The document summarizes a study that investigated whether various fitness tests could predict 2000m rowing performance in tertiary students. 81 exercise science students completed tests including VO2 max testing, Wingate cycling tests, strength testing, vertical jumps, and sprint times. They then performed two 2000m rowing time trials. Results showed height, weight, VO2 max, and power output strongly correlated with rowing performance. Stepwise regression predicted rowing times within 4.84%. The study supports using non-rowing tests to predict rowing performance in inexperienced populations.

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PHYSIOLOGICAL PREDICTORS OF 2000M ROWING
PERFORMANCE IN TERTIARY STUDENTS
Ben King: 212194153
Brett Cranage: 213089031
Pete Greenway: 212260431
Nathan Reid: 212171909
ABSTRACT
KING, W,B., CRANAGE, E,B,. GREENWAY, W,P,. REID, T,N. Physiological Predictors of
2000m Rowing Performance in Tertiary Students. Purpose: Rowing popularity alongside
equipment scarcity often results in predicting potential performance through non-rowing testing.
This has become an integral part of talent identification. This study investigated a variety of testing
methods to identify any potential correlations between this testing and actual 2000m rowing
perfomance. Method: A cohort of exercise science students (N=81, Age=21.94±3.36,
Height=175cm±9.8, Weight=73.8kg±13.12) completed anaerobic/aerobic testing before attempting
two 2000m rowing efforts in consecutive weeks. The study commenced with participants
completing VO2 max test, with further testing including 30 second Wingate cycling, isokinetic
hamstring and quadriceps strength/power, maximal vertical jump and 5/20 meter sprint times. These
results were analysed through bivariate/stepwise regression analysis to predict 2000m rowing
performance. Results- Anthropometric characteristics such as height and mass showed moderate
negative relationships in regards to 2000m rowing performance (r=-0.628, p<0.00 and r=-0.471 and
p<0.00 respectively). The testing results of Maximal Oxygen Uptake (L.Min)(r=-0.699) and Peak
Power Output for the Maximal Oxygen Uptake Test (W)(r=-0.680) showed the greatest correlation,
while percentage VO2 max corresponding to 4mmol of blood lactate (r=0.152) showed the weakest.
Further stepwise linear regression analysis was used in order to predict rowing performance, and a
4.84% difference between predicted and actual times were examined. Conclusions – The results
obtained in this study support the ideology that 2000m rowing can be predicted from non-rowing
testing. Further testing is required to see this testing applied to alternate age groups, fitness and
rowing abilities.
KEY WORDS- Rowing, Predictors, Tertiary Students, Aerobic, Aanaerobic, Linear Regression.

