Ventilatory responses at submaximal exercise intensities in healthy children and adolescents during the growth spurt period: a semi-longitudinal study.

PURPOSE: To identify the changes of ventilation ([Formula: see text]E), tidal volume (VT) and respiratory frequency (fr) at different incremental step test intensities during maturation of children and adolescents. METHODS: A semi-longitudinal study was conducted on 68 healthy untrained boys and girls aged 11-17 years. The subjects were separated into three distinct age groups. [Formula: see text]E, VT and fr parameters were evaluated annually during 3 years by modifying incremental step test intensities according to ventilatory threshold (VTh) level (30, 60 and 90% of [Formula: see text]O2max). Absolute and relative values of ventilatory responses were analyzed and compared according to age and developmental phase. RESULTS: (1) Height, weight, lean body mass and vital capacity increased significantly from 11 to 17 years of age. (2) [Formula: see text]O2max, [Formula: see text]E, and VT increased during maturation even when exercise intensity changed, especially from 11 to 15 years of age. On the other hand, fr showed a decreasing trend. CONCLUSION: Increases of VT are the main reason for [Formula: see text]E increases during maturation of children. fr decreased independently of total body mass during maturation. [Formula: see text]E.kg-1 was stable despite intensity variations. VT.kg-1 increased significantly from 11 to 15 years then stabilized at 17 years. Lean body mass seems to explain the evolution of VT.kg-1 during maturation.

Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning.

PURPOSE: The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. METHOD: We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12  km [Formula: see text] and ergometer cycling at 90 and 110 W). RESULTS: The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L). CONCLUSION: Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.

Effects of recovery mode (active vs. passive) on performance during a short high-intensity interval training program: a longitudinal study.

The aim of this longitudinal study was to compare two recovery modes (active vs. passive) during a seven-week high-intensity interval training program (SWHITP) aimed to improve maximal oxygen uptake ([Formula: see text]), maximal aerobic velocity (MAV), time to exhaustion (t lim) and time spent at a high percentage of [Formula: see text], i.e., above 90 % (t90 [Formula: see text]) and 95 % (t95 [Formula: see text]) of [Formula: see text]. Twenty-four adults were randomly assigned to a control group that did not train (CG, n = 6) and two training groups: intermittent exercise (30 s exercise/30 s recovery) with active (IEA, n = 9) or passive recovery (IEP, n = 9). Before and after seven weeks with (IEA and IEP) or without (CG) high-intensity interval training (HIT) program, all subjects performed a maximal graded test to determine their [Formula: see text] and MAV. Subsequently only the subjects of IEA and IEP groups carried out an intermittent exercise test consisting of repeating as long as possible 30 s intensive runs at 105 % of MAV alternating with 30 s active recovery at 50 % of MAV (IEA) or 30 s passive recovery (IEP). Within IEA and IEP, mean t lim and MAV significantly increased between the onset and the end of the SWHITP and no significant difference was found in t90 VO2max and t95 VO2max. Furthermore, before and after the SWHITP, passive recovery allowed a longer t lim for a similar time spent at a high percentage of VO2max. Finally, within IEA, but not in IEP, mean VO2max increased significantly between the onset and the end of the SWHITP both in absolute (p < 0.01) and relative values (p < 0.05). In conclusion, our results showed a significant increase in VO2max after a SWHITP with active recovery in spite of the fact that t lim was significantly longer (more than twice longer) with respect to passive recovery.

Energy Expenditure Estimation From Respiratory Magnetometer Plethysmography: A Comparison Study.

Physical activity (PA) quantification by estimating energy expenditure (EE) is essential to health. Reference methods for EE estimation often involve expensive and cumbersome systems to wear. To address these problems, light-weighted and cost-effective portable devices are developed. Respiratory magnetometer plethysmography (RMP) is among such devices, based on the measurements of thoraco-abdominal distances. The aim of this study was to conduct a comparative study on EE estimation with low to high PA intensity with portable devices including the RMP. Fifteen healthy subjects aged 23.84±4.36 years were equipped with an accelerometer, a heart rate (HR) monitor, a RMP device and a gas exchange system, while performing 9 sedentary and physical activities: sitting, standing, lying, walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 and 110 W. An artificial neural network (ANN) as well as a support vector regression algorithm were developed using features derived from each sensor separately and jointly. We compared also three validation approaches for the ANN model: leave one out subject, 10 fold cross-validation, and subject-specific. Results showed that 1. for portable devices the RMP provided better EE estimation compared to accelerometer and HR monitor alone; 2. combining the RMP and HR data further improved the EE estimation performances; and 3. the RMP device was also reliable in EE estimation for various PA intensities.

