing performance on an ergometer (19, 23). Inclusion of

bilateral leg extension power improves the estimate of power output and performance by 5% (in the

current study, the performance prediction was improved by 9%), due to the ability to powerfully

extend the legs bilaterally which is a key physiological aspect of rowing (23). Parkin et al. found

that the rowing side did not lead to any asymmetries in left and right leg strength (14), however

there were a number of participants in the current study that possessed dominant leg quadriceps that

were significantly stronger than the non-dominant. This difference may be due to level and type of

training and experience, potentially influencing the variance of results (12). Upon a review of

previous studies, it could not be concluded that technological advances in strength and conditioning

provide additional validity of testing data when predicting rowing performance. Some researchers

found, when considering interrelationships between tests, that isokinetic leg strength was strongly

correlated with isoinertial leg strength (r = 0.75, p<0.05) (10, 11). These findings confirm that

isokinetic quadriceps strength can be used to accurately assist in predicting rowing time, in both

elite and sub-elite rowers. (11, 12). Further studies would need to test and report upon unilateral leg

power in order to validate our findings.

Paragraph 22. The moderate correlation of %VO2max corresponding to 4mmol.L-1 [BLac] (r=

-0.152) falls in line with the results of Cosgrove et al. (1999), emphasising that individuals with

high VO2max at a [BLac] concentration of 4mmol.L-1 generally produced faster times over a 2000-m

rowing race (4). This confirms that, given all subjects in this study had relatively similar rowing

ability, those with a higher VO2max would have reached 4mmol.L-1 [BLac] at a higher workload,

producing a faster finishing time. Those participants with lower VO2max exhibit the onset of fatigue

earlier, due to the metabolic acidosis resulting from increased anaerobic glycolysis and H+

concentrations (4). Metabolic adaptations and capacities are reflected in relation to blood lactate

formation (21). Additional research has found endurance capacity, measured through power at

4mmol.L-1 [BLac] is the most predicative variable for performance in trained rowers (21). The

lactate performance curve gives coaches and trainers an insight to athletes resistance to fatigue (2,

21). In this study, there was a strong correlation between percentage of VO2max corresponding to

4mmol.L-1 [BLac] across the group data (71.20mmol.L-1 ± 14.19). Given that highly trained

athletes attain this [BLac] at around 80-90% of their VO2max (21), whilst untrained individuals reach

it around 55% (2), this indicates untrained subjects would reach 4mmol.L-1 of [BLac] earlier than

the highly trained (19). This could be largely due to the fact that, with incremental exercise seen in a

VO2max test, those who are less trained experience a reduced ability of oxidative metabolism to

remove or resynthesise lactate (2).

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Paragraph 23. Current literature reports that there is a strong positive correlation between

VJ performance and 2000-m rowing performance. This was particularly displayed by Huang,

Nesser and Edwards, who used a VJ test in their study to assess lower body power, emphasising

that bilateral leg extension power was crucial to rowing performance (6). As with the current study,

they used stepwise linear regression analysis to determine predictors of 2000m rowing time, while

Pearson correlation coefficients were used to determine relationships between performance and

independent variables (6). The authors found that VJ had a strong correlation (r=0.542) with 2000-

m rowing time, after the exclusion of height as one of the leading determinants of performance due

to the inability to train this aspect (6). Along with VJ, weight (kg) and age were the best predictors

of rowing performance (6). The current study found a large correlation (r=-0.500) between VJ and

FT for the 2000-m rowing performance, along with a large correlation (r=0.536) for MPO. This

falls in line with current literature that finds VJ to be a reliable indicator of rowing performance, as

the explosive, powerful nature of VJ mimics the use of the legs during a rowing performance (6).

The possible mechanisms responsible for this are likely linked to coordination of the segmental

actions influenced by the timing, sequence and amplitude of the muscle activation and joint

movement (16).

Paragraph 24. In general, the remaining percentage of variance not included in the

regression equations may have been due to factors such as motivation, technique, fatigue,

experience and environmental factors for the participants in the current study. Furthermore, the

accuracy of the regression equations could account for some of the variance. Factors such as input

error, statistical analysis error and rounding of figures could be accountable.

Paragraph 25. Practical implications of this study might focus on employing more lactate

threshold training to evoke metabolic acidosis, thereby increasing anaerobic glycolysis and H+

concentrations (4). Through this, metabolic adaptations will occur, thus promoting an increased

lactate threshold, and consequently blood lactate buffering. This will allow 4mmol.L -1 to be reached

at a greater workload resulting in a faster finishing time for the 2000m row.