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INTRODUCTION
Rowing is a physically taxing sport which demands involvement of almost all muscles as well as
the contribution of both the anaerobic and aerobic energy systems 2, 3, 5, 7, 18 Over the 2000m
distance, individuals require both aerobic endurance and muscular strength due to the 6-8 minute
event duration. 2, 7, 16 Several studies have investigated physiological determinants and
anthropometric parameters of rowing performance to predict 2000m rowing ergometer performance
in experienced rowers. 1, 2, 8 VO2max and anaerobic threshold have been regularly used as accurate
predictors of 2000-metre rowing performance in elite rowers. 1-3, 6, 12, 14Another study discovered a
strong correlation between height (r = -0.815), lean mass (r = - 0.723) and VO2 at the lactate
threshold (r= - 0.765) in 12 competitive female rowers. 12 Other studies also demonstrate that
specific anthropometric parameters, namely height and body weight affect rowing performance 2, 18
Elite level rowing performance has been strongly correlated with maximal strength, greater body
mass and fat free body mass in comparison with inexperienced rowers. 5 When comparing elite
rowers to amateur’s, elite rowers display a greater VO2 max, accompanied by larger body mass and
a greater VCO2 max at a blood lactate concentration of 4 mmol. 3, 8, 10, 13 Large fat-free mass and
high VO2 max results in greater rowing performance, emphasizing the demand of aerobic
metabolism and activation of muscles within the body. 12, 18 One study suggested that critical
velocity may be a more appropriate predictor of rowing performance for amateur rowers. 7 The
correlation between VO2max and rowing performance is openly accepted in elite and experienced
rowers. 11, 17 Previous literature has conducted testing of physiological variables mainly among elite
experienced rowers as opposed to inexperienced rowers. 3, 5-9, 15 This limits the transferability to a
general population where training status and overall fitness is markedly lower.
Our study conducted specific fitness tests among tertiary university students to predict rowing
performance which to our knowledge has not been investigated previously. While this research was
conducted on generally healthy exercise science students with little testing variability, future
research that better reflects the general population could produce greater sourcing of potential elite
athletes. We also examined the influence of anthropometric parameters and physiological variables
on 2000-metre rowing performance among a group that is potentially not applicable and suitable to
the general population. The aim of the current study was to uncover additional predictors of 2000-
metre rowing ergometer performance by conducting various fitness tests not used in previous
literature. Consistent with previous work, our study found there to be no single predictor which can
be utilised to determine results in a 2000-metre rowing ergometer performance. 7 We found a
moderately strong correlation between leg extension testing and rowing performance which is
consistent with previous work. 15 By conducting unorthodox fitness tests among tertiary university

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students that has not been previously used, our study attempted to fill gaps in previous literature that
predominantly focused on elite and experienced rowers. In doing so our study has provided further
scope into additional predictors of 2000-metre rowing performance which could further assist in
talent identification.
Method
This study investigated the ability of aerobic/anaerobic testing to predict 2000m rowing
performance, over a four week period a cohort of exercise science students (N=81, Age=21.94 ±
3.36, Height = 175cm ± 9.8, Weight =73.8kg ± 13.12) were studied. Comparisons were made using
both bivariate and stepwise regression analysis. Following completion of two tailed t-testing, a
significance level of p<0.05 was set, all analysis was completed through the SPSS program (SPSS
version 23, IMB, USA).
Participants gave prior consent to all testing methods and the study was approved by the Deakin
ethics board.
Study protocol – This study focused on the ability of aerobic/anaerobic measures of power, capacity
and speed to predict all-out 2000m rowing performance. The following tests were completed by all
participants in successive weeks;
Week 1
• Lactate threshold and VO2 max testing
Participants completed a 3 minute warm up on a cycling ergometer (828E, ergomedic testing bike,
Monark , Sweden) before testing. Expired air was recorded through the Lab Chart Pro 2 software
(Lab Pro 2, Adinstuments, Australia) whilst participants exhaled through a mouth piece into an
oxygen analyser. A nose clip was used to ensure all expired air was recorded. Heart rate (T31 coded
transmitter, Polar,USA) and lactate readings (Lactate Pro 2 , Arkray, Japan) were also recorded.
Testing commenced with participants cycling at a 50 Watt output, witht intensity increasing by 25
Watts every three minutes until exhaustion. In the last 30 seconds of each incremental intensity,
heart rate was recorded along with blood lactate from a blood sample drawn from the earlobe
ensuring minimal athlete disturbance. A final measure was taken at the tests completion.
A period of three minute active recovery followed. The recorded data was then analysed to
determine the percentage of maximal oxygen uptake equivalent to 4mmol.L of blood lactate.

