Relationship between oxygen consumption kinetics and BODE Index in COPD patients

Predictors of cardiopulmonary exercise testing in COPD patients according to the Weber classification

BACKGROUND

Weber classification stratifies cardiac patients based on peak oxygen consumption (V̇O2), the gold-standard measure of exercise capacity.

OBJECTIVE

To determine if Weber classification is a useful tool to discriminate clinical phenotypes in COPD patients and to evaluate if disease severity and other clinical measures can predict V̇O2peak.

METHODS

Three hundred and six COPD patients underwent cardiopulmonary exercise testing (CPX) and were divided according to Weber class: 1) Weber A (n = 34); 2) Weber B (n = 88); 3) Weber C (n = 138); and 4) Weber D (n = 46).

RESULTS

Weber class D patients demonstrated a reduced V̇O2 peak, heart rate (HR), minute ventilation (V̇E), oxygen (O2) pulse, circulatory power (CP), oxygen uptake efficiency slope (OUES), oxygen saturation (SpO2%), delta (Δ)HR and ΔSpO2 when compared to Weber A and B (p<0.05). Moreover, Dyspnea and the V̇E/carbon dioxide production (V̇CO2) slope were higher in Weber D compared with Weber C and A (p<0.001). Hierarchical regression analysis demonstrated significant predictors of V̇O2peak (R2= 0.131; Adj R 2 = 1.25), including HR (β=0.5757; t = 5.7; P<0.001) and forced expiratory volume in one second (FEV1) (β=0.119; t = 2.16; P<0.03). Among the Weber C + D groups, predictors of V̇O2peak (R = 0.78; R2= 0.60; Adj R2 =0.59), dyspnea (β=0.076; t = 1.111; P<0.27) and maximal voluntary ventilation (MVV) (β=0.75; t = 1.14; P<0.00).

CONCLUSION

Weber classification may be a useful tool to stratify cardiorespiratory fitness in COPD patients. Other clinical measures may be useful in predicting peak V̇O2 in mild-to-severe COPD, moreover different phenotypes may be important tool to improve physical capacity of chronic disease patients.

The role of the microcirculation and integrative cardiovascular physiology in the pathogenesis of ICU-acquired weakness

Skeletal muscle dysfunction after critical illness, defined as ICU-acquired weakness (ICU-AW), is a complex and multifactorial syndrome that contributes significantly to long-term morbidity and reduced quality of life for ICU survivors and caregivers. Historically, research in this field has focused on pathological changes within the muscle itself, without much consideration for their in vivo physiological environment. Skeletal muscle has the widest range of oxygen metabolism of any organ, and regulation of oxygen supply with tissue demand is a fundamental requirement for locomotion and muscle function. During exercise, this process is exquisitely controlled and coordinated by the cardiovascular, respiratory, and autonomic systems, and also within the skeletal muscle microcirculation and mitochondria as the terminal site of oxygen exchange and utilization. This review highlights the potential contribution of the microcirculation and integrative cardiovascular physiology to the pathogenesis of ICU-AW. An overview of skeletal muscle microvascular structure and function is provided, as well as our understanding of microvascular dysfunction during the acute phase of critical illness; whether microvascular dysfunction persists after ICU discharge is currently not known. Molecular mechanisms that regulate crosstalk between endothelial cells and myocytes are discussed, including the role of the microcirculation in skeletal muscle atrophy, oxidative stress, and satellite cell biology. The concept of integrated control of oxygen delivery and utilization during exercise is introduced, with evidence of physiological dysfunction throughout the oxygen delivery pathway - from mouth to mitochondria - causing reduced exercise capacity in patients with chronic disease (e.g., heart failure, COPD). We suggest that objective and perceived weakness after critical illness represents a physiological failure of oxygen supply-demand matching - both globally throughout the body and locally within skeletal muscle. Lastly, we highlight the value of standardized cardiopulmonary exercise testing protocols for evaluating fitness in ICU survivors, and the application of near-infrared spectroscopy for directly measuring skeletal muscle oxygenation, representing potential advancements in ICU-AW research and rehabilitation.

