Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body segmental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, it has not been clarified whether GV is associated with walking speed, a clarification needed to exploit effective better bilateral coordination level. For this aim, fourteen male students (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, and body height 1.78 ± 0.05 m) took part in this study. After three days of walking 1 km each day at a self-selected speed (SS) on asphalt with an Apple Watch S. 7 (Apple^TM, Cupertino, CA, USA), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 min at each one, SS − 20%/SS + 20%/ SS, with 5 min of passive recovery in-between. Heart rate (HR) was monitored and normalized as %HR_max, while the rate of perceived exertion (RPE) (CR-10 scale) was asked after each trial. Kinematic analysis was performed, assessing the Contact Time (CT), Swing Time (ST), Stride Length (SL), Stride Cycle (SC), and Gait Variability as Phase Coordination Index (PCI). RPE and HR increased as the walking speed increased (p = 0.005 and p = 0.035, respectively). CT and SC decreased as the speed increased (p = 0.0001 and p = 0.013, respectively), while ST remained unchanged (p = 0.277). SL increased with higher walking speed (p = 0.0001). Conversely, PCI was 3.81 ± 0.88% (high variability) at 3.96 ± 0.47 km·h⁻¹, 2.64 ± 0.75% (low variability) at SS (4.94 ± 0.58 km·h⁻¹), and 3.36 ± 1.09% (high variability) at 5.94 ± 0.70 km·h⁻¹ (p = 0.001). These results indicate that while the metabolic demand and kinematics variables change linearly with increasing speed, the most effective GV was observed at SS. Therefore, SS could be a new methodological approach to choose the individual walking speed, normalize the speed intensity, and avoid a gait pattern alteration.

The rhythmic control of the lower limb muscles influences the cycle-to-cycle variability during a walking task. The benefits of insoles, commonly used to improve the walking gait, have been little studied. Therefore, the aim of this study was to assess the walking gait variability and stability on different walking conditions (without shoes, WTS, with shoes, WS, with shoes and insoles, WSI) related to brain activity. Twelve participants randomly (WTS/WS/WSI) walked on a treadmill at 4 km/h for 10 min. Kinematic analysis (i.e., footstep and gait variability), brain activation (beta wave signal), rating of perceived exertion (RPE, CR-10 scale), and time domain measures of walking variability were assessed. The maximum Lyapunov exponent (LyE) on the stride cycle period’s datasets was also calculated. Stride length and cycle calculated for all walking conditions were 61.59 ± 2.53/63.38 ± 1.43/64.09 ± 2.40 cm and 1.11 ± 0.03/1.14 ± 0.03/1.15 ± 0.04 s (F_1,10 = 4.941/p = 0.01, F_1,10 = 4.938/p = 0.012) for WTS, WS, WSI, respectively. Beta wave (F_1,10 = 564.201/p = 0.0001) was higher in WTS compared to WS and WSI. Analysis of variance’s (ANOVA) LyE showed a F_1,10 = 3.209/p = 0.056, while post hoc analysis showed a significant effect between WS and WSI with p = 0.023, and nonsignificant effects between WTS and WS/WSI (p = 0.070/0.607), respectively. Small perturbations of the foot can influence the control of gait rhythmicity by increasing the variability in a dissipative deterministic regimen.

The present study investigated whether or not verbal instruction affects the electromyographic (EMG) amplitude of back-squat prime movers. Fifteen resistance-trained men performed back-squat at 50%1-RM and 80%1-RM and received external (EF) or internal focus (IF) on lower-limb posterior muscles. EMG amplitude of gluteus maximus, biceps femoris, gastrocnemius medialis, vastus lateralis, and tibialis anterior was recorded during both concentric and eccentric phases. During the concentric phase, the gluteus maximus and biceps femoris EMG amplitude was greater in IF vs EF at 50% [effect size (ES): 0.63 (95%CI 0.09/1.17) and 0.49 (0.10/0.78), respectively] and 80% [ES: 1.30 (0.29/2.21) and 0.59 (0.08/1.10)]. The gastrocnemius medialis EMG amplitude was greater in IF vs EF during the eccentric phase at 50% [ES: 0.73 (0.13/1.33)] and at 80% [ES: 0.72 (0.10/1.34)]. Concomitantly, vastus lateralis EMG amplitude was lower at 50% [ES: −0.71 (−1.38/-0.04)] and 80% [ES: −0.68 (−1.33/-0.03)]. During the eccentric phase, the tibialis anterior EMG amplitude was greater in IF vs EF at 50% [ES: 0.90 (0.12 to 1.68)] and 80% [ES: 0.74 (0.13/1.45)]. Irrespective of the load, in the thigh muscles the internal focus promoted a different motor pattern, increasing the hip extensors and reducing the knee extensor excitation during the concentric phase. Concomitantly, both ankle muscles were more excited during the eccentric phase, possibly to increase the anterior–posterior balance control. The internal focus in back-squat seems to have phase-dependent effects, and it is visible at both moderate and high loads.