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Week 2- Strength, Speed and Power Testing
• Isokinetic quadriceps and hamstring strength and power.
Participants undertook a three minute cycling warm up before hamstring and quadriceps power
(240⁰.s-1) and strength (60⁰.s-1) were measured for dominant and non-dominant limbs on an
Isokinetic dynamometer (Isokinetic dynamometer, Humac, USA). Each measurement required three
maximal attempts with peak torque recorded for all measurements.
• 30 second Wingate maximal sprint cycling
In order to determine anaerobic power, athletes completed one thirty second Wingate sprint test on
a cycle ergometer (Cycling ergometer 894E, Monark, Sweden), with resistance set at 70 grams per
kilogram body mass for males and 65 grams per kilogram body mass for females. Participants were
instructed to cycle at 50 rpm before the commencement of the 30 second sprint with peak power,
mean power and power drop percentage measured and recorded through the use of the Monark
Anaerobic test software (Monark Anerobic test software 3.0, Monark, Sweden).
• Maximal vertical jump
Participants were required to stand under a measuring apparatus (Yardstick, Swift Performance
Equipment, Wacol, QLD) before completing 3 maximal counter-movement jumps, with a short
break following each effort. The highest score was recorded for each participant.
• Five-Meter and 20-meter sprint time.
A 20 meter sprint test was undertaken. Data was collected through the smart speed timing gates
software (Smart Speed timing gates, Fusion Sports, USA). Each individual completed three 20m
meter sprints with the 5 meter split time also recorded, the fastest individual times trial time over
both distances used for analysis.
Week 3 and 4.
Performance testing – 2000-meter rowing ergometer time-trails
Participants were required to complete a 2000m rowing effort on a rowing ergometer in two
successive weeks (Model D rowing ergometer, Concept2, Australia). In each week, participants
were allowed an identical three minute warm up on a cycle ergometer.
Once seated on the rowing machine, feet were secured and participant comfort was ensured.
Throughout both weeks, the participant was able to see elapsed time as well as distance remaining,
mean power and overall time was recorded. A period of active rest followed the test.

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Results
Anthropometry
The anthropometric characteristics of the subjects are displayed in table 1. Height (174.99±9.791)
was found to have a moderate, negative correlation with 2000m rowing time (r= -0.626, p≤0.00).
This was a similar finding to mass (73.829±13.1229), which had a moderate, negative correlation
(r= -0.471, p≤0.00).
Vo2 Max Ergometer Cycle Test
Absolute oxygen uptake (2.8618±.76395) was strongly, negatively correlated with 2000m rowing
time, shown in Table 2 (r= -0.699, p≤0.00), whereas oxygen uptake relative to body mass
(mL.kg.min) showed a moderate, negative correlation (r= -0.480, p≤0.00). Those who were able to
take in a higher absolute volume were able to perform better. This was reflected in the power output
measured during the test, as absolute power output produced a strong, negative correlation (r=-
0.680, p≤0.00), whereas power output relative to body mass produced a weak, negative correlation
(r=-0.332, p≤0.00).
20m Max Sprint Test
20m sprint time (3.3676±.29500), shown in Table 2, had a moderate, positive correlation (r=0.527,
p≤0.00) with finishing time, with participants achieving quicker times in the sprint conversely
demonstrating increased 2000m rowing time. This was the same with 5m sprint time
(1.1115± .10508), a split taken from the 20m test (r=.398, p≤0.00), despite being a weaker
correlation.
Max Vertical Leap Test
Those participants with a higher vertical jump (50.62±10.738) generally produced a lower finishing
2000m row time, as shown in the moderate negative correlation (r-0.500, p≤0.00).
Cybex Dominant and Non dominant leg Strength
At 60˚/second, quadriceps strength, on both dominant (r=-0.494. p≤0.00, 192.47±54.310 ˚/second)
and non-dominant (r=-0.516, p≤0.00, 182.97±55.877 ˚/second) legs, were shown to have a
moderate, negative correlation. Hamstring strength, on both the dominant (r=-0.579, p≤0.00) and
non-dominant (r=-0.585, p≤0.00) leg showed very similar findings. At 240 ˚/second, quadriceps
(dominant- r=-0.559, p≤0.00, non-dominant r=-0.585) were negatively correlated and were
measured much lower than at 60˚/second (dominant- 101.97±36.034 ˚/second, non-dominant-
101.78±38.066 ˚/second). Hamstring strength was also much lower at 240 ˚/second (dominant-

