Various metabolic, biomechanical and physical parameters are used in literature to predict oxygen consumption through regression methods. These prediction models can be the means to overcome, the use of much costly and time-consuming ways to measure calories and other associated variables of performance. Indoor rowing is a fascinating endurance and strength training exercise type. Various indoor ergometers are used now a days.
The present study aimed to estimate the oxygen consumption or calories consumed through the prediction models developed by various parameters like distance covered (m), movement speed (m/s), ergometer resistance force (kg), handle & feet forces (N). The secondary aim was to calculate and validate the regression model for speed, distance and power for newly developed ergometer by comparing parameters from Concept II ergometer.
This study recruited ten healthy participants and basketball athletes. The study used cortex Metalyzer for VO2 recording, polar sensor for heart rate, motion capture to measure the movement velocity and distance, handle and feet load cells for power output. The study predicted VO2 and distance prediction models for newly developed ergometer through linear multiple regression method with combination of various variables in statistical package for social sciences (SPSS) software version 21.
The better valid and reliable predictor for distance estimation are from eccentric (R=0.81, adjusted R2=0.63, SEE=0.41 & P-value=0.000) and constant (R=0.86, adjusted R2=0.73, SEE=0.58 & P-value=0.000) resistance modes with parameters like velocity, handle force and resistance force. The eccentric mode predicts better power output from pace than other modes. The results of this study showed that distance can predict the VO2 with R=0.71, adjusted R2=0.51, standard error of estimate (SEE)=2.55 & P-value=0.000 while including all parameters improved prediction model with R=0.87, adjusted R2=0.75, SEE=0.18 & P-value=0.000.
The movement velocity has good reliability for distance prediction. The prediction of power from pace is also reliable based on the findings of this study. The results of this study concluded that movement velocity and distance travelled are good predictor of oxygen consumption along with resistance force or total force.

Various metabolic, biomechanical and physical parameters are used in literature to predict oxygen consumption through regression methods. These prediction models can be the means to overcome, the use of much costly and time-consuming ways to measure calories and other associated variables of performance. Indoor rowing is a fascinating endurance and strength training exercise type. Various indoor ergometers are used now a days.
The present study aimed to estimate the oxygen consumption or calories consumed through the prediction models developed by various parameters like distance covered (m), movement speed (m/s), ergometer resistance force (kg), handle & feet forces (N). The secondary aim was to calculate and validate the regression model for speed, distance and power for newly developed ergometer by comparing parameters from Concept II ergometer.
This study recruited ten healthy participants and basketball athletes. The study used cortex Metalyzer for VO2 recording, polar sensor for heart rate, motion capture to measure the movement velocity and distance, handle and feet load cells for power output. The study predicted VO2 and distance prediction models for newly developed ergometer through linear multiple regression method with combination of various variables in statistical package for social sciences (SPSS) software version 21.
The better valid and reliable predictor for distance estimation are from eccentric (R=0.81, adjusted R2=0.63, SEE=0.41 & P-value=0.000) and constant (R=0.86, adjusted R2=0.73, SEE=0.58 & P-value=0.000) resistance modes with parameters like velocity, handle force and resistance force. The eccentric mode predicts better power output from pace than other modes. The results of this study showed that distance can predict the VO2 with R=0.71, adjusted R2=0.51, standard error of estimate (SEE)=2.55 & P-value=0.000 while including all parameters improved prediction model with R=0.87, adjusted R2=0.75, SEE=0.18 & P-value=0.000.
The movement velocity has good reliability for distance prediction. The prediction of power from pace is also reliable based on the findings of this study. The results of this study concluded that movement velocity and distance travelled are good predictor of oxygen consumption along with resistance force or total force.

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