Many competitive endurance sports are based upon athletes performing a given distance in the fastest time possible. While there have been studies examining physiological and environmental factors that influence finishing time, few studies have focused on the impact of pacing strategy, which is considered an important performance factor. Specific pacing strategies have been shown to benefit different race durations ranging from 800 m to ultraendurance events (1,30). Since the top finishers in elite races finish within seconds of each other, it is important to closely consider factors affecting finishing time.
Athletes will use different pacing strategies in competition depending on the distance covered. For the purpose of this paper, distances will be defined by the following, commonly accepted ranges: short duration/sprint event (<30 seconds), middle distance event (1.5-2 minutes), prolonged/endurance event (>2 minutes but <4 hours), ultraendurance event (>4 hours) (1). An athlete might use a pacing strategy termed “negative race pace.” A performance is defined as having negative pacing when the average speed of the athlete gradually increases throughout the duration of the event. Negative refers to the fact that split times with this strategy continue to decrease as the race continues. An “all-out” pacing strategy is when an athlete spends the majority of the race accelerating and eventually reaches peak speed and gradually but slightly slows until the finish. An athlete utilizes a positive pacing strategy when his or her speed gradually decreases over the duration of the event. Lastly, even pacing occurs when an athlete performs at a constant speed throughout (1).
In endurance events, it has been widely demonstrated that an even pacing strategy is the most effective in both cycling (2,13,26) and running (10,16). Previous studies have indicated that even pacing produces the fastest times (10,13,16,26). When Padilla et al. (26) examined cyclists on a 1-hour world record performance attempt, cyclists maintained a pace within an average range of 2 km·hr-1 from their average pace. Foster et al. (13) determined that during a 2-km bike race the overall finishing difference between a slow start and an even start averaged 7.2 seconds or a 4.3% difference. In the world of running, such as during the Olympic 1.5 km, this would translate to the difference between first place and last place.
While a relatively constant pace has been shown to be effective, just how little variability there is within that pace can be a concern. Billat et al. (6) demonstrated that while runners completed a 10 km run to exhaustion, a greater physiological strain was demonstrated during a constant paced run, as opposed to when the runners were allowed to freely pace themselves. Allowing the runners to freely pace themselves resulted in slight variations in pace and a better performance, however it may be important to note that this study only included three subjects. In another study by Cottin et al. (10) world record performances ranging from the 1.5 km to the 10 km were analyzed. It was determined that the range of coefficients of variation in velocity was 1%-5%. This demonstrates that a perfectly constant pace may not be completely efficient and therefore is not necessary to result in world record performances. Physiologically speaking, it is unclear as to why a perfectly constant pace may not be optimal but straying too far from this is detrimental. However, few studies have examined this point. Other factors including terrain, wind, or competition could potentially influence pacing.
In further analyzing variations in pace, Gosztyla et al.(15) performed a study with 5 km runners starting at different speeds (3% faster, 6% faster, even pace, 3% slower, or 6% slower than average pace). These athletes were able to start from 3%-6% faster than their average 5 km pace without performance decrements. Some studies have further supported a slightly varied pace (4), while others have demonstrated decreases in performance from such pacing strategies (16).
One last option that has shown to be effective for distance runners is a negative pacing strategy. Studies by both Noakes (24) and Robinson (29) demonstrate that when completing a race between 1200 m and 1600 m, athletes were more successful when using a negative pacing strategy while deviating from this resulted in slower finishing times.
Accumulating evidence supports endurance runners utilizing either a constant or possibly a negative pacing strategy (6,15,24,29). There is also the possibility that a negative pace strategy becomes more effective as the event decreases in length (30). However, the impact that uncontrollable factors, such as the environment, might have on pacing ability must also be considered. Martin et al. (19) discusses the evident decrease in pace during the 10-km run at the World University Games in Kobe, Japan in 1985. It was 32°C dry bulb with 73% relative humidity that day. Given ideal conditions running at the ideal pace would not be a problem, however in such a stressful environment this pace would have led to intolerable heat accumulation. Therefore, pacing strategies were forced to change and consequently the overall pace slowed. There are many other examples of slow race finishes due to nonideal environmental conditions (12,21). However, we know of no studies that have examined the effect of hydration status on the ability of runners to properly pace themselves.
