Abstract
ound and Objectives: Pacing analyses for increasingly popular long-distance running disciplines have been in researchers’ spotlight for several years. In particular, assessing pacing variability in long-distance running was hardly achievable since runners must repeat long-running trials for several days. Potential solutions for these problems could be multi-stage long-distance running disciplines. Therefore, this study aimed to assess the long-distance running variability as well as the reliability, validity, and sensitivity of the variables often used for pacing analyses.Materials and Methods: This study collected the split times and finish times for 20 participants (17 men and three women; mean age 55.5 years±9.5 years) who completed the multiday marathon running race (five marathons in 5 days), held as part of the Bretzel Ultra Tri in Colmar, France, in 2021. Seven commonly used pacing variables were subsequently calculated: Coefficient of variation (CV), Change in mean speed (CS), Change in first lap speed (CSF), Absolute change in mean speed (ACS), Pace range (PR), Mid-race split (MRS), and First 32 km–10 km split (32-10).Results: Multi-stage marathon running showed low variability between days (Intraclass correlation coefficient (ICC) > 0.920), while only the CV, ACS, and PR variables proved to
subsequently calculated: Coefficient of variation (CV), Change in mean speed (CS), Change in first lap speed (CSF), Absolute change in mean speed (ACS), Pace range (PR), Mid-race split (MRS), and First 32 km–10 km split (32-10).Results: Multi-stage marathon running showed low variability between days (Intraclass correlation coefficient (ICC) > 0.920), while only the CV, ACS, and PR variables proved to have moderate to good reliability (0.732 < ICC < 0.785). The same variables were also valid (r > 0.908), and sensitive enough to discern between runners of different performance levels (p< 0.05).Conclusions: Researchers and practitioners who aim to explore pacing in long-distance running should routinely utilize ACS, CV, and PR variables in their analyses. Other examined variables, CS, CSF, MRS, and 32-10, should be used cautiously. Future studies might try to confirm these results using different multi-stage event’s data as well as by expanding sensitivity analysis to age and gender differences. Keywords:endurance; speed; long-distance running; evaluation; coefficient of variation 1. Introduction Optimally performed endurance activities require efficient use of available energetic re- sources [1]. Consequently, participants in such activities (i.e., elite and recreational athletes) must decide how and when to invest their available energy. This process is continuous, and it is known as pacing [2]. In numerous endurance sports disciplines, the optimal pacing of elite athletes is often based on the drafting possibilities and expectations/actions of their direct opponents [3–5]. As a result, pacing behavior adjusts towards winning a specific position in the race rather than achieving the best finish time [6]. On the other hand, recreational endurance athletes like marathoners often aim to enjoy running, avoid injuries, and (if possible) achieve the fastest race time [7]. As a result, they have more choices in selecting the best pacing strategy in accordance with their goal. Several pacing strategies were previously reported in the scientific literature: (a) pos- itive pacing (i.e., decreasing in speed over time), (b) negative pacing (i.e., increasing in Medicina2024,60, 218.
in accordance with their goal. Several pacing strategies were previously reported in the scientific literature: (a) pos- itive pacing (i.e., decreasing in speed over time), (b) negative pacing (i.e., increasing in Medicina2024,60, 218.
