ECSS Paris 2023: OP-AP30
INTRODUCTION: The physiological underpinnings of successful middle distance (MD) running performance have proven challenging to determine and quantify. This is a consequence of the heavy reliance on both the mitochondrial (aerobic system) and glycolytic and high-energy phosphate (anaerobic system) contributions to energy transfer. Furthermore, most of the previous research on MD running performance has been conducted with male athletes. Consequently, we sought to investigate the physiological variables associated with male and female 1500 m running performance, with the aim of producing a mathematical model which enables the prediction of 1500 m performance. METHODS: Twenty-one (n = 11 male, 1500 m personal best: 242.9 ± 5.8 s; n = 10 female, 1500 m personal best: 267.4 ± 7.1 s) MD athletes completed one laboratory visit and six field-based track assessments. We collected data on over seventy variables in total. During the laboratory visit, an incremental step test was conducted to determine maximal oxygen uptake (V̇O2max), running economy (RE), lactate threshold (LT) and lactate turnpoint (LTP). The six field-based performance tests included: a 10 m maximal sprint speed (MSS) test; a 20 s maximal rate of lactate accumulation (VLamax) sprint test; and 2, 4, 6 and 12-minute time trials for estimation of critical speed and D′. The participants’ 1500 m seasons best (1500SB) speed was the performance outcome. Pearson’s correlation and stepwise multiple linear regression were conducted to assess the strength of the relationship between the physiological and performance outcomes with 1500SB. RESULTS: The mean V̇O2max, RE, LT and LTP were 65.5 ± 5.2 mL·kg−1·min−1 (male), 62.9 ± 4.8 mL·kg−1·min−1 (female), 221 ± 17 mL·kg−1·km−1, 14.5 ± 1.0 km·h−1 and 16.8 ± 1.2 km·h−1, respectively. CS was 4.8 ± 0.3 m·s−1 and D′ was 161 ± 40 m. MSS and 20 s sprint speed were 8.0 ± 0.6 m·s−1 and 7.3 ± 0.5 m·s−1 , respectively. 20 s sprint speed had the strongest correlation with 1500SB (r2 = 0.76, P < 0.001). The fitted regression model was: 1500SB (m·s−1) = .43 (20 s sprint speed) + .144 (LTP) + 0.335. The model explained 81.9% of variance in 1500SB (F (2,14) = 37.11, P < 0.001). Mean 20 s sprint speed (m·s−1) and LTP (km·h−1) were both significant predictors in the model (20 s sprint speed: t = 5.88, P < 0.001; LTP: t = 4.87, P < 0.001). CONCLUSION: These findings indicate that 20 s sprint speed (m·s−1), and LTP (km·h−1) are the two most important physiological determinants of 1500 m running performance. These two variables incorporate both the aerobic and anaerobic contribution to energy transfer, highlighting that for successful MD performance, it is imperative that athletes train to improve energy transfer from all three energy systems (mitochondrial, glycolytic and high-energy phosphate).
Read CV Rebekah OsborneECSS Paris 2023: OP-AP30
INTRODUCTION: Long-distance triathlons are ultra-endurance multisport events that require sustaining high training volumes in the “moderate-to-heavy” intensity domain (MTHID)1. Recently, the concept of durability, defined as the time of onset and magnitude of physiological decline over time during prolonged exercise, has been studied in isolated cycling and running activities2. However, changes in running profiling when transitioning from prolonged cycling have yet to be studied. The aim of this study was to evaluate physiological changes while running at the MTHID after prolonged cycling and changes in performance during a 5-min time trial (TT) to better understand specific training and racing demands. METHODS: Seven trained male triathletes (age: 36±8 y, relative VO2max: 61.7±4.9 ml·kg-1·min-1) completed two running tests on separate days, one performed in a “fresh” (FRE) and one in “fatigued” (FAT) condition. Fatigue was induced by performing a prolonged 150-min indoor cycling at 90% of LT1 power. The testing protocol was a submaximal incremental test on a treadmill with 5-min steps until the second lactate threshold (LT2), followed by an outdoor all-out 5-min TT. VO2, respiratory exchange ratio (RER), ventilation (VE), respiratory frequency (RF), HR, BLa and RPE were determined during the steps below (UNDER) and just above (OVER) the LT1FRE and compared to the same speed in the FAT condition. In addition, distance completed during the 5-min TT were compared between the conditions. Due to the small sample size, data were analyzed with a Wilcoxon signed rank test (p < 0.05) and presented as median (interquartile range). RESULTS: Differences in both the UNDER and OVER period between the FRE and FAT condition were found in VO2 [UNDER: VO2FRE: 45.3(42.0-46.8) vs VO2FAT: 47.5(46.1-53.2) p=0.02; OVER: VO2FRE 47.7(46.3-51.2) vs VO2FAT 52.0(47.8-55.1) ml·kg-1·min-1; p=0.02], HR [UNDER: HRFRE 153(142-154) vs HRFAT 157(147-161); p= 0.02; OVER: HRFRE:160(150-163) vs HRFAT:163(153-168) bpm; p=0.02], BLa [UNDER: BLaFRE 1.6(1.5-1.7) vs BLaFAT 2.2(2.2-2.6); p=0.02; OVER: BLaFRE 2.1(1.9.