Free Access
Issue
Mov Sport Sci/Sci Mot
Number 104, 2019
Masters athletes: Age is just a number / Les athlètes masters : l’âge n’est qu’un nombre
Page(s) 37 - 44
DOI https://doi.org/10.1051/sm/2019016
Published online 04 July 2019

© ACAPS, 2019

1 Introduction

Over the past decades, the half marathon has become one of the most popular events for both elite and recreational distance runners (Aschmann et al., 2013; Hanley, 2015). For example, the most popular half marathon in Italy (Roma-Ostia) sees each year more than 11,000 participants, 87% of whom are recreational or non-professionals and in the past 10 years, master participants increased by 62% (http://www.tds-live.com).

Independently of the athletic level, in order to achieve best results, athletes need to be able to distribute their effort correctly. Much research has concentrated on the optimal pacing strategy adopted by athletes in events of different durations, and for long lasting events, it has been suggested that even pacing strategies may be optimal (Foster, Schrager, Snyder, & Thompson, 1994). Pacing in athletic performance describes how an athlete controls and distributes work and energy throughout an exercise task (Abbiss & Laursen, 2005, 2008), by controlling the intensity of effort early in competition in order not to develop such profound fatigue that success (or target time) will not be attained (Foster et al., 2014). More specifically, it allows the athlete to ensure the best possible performance outcome (Thompson, 2014).

Pacing is a learned process and a variety of elements including prior competitive experience and race simulations performed in training contribute to developing a sense of pace optimal for performance (Micklewright, Papadopoulou, Swart, & Noakes, 2010). Recent research showed that both the distance knowledge and prior experience are fundamental for a correct pacing strategy and could explain some of the pacing differences observed during a 100 km in both faster and slower runners (Lambert, Dugas, Kirkman, Mokone, & Waldeck, 2004).

From a practical perspective, pre-planning a race strategy may be a useful way to distribute effort and optimize performance for that event (Edwards & Polman 2012). Athletes train specifically to set a certain time in competition therefore a target finish time can be the ultimate goal on how to set the pace during the race. Pacing ability is in fact a skill and would benefit from repetition as an individual becomes better able to regulate effort to evenly pace or achieve desired splits (Green, Sapp, Pritchett & Bishop, 2010).

Although for endurance competitions athletes will benefit from an even or a negative pacing strategy (Tucker, Lambert & Noakes, 2006), a recent paper showed that nearly all athletes who competed in the IAAF world half marathon championship showed a parabolic shaped (reverse-J) pacing profile (Hanley, 2015). It therefore seems that while elite athletes often deviate from an optimal distribution of effort for tactical reasons (Hanley, 2013), master and amateur athletes, who mainly race to achieve a specific target time, should benefit from an even or a negative pacing profile. Two recent studies found that, independently of age, the best performers of each age category demonstrate a more even pacing strategy during the New York City marathon (Breen et al., 2018) or a 100 km (Renfree et al., 2016) compared to lower level runners. Especially for these athletes, pacing accuracy may be a critical component in achieving a desired performance irrespective of ideal effort allocation (Green et al., 2010).

Moreover, rating of perceived exertion (RPE) is considered the main mediator utilized by athletes to modify pacing (Roelands, De Koning, Foster, Hettinga & Meeusen, 2013). It has been shown that RPE increases in a linear manner as a function of the proportion of an event completed (Foster et al., 2009; Joseph et al., 2008).

In particular, athletes change their pace to match their RPE in a specific moment integrating this information with the time remaining to complete the event (Roelands et al., 2013). To study this particular aspect, de Koning et al. (2011) integrated the velocity and RPE data of different laboratory experiments of different time and/or distance durations and calculated the hazard score (HS) by multiplying the momentary RPE by the time remaining to complete the event. They found that there was a significant likelihood that the change in pace would be positive when the HS was less than 1.5, negative (deceleration) when the HS was greater than 3 and unchanged with in between values (De Koning et al., 2011). Therefore, starting faster than predicted would bring to a corresponding higher RPE and a higher risk of decreasing pace. Recently, Micklewright et al. (2015) reported that athletes with a higher perception of risk adopted a more conservative initial strategy during a time trial, but no difference in RPE or HS was observed.

However, the precise pacing strategies that ensure the best possible performance outcome under the variety of existing athletic competitions are not clear and the HS has been calculated (and utilized to predict changes in pace) mainly in laboratory settings.

Therefore, the aim of the study was to analyze the ability of recreational master runners to accurately maintain the desired pace, according to their pre-race target time. We hypothesized that athletes who ran slower than their predicted time would adopt a positive pacing strategy, starting at or faster than predicted pace and thereafter decreasing speed because of high RPE and HS early in the event.