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67.56±31.422, non-dominant-64.59±27.269 ˚/second) yet showed almost identical correlation
values as at 60˚ (Dominant- r= -0.573, p≤0.00, non-dominant r=-0.585, p≤0.00)
30 sec Wingate sprint bike test
30 sec wingate sprint power output had a negative, moderate correlation for both absolute mean
(553.03±136.803) (r=-.0589, p≤0.00) and peak (797.60±200.671) (r=-0.543, p≤0.00). When
factoring in weight, both relative mean (7.616±1.5426) (r-0.466, p≤0.00) and peak power
(11.013±2.4021) (r=-0.373, p≤0.00) showed slightly weaker, negative correlations. Wingate fatigue
index (58.590±12.9101) was shown to be weakly, positively correlated (r=0.244, r=0.041).
Statistical Analyses
Anthoprometic values were analysed, in mean and SD, to give an indication into the group as a
whole, which is shown in Table 2. Further analysis, shown in Table 3, presented correlation
coefficients and P-values for the variables tested as predictors of a 2000m rowing ergometer time-
trial time, therefore displaying the strength and direction of the relationship between variables and
the actual rowing time. The relationships that demonstrated the strongest relationship with the 2000-
metre rowing ergometer finishing time was Maximal Oxygen Uptake (L/min) (r =-0.699) and Peak
Power Output for Maximal Oxygen Uptake Test (W) (r =-0.680), while Percentage of VO2max
corresponding to 4 mmol/L of Blood Lactate (r=0.152) and Fatigue Index for 30s Wingate Sprint
(r=0.246) displayed the lowest correlation. A stepwise multiple regression analysis was able to
produce findings that lead to the creation of a linear prediction equation that was used to predict
2000m ergometer rowing time of participants (n=65). While 81 participants participated in the
study, 16 were removed from statistical analysis, having not completed at least one of the prediction
tests used to develop the linear prediction equation. The equation resulted in an average difference
of 4.86 ± 4.84% between the time-trial finishing time, and the predicted time using the equation.
Regression analysis results also demonstrate that, as shown in Table 3, 65.2% of the variance of the
2000m rowing ergometer time-trial finishing time can be explained by variance in the four included
variables.
Discussion
The research topic focused on the rowing performance of participants in a standardised ergometer
test of 2000m. The purpose was to find physiological predictors to performance, as to undertake a
battery of tests, run a statistical analysis and be able to predict 2000m rowing performance, without
having to undertake the actual test. As previously discussed, Maximal Oxygen Uptake (L/min) (r =-
0.699) and Peak Power Output for Maximal Oxygen Uptake Test (W) (r =-0.680) were found to
show the strongest relationship with 2000m rowing ergometer performance. These variables should

