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Overall performance of different models based exclusively on CS and D′, as well as parameters related to the decoupling of the internal-to-external workload ratio

Overall performance of different models based exclusively on CS and D′, as well as parameters related to the decoupling of the internal-to-external workload ratio

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Aim This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance. Methods The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determin...

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... Further, DFAa1 has been shown to be useful as an additional marker of acute fatigue in terms of systemic perturbation patterns in HR time series (Nuuttila et al., 2024;Rogers, Giles, Draper, Hoos, & Gronwald, 2021;Schaffarczyk et al., 2022;Van Hooren, Mennen, et al., 2023) or as a measure of fatigue resistance in studies with prolonged exercise (Gronwald et al., 2018(Gronwald et al., , 2019Gronwald, Berk, et al., 2021;Nuuttila et al., 2024). Therefore, expanding these findings to future approaches of real-time monitoring of steadystate exercises typically used in training sessions seems to be promising, as the DFAa1 marker might bear the potential to mirror decoupling mechanisms as alterations of external-to-internal-load relationships (Maunder et al., 2021;Smyth et al., 2022). ...
... The difference of the EF between the second and the fourth running bout was calculated and divided by the EF from the second running bout multiplied by 100 to get a percentage of alteration (%). Thus, for example, a value of 10% indicates that internal-to-external ratio was 10% greater during the fourth running bout compared to that observed in the second running bout (Maunder et al., 2021;Smyth et al., 2022). ...
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Aim was to evaluate alterations of the non‐linear short‐term scaling exponent alpha1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability (HRV) as a sensitive marker for assessing global physiological demands during multiple running intervals. As a secondary analysis, agreement of ECG‐derived respiratory frequency (EDR) compared to respiratory frequency (RF) derived from the metabolic cart was evaluated with the same chest belt device. Fifteen trained female and male long‐distance runners completed four running bouts over 5 min on a treadmill at marathon pace. During the last 3 min of each bout gas exchange data and a single‐channel ECG for the determination of HR, DFAa1 of HRV, EDR and RF were analyzed. Additionally, blood lactate concentration (BLC) was determined and rating of perceived exertion (RPE) was requested. DFAa1, oxygen consumption, BLC, and RPE showed stable behaviors comparing the running intervals. Only HR (p < 0.001, d = 0.17) and RF (p = 0.012, d = 0.20) indicated slight increases with small effect sizes. In addition, results point towards inter‐individual differences in all internal load metrics. The comparison of EDR with RF during running revealed high correlations (r = 0.80, p < 0.001, ICC3,1 = 0.87) and low mean differences (1.8 ± 4.4 breaths/min), but rather large limits of agreement with 10.4 to −6.8 breaths/min. Results show the necessity of EDR methodology improvement before being used in a wide range of individuals and sports applications. Relationship of DFAa1 to other internal load metrics, including RF, in quasi‐steady‐state conditions bears the potential for further evaluation of exercise prescription and may enlighten decoupling mechanisms during prolonged exercise bouts.
... Examples include salivary hormone markers (Deneen and Jones 2017), muscle enzyme elevation (Martínez-Navarro et al. 2019), blood lactate concentration (Jastrzębski et al. 2015), markers of substrate availability (Schader et al. 2020), cortical activity (Ludyga et al. 2016), functional testing, such as the counter movement jump (Wu et al. 2019) and measures of running economy (Scheer et al. 2018), with few being practical for ongoing activity. One commonly used field-based method is the upward "drift" in heart rate (HR) that occurs with prolonged exercise (Maunder et al. 2021;Smyth et al. 2022). Heart rate drift is a complex process dependent on multiple factors including fluid balance, skin or core temperature, cardiac preload dynamics and stroke volume change (Souissi et al. 2021;Billat et al. 2022). ...
... Therefore, comparison of DFA a1 values (over similar time/workloads) through training cycles could be followed for assessment of autonomic durability as a performance metric. This type of monitoring has been previously employed using HR (Smyth et al. 2022) but can now be extended to both DFA a1 and fB. This close association should not be surprising as there may be some commonalities in both HRV and fB regulation. ...