2 Methods

2.1 Experimental approach to the problem

To test the hypothesis that athletes who ran slower than their predicted time would adopt a positive pacing strategy, starting at or faster than predicted pace and thereafter decreasing speed because of high RPE and HS early in the event, we recruited experienced master/recreational athletes in 4 different editions of a competitive half marathon specifically organized by the University. We asked athletes to write the time they thought they would be able to finish the half marathon. During the 21.1 km race, RPE and speed were collected every 7 km and thereafter the HS and pacing were calculated.

2.2 Subjects

One hundred and seventy trained experienced master athletes (males, n = 135, females, n = 35) were recruited from local clubs (Table 1) to participate at the “Run For Science” race, a competitive half marathon specifically organized by the University. The study was approved by the local ethical committee. Subjects were informed of the benefits and risks of the investigation prior to signing an institutionally approved informed consent document to participate in the study.

Table 1

Anthropometric characteristics of all participants and divided by groups.

2.3 Procedures

After signing a written informed consent, athletes arrived on the local track behind the University, the morning of the experiment and were asked to declare the time they thought they were able to finish the half marathon based on their training. Thereafter, they were required to run a half marathon as fast as possible and split times and RPE were collected every 7 km throughout the event. The event consisted in a competitive half marathon certified by the Italian Athletic Federation (FIDAL), on a flat course around the city of Verona. Data were collected over 4 different editions of “Run For Science”, a half marathon specifically organized each year by different Universities in Europe in order to investigate different aspects of endurance performance and ensure a high ecological validity because of the competitive nature of the event. Athletes were informed about the different scopes of the research groups involved, including the present one investigating pacing ability, and were therefore asked to run at their best.

Athletes were in fact instructed to self-regulate their pace throughout the entire 21 km as they would normally do in a competition. The half marathon started from the 400 m track for 3 laps of 7 km each, each lap finishing inside the track. Since we wanted to avoid packing and/or following pace makers that could interfere with our research question, runners started in a time trial mode (every 2 minutes), and had to rely on their ability to pace.

For data analysis, athletes were divided in three groups (slower [SL], target [TAR] and faster [FA]) according to the difference between the predicted (OBJ) and real race time. SL (n = 52) included athletes whose real time was slower than OBJ time, TAR (n = 57) included athletes who ran within 2 minutes from OBJ and FA (n = 61) included athletes whose real time was faster compared to OBJ.

2.4 RPE

To monitor the perceived exertion (RPE), a 2-m high Borg CR-100 scale (Borg & Borg, 2001) was positioned inside the track in a visible position and athletes reported their RPE every 7 km entering the track. This numeric scale goes from 0 (nothing at all) to 100 (maximal).

2.5 Pacing and hazard score

In order to analyze pacing and the hazard score time splits were collected every 7 km and compared to the average speed maintained throughout the race.

The hazard score (HS) was calculated by multiplying the momentary RPE by the remaining fraction of the race (de Koning et al., 2011).

2.6 Statistical analysis

The statistical package IBM SPSS version 20 (IBM, Chicago, IL, USA), was used for the analysis. All data are expressed as means ± SD. The Shapiro-Wilk test was applied, before the analysis, to test the normal distribution of the data, and for all the variables no outliers or non-normal distribution were detected.

For all variables (i.e. average running speed, pacing, RPE and hazard score) separate mixed ANOVAs with repeated measures were used considering the splits (i.e, 7, 14, 21 km) as within factor and the group (i.e.SL, TAR, FA) as between factor.

Before the analysis the Levene’s test for homogeneity of variance and the Mauchly’s sphericity test were also performed to verify the assumptions underlying this statistical test.

When significant interactions were observed, follow-up tests were conducted by splitting the sample in the two subgroups (i.e. slower, target and faster) and running separate repeated measures ANOVAs to explore the different effect of split on the two groups. Multiple comparisons were performed using post-hoc Fisher’s protected least significant difference (LSD) test with Bonferroni correction was used.

The significance level for all comparisons was set at p ≤ 0.05. In addition, effect size (ES) was calculated for all variables as partial eta-squared (η2 p). Partial eta-squared values below 0.01, between 0.01 and 0.06, between 0.06 and 0.14, and above 0.14 were considered to have trivial, small, medium, and large effect sizes, respectively (Cohen, 1988).

