Systematic Review and Regression Modeling of the Effects of Age, Body Size, and Exercise on Cardiovascular Parameters in Healthy Adults

Purpose Blood pressure, cardiac output, and ventricular volumes correlate to various subject features such as age, body size, and exercise intensity. The purpose of this study is to quantify this correlation through regression modeling. Methods We conducted a systematic review to compile reference data of healthy subjects for several cardiovascular parameters and subject features. Regression algorithms used these aggregate data to formulate predictive models for the outputs—systolic and diastolic blood pressure, ventricular volumes, cardiac output, and heart rate—against the features—age, height, weight, and exercise intensity. A simulation-based procedure generated data of virtual subjects to test whether these regression models built using aggregate data can perform well for subject-level predictions and to provide an estimate for the expected error. The blood pressure and heart rate models were also validated using real-world subject-level data. Results The direction of trends between model outputs and the input subject features in our study agree with those in current literature. Conclusion Although other studies observe exponential predictor-output relations, the linear regression algorithms performed the best for the data in this study. The use of subject-level data and more predictors may provide regression models with higher fidelity. Significance Models developed in this study can be useful to clinicians for personalized patient assessment and to researchers for tuning computational models

Application of Functional Data Analysis for the Prediction of Maximum Heart Rate

Maximum heart rate (MHR) is widely used in the prescription and monitoring of exercise intensity, and also as a criterion for the termination of sub-maximal aerobic fitness tests in clinical populations. Traditionally, MHR is predicted from an age-based formula, usually 220−age. These formulae, however, are prone to high predictive errors that potentially could lead to inaccurately prescribed or quantified training or inappropriate fitness test termination. In this paper, we used functional data analysis (FDA) to create a new method to predict MHR. It uses heart rate data gathered every 5 seconds during a low intensity, sub-maximal exercise test. FDA allows the use of all the information recorded by monitoring devices in the form of a function, reducing the amount of information needed to generalize a model, besides minimizing the curse of dimensionality. The functional data model created reduced the predictive error by more than 50% compared to current models within the literature. This new approach has important benefits to clinicians and practitioners when using MHR to test fitness or prescribe exerciseThis work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project TIN2015-73566-JIN, in part by the European Regional Development Fund (ERDF/FEDER), the Consellería de Cultura, Educación e Ordenación Universitaria (2016-2019), under Grant ED431G/08, in part by the Reference Competitive Group (2014-2017) under Grant GRC2014/030, in part by the 2016 Postdoctoral Training Grants, and in part by the European Regional Development Fund (ERDF)S

Growth, Somatic Maturation and Their Impact on Physical Health and Sports Performance

A total of 11 manuscripts focused on different topics related to youth and sports practice are published in this book. Three papers focus on aspects of physical performance, five papers provide innovative findings in relation to anthropometry and body composition features, one paper examines the difficulties in running online physical education classes in the context of COVID-19, and two focus on the influence of training strategies on muscle strength and blood pressure

REGRESSION ALGORITHMS IN ASSESSING THE IMPACT OF MORPHOLOGICAL AND MOTOR CHARACTERISTICS ON 60-M SPRINT

The aim of the present study was to identify the relationship between morphological parameters and motor skills that are important for sprint performance in children aged 8 to 16 years divided into four age groups (U10, U12, U14, U16) in both genders. The sample consisted of two hundred eighty one participant who trained sprinting in various athletic clubs. A prediction set of twenty-five variables for assessing morphological characteristics and motor skills was applied, and the criterion variable was a sprint at 60m. Using multiple correlation, it has been established that a large number of morphological characteristics are statistically significant positive correlation with the sprint, especially the longitudinal variables, while the variables of skinfolds showed a low negative statistical significance in relation to the given criterion. In the field of motor skills, the highest number of positive statistically significant correlations were found in the tests of explosive power of the upper and lower extremities, agility test and horizontal and vertical jump tests. In order to determine which morphological features and motor skills should be applied in sprint running training, we tested related attributes using different algorithms for data mining (LR, M5, KNN, SVM, MLP, RBF). The results suggests that the predictors that we use can continue to be applied with high reliability in assessing sprint performance, but also in the monitoring of the training process in order to profile the better sprint achievements.The aim of the present study was to identify the relationship between morphological parameters and motor skills that are important for sprint performance in children aged 8 to 16 years divided into four age groups (U10, U12, U14, U16) in both genders. The sample consisted of two hundred eighty one participant who trained sprinting in various athletic clubs. A prediction set of twenty-five variables for assessing morphological characteristics and motor skills was applied, and the criterion variable was a sprint at 60m. Using multiple correlation, it has been established that a large number of morphological characteristics are statistically significant positive correlation with the sprint, especially the longitudinal variables, while the variables of skinfolds showed a low negative statistical significance in relation to the given criterion. In the field of motor skills, the highest number of positive statistically significant correlations were found in the tests of explosive power of the upper and lower extremities, agility test and horizontal and vertical jump tests.In order to determine which morphological features and motor skills should be applied in sprint running training, we tested related attributes using different algorithms for data mining (LR, M5, KNN, SVM, MLP, RBF). The results suggests that the predictors that we use can continue to be applied with high reliability in assessing sprint performance, but also in the monitoring of the training process in order to profile the better sprint achievements.Key words: youth athletes, sprint running, morphological characteristics, motor skills, regression algorithm

