Test of Physiological Performance: Rationale and Feasibility

Journal of Strength and Conditioning Research, 2004

Lamberts, R.P., K.A.P.M. Lemmink, J.J. Durandt, and M.I. Lambert. Variation in heart rate during submaximal exercise: implications for monitoring training. J. Strength Cond. Res. 18(3):641-645. 2004.-A change in heart rate at a controlled submaximal exercise intensity is used as a marker of training status. However, the standard error of measurement has not been studied systematically, and therefore a change in heart rate, which can be considered relevant, has not been determined. Forty-four subjects (26.5 Ϯ 5.4 years; mean Ϯ standard deviation) participated in a submaximal running test at the same time of day for 5 consecutive days. Heart rates were determined during each of the 4 exercise intensities (2 minutes each) of increasing intensity and during the 1-minute recovery period after each stage. The repeatability of the heart rate on a day-to-day basis during the stages and recovery periods were high (intraclass correlation coefficient: 95% confidence interval R ϭ 0.94-0.99). The lowest variation in heart rate occurred in the fourth stage (ഠ90% maximum heart rate) with heart rate varying 5 Ϯ 2 b·min Ϫ1 (95% confidence interval for coefficient of variation ϭ 1.1-1.4%). In conclusion, the standard error of measurement of submaximal heart rate is 1.1-1.4%. This magnitude of measurement error needs to be considered when heart rate is used as a marker of training status.

In this study we aimed to establish the usefulness of the time constant of heart rate recovery (Tr) in the evaluation of cardiovascular fitness. 15 male subjects exercised on recumbent bicycle at three different workloads (75W, 100W 125W) where R-R intervals were monitored to determine Tr. In order to find the maximal oxygen uptake ( max 2 O V ) of each subject, oxygen consumption rate ( 2 O V ) was recorded throughout the treadmill exercise (10km/h). Based on max 2

2004

A recently proposed procedure for checking the cardiovascular steady state from beat-by-beat values of blood pressure and heart rate was used for investigating incremental exercise tests. We measured continuous blood pressure in 5 volunteers during rest, incremental exercise until volitional exhaustion, and recovery, and derived a "steadiness profile" over the whole recording. During exercise, the level of steadiness was higher in the best trained subjects. Steady state was lost at the maximum workload, WL max , in all the subjects. Recovery time, measured as the period to reach a new steady state after exercise, seems to be inversely related to WL max , ranging from 254s for the best trained subject (WL max = 275W) to 350s for the less trained one (WL max = 175W). The analysis showed that the procedure can check the hypothesis of steady state during exercise tests, and provide useful information to better describe the level of physical fitness.

Health, 2019

The way in which the Root Mean Square of the Successive Differences between adjacent RR intervals (RMSSD) recovers immediately after exercise could be a good indicator of internal training load (ITL). The aim of this study is to design a recovery index based on RMSSD. Forteen healthy men took part in this study. The experiment lasted 2 weeks, with 4 separate (48 -72 h) sessions. First session was an incremental treadmill test to determine ventilatory thresholds (VT1 and VT2) and maximal aerobic speed (MAS). Each subject ran at VT1 speed (second day), VT2 speed (third day) and a time-to-exhaustion test at MAS (fourth day). The duration of VT1 and VT2 loads was selected in such a way that the product intensity-duration (training load) was the same. HRV was measured from 10' prior to test (Rest) to 30' after completed (Recovery). Recovery slopes were calculated from RMSSD values at 10 and 30 minutes. Borg scale was recorded at the end of every test and the Training Impulse (TRIMP) values were calculated using Banister equations. The RMSSD values dropped substantially regardless of the intensity and the duration of exercise (average 4 ms). The RMSSD recovery was linear during the 30 min and different depending on the intensity of exercise. To propose a recovery index, we calculated the slope of RMSDD over the 30 minutes (slope-30) and also the first 10 minutes (slope-10). Given that the slopes presented an exponential behavior in relation with effort intensity, three curves were obtained (average values, plus SD and minus SD) defining a nomogram. For practical application, we propose: 1) to measure RMSSD the last 5 minutes of exercise and any period of 5 minutes during the first 30 minutes recovery; 2) to calculate the slope of RMSSD between exercise and recovery; 3) to compare with the nomogram.

Annales Universitatis Mariae Curie Sklodowska Sectio D Medicina, 2005

Siedlik, JA, Harrison, G, Brigman, R, Graham, ZA, Weir, JP, Gallagher, PM, and Vardiman, JP. Methods comparison: Assessing agreement of physiological parameters obtained from exercise on two different cycle ergometers. J Strength Cond Res 29(4): 1139-1145, 2015-The purpose of this study was to determine the agreement of physiological parameters measured during exercise testing on 2 devices at established power outputs (POs). Ten trained male cyclists were recruited. The devices used for comparison were the Lode stationary bicycle ergometer (SBE) (Lode Excalibur) and the PowerTap Pro+ (BPT) (Saris Cycling Group) mobile ergometer. The physiological parameters recorded at established PO (50, 100, 150, 200, 250 W) were heart rate (HR), oxygen consumption (V _ O 2 ), pulmonary ventilation (V E ), blood lactate, and ratings of perceived exertion (RPE). Subjects were randomized once to the SBE and 3 times to a bicycle equipped with a BPT. After 15 minutes of unloaded pedaling, the trial began with 5 minutes of cycling at 50 W. Intensity increased by 50 W every 5 minutes up to 250 W. Physiological measures were recorded at each PO. Bland-Altman plots were constructed including computation of the ratio of half the range of limits of agreement and the mean of the pairwise means along with bivariate regression calculations for analysis of the linear association between device measurements. Moderate to good agreement was found for HR with agreement improving as PO increased. Comparisons of V _ O 2 found agreement increased as the PO increased. V E , RPE, and lactate did not consistently provide similar measures across trials. The level of agreement between HR and V _ O 2 when comparing the SBE and BPT suggests that the PowerTap Pro+ is a sufficient tool for estimating PO and associated physiological parameters in the field. *LOA = limits of agreement; HR = heart rate; MPM = mean of the pairwise means; CI = confidence interval. Journal of Strength and Conditioning Research the TM | www.nsca.com