The current study aimed to verify whether or not passive static stretching affects balance control capacity. Thirty-eight participants (19 women and 19 men) underwent a passive static stretching session, involving the knee extensor/flexor and dorsi/plantarflexor muscles, and a control session (no stretching, CTRL). Before (PRE), immediately after (POST), after 15 (POST₁₅) and 30 min (POST₃₀) from stretching (or rest in CTRL), balance control was evaluated under static and dynamic conditions, with open/closed eyes, and with/without somatosensory perturbation (foam under the feet). During tests, centre of pressure (CoP) sway area and perimeter and antero-posterior and medio-lateral sway mean speed were computed. Surface electromyography root mean square (sEMG RMS) was calculated from the vastus lateralis, biceps femoris, gastrocnemius medialis, and tibialis anterior muscles during MVC and during the balance tests. Hip flexion/extension and dorsi/plantarflexion range of motion (ROM), maximum voluntary contraction (MVC) and sEMG RMS during MVC were measured at the same time points. After stretching, ROM increased (≈6.5%; P<0.05), while MVC and sEMG RMS decreased (≈9% and ≈7.5%, respectively; P<0.05). Regardless of the testing condition, CoP sway area and the perimeter remained similar, while antero-posterior and medio-lateral sway mean speed decreased by ≈8% and ≈12%, respectively (P<0.05). sEMG RMS during the balance tests increased in all muscles in POST (≈7%, P<0.05). All variables recovered in POST₃₀. No changes occurred in CTRL. Passive static stretching did not affect the overall balance control ability. However, greater muscle activation was required to maintain similar CoP sway, thus suggesting a decrease in muscle efficiency.

The present study investigated the relationship between local fat percentage (SK_fat) and muscle quality (MQ) estimated by a new hand‐held electrical impedance myography (hEIM) device or derived from ultrasound and strength assessments. The right anterior thigh of 90 healthy participants (mean ± SD; age=22.9 ± 2.9 years; 45 men: BMI = 23.9 ± 2.4 kgm⁻²; 45 women: BMI = 21.1 ± 1.9 kgm⁻²) was scanned by hEIM and ultrasound. Correlations between SK_fat, local subcutaneous fat (SUB_fat), and echo intensity (EI_us) were explored. Correlations between MQ, EI_us, quadriceps femoris anatomical cross‐sectional area (ACSA_QF), knee extensors maximum voluntary isometric torque (T), T/ACSA_QF, EI_us/SUB_fat, and ACSA_QF/SUB_fat were also assessed. SK_fat correlated with SUB_fat (r = 0.88; p < 0.001) and EI_us (r = 0.64; p < 0.001). MQ correlated with EI_us (r = −0.66; p < 0.001), ACSA_QF (r = 0.37; p < 0.001), EI_us/SUB_fat (r = 0.37; p < 0.001), and ACSA_QF/SUB_fat (r = 0.81; p < 0.001). Multiple regression analysis showed that SUB_fat, EI_us, and sex explained 86% of SK_fat variance, whereas ACSA_QF/SUB_fat, sex and EI_us explained 75% of MQ variance. In conclusion, high hEIM local fat percentage relates to greater subcutaneous fat and intramuscular non‐contractile tissue content. High hEIM muscle quality relates to greater muscle‐size:subcutaneous‐fat ratio and contractile tissue content. Sex influences the prediction of both parameters. This hEIM device seems to be useful to estimate local thigh composition.

Purpose: Continuous incremental protocols (CP) may misestimate the maximum aerobic velocity (V_max) due to increases in running speed faster than cardiorespiratory/metabolic adjustments. A higher aerobic capacity may mitigate this issue due to faster pulmonary oxygen uptake ( $$\dot{V}$$V ˙ O₂) kinetics. Therefore, this study aimed to compare three different protocols to assess V_max in athletes with higher or lower training status. Methods: Sixteen well-trained runners were classified according to higher (HI) or lower (LO) $$\dot{V}$$V ˙ O_2max $$\dot{V}$$V ˙ O₂-kinetics was calculated across four 5-min running bouts at 10 km·h⁻¹. Two CPs [1 km·h⁻¹ per min (CP1) and 1 km·h⁻¹ every 2-min (CP2)] were performed to determine V_max $$\dot{V}$$V ˙ O_2max, lactate-threshold and submaximal $$\dot{V}$$V ˙ O₂/velocity relationship. Results were compared to the discontinuous incremental protocol (DP). Results: V_max, $$\dot{V}$$V ˙ O_2max, $$\dot{V}$$V ˙ CO₂ and VE were higher [(P < 0.05,(ES:0.22/2.59)] in HI than in LO. $$\dot{V}$$V ˙ O₂-kinetics was faster [P < 0.05,(ES:-2.74/ − 1.76)] in HI than in LO. $$\dot{V}$$V ˙ O₂/velocity slope was lower in HI than in LO [(P < 0.05,(ES:-1.63/ − 0.18)]. V_max and $$\dot{V}$$V ˙ O₂/velocity slope were CP1 > CP2 = DP for HI and CP1 > CP2 > DP for LO. A lower [P < 0.05,(ES:0.53/0.75)] V_max-difference for both CP1 and CP2 vs DP was found in HI than in LO. V_max-differences in CP1 vs DP showed a large inverse correlation with V_max, $$\dot{V}$$V ˙ O_2max and lactate-threshold and a very large correlation with $$\dot{V}$$V ˙ O₂-kinetics. Conclusions: Higher aerobic training status witnessed by faster $$\dot{V}$$V ˙ O₂ kinetics led to lower between-protocol V_max differences, particularly between CP2 vs DP. Faster kinetics may minimize the mismatch issues between metabolic and mechanical power that may occur in CP. This should be considered for exercise prescription at different percentages of V_max.