Non-invasive ventilatory support accelerates the oxygen uptake and heart rate kinetics and improves muscle oxygenation dynamics in COPD-HF patients

BACKGROUND

The aim of this study was to explore the effects of non-invasive positive pressure ventilation (NIPPV) associated with high-intensity exercise on heart rate (HR) and oxygen uptake (V̇O₂) recovery kinetics in in patients with coexistence of chronic obstructive pulmonary disease (COPD) and heart failure (HF).

METHODS

This is a randomized, double blinded, sham-controlled study involving 14 HF-COPD patients, who underwent a lung function test and Doppler echocardiography. On two different days, patients performed incremental cardiopulmonary exercise testing (CPET) and two constant-work rate tests (80% of CPET peak) receiving Sham or NIPPV (bilevel mode - Astral 150) in a random order until the limit of tolerance (Tlim). During exercise, oxyhemoglobin and deoxyhemoglobin were assessed using near-infrared spectroscopy (Oxymon, Artinis Medical Systems, Einsteinweg, Netherland).

RESULTS

The kinetic variables of both V̇O₂ and HR during the high-intensity constant workload protocol were significantly faster in the NIPPV protocol compared to Sham ventilation (P<0.05). Also, there was a marked improvement in oxygenation and lower deoxygenation of both peripheral and respiratory musculature in TLim during NIPPV when contrasted with Sham ventilation.

CONCLUSIONS

NIPPV applied during high-intensity dynamic exercise can effectively improve exercise tolerance, accelerate HR and V̇O₂ kinetics, improve respiratory and peripheral muscle oxygenation in COPD-HF patients. These beneficial results from the effects of NIPPV may provide evidence and a basis for high-intensity physical training for these patients in cardiopulmonary rehabilitation programs.

Obstructive sleep apnea reduces functional capacity and impairs cardiac autonomic modulation during submaximal exercise in patients with chronic obstructive pulmonary disease: A follow-up study

BACKGROUND

Functional capacity and heart rate variability (HRV) are important prognostic markers in chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea syndrome (OSA). However, the impact of the overlap of these diseases and the one-year clinical follow-up has not yet been evaluated.

OBJECTIVES

To assess whether the presence of OSA can impair functional performance and cardiac autonomic control during exercise in patients with COPD; and to verify whether the overlap of these diseases could lead to worse clinical outcomes during the one-year follow-up.

METHODS

Thirty-four patients underwent pulmonary function tests, echocardiography and polysomnography for diagnostic confirmation, disease staging, exclusion of any cardiac changes, and allocation between groups. The patients underwent the six-minute walk test (6MWT) to assess functional capacity and HRV during exercise. Subsequently, patients were followed up for 12 months to record outcomes such as exacerbation, hospitalization, and deaths. At the end of this period, the patients were revaluated to verify the hypotheses of the study.

RESULTS

The OSA-COPD group showed greater functional impairment when compared to the COPD group (p=0.003) and showed worse cardiac autonomic responses during the 6MWT with greater parasympathetic activation (p=0.03) and less complexity of the autonomic nervous system, in addition to being more likely to exacerbate (p=0.03) during one year of follow-up.

CONCLUSION

OSA-COPD produces deleterious effects on functional performance and a greater autonomic imbalance that impairs clinical outcomes.

Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities

Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min^-1, [-262, 218]), spanning transitions from low-moderate (-23 ml min^-1, [-250, 204]), low-high (14 ml min^-1, [-252, 280]), ventilatory threshold-high (-49 ml min^-1, [-274, 176]), and maximal (-32 ml min^-1, [-261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted [Formula: see text] was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.

