scholarly journals An Experimental Study of Step Test Index Combined With Heart Rate Variability in Estimating Maximum Oxygen Uptake in Women With Drug Use Disorder

2020 ◽  
Vol 11 ◽  
Author(s):  
Kun Wang ◽  
Tingran Zhang ◽  
Yiyi Ouyang ◽  
Haonan Jiang ◽  
Meichen Qu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Experimental Study ◽  
Heart Rate Variability ◽  
Drug Use ◽  
Oxygen Uptake ◽  
Maximum Oxygen Uptake ◽  
Step Test ◽  
Drug Use Disorder

scholarly journals Cardiopulmonary fitness test among Nepalese students

2013 ◽  
Vol 1 (1) ◽  
pp. 3-8
Author(s):  
S Hada ◽  
S Amatya ◽  
K Gautam
Keyword(s):  
Heart Rate ◽  
Oxygen Uptake ◽  
Total Duration ◽  
Prediction Equation ◽  
Maximum Oxygen Uptake ◽  
Step Test ◽  
Vo2 Max ◽  
Cardiopulmonary Fitness ◽  
Medical College ◽  
Rate Maximum

Background and Objectives: Maximum Oxygen uptake (VO2 max) is a good predictor of cardiopulmonary and muscle fitness. Maximum oxygen uptake is defined as the highest rate at which oxygen can be taken up and utilize by body during severe exercise. The present study aims to find out the level of VO2 max using Mc Ardle equation and to compare obtained values from Chatterjee’s equation in Nepalese population. Methodology: Maximum oxygen uptake was determined by using the Queen’s college step-stool of 16.25 inches and popular Mc Ardle equation. Stepping was done for a total duration of 3 minutes at the rate of 24 cycles per minute for males and 22 cycles per minute for females. After completion of the exercise, subjects remained standing while the carotid pulse rate was taken as heart rate. Maximum oxygen uptake was calculated using obtained heart rate. Results: Queen’s college step test (QCT) was used as a submaximal exercise and the estimated VO2 max in boys and girls was 48.8± 7.3 ml/kg/min and 37.4± 2.7 ml/kg/min respectively with Mc Ardle equation and the value was higher when compared with Chatterjee’s equations. The value of VO2 max was observed and found to be less in smokers and sedentary individuals. Conclusion: As the values of VO2 max obtained from different equations are different, this research strongly argues the need of developing a prediction equation specifically for the Nepalese population.DOI: http://dx.doi.org/10.3126/jmcjms.v1i1.7879 Janaki Medical College Journal of Medical Sciences (2013) Vol. 1 (1):3-8

scholarly journals A new analysis of heart rate variability in the assessment of fetal parasympathetic activity: An experimental study in a fetal sheep model

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0180653 ◽  
Author(s):  
C. Garabedian ◽  
C. Champion ◽  
E. Servan-Schreiber ◽  
L. Butruille ◽  
E. Aubry ◽  
...  
Keyword(s):  
Heart Rate ◽  
Experimental Study ◽  
Heart Rate Variability ◽  
Parasympathetic Activity ◽  
Sheep Model ◽  
Fetal Sheep

scholarly journals Validation of the Software-Hardware Complex “Rytm” for Measurement of the RR Intervals and Heart Rate Variability Analysis During Exercise and Recovery Period

2021 ◽  
Vol 21 (1) ◽  
pp. 61-68
Author(s):  
Lyubomyr Vovkanych ◽  
Yuriy Boretsky ◽  
Viktor Sokolovsky ◽  
Dzvenyslava Berhtraum ◽  
Stanislav Kras
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Recovery Period ◽  
Intraclass Correlation ◽  
Step Test ◽  
Heart Rate Variability Analysis ◽  
Test Protocol ◽  
Rr Intervals ◽  
Significant Difference ◽  
Very High