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Purpose Field-based measures of durability (exercise-related physiologic deterioration over time) for assessing athletic fitness often rely on changes in maximal power profiles or heart rate (HR) drift. This study aimed to determine whether an index of HR variability based on the short-term exponent of Detrended Fluctuation Analysis (DFA a1) along with respiratory frequency (fB) could demonstrate changes in durability during a Time to Task Failure (TTF) Trial. Methods Ten participants performed a cycling TTF at an intensity of 95% of the respiratory compensation point (RCP) on two occasions, Control and a “Reward” where a monetary incentive was offered when task failure was signaled. Metabolic responses including oxygen uptake (V˙O2{\dot{\text{V}}\text{O}}_{{2}}), lactate and glucose along with HR, DFA a1 and fB were measured and compared over each quarter of the TTF up to the time of signaling (Q1, Q2, Q3, and Q4). Results The elapsed time of TTF sessions was statistically similar (p = 0.54). After initial equilibration, metabolic responses remained largely stable over Q2–Q4. HR, DFA a1 and fB displayed drift over Q2–Q4 with significant ANOVA. Repeatability of quarterly HR, DFA a1, and fB between Control and Reward sessions was high with ICC between 0.73 and 0.94, Pearson’s r was between 0.83 and 0.98 with no difference in mean values by paired t testing. Conclusion HR, fB and DFA a1 are useful metrics representing alteration in physiologic characteristics demonstrating durability loss during an endurance exercise session. These measures were repeatable across sessions and have the potential to be monitored retrospectively or in real time in the field with low-cost consumer equipment.
... Postural and orthostatic challenges during exercise can also influence sympathetic outflow via the baroreflex. Recently, a more crude coupling concept, the ratio of internal (%HR max ) and external workload (% of critical speed, CS) (Smyth et al., 2022), was effectively applied in an analysis of >80,000 marathon runners. Low versus high 'decouplers' ran faster by 0.3 m/s on average, experienced decoupling onset ∼14 km later (33 vs. 19 km) and ran at a higher fraction of CS (88 vs. 82%). ...
... This new model of physiological analysis and cardiomuscular synchrony opens the door for new ideas involving its trainability, application to training prescription, modification by disease and extreme environments, recovery from asynchrony to synchrony, changes during work rate transitions and alignment with other endurance parameters. It is likely that stronger network connectivity, complexity and diversity is a feature of high 'resilience' or 'durability' in athletes (Smyth et al., 2022) and by extension the general population, making these metrics relevant. The growing body of work in the Network Physiology field suggests it can be applied to nearly all health and disease states, and the future of its application to exercise may lead us to novel findings that are unattainable by traditional methods. ...
... Similarly, when considering results-proven practice of 59 world-leading athletes, only 17 (~ 29%) athletes were female [18]. Observational studies using large databases have previously been used to identify determinants of marathon success [21][22][23], but an analysis of TID in a large sample of marathon runners with heterogeneous levels of performance is lacking. ...
... We used CS to identify the transition from heavy to severe exercise domains, and thus the boundary between Z2 and Z3, as it has been shown that CS represents the highest intensity at which a metabolic steady state may be achieved [9,25]. Critical speed was estimated for each runner using raw training data, as previously described [21,22]. In brief, the best performances recorded for each runner over a range of distances (400-5000 m) were used to construct the distance-time relationship, where the slope estimates CS [21,22]. ...
... Critical speed was estimated for each runner using raw training data, as previously described [21,22]. In brief, the best performances recorded for each runner over a range of distances (400-5000 m) were used to construct the distance-time relationship, where the slope estimates CS [21,22]. The boundary between the moderate and heavy domains, thus demarcating the boundary between Z1 and Z2 in the present study, is normally determined as the lactate threshold or gas exchange threshold [26,27], and therefore cannot be derived directly from the dataset used in the current study. ...