3 Results

3.1 Average running speed

Average running speed showed a significant main effect for split (F2;334 = 77.39; p < 0.001; ES = 0.317) and for the interaction effect for splits by groups (F2;334 = 11.13; p < 0.001; ES = 0.180).

Post-hoc showed an overall average running speed decrease between the three splits (p < 0.001). Follow-up analysis presented a significant decrease between the three splits in the SL group (p < 0.001), conversely the TAR and the FA group decreased average speed only between the first two splits (p < 0.001) (Fig. 1).

Additional analysis showed that in the first split, there were no differences between the three groups while there were differences in the second (p = 0.033) and in the third split (p = 0.001). In particular, in the second split (km 14) SL group had significantly lower running speeds than TAR (p = 0.035) while in the third split (km 21), SL group showed significantly lower running speeds from TAR (p = 0.001) and FA (p = 0.003).

thumbnail Fig. 1

A. Faster, target and slower runners average running speed in the different splits [* difference between km 7 and km 14 (all groups), p < 0.001; # difference between km 14 and 21 (all groups), p < 0.001; & Slower group difference between km 14 and km 21, p < 0.001; § Difference from slower in the split (p = 0.001)]. B. Runners’ average RPE. C. Runners’ average hazard score.

3.2 Pacing

Pacing (% difference compared to average running speed) showed a significant main effect for split (F2;334 = 36.79; p < 0.001; ES  = 0.181) and for the interaction effect for splits by groups (F2;334 = 20.93; p < 0.001; ES = 0.200).

Post-hoc showed that pace decreased compared to average running speed between the three splits (p < 0.001). Follow-up analysis showed that this decrease was significant only in the SL group (p < 0.001), while the TAR group decreased speed from average speed only between the two first splits (p < 0.001). There were no differences between the splits in the FA group (Fig. 2A).

Additional analysis showed that in the first (p = 0.001) and in the third (p < 0.001) splits, there were differences between SL and the other two groups while no difference was found in the second (km 14).

thumbnail Fig. 2

Pacing (A: percent difference from overall running speed; B: percent difference from overall OBJ speed). * SL group difference between km 7 and km 14 p < 0.001; & SL group difference between km 14 and km 21, p < 0.001; # TAR group difference between km 7 and km 14, p < 0.001; § Difference from SL (p = 0.001).

3.3 Pacing related to predicted average speed (OBJ)

Pacing OBJ showed a significant main effect for split (F2;334 = 34.39; p < 0.001; ES = 0.171) and for the interaction effect for splits by groups (F2;334 = 18.84; p < 0.001; ES = 0.184).

Post-hoc showed that overall pacing OBJ decreased between the three splits (p < 0.001). Follow-up analysis showed a significant decrease between the three splits only in the SL group (p < 0.001), contrariwise the TAR group decreased pacing OBJ only between the first two splits (p < 0.006). There were no differences between the splits in the FA group (Fig. 2B).

Additional analysis showed that pacing OBJ was different between the three groups in all the splits (p < 0.001).

3.4 Rate of perceived exertion

RPE presented a significant main effect for split (F2;332 = 341.42; p < 0.001; ES = 0.673) while no interaction effect for splits by groups was found.

Overall, RPE increased in the three splits (p < 0.001) with no differences between groups (Fig. 1).

3.5 Hazard score

Hazard score showed a significant main effect for split (F1;164 = 314.42; p < 0.001; ES = 0.657) while no interaction effect for splits by groups was found (Fig. 1).

4 Discussion

The purpose of the present study was to evaluate pacing abilities of recreational athletes with the hypothesis that athletes who ran slower than their predicted time would adopt a positive pacing strategy (decrease in speed throughout the event) with higher RPE early in the event. Different studies have tried to understand the pacing profile of faster and slower runners during official events both at the elite level (Hanley, 2013, 2015, 2016; Renfree & Gibson, 2013) or at the amateur level (Hubble & Zhao, 2016; Lambert et al., 2004; Renfree et al., 2016; Nikolaidis et al., 2018; Breen et al., 2018). In these studies, slower and faster runners were divided based on their finish time or classification (medallists or non-medallists) and final ranking. Our novel approach instead was to divide individuals based on their ability to run their half marathon as predicted (TAR), faster than predicted (FA) or slower than predicted (SL).

As hypothesized, SL group, despite starting at OBJ pace, decreased speed throughout the event, while TAR started faster than average speed, slowed down and thereafter maintained an even pace while the FA runners started at higher speeds than OBJ and maintained an even pace throughout the event. Contrary to our hypothesis, RPE and the HS were similar between the 3 groups and were not able to discriminate between faster and slower groups (according to their predicted time) or predict changes in pace.