The study purpose was estimation of the accuracy of RR time series measurements by SHC “Rytm” and validity of derived heart rate variability (HRV) indexes under physical loads and recovery period. Materials and methods. The participants were 20 healthy male adults aged 19.7 ± 0.23 years. Data was recorded simultaneously with CardioLab CE12, Polar RS800, and SHC “Rytm”. Test protocol included a 2 minute step test (20 steps per minute, platform height – 40 cm) with the next 3 minute recovery period. HRV indexes were calculated by Kubios HRV 2.1. Results. The RR data bias in the case of physical loads was -0.06 ms, it increased to 0.09-0.33 ms during the recovery period. The limits of agreement for RR data ranged from 3.7 ms to 22.8 ms, depending on the period of measurements and pair of compared devices. It is acceptable for the heart rate and HRV estimation. The intraclass correlation coefficients (0.62–1.00) and Spearman correlation coefficient (0.99) were high enough to suggest very high repeatability of the data. We found no significant difference (p > 0.05) and good correlation (r = 0.94-1.00) between the majority of HRV indexes, calculated from data of Polar RS800 and SHC “Rytm” in conditions of physical loads (except for LF/HF ratio) and in the recovery period. The only one index (RMSSD) was different (p < 0.05) in case of Polar RS800 and SHC “Rytm” data, obtained in the recovery period. The largest numbers of different HRV indexes have been found during the comparison of CardioLab CE12 and Polar RS800 – RMSSD, pNN50, and SD1. Correlation between HRV indexes (r = 0.81-1.00) was very high in all pairs of devices in all periods of measurements. Conclusions. The SHC “Rytm” appears to be acceptable for RR intervals registration and the HRV analysis during physical loads and recovery period.

scholarly journals Predicting maximal oxygen uptake from a 3-minute progressive knee-ups and step test

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10831
Author(s):  
Yu-Chun Chung ◽  
Ching-Yu Huang ◽  
Huey-June Wu ◽  
Nai-Wen Kan ◽  
Chin-Shan Ho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Composition ◽  
Oxygen Uptake ◽  
Cardiorespiratory Fitness ◽  
Peak Oxygen Uptake ◽  
Step Test ◽  
Fat Percentage ◽  
Fitness Assessment ◽  
Adult Participants ◽  
Health Related

Background Cardiorespiratory fitness assessment is crucial for diagnosing health risks and assessing interventions. Direct measurement of maximum oxygen uptake (V̇O2 max) yields more objective and accurate results, but it is practical only in a laboratory setting. We therefore investigated whether a 3-min progressive knee-up and step (3MPKS) test can be used to estimate peak oxygen uptake in these settings. Method The data of 166 healthy adult participants were analyzed. We conducted a V̇O2 max test and a subsequent 3MPKS exercise test, in a balanced order, a week later. In a multivariate regression model, sex; age; relative V̇O2 max; body mass index (BMI); body fat percentage (BF); resting heart rate (HR0); and heart rates at the beginning as well as at the first, second, third, and fourth minutes (denoted by HR0, HR1, HR2, HR3, and HR4, respectively) during a step test were used as predictors. Moreover, R2 and standard error of estimate (SEE) were used to evaluate the accuracy of various body composition models in predicting V̇O2max. Results The predicted and actual V̇O2 max values were significantly correlated (BF% model: R2 = 0.624, SEE = 4.982; BMI model: R2 = 0.567, SEE = 5.153). The BF% model yielded more accurate predictions, and the model predictors were sex, age, BF%, HR0, ΔHR3−HR0, and ΔHR3−HR4. Conclusion In our study, involving Taiwanese adults, we constructed and verified a model to predict V̇O2 max, which indicates cardiorespiratory fitness. This model had the predictors sex, age, body composition, and heart rate changes during a step test. Our 3MPKS test has the potential to be widely used in epidemiological research to measure V̇O2 max and other health-related parameters.

scholarly journals HRV (Heart Rate Variability) as a non-invasive measurement method for performance diagnostics and training control

2019 ◽  
Vol 5 (1) ◽  
pp. 97-100 ◽  
Author(s):  
Matthias Scherer ◽  
Johannes Martinek ◽  
Winfried Mayr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Athletic Performance ◽  
Lactate Concentration ◽  
Bicycle Ergometer ◽  
Step Test ◽  
Total Power ◽  
Plateau Phase ◽  
Test Procedures ◽  
Non Invasive