The article describes the results of the application of heart rate monitors in the physical education course, namely a command system that can record the heart rate of up to 20 people at a time. The study was conducted at the premises of the Physical Education Department of National Research Irkutsk State Technical University with the male students of the first functional health group. There was described a method of determining the functional capabilities of students at physical education lessons with the use of heart rate monitors. In addition, there were considered several methods of recording the intensity and impact of physical exercises on the student's body, and some regular human body's responses to physical exercises in case of using such a method were identified. Computer processing of the functional test (20 sit-ups) results was performed to determine the exact recovery time in seconds after a standard exercise.

International Journal of Sports Physiology and Performance, 2014

The authors directly compared 3 frequently used methods of heart-rate-based training-intensitydistribution (TID) quantification in a large sample of training sessions performed by elite endurance athletes. Methods: Twenty-nine elite cross-country skiers (16 male, 13 female; 25 ± 4 y; 70 ± 11 kg; 76 ± 7 mL · min -1 · kg -1 VO 2max ) conducted 570 training sessions during a ~14-d altitude-training camp. Three analysis methods were used: time in zone (TIZ), session goal (SG), and a hybrid session-goal/time-in-zone (SG/TIZ) approach. The proportion of training in zone 1, zone 2, and zone 3 was quantified using total training time or frequency of sessions, and simple conversion factors across different methods were calculated. Results: Comparing the TIZ and SG/TIZ methods, 96.1% and 95.5%, respectively, of total training time was spent in zone 1 (P < .001), with 2.9%/3.6% and 1.1%/0.8% in zones 2/3 (P < .001). Using SG, this corresponded to 86.6% zone 1 and 11.1%/2.4% zone 2/3 sessions. Estimated conversion factors from TIZ or SG/TIZ to SG and vice versa were 0.9/1.1, respectively, in the low-intensity training range (zone 1) and 3.0/0.33 in the high-intensity training range (zones 2 and 3). Conclusions: This study provides a direct comparison and practical conversion factors across studies employing different methods of TID quantification associated with the most common heart-rate-based analysis methods.

PubMed, 2015

The Astrand-Rhyming cycle ergometer test (ARCET) is a commonly administered submaximal test for estimating aerobic capacity. Whereas typically utilized in clinical populations, the validity of the ARCET to predict VO2max in a non-clinical population, especially female, is less clear. Therefore, the purpose of this study was to determine the accuracy of the ARCET in a sample of healthy and physically active college students. Subjects (13 females, 10 males) performed a maximal cycle ergometer test to volitional exhaustion to determine VO2max. At least 48 hours later, subjects performed the ARCET protocol. Predicted VO2max was calculated following the ARCET format using the age corrected factor. There was no significant difference (p=.045) between actual (41.0±7.97 ml/kg/min) and predicted VO2max (40.3±7.58 ml/kg/min). When split for gender there was a significant difference between actual and predicted VO2 for males, (45.1±7.74 vs. 42.7±8.26 ml/kg/min, p=0.029) but no significant difference observed for females, (37.9±6.9 vs. 38.5±6.77 ml/kg/min, p=0.675). The correlation between actual and predicted VO2 was r=0.84, p<0.001 with an SEE= 4.3 ml/kg/min. When split for gender, the correlation for males was r=0.94, p<0.001, SEE=2.72 ml/kg/min; for females, r=0.74, p=0.004, SEE=4.67 ml/kg/min. The results of this study indicate that the ARCET accurately estimated VO2max in a healthy college population of both male and female subjects. Implications of this study suggest the ARCET can be used to assess aerobic capacity in both fitness and clinical settings where measurement via open-circuit spirometry is either unavailable or impractical.

2010 Computing in Cardiology, 2010

Ventilatory thresholds (VT1 and VT2) are useful in many fields of medicine and sports. Nevertheless, their measurement is cumbersome and needs trained personnel. This work proposes an alternative method to predict VT1, VT2 and maximum loads in incremental maximal tests based on heart rate variability (HRV) analysis. Twelve competitive male cyclists executed an incremental exhaustive test. During the test, RR time series and gas concentrations were recorded. After artifact correction the power spectrum was estimated in a sliding window, and central frequency (CF) and bandwidth that contains half the total power (BW) were computed. An automatic algorithm recognized the loads where CF and BW undergo a significant change. These loads were used as inputs in linear regression models to predict VT1, VT2 and maximum loads. The errors of the predictions are similar to the load resolution.