Energy expenditure estimate by heart-rate monitor and a portable electromagnetic coils system.

The aim of this article was to compare 2 portable devices (a heart-rate monitor and an electromagnetic-coil system) that evaluate 2 different physiological parameters--heart rate (HR) and ventilation (VE)--with the objective of estimating energy expenditure (EE). The authors set out to prove that VE is a more pertinent setting than HR to estimate EE during light to moderate activities (sitting and standing at rest and walking at 4, 5, and 6 km/hr). Eleven healthy men were recruited to take part in this study (27.6 ± 5.4 yr, 73.7 ± 9.7 kg). The authors determined the relationships between HR and EE and between VE and EE during light to moderate activities. They compared EE measured by indirect calorimetry (EEREF) with EE estimated by HR monitor (EEHR) and EE estimated by electromagnetic coils (EEMAG) in upright sitting and standing positions and during walking exercises. They compared EEREF with EEHR and EEMAG. The results showed no significant difference between the values of EEREF and EEMAG. However, they showed several significant differences between the values of EEREF and EEHR (for standing at rest and walking at 5 and 6 km/hr). These results showed that the electromagnetic-coil system seems to be more accurate than the HR monitor to estimate EE at rest and during exercise. Taking into consideration these results, it would be interesting to associate the parameters VE and HR to estimate EE. Furthermore, a new version of the electromagnetic-coil device was recently developed and provides the possibility to perform measurement under daily life conditions.

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model. METHODS: Twenty-three healthy subjects in three groups (nine adults (A), eight post-pubertal (PP) males and six pubertal (P) females) first sat or stood for six minutes and then performed a maximal graded test on a bicycle ergometer until exhaustion. We measured energy expenditure, oxygen uptake, ventilatory thresholds 1 and 2 and maximal oxygen uptake. The respiratory magnetometer plethysmography system measured four chest and abdomen distances using magnetometers sensors. We trained the models to predict energy expenditure based on the temporal convolutional networks model. RESULTS: The respiratory magnetometer plethysmography system provided accurate energy expenditure estimation in groups A (R2 = 0.98), PP (R2 = 0.98) and P (R2 = 0.97). The temporal convolutional networks model efficiently estimates energy expenditure under sitting, standing and high levels of exercise intensities. CONCLUSION: Our results proved the respiratory magnetometer plethysmography system's effectiveness in estimating energy expenditure for different age populations across various intensities of physical activity.

Chronic Fatigue in Cancer, Brain Connectivity and Reluctance to Engage in Physical Activity: A Mini-Review.

A large amount of evidence shows that after a cancer diagnosis, patients significantly reduce their level of physical activity. Usually, this reduction is attributed to cancer-related fatigue. However, to our knowledge, no study has clearly demonstrated that fatigue alters effort-based decision-making in cancer. This mini-review aimed to provide evidence that chronic fatigue in cancer patients causes changes in brain connectivity that impact effort-based decision-making. Indeed, three patterns of activation to compensate for dysfunctional networks have been reported: greater variability in the executive network and hyperactivation in the executive network, which account for less efficient and costly processes in the frontal cortex, and reduced deactivation in the default mode network. Nevertheless, these activation patterns are also observed with other factors, such as anticipatory stressors (worry, rumination or sleep loss), that might also cause reluctance to engage in physical activity. Effort-based decision-making involving weighing costs against benefits and physical activity interventions should increase immediate benefits to facilitate engagement in effortful activities.

A novel algorithm for minute ventilation estimation in remote health monitoring with magnetometer plethysmography.

PURPOSE: The purpose of this study was to evaluate the accuracy of minute ventilation (V˙E) estimation using a novel method based on a non-linear algorithm coupled with cycle-based features. The experiment protocol was well adapted for remote health monitoring applications by exploiting data streams from respiratory magnetometer plethysmography (RMP) during different physical activity (PA) types. Methods Thirteen subjects with an age distribution of 24.1±3.4 years performed thirteen PA ranging from sedentary to moderate intensity (walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 W and 110 W). In total, 3359 temporal segments of 10s were acquired using the Nomics RMP device while the iWorx spirometer was used for reference V˙E measurements. An artificial neural network (ANN) model based on respiration features was used to estimate V˙E and compared to the multiple linear regression (MLR) model. We also compared the subject-specific approach with the subject-independent approach. Results The ANN model using subject-specific approach achieved better accuracy for the V˙E estimation. The bias was between 0.20±0.87 and 0.78±3 l/min with the ANN model as compared to 0.73±3.19 and 4.17±2.61 l/min with the MLR model. Conclusion Our results demonstrated the pertinence of processing data streams from wearable RMP device to estimate the V˙E with sufficient accuracy for various PA types. Due to its low-complexity and real-time algorithm design, the current approach can be easily integrated into most remote health monitoring applications coupled with wearable sensors.