-2.2) vs BLaFAT 2.4(2.2-2.8) mmol·L-1; p=0.02] and RER [UNDER: RERFRE 0.92(0.88-0.95) vs RERFAT 0.84 (0.83-0.87); p=0.04; OVER: RERFRE 0.92(0.90-0.96) vs RERFAT 0.87(0.84-0.89); p=0.02] Total distance covered during the 5-min TT, was less in the FAT condition [FRE:1483(1405-1538) vs FAT:1406(1374-1500) m; p=0.02]. CONCLUSION: Induced fatigued, resulted in higher VO2 costs, HR and BLa levels, with lower RER levels in data UNDER and OVER LT1 during a standardized submaximal test. In addition, the mean distance covered during a 5-min TT was lower in the FAT condition. These findings indicate that physiological markers change with increasing levels of accumulating fatigue, which should be considered when prescribing training and determining racing strategies in triathlons.
Read CV Andrea FukECSS Paris 2023: OP-AP30
INTRODUCTION: Modifiable stride components—ground contact time (GCT), ground reaction force (GRF), flight time (FT), and stride frequency (SF)—substantially influence running economy. Less is known about how these parameters are related to force-time characteristics of dynamic movements like the countermovement vertical jump (CMJ) or isometric tests like the isometric mid-thigh pull (IMTP). This exploratory study examined the associations between neuromuscular performance measures and running stride parameters at three individualized paces in well-trained male and female runners. METHODS: Stride parameters were assessed via a validated 2D video app [1] as participants (N = 16, age = 34.6 ± 5.7y, height = 171.7 ± 9.4 cm, mass = 66.5 ± 11.5 kg, VO₂max = 58.8 ± 5.6 ml·kg⁻¹·min⁻¹, 10 males, 6 females) ran on a treadmill at 3 stages (warm-up, easy aerobic, and half-marathon pace, individualized based on recent race performance). On a separate day, CMJ and IMTP tests were completed on force plates. RESULTS: MANOVA revealed a significant multivariate effect of pace (Wilks’ Λ = 0.517, F(10,82) = 3.205, p < 0.001), with GCT decreasing from stage 1 to 3 (diff = –0.044, p < 0.001) and stage 2 to 3 (diff = –0.031, p = 0.0025), while relative GRF increased from stage 1 to 3 (diff = 0.3023, p = 0.0049). FT, SF, and VO did not change significantly (p > 0.06). Correlational analysis revealed that GCT positively correlated with rate of force development (RFD) (r = 0.29–0.40) and concentric impulse (r ≈ 0.28), while FT correlated with normalized peak force (PF) (r = 0.39–0.54). SF correlated inversely with force and impulse measures (up to r = –0.64), whereas VO correlated positively with force production metrics (r ≈ 0.46–0.56). CONCLUSION: Neuromuscular contributions to running mechanics arise from the interplay between lower-limb force, impulse, and stride parameters. Force, impulse, and RFD metrics are moderately associated with reduced GCT and enhanced relative GRF, demonstrating the neuromuscular system’s role in optimizing the GRF/GCT relationship. In particular, the inverse association between SF and impulse measures indicates how efficient neuromuscular function supports more economical running mechanics, though this may be confounded by athlete height, as taller runners with greater mass tend to generate larger impulse values and longer legs may influence flight times. Together, these results indicate that power, force, and impulse capacities are associated with and may influence biomechanical factors related to running economy. Future research should further delineate these relationships by employing longitudinal studies to investigate causal links between targeted strength, power, and plyometric training and subsequent changes in stride characteristics over time. REFERENCES 1. Balsalobre-Fernández, C., Agopyan, H., & Morin, J. B. (2017). The Validity and Reliability of an iPhone App for Measuring Running Mechanics. Journal of Applied Biomechanics, 33(3), 222–226.
Read CV Jacob GoodinECSS Paris 2023: OP-AP30