Energy expenditure and accelerometer cut-points for sedentary behavior and physical activity in spinal cord injury : implication for guiding and prevention

Background: A motor-complete spinal cord injury (SCI) alters the prerequisites for physical activity (PA) and subsequently energy expenditure. Persons with SCI above thoracic level six have a compromised physiological response, which further compromises energy expenditure, during exercise. Weight gain and lower levels of PA increase the risk of lifestyle-related diseases and their risk factors. Yet little is known about energy expenditure and the intensity of different activities from rest to maximal effort, nor about clinically useful cut-points for accelerometer in motor-complete SCI. Aim: The aim of the work reported in this dissertation was to extend knowledge about energy expenditure, oxygen consumption and heart rate during rest, standardized activities and peak capacity in people with motor-complete SCI. A further aim was to evaluate how clinically accessible methods can be used to measure and describe activity patterns and intensity levels. Methods: Participants were 64 persons with motor-complete SCI. Seventeen were women. Twenty-six had tetraplegia (C5-C8) and 38 (T7-T12) had paraplegia. Studies I and II are based on data from indirect calorimetry during rest and during standardized activities. In study III data from peak capacity (VO2peak), peak heart rate (HRpeak) and Borg rating of perceived exertion (RPE) were used for categorizing standardized activities into different levels of intensity. In study IV dominant-wrist-worn accelerometer (ActiGraph GT3X+) cutpoints were created by using receiver operating characteristic (ROC) curves, and the relative intensities established in Study III. Results: Studies I and II showed that mean resting oxygen consumption for the whole group, no gender differences, was 2.52 ml·kg-1 ·min-1 and the variable that best explained the variance for energy expenditure during rest (24 hours) was bodyweight r 2= 0.37 for the total cohort. During non-exercise activities (wheeling indoors/outdoors Borg RPE 10-11 and setting table), the activity energy expenditure (total energy expenditure minus resting energy expenditure) for tetraplegia increased between two and four times compared to sedentary, and between three and five times during exercise activities. Motor-complete paraplegia could increase energy expenditure between three and six times during non-exercise activities and between 6 and 14 times during exercise. In study III absolute VO2peak was 0.76 L∙min-1 in tetraplegia and 1.36 L∙min-1 in paraplegia, differing significantly between men and women for both tetraplegia and paraplegia (p≤0.001). The significant difference disappeared for both groups when the VO2 was related to body weight (p=0.43). Further, in study III all activities were categorized into sedentary, light, moderate and vigorous levels of intensity, based on percentage of VO2peak, heart rate and Borg. Thus, many of the non-exercise physical activities (NEPA) were categorized as moderate or vigorous for persons with tetraplegia. Study IV showed a high correlation of 0.8-0.9 between percentage of VO2peak, absolute VO2 (MET) and accelerometer vector magnitude counts (VMC). The ROC curve analysis showed an area under the curve (AUC) of 0.8, which resulted in cut-points for different intensity levels such as, moderate-to-vigorous intensity of 4887 VMC (tetraplegia) and 9515 VMC (paraplegia). Conclusion: Given the large inter-individual differences, person-specific information regarding RMR is crucial. The VO2peak was lower for person with tetraplegi, affecting the relative intensity level for activities of daily living. Activity energy expenditure, especially during daily activities, may increase total daily energy expenditure since daily activities are easily accessible and can be performed for long periods. Specific accelerometer cut-points for motor-complete tetraplegia and paraplegia from ROC curve analysis may be used in rehabilitation and research to capture activity patterns objectively

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices

Body Composition and Physical Health in Sports Practice

Research on human body composition has gained relevance given the recognized health impact of several body components. Many contemporary scientists have contributed to the field of body composition research as it exists today, even though interest in the topic extends back several thousand years. Quantifying human body composition in sports practice plays an important role in monitoring athletes' health status, performances, and training regimens. Such analysis can be performed in different contexts and with different approaches—e.g., in cross-sectional studies that aim to characterize sporting group samples and in longitudinal research finalized to define short-term or long-term changes and implications for physical health and performance. Body composition is also fundamental for a correct interpretation of body mass and weight status to plan specific interventions. This book adds new information on the effect of body composition on physical health and sport performance, current body composition measurement techniques and strategies for improving physical health through sports practice