Prediction of 2000-m on-water rowing performance with measures derived from instrumented boats.

PURPOSE: Rowing instrumentation systems provide measures of stroke power, stroke rate, and boat velocity during rowing races, but how well these measures predict race performance has not been reported previously. METHODS: Data were collected per stroke from 45 2000-m races using Peach PowerLine and OptimEye S5 GPS units. The boat classes assessed were nine male singles, eight female singles, three male pairs, and six female pairs. Random effects and residuals from general linear mixed modeling of stroke velocity adjusted for stroke power, stroke rate, and mean headwind provided measures interpreted as technical efficiency, race conditions, and stroke-velocity variability. These measures, along with mean race power, mean stroke rate, and mean headwind were then included in multiple linear regressions to predict race velocity from official race times. Effects were assessed for 2 SD changes in predictors and interpreted using interval hypothesis tests. RESULTS: Effects of mean race power, mean stroke rate, and mean headwind on race velocity ranged from small to extremely large and were mostly decisively substantial. Effects of technical efficiency and race conditions ranged from trivial to extremely large but were generally unclear, while stroke-velocity variability had trivial-small and mostly unclear effects. Prediction error was small to moderate and decisively substantial. Men's pairs lacked sufficient data for analysis. CONCLUSION: On-water rowing race performance can be predicted with mean race values of power, stroke rate, and headwind. Estimates from stroke data are potentially useful predictors but require impractical numbers of boats and races to reduce their uncertainty.

Relationships between measures of boat acceleration and performance in rowing, with and without controlling for stroke rate and power output.

PURPOSE: Boat acceleration profiles provide a valuable feedback tool by reflecting both rower technique and force application. Relationships between measures of boat acceleration and velocity to inform interpretation of boat acceleration profiles in rowing were investigated here. METHODS: Thirteen male singles, nine female singles, eight male pairs, and seven female pairs participated (national and international level, age 18-27 y). Data from each stroke for 74 2000-m races were collected using Peach PowerLine and OptimEye S5 GPS units. General linear mixed modelling established modifying effects on velocity of two within-crew SD of boat acceleration variables for each boat class, without and with adjustment for stroke rate and power, to identify potential performance-enhancement strategies for a given stroke rate and power. Measures of acceleration magnitude at six peaks or dips, and six measures of the rate of change (jerk) between these peaks and dips were analyzed. Results were interpreted using rejection of non-substantial and substantial hypotheses with a smallest substantial change in velocity of 0.3%. RESULTS: Several boat acceleration measures had decisively substantial effects (-2.4-2.5%) before adjustment for stroke rate and power. Most effect magnitudes reduced after adjustment for stroke rate and power, although maximum negative drive acceleration, peak drive acceleration, jerk during the mid-drive phase, and jerk in the late recovery remained decisively substantial (-1.8-1.9%) in some boat classes. CONCLUSION: Greater absolute values of maximum negative drive acceleration and jerk in the late recovery are related to improved performance, likely reflecting delayed rower centre-of-mass negative acceleration in preparation for the catch. Greater absolute values of peak drive acceleration, first peak acceleration, and jerk in the early and mid-drive are also associated with improved performance, likely reflecting propulsive force during the drive. These proposed mechanisms provide potential strategies for performance enhancement additional to increases in stroke rate and power output.

Concurrent Validity of Power From Three On-Water Rowing Instrumentation Systems and a Concept2 Ergometer.

Purpose: Instrumentation systems are increasingly used in rowing to measure training intensity and performance but have not been validated for measures of power. In this study, the concurrent validity of Peach PowerLine (six units), Nielsen-Kellerman EmPower (five units), Weba OarPowerMeter (three units), Concept2 model D ergometer (one unit), and a custom-built reference instrumentation system (Reference System; one unit) were investigated. Methods: Eight female and seven male rowers [age, 21 ± 2.5 years; rowing experience, 7.1 ± 2.6 years, mean ± standard deviation (SD)] performed a 30-s maximal test and a 7 × 4-min incremental test once per week for 5 weeks. Power per stroke was extracted concurrently from the Reference System (via chain force and velocity), the Concept2 itself, Weba (oar shaft-based), and either Peach or EmPower (oarlock-based). Differences from the Reference System in the mean (representing potential error) and the stroke-to-stroke variability (represented by its SD) of power per stroke for each stage and device, and between-unit differences, were estimated using general linear mixed modeling and interpreted using rejection of non-substantial and substantial hypotheses. Results: Potential error in mean power was decisively substantial for all devices (Concept2, -11 to -15%; Peach, -7.9 to -17%; EmPower, -32 to -48%; and Weba, -7.9 to -16%). Between-unit differences (as SD) in mean power lacked statistical precision but were substantial and consistent across stages (Peach, ∼5%; EmPower, ∼7%; and Weba, ∼2%). Most differences from the Reference System in stroke-to-stroke variability of power were possibly or likely trivial or small for Peach (-3.0 to -16%), and likely or decisively substantial for EmPower (9.7-57%), and mostly decisively substantial for Weba (61-139%) and the Concept2 (-28 to 177%). Conclusion: Potential negative error in mean power was evident for all devices and units, particularly EmPower. Stroke-to-stroke variation in power showed a lack of measurement sensitivity (apparent smoothing) that was minor for Peach but larger for the Concept2, whereas EmPower and Weba added random error. Peach is therefore recommended for measurement of mean and stroke power.