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Background The training characteristics and training intensity distribution (TID) of elite athletes have been extensively studied, but a comprehensive analysis of the TID across runners from different performance levels is lacking. Methods Training sessions from the 16 weeks preceding 151,813 marathons completed by 119,452 runners were analysed. The TID was quantified using a three-zone approach (Z1, Z2 and Z3), where critical speed defined the boundary between Z2 and Z3, and the transition between Z1 and Z2 was assumed to occur at 82.3% of critical speed. Training characteristics and TID were reported based on marathon finish time. Results Training volume across all runners was 45.1 ± 26.4 km·week⁻¹, but the fastest runners within the dataset (marathon time 120–150 min) accumulated > three times more volume than slower runners. The amount of training time completed in Z2 and Z3 running remained relatively stable across performance levels, but the proportion of Z1 was higher in progressively faster groups. The most common TID approach was pyramidal, adopted by > 80% of runners with the fastest marathon times. There were strong, negative correlations (p < 0.01, R² ≥ 0.90) between marathon time and markers of training volume, and the proportion of training volume completed in Z1. However, the proportions of training completed in Z2 and Z3 were correlated (p < 0.01, R² ≥ 0.85) with slower marathon times. Conclusion The fastest runners in this dataset featured large training volumes, achieved primarily by increasing training volume in Z1. Marathon runners adopted a pyramidal TID approach, and the prevalence of pyramidal TID increased in the fastest runners.
... Additionally, males exhibit higher sweat production under heat stress conditions [64]. While sweating helps dissipate heat, excessive sweating can result in fluid loss and dehydration, ultimately impacting bodily functions and concentration [65]. These factors may contribute to decreased work efficiency among male petrochemical workers in high-temperature environments. ...
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Background Workplace may not only increase the risk of heat-related illnesses and injuries but also compromise work efficiency, particularly in a warming climate. This study aimed to utilize machine learning (ML) and deep learning (DL) algorithms to quantify the impact of temperature discomfort on productivity loss among petrochemical workers and to identify key influencing factors. Methods A cross-sectional face-to-face questionnaire survey was conducted among petrochemical workers between May and September 2023 in Fujian Province, China. Initial feature selection was performed using Lasso regression. The dataset was divided into training (70%), validation (20%), and testing (10%) sets. Six predictive models were evaluated: support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP), and logistic regression (LR). The most effective model was further analyzed with SHapley Additive exPlanations (SHAP). Results Among the 2393 workers surveyed, 58.4% (1,747) reported productivity loss when working in high temperatures. Lasso regression identified twenty-seven predictive factors such as educational level and smoking. All six models displayed strong prediction accuracy (SVM = 0.775, RF = 0.760, XGBoost = 0.727, GNB = 0.863, MLP = 0.738, LR = 0.680). GNB model showed the best performance, with a cutoff of 0.869, accuracy of 0.863, precision of 0.897, sensitivity of 0.918, specificity of 0.715, and an F1-score of 0.642, indicating its efficacy as a predictive tool. SHAP analysis showed that occupational health training (SHAP value: -3.56), protective measures (-2.61), and less physically demanding jobs (-1.75) were negatively associated with heat-attributed productivity loss, whereas lack of air conditioning (1.92), noise (2.64), vibration (1.15), and dust (0.95) increased the risk of heat-induced productivity loss. Conclusions Temperature discomfort significantly undermined labor productivity in the petrochemical sector, and this impact may worsen in a warming climate if adaptation and prevention measures are insufficient. To effectively reduce heat-related productivity loss, there is a need to strengthen occupational health training and implement strict controls for occupational hazards, minimizing the potential combined effects of heat with other exposures.
... Consequently, fatigue detection using biomechanical variables could have more universal applicability, potentially within a shorter detection window than physiological measures. Alternatively, changes in the relations between the two could indicate fatigue, with the decoupling of internal and external measures of load associated with a lack of fatigue resistance in distance runners [86]. Future research should strive to integrate both biomechanical and physiological measures to gain a more comprehensive understanding of the interplay of different measures under fatiguing conditions. ...