It has been shown that dehydration negatively affects performance and increases core body temperature, possibly by reaching a “limiting core body temperature” and causing performance decrements (5,28,31). Performance decrements are seen as early as 2% dehydration and are reflected in a 3%-6% decrease in running velocity (5). This has been mirrored in ratings of perceived exertion (RPE) values. As dehydration increases RPE values increase and are significantly greater than the RPE values reported in a hydrated state (17). This increased RPE along with greater core temperatures associated at a given intensity could play a part in the chosen pace and the variability of that pace throughout competition. Another proposed mechanism discussed later is the influence of thirst and anticipatory regulation on performance.
Both pacing and hydration have been shown to be important factors in performance. However the factors that may positively or negatively affect an athlete's ability to pace themselves is not clear. Hydration has been shown to effect areas such as RPE (17), heart rate, and core body temperature (5,29,31), which may then effect cognitive functioning, perception of effort, and ultimately the pace at which the athlete participates. Therefore dehydration may affect a runner's ability to properly judge effort and therefore a pace that would optimize performance. However, studies have yet to examine the effect of hydration on the ability of runners to accurately pace themselves during race conditions. Accordingly, the purpose of this study was to examine the effects of hydration status on running pace during maximal effort performances.
Experimental Approach to the Problem
Subjects reported to a nearby state park for familiarization and baseline measurements. In order to familiarize the subjects with the 12-km course, they completed two practice runs on the course with a researcher familiar with the course. These runs were performed between 2 and 4 weeks before trial 1. One of these familiarization runs included a 4-km time trial to gauge their ability as a runner. This timed practice run was used to group subjects into three groups of four and one group of five runners with similar running abilities. Based on these groups monetary incentives were included to encourage maximal effort. The experimental trials consisted of completing two 12-km timed trail runs on 2 separate days 2 weeks apart: a) maximal (race effort) 12-km trial beginning euhydrated (HYR); and b) maximal (race effort) 12-km trial beginning hypohydrated (DYR). Note that 400 mL of water was provided at the 4-km and 8-km points for the euhydrated trials.
A 12-km distance was chosen consisting of three 4-km loops, which allowed for data collection when subjects ran by the start/finish area. This distance is also similar to common racing distances of 10 km and 15 km. It was also calculated that this distance would allow for subjects to become sufficiently dehydrated for comparison with a hydrated trial. The trails consisted of some single track, and some technical portions with rocky/root filled paths. Net elevation was 0 since this was a loop course with elevation gains of no more than 50 ft.
A randomized, crossover, counterbalanced design was used. All subjects were randomly assigned to either a euhydrated or a hypohydrated protocol for each trial. Half of the participants were randomly assigned to a hypohydration protocol for the first race. These participants then followed a hydration protocol for the second race and vice versa. All subjects received a calibrated scale (BWB-800 A, Tanita, Tokyo, Japan) to record body mass measurements for the duration of the study. Subjects took their own nude body mass measures each morning for the three days before the first trial which served as a baseline body mass throughout the study. Subjects continued to monitor their body mass every day until the last trial.
Seventeen (9 men, 8 women) competitive, heat acclimatized, well-trained distance runners (men: age 28 ± 9 years, height 178 ± 4.6 cm, weight 69.2 ± 5.6 kg, body fat 10.2 ± 2.5%; women: age 26.8 ± 4 years, height 164.2 ± 6.8 cm, weight 58.6 ± 7.9 kg, body fat 19.4 ± 3.5%; overall: age 27 ± 7 years, height 171 ± 9 cm, weight 64.2 ± 9.0 kg, body fat 14.6 ± 5.5%) participated. All subjects had a minimum of 3 consistent years of running experience and reported an exercise history of running a minimum of 30 minutes 4 times a week for the past 3 months. The majority of subjects were either on a college cross-country team or had participated on college teams; however, the average participant would not be considered an elite running competitor. Participants were excluded if they had any of the following: a disorder that could cause complications from taking Cor-Temp Disposable Temperature Sensor (HQinc, Palmetto, Florida, USA), any woman that was pregnant at the time of either trial or had the possibility of being pregnant; a history of exertional heat stroke or heat exhaustion within the last 3 years; musculoskeletal injury during the time of the running trials; an exercise/activity of less than 30 minutes per day, 4 times per week, at a moderate intensity for the past 3 months; chronic health problems; a history of cardiovascular, metabolic, or respiratory disease; fever or other current illness; or outside the age range of 18-59 years. Participants completed a running history questionaire and a medical history questionaire prior to being accepted into the study. All participants read and signed a written informed consent form. This study was approved by the University of Connecticut's institutional review board.
