ECSS Paris 2023: CP-AP31
INTRODUCTION: The six minute walking test (6MWT) is a cost-effective and easy to administer submaximal test used to evaluate functional capacity and estimate aerobic fitness in both clinical and healthy populations [1]. However, because it is self-paced, test intensity can vary significantly between individuals, ranging from moderate to vigorous, potentially affecting the reliability of the maximum walking distance achieved during the test [2][3]. This variation raises concerns about whether test outcomes accurately reflect an individuals functional capacity in a healthy population or are largely influenced by motivation. This study aimed to evaluate whether walking speed and heart rate measures can help assess the quality of each individuals test results by identifying the impact of motivation on performance. METHODS: Seventy overweight participants (84.1±11.9 kg; 170.6±9.0 cm; BMI: 28.8±2.3 kg/m²) performed the 6MWT on a 20 m indoor track. Participants were instructed to walk as fast as possible to cover the greatest distance in six minutes. Time updates were provided every minute without additional encouragement. HR was recorded using a Polar H10 chest strap, and lap counts and time were tracked with a Polar Pacer Pro watch. Pearson correlation was used to analyze the relationship between average relative HR expressed as a percentage of predicted max HR (220 – age) and maximum walking distance. Hierarchical agglomerative clustering using Wards minimum variance linkage method and Euclidean distance was applied to classify participants based on their average walking speed and average relative HR. Group comparisons were conducted using ANOVA or the Kruskal-Wallis test, depending on normality (Shapiro-Wilk test), with appropriate post hoc tests applied when significant differences were detected. RESULTS: A moderate positive correlation was observed between average relative HR and maximum walking distance (r=0.36, p=0.002). Based on average walking speed and average relative HR, four distinct clusters were identified: Slow-LowHR (n=11), and Slow-ModerateHR (n=19), Fast-ModerateHR (n=9), Fast-HighHR (n=31). Significant differences in average walking speed were observed between all groups except Slow-LowHR (1.66±0.09 m/s) and Slow-ModerateHR (1.74±0.12 m/s) as well as Fast-ModerateHR (2.01±0.09 m/s) and Fast-HighHR (1.93±0.15 m/s). Average relative HR differed significantly between all groups (Slow-LowHR: 64.2±2.4 %HRmax, Slow-ModerateHR: 74.4±3.4 %HRmax, Fast-ModerateHR: 70.7±5.0 %HRmax, Fast-HighHR: 84.4±3.6 %HRmax), except between Slow-ModerateHR and Fast-ModerateHR. CONCLUSION: In the 6MWT, evaluating the interaction between walking speed and heart rate response can support interpretation and help identify individuals whose slow pace and low HR may indicate low motivation, suggesting that their results could be unreliable and a repeat assessment may be needed. 1. Dawn et al. (2022) 2. Burr et al. (2011) 3. Mänttäri et al. (2018)
Read CV Zane ŠmiteECSS Paris 2023: CP-AP31
INTRODUCTION: Monitoring training load is essential for understanding individual responses to training, managing fatigue, optimizing recovery, and minimizing the risk of nonfunctional overreaching and injury. Training load is typically classified into internal load (IL) and external load (EL) (1). Despite the importance of these monitoring methods, standardized methods for longitudinal evaluation remain limited. This study aimed to explore an effective condition assessment framework for optimizing elite athlete performance using a single-case experimental design (SCED) in preparation for the Paris Olympic Games. METHODS: A single elite athlete was monitored over one year, tracking daily sleep parameters, lowest heart rate (HR), and heart rate variability (HRV), which were measured using the OURA Ring (Ōura Health, Oulu, Finland). Subjective data, including subjective fatigue and training load indicators such as session rating of perceived exertion (sRPE), calculated as training time x RPE, were collected via AthletesPort developed by JISS, while both subjective and objective data were managed using a centralized tool (2). Preparedness was estimated using a fitness-fatigue mathematical model incorporating sRPE (3). Tau-U was employed to compare various indices during the training and tapering phases. Effect sizes (ES) were classified as follows: 0.80–1.00 (very large change), 0.60–0.80 (large change), 0.20–0.60 (moderate change), and 0.00–0.20 (small