Medicina2024,60, 218 2 of 13 speed over time), (c) all-out pacing (i.e., maximal possible speed from the start), (d) even pacing (i.e., constant speed over time), (e) variable pacing (i.e., significant race speed fluc- tuations), and (f) parabolic-shaped pacing (i.e., positive and negative pacing in different segments of the race), with no unanimous conclusion which one is the most optimal in all competitions [8]. Although, homeostasis maintenance could be a fundamental requirement for optimal performance during exercise [9]. As events take increasing time to complete (>2 min or >800 m), the optimal pacing profile appears to be more even [10,11]. Therefore, even pacing (with less speed variation) seems an obvious candidate for the optimal pacing strategy, particularly for recreational runners. It applies explicitly in prolonged locomo- tive events under stable external conditions (i.e., environmental and geographic), such as long-distance running, swimming, rowing, skiing, speed skating, and cycling [8]. In long- distance running, the even pacing strategy might help runners achieve a faster race time, decrease the risk of musculoskeletal injuries, and increase the pleasure of running [7,12]. In addition, with an increase in velocity, athletes use a more significant percentage of the power to overcome resistance (i.e., air or water) rather than producing forward motion [13]. Therefore, even minor speed fluctuations can result in a more significant energy cost, mainly due to acceleration [14,15]. At the same time, frequent deceleration can increase the risk of injury due to greater impact forces on the musculoskeletal apparatus [16]. Consequently, several recent studies assessed pacing in long-distance running as variability from even pacing (expressed as a percentage of speed change) [17–20]. A single dependent variable (depicting pacing variability for the entire race) was often used rather than the mean running speed for each race segment. Several reasons for such a decision can be acknowledged: (a) more complex statistical procedures can be performed to achieve robust results [18,19]; (b) pacing on two or more events held on the same track at the same time can be compared (e.g., half-marathon and marathon [18,21] or ultra-marathons of 6, 12, and 24 h [22]); and (c) the
race segment. Several reasons for such a decision can be acknowledged: (a) more complex statistical procedures can be performed to achieve robust results [18,19]; (b) pacing on two or more events held on the same track at the same time can be compared (e.g., half-marathon and marathon [18,21] or ultra-marathons of 6, 12, and 24 h [22]); and (c) the same events held in different years with a different number of checkpoints or their positions can be compared [7,17,19]. However, a potential problem with this approach is that various studies have utilized different variables to assess pacing variability in long-distance running [17,18,21,23]. At the same time, the reliability, validity, and sensitivity, as well as pacing variability, have not been evaluated in long-distance running, although pacing analysis for long-distance running disciplines has been in researchers’ spotlight for several years [18,24]. In addition, half-marathons and marathons are increasingly popular among recreational and professional runners [25]. Contrary to long-distance running, indices of pacing variability have been evaluated in other endurance sports, such as kayaking [26], swimming [27–29], cycling [30,31], and middle-distance running [32]. Assessing pacing variability in long-distance running is hardly achievable since run- ners must repeat long-running trials for several days. Furthermore, all studies mentioned above were performed in laboratory settings (e.g., treadmill running or kayak ergometer) or in time trial settings (swimming and cycling) instead of real race situations. Potential solutions for these problems could be multi-stage long-distance running disciplines. These running events are similar to Grand Tours in cycling (e.g., Tour de France, Giro d’ Italia). They include daily running a marathon for several consecutive days [33]. Multi-stage running events have a long tradition, with a recent increase in popularity [34]. Therefore, this study aims to assess the long-distance running variability as well as the reliability, validity, and sensitivity of commonly used pacing variables. For that purpose, this study used the official results of the Bretzel Ultra Tri Challenge Marathon (i.e., multi-stage marathons). We hypothesized that the results would show low pacing variability because of the athletes’ experience and stable weather throughout racing days. We also hypothesized that some pacing
variability as well as the reliability, validity, and sensitivity of commonly used pacing variables. For that purpose, this study used the official results of the Bretzel Ultra Tri Challenge Marathon (i.e., multi-stage marathons). We hypothesized that the results would show low pacing variability because of the athletes’ experience and stable weather throughout racing days. We also hypothesized that some pacing variables could present higher reliability, sensitivity, and validity than others, thus providing a fundamental tool for future studies interested in pacing strategies. Using potentially reliable, valid, and sensitive variables can help to obtain more robust results of the pacing analysis in long-distance running. As a result,