The day before each trial, subjects were informed of which hydration group they were in via individual phone calls. Subjects were randomly grouped by treatment (hydrated or hypohydrated). Whichever group a subject was assigned to for the first trial, the subject then followed the other protocol for the second trial (i.e., those that were assigned to the hydrated group for the first trial followed the dehydration protocol for the second trial). Subjects in the hypohydrated group were instructed to start fluid restriction 22 hours before their individual start time. Subjects in the hydrated group were allowed to consume fluids ad libitum. All subjects were required to perform a typical training run (60-minute run or 90 minutes with a combination of jogging or walking/hiking) after 14:00 hours, which was replicated on the days prior to both trials. All subjects were instructed to consume the same dinner the night before each trial, and the same breakfast and snack on the mornings of the two testing days. Subjects were also asked to wear the same shoes and the same or similar clothing for each of the trials. It was projected that the dehydrated group would start the trial about 2% dehydrated and finish about 4% dehydrated, while the hydrated group would finish about 2% dehydrated. These values where chosen because it is not uncommon for runners to finish races ∼2% dehydrated; however, for longer races runners may finish upwards of ∼4% dehydrated depending on sweat rate, water intake, and other variables. In addition, other research has shown that as little as 2% dehydration can impair performance (5).
Before the start of each trial, baseline measurments were taken, including body mass as measured by a scale to determine percent dehydration, heart rate (HR), perceived thirst (28), and perceived thermal sensations (32). Urine was used as a marker of dehydration, including urine color, urine specific gravity (Usg), urine osmolality (Uosm). At this time, subjects were also given instruction on how to use the perceptual scales. A standardized set of instructions including how to respond to each question was given for each scale. We used the Borg RPE scale, which ranges from 6-20 points (7). After baseline measurements, subjects began each trial individually with 4-minute intervals separating each subject's start time. Subjects wore a heart rate monitor throughout each trial. At the 4-km and 8-km mark, there was a mandatory 4-minute break for all subjects, where RPE, HR, perceived thirst, and perceived thermal sensations were measured. If at any point during a trial a subject needed to urinate, urine was collected into a jug, measured, and calculated into the subject's body mass loss.
At the conclusion of each trial, immediate postrun measurements included HR, perceived thirst, perceived thermal sensation, and RPE. Core body temperature was taken immediately post run, however these measures are reported elsewhere (9). Ten minutes after the run, HR was taken. Twenty minutes after the subject completed the trial, perceived thirst, perceived thermal sensation, and HR were measured. Participants in the hypohydrated trial remained on site until they were rehydrated to less than 2% of baseline body mass measures. Subjects received monetary compensation if the entire study was completed. Subjects received additional compensation based on their performances during the two trials. Subjects were placed into four groups according to ability (based on the times run during one all out loop which was performed during the familiarization runs). Time to complete the course for both trials was calculated and incentives were based off performance within each group. The average pace for each loop of every trial was calculated. Each loop was then analyzed to determine if a subject ran the loop closer to his/her average pace for the hydration trial or for the average pace during the DHR.
Wet bulb globe temperature (WBGT) was calculated for all hydrated and dehydrated trials occurring on the separate days. WBGT was taken every 20 min. These values were then averaged in appropriate time segments and averaged for each subject.
Each subject's percent body fat was calculated using three site skinfold measurements and the Jackson-Pollock equation (27). Duplicate measures (Uosm) were averaged. Uosm was determined via freezng point depression using an osmometer (Model 3DII, Advanced Instruments, Needham Heights, MA, USA). Urine color was determined by the urine color chart (8). HR was measured using Polar HR monitors (Polar E40, Polar Electro, Lake Success, NY, USA).
