Recent evidence shows that simple and inexpensive anthropometric measurements can be used to identify, at an early stage, women with obesity at increased risk of developing metabolic syndrome (MetS). Thus, the aim of this study was to compare the accuracy of five different indexes of adiposity and/or body composition in identifying MetS in a group of 876 women (mean age ± SD: 52.1 ± 13.8 years; body mass index (BMI): 43.6 ± 6.1 kg m⁻²). The following indexes were determined for each subject: waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), body mass fat index (BMFI), visceral adiposity index (VAI), and cardiometabolic index (CMI). Overall, the presence of MetS was detected in 544 patients (62%). Pearson correlation coefficients were calculated to evaluate the relationships between body composition indexes and metabolic characteristics of the women. Receiver operating characteristic (ROC) analysis was used to determine the best predictor for each adiposity index among metabolic risk factors. The ROC analysis showed VAI (AUC = 0.84) and CMI (AUC = 0.86) showed the best performance in predicting MetS. Differences were found between the ROC area of CMI and VAI with all other indexes (p < 0.05). The optimal cutoff point for early diagnosis of MetS was >0.92 for WHR, >0.76 for WtHR, >30.1 kg m⁻¹ for BMFI, >1.94 for VAI, and >0.84 for CMI. In addition, VAI and CMI were the most sensitive and specific indexes compared with other indexes. In conclusion, VAI and CMI represent the most useful and reliable indexes to be used for detecting MetS in women suffering from obesity in clinical practice.

Purpose: To investigate the effects of an uphill marathon (43 km, 3063-m elevation gain) on running mechanics and neuromuscular fatigue in lower-limb muscles.Methods: Maximal mechanical power of lower limbs (MMP), temporal tensiomyographic (TMG) parameters, and muscle-belly displacement (D_m) were determined in the vastus lateralis muscle before and after the competition in 18 runners (age 42.8 ± 9.9 y, body mass 70.1 ± 7.3 kg, maximal oxygen uptake 55.5 ± 7.5 mL · kg⁻¹ · min⁻¹). Contact (t_c) and aerial (t_a) times, step frequency (f), and running velocity (v) were measured at 3, 14, and 30 km and after the finish line (POST). Peak vertical ground-reaction force (F_max), vertical displacement of the center of mass (Δz), leg-length change (ΔL), and vertical (k_vert) and leg (k_leg) stiffness were calculated.Results: MMP was inversely related with race time (r = –.56, P = .016), t_c (r = –.61, P = .008), and Δz (r = –.57, P = .012) and directly related with F_max (r = .59, P = .010), t_a (r = .48, P = .040), and k_vert (r = .51, P = .027). In the fastest subgroup (n = 9) the following parameters were lower in POST (P < .05) than at km 3: t_a (–14.1% ± 17.8%), F_max (–6.2% ± 6.4%), k_vert (–17.5% ± 17.2%), and k_leg (–11.4% ± 10.9%). The slowest subgroup (n = 9) showed changes (P < .05) at km 30 and POST in F_max (–5.5% ± 4.9% and –5.3% ± 4.1%), t_a (–20.5% ± 16.2% and –21.5% ± 14.4%), t_c (5.5% ± 7.5% and 3.2% ± 5.2%), k_vert (–14.0% ± 12.8% and –11.8% ± 10.0%), and k_leg (–8.9% ± 11.5% and –11.9% ± 12%). TMG temporal parameters decreased in all runners (–27.35% ± 18.0%, P < .001), while D_m increased (24.0% ± 35.0%, P = .005), showing lower-limb stiffness and higher muscle sensibility to the electrical stimulus.Conclusions: Greater MMP was related with smaller changes in running mechanics induced by fatigue. Thus, lower-limb power training could improve running performance in uphill marathons.

The prevalence of obesity in children has increased dramatically during the past decades in Europe and understanding physical fitness and its components in children is critical to design and implement effective interventions. The objective of the present study was to analyse the association between physical fitness (aerobic, speed, agility, power, flexibility and balance) and body mass index (BMI) in pre‐pubertal children. A total of 2411 healthy schoolchildren (7–11 years) participated in this study. Anthropometric characteristics and body composition were assessed by skinfold thickness. Physical fitness was measured by nine physical fitness tests: endurance running, 20 m running speed, agility, handgrip strength, standing long jump and squat jump, sit and reach, medicine ball forward throw and static balance. No relevant differences were observed between boys and girls regarding anthropometric characteristics, body composition and physical fitness. However, overweight and obese children showed significantly lower physical fitness levels in endurance running, speed and agility (mean: +18.8, +5.5 and +14.5% of time to complete tasks, respectively), lower limb power normalised to body mass (−23.3%) and balance tests (number of falls: +165.5%) than their normal weight counterparts. On the other hand, obesity did not affect handgrip, throwing and flexibility. In conclusion, increased BMI was associated with lower performance capabilities limiting proper motor skill development, which directly affects the ability of children to take on sports skills. Actions undertaken to promote children's wellness and fitness should be prioritised and introduced early in life with the aim of enhancing physical fitness as well as preventing overweight and obesity.

In recent years, different companies have developed devices that estimate “running power”. The main objective of this paper is to evaluate the effect of running speed on aerobic and running powers measured using force plates and by different devices. The second objective is to evaluate the relationship between aerobic power and running powers measured using force plates and by different devices. We enrolled 11 subjects in the study, they performed 5‐min running trials at 2.22, 2.78, 3.33, 3.89 and 4.44 m/s respectively on a force‐measuring treadmill while we collected metabolic data. We calculated running power as the dot product of ground reaction force and velocity of the centre of mass and compared it to the running power estimates of three devices: Skillrun (Technogym), Stryd Summit Powermeter (Stryd) and Garmin HRM‐Run (Garmin). We found statistically significant linear correlations with running powers measured by all devices and running speed. Although absolute running power measurements were different among devices, an increase of 1 m/s in running speed translated to an increase of 0.944 W/kg in running power (p < 0.001). We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices (p < 0.001). For level treadmill running, across speeds, running power measured by commercially available devices reflects force‐based measurements and it can be a valuable metric, providing quasi real‐time feedback during training sessions and competitions.Highlights We evaluated the effect of running speed on aerobic and running powers measured using force plates and by different devices. We also compared the relationship between aerobic power and running powers measured using force plates and by different devices. We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices. For level treadmill running, across speeds, running power measured by commercially available devices reflects force‐based measurements and it can be a valuable metric, providing quasi real‐time feedback during training sessions and competitions.