Medicina2024,60, 218 3 of 13 sports scientists and coaches could help runners manage their energy better, thus avoiding burnout and injuries and achieving their running goals. 2. Materials and Methods The study design and methodology are graphically presented in Figure. Figure 1.Flow chart of the study design and methodology. 2.1. Ethical Approval This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants as the study involved the analysis of publicly available data (EKSG 01/06/2010). The study was conducted following recognized ethical standards according to the Declaration of Helsinki adopted in 1964 and revised in 2013. 2.2. Participants We conducted a sample size estimate based on Cohen’s guidelines [35]. With an alpha level of 0.05, a power of 0.8, and 4 repeated measurements, 10 to 13 participants appear to be necessary for further analyses. Additionally, we also searched for previous methodological studies evaluating variability in other endurance sports disciplines. These studies included 10 to 20 participants, both men and women [26,29,30,32], and our sample size analysis confirmed that this number of participants is sufficient to test the proposed hypotheses. Therefore, this study collected each day’s split times and finish times for 20 participants (17 men and three women; mean age 55.5 years±9.5 years) who completed the entire race (i.e., “Challenge Marathon”). All participants‘ split and daily race times were obtained from the race director. 2.3. The Race The multiday marathon running race was held as part of the Bretzel Ultra Tri in Colmar, France, in 2021 (https://bretzelultratri.com/challenge-marathon/). Results from
Medicina2024,60, 218 4 of 13 four days were obtained for further analysis. The race started at 10:30 am on the first day and 10:00 am on each consecutive day. The race was held on a flat asphalt road with no elevation along a river, mainly in the forest shade on the loop track. Each lap was approximately 1279 m long; thus, 33 laps were needed to complete a marathon (42,195 m). The average 4-day temperature (hourly from 10 am to 4 pm) was 19.18 ◦ Celsius (ranging from 16 to 22 ◦ ), with 66.71% humidity (ranging from 46 to 91%) and no rain. 2.4. Data Analysis Individual split times and finish times for each day were initially acquired from the official race website on 25 September 2021). Thirty-three split times were obtained for each marathon (approx- imately 1279 m per split). Each 1279 m split time was measured electronically using a shoe chip. The race weather data were obtained from messwerte/haut-rhin 10 am to 4 pm. This time interval was selected because all runners ran all marathons in less than 6 h. The final phase of data analysis consisted of calculating different pacing variables. 2.4.1. Dependent Variables Note that all dependent variables were calculated independently for each runner: • Mean speed (MS). The MS for each lap and the MS for each marathon race were calculated based on the time needed to complete the distance. • Coefficient of variation (CV). CV was calculated as the standard deviation of the participant’s laps MS divided by race MS times 100 [22,36,37]. • Change in mean speed (CS). The percentage of MS change in each lap in relation to the race MS was calculated. CS was calculated as the mean of all lap percentages [20,38]. • Change in first lap speed (CSF). The percentage of MS change in each lap in rela- tion to the first lap MS was calculated. CSF was calculated as the mean of all lap percentages [3,39]. • Absolute change in mean speed (ACS). Percentage of MS change in each lap in relation to the race MS was
lap percentages [20,38]. • Change in first lap speed (CSF). The percentage of MS change in each lap in rela- tion to the first lap MS was calculated. CSF was calculated as the mean of all lap percentages [3,39]. • Absolute change in mean speed (ACS). Percentage of MS change in each lap in relation to the race MS was calculated, where all percentages were presented in absolute (i.e., positive) values. ACS was calculated as the mean of all lap percentages [18,19]. • Pace range (PR). The fastest and slowest lap MS were identified. These laps were then expressed as a percentage faster or slower than the race MS. Each individual’s fastest lap MS was named “positive range”, while the slowest lap MS was called “negative range”. The absolute sum of the positive and negative ranges was calculated to obtain PR [23,40]. • Mid-race split (MRS). MRS was calculated as a percentage of MS change in the second half of the race in relation to the MS of the first half of the race [7,41,42]. • First 32-10 split (32-10). The 32-10 split was calculated as a percentage of MS change in the last 10 km in relation to the MS of the prior 32 km of the race [17]. 2.4.2. Independent Variable Three performance groups were established to test the different pacing variables’ sen- sitivity. Groups were visually binned based on the mean speed of all four marathons and named: Fast (mean marathon time 3:46:24 h:min:s, ranging from 3:29:57 h:min:s to 4:01:17 h:min:s), Medium (mean marathon time 4:17:49 h:min:s, ranging from 4:01:17 h:min:s to 4:37:08 h:min:s), and Slow (mean marathon time 4:59:05 h:min:s, ranging from 4:37:08 h:min:s to 5:49:55 h:min:s) [ 2.5. Statistical Analysis Descriptive statistics were calculated as mean and standard deviation before all statis- tical tests. Data distribution normality was confirmed by the Kolmogorov–Smirnov test and visual inspection of histograms and QQ plots.