change) (4). The Bayesian hierarchical piecewise regression model (BHPRM) with AR1 was employed to assess athlete-specific indicators and evaluate daily condition fluctuations in the year before the Paris Olympic Games. RESULTS: A comparison of changes in resting HRV between the training and tapering phases of hypoxic training at 2,000 m, conducted three times a year, showed that resting HRV increased significantly from the first training to the tapering phase (ES = 0.71), with moderate increases in the second (ES = 0.27) and third (ES = 0.43). Based on BHPRM estimation, preparedness, calculated using a 5-day moving average, differed significantly between the training and tapering phases within the three weeks before each race (level difference: 0.26 [95% CI: 0.09 – 0.41], slope difference: 0.09 [95% CI: 0.03 – 0.16]). Both the level and slope improved during tapering, with significant between-race variability in the training phase slope (0.05, [ 95% CI: 0.02 – 0.10]). CONCLUSION: Monitoring daily IL demonstrated its potential for assessing training adaptation and competition readiness in elite athletes. Furthermore, the results of the present study suggested that IL data collection and SCED-based statistical analysis may be useful for tracking intra-individual conditions in athletes. Reference 1. Bourden et al. (2017). IJSPP, 12, S2-161-S2-170. 2. Shimizu et al. (2024), Jpn J Phys Educ Health Sport Sci, 825. 3. Suzuki et al. (2006), J Strength Cond Res, 20(1) 36-42. 4. Vannest and Ninci (2015), J Couns Dev, 9, 403-411.
Read CV Mariko NAKAMURAECSS Paris 2023: CP-AP31
INTRODUCTION: Refractive error is one of the most prevalent eye disorders worldwide, occurring when light fails to focus accurately on the retina, thereby reducing visual clarity [1]. The visual system plays a vital role in dynamic tasks, and visual blur can significantly impair athletic performance. Despite the importance of vision in sports, few studies in sports biomechanics have explored how blurred vision affects motor tasks. This study aims to address this gap by examining lower limb joint kinematics and foot plantar pressure characteristics during slow running under varying degrees of refractive error. METHODS: This study recruited 5 male collegiate athletes. Blurred vision was induced by wearing lenses with different diopters (D) to simulate three visual conditions: myopia (-3D), full correction (0D), and hyperopia (+3D). Participants performed a 15-second slow run at a speed of 6 km/h. The gait parameters, lower limb joint angles, and foot plantar pressure during the stance and swing phases were analyzed. One-way ANOVA repeated measures was used to compare kinematic and kinetic differences among the three visual conditions. RESULTS: The results revealed no significant differences in foot plantar pressure, gait parameters, or joint angles among the visual conditions. However, descriptive statistics revealed some noteworthy trends. Full-foot pressure during the stance phase showed higher in the -3D and +3D conditions compared to 0D. Specifically, midfoot and heel pressures were higher in both the -3D and +3D conditions compared to 0D, with midfoot pressure increasing by 3.29% (-3D) and 5.05% (+3D), and heel pressure increasing by 12.53% (-3D) and 11.8% (+3D), respectively. During the swing phase, participants exhibited greater hip and knee flexion and increased ankle abduction in the -3D and +3D conditions. Additionally, gait analysis showed that the -3D condition demonstrated a wider step width compared to the other two conditions. CONCLUSION: The findings suggest that blurred vision reduces athletes environmental perception, prompting them to adopt more stable gait strategies, such as increased hip and knee flexion and greater ankle abduction during the swing phase to improve swing foot safety and center of mass adjustment. Moreover, blurred vision (-3D and +3D) resulted in increased midfoot and heel pressures during the stance phase. Under the -3D condition, athletes compensated by increasing step width, thereby enhancing balance support [2]. Although these compensatory strategies help mitigate the effects of visual blur on motor control, they may also increase the mechanical load on the hip and knee joints, which potentially affecting gait patterns and athletic performance over time. Therefore, it is recommended to fully correct refractive errors to ensure clear visual feedback and reduce the need for compensatory gait adjustments.
Read CV CHI-WEI CHANGECSS Paris 2023: CP-AP31