A Multilevel Examination of Individual Differences in Rowing Pace: Associations with Gender, Weight Class, and Age

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1 A Multilevel Examination of Individual Differences in Rowing Pace: Associations with Gender, Weight Class, and Age Robert D. Dvorak 1 William E. Schweinle 1 Paul Geoghegan 2 Alison K. Irvine 1 The University of South Dakota 1 Normandeau Associates Inc. 2 Key words: rowing, ergometer, multi-level modeling ABSTRACT Rowing is one of the most challenging sports due to its simultaneous aerobic and anaerobic requirements. There is a considerable amount of research on rowing; however it focuses primarily on elite rowers. The current study examines variation in the rowing ability of non-elite rowers. The analysis tested individual differences that may explain differences in rowing ergometer pace (time/500 m) across varying distances. Results show that age, weight class, and gender affected the speed of a 500 m sprint. Additionally, age and weight class affected differences in rowing pace as distances longer than 500 m were rowed. Both age and weight moderated the effect of distance on rowing pace trajectories. Three important findings are discussed: (1) as distance increases, the advantage shared by heavyweight rowers over lightweight rowers tends to decrease; (2) after controlling for weight and age, gender had no effect on the decrease in pace across successively longer distances; (3) changes in pace decreased with increasing rowing interval distance as age increased. Rowing, whether for sport or fitness, represents one of the most challenging exercises in fitness, because it is associated with both aerobic and anaerobic challenge (Mäestu, Jürimäe, & Jürimäe, 2005). Distances of 500 m or less are generally considered brief sprints and require more anaerobic power than longer distances. Beyond 500 m, it is generally accepted that rowers increasingly rely on aerobic capacity, as energy stores and glycolysis diminish rapidly (Steinacker, 1993). To date, most research on rowing focuses on physiological aspects associated with training, various techniques acquired through training, and the overall ergonomics of the equipment (Mayberry, 2002; Nolte, 2005). However, less attention has been directed at the descriptive nature of rowing performance based on characteristics that remain relatively constant or are unaffected by training (e.g., gender, age, weight class, etc.). Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 1

2 Research in anthropometry has shown that elite rowers differ from average rowers in several key physical features (Kerr et al., 2007). Elite rowers tend to be heavier, taller, have longer forearms and thighs, greater bicep girth, lower skin-fold measurements (i.e., proportional body fat), and smaller hip to shoulder ratio (Ackland et al., 2001; Ackland, 2002; Kerr et al., 2007). Thus, as can be expected, larger, leaner, more physically fit individuals tend to make better rowers. Yet, size may also have drawbacks for rowers. Higher weight in rowers is typically indicative of higher muscle mass (Jürimäe, & Jürimäe, 2002), which may be helpful over short distances, e.g. 500 m or 1000 m, but may actually hinder a rower at very long distances due to the increased aerobic load that greater muscle mass includes. To date, however, this hypothesis has not been examined. Gender differences in rowing performance have also been researched, however to a lesser extent than weight. In general, findings suggest that the same basic anthropometric properties that produce a successful male rower also contribute to success among female rowers (Stefani, 2006). However, there has been some evidence that contradicts this claim. For example, Hahn (1990) reported that there were no significant anthropometric differences among top ranked and bottom ranked elite female rowers. While, in general, male rowers typically row faster than female rowers (Yoshiga & Higuchi, 2003; Stefani, 2006), this may be attributable to faster starts due to considerably higher proportions of muscle tissue in male rowers. Although individual times for a 500 m sprint (a distance associated with anaerobic capacity) seem to vary by gender, it is unknown whether changes in pace (time/500 m or 500 m split) across increasing distances rowed (pace trajectory) vary between genders. For example, men may have a faster 500 m time, but show a steeper decline in pace due to higher levels of muscle bulk. Alternatively, men may have a faster 500 m sprint and be able to maintain an increasingly stronger pace for the same reason (though this would seem somewhat counterintuitive). In contrast to both of these hypotheses, men may row a faster 500 m sprint while changes in pace trajectory do not vary by gender. This would suggest that aerobic capacity, rather than gender or muscle mass, is more important. Finally, there is very little evidence of the effect of age on rowing performance. This is likely due to two factors: (1) most of the rowing literature focuses on elite rowers who tend to fall in a very specific age range, and (2) age is often expressed in terms of training years. However, age may be a very important factor, particularly for the fitness or recreational rower (in comparison to the elite or competitive rower). It is well documented that both anaerobic strength and aerobic fitness decline with age (Fleg et al., 2005; Spirduso, Anton, & Tanaka, 2004). Thus, one may expect age to impact sprint (500 m) rowing speed as well as rowing speed over longer distances (e.g. 5,000 m or 10,000 m); however, to the best of our knowledge this hypothesis has not been tested. The current study sought to examine specific hypotheses with regard to average rowing pace across and changes in rowing pace trajectory from a 500 m sprint to increasingly longer rowing distances and based on individual differences in gender, age, and weight class. We proposed that as the distance for a given rowing interval increases, pace would decrease and that there would be a significant positive association between time for a 500 m sprint and change in rowing pace across progressively longer distances. Additionally, we posited that several individual difference characteristics would moderate the association between distance and change in pace. Specifically, age and gender would be positively associated with 500 m sprint times, and weight class would be negatively associated with 500 m sprint times. Lastly, we proposed that gender would not be associated with differences in the slope of rowing pace (time / Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 2