Repeated measures analysis of variance (ANOVA) was used to compare differences for performance in relation to dehydration level, intensity, and time. Post-hoc statistical analysis for pre- and post-values (body mass, urine color, Uosm,) during trials was determined using a paired sample t-test with a Bonferroni correction. Pearson's bivariate correlations were used to determine relationships among variables and to assess reliability. Total variation from pace was calculated as the sum of the difference from the mean pace at each time point. Percent of the course completed was calculated to further express variation in pace in terms of how much time was spent on each loop in relation to total time. Therefore, the percent of time spent on each loop in relation to total time is represented. Percent of course completed was calculated based on the average loop time and the actual time run per loop and determining the percentage this represented. Average percent of variance was calculated by determining the percent difference that existed between actual pace and average pace by loop and taking the average from the three loops. Significance was set at p ≤ 0.05. To further quantify the amount of variability between groups, a nonparametric chi-squared analysis was performed. The number of subjects in this study is similar or greater than previous papers that have found significant differences in pacing (13,14,16,29) and was therefore considered an acceptable n size for statistical power. All data analyses were performed using SPSS version 10.0.
Body Mass Changes
The DHR produced a significantly greater body mass loss when comparing pre and post race (−2.05 ± 1.25, −4.3 ± 1.25%) in relation to a 3-day baseline vs. HYR (-0.79 ± 0.95,−2.05 ± 1.09%, Figure 1). Urine color, Usg, and Uosm for the HYR were significantly lower (p ≤ 0.05) at both the pre and post time points when compared to the DHR.
Subjects ran significantly faster (p < 0.001) for the entire 12 km during the HYR vs. DHR (Figure 2). Differences between fastest and slowest loops during HYR (54 ± 40 seconds) were significantly smaller than DHR (111 ± 93 seconds; p = 0.041). A two-way ANOVA revealed a significant interaction affect over time (p = 0.024) when percentage of the course completed at the end of each loop was compared. A follow-up paired-samples t-test revealed significant differences in HYR (vs. DHR) after loop 2 (33.6 ± 0.36 vs. 33.1 ± 0.35%, respectively; p = 0.002) and loop 3 (33.8 ± 0.75 vs. 34.9 ± 1.35%, respectively; p = 0.01) (Figure 3). A two-way ANOVA was performed separately for men and women, which revealed a significant interaction over time for both the men and women (p = 0.001, p = 0.001, respectively). A follow-up paired samples t-test revealed significant differences in the HYR vs. the DHR on loops 1, 2, and 3 for men (32.6 ± 0.61% vs. 33.5 ± 0.28%, p = 0.005; 33.9 ± 0.60% vs. 32.4 ± 1.31%, p = 0.03; 33.1 ± 0.44 vs. 34.5 ± 1.08, p = 0.005, respectively) and women (32.6 ± 0.46 vs. 33.6 ± 0.46, p = 0.027; 33.8 ± 0.93 vs. 31.6 ± 1.39, p = 0.008; 33.1 ± 0.24 vs. 35.4 ± 1.53, p = 0.006, respectively; Figure 3). Fastest individual loop times were significantly faster (p = 0.028) for HYR (1,036 ± 116 seconds) than fastest loop times in DHR (1,060 ± 131 seconds). Slowest individual loop times for HYR were significantly faster (1,090 ± 132 seconds) when compared to the slowest DHR loops (1,172 ± 184 seconds; p = 0.004; Figure 4). Total variation from the mean pace between HYR and DHR approached significance (p = 0.064, Figure 5). When men and women were compared separately total variation from mean pace between HYR and DHR was significant for the men (206 ± 23.9 vs. 240 ± 49.0 seconds, p = 0.042) but not the women (291 ± 45.5 vs. 337 ± 101.2 seconds, p = 0.296). The mean percent of variance from average velocity approached significance between HYR (1.7 ± 1.3%) and DHR (3.3 ± 2.5%, p = 0.057). When men and women were compared separately, there was not a significant difference (p = 0.226, p = 0.155, respectively). No significant differences were found between the number of subjects that ran closer to his/her pace for the HYR vs. the DHY on loop 1 (p = 0.09) or loop 2 (p = 0.467), but significant differences occurred on loop 3 (p = 0.046, Table 1) and total time (p = 0.001).