Paragraph 26. There are several limitations associated with this study. Firstly, testing was

conducted over four weeks, which perhaps caused each subject’s physical wellbeing or influence of

other physical training within this period to have a confounding effect. Additionally, the level of

experience varied greatly between participants, which would influence results and cause greater

variance. Those who were less experienced and possessed a lower level of fitness potentially would

see greater improvements in their testing results over the four weeks if additional activities were

being conducted outside of testing, meaning the current study wasn’t standardised

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Paragraph 27. In this study, the strongest correlations with rowing performance were

VO2max (L.min-1), dominant quadriceps torque at 240°.s-1, % VO2max at 4mmol.L-1 [BLac] and

vertical jump (cm) for predicting 2000m ergometer rowing performance. Variables VO2max (L.min-

1), relative oxygen uptake (mL.kg.min-1) and vertical jump were strong predictors of PPO during

the final time trial. From this study, we conclude that movements and exercises mimicking the

movement and aerobic nature of rowing are excellent indicators of rowing performance.

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Table 1. Descriptive statistics from group physiological and performance testing.

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Variable Mean ± SDMaximal Oxygen Uptake (L/min) 2.86 ± 0.76Maximal Oxygen Uptake (mL/kg/min) 38.95 ± 8.59

Peak Power Output for Maximal Oxygen Uptake Test (Watts) 216.76 ± 50.08Peak Power Output for Maximal Oxygen Uptake Test (Watts/kg body mass) 2.97 ± 0.61

Percentage of VO2max corresponding to 4 mmol.L-1 of blood lactate 71.20 ± 14.19

Dominant Quadriceps Torque at 60 degrees per second 192.47 ± 54.31

non-Dominant Quadriceps Torque at 60 degrees per second 182.97 ± 55.88

Dominant Hamstring Torque at 60 degrees per second 108.52 ± 36.50

non-Dominant Hamstring Torque at 60 degrees per second 105.12 ± 35.23

Dominant Quadriceps Torque at 240 degrees per second 101.97 ± 36.03

non-Dominant Quadriceps Torque at 240 degrees per second 101.78 ± 38.07

Dominant Hamstring Torque at 240 degrees per second 67.56 ± 31.42

non-Dominant Hamstring Torque at 240 degrees per second 64.59 ± 27.27

Peak Power (W) for 30 s Wingate Sprint 797.60 ± 200.67

Peak Power (W/kg) for 30 s Wingate Sprint 11.01 ± 2.40

Mean Power (W) for 30 s Wingate Sprint 553.03 ± 136.80

Mean Power (W/kg) for 30 s Wingate Sprint 7.62 ± 1.54

Fatigue Index for 30 s Wingate Sprint 58.59 ± 12.91

Vertical Jump (cm) 50.63 ± 10.745-metre split time for 20 m sprint (seconds) 1.11 ± 0.1120-metre sprint time (seconds) 3.37 ± 0.30

Finishing time in 2000-metre rowing trial two (seconds) 500.98 ± 52.15

Mean Power in 2000-metre rowing trial two (seconds) 186.85 ± 54.82

Table 2. Predicted Regression Equations

Outcome Regression EquationPredicted mean power output = 22.158 + (59.998 x VO2max (L.min-1)) + (1.541 x VJ) + (-2.094 x

VO2max (ml.kg.min-1)Predicted Finishing Time = 723.459 + (-28.215 x VO2max (L.min-1) + (-0.288 x Dom. Quad Torque

240 deg) + (-0.767 x % VO2 max at 4mmol/L lactate) + (-1.231 x VJ)

DEFINITIONS: VO2max (L.min-1) = absolute maximum oxygen consumption during incremental exercise test. VJ = Vertical Jump. VO2max (ml.kg.min-1) = absolute maximum oxygen consumption during incremental exercise test, when each participant’s mass was accounted for. Dom. Quad Torque 240 deg = quadriceps torque for dominant leg, measured at 240O/s on an isokinetic dynamometer.

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FIGURE LEGEND

FIGURE 1 – Correlation of Maximal Oxygen Uptake during incremental exercise test to actual 2000-m rowing finish time. VO2max = Rate of maximal oxygen uptake. L/min = Litres per minute. A-FT = Actual Finishing Time. S = Seconds.

FIGURE 2 – Correlation of Maximal Oxygen Uptake during incremental exercise test to actual 2000-m rowing mean power output. VO2max = Rate of maximal oxygen uptake. L/min = Litres per minute. A-MPO = Actual Mean Power Output. W = Watts

FIGURE 3 – Correlation of peak power reached during the incremental exercise test to the actual 2000-m rowing finish time. PPO = Peak Power Output. W = Watts. A-FT = Actual Finishing Time. S = Seconds.

FIGURE 4 – Correlation of peak power reached during the incremental exercise test to actual 2000-m rowing mean power output. PPO = Peak Power Output. W = Watts. A-MPO = Actual Mean Power Output

FIGURE 5 – Actual Finishing Time of 2000-m ergometer rowing test compared to finishing time predicted by regression equation. A-FT = Actual Finishing Time. P-FT = Predicted Finishing Time. S = Seconds.

FIGURE 6 – Actual power output of 2000-m ergometer rowing test compared to power output predicted by regression equation. A-MPO = Actual Mean Power Output. P-MPO = Predicted Mean Power Output. W = Watts

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FIGURE 1

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FIGURE 2

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FIGURE 3

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FIGURE 4

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FIGURE 5

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FIGURE 6

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