ECSS Paris 2023: OP-BM12
INTRODUCTION: The metabolic cost (C) of walking and running is reported to increase with headwind and decrease with tailwind (1). In analogy with gradient locomotion, air drag may increase C by impacting the mechanical work done on the body center of mass and the proportion between its positive and negative fractions (2-4), although limited evidence exists on the underlying mechanisms. Elucidating the relationship between wind speed and C would shed further light on the energy demands of overground locomotion, where people move through air, and the generalizability of treadmill studies, where relative wind speed is nil. This study aimed to assess how air drag affects the metabolic and mechanical demands of walking and running. METHODS: After sample size estimation, eight male endurance athletes (age: 32±6 y, mass: 63.2±6.6 kg; height: 1.77±0.05 m, PB 10000 m: 31:20±01:12 min:s) were recruited. Participants walked at 1.5 m/s and ran at 4.0 m/s on an instrumented treadmill in a wind tunnel. Wind speeds (v) ranged from −12.5 to 12.5 m/s, where negative and positive signs indicate wind from the back (tailwind) or the front (headwind) of participants, respectively. A portable metabograph measured steady-state gas exchanges, and an eight-camera optoelectronic system recorded the position of reflective markers on the main body segments. This allowed calculating C (J/(kg*m)), drag force (Fd, N/kg), internal kinetic mechanical work (Wintk), positive and negative external mechanical work (Wext+ and Wext−). Mixed-effects models regressed such variables over v and v^2: those with the lowest Akaike Information Criterion were reported with their fixed effects and t-values. RESULTS: Headwind increased C for walking (C=2.72−0.05*v+0.01*v^2; t_v=−1.5; t_v^2=3.1) and running (C=4.28−0.08*v+0.01*v^2; t_v=−3.3; t_v^2=6.4), while tailwind decreased it (C=2.72+0.12*v; t_v=14.7 and C=4.28+0.15v; t_v=19.3, respectively). Similarly, Wext+ increased with headwind and decreased with tailwind; the opposite was observed for Wext−, whereas Wintk was negligibly affected by wind. Across the whole range of wind speeds (−12.5 to 12.5 m/s), variations in C followed linearly those in Fd (C=2.52+1.9*Fd; t_Fd=17.2 for walking, and C=4.0+2.5*Fd; t_Fd=20.1 for running). CONCLUSION: Our study confirms that the C of walking and running increases with headwind and decreases with tailwind; variations in C have similar magnitude in walking and running, and parallel those in Fd. The relations between C, Wext+ and Wext− with a head- and tailwind align with those observed in uphill and downhill locomotion, respectively (2). As for this case, variations in C may be determined by the partitioning between positive and negative work, together with their different efficiencies. REFERENCES: (1) Davies, J Appl Physiol, 1980 (2) Minetti et al., J Physiol, 1993 (3) Mesquita et al., Eur J Appl Physiol, 2020 (4) Dewolf et al., Eur J Appl Physiol, 2020
Read CV Francesco LucianoECSS Paris 2023: OP-BM12
INTRODUCTION: Analysing walking and running gait is pivotal for clinical diagnosis and athletic performance assessments. Motion capture (MOCAP) systems are recognized as the gold standard for gait analysis; however, their complexity, cost, and setup time limit their widespread use. The Optogait (OP) system, known for its portability, ease of setup, and user-friendly interface, has emerged as a popular alternative [1, 2]. However, its validity and reliability remain under scrutiny. Therefore, this study aimed to assess the validity and reliability of selected gait parameters during walking and running derived from the OP system in comparison to a reference MOCAP system. METHODS: Twenty-four asymptomatic participants were recruited from Potsdam and its surroundings. Participants underwent two walking (4.75 km/h) and two running (9.00 km/h) trials on a treadmill, during two consecutive measurements (M1 and M2) in a single day, for a test-retest setup, with a 5-minute break in between. Both on M1 and M2 the trial started with walking and was followed by running. Data were simultaneously captured using a 13-camera MOCAP system (500 Hz) and the OP system (1000 Hz). After an initial familiarization, recordings were analysed for specific intervals (0-15 seconds and 90-105 seconds), during both measurements. The outcome variables for walking were: cycle, stance, swing, step times (seconds); step, stride lengths (meters); and for running: stance, swing times (seconds); step, stride lengths (meters). Bland-Altman with limits of agreements, intra-class correlation coefficients (ICC), paired t-tests, and test-retest variability percentages were estimated to compute the Optogait’s validity at M1 and M2 and its reliability (M2-M1). RESULTS: Data from 17 out of 24 participants were suitable for analysis (28.0±5.4 years old, 173.2±10.0 cm height, 69.9±9.0 kg weight, 6 females/11 males). In walking, the OP demonstrated moderate to excellent reliability and validity for most variables except for stance time (M2: ICC [95% CI]: 0.257 [-0.780-0.716], p=.261, M2-M1: 0.138 [-1.341-0.686], p=.384 during the first 15 seconds; and M2-M1: 0.457 [-0.430-0.800], p=.111 during the second 15-seconds period). Also, the OP mean values deviated slightly from the MOCAP values (-2% to +5%). In contrast, running trials exhibited weak or poor agreement between OP and MOCAP across all variables. Moreover, the OP mean values deviated from the MOCAP values (-33% to -76% for temporal parameters and -3 to +5% for spatial parameters). CONCLUSION: The Optogait system proved a valid and reliable tool for analysing walking gait, offering a feasible alternative to MOCAP technology. Nevertheless, its application in running gait analysis did not meet the criteria for reliability or validity, indicating a need for further refinement of the system for comprehensive gait analysis across different modalities. REFERENCES [1] Lee M et al. Med. Sci. Mon. 20, 2014. [2] Healy A et al. J. Biom. Eng. 141, 2019.