Corrigendum: Technical Determinants of On-Water Rowing Performance.

[This corrects the article DOI: 10.3389/fspor.2020.589013.].

Technical Determinants of On-Water Rowing Performance.

Purpose: Research establishing relationships between measures of rowing technique and velocity is limited. In this study, measures of technique and their effect on rowing velocity were investigated. Methods: Ten male singles, eight female singles, three male pairs, and six female pairs participated. Data from each stroke for forty-seven 2,000 m races were collected using Peach PowerLine and OptimEye S5 GPS units. General linear mixed modeling established modifying effects on velocity of two within-crew SD of predictor variables for each boat class, with subsequent adjustment for power, and for power and stroke rate in separate analyses. Twenty-two predictor variables were analyzed, including measures of boat velocity, gate force, and gate angle. Results were interpreted using superiority and inferiority testing with a smallest important change in velocity of 0.3%. Results: Substantial relationships with velocity were found between most variables assessed before adjustment for power, and for power and stroke rate. Effect magnitudes were reduced for most variables after adjustment for power and further reduced after adjustment for stroke rate and power, with precision becoming inadequate in many effects. The greatest modifying effects were found for stroke rate, mean and peak force, and power output before adjustment, and for catch angle after adjustment for stroke rate and power. Substantial between-crew differences in effects were evident for most predictors in some boat classes before adjustment and in some predictors and some boat classes after adjustment for stroke rate and power. Conclusion: The results presented reveal variables associated with improvements in rowing performance and can be used to guide technical analysis and feedback by practitioners. Higher stroke rates and greater catch angles should be targeted to improve rowing performance, and rower force development for the improvement of power output. Relationships between rowing technique and velocity can be crew-dependent and are best assessed on an individual basis for some variables.

Cardiac Parasympathetic and Anaerobic Performance Recovery After High-Intensity Exercise in Rowers.

PURPOSE: To determine the effect of different high-intensity interval-training (IT) sessions on the postexercise recovery response and time course across varying recovery measures. METHODS: A total of 13 highly trained rowers (10 male and 3 female, peak oxygen uptake during a 6-min maximal test 4.9 [0.7] L·min-1) completed 3 IT sessions on a rowing ergometer separated by 7 d. Sessions consisted of 5 × 3.5 min, 4-min rest periods (maximal oxygen uptake [VO2max]); 10 × 30 s, 5-min rest periods (glycolytic); and 5 × 10 min, 4-min rest periods (threshold). Participants were instructed to perform intervals at the highest maintainable pace. Blood lactate and salivary cortisol were measured preexercise and postexercise. Resting heart-rate (HR) variability, post-submaximal-exercise HR variability, submaximal-exercise HR, HR recovery, and modified Wingate peak and mean power were measured preexercise and 1, 10, 24, 34, 48, 58, and 72 h postexercise. Participants resumed training throughout the measurement period. RESULTS: Between-groups short-term response differences (1 h post-IT) across IT sessions were trivial or unclear for all recovery variables. However, post-submaximal-exercise HR variability demonstrated the longest recovery time course (threshold = 37.8 [14.2], glycolytic = 20.2 [11.0], and VO2max = 20.6 [15.2]; mean [h] ± confidence limits). CONCLUSION: Short-term responses to threshold, glycolytic, and VO2max IT in highly trained male and female rowers were similar. Recovery time course was greatest following threshold compared with glycolytic and VO2max-focused training, suggesting a durational influence on recovery time course at HR intensities ≥80% HRmax. As such, this provides valuable information around the programming and sequencing of high-intensity IT for endurance athletes.