Frequency domain analysis to extract dynamic response characteristics for oxygen uptake during transitions to moderate- and heavy-intensity exercises

At the onset of an exercise transition, exponential modeling to calculate a time constant (τ) is the conventional method to analyze pulmonary oxygen uptake (V̇O_2p) kinetics for moderate and heavy exercises. A new frequency domain analysis technique, mean normalized gain (MNG), has been used to analyze V̇O_2p kinetics during moderate exercise, but has not been evaluated for its ability to detect differences in kinetics between moderate and heavy exercises. This study tested the hypothesis that MNG would detect smaller amplitude V̇O_2p responses in the heavy-exercise domain compared with moderate-exercise domain. Eight young healthy adults (3 female; age: 27 ± 6 yr; peak V̇O_2p: 43 ± 6 mL·min^-1·kg^-1; means ± SD) performed three bouts of pseudorandom binary sequence (PRBS) exercise for frequency analysis, with the work rate (WR) changing between 25 W and 90% ventilatory threshold (VT; L → M_PRBS), 25 W and 50% of the difference between VT and peak V̇O_2p (Δ50%; L → H_PRBS), and VT to Δ50% (VT → H_PRBS). Step exercise tests with equivalent changes in WR to the PRBS tests were performed to facilitate the comparison between MNG and τ. MNG was the highest for L → M_PRBS (59 ± 7%), then L → H_PRBS (52 ± 6%), and the lowest for VT → H_PRBS (38 ± 7%, F_(2,14) = 129.755, P < 0.001) exercise conditions indicating slower kinetics with increasing exercise intensity that correlated strongly in repeated measures with τ from step transitions (r_rm = -0.893). These results indicate that frequency domain analysis and MNG reliably detect differences in V̇O_2p kinetics observed across exercise intensity domains.NEW & NOTEWORTHY Mean normalized gain is able to detect differences in V̇O_2p kinetics between moderate-, heavy-, and heavy-intensity exercises from a raised WR within the same individuals. This new method of kinetic analysis may be advantageous compared with conventional V̇O_2p curve fitting, as it is less sensitive to breath-by-breath noise, it can provide useful information from a single exercise testing session, and it can be applied to nonconstant work rate exercise situations.

Association Between the Predictors of Functional Capacity and Heart Rate Off-Kinetics in Patients with Chronic Obstructive Pulmonary Disease

Background

The heart rate (HR) kinetics as well as other predictors of functional capacity such as the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test (CAT), the Duke Activity Status Index (DASI) and the handgrip strength (HGS) represent important tools in assessing the impact of COPD on exercise performance and health status of individuals with COPD.

Purpose

To verify the relationship between functional capacity, measured using the six-minute walking test (6MWT), with the HR off-kinetics, HGS and the DASI and CAT scores.

Methods

For this cross-sectional study, 29 subjects with COPD underwent body composition, pulmonary function and cardiac function tests. Subsequently, the DASI and CAT questionnaires and HGS test were performed. The beat-to-beat R-R intervals (IRR) were collected in rest, during the test and in recovery after the 6MWT. The HR off-kinetics was obtained during a 360-second period in post-exercise recovery through the HR mono-exponential decay.

Results

Moderate correlations were observed between: 1) walked distance (WD) in the 6MWT and the CAT and DASI scores (r= −0.58, p=0.001 and r= 0.58, p=0.001, respectively); 2) WD and HGS (r=0.37, p=0.05); 3) and WD and HR off-kinetics (τ; r= −0.54, p=0.002 and MRT; r= −0.55, p=0.002, respectively).

Conclusion

The 6MWT performance is a direct measurement to evaluate functional capacity. Additionally, it is related to other direct and indirect markers for functional evaluation in patients with COPD. These results suggest the application of these tools in clinical practice, based on the accessibility, non-invasive character and easy applicability of these methods.

Relationship between maximal aerobic power with aerobic fitness as a function of signal-to-noise ratio

Efforts to better understand cardiorespiratory health are relevant for the future development of optimized physical activity programs. We aimed to explore the impact of the signal quality on the expected associations between the ability of the aerobic system in supplying energy as fast as possible during moderate exercise transitions with its maximum capacity to supply energy during maximal exertion. It was hypothesized that a slower aerobic system response during moderate exercise transitions is associated with a lower maximal aerobic power; however, this relationship relies on the quality of the oxygen uptake data set. Forty-three apparently healthy participants performed a moderate constant work rate (CWR) followed by a pseudorandom binary sequence (PRBS) exercise protocol on a cycle ergometer. Participants also performed a maximum incremental cardiopulmonary exercise testing (CPET). The maximal aerobic power was evaluated by the peak oxygen uptake during the CPET, and the aerobic fitness was estimated from different approaches for oxygen uptake dynamics analysis during the CWR and PRBS protocols at different levels of signal-to-noise ratio. The product moment correlation coefficient was used to evaluate the correlation level between variables. Aerobic fitness was correlated with maximum aerobic power, but this correlation increased as a function of the signal-to-noise ratio. Aerobic fitness is related to maximal aerobic power; however, this association appeared to be highly dependent on the data quality and analysis for aerobic fitness evaluation. Our results show that simpler moderate exercise protocols might be as good as maximal exertion exercise protocols to obtain indexes related to cardiorespiratory health.NEW & NOTEWORTHY Optimized methods for cardiorespiratory health evaluation are of great interest for public health. Moderate exercise protocols might be as good as maximum exertion exercise protocols to evaluate cardiorespiratory health. Pseudorandom or constant workload moderate exercise can be used to evaluate cardiorespiratory health.

COPD Patients Have a Restricted Breathing Pattern That Persists with Increased Metabolic Demands

A healthy respiratory system has variability from breath-to-breath and patients with COPD (PwCOPD) have abnormal variability in breath cycles. The aim of this study was to determine if interbreath-interval and tidal-volume variability, and airflow regularity change as metabolic demands increase (seated, standing, and walking) in PwCOPD as compared to controls. Sixteen PwCOPD (64.3 ± 7.9 yr, 61.3 ± 44.1% FEV₁%predicted) and 21 controls (60.2 ± 6.8 yr, 97.5 ± 16.8% FEV₁%predicted) sat, stood, and walked at their preferred-pace for five-minutes each while breathing patterns were recorded. The mean, standard deviation, and coefficient of variation of interbreath-intervals and tidal-volume, and the regularity (sample entropy) of airflow were quantified. Results were subjected to ANOVA analysis. Interbreath-interval means were shorter in PwCOPD compared to controls (p = 0.04) and as metabolic demand increased (p < 0.0001), standard deviation was decreased in PwCOPD compared to controls during each condition (p's < 0.002). Mean tidal-volume did decrease as metabolic demand increased across groups (p < 0.0001). Coefficient of variation findings (p = 0.002) indicated PwCOPD decline in tidal-volume variability from sitting to standing to walking; whereas, controls do not. There was an interaction for airflow (p = 0.02) indicating that although, PwCOPD had a more regular airflow across all conditions, control's airflow became more irregular as metabolic demand increased. PwCOPD's airflow was always more regular compared to controls (p = 0.006); although, airflow became more irregular as metabolic demand increased (p < 0.0001). Healthy respiratory systems have variability and irregularity from breath-to-breath decreases with adaptation to demand. PwCOPD have more regular and restricted breathing pattern that may affect their ability to adjust in demanding situations.

Mean Normalized Gain: A New Method for the Assessment of the Aerobic System Temporal Dynamics during Randomly Varying Exercise in Humans

The temporal dynamics of the oxygen uptake ([Formula: see text]) during moderate exercise has classically been related to physical fitness and a slower [Formula: see text] dynamics was associated with deterioration of physical health. However, methods that better characterize the aerobic system temporal dynamics remain challenging. The purpose of this study was to develop a new method (named mean normalized gain, MNG) to systematically characterize the [Formula: see text] temporal dynamics. Eight healthy, young adults (28 ± 6 years old, 175 ± 7 cm and 79 ± 13 kg) performed multiple pseudorandom binary sequence cycling protocols on different days and time of the day. The MNG was calculated as the normalized amplitude of the [Formula: see text] signal in frequency-domain. The MNG was validated considering the time constant τ obtained from time-domain analysis as reference. The intra-subject consistency of the MNG was checked by testing the same participant on different days and times of the day. The MNG and τ were strongly negatively correlated (r = -0.86 and p = 0.005). The MNG measured on different days and periods of the day was similar between conditions. Calculations for the MNG have inherent filtering characteristics enhancing reliability for the evaluation of the aerobic system temporal dynamics. In conclusion, the present study successfully validated the use of the MNG for aerobic system analysis and as a potential complementary tool to assess changes in physical fitness.

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o₂) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o₂ kinetics). This study evaluated aerobic system dynamics based on predicted V̇o₂ data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC_HIP), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o₂. The aerobic system analysis was based on the frequency-domain analysis of ACC_HIP and predicted V̇o₂ data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC_HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o₂ data during μADL correlated with the temporal characteristics of measured V̇o₂ data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Linear and non-linear contributions to oxygen transport and utilization during moderate random exercise in humans

NEW FINDINGS

What is the central question of this study? The pulmonary oxygen uptake (pV̇O2) data used to study the muscle aerobic system dynamics during moderate-exercise transitions is classically described as a mono-exponential function controlled by a complex interaction of the oxygen delivery-utilization balance. This elevated complexity complicates the acquisition of relevant information regarding aerobic system dynamics based on pV̇O2 data during a varying exercise stimulus. What is the main finding and its importance? The elevated complexity of pV̇O2 dynamics is a consequence of a multiple-order interaction between muscle oxygen uptake and circulatory distortion. Our findings challenge the use of a first-order function to study the influences of the oxygen delivery-utilization balance over the pV̇O2 dynamics. The assumption of aerobic system linearity implies that the pulmonary oxygen uptake (pV̇O2) dynamics during exercise transitions present a first-order characteristic. The main objective of this study was to test the linearity of the oxygen delivery-utilization balance during random moderate exercise. The cardiac output (Q̇) and deoxygenated haemoglobin concentration ([HHb]) were measured to infer the central and local O₂ availability, respectively. Thirteen healthy men performed two consecutive pseudorandom binary sequence cycling exercises followed by an incremental protocol. The system input and the outputs pV̇O2, [HHb] and Q̇ were submitted to frequency-domain analysis. The linearity of the variables was tested by computing the ability of the response at a specific frequency to predict the response at another frequency. The predictability levels were assessed by the coefficient of determination. In a first-order system, a participant who presents faster dynamics at a specific frequency should also present faster dynamics at any other frequency. All experimentally obtained variables (pV̇O2, [HHb] and Q̇) presented a certainly degree of non-linearity. The local O₂ availability, evaluated by the ratio pV̇O2/[HHb], presented the most irregular behaviour. The overall [HHb] kinetics were faster than pV̇O2 and Q̇ kinetics. In conclusion, the oxygen delivery-utilization balance behaved as a non-linear phenomenon. Therefore, the elevated complexity of the pulmonary oxygen uptake dynamics is governed by a complex multiple-order interaction between the oxygen delivery and utilization systems.

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min⁻¹·kg⁻¹. The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

Aerobic system analysis based on oxygen uptake and hip acceleration during random over-ground walking activities

Deteriorated aerobic response to moderate exercise might precede the manifestation of clinical symptoms of noncommunicable diseases. The purpose of the current study was to verify that the use of current wearable technologies for analysis of pulmonary oxygen uptake (V̇o₂) dynamics during a pseudorandom ternary sequence (PRTS) over-ground walking protocol is a suitable procedure for the investigation of the aerobic response in more realistic settings. A wearable accelerometer located at the hip assessed the magnitude of the input changes delivered to the aerobic system. Eight adults (24 ± 4 yr old, 174 ± 7 cm, and 71.4 ± 7.4 kg) performed two identical PRTS over-ground walking protocols. In addition, they performed on the cycle ergometer two identical pseudorandom binary sequence (PRBS) protocols and one incremental protocol for maximal V̇o₂ determination. In the frequency domain, mean normalized gain amplitude (MNG in %) quantified V̇o₂ dynamics. The MNG during PRTS was correlated (r = -0.80, P = 0.01) with the V̇o₂ time constant (τ) obtained during cycling. The MNG estimated during PRBS was similar to the MNG estimated during PRTS (r = 0.80, P = 0.01). The maximal V̇o₂ correlated with the MNG obtained during the PRBS (r = 0.79, P = 0.01) and PRTS (r = 0.78, P = 0.02) protocols. In conclusion, PRTS over-ground walking protocol can be used to evaluate the aerobic system dynamics by the simultaneous measurement of V̇o₂ and hip acceleration. In addition, the aerobic response dynamics from PRBS and PRTS were correlated to maximal V̇o₂ This study has shown that wearable technologies in combination with assessment of MNG, a novel indicator of system dynamics, open new possibilities to monitor cardiorespiratory health under conditions that better simulate activities of daily living than cardiopulmonary exercise testing performed in a medical environment.

Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs

The study of oxygen uptake (V̇o₂) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o₂ measurements are not practical for general population under realistic settings. Devices to measure V̇o₂ are associated with elevated cost, uncomfortable use of a mask, need of trained technicians, and impossibility of long-term data collection. The objective of this study was to predict the V̇o₂ dynamics from heart rate and inputs from the treadmill ergometer by a novel artificial neural network approach. To accomplish this, 10 healthy young participants performed one incremental and three moderate constant work rate treadmill walking exercises. The speed and grade used for the moderate-intensity protocol was related to 80% of the V̇o₂ response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o₂ was used to train an artificial neural network to create an algorithm able to predict the V̇o₂ based on easy-to-obtain inputs. The dynamics of the V̇o₂ response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o₂ was strongly correlated to the measured V̇o₂ ( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o₂ dynamics during treadmill walking exercise. The proposed algorithm could be incorporated into smart devices and fitness equipment, making them suitable for tracking changes in aerobic fitness and physical health beyond the infrequent monitoring of patients during clinical interventions and rehabilitation programs.

Oxygen kinetics during 6-minute walk tests in patients with cardiovascular and pulmonary disease

BACKGROUND

The 6-Minute Walk Test (6MWT) is representative of daily-life activities and reflects the functional capacity of patients. The change of oxygen uptake (VO2) in the initial phase of low-intensity exercise (VO2 kinetics) can be used to assess submaximal exercise performance of patients.The objective of the following study was to analyse VO2 kinetics in patients with different pulmonary and cardiovascular diseases. In addition, we investigated the extent to which VO2 kinetics at the onset of the 6MWT were associated with exercise capacity, morbidity and mortality.

METHODS

VO2 kinetics of 204 patients and 16 healthy controls were obtained using mobile telemetric cardiopulmonary monitoring during a 6MWT. A new mean response time (MRT) index (wMRT) was developed to quantify VO2 kinetics by correcting MRT for work rate. The differences in wMRT between disease categories as well as the association between wMRT and patients' exercise capacity and outcome - time to hospitalization/death- were tested.

RESULTS

The assessment of a robust wMRT was feasible in 86% (244/284) patients. wMRT was increased in patients compared to healthy controls (p <0.001). wMRT was largest in patients with pulmonary arterial hypertension (PAH). There were significant associations between wMRT and exercise capacity in all patients. High wMRT was found to be associated with a high rate of death and re-hospitalization in patients with CHF (p = 0.024). In patients with pulmonary diseases and pulmonary hypertension wMRT was not associated with outcome (p = 0.952).

CONCLUSIONS

Submaximal exercise performance of patients is reduced. O2 kinetics at the onset of exercise are associated with exercise capacity in all patients. wMRT was found to be an important prognostic factor in patients with congestive heart failure (CHF), but not with pulmonary diseases.