AbstractThe aim of this study was to determine whether non-invasive heart rate variability (HRV) recordings can be used to monitor training exercises and to estimate athletic performance. Thus far, condition and performance have been evaluated with lactate test procedures and spirometry. Several tests were conducted to determine the relationship of data from lactate test samplings, spirometry and HRV recordings. Four groups of professional athletes in different disciplines such as ball sports (n=15), martial arts (n=17), endurance sports (n=8) and hobby athletes (n=6) underwent a standardized treadmill or bicycle ergometer step test while increasing load rates, e.g. 2 km/h or 20-50 Watt every 3.5 minutes, synchronized with standardized series of lactate test sampling, spirometry and ECG recording. An inclusion criterion for all athlete groups was a minimum training frequency of an hour, five days a week focusing on continuous performance improvement. Evidence shows that offline analysis of ECG data allows conclusions on actual individual athletic performance without the need for complex instrumentation and laboratory environment. The total power parameter of the HRV reaches a plateau phase in all tested subjects and this plateau phase reaches zero near the 2 mmol threshold of lactate concentration in all subjects recorded on a bicycle ergometer. Nine out of ten subjects measured on the bicycle ergometer had negatively correlating data of lactate concentration and total power of HRV (α < 0.05). Lactate measurements using treadmills require resting periods for blood sampling. As the HRV increases instantly in these resting periods, the use of bicycle ergometers, where no testing breaks are needed, is recommended for further research.

scholarly journals Depressive Symptoms are Associated with Heart Rate Variability Independently of Fitness: A Cross-Sectional Study of Patients with Heart Failure

2019 ◽  
Vol 53 (11) ◽  
pp. 955-963 ◽  
Author(s):  
Fawn A Walter ◽  
Emily Gathright ◽  
Joseph D Redle ◽  
John Gunstad ◽  
Joel W Hughes
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Depressive Symptoms ◽  
Physical Fitness ◽  
Secondary Analysis ◽  
Small Sample ◽  
Step Test ◽  
Cross Sectional ◽  
The Relationship

Abstract Background Depression is associated with reduced heart rate variability (HRV) in healthy and cardiac samples, which may be accounted for by physical fitness. In a small sample of cardiac patients, activity and fitness levels attenuated the relationship between HRV and depression. In the current study of heart failure (HF) patients, we hypothesized that depressive symptoms and HRV would be inversely related and physical fitness would attenuate this association. Purpose To determine if previous associations among depressive symptoms, physical fitness, and HRV would replicate in a sample of HF patients. Methods The sample consisted of HF patients (N = 125) aged 68.55 ± 8.92 years, 68.8% male, and 83.2% Caucasian. The study was cross-sectional and a secondary analysis of a nonrandomized clinical trial (Trial Identifier: NCT00871897). Depressive symptoms were evaluated using the Beck Depression Inventory (BDI)-II, fitness with the 2 min step test (2MST), and HRV during a 10 min resting laboratory psychophysiology protocol. The dependent variable in hierarchical linear regressions was the root mean square of successive differences. Results Controlling for sex, age, β-blocker use, hypertension, and diabetes, higher BDI-II scores significantly predicted lower HRV, β = −.29, t(92) = −2.79, p < .01. Adding 2MST did not attenuate the relationship in a follow-up regression. Conclusion Depressive symptoms were associated with lower HRV in HF patients, independent of physical fitness. Given the prevalence of depression and suppressed HRV common among HF patients, interventions addressing depressive symptoms and other predictors of poor outcomes may be warranted.

scholarly journals Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 67
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
María Vanessa Villasana ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Heart Rate ◽  
Experimental Study ◽  
Heart Rate Variability ◽  
Mobile Devices ◽  
Brain Activity ◽  
Health Conditions ◽  
Qrs Complex ◽  
Timed Up And Go ◽  
Eeg Data ◽  
Ecg Data

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

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