ECSS Paris 2023: CP-AP01
INTRODUCTION: Maximal vertical jump height is a key performance metric in research and athlete testing. Force platforms are the most common measurement tool, but systems vary across and within laboratories and sports clubs. Different force platform systems give rise to hardware and calculation (software) differences, which may impact data comparability. While prior research has examined force platform differences for some hardware solutions, to our knowledge, no studies have quantified discrepancies across multiple hardware and software solutions for several jump modalities. This study aimed to compare jump height measurements across four portable force platform systems for countermovement jumps (CMJs), squat jumps (SJs), and drop jumps (DJs). METHODS: Twenty-seven participants (26.2 ± 3.8 yrs, 1.76 ± 0.08 m, 73.2 ± 11.2 kg) were included in this study. Four portable force platform systems were tested: Kistler, ForceDecks, MuscleLab, and HurLabs. Each force platform was placed on top of an in-ground reference force platform (AMTI) to concurrently measure ground reaction forces (GRFs) during SJs (n=678), CMJs (n=667), and DJs (n=998). 3D motion capture was collected for 14 of the participants (1426 jumps) as a secondary reference. Jump height was estimated using two analytical approaches. In the Hardware analysis, GRF data from each force platform (reference and portable) were processed using the same standardized algorithm. In the Software analysis, jump height was computed from the portable system using each system´s built-in (proprietary) software, comparing it to the standardized algorithm. System differences were assessed using Bland-Altman comparisons, and typical error of estimate was evaluated. A one-way ANOVA detected significant differences between force platform systems across all jump modalities for both analyses, followed by a Tukey-Kramer post hoc test. RESULTS: Across all jump types, systematic differences in jump height between portable systems and the reference system ranged from -2.9% to 3.8% for the Hardware analysis, with a typical error of 1.1% to 5.0%. The Software analysis showed larger systematic differences (-11.6% to 11.1%) and greater typical error (3.2% to 22.3%). Similar trends appeared when using kinematics as reference. The Tukey-Kramer post hoc test found significant differences (p < 0.05) across all force platforms and jump modalities in both analyses. CONCLUSION: Jump height measurements varied significantly across different force platform systems, even when using identical jump protocols and calculation methods. Different force platform systems should be used interchangeably in this way with caution. Proprietary software calculations should not be assumed valid, and different software cannot be used interchangeably for jump height measures if accuracy below 2 ± 2 cm is important. The results of this study are useful to understand and interpret jump height measured from different commercial force platform suppliers.
Read CV Ingrid EythorsdottirECSS Paris 2023: CP-AP01
INTRODUCTION: Monitoring loads and energy costs (EC) is important, but obtaining EC via spiroergometry (gold standard) during competitions is not feasible, especially not in contact game sports. A unique method (metabolic power model) addressed this problem by estimating EC based on players’ acceleration derived from a local position system (LPS) [1]. Since the method’s accuracy has been debated despite model updates [2], inertia measurement units (IMU; often incorporated in LPS) may be an alternative or addition to estimate EC more accurately. The study aimed to compare EC estimations derived from spiroergometry, LPS, and IMU during a validated game sports test. METHODS: Eleven professional female handball players completed a validated, team handball game-based performance test [3]. Data were collected via a portable spiroergometry system (K5 Cosmed) and an LPS with integrated IMU (Catapult ClearSky T6). Indirect calorimetry derived ECSpiro from spiroergometry data, and the updated metabolic power model estimated ECLPS based on position data (10 Hz). IMU data (100 Hz) were used to calculate horizontal, vertical, and total PlayerLoad [4]. Differences between ECs were examined via repeated-measures ANOVA (pη2). Pearson correlation coefficients (r) expressed the relationship between time- and body mass-normalized spiroergometry, LPS, and PlayerLoad data. Stepwise regression analysis (R2) explored the predictability of ECSpiro via metabolic power model and PlayerLoad. Significance level was set