The human locomotion has been constantly analysed from both bioenergetics and biomechanical point of views (Saibene & Minetti, 2003; Cavagna, 2010). Since earliest times, hunting for food and escaping from predators already has proven how important is to comprehend this complex engineering that is our locomotor machine. Gradient locomotion has been investigated in the past, and the concept of the optimum gradient for walking, running and mountain paths are well known in the literature. The existence of an optimum gradient is based on the different partitioning between positive and negative mechanical work (that mirrors concentric and eccentric muscular activity) and the related metabolic demand. In the literature, the ratio between negative and positive work efficiency during unloaded locomotion was found to be 5/1. The purpose of this new study is to analyse the mechanical, metabolic and electromyography parameters during gradient loaded walking in order to understand how an extra-load can affect locomotion and especially the efficiency of positive and negative work. Still, another important topic referred to the human locomotion physiology is about the cardiovascular system. Related to this, oxygen uptake (V'O2) refers to the product of cardiac output and the volume of oxygen extracted from the blood, and its maximal value (V'O2peak) strengthen the maximal capacity of the cardiovascular system to provide O2 to muscle cells during continued exercise, being the most widely used measure of physical fitness (Plasqui & Westerterp, 2005; Koeneman et al., 2011). Although there is a large genetic component, it is mainly determined by a person’s activity level, and inversely related to several health outcomes such as cardiovascular disease (Daanen et al., 2012). Besides of heart rate (HR) control and its relationship with V'O2, the HR recovery (off transient, after exercise) has received more attention by current researchers (Myers et al., 2007; Dupuy et al., 2012; Haddad et al., 2012). The rate of decline in HR following termination of exercise, which is regulated by the autonomic nervous system and thereby, provides information concerning sympathetic and parasympathetic activity (Daanen et al., 2012). In general, the more rapid the HR recovery, the better the fitness (Daanen et al., 2012; Buchheit, 2014). While exercise-training studies usually report HR values at a given time during the recovery period (Daanen et al., 2012), in most clinical studies, HR recovery is defined as the difference between HR at the end of exercise and HR at a given time during the recovery period (Otsuki et al., 2007; Dupuy et al., 2012). Moreover, in some studies a mono-exponential model fit the HR off-kinetics to derive global parameters of HR recovery kinetics such as the time constant or the asymptotic value (Stanley et al., 2013; Peinado et al., 2014). Based on these results and the growing interest in new smart devices for health monitoring, here we aimed to estimate V'O2peak from a short test (60 m) with variables that can be detected by the smart sensors. We ask to 25 healthy subjects to perform a maximal sprint over 60-m. Beat-by-beat HR was recorded by a chest belt during the whole test including resting period before and recovery post sprint. (n = 25). HR off kinetics was fitted by a mono-exponential function and tau value was calculated in order to obtain a velocity of HR decrement post exercise. V'O2peak was then estimate with a multiple regression analysis: V'O2peak = 7.46∙vtest + 261.4∙voff - 0.19∙∆HR (R2= 0.65, p<0.001). Where vtest represents the velocity performed during the 60-m test, voff corresponds to the velocity of HR decreasing during off-transient (recovery phase), and "∆HR" is the difference of HR during on-transient of exercise and it is the difference between maximum and the resting value. This new equation does not aim to replace the laboratory-standard protocols for V'O2peak determination, but it can give an insight about fitness level to laymen that use smart devices for monitoring their physical activity. Whenever these new models (smart watches) would perform a beat-by-beat analysis this equation could be introduced to the software and give a first general estimate of the user's fitness level.

CURRENT TOPICS IN LOCOMOTION PHYSIOLOGY: A) MUSCLE EFFICIENCY IN HEAVILY LOADED GRADIENT WALKING AND B) HEART RATE OFF-KINETICS AS A PREDICTOR OF VO2MAX / J.l. Lopes Storniolo Junior ; tutor: A. Minetti ; coordinatore: C. Sforza. - : . DIPARTIMENTO DI FISIOPATOLOGIA MEDICO-CHIRURGICA E DEI TRAPIANTI, 2018 Nov 07. ((30. ciclo, Anno Accademico 2017. [10.13130/lopes-storniolo-junior-jorge-luiz_phd2018-11-07].