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Background: Fatigue manifests as a decline in performance during high-intensity and prolonged exercise. With technological advancements and the increasing adoption of inertial measurement units (IMUs) in sports biomechanics, there is an opportunity to enhance our understanding of running-related fatigue beyond controlled laboratory environments. Research question: How have IMUs have been used to assess running biomechanics under fatiguing conditions? Methods: Following the PRISMA-ScR guidelines, our literature search covered six databases without date restrictions until September 2024. The Population, Concept, and Context criteria were used: Population (distance runners ranging from novice to competitive), Concept (fatigue induced by running a distance over 400 m), Context (assessment of fatigue using accelerometer, gyroscope, and/or magnetometer wearable devices). Biomechanical outcomes were extracted and synthesised, and interpreted in the context of three main study characteristics (cohort ability, testing environment, and the inclusion of physiological outcomes) to explore their potential role in influencing outcomes. Results: A total of 88 articles were included in the review. There was a high prevalence of treadmill-based studies (n=46, 52%), utilising only 1-2 sensors (n=69, 78%), and cohorts ranged in experience, from sedentary to elite-level runners, and were largely comprised of males (69% of all participants). The majority of biomechanical outcomes assessed showed varying responses to fatigue across studies, likely attributable to individual variability, exercise intensity, and differences in fatigue protocol settings and prescriptions. Spatiotemporal outcomes such as stride time and frequency (n=37, 42 %) and impact accelerations (n=55, 62%) were more widely assessed, with a fatigue response that appeared population and environment specific. Significance: There was notable heterogeneity in the IMU-based biomechanical outcomes and methods evaluated in this review. The review findings emphasise the need for standardisation of IMU-based outcomes and fatigue protocols to promote Interpretable metrics and facilitate inter-study comparisons
... Durability has basically been defined as an individual's capability to resist and delay deteriorations (magnitude and time of onset) in physiological profiling characteristics during prolonged exercise (Maunder et al. 2021). In practice, the capability has been assessed through changes in physiological parameters, such as heart rate (HR) (Matomäki et al. 2023;Smyth et al. 2022), HR variability (HRV) Matomäki et al. 2023), or energy expenditure (EE) (Matomäki et al. 2023) during long-duration exercise. Other assessment options have included changes in threshold (Gallo et al. 2024;Hamilton et al. 2024;Stevenson et al. 2022) or maximal performance (Hamilton et al. 2024;Noordhof et al. 2021) after prolonged endurance exercise. ...
... Another aspect that has not yet been examined is the possible difference between the sexes. Some studies have suggested that females may have greater "durability", namely lower magnitude and later occurring decoupling of HR during marathon (Smyth et al. 2022) and less neuromuscular fatigue after strenuous exercise protocols (Ansdell et al. 2019(Ansdell et al. , 2020 or trail-running races (Besson et al. 2021;Temesi et al. 2015). In support of potential sex differences, the analyses of Le Mat et al. (2023) suggested that the longer the running distance in trail-running competitions, the smaller the gap between males and females. ...
... Although it has been suggested that females could be more endurant than males in regard to HR decoupling during a marathon (Smyth et al. 2022) or neuromuscular fatigability after trail-running races (Besson et al. 2021;Temesi et al. 2015), the current study did not find significant differences in any of the primary outcomes. It has been speculated that potential explanatory mechanisms may relate to higher percentage of type 1 fibers and the ability to oxidize fat at given intensity (Besson et al. 2022;Tiller et al. 2021). ...
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Purpose Recent studies have suggested that the capability to resist deterioration of physiological characteristics could be an independent factor contributing to endurance performance. This study aimed at investigating whether prolonged low-intensity exercise induces shifts in the lactate threshold, and whether fatigue-induced changes differ between the sexes. Methods A total of 31 (15 females) recreational runners performed an incremental treadmill test and a 90-min low-intensity exercise (LIT90) on two separate occasions. The LIT90 was performed at 90% of the first lactate threshold speed (LT1v), derived from the incremental treadmill test. The LT1v was determined from a 5-stage (3 min) submaximal threshold test (SubmaxLT), performed before and after LIT90. The SubmaxLTs were followed by a 10/5 reactivity jump test. Respiratory gases, heart rate (HR), and HR-derived detrended fluctuation analysis alpha 1 (DFA-a1) were assessed every 15 min during the LIT90. Results A significant decrease (p < 0.01) was observed in the LT1v in females (− 5.8 ± 4.4%) and in males (− 5.3 ± 6.4%). The HR increased (p < 0.001) similarly in females (5.9 ± 3.1%) and in males (5.5 ± 3.6%) during the LIT90, while energy expenditure increased (3.1 ± 4.5%, p = 0.013) in females but remained unchanged in males (0.9 ± 3.1%). Change in DFA-a1 during the LIT90 was the only marker that correlated significantly with the relative change of LT1v (r = 0.463, p = 0.013). Conclusion LIT90 induced significant decreases in the LT1v, and the changes were comparable between sexes. DFA-a1 could be a potential intra-session marker of durability.
