This study investigated differences in lower-limb coordination and coordination variability between experienced and novice runners during a prolonged run. Thirty-four participants were categorised as either experienced (n = 17) or novice runners (n = 17). All participants performed a 31-min treadmill run at their individual anaerobic threshold speed, and lower-limb kinematic data were acquired in the sagittal plane at the beginning, middle, and end of the run. Lower-limb coordination and variability during the stance phase were quantified using a vector coding technique for hip-knee, knee-ankle, pelvis-thigh, thigh-shank, and shank-foot couplings. Repeated-measure analysis of covariance revealed that running experience and time had significant interactions on the coordination patterns for hip-knee and pelvis-thigh couplings. During the midstance, experienced runners exhibited a higher percentage of in-phase motion for pelvis-thigh and knee-ankle couplings while novice runners displayed a higher percentage of distal motion for pelvis-thigh coupling and anti-phase motion for hip-knee coupling. Experienced runners displayed more variability in hip-knee and shank-foot couplings, and novice runners had more variability in hip, knee, and thigh motion. Experienced and novice runners adapted to progressive fatigue through different lower-limb coordination patterns. Throughout the prolonged run, experienced runners demonstrated greater coordination variability and novice runners displayed greater joint and segment variability.

The surface effects on running biomechanics have been greatly investigated. However, the effects on rearfoot strike runners remain unknown. The purpose of this study was to investigate the effects of surfaces on the running kinematics, kinetics, and lower-limb stiffness of habitual rearfoot strikers. Thirty healthy male runners were recruited to run at 3.3 ± 0.2 m/s on a customized runway covered with three different surfaces (artificial grass, synthetic rubber, or concrete), and their running kinematics, kinetics, and lower-limb stiffness were compared. Differences among the three surfaces were examined using statistical parametric mapping and one-way repeated-measure analysis of variance. There were no statistical differences in the lower-limb joint motion, vertical ground reaction force (GRF), loading rates, and lower-limb stiffness when running on the three surfaces. The braking force (17%–36% of the stance phase) and mediolateral GRF were decreased when running on concrete surface compared with running on the other two surfaces. The moments of ankle joint in all three plane movement and frontal plane hip and knee joints were increased when running on concrete surface. Therefore, habitual rearfoot strikers may expose to a higher risk of running-related overuse injuries when running on a harder surface.

This study investigated treadmill familiarisation time in different shoe conditions by comparing lower limb consecutive kinematics waveforms using a trend symmetry method to calculate trend symmetry index, range amplitude ratio and range offset. Eighteen young adults (26.6 ± 3.3 years, 7 females) completed three 10-minute running trials at their preferred running speed (2.30 ± 0.17 m/s) on a treadmill with three shoe conditions (i.e., usual, minimalist and maximalist shoes) in a random order. Sagittal lower limb kinematic data were recorded using inertial measurement units. The results showed that sagittal-plane kinematic waveforms in the hip, knee and ankle remained consistent (trend symmetry > 0.95) without extreme excursions (range amplitude ratio ≈ 1) over 10 minutes within each testing shoe condition. Significant time × shoe interaction effect was observed in range offset (i.e., absolute differences in the average degree of kinematic waveforms between consecutive minutes) at ankle (p = 0.029, ŋ_p² = 0.096) and knee (p = 0.002, ŋ_p² = 0.126). Post-hoc analysis suggested that running with novel shoes required a shorter time to achieve stable lower limb kinematics (2 to 3 minutes) compared with usual shoes (7 minutes). In conclusion, young healthy adults need up to 3 and 7 minutes to familiarise to the treadmill when running at their preferred speed with their novel and usual running shoes.

Footstrike angle (FSA) has been widely used to classify footstrike pattern (FSP). However, inconsistent FSA cut-off values were adopted in previous studies. This study aimed to validate the FSA cut-off values in runners. Stride index, the gold standard to determine FSP, and FSA were obtained when 15 experienced runners, 14 novice runners and 14 untrained individuals performed 3-min run on an instrumented treadmill at their preferred running speeds in habitual, rearfoot, midfoot and forefoot strike patterns. According to the receiver operating characteristic curve associated with the Youden index, the optimal FSA cut-off values were −0.8° (i.e., cut-off angle for forefoot strike) –7.4° (i.e., cut-off angle for rearfoot strike) for runners. We observed minor differences in the FSA cut-off values across runners with various running experience and a wider cut-off range for midfoot strikers when a modified strike index was utilized. This validation study established cut-off footstrike angles for runners’ FSP classification.

Exercise-induced bronchoconstriction (EIB) is highly prevalent in athletes. The objective of this study was to assess the therapeutic efficacy of daily tangeretin combined with whey protein supplementation over a period of 4 weeks in professional athletes with EIB. Using a placebo-controlled, double-blind, paired, randomized trial design, a cohort of 30 professional athletes with EIB, consisting of 14 females and 16 males, was divided into two groups: the tangeretin combined with whey protein intervention group (TIG), and the placebo control group (PCG). Both the TIG and PCG underwent exercise challenge tests (ECT) and VO_2max tests before (ECT₁, V1) and after (ECT₂, V2) the intervention. Blood (eosinophils, neutrophils, and basophils) and serum (interleukin-5, IL-5; interleukin-8, IL-8; Clara cell secretory protein-16, CC16; immunoglobulin E, IgE) levels were measured early in the morning of ECT₁ and ECT₂, respectively. Lung function was assessed immediately before and post-ECT immediately. Tangeretin combined with whey protein use for 4 weeks attenuated the decrease in forced expiratory volume in 1 s (FEV₁) post trials (∆FEV₁(ECT1-ECT2): mean (SD) TIG −7.51(6.9)% vs. PCG −2.33(11.49)%, p = 0.013). Tangeretin also substantially attenuated IL-5 concentration (∆IL-5(T₁-T₅): Tangeretin −19.4% vs Placebo + 8.37%, p = 0.022); IL-8 concentration (∆IL-8(T₁-T₅): Tangeretin −17.28% vs Placebo + 6.1%, p = 0.012); CC16 concentration (∆CC16(T₁-T₅): Tangeretin −11.77% vs Placebo + 24.19%); and IgE concentration in the serum (∆IgE(T₁-T₅): Tangeretin −24.1% vs Placebo −3.9%), and significantly decreased neutrophil count (∆N(T₁-T₅): Tangeretin −11.34% vs Placebo + 0.3%) and eosinophil count in blood (∆N(T₁-T₅): Tangeretin −38.5% vs Placebo + 4.35%). Compared with V1, VO_2max (p = 0.042) and TLim (p = 0.05) of V2 were significantly increased in the TIG, and there was no significant change in the PCG. Meanwhile, six athletes in the TIG and 0 athletes in the PCG became EIB-negative at ECT₂; the overall negative conversion rate of EIB was 40.00% in TCG. Additionally, the number of cough symptoms decreased from 9 to 3 and dyspnea from 4 to 2 in the TIG. After high-intensity exercise, athletes with EIB achieved significant improvements in lung function and blood inflammatory factors by combining tangeretin and whey protein supplementation. EIB athletes also showed longer exercise endurance and VO_2max at 4 weeks after TI. In addition, some patient symptoms disappeared after combination supplementation. The effect of this treatment on professional athletes with EIB was beneficial.