A comparison between ventilation and heart rate as indicator of oxygen uptake during different intensities of exercise.

The aim of this study is to compare the relation between ventilation (VE) and oxygen uptake (VO2) [VO2=ƒ(VE)] and between heart rate (HR) and VO2 [VO2=ƒ(HR)]. Each one of the subjects performed three types of activities of different intensities (walking without load, walking with load and intermittent work). VO2, VE, and HR were measured continuously by using indirect calorimetry and an electrocardiogram. Linear regressions and coefficients of determination (r(2)) were calculated to compare the relation VO2 =ƒ(VE) and VO2 =ƒ(HR) for two different regroupings: by session duration (r(2) session) and by subject (r(2) subject). Results showed that r(2) session of the relation VO2 =ƒ(VE) were significantly higher than those of the relation VO2 =ƒ(HR) for steady state activities (walking with or without load during 3 or 6 min, p < 0.01) and for activities without oxygen consumption steady state (walking with or without load during 1 min, p < 0.01 and intermittent work, p < 0.05). VE is more strongly correlated with VO2 than with HR. This is a very promising approach to develop a new method to estimate energy expenditure. Key pointsVentilation is more strongly correlated with oxygen uptake than heart rate during physical activities of different intensities.This study shows the interest to looking for ventilation to estimate energy expenditure.This study is a promising approach to develop a new method to estimate energy expenditureAn interesting perspective could be to develop a light and portable device to measure ventilation based on the coupling of four magnetometers.

Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods.

The precise measurement of respiratory variables, such as tidal volume, minute ventilation, and respiratory rate, is necessary to monitor respiratory status, overcome several diseases, improve patient health conditions and reduce health care costs. This measurement has conventionally been performed by breathing into a mouthpiece connected to a flow rate measuring device. However, a mouthpiece can be uncomfortable for the subject and is difficult to use for long-term monitoring. Other noninvasive systems and devices have been developed that do not require a mouthpiece to quantitatively measure respiratory variables. These techniques are based on measuring size changes of the rib cage (RC) and abdomen (ABD), as lung volume is known to be a function of these variables. Among these systems, we distinguish respiratory inductive plethysmography (RIP), respiratory magnetometer plethysmography (RMP), and optoelectronic plethysmography devices. However, these devices should be previously calibrated for the correct evaluation of respiratory variables. The most popular calibration methods are isovolume manoeuvre calibration (ISOCAL), qualitative diagnostic calibration (QDC), multiple linear regression (MLR) and artificial neural networks (ANNs). The aim of this review is first to present how thoracoabdominal breathing distances can be used to estimate respiratory variables and second to present the different techniques and calibration methods used for this purpose.

A review of the evidence for the use of ventilation as a surrogate measure of energy expenditure.

Precise measurement of sedentary behavior and physical activity is necessary to characterize the dose-response relationship between these variables and health outcomes. The most frequently used methods employ portable devices to measure mechanical or physiological parameters (eg, pedometers, heart rate monitors, accelerometers). There is considerable variability in the accuracy of total energy expenditure (TEE) estimates from these devices. This review examines the potential of measurement of ventilation (VE) to provide an estimate of free-living TEE. The existence of a linear relationship between VE and energy expenditure (EE) was demonstrated in the mid-20th century. However, few studies have investigated this parameter as an estimate of EE due to the cumbersome equipment required to measure VE. Portable systems that measure VE without the use of a mouthpiece have existed for about 20 years (respiratory inductive plethysmography). However, these devices are adapted for clinical monitoring and are too cumbersome to be used in conditions of daily life. Technological innovations of recent years (small electromagnetic coils glued on the chest/back) suggest that VE could be estimated from variations in rib cage and abdominal distances. This method of TEE estimation is based on the development of individual/group calibration curves to predict the relationship between ventilation and oxygen consumption. The new method provides a reasonably accurate estimate of TEE in different free-living conditions such as sitting, standing, and walking. Further work is required to integrate these electromagnetic coils into a jacket or T-shirt to create a wearable device suitable for long-term use in free-living conditions.