... Recent work by Unhjem [76] demonstrated that active adults (Tier 1) exhibited a greater increase inVO 2 during a 1 h run at 70%VO 2max compared to trained runners (Tier 2-3), resulting in a greater increase in the relative intensity of exercise across the bout. Consistent with this finding, an analysis of more than 82,000 recreational runners found faster marathon performances in individuals who had a lower ratio of internal to external work decoupling (<10%) across the race than those with higher rates (>2%) [77]. ...
... Further, DFAa1 has been shown to be useful as a marker of acute fatigue in terms of a systemic perturbation van Hooren, Mennen, et al., 2023) or as an internal load measure of fatigue resistance in studies with prolonged exercise (Gronwald et al., 2018(Gronwald et al., , 2019. Therefore, expanding these findings to future approaches of real-time monitoring of prolonged exercise seems to be promising, as the DFAa1 marker might bear the potential to mirror decoupling mechanisms as alterations of external-to-internal load relationships or "durability" aspects of endurance performance that were recently described as "the time of onset and magnitude of deterioration in physiologicalprofiling characteristics over time during prolonged exercise" (Maunder et al., 2021;Smyth et al., 2022). Jones (2023) recently introduced this construct in terms of physiological resilience constituting an independent, fourth dimension of endurance exercise performance in addition to the classical ones of maximal oxygen uptake (VO 2MAX ), economy or efficiency, and fractional utilization of VO 2MAX (e.g., Joyner, 1991;Joyner & Coyle, 2008). ...
... This alteration of internal-to-external load relationship could be also shown from an internal load perspective with fixed external load in the moderate intensity domain according to HR metrics and DFAa1 van Hooren, Mennen, et al., 2023). Here, it should be still differentiated between pre and post comparisons (e.g., Hamilton et al., 2024;Stevenson et al., 2024) and alterations during continuous prolonged exercise bouts (e.g., Gronwald et al., 2018;Maunder et al., 2021;Smyth et al., 2022). ...
... The difference of the EF from the start and end was calculated and divided by the EF from the start multiplied by 100 to get a percentage of alteration (%). Thus, a value of 10% indicates that internalto-external ratio was 10% greater at the end segment compared to that observed in the start segment (Maunder et al., 2021;Smyth et al., 2022). ...