at p<.05. RESULTS: ECSpiro (727±87 J/kg/min) differed from ECLPS (266±28 J/kg/min) (pη2=.98, p<.001) and did not significantly correlate with ECLPS (r=.56, p=.07). ECSpiro correlated with horizontal (r=.68, p<.05) but not with vertical (r=.47, p=.15) and total (r=.57, p=.07) PlayerLoad. A final regression model included only horizontal PlayerLoad (R2=.46, p<.05). CONCLUSION: The large difference and insufficient correlation between ECSpiro and ECLPS advised against using ECLPS as the only estimate. PlayerLoad metrics demonstrated stronger correlations, suggesting greater potential for EC estimations than the metabolic power model. Combining ECLPS with PlayerLoad did not improve regression predictions due to strong co-linearity. Regression analysis was statistically underpowered due to the limited sample size including only elite athletes to capture realistic dynamics. Nevertheless, the findings highlighted the significant discrepancy between ECSpiro and ECLPS, suggesting ECLPS unsuitable as a direct proxy for EC but not undermining its value for intra- and inter-subject comparisons. References 1. di Prampero, Fusi, Sepulcri, Morin, Belli, Antonutto. (2005). J Exp Biol, 208(14), 2809–16. 2. Fuchs, Fuchs, von Duvillard, Wagner, Shiang. (2022). J Sport Health Sci, 11(6), 641–3. 3. Wagner, Orwat, Hinz, Pfusterschmied, Bacharach, von Duvillard, Müller. (2016). J Strength Cond Res, 30(10), 2794–801. 4. Barrett, Midgley, Lovell. (2014). Int J Sports Physiol Perf, 9(6), 945–52.
Read CV I-CHENG HUANGECSS Paris 2023: CP-AP01
INTRODUCTION: Field-based research highlights the susceptibility of elite team-sport athletes to irregular sleep patterns, particularly during competition due to travel and matches. Currently, field-based data is derived from wrist-worn activity monitors (tracking sleep-wake patterns) and subjective assessments, however, they cannot measure sleep architecture (i.e., sleep-staging). While polysomnography is the gold standard for sleep assessment, its use in ecological settings remains impractical. Advancements in wireless home-based polysomnography and commercial sleep technology now enable the measurement of sleep architecture outside of laboratory environments. Given that each sleep stage plays a role on next-day function and performance, assessing sleep architecture could offer valuable insights into the consequences of travel and matches during competition. Therefore, the aim of this study was to examine the effects of travel and matches by comparing the sleep quantity, quality, and architecture of professional male rugby players during a home and away match fixture. METHODS: Twenty-six players, contracted for the Super Rugby season, were recruited for a prospective observational study. The experiment was conducted during the first two rounds of the competition to compare sleep (quantity, quality and architecture) across two conditions: a home (no travel; HOME) and an away (eastbound travel across three time zones; AWAY) match fixture, separated by eight days. Sleep data was collected using home-based polysomnography over three nights per condition: two nights preceding the match (MD-2), match night (MD), and the following night (MD+1). Sleep quantity (sleep duration), quality (sleep efficiency), and architecture (sleep onset/offset, latency, wake after sleep onset, awakenings, and sleep stages) were monitored. Sleep stages (light, deep and rapid eye movement [REM] sleep) were evaluated as proportion (%) and duration (min). RESULTS: Twenty athletes participated in the study (age: 27.5±4.1 years; height: 185.4±7.1 cm; body weight: 99.6±12.3 kg; positions: 9 forwards and 11 backs), with sleep data collected from 69 out of the 120 nights. Compared to HOME, AWAY increased sleep onset latency (+12±23 min; p<0.01) and altered sleep architecture (light: -6.2±7.1%; p<0.01). Regardless of condition, MD incurred delayed bedtime (+71±135 min; p<0.001), reduced sleep duration (-94±120 min; p<0.05) and altered sleep architecture (deep: +9.8±10.0%; REM: -6.9±8.3%; both p<0.05) compared to MD-2. On MD+1, sleep duration (+96±147 min; p<0.01), and time in light and REM sleep all rebounded (light: +52±44 min; REM: +39±52 min; both p<0.05) in both conditions. CONCLUSION: Professional male rugby union players experience alterations in sleep quantity, quality, and architecture on nights surrounding both home and away matches, with variability among players. Teams should arrange in-season training and flight schedules to ensure adequate sleep opportunity to optimise recovery.
Read CV Kanon UchiyamaECSS Paris 2023: CP-AP01