CURRENT TOPICS IN LOCOMOTION PHYSIOLOGY: A) MUSCLE EFFICIENCY IN HEAVILY LOADED GRADIENT WALKING AND B) HEART RATE OFF-KINETICS AS A PREDICTOR OF VO2MAX

J.L. LOPES STORNIOLO JUNIOR
2018

Abstract

The human locomotion has been constantly analysed from both bioenergetics and biomechanical point of views (Saibene & Minetti, 2003; Cavagna, 2010). Since earliest times, hunting for food and escaping from predators already has proven how important is to comprehend this complex engineering that is our locomotor machine. Gradient locomotion has been investigated in the past, and the concept of the optimum gradient for walking, running and mountain paths are well known in the literature. The existence of an optimum gradient is based on the different partitioning between positive and negative mechanical work (that mirrors concentric and eccentric muscular activity) and the related metabolic demand. In the literature, the ratio between negative and positive work efficiency during unloaded locomotion was found to be 5/1. The purpose of this new study is to analyse the mechanical, metabolic and electromyography parameters during gradient loaded walking in order to understand how an extra-load can affect locomotion and especially the efficiency of positive and negative work. Still, another important topic referred to the human locomotion physiology is about the cardiovascular system. Related to this, oxygen uptake (V'O2) refers to the product of cardiac output and the volume of oxygen extracted from the blood, and its maximal value (V'O2peak) strengthen the maximal capacity of the cardiovascular system to provide O2 to muscle cells during continued exercise, being the most widely used measure of physical fitness (Plasqui & Westerterp, 2005; Koeneman et al., 2011). Although there is a large genetic component, it is mainly determined by a person’s activity level, and inversely related to several health outcomes such as cardiovascular disease (Daanen et al., 2012). Besides of heart rate (HR) control and its relationship with V'O2, the HR recovery (off transient, after exercise) has received more attention by current researchers (Myers et al., 2007; Dupuy et al., 2012; Haddad et al., 2012). The rate of decline in HR following termination of exercise, which is regulated by the autonomic nervous system and thereby, provides information concerning sympathetic and parasympathetic activity (Daanen et al., 2012). In general, the more rapid the HR recovery, the better the fitness (Daanen et al., 2012; Buchheit, 2014). While exercise-training studies usually report HR values at a given time during the recovery period (Daanen et al., 2012), in most clinical studies, HR recovery is defined as the difference between HR at the end of exercise and HR at a given time during the recovery period (Otsuki et al., 2007; Dupuy et al., 2012). Moreover, in some studies a mono-exponential model fit the HR off-kinetics to derive global parameters of HR recovery kinetics such as the time constant or the asymptotic value (Stanley et al., 2013; Peinado et al., 2014). Based on these results and the growing interest in new smart devices for health monitoring, here we aimed to estimate V'O2peak from a short test (60 m) with variables that can be detected by the smart sensors. We ask to 25 healthy subjects to perform a maximal sprint over 60-m. Beat-by-beat HR was recorded by a chest belt during the whole test including resting period before and recovery post sprint. (n = 25). HR off kinetics was fitted by a mono-exponential function and tau value was calculated in order to obtain a velocity of HR decrement post exercise. V'O2peak was then estimate with a multiple regression analysis: V'O2peak = 7.46∙vtest + 261.4∙voff - 0.19∙∆HR (R2= 0.65, p<0.001). Where vtest represents the velocity performed during the 60-m test, voff corresponds to the velocity of HR decreasing during off-transient (recovery phase), and "∆HR" is the difference of HR during on-transient of exercise and it is the difference between maximum and the resting value. This new equation does not aim to replace the laboratory-standard protocols for V'O2peak determination, but it can give an insight about fitness level to laymen that use smart devices for monitoring their physical activity. Whenever these new models (smart watches) would perform a beat-by-beat analysis this equation could be introduced to the software and give a first general estimate of the user's fitness level.
MINETTI, ALBERTO ENRICO
SFORZA, CHIARELLA
7-nov-2018
Walking; Gradients; Mechanical efficiency; Maximum aerobic power; Heart Rate; Off-kinetics; Sprint running; Smart devices
Settore BIO/09 - Fisiologia
Settore M-EDF/02 - Metodi e Didattiche delle Attivita' Sportive
CURRENT TOPICS IN LOCOMOTION PHYSIOLOGY: A) MUSCLE EFFICIENCY IN HEAVILY LOADED GRADIENT WALKING AND B) HEART RATE OFF-KINETICS AS A PREDICTOR OF VO2MAX / J.l. Lopes Storniolo Junior ; tutor: A. Minetti ; coordinatore: C. Sforza. - : . DIPARTIMENTO DI FISIOPATOLOGIA MEDICO-CHIRURGICA E DEI TRAPIANTI, 2018 Nov 07. ((30. ciclo, Anno Accademico 2017. [10.13130/lopes-storniolo-junior-jorge-luiz_phd2018-11-07].
Doctoral Thesis
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