Background: The 6-Min Walking Test (6MWT) has been proposed to assess functional aerobic capacity in patients with heart failure, but many older adults with heart failure cannot complete it. The adequacy of the 5-repetition Sit-To-Stand (5-STS), a simpler test than 6MWT, to assess the functional aerobic capacity in older adults with heart failure has not been evaluated. Objectives: This study aimed to assess the usefulness of 5-STS in estimating maximal oxygen uptake (VO2 peak) in older adults with heart failure with preserved ejection fraction (HFpEF). Methods: A cross-sectional study was carried out. Patients 70 years and older with HFpEF were included. A bivariant Pearson correlation and subsequent multivariate linear regression analysis were used to analyze the correlations between the 5-STS and the estimated VO2 peak. Results: Seventy-six patients (80.74 (5.89) years) were recruited. The 5-STS showed a moderate and inversely correlation with the estimated VO2 peak (r = −0.555, p < 0.001). The 5-STS explained 40.4% of the variance in the estimated VO2 peak, adjusted by age, sex, and BMI. When older adults were stratified by BMI, the 5-STS explained 70% and 31.4% of the variance in the estimated VO2 peak in older adults with normal weight and overweight/obesity, respectively. Conclusions: The 5-STS may be an easy tool to assess functional aerobic capacity in older adults with HFpEF, especially for those with normal weight.
It is a challenge to manage and assess heart failure with preserved left ventricular ejection fraction (HFpEF) patients. Six-Minute Walk Test (6MWT) is used in this clinical population as a functional test. The objective of the study was to assess gait and kinematic parameters in HFpEF patients during the 6MWT with an inertial sensor and to discriminate patients according to their performance in the 6MWT: (1) walk more or less than 300 m, (2) finish or stop the test, (3) women or men and (4) fallen or did not fall in the last year. A cross-sectional study was performed in patients with HFpEF older than 70 years. 6MWT was carried out in a closed corridor larger than 30 m. Two Shimmer3 inertial sensors were used in the chest and lumbar region. Pure kinematic parameters analysed were angular velocity and linear acceleration in the three axes. Using these data, an algorithm calculated gait kinematic parameters: total distance, lap time, gait speed and step and stride variables. Two analyses were done according to the performance. Student’s t-test measured differences between groups and receiver operating characteristic assessed discriminant ability. Seventy patients performed the 6MWT. Step time, step symmetry, stride time and stride symmetry in both analyses showed high AUC values (>0.75). More significant differences in velocity and acceleration in the maximum Y axis or vertical movements. Three pure kinematic parameters obtained good discriminant capacity (AUC > 0.75). The new methodology proved differences in gait and pure kinematic parameters that can distinguish two groups according to the performance in the 6MWT and they had discriminant capacity.
Background: Malnutrition is a prevalent condition associated with adverse health outcomes, requiring the accurate assessment of muscle composition and fat distribution. Methods: This study presents a novel method for the automatic analysis of ultrasound images to estimate subcutaneous and visceral fat, as well as muscle, in patients with suspected malnutrition. The proposed system utilizes computer vision techniques to segment regions of interest (ROIs), calculate relevant variables, and store data for clinical evaluation. Unlike traditional segmentation methods that rely solely on thresholding or pre-defined masks, our method employs an iterative hierarchical approach to refine contour detection and improve localization accuracy. A dataset of abdominal and leg ultrasound images, captured in both longitudinal and transversal planes, was analyzed. Results: Results showed higher precision for longitudinal scans compared to transversal scans, particularly for length-related variables, with the Y-axis Vastus intermediate achieving a precision of 92.87%. However, area-based measurements demonstrated lower precision due to differences between manual adjustments by experts and automatic geometric approximations. Conclusions: These findings highlight the system’s potential for clinical use while emphasizing the need for further algorithmic refinements to improve precision in area calculations.
Running is one of the most popular sports practiced today and biomechanical variables are fundamental to understanding it. The main objectives of this study are to describe kinetic, kinematic, and spatiotemporal variables measured using four inertial measurement units (IMUs) in runners during treadmill running, investigate the relationships between these variables, and describe differences associated with different data sampling and averaging strategies. A total of 22 healthy recreational runners (M age = 28 ± 5.57 yrs) participated in treadmill measurements, running at their preferred speed (M = 10.1 ± 1.9 km/h) with a set-up of four IMUs placed on tibias and the lumbar area. Raw data was processed and analysed over selections spanning 30 s, 30 steps and 1 step. Very strong positive associations were obtained between the same family variables in all selections. The temporal variables were inversely associated with the step rate variable in the selection of 30 s and 30 steps of data. There were moderate associations between kinetic (forces) and kinematic (displacement) variables. There were no significant differences between the biomechanics variables in any selection. Our results suggest that a 4-IMU set-up, as presented in this study, is a viable approach for parameterization of the biomechanical variables in running, and also that there are no significant differences in the biomechanical variables studied independently, if we select data from 30 s, 30 steps or 1 step for processing and analysis. These results can assist in the methodological aspects of protocol design in future running research.
About Me
Grupo de Investigación Clinimetría CTS-631, Departamento de Fisioterapia, Universidad de Málaga.
Grupo de Investigación Clinimetría CTS-631, Departamento de Fisioterapia, Universidad de Málaga.