Running Efficiency Of A Trans-tibial Amputee With Different Prosthetic Stiffness: A Case Study

It has been recognized that that the design and prescription of Energy Storing and Returning prosthetic running feet are not well understood and that further information on their performance would be beneficial to increase this understanding. Dynamic analysis of an amputee wearing a prosthetic foot is typically performed using reflective markers and motion-capture systems. High-speed cameras and force plates are used to collect data of a few strides. This requires specialized and expensive equipment in an unrepresentative environment within a large area. Inertial Measurement Units are also capable of being used as wearable sensors but suffer from drift issues. This paper presents the development of a wearable sensing system that records the action of an Energy Storing and Returning prosthetic running foot (sagittal plane displacement and ground contact position) which could have research and/or clinical applications. This is achieved using five standalone pieces of apparatus including foot-mounted pressure sensors and a rotary vario-resistive displacement transducer. It is demonstrated, through the collection of profiles for both foot deflection and ground contact point over the duration of a stride, that the system can be attached to an amputee’s prosthesis and used in a non-laboratory environment. It was found from the system that the prosthetic ground contact point, for the amputee tested, progresses along the effective metatarsal portion of the prosthetic foot towards the distal end of the prosthesis over the duration of the stride. Further investigation of the effective stiffness changes of the foot due to the progression of the contact point is warranted.

Background: We investigated the relationship of data from the pulmonary function test and 6-minute walking test (6MWT) to maximal oxygen consumption (VO2 max) and predicted VO2 max in patients with pneumoconiosis, with the goal of being able to evaluate the functional status of pneumoconiosis patients with a test, which is less expensive and much safer for the patient. Methods: This study included 37 patients with pneumoconiosis who had performed the 6-minute walking test, and resting pulmonary function test, and from whom the VO2 max was obtained from an exercise cardiopulmonary function test. Lineal regression analysis of multiple variables, involving body height (BH), body weight (BW), patient age, DLCO, DLCO/VA, distance walked during the 6MWT, changes in SpO2 (△SpO2), changes in heart rate (△HR) during the 6MWT, Borg scale, FEV1, FVC, and FEV1/FVC, was done. Results: The distance of the 6MWT was the strongest independent variable to predict VO2 max, with a correlation coefficient (R) of 0.644 (p＜0.001). The predicted equation was VO2 max = 2.488 x distance of the 6MWT - 85.85. The factors of greatest correlation to predict VO2 max were the distance of the 6MWT, FVC, DLCO, BH,△SpO2, and BW with a R of 0.874 (p＜0.001). The equation was VO2 max = 1.484 x distance of the 6MWT + 105.242 x FVC + 9.138 x DLCO + 7.484 x BH + 26.756 x△SpO2 + 3.563 x BW - 1316.316. Conclusions: The distance of the 6MWT can be used to predict VO2 max in patients with pneumoconiosis. The addition of the patients’ body height and weight, and data from the pulmonary function tests, can increase the accuracy of predicting VO2 max.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work. Editor's evaluation The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appropriate and validated methodology in line with the current state-of-the-art, as shown by references to common exercise books. https://doi.org/10.7554/eLife.86291.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The oxygen uptake (VO2) is considered an important metric in assessing cardiorespiratory fitness, health status, or endurance performance potential (Guazzi et al., 2012). With the application of standardised procedures and interpretation protocols, during graded exercise tests (GXT), the (maximal oxygen uptake) VO2max can be established (Bentley et al., 2007). GXT is the most widely used assessment to examine the dynamic relationship between exercise and integrated physiological systems (Albouaini et al., 2007; Bentley et al., 2007). The information from GXT during cardiopulmonary exercise testing (CPET) can be applied across the spectrum of sport performance, occupational safety screening, research, and clinical diagnostics (Guazzi et al., 2017). VO2 max is often used as a boundary between severe and extreme intensity domains and by definition requires maximal effort from the tested subject (Gaesser and Poole, 1996). However, it is not always recommended or possible to undertake a test to exhaustion (Guazzi et al., 2012). For the athletes, the proximity of competition or injury history can allow submaximal testing, but not testing to exhaustion (Sassi et al., 2006). Testing that requires maximal effort may be disruptive to the training process or interfere with race performance (Coutts et al., 2007; Lamberts et al., 2011). Due to practical constraints, tests to exhaustion or peak-power-output tests are often performed only two or three times a year (Coutts et al., 2007). However, VO2 values are widely used in sport science and the decision-making process (Mann et al., 2013). VO2 is widely considered one of the major endurance performance determinants (Joyner and Coyle, 2008). Using VO2max to guide the selection process, prescribing training intensity, assessing training adaptations, or predicting race times is a common practice in high-performance sports (Bassett and Howley, 2000; Bentley et al., 2007; Hawley and Noakes, 1992; Noakes et al., 1990). VO2max is also one of the critical vital signs coordinating the