Perceived thirst was significantly greater (p ≤ 0.05) at all time points during the DHR. Thermal sensations were significantly greater (p ≤ 0.05) in the DHR after loop 2 (p = 0.027) and at 20 minutes after (p = 0.018). RPE was significantly greater (p ≤ 0.05) in the DHR after loop 2 and loop 3.
WBGTs for the hydrated (26.1 ± 1.9°C) and dehydrated (26.3 ± 1.9°C) trials were not significantly different.
The purpose of this study was to determine the influence of hydration status on pacing during trail running in the heat. While previous studies have examined the importance of pacing on performance, few have examined other variables-specifically hydration status-that could alter the ability of athletes to appropriately pace themselves. There are four previously identified pacing strategies. While each one is usually associated with specific race distances, some pacing strategies vary depending on the skill of the athlete, environmental factors, age, and competition. The main strategies that this study examined were negative pace, even pace, or positive pace. A negative pace occurs when the athlete's speed gradually increases throughout the duration of the event. A positive pace is when an athlete's speed will gradually decrease throughout the event. Lastly, an athlete can pace himself or herself evenly by maintaining a constant speed throughout the event.
The main finding from our study was the differences identified between the fastest and slowest loop times during a race situation. Additionally, the difference between the fastest and slowest loops was significantly (p ≤ 0.05) different between hydration states. While there was not a statistical difference between pace variability at different hydration states, it did approach significance (p = 0.064, Figure 4) and while variability was significant for the men, the variation present in the women's times may explain the lack of significance in the overall variability analysis and may still have clinical significance. When percent of the trial completed was calculated, there were significant differences on loop 2 and loop 3 demonstrating a more even pace run in the hydrated trial. In a hydrated state, subjects demonstrated that a pace closer to the overall average pace was more effective as opposed to a positive pacing strategy, which was utilized by the dehydrated subjects (Figure 2). This demonstrates that when euhydrated and allowed to pace themselves freely without influence from other runners, subjects attempted to run at an even pace to attain optimal performance. The subjects reported significantly decreased RPE values in a hydrated state after loop 2 and at the finish, once again demonstrating the increased strain the fluid restricted runners were experiencing.
Since there are limited publications on the impact of hydration on pacing, it is difficult to compare this with previous observations. Publications on pacing support an even pacing strategy that the hydrated runners chose for this distance (14,16,26,30). Cottin et al. (10) and Gosztyla et al. (15) demonstrated that variations of up to 5% of the average velocity have no negative impact on performance and perhaps are actually beneficial compared to a perfectly constant pace. While both trials resulted in average velocity variations of less than 5%, the hydrated trial resulted in significantly less variation in velocity. Maughan et al. (20) examined pacing strategy of marathon runners and found that the slowest runners ran a positive pace while the fastest runners ran at a constant pace. Based on the dehydrated runners positive pacing strategy and past literature, it would seem that dehydrated runners paced themselves in accordance for completing an ultraendurance event or were not in the physical condition to perform at the initial pace selected (1). This may suggest that dehydrated runners pace themselves for a more strenuous distance than the actual task at hand.
It has been published (18,22,23,33) that our brain can anticipate how much work there is to be accomplished and therefore regulates an appropriate pace. Noakes (22) stated that the brain is responsible for skeletal muscle motor unit recruitment and therefore the amount of working muscle that can cause an increase in heat production. He also argued that the mechanism of control for this appears to be a preset rating of perceived exertion at which exercise terminates before dangerously high temperatures are accumulated. However, very little discussion on this theory has focused on hydration status and other outside factors (e.g., competition, pressure) that could play a role. At the start of the trial, our subjects knew they were dehydrated and had a significantly greater level of thirst. However, despite starting an average of 27 seconds slower for the first loop when compared to the HYR, dehydrated subjects tended to slow over the next two loops. Near the end of the race, 14 of the 17 subjects had slowed greatly and acquired a positive pacing strategy, suggesting that they were unable to pace themselves appropriately while dehydrated. Only one subject ran the last loop the fastest while dehydrated. Despite not finding statistical significance between hydration states and race variability, it did approach significance and can have practical applicability. As mentioned previously, the difference between first and last place could be a few seconds. This may not be statistically significant, but when competing at an elite level with other highly competitive athletes any small decrease in finishing time is imperative for success.
