Arterial stiffness assessment by pulse wave velocity in postmenopausal women: comparison between noninvasive devices.

OBJECTIVE: This study aimed to ascertain the accuracy of measure arterial stiffness using the HUAWEI GT 3 Pro smartwatch and pOpmètre device against the SphygmoCor (algorithms: intersect tangent and maximum of the second derivate). METHODS: Twenty-three physically active postmenopausal women (age: 58.9 ± 3.2 years; body mass index: 26.3 ± 4.8 kg/m 2 ) were recruited. Carotid-femoral pulse wave velocity, finger-toe pulse wave velocity, and wrist-finger pulse wave velocity were obtained using SphygmoCor, pOpmètre and HUAWEI GT 3 Pro devices in a randomized order. Additionally, the pulse mean carotid-femoral and finger-toe pulse transit time was registered for SphygmoCor and pOpmètre, respectively. RESULTS: Lower values of pulse wave velocity were recorded by HUAWEI in comparison with SphygmoCor with both algorithms, whereas no significant differences were detected between SphygmoCor and pOpmètre results. Pulse wave velocity values from SphygmoCor were positively correlated with pOpmètre results ( r = 0.464 and r = 0.451 using intersect tangent and second derivative algorithms), whereas this was not the case with those obtained from HUAWEI. Coefficients of bias of Lin's concordance coefficients close to 1 (0.832 and 0.831 for intersect tangent and second derivative algorithm, respectively) and mean bias close to 0 from Bland-Altman analysis suggested an acceptable agreement between pulse wave velocity obtained from SphygmoCor and pOpmètre. CONCLUSIONS: Our results suggest an acceptable concordance of pulse wave velocity values recoded by SphygmoCor and pOpmètre, whereas this was not the case for data obtained from HUAWEI GT 3 Pro smartwatch. Therefore, the pOpmètre may be a viable alternative for assessing arterial stiffness, but measurement via the smartwatch device cannot be recommended.

Face Masks at the Gymnasium: Physiological Responses and Mechanical Performance Are Not Compromised by Wearing Surgical or Filtering Facepiece 2 Masks in Healthy Subjects.

ABSTRACT: Rial-Vázquez, J, Nine, I, Guerrero-Moreno, JM, Rúa-Alonso, M, Fariñas, J, Márquez, G, Giráldez-García, MA, Méndez-Bouza, KY, López-Pillado, H, Coutado-Sánchez, E, Losada-Rodríguez, A, and Iglesias-Soler, E. Face masks at the gym: physiological responses and mechanical performance are not compromised by wearing surgical or filtering facepiece 2 masks in healthy subjects. J Strength Cond Res 37(7): 1404-1410, 2023-This study explored the effects of wearing 2 types of face masks on mechanical performance and physiological responses during high-intensity resistance exercise. Twelve healthy men performed 3 workout protocols in a randomized order: wearing a surgical or filtering facepiece 2 (FFP2) mask or without a mask. Each workout consisted of 3 sets of 10 repetitions of bench press (BP) and parallel squat (SQ) with a 12 repetition maximum load, including 2 minutes of recovery between sets and exercises. Mechanical performance was evaluated through the mean propulsive velocity and the number of repetitions completed during each session. Physiological responses were the oxygen saturation (SpO2), blood lactate concentration, heart rate (HR), and HR variability. Perceived exertion was recorded after each set, and The Beck Anxiety Inventory scale was completed at the end of each workout. The number of repetitions completed and the session mean propulsive velocity {(BP [m·s-1]: surgical: 0.35 ± 0.05; FFP2: 0.36 ± 0.04; nonmask: 0.38 ± 0.06) and (SQ: surgical: 0.43 ± 0.05; FFP2: 0.40 ± 0.07; nonmask: 0.41 ± 0.05)} were similar between conditions (p > 0.05). Heart rate recorded during sessions was similar across conditions: surgical: 119 ± 14, FFP2: 117 ± 13, and nonmask: 118 ± 10 bpm (p = 0.919). Face masks had no effect on SpO2, blood lactate concentration, HR variability, perceived exertion, and anxiety values (p > 0.05). Face masks do not compromise strength performance, physiological parameters, and perceived comfort of young and healthy individuals during a high-intensity resistance training session.