3 500 m) across longer distances. Instead, age would be positively associated with rowing pace, whereas weight class would be negatively associated with changes in pace as interval distances increase. METHOD Participants There were 2,411 total rowers included in the analysis. The analysis sample ranged in age from (M = 41.61, SD =11.53). The sample was comprised of 2,026 males (84.03%) and 385 females (15.97%). Measures 500 m Split time. The dependent variable of interest was the rowers pace (i.e. 500 m splits) for each of the distances of interest. Rowing pace is typically presented as time/500 m. Distance units. This research involved a dependent-measures design, such that there were multiple pace scores for each participant. Specifically, we looked at rowers average pace for each of several distance intervals: 500 m, 1 km, 2 km, 5 km, and 10 km. These distances are standard rowing test distances and they, very effectively, capture the anaerobic and aerobic range of rowing effort. Weight class. Because there is a significant performance advantage for taller indoor rowers, there are two weight classes in rowing, heavy and light. Lightweight female rowers must weigh less than 62.5 kg (135 lbs.), and male lightweight rowers weigh less than 75 kg (165 lbs.) Procedure Data were obtained from Concept 2, manufacturer of the most widely used indoor rowing machine, i.e., ergometer. The Concept 2 ergometer is also used world-wide to test and select national team athletes and is used in indoor rowing races. Although there are differences between ergometer rowing and actual rowing on water (e.g., the skill required), ergometer scores correlate highly with actual on-the-water performance (Lamb, 1989). Permission to use this preexisting, publicly available data was obtained from the University of South Dakota Institutional Research Board and Concept 2. The initial database consisted of self-report times (in seconds) for numerous distances. There were 26,291 participants with 65,535 entries. To clean the dataset and achieve interpretable results, we reduced the analysis sample to individuals with 3 or more entries, across the distances of interest (500 m, 1 km, 2 km, 5 km, and 10 km). We also eliminated individuals under 14 years of age and over 65 years of age. Because several rowers entered multiple times for a given distance, we used the fastest time each rower reported for each distance of interest. Finally, we examined outliers on the split times for all distances and eliminated those individuals whose split times fell outside the 99 th percentiles for each distance. This left us with a final analysis sample of 2,411 rowers with a cumulative 9,975 observations, i.e., 4.14 observations per rower on average. After cleaning the data, we used descriptive analyses, univariate analyses and multilevel analyses. The primary analysis utilized multilevel modeling with robust standard errors conducted in HLM 6.0 (Raudenbush, Bryk, Cheong, & Congdon, 2000). In simple terms, multilevel modeling allowed us to estimate the slope or quantitative increase of a group s pace (Time / 500 m) as the rowing intervals increase in length. We could then look at the differences in slopes between groups. It is the slopes that are of particular interest here. Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 3