It is possible that the runners did not expect the dehydrated state to impact their performance as greatly as it did; therefore, they did not initially start at a slower, more appropriate pace. It is important to note that the dehydrated runners did start at a slower pace; however, their pace still tended to slow, as they did not continue this pace throughout the race. The runners may also normally maintain a euhydrated state, and were not able to account for the performance decrements associated with dehydration. The results of this study lend support to the anticipatory regulation theory in that with a small amount of dehydration subjects had a slightly decreased pace on loop 1 and then with progressively worsening dehydration the pace slowed at an exaggerated rate. It is possible that the decrement in pace noted in the dehydrated group was due to the greater physiological response (core body temperature increases and cardiovascular strain, published in another article from the same study ).
This study has some limitations. All subjects started the races individually with 4 minutes between each person. Subjects also rested for 4 minutes after each loop. While this rest was necessary to gain valuable data (9), this is not practical for a real race scenario where all participants run together. This 4-minute rest was imposed on every trial so that they were identical; however, it is unusual for runners to be allowed this small recovery, so they may have therefore changed their pacing strategies. Instead of approaching the three loops as one race, participants may have paced themselves according to each loop individually. Fluid ingestion during the trials may have also affected pacing. Knowing that water would be provided (vs. knowing that no consumption of water would be allowed) may have changed the approach taken to pacing. A study by Dugas et al. (11) observed cyclists during an 80-km race in the heat with low vs. high fluid intake. They reported that overall power output was significantly lower in the low-fluid trial starting at 10 km. The authors concluded that simply knowing the amount of fluid intake impacted the pace selected by subjects (11).
Further studies should focus on pacing while dehydrated and with ad libitum fluid intake. Studies may also look at pacing strategies of athletes and correlate the level of dehydration at the finish to the variability of a chosen pacing strategy. The effects of dehydration on pacing may vary based on race distance, experience, and level of competition, which other studies should examine.
Overall, our results demonstrate that dehydrated runners may have a slight decrease in their ability to evenly pace themselves during an endurance event. It has been consistently shown that for a race at this distance, an even pace or a pace that with less than 5% variability has the best performance outcomes (2,10,13). The fastest recorded marathon times have also been performed at a constant pace, while the slowest utilize a positive pace (20). It has also been shown that as little at 2% dehydration can have a negative impact on race performance. It is not uncommon for runners to reach this level of dehydration at the end of a race; however, the impact of this upon the ability of runners to correctly pace themselves had not previously been examined. The combination of dehydration and variability in pace could therefore further hinder overall performance.
Despite knowing that they were dehydrated and starting at a slower pace, dehydrated runners still adopted a positive pace. Therefore, not only did dehydration hinder performance, but it also affected the runner's ability to adopt an optimal pace. Clinically, this could explain another portion of the performance decrements classically seen in dehydrated athletes. Athletes may gradually become dehydrated either from not replacing the appropriate fluids prior to or during a competition or simply due to an extended length of competition leading to more fluid loss. Dehydration will decrease the performance of the athlete, but this study demonstrates that it also effects variability in pace, which has also been shown to decrease performance.
The authors thank the Gatorade Sports Science Institute for funding this study, as well as the following people for all of the time and effort throughout the data collection process: Jen Klau, Elaine Lee, Melissa Roti, Paul Boyd, Ben St. Martin, Mike Eckert, Stephanie Mazerolle, Brittanie Volk, Ben Keegan, Kristoffer Friend, Liz Silverberg, Kate Sanders, Kevin Ballard, Erin Quann, Heather Mispagel, Linda Yamamoto, Tutita Casa, Patrick Austin, Holly Wisehart, Kelli Christensen.
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