A Simple Equation to Estimate Half-Marathon Race Time From the Cooper Test

BACKGROUND: Half-marathon races have become increasingly more popular with many recreational athletes all around the world. New and recreational runners are likely to have the greatest need for training advice to set running paces during long-distance races. PURPOSE: To develop a simple equation to estimate half-marathon time from the Cooper test and verify its validity. METHODS: One hundred ninety-eight recreational runners (177 men and 21 women, 40 [6.8] years and 33.7 [8] years, respectively) participated in this study. All runners completed the Cooper test 7 to 10 days prior to races. A stepwise multiple regression analysis was performed to select the main predictors of half-marathon time. RESULTS: Simple correlation analysis showed that Cooper test performance (distance) was a good construct to estimate half-marathon time (r = -.906; 95% confidence interval, -0.927 to -0.877; P < .0001). The authors also derived an equation with a high predictive validity (R2 = .82; standard error of estimation = 5.19 min) and low systematic bias (mean differences between the predicted value and the criterion of 0.48 [5.2] min). Finally, the concordance coefficient of correlation (.9038) and proportional bias analysis (Kendall τ = -.0799; 95% confidence interval, -0.184 to 0.00453; P = .09) confirmed a good concurrent validity. CONCLUSION: In this study, the authors derived an equation from the Cooper test data with a high predictive and concurrent validity and low bias.

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests.

This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper's original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = -0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = -0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO2max), (r = -0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO2VT2) (r = -0.79; p < 0.0001), and VO2max (r = -0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 - 0.03433 (Cooper test in m) (R 2 = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R 2 = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 - 4.7194 (vVO2max) - 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.

El ejercicio físico, un recurso preventivo y terapéutico muy valioso y científicamente probado que deberíamos aprovechar mejor / Exercise is a scientifically-proven and valuable therapeutic and preventive tool, that everyone should take part in

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["In vivo" body composition assessment; part I: a historic overview]. / LA EVALUACIÓN DE LA COMPOSICIÓN CORPORAL "IN VIVO"; PARTE I: PERSPECTIVA HISTÓRICA.

The study of body composition (BC) has gained in relevance over the last decades, mainly because of its important health- and disease- related applications within both the clinical and the sports setting. It is not a new area, and its especial relevance as an area of biology dates from the second half of the nineteenth century. In this paper, we have reviewed the three historic periods of BC, with special reference to the most important advances in in vivo assessment. Even though the earliest findings about human BC date from antiquity, the first (or 'early') stage of discovery began in 1850. Said early stage was mainly characterized by data obtained from the dissection of cadavers and by the application of biochemical methods in vivo. Longitudinal changes in body composition were also a concern. The second (so called 'recent') stage, in the second half of the twentieth century, was marked by milestones such as the formulation of the first mathematical models for the estimation of body components, and technological advances. Within the third ('contemporary' or 'current') stage of research, several groups have focused on validating the classical BC models in specific populations, on analysis of the genetic determinants (i.e. phenotypes and, more recently genotypes) of body composition, and on re-instigating the study of dynamic BC.

El estudio de la composición corporal humana ha cobrado una relevancia creciente en las últimas décadas, debido a sus enormes aplicaciones en los terrenos clínico, deportivo y de la actividad física saludable. Sin embargo, no es un área de conocimiento de reciente creación, y su estudio dentro de la biología data ya de la segunda mitad del siglo xix. En este documento resumiremos los tres grandes periodos en los que se divide la investigación de la composición corporal humana, dando especial relevancia a los descubrimientos y avances para el estudio in vivo. Aunque históricamente podemos situar los primeros descubrimientos en la antigüedad, la primera etapa (temprana) comienza en el 1850, y está caracterizada principalmente por los datos obtenidos de la disección de cadáveres, y la utilización de los primeros métodos bioquímicos para el estudio in vivo y la observación de las alteraciones. En la segunda etapa (reciente) está protagonizada por el desarrollo de los primeros modelos matemáticos para estimar componentes corporales y por los grandes desarrollos tecnológicos de la segunda parte del siglo xx. En la tercera etapa (composición corporal en el siglo xxi o contemporánea) los estudios se están centrando en validar los modelos clásicos para poblaciones específicas, conocer los determinantes genéticos de la composición corporal (primero fenotipos y recientemente genotipos) y recuperar el estudio de la composición corporal dinámica.