4 In more complex terms, multilevel modeling allows for a single level 1 regression equation to predict the intercept and slope for a group on a specific criterion. It then allows researchers to examine whether there is significant variance in the intercept and slope and if individual differences between subjects can account for this variance. The individual difference variables (e.g., age, gender, weight class) are then added as level 2 equations predicting the level 1 regression coefficients for slope and intercept. Multilevel analysis is a relatively new and powerful statistical technique that allowed us to compare rowers performances at a given distance and also to compare changes, i.e. the slopes, of rowers pace across various distance intervals. Thus, it allows us to examine possible differences between anaerobically but not aerobically fit rowers and aerobically but not anaerobically fit rowers. Multilevel analyses let us look for such differences between these rowers across gender, weight class, age, etc. Results Descriptive Statistics Seventy-nine percent were classified as being in the heavy weight class. Split times (pace) in seconds ranged from 75.90s/500m s/500m (M = , SD = 15.51). There were 9,975 observations out of a possible 12,055 (82.75%). The number of observations per participant ranged from 3 to 5 (M = 4.14, SD = 0.74). There were 1,925 observations from women (15.97%) and 10,130 observations from men (84.03%). Univariate and Bivariate analyses There was no difference in number of observations in age by gender, t(2409) = 0.26, p =.79. There were differences in gender distribution by weight class, with men being overrepresented in the heavyweight class, χ 2 (1) = 62.47, p <.001. Additionally, men reported more observations than women across the distances, χ 2 (3) = 9.77, p =.02. The bivariate correlations are reported in Table 1. Gender (1 = female, 0 = male) was negatively associated with weight class and positively associated with pace. Weight class (1 = heavy, 0 = light) was positively associated with age and negatively associated with pace. In other words the men rowed faster than the women, and heavyweight rowers rowed faster than lightweight rowers. Finally, as distance increased, rowers pace tended to decrease. All of these are intuitively appropriate findings. Primary analysis The following is a more detailed description of the multilevel analysis we conducted. These level 1 and corresponding level 2 equations were specified to test our hypotheses: Level 1: Split it = π 0i + π 1i (Distance Unit it ) + e it Level 2: π 0i = B 00 + B 01 (Gender) +B 02 (Age) + B 03 (Weight Class) + u 0i π 1i = B 10 + B 11 (Gender) +B 12 (Age) + B 13 (Weight Class) +u 1i In the above level 1 equation, Distance Unit is the actual distance in meters divided by 500 to produce units of 500 meters for person i at Distance Unit t. Split is person i s 500 m split time in seconds over Distance Unit t; π 0i is the person i s mean predicted initial value, or their time for a single 500 m; π 1i is the effect on 500 m split time at one increase of Distance Unit t for person i; e it is the residual variance component for person i at Distance Unit t. In other words, a Distance Unit of 1 is equal to a single 500m sprint, while a distance unit of 20 would equate to 10k. The level 2 equations represent cross-level interactions of the individual difference variables on the slope and intercept of the level 1 equation. Thus, significant cross- Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 4