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The study explores the validity of the nonlinear index alpha 1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability for exercise prescription in prolonged constant load running bouts of different intensities. 21 trained endurance athletes (9 w and 12 m) performed a ramp test for ventilatory threshold (vVT1 and vVT2) and DFAa1‐based (vDFAa1‐1 at 0.75 and vDFAa1‐2 at 0.5) running speed detection as well as two 20‐min running bouts at vDFAa1‐1 and vDFAa1‐2 (20‐vDFAa1‐1 and 20‐vDFAa1‐2), in which HR, oxygen consumption (VO2), respiratory frequency (RF), DFAa1, and blood lactate concentration [La‐] were assessed. 20‐vDFAa1‐2 could not be finished by all participants (finisher group (FG), n = 15 versus exhaustion group (EG), n = 6). Despite similar mean external loads of vDFAa1‐1 (10.6 ± 1.9 km/h) and vDFAa1‐2 (13.1 ± 2.4 km/h) for all participants compared to vVT1 (10.8 ± 1.7 km/h) and vVT2 (13.2 ± 1.9 km/h), considerable differences were present for 20‐vDFAa1‐2 in EG (15.2 ± 2.4 km/h). 20‐vDFAa1‐1 and 20‐DFAa1‐2 yielded significant differences in FG for HR (76.2 ± 5.7 vs. 86.4 ± 5.9 %HRPEAK), VO2 (62.1 ± 5.0 vs. 77.5 ± 8.6 %VO2PEAK), RF (40.6 ± 11.3 vs. 46.1 ± 9.8 bpm), DFA‐a1 (0.86 ± 0.23 vs. 0.60 ± 0.15), and [La‐] (1.41 ± 0.45 vs. 3.34 ± 2.24 mmol/L). Regarding alterations during 20‐vDFAa1‐1, all parameters showed small changes for all participants, while during 20‐vDFAa1‐2 RF and DFAa1 showed substantial alterations in FG (RF: 15.6% and DFAa1: −12.8%) and more pronounced in EG (RF: 20.1% and DFAa1: −35.9%). DFAa1‐based exercise prescription from incremental testing could be useful for most participants in prolonged running bouts, at least in the moderate to heavy intensity domain. In addition, an individually different increased risk of overloading may occur in the heavy to severe exercise domains and should be further elucidated in the light of durability and decoupling assessment.
... Running performance is affected by the maximal oxygen uptake (Noakes, 1988), running economy (Conley & Krahenbuhl, 1980), and critical velocity (Noakes, 1988), but a review of Denadai and Greco (2022) revealed that performance predictors depend on the running distance, for example, for longer distance events (5000 m, 10,000 m, marathon, and ultramarathon), blood lactate response to exercise seem to be the main predictor of performance. More recently, one pivotal parameter that emerged is durability, which can play a significant role in predicting an athlete's performance during long endurance events (Smyth et al., 2022). Durability or resilience refers to the ability of an individual to withstand functional decline following acute and/or chronic stressors (Jones, 2023). ...
... Notably, this metric exhibits significant interindividual variability in terms both of the magnitude and onset of decoupling, and when classified as low, moderate, and high decoupling, athletes experiencing low decoupling had better marathon performance (Smyth et al., 2022). ...
... One main finding of the current study is the variation in physiological resilience among individuals who completed fewer than or more than 35 laps. Smyth et al. (2022) reported substantial F I G U R E 1 Resilience of participants running <35 and >35 laps considering HR/running speed (*p ≤ 0.028). interindividual variability in the magnitude and timing of the decoupling between the internal and external workload as reflected by the HR and running speed. ...
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Ultrarunning is gaining in popularity but no information is available on the physiological and psychological responses during backyard ultrarunning events. The aim of this study was to determine changes in cognitive function, markers of physiological resilience, and running performance during a backyard‐running event. Twelve male ultrarunners (38 ± 8 years old, BMI: 23.5 ± 1.6 kg/m², and VO2max: 60.8 ± 4.7 mL/min/kg) were monitored before, during, and after the event. Cognitive performance was determined using a cognitive test battery before, during, and after the event. During the event, the rating of perceived exertion (RPE), blood lactate concentration, and heart rate (HR) were assessed. Physical performance was investigated using the total number of completed laps and running speed per lap. Athletes completed 34 ± 17 laps equaling 227.8 ± 113.9 km with average speeds starting at 9.0 km/h and slowing down to 7.5 km/h at the end of the event. Physiological resilience (estimated using HR/speed) varied between athletes, with significantly lower values in the more proficient backyard runners at the end of the event (p < 0.05). HR and lactate levels remained constant, whereas a progressive increase in RPE was noticed (p ≤ 0.001). A significantly worsened reaction time was observed for several cognitive tasks after the event compared to baseline measures (p ≤ 0.05). These observations show that physiological resilience differs depending on the level of endurance performance of the athletes. Furthermore, the backyard ultrarunning event negatively impacted psychomotor speed. Therefore, the results suggest that implementing strategies that enhance physiological resilience and/or psychomotor speed could potentially have a positive effect on performance in ultraendurance activities.