Read CV Alberto Sanchez-AlvaradoECSS Paris 2023: OP-BM12
INTRODUCTION: In sports, the foot is the first organ to contact the ground, and can continuously adjust according to the movement state and the ground environment to reduce the impact on the human body. Due to the complex internal structure of the foot, the movement characteristics of each joint in different movement modes are not clear, which needs to be further explored. Therefore, the IOR Foot Model (1) was used in this study to build a three-segment foot model, and the segments were defined as Metatarsus (Met), Mid-foot (Mid), Calcaneus (Cal), and Hallux (Hal). Compare the motion characteristics of the internal foot joints under walking and running, to provide a theoretical reference for scientific running, but also to provide a reference for the study of pathological foot movement performance. METHODS: Fourteen healthy male runners with normal feet who ran more than 20km per week were involved in this study (age 21.5 ± 2.2 years; height 1.75 ± 0.03 m; BMI 22.3 ± 0.98 kg/m). The Qualisys capture system was used to track the trajectory of the marker at 200Hz. The Kistler force platform was used to simultaneously collect the ground reaction force at 1000Hz and the Smartspeed system was used to test walking (1.25 m/s ± 10%) and running speed (3.33 m/s ± 10%). Visual3D (v3.0, C-Motion, USA) software was used to calculate the angle and angular velocity of each joint. The parameters were expressed as mean ± standard deviation. RESULTS: For a better clinical interpretation, the joint between Cal and Mid will be also referred to as a midtarsal (MT) joint, the joint between Mid and Met as tarsometatarsal (TMT) joint, and the joint between Met and Hal as metatarsophalangeal (MTP) joint (2). The MT joint angle was found more dorsiflexed (walking: 38.44 ± 10.94 vs. running: 40.68 ± 17.91 deg) and had a greater adduction angle (walking: -6.54 ± 18.52 vs. running: -7.10 ± 20.61 deg) during running. The TMT joint was found to have smaller angles in the three planes compared to walking. A larger peak of abduction (walking: 25.05 ± 4.68 vs. running: 25.82 ± 4.31 deg) was observed at the MTP joint, but smaller dorsiflexion (walking: 93.66±5.04 vs. running: 90.19 ± 4.52 deg) and inversion (walking: -6.84 ± 5.79 vs. running: -6.32 ± 6.05 deg) angles. Moreover, the peak angular velocity of the internal foot joints increased significantly in the running condition. Among them, the peak dorsiflexion angular velocity of the MTP joint during running was 90°/s more than that of walking, and peak dorsiflexion arrival time was 6% earlier than during walking. CONCLUSION: There were significant differences in the kinematic characteristics of internal foot joints between walking and running conditions. A greater dorsiflexion angle was observed at the internal foot joints. With the increase in motion speed, the peak angular velocity of the joint increased significantly. This suggests that the internal foot joint movement pattern during running needs to be attended to. These findings would further understand the kinematic features of the internal foot joints and provide a reference for the future study of foot kinematics in the non-healthy foot population. REFERENCES: 1)Leardini et al., Gait Posture, 2007 2)Arnold et al., J Foot Ankle Res, 2017
Read CV Ye DiECSS Paris 2023: OP-BM12