5 level interactions of individual difference variables on the intercept would suggest variability in initial status based on gender, age, and/or weight class. Significant cross-level interactions of individual difference variables on the slope would suggest that changes in 500 m split time across Distance Units of 500 m vary as a function of the individual differences. For example, an aerobically fit individual may show a lower slope as their pace (split time) across distances may be more constant. In contrast, an anaerobically fit individual may have a steep slope, suggesting that their primary strength relies on sprints over short distances. Similarly, intercepts may vary considerably between aerobic and anaerobic individuals. For example, individuals who are aerobically fit may have initial starting values (e.g., higher intercepts) as pace is more constant. In contrast, anaerobically fit individuals may have lower intercepts (e.g., faster values at the lowest level) due to faster times during 1 Distance Unit (i.e., 500 m) sprints. We estimated the model coefficients and examined variability in the level 1 slope and intercept coefficients. There was significant variance in both the intercept (χ 2 [2407] = , p <.001) and the slope (χ 2 [2407] = , p <.001) coefficients, thus these parameters were allowed to vary randomly. It is often helpful to present multilevel models in different ways. One way to present them is in the equations above. Another way is to present the models as a structural equation model as presented in Figure 1. The average 500 m sprint time was seconds. The average person increased their split time by 0.98 seconds per unit (500 m) of interval distance. So, for example, if a person were to row 5,000 meters, their total time would be, on average, 10 times their time for a 500 m sprint plus 98 (i.e..98 x 10) additional seconds. Thus, a person s pace slows at a rate of approximately 1 second for each 500 m as the length of the interval increases. Finally, there was a significant association between the slope and intercept (r =.28, p <.001). Thus, as participants 500 m sprint time (anaerobic ability) increased, so does the slope across distance (i.e., individuals who row a slower 500 m sprint, will also be progressively slower than faster 500 m sprinters as the distance they row gets longer). There were significant effects of all three individual difference variables on the 500 m sprint. Figure 2 shows that women s time for a 500 m sprint was, on average, seconds slower than men s. However, there were no significant cross-level gender effects (b = 0.05, p =.09), as can be observed in Figure 2. For each year increase in age there was a slight increase in 500 m sprint time of 0.24 seconds; thus, the younger a person, the faster their time for a 500 m sprint. Finally, there was a significant effect of weight class on 500 m sprint time, with heavyweight rowers having a faster time for a 500 m piece by approximately 1.69 seconds. In addition to the effects on the intercept, there were also significant cross-level interactions of age and weight class on the slope. Accordingly we further probed the simple slopes of 500 m split times (pace) on Distance Units for the individual difference moderators (Aiken & West, 1991). Figure 3 shows that at -1 SD on the age moderator there was a strong effect of 500 m split time on Distance (b = 1.05, p <.001). However, at +1 SD age the effect was attenuated (b = 0.91, p <.001). In other words, the advantage of younger rowers begins to decline as distance increases. The slope of 500 m split times on Distance Units was potentiated by weight class. Figure 3 shows that split times increased across distance for those in the heavy weight class (b = 1.02, p <.001) relative to those in the light weight class (b = 0.98, p <.001); meaning, that while individuals in the heavy weight class tended to row an initial 500 m segment faster than lightweight rowers, the heavyweight rowers tended to have slower 500 m splits as distance Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 5

6 increased beyond 500 m. These results ultimately suggest that as distance increases, the advantage of heavyweight rowers over lightweight rowers tends to decrease. Note that we conducted several additional analyses in which we tested the fit between the predictors and the criterion as exponential relationships, e.g. log transform and quadratic relationship. These models did not significantly improve the fit of the prediction models over the linear models that are presented in this paper. DISCUSSION The current study examined variation in rowing ability of typical, non-elite indoor rowers from a large sample of self-report data. All of the initial hypotheses were supported. Previous research has suggested that gender and weight may exert influences on rowing pace; however, the majority of this research has been conducted with elite rowers, leading to difficulties generalization to average or recreational rowers. Furthermore, the effect of age on rowing has not been previously examined due to the fact that elite rowers tend to fall in a narrow age range, though there are, as always, exceptions. The current study fills this gap by testing individual differences that may explain variation in change in rowing pace across distance for the typical rower. Results suggest that age, weight class, and gender affected anaerobic capacity as measured by the 500 m sprint. Additionally, multilevel analysis revealed that age and weight class affected change in rowing splits across interval distances that were progressively longer than 500 m, i.e., 1 km, 2 km, 5 km, and 10 km. Each of these findings is discussed in turn below. The current findings support previous research showing that aerobic abilities decline with age (Spirduso, Anton, Tanaka, 2004). Age was associated with a slower 500 m sprint time, suggesting less anaerobic strength. Interestingly, as age increased, longer distance exerted less of an effect. This may be a result of two separate processes. First, younger rowers may simply have a faster sprint time, and as such they may have faster initial times which then takes a toll on their pace at longer distances due to fatigue (i.e., a stronger decline in pace across distance). Alternatively, perhaps older rowers are simply more experienced, and thus have slower initial sprint times accompanied by less overall variance in pace across distances. Future research should seek to further evaluate this phenomenon. In support of previous findings, gender exerted a strong effect on 500 m sprint times, with females having a slower 500 m sprint time than males. This is representative of the difference in anaerobic strength between males and females. In contrast, females and males did not differ in their change in rowing pace trajectories (i.e., pace slope) across progressively longer interval distances beyond 500 m. This suggests that although females tended to have less anaerobic strength than males, there is no significant difference in the decay of the pace trajectory between males and females. This seems to contradict research showing fairly robust differences in aerobic capacity by gender (see Weiss, Spina, Holloszy, & Ehsani, 2006). Future research should seek to better delineate this relationship. Weight class exerted two distinct effects. Consistent with hypothesis, heavier rowers, row significantly faster 500 m sprints. This supports research showing that heavier rowers have more muscle mass (Jürimäe, & Jürimäe, 2002), which translates into greater anaerobic strength and, consequently, faster 500 m sprints. However, weight class moderated the effect of distance on pace trajectory, such that the change in pace trajectory (i.e., the increase in split times as distance increased) was attenuated for lighter rowers. These results ultimately suggest that as distance Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 6