4.1 Pacing in endurance events

The ability to accurately self-pace is an important feature for successfully completing an endurance race (Abbiss & Laursen, 2008). Generally, the study of pacing has been conducted in controlled laboratory settings where workload was imposed and behavioural processes are altered (Smits, Pepping & Hettinga, 2014). In real races, athletes decide how to best approach the race based on transient sensations of wellbeing, responding with small variations in speed or power output (Edwards & Polman, 2012). The ability to pace is not the same as ability to race (Hanley & Hettinga, 2018) therefore we decided to eliminate any tactical conditioning of pacing by having runners start separated by 2 minutes. It has been in fact shown that the behaviour of an opponent can improve performance (Williams et al., 2014) or alter pacing strategy both in laboratory settings (Konings, Schoenmakers, Walker & Hettinga, 2016) and during the half marathon world championships (Hanley, 2015) because athletes tend to pack following the lead pace especially during early stages of competition before slowing to more sustainable speeds (Hanley, 2015). Specifically, Konings et al. (2016) reported how pacing behaviour is altered depending on the pacing profile of a virtual opponent. In this case in fact, the decision to start fast or slow was based on the virtual opponent’s strategy emphasizing that the interaction with environment is a crucial determinant of pacing. On the other hand, Bath et al. (2012) investigated the effects of a “pacer” running before, behind or aside an athlete during a 5-km time trial. They found no difference in time trial performance, pacing or RPE suggesting that an athlete’s pacing strategy might be robust or follow a template, however, most athletes found it easier to race with the pacer.

In most solo competitions such as time trials, fluctuations of pace are driven by sensations occurring during the event while if racing against competitors, changes in pace may be forced by opponents (Edwards & Polman 2012). Therefore, given the different responses of athletes to opponents or pacers, we chose to study pacing in an observational study during an official event. We adopted a design that eliminated any true race component that could have confounded our results, (opponents, pacers) and we did not want athletes to race one against each other but specifically asked individuals to run as fast as possible.

In our study, the FA group started much faster than predicted pace and was able to maintain the selected speed throughout the 21 km while the TAR group slowed down after 7 km and thereafter maintained OBJ pace. The SL group started at OBJ pace but was forced to decrease pace throughout the race adopting a positive strategy.

4.2 RPE and the hazard score (HS) during endurance events

Foster et al. (2014) divided athletes according to 3000 m race time and asked them to stay with the pack as long as possible. During the race, runners who could stay with the pack (at pre-set pace) showed a normal increase in RPE throughout the event while for those who dropped back from the pack, RPE increase was accelerated (meaning higher midpoint RPE) (Foster et al., 2014). RPE is considered in fact one of the main mediators utilized by athletes to modify pacing. In particular, athletes change their pace to match their RPE in a specific moment integrating this information with the time remaining to complete the event (Roelands et al., 2013). This is reflected in a higher hazard score. Moreover, it has been recently shown that imposing a protocol (or a pace as in the above mentioned study) exercise results much harder than conducting exactly the same activity when self-paced (Lander, Butterly & Edwards, 2009) while in solo competitions (as ours), fluctuations of pace may be driven mainly by temporary sensations of comfort/discomfort (Foster et al., 2014). Although the authors acknowledge that the HS is more reliable in explaining group data rather that individual behaviour, we failed to find a difference between groups both in HS and in RPE.

The HS is representative of the riskiness of adopting a fast pace early in competition, corresponding to a high RPE at the beginning or midway through the event (Micklewright et al., 2015). It represents the continuous comparison that athletes perform in each moment of the race, comparing how they feel and how they expected to feel at that moment of the race (de Koning et al., 2011). Therefore, hypothetically, an athlete performing a fast start will have higher RPE values throughout the race compared to even pacers (de Koning et al., 2011) resulting for the fast starters in a higher “hazard” and an obligation to reduce speed throughout the remaining part of the race. Therefore, the HS indicates the likelihood of a change in pace.

de Koning et al. (2011) tested this hypothesis taking laboratory data from different experiments lasting from 4–60 minutes calculating the HS against pacing and computed that if the HS was less than 1.5, there would be an acceleration, and if greater than 3 there would be a decrease in speed. In a recent study (De Ioannon et al., 2015), RPE and HS were calculated during a 78-km solo swim and the high RPE midway through the event predicted the observed change in pace. Another interesting recent study (Micklewright et al., 2015) evaluated how pacing, RPE and the HS are influenced by individual perception of risk (risk perception) and individual’s propensity to take risk (risk taking). Athletes with a higher perception of risk adopted a more conservative initial pacing strategy and the athletes with a higher propensity of taking risks adopted a faster start. However, there was no difference in RPE or HS between risk groups (therefore pacers) despite a difference in pace adopted. It can be speculated that our FA group was formed by low risk perceivers, but this was not measured. As already pointed out by Micklewright et al. (2015), no differences in RPE or HS observed between groups indicated that for these athletes there was no “riskiness” in the initial adopted pace, producing the same RPE. It must be pointed out in fact, that SL group did not start too fast but adopted a speed that corresponded to predicted pace and might have momentarily felt comfortable, and thereafter had to decrease pace in order to maintain values of RPE that would allow them to finish the race.