function of the cardiovascular, respiratory, and muscular systems, it is an indicator of overall body health status (Kaminsky et al., 2017). Quantifying VO2max provides additional input regarding clinical decision-making, risk stratification, evaluation of therapy, and physical activity guidelines (Guazzi et al., 2012). For patients undertaking a test to exhaustion is rarely needed or possible due to health restraints or cardiac risk (Guazzi et al., 2016). For many years researchers have studied indirect methods of estimating VO2max(Sartor et al., 2013). Protocols such as the Astrand-Ryhming Test, Six-Minute Walk Test, or YMCA Step Test have been established and validated (Astrand and Ryhming, 1954; Beutner et al., 2015; Carey, 2022; Jalili et al., 2018). Moreover, estimation of the VO2 and heart rate (HR) values below the ventilatory threshold can be based on cardiorespiratory kinetics assessment using randomised changes in the work rate known as a pseudo-random binary sequences testing (Hoffmann et al., 2022). However, with the development of technology, the accessibility of laboratory testing and mobile testing improved (Montoye et al., 2020; Pritchard et al., 2021). Therefore, new opportunities to develop more precise yet simple and accessible methods and models to assess VO2max occur (Jurov et al., 2023). This appears to be especially important considering the low prediction accuracy of most of the VO2max formulae that were validated in our previous study (Wiecha et al., 2023). Recently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Ashfaq et al., 2022). Both ML and AI are used in sport science as forecasting and decision-making support tools (Abut and Akay, 2015; Bobowik and Wiszomirska, 2022; Chmait and Westerbeek, 2021; Hammes et al., 2022; Rossi et al., 2021). There is growing evidence that VO2max prediction based on ML models, especially support vector ML and artificial neural network models, exhibits more robust and accurate results compared to MLR only (Abut and Akay, 2015; Ashfaq et al., 2022). Therefore, in this research, with the support of ML, we look for algorithms and prediction patterns that allow us to use values obtained during submaximal CPET and somatic measurements to estimate maximal VO2max values in male runners and cyclists. We stipulate that prediction models allow for accurate calculation of VO2max based on somatic or submaximal CPET variables. Materials and methods We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) (Collins et al., 2015). The study is based on retrospective data analysis from the CPET registry collected from 2013 to 2021 at the medical clinic (Sportslab, Warsaw, Poland). All CPET have been performed at the individual request of participants, as a part of regular training monitoring or performance assessment. Ethical approval Request a detailed protocol The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the study. Derivation cohort Request a detailed protocol We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Martens and Logan, 2021). Out of 6439 healthy, adult male cyclists and long-distance runners that undergone CPET, 4423 met the criteria as further: (1) age ≥18 years, (2) declared regular cycling or running training for ≥3 months, (3) had no extreme outliers ≤ or ≥±3 standard deviations (SD) from mean for all of the testing variables (beyond ≥±3 SD in VO2max), (4) lack of any injury, medical condition, or addiction in medical history that may affect exercise capacity, (5) not taking any medications with a modifying effect on exercise capacity, (6) maximum exertion achieved during CPET. We defined the maximum exertion in CPET as the fulfilment of the minimum six of the following criteria: (1) respiratory exchange ratio (RER) ≥1.10, (2) present VO2 plateau (growth <100 mL·min–1 in VO2 despite increased running speed or cycling power), (3) respiratory frequency (fR) ≥45 breaths·min–1, (4) declared subjective exertion intensity during CPET ≥18 in the Borg scale (Borg, 1970), (5) blood lactate concentration [La-]b ≥8 mmol·L–1, (6) growth in speed/power ≥10% of respiratory compensation point (RCP) values after exceeding the RCP, (7) peak heart rate (HRpeak) ≥15 beats·min–1 below predicted maximal heart rate (HRmax) (Lach et al., 2021). Participants’ selection procedure has been shown in Figure 1. Figure 1 Download asset Open asset Flowchart of the preliminary inclusion and exclusion process. Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO2, oxygen uptake (mL·min−1·kg−1); [La−]b, lactate concentration (mmol·L−1); fR, breathing frequency (breaths·min−1); RCP, respiratory compensation point; HRpeak, peak heart rate (beats·min−1); HRmax, maximal heart rate (bpm). At both stages of the selection, some participants met several (>1) exclusion criteria. Somatic measurements and CPET protocols Request a detailed protocol Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants received instructions to refrain from exercising for 2 hr, consume a light meal rich in carbohydrates 2–3 hr beforehand, and maintain hydration by drinking isotonic beverages. Additionally, they were advised to abstain from medications, caffeine, and cigarettes on the day of the test. Running CPET (TE) was performed on a mechanical treadmill (h/p/Cosmos Quasar, Germany). Cycling CPET (CE) was performed on Cyclus-2 (RBM elektronik-automation GmbH, Leipzig, Germany). Hans Rudolph V2 mask (Hans Rudolph, Inc, Shawnee, KS, USA), breath-by-breath method with Cosmed Quark CPET gas exchange analysing device (Cosmed Srl, Rome, Italy), and Quark PFT Suite to Omnia 1.6 software were utilised. The gas analyser device was regularly calibrated with the reference gas (16% O2; 5% CO2) in accordance with the manufacturer’s instructions (Airgas USA, LLC, Plumsteadville, PA, USA). From 2013 to 2021, