Validation of the two-minute step test in obese with comorbibities and morbidly obese patients

Cardiopulmonary fitness assessment is a valuable resource to obtain quantitative indicators of an individual's physical performance. The cardiopulmonary exercise test (CPX), considered the gold standard test for this evaluation, is costly and difficult to be accessed by the general population. In order to make this evaluation more accessible, and to better reflect the performance of daily life activities, alternative tests were proposed. Morbidly obese patients present limitations that impair physical performance assessment and could benefit from a test of shorter duration, provided it is validated. This observational study aimed to validate the two-minute step test (2MST) as a tool to evaluate functional capacity (FC) in obese with comorbidities and morbidly obese patients, compared the 2MST with CPX as a measure of physical performance, and developed a predictive equation to estimate peak oxygen uptake (VO₂) in the 2MST. The CPX and the 2MST were performed and metabolic and ventilatory parameters were recorded in 31 obese individuals (BMI>35 kg/m²). Pearson correlation and multiple linear regression analyses were performed to evaluate the peak VO₂ best predictors. Bland-Altman analysis was performed to assess the agreement between the two methods. Peak VO₂ measured by CPX and 2MST showed a strong correlation (r=0.70, P<0.001) and there was a moderate correlation between peak VO₂ of the 2MST and the number of up-and-down step cycles (UDS) (r=0.55; P=0.01). The reference equation obtained was: VO₂ (mL·kg^-1·min^-1) = 13.341 + 0.138 × total UDS – (0.183 × BMI), with an estimated standard error of 1.3 mL·kg^-1·min^-1. The 2MST is a viable, practical, and easily accessible test for FC. UDS and BMI can predict peak VO₂ satisfactorily.

Ultra-short-term heart rate variability during resistance exercise in the elderly

Despite the appeal of ultra-short-term heart rate variability (HRV) methods of analysis applied in the clinical and research settings, the number of studies that have investigated HRV by analyzing R-R interval (RRi) recordings shorter than 5 min is still limited. Moreover, ultra-short-term HRV analysis has not been extensively validated during exercise and, currently, no indications exist for its applicability during resistance exercise. The aim of the present study was to compare ultra-short-term HRV analysis with standard short-term HRV analysis during low-intensity, dynamic, lower limb resistance exercise in healthy elderly subjects. Heart rate (HR) and RRi signals were collected from 9 healthy elderly men during discontinuous incremental resistance exercise consisting of 4-min intervals at low intensities (10, 20, 30, and 35% of 1-repetition maximum). The original RRi signals were segmented into 1-, 2-, and 3-min sections. HRV was analyzed in the time domain (root mean square of the of differences between adjacent RRi, divided by the number of RRi, minus one [RMSSD]), RRi mean value and standard deviation [SDNN] (percentage of differences between adjacent NN intervals that are greater than 50 ms [pNN50]), and by non-linear analysis (short-term RRi standard deviation [SD1] and long-term RRi standard deviation [SD2]). No significant difference was found at any exercise intensity between the results of ultra-short-term HRV analysis and the results of standard short-term HRV analysis. Furthermore, we observed excellent (0.70 to 0.89) to near-perfect (0.90 to 1.00) concordance between linear and non-linear parameters calculated over 1- and 2-min signal sections and parameters calculated over 3-min signal sections. Ultra-short-term HRV analysis appears to be a reliable surrogate of standard short-term HRV analysis during resistance exercise in healthy elderly subjects.

Relationship between aerobic capacity and pelvic floor muscles function: a cross-sectional study

The objective of this study was to evaluate the relationship between aerobic capacity and pelvic floor muscles (PFM) function in adult women. Women aged 18 or over and without urinary dysfunction or other chronic diseases were eligible to participate. They completed the habitual physical activity (HPA) questionnaire, underwent a PFM functional evaluation by palpation and perineometry, and performed a submaximal (between 75 and 85% of maximum heart rate) cardiopulmonary exercise (CPX) test to determine the ventilatory anaerobic threshold (VAT). Forty-one women were included (35±16 years, 75% physically active, 17% very active, and 8% sedentary and 17% presented grade 1 PFM contraction, 31.8% grade 2, 26.8% grade 3, and 24.4% grade 4, according to the modified Oxford Scale). The average PFM contraction pressure obtained by perineometer was 53±26 cmH2O and the average oxygen consumption at VAT (VO2VAT) obtained from CPX was 14±2 mL·kg-1·min-1. Significant correlations were found between PFM contraction pressure and VO2VAT (r=0.55; P<0.001); between PFM contraction pressure and HPA score (r=0.38; P=0.02); between age and VO2VAT (r=-0.25; P=0.049); and between VO2VAT and HPA score (r=0.36; P=0.02). An age-adjusted multiple linear regression equation (R2=0.32) was derived to estimate VO2VAT from the contraction value obtained by perineometer, so that the PFM contraction pressure was able to predict VO2VAT. The equation was validated using data from another group of 20 healthy women (33±12 years; PFM contraction: 49±23 cmH2O) and no significant difference was found between actual VO2VAT and predicted VO2VAT (13.1±1.9 vs 13.8±2.0 mL·kg-1·min-1). In conclusion, PFM function is associated with aerobic capacity in healthy women and PFM contraction pressure may be used to estimate VO2VAT in this population.

A non-exercise based V02max prediction using FRIEND dataset with a neural network

The main goal of this work is the development of models, based on computational intelligence techniques, in particular neural networks, to predict the maximum oxygen consumption value. While the maximum oxygen consumption is a direct mark of the cardiorespiratory fitness, several studies have also confirmed it also as a powerful predictor of risk for adverse outcomes, such as hypertension, obesity, and diabetes. Therefore, the existence of simpler and accurate models, establishing an alternative to standard cardiopulmonary exercise tests, with the potential to be employed in the stratification of the general population in daily clinical practice, would be of major importance. In the current study, different models were implemented and compared: 1) the traditional Wasserman/Hansen equation; 2) linear regression and; 3) non-linear neural networks. Their performance was evaluated based on the "FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base" [1] being, in the present study, a subset of 12262 individuals employed. The accuracy of the models was performed through the computation of sensitivity and specificity values. The results show the superiority of neural networks in the prediction of maximum oxygen consumption.

Assessment of cardiovascular risk based on a data-driven knowledge discovery approach

The cardioRisk project addresses the development of personalized risk assessment tools for patients who have been admitted to the hospital with acute myocardial infarction. Although there are models available that assess the short-term risk of death/new events for such patients, these models were established in circumstances that do not take into account the present clinical interventions and, in some cases, the risk factors used by such models are not easily available in clinical practice. The integration of the existing risk tools (applied in the clinician's daily practice) with data-driven knowledge discovery mechanisms based on data routinely collected during hospitalizations, will be a breakthrough in overcoming some of these difficulties. In this context, the development of simple and interpretable models (based on recent datasets), unquestionably will facilitate and will introduce confidence in this integration process. In this work, a simple and interpretable model based on a real dataset is proposed. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The validation is performed using a recent dataset provided by the Portuguese Society of Cardiology (11113 patients), which originally comprised 77 risk factors. A sensitivity, specificity and accuracy of, respectively, 80.42%, 77.25% and 78.80% were achieved showing the effectiveness of the approach.