Similarly, Hubble and Zhao (2016) evaluated gender differences in marathon pacing, comparing times retrieved form the Chevron Houston Marathon to the registration times. Overall, they found that men overestimate their abilities compared to women who are more conservative and consider this overconfidence as a reflection of different risk attitudes between genders. We found no gender difference in over or under estimation of half marathon target time. However, compared to the study of Hubble, where the authors took into consideration registration marathon times which most of the time are a prediction 3 or 4 months ahead of the event, we asked athletes to state their predicted target time the day before the race, which might be a more realistic estimate.

Although the benefits of an even pacing strategy are evident (Abbiss & Laursen, 2008), most athletes start quickly because they follow the leader group regardless their ability. Hanley (2015) evaluated the pacing profiles of the IAAF half marathon world championships and independently of level, (medalists or non medalists) the pacing profile was a parabolic shaped (reverse-J) profile. This occurred because all athletes packed together for the first 5-km running relatively quickly. Recently, pacing strategies have been shown to be influenced by a number of situational factors, such as course topography, knowledge of the task duration/distance remaining, memory of past similar experiences motivation and mood (Abbiss, Peiffer, Meeusen, & Skorski, 2015). Practicing pacing in training and competition increases pacing abilities of athletes. Green et al. (2010) saw that pacing accuracy in performing specific splits is different between recreational and collegiate athletes probably this difference driven also by experience. Recently, Breen et al. (2018) analysed pacing of the participants in the New York city marathon and reported that, independently of age, the best performers of each age category demonstrate a more even pacing strategy compared to lower level runners. Similar results were reported in the analysis of longer races (100 km) where the best performers of each age category started with a slower relative speed and finished with a higher relative speed compared to slower runners in the same age category (Renfree et al., 2016). The athletes of our study were amateur/master athletes with an overall average racing time of 1 h 48 min. We could speculate that the FA group were athletes with more experience in running half marathons and they were in fact better (6 minutes faster than the SL) and that a certain experience is required to confront momentary RPE with remaining part of the race in order to adopt an optimal pacing strategy.

4.3 Practical applications

The results of the present study demonstrate different pacing strategies adopted by athletes who raced faster than predicted time or slower, despite no differences in RPE or HS. Specifically, recreational runners, who start training and racing in older age compared to younger and more expert athletes, could benefit from more sessions specifically designed to mimic race pace and learn to train and race by feeling more than by pre-selected time.

Author contributions

Conceptualization: C.T.; methodology: C.T., K.S., F.S.; formal analysis: C.M.; investigation: K.S., C.T., L.F., D.R. and R.B.; data curation: C.M.; writing–original draft preparation: M.F.P.; writing–review and editing: M.F.P.; supervision: M.F.P., A.L.T. and F.S.; project administration: F.S.

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Cite this article as: Piacentini MF, Reda D, Minganti C, Baldassarre R, Tarperi C, Festa L, Skroce K, Schena F, & La Torre A (2019) Pacing profiles of master athletes according to their predicted finishing time. Mov Sport Sci/Sci Mot, 104, 37–44

All Tables

Table 1

Anthropometric characteristics of all participants and divided by groups.

All Figures

thumbnail Fig. 1

A. Faster, target and slower runners average running speed in the different splits [* difference between km 7 and km 14 (all groups), p < 0.001; # difference between km 14 and 21 (all groups), p < 0.001; & Slower group difference between km 14 and km 21, p < 0.001; § Difference from slower in the split (p = 0.001)]. B. Runners’ average RPE. C. Runners’ average hazard score.

In the text
thumbnail Fig. 2

Pacing (A: percent difference from overall running speed; B: percent difference from overall OBJ speed). * SL group difference between km 7 and km 14 p < 0.001; & SL group difference between km 14 and km 21, p < 0.001; # TAR group difference between km 7 and km 14, p < 0.001; § Difference from SL (p = 0.001).

In the text

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