Medicina2024,60, 218 5 of 13 To assess day-to-day running variability, individual linear regressions were applied on mean speed for each of the 33 laps, separately for each day, to obtain pacing profiles. A linear regressions coefficient (r) was used to present each participant’s regression. Further- more, one-way repeated ANOVA was performed on the Z-transformed [43] coefficients mentioned above to test differences between days. To assess the reliability of the mean speed and pacing variables obtained from the four marathons, standard error of measurement (SEM), Coefficient of variation (CV), Intraclass correlation coefficients (ICC), and repeated one-way ANOVA were performed. Since all pacing variables were expressed as percentages, data were log-transformed for the analyses and then back-transformed according to existing methods [27,44]. The Pearson correlation coefficient was performed to assess the concurrent validity of pacing variables with regard to the “golden standard” (i.e., CV) for measuring variability in sports [45]. The Pearson correlation coefficient was also used to assess the sensitivity of the pacing analytical techniques—in particular, the relationship between different pacing analytical techniques and the mean race speed. Furthermore, one-way ANOVA with LSD post-hoc test was applied to test differences in pacing (assessed by different variables) between 3 performance groups (i.e., Fast, Medium, and Slow). Eta squared (n 2) was calculated for the ANOVAs where the effect sizes 0.01, 0.06, and above 0.14 were considered small, medium, and large, respectively [35]. All correlation coefficients were interpreted as small,r = 0.10–0.29;moderate, r = 0.30–0.49; and large, r = 0.50–1.0[35], whereas values of ICC less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 were indicative of poor, moderate, good, and excellent reliability, respectively [46]. The level of statistical significance was set atp< 0.05. All statistical tests were performed using Microsoft Office Excel 2017 (Microsoft Corporation, Redmond, WA, USA) and SPSS 26 (IBM, Armonk, NY, USA). 3. Results 3.1. Day-to-Day Running Variability Day-to-day mean speed variability shows a positive pacing strategy on all four days, with a low running variability between days (Figure). The frequency of participants’ positive, even, and negative pacing strategies based on the individual linear
performed using Microsoft Office Excel 2017 (Microsoft Corporation, Redmond, WA, USA) and SPSS 26 (IBM, Armonk, NY, USA). 3. Results 3.1. Day-to-Day Running Variability Day-to-day mean speed variability shows a positive pacing strategy on all four days, with a low running variability between days (Figure). The frequency of participants’ positive, even, and negative pacing strategies based on the individual linear regression profiles are displayed in Figure. One-way repeated ANOVA performed on Fisher Z-transformed individual linear regression coefficients showed no statistical differences between the four days (F = 1.440,p= 0.241), thus in- dicating low day-to-day pacing variability. In addition, all participants had the same pacing profile in four days (55%) or the same pacing profile in 3 out of 4 days (45%). Finally, the mean running speed showed no statistical differences between the four days (Table). Figure 2.Cont.
Medicina2024,60, 218 6 of 13 Figure 2.Day-to-day pacing variability presented in mean running speed (a–d). Dashed lines = mean linear regressions; r = mean linear regressions coefficient. Figure 3.The frequency of even, negative, and positive pacing strategies adopted by 20 participants based on the individual linear regression profiles. r = mean individual linear regressions coefficient. r between−0.1 and 0.1 defines even pacing profile; r lower than−0.1 defines positive pacing profile; r higher than 0.1 defines negative pacing profile. Table 1.Indices of reliability of mean running speed and the commonly used pacing variables. Variable Day 1 Day 2 Day 3 Day 4 CV% SEM ICC (95% CI) F p MS 2.72 ±0.32 2.74±0.36 2.73±0.36 2.69±0.38 4.10 0.11 0.920 (0.862, 0.958) 0.731 0.538 CV 6.50 ±1.47 6.85±1.64 6.68±1.76 7.44±1.58 23.22 1.26 0.785 (0.650, 0.884) 1.244 0.302 CS 5.58 ±2.30 4.27±2.47 5.17±2.76 3.52±3.17 77.09 2.16 0.404 (0.190, 0.625) 1.110 0.353 CSF 9.69 ±1.61 7.49±1.49 9.02±1.99 7.46±1.51 43.64 1.55 0.267 (0.06, 0.509) 2.109 0.130 ACS 5.84 ±1.47 5.32±1.69 5.25±1.76 5.69±1.67 24.42 1.28 0.781 (0.645, 0.882) 0.775 0.513 PR 26.78 ±1.56 28.70±1.65 26.62±1.77 31.20±1.51 26.10 1.30 0.732 (0.576, 0.853) 1.721 0.173 MRS 4.05 ±3.24 4.21±4.10 6.32±2.45 4.55±2.01 93.84 2.56 0.258 (0.052, 0.501) 0.864 0.465 32-10 5.45 ±2.59 3.96±2.94 4.87±2.51 3.24±3.57 82.54 2.28 0.418 (0.205, 0.637) 1.112 0.342 Abbreviations: SEM—Standard error of measurement; CV%—Coefficient of variation; ICC—Intraclass corre- lation coefficient; CI—Confidence Interval; F—ANOVA;p—Level of statistical significance; MS—Mean speed; CV—Coefficient of variation; CS—Change in mean speed; CSF—Change in the first lap speed; ACS—Absolute change in mean speed; PR—Pace range; MRS—Mid-race split; 32-10—First 32-10 split.
Description
The study evaluates pacing variability in multi-stage marathon running.