7 increases, there is a diminished advantage to the physical strength associated with the heavyweight rower (i.e. as distance increases, the advantage of increased strength at the beginning of a race becomes less important and may actually be a hindrance at great distances). This has apparently been, at least anecdotally, recognized by elite rowers, since long distance rowers are considerably lighter than shorter distance rowers (cf. Kerr et al., 2007). Finally, there are several limitations of this study that should be noted. First, this data was obtained by self-report, and as such may not always represent accurate measures. In fact, there were numerous inconsistencies in the original dataset. The data preparation was guided by both analytic techniques (e.g., outlier identification, leverage, residual distributions, etc.) as well as heuristic knowledge of rowing by the authors, and as such we hope that the dataset is an accurate representation of associations among typical rowers. Additionally, there were considerably fewer women than men, and the women were underrepresented in both the heavyweight class (although this might be reasonably expected) and in the number of reported observations. This may have contributed to our inconsistent findings with regard to a lack of gender differences in pace trajectory across distance units. Finally, these results were based on typical rowers rather than elite rowers. Although we see this as a strength of these findings, it may not help inform the training of elite athletes. It may be interesting to build on these results by examining pooled performance data from elite athletes across numerous races. An important aspect of the present study is that it describes a relatively new, elegant analytic framework for examining such data: multilevel modeling or growth-curve analysis. In conclusion, this study examined individual differences in rowing a 500 m sprint and rowing pace trajectory across distances greater than 500 m. The results suggest that variability in rowing sprint (500 m) pace and rowing pace trajectories across various distances can be accounted for by several, relatively constant, individual difference variables. Three important findings emerged. First, heavyweight rowers tend to perform better at shorter distances, but as distance increases, this advantage tends to decrease. Second, after controlling for weight and age, gender had no effect on the decrease in speed as the distance of a rowed interval increases, although, in general men tend to have faster average paces at any given distance. Finally, changes in pace decrease with distance as age increases. These findings highlight the importance of considering individual difference factors when comparing rowing performance between individuals. REFERENCES Ackland, T. R., Kerr, D., Hume, P., Norton, K., Ridge, B., Clark, S., Broad, E., Ross, W., (2001). Anthropometric normative data for Olympic rowers and paddlers. Australian Conference of Science and Medicine in Sport, October Ackland, T. R., (2002). Optimizing body size and equipment set-up for success in rowing and kayaking competition. Medicine & Science in Sports & Exercise, 34, 5, 108. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage. Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 7