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theoretically show such a strong correlation, as they analyse the ability to sustain aerobic
performance, a critical attribute required for elite performance in a 2000m row time-trial3-4.
The review of multiple studies found that many had a similar aim as the current study, being to
predict 2000m rowing ergometer performance, and while using a slightly different testing battery,
found results that are comparable to those found throughout our analysis. One study produced
findings that accurately reflect results in the current study, with Maximal Oxygen Uptake (L/min)
strongly correlating (r=0.85) with the 2000m rowing time-trial3. Another study used participants of
slightly higher maximal oxygen uptake capacities (3.18± 0.35 vs. 2.86± 0.76) and found a
moderately correlated relationship to 2000m rowing performance (r=-0.502)12. With these studies,
in conjunction with the current study, all suggesting at least a moderate strength relationship
between VO2max and 2000m ergometere performance, absolute maximal oxygen uptake can be
assumed to have an effect on the finishing time of a 2000m-ergometer time-trial.
Using a stepwise method of regression analysis, the four variables used to develop the prediction
equation, Maximal Oxygen Uptake (L/min), Dominant Quadricep Torque at 240 degrees per second,
Percentage of VO2max corresponding to 4 mmol/L of Blood Lactate and Vertical Jump,
demonstrated a strong relationship with the 2000m rowing ergometer time-trial time (r=0.81,
p≤0.05). Other variables were found to have a stronger relationship with the 2000m rowing
ergometer time-trial result independently, such as Peak Power Output for Maximal Oxygen Uptake
test (W) (r=-0.680), but if the addition of a variable to the regression analysis does not increase its
precision, then that variable was not included in the equation. As the Peak Power Output for
Maximal Oxygen Uptake test (W) variable is likely to represent the same statistics as the Maximal
Oxygen Uptake (L/min) variable, and would therefore not have added much to the precision of the
equation, it was excluded.
The linear prediction equation, developed following the undertaking of regression analysis, was
able to, on average, provide a prediction that was 24.7 seconds from the actual finishing time
(503.1±52.06, n=65), equating to a difference of 4.91%. The prediction equation generally more
accurately predicted time for individuals with a lower 2000m rowing ergometer time when
compared to those who finished with a higher time, possibly suggesting that the prediction equation
may be more useful for high-level and elite athletes, rather than the general population. While this
may have some relevance with talent identification for people with a similar profile to those tested
in this study, talent identification generally targets children of a young age so that training and
subsequent physiological adaptations can occur as they mature into their adult bodies throughout
puberty. This study’s use of participants aged primarily between 20-25 years old would therefore

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not accurately provide a prediction of 2000 metre rowing ergometer performance of a younger age.
Further studies targeting a younger sample of participants would allow for greater understanding of
how this prediction tool could be effectively utilised to discover potentially elite-level rowers at a
young age.
Despite results that suggest the study can be largely applicable to various settings, there are a
number of limitations to the study that must be considered. Firstly, all of the participants used in the
study were considered fit, and all were undertaking studies in a sporting field that provided greater
knowledge to health outcomes. Also, the battery of participants consisted of very little variation in
age, with only 6 participants (7.4%) outside of the age range of 20-25 years. With both of these in
mind, it is hard to generalise the results to a wider population beyond 20-25 years of age, who are
undertaking studies in the sporting field, therefore severely limiting the applicability of the study in
the form of talent identification, as it generally targets people of a much younger age. During the
undertaking of the tests used to predict 2000m rowing performance, participants completed tests in
no particular order. This limits the results as one test may have affected the results for a subsequent
test for one participant, more than it may have for another. Finally, not all participants were able to
perform all tests, or were not able to obtain statistics from particular tests, meaning that they had to
be removed from the statistical analysis, as well as errors in the consistency of collecting, recording
and entering data into the database. This meant that the analysis was performed on a smaller sample
size, potentially decreasing the accuracy of both the correlation and regression analysis.
While the study was largely successful in answering the questions intended, some questions were
still left unanswered.
As it exists, there is limited research on the transferability of ergometer tests to competitive rowing,
as there are factors such as technique, paddle length, boat position, direction and environmental
factors that all significantly impact performance. With the potential benefit of effective predictors
being the heightened ability to practice talent identification away from the natural setting of rowing,
further research should be directed towards answering questions relating to the application of
predictors to a real sporting setting.

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References
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Sport, 23, 107-114
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Tables and Figures
Variable Mean ± SD Range
Age (Years) 21.94 ± 3.35 19-43
Height (cm) 173.99 ± 73.82 153-199
Body Mass (kg) 73.82 ± 13.12 49-113
Table 1- Anthropometric Characteristics of the Tertiary Students who undertook the study (n=81)