8 Fleg, J. L., Morrell, C. h., Bos, A. G., Brant, L. J., Talbot, L. A., Wright, J. G., Lakatta, E. G., Accelerated longitudinal decline of aerobic capacity in healthy older adults, American Heart Association Inc., 112, Hahn A. (1990). Identification and selection of talent in Australian rowing. Excel, 6, Jürimäe J. & Jürimäe T. A, (2002). Comparison of selected anthropometric, metabolic and hormone parameters in lightweight and open- class rowers. Biology of Sport, 19, Kerr, D. A., Ross, W. D., Norton, K., Hume, P., Kagawa, M., (2007) Olympic lightweight and open-class rowers possess distinctive physical and proportionately characteristics. Journal of Sports Sciences, 25, Lamb, D. H. (1989). A kinematic comparison of ergometer and on-water rowing. American Journal of Sports Medicine, 17, Mäestu, Jürimäe, & Jürimäe (2005). Monitoring of performance and training of rowing. Sports Medicine, 35, Mayberry, K., (2002), Rowing. Stackpole Books. Nolte, V., (2005). Rowing Faster: training, rigging, technique, racing. Human Kinetics. Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., & Congdon, R. (2000). HLM: Hierarchical linear and nonlinear modeling (Version 5) [Computer software]. Chicago, IL: Scientific Software International. Spirduso, W., Anton, M., & Tanaka, H., (2004). Age related declines in anaerobic muscular performance: weightlifting and powerlifting. Medicine and Science in Sports and Exercise, 36, Stefani, R. T., (2006). The relative power output and relative lean body mass of world and Olympic male and female champions with implications for gender equity. Journal of Sports Medicine, 24, Steinacker, J. M. (1993). Physiological aspects of rowing. International Journal of Sports Medicine, 1, Weiss, E. P., Spina, R. J., Holloszy, J. O., & Ehsani1, A. A. (2006). Gender differences in the decline in aerobic capacity and its physiological determinants during the later decades of life. Journal of Applied Physiology, 101, Yoshiga, C. C., Higuchi, M., (2002). Rowing performance of female and male rowers. Scandinavian Journal of Medicine & Science in Sports, 13, 5, Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 8

9 Table 1. Correlation of table of all variables in analysis Gender -.07 * * 2. Weight Class * * 3. Age * 4. Distance Units * m Split --- Note. * p <.001. Gender coded 1 = female, 0 = male. Weight Class coded 0 = light, 1 = heavy. Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 9

10 Figure Captions Figure 1. Multilevel Model of Distance Predicting 500 m Split Time at Level 1 with Age, Gender and Weight Class at Level 2. Figure 2. Trajectory of Women s and Men s 500 m split times across distance. Figure 3. Simple slopes of 500 m split time on distance at +/- 1 SD age. Figure 4. Simple slopes of 500 m split time on distance in the heavy and light weight classes. Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 10

11 Figure 1. Multilevel Model of Distance Predicting 500 m Split Time at Level 1 with Age, Gender and Weight Class at Level Age Gender Weight Class (0.05) Distance m Split Time Note. Rectangles represent the Level 1 model with random intercept and slope represented by circles. Squares represent Level 2 Variables. Triangles represent Level 1 and Level 2 intercepts. Dashed lines represent significant cross-level interactions of Level 2 variables on the random slope. Coefficient in parenthesis is non-significant (p =.09), all other coefficients significant at p <.05. Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 11

12 500 m Rowing Pace in Seconds Figure 2. Trajectory of Women s and Men s 500 m split times across distance Women Men m 10,000 m Distance Rowed Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 12

13 500 m Rowing Pace in Seconds Figure 3. Simple slopes of 500 m split time on distance at +/- 1 SD age SD Age Mean Age +1 SD Age m 10,000 m Distance Rowed Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 13

14 500 m Rowing Pace in Seconds Figure 4. Simple slopes of 500 m split time on distance in the heavy and lightweight classes Light weight Heavy weight 500 m 10,000 m Distance Rowed Volume 1 Number 1 June 2013 Journal of Athletic Medicine Page 14