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Table 2- Physiological and performance characteristics of tertiary students undertaking study (n-81)
Variable N Mean± Std. Deviation Range
Anthropometry
Age 81 21.94±3.355 19-43
Height (cm) 81 174.99±9.791 153-199
Mass (kg) 81 73.829±13.1229 49.0-113.0
Aerobic Endurance
Maximal OxygenUptake(L/min) 76 2.8618±.76395 1.24-4.87
Maximal OxygenUptake(mL/kg/min) 76 38.945±8.5928 20.7-58.9
Peak Power Output forMaximal Oxygen UptakeTest (Watts) 74 216.76±50.078 125-350
Peak Power Output forMaximal Oxygen UptakeTest (Watts per kilogrambody mass)
74 2.9681±.60929 1.48-5.00
Percentage ofVO2maxcorrespondingto4 mmol/L ofbloodlactate 73 71.202±14.1871 29.5-99.4
Dynamic Leg Strength
Dominant Quadricep Torque at 60degrees per second 80 192.47±54.310 82-377
non-Dominant QuadricepTorque at 60 degrees persecond 80 182.97±55.877 84-386
Dominant HamstringTorque at 60 degrees persecond 80 108.52±36.499 38-206
non-Dominant HamstringTorque at 60degrees per second 80 105.18±35.225 49-216
Dominant Quadricep Torque at 240degrees per second 80 101.97±36.034 23-200
non-Dominant QuadricepTorque at 240degrees persecond 80 101.78±38.066 20-240
Dominant HamstringTorque at 240degrees persecond 80 67.56±31.422 16-214
non-Dominant HamstringTorque at 240degrees per second 80 64.59±27.269 14-137
SprintPerformance
Peak Power (W) for 30s Wingate Sprint 79 797.60±200.671 427-1347
Peak Power (W/kg) for30 s WingateSprint 79 11.013±2.4021 7.1-19.2
Mean Power (W)for30 s Wingate Sprint 79 553.03±136.803 299-843
Mean Power (W/kg) for 30s Wingate Sprint 79 7.616±1.5426 4.6-11.7
Fatigue Index for 30s Wingate Sprint 76 58.590±12.9101 31.7-99.0
Leg Power
Vertical Jump 80 50.62±10.738 21-76
5-metre split time for 20m sprint 79 1.1115± .10508 .91-1.41
20-metre sprint time 79 3.3676±.29500 2.89-4.10
Overall Performance
Finishingtime in 2000-metre rowingtrial two 74 500.98±52.151 413-640
Mean Power in 2000-metre rowingtrial two 74 186.85±54.820 86-318

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 = Denotes significance
Table 3- Pearsons correlation co-efficients [r] for each variable compared with 2000m rowing time,
statistical significance set at P=0.0
Variables r P
Anthropometry
Height (cm)
Mass (kg)
Aerobic Endurance
Maximal Oxygen Uptake (L/min)
Maximal Oxygen Uptake (mL/kg/min)
Peak Power Output for Maximal Oxygen Uptake Test
(Watts)
Peak Power Output for Maximal Oxygen Uptake Test (Watts
per kilogram body mass)
Percentage of VO2max corresponding to 4 mmol/L of blood
lactate
Dynamic Leg Strength (60°/sec)
Dominant Quadricep Torque
Non-Dominant Quadricep Torque
Dominant Hamstring Torque
Non-Dominant Hamstring Torque
Dynamic Leg Strength (240°/sec)
Dominant Quadricep Torque
Non-Dominant Quadricep Torque
Dominant Hamstring Torque
Non-Dominant Hamstring Torque
Sprint Performance
Peak Power (W) for 30s Wingate Sprint
Peak Power (W/kg) for 30s Wingate Sprint
Mean Power (W) for 30s Wingate Sprint
Mean Power (W/kg) for 30s Wingate Sprint
Fatigue Index for 30s Wingate Sprint
Leg Power
Vertical Jump
5-metre split time for 20m sprint
20-metre sprint
-0.626
-.0471
-0.699
-0.480
-0.680
-0.332
-0.152
-0.494
-0.516
-0.579
-0.585
-0.559
-0.585
-0.573
-0.585
-0.543
-0.373
-0.589
-0.466
0.246
-0.500
0.398
0.527
0.000*
0.000*
0.000*
0.000*
0.000*
0.006*
0.225
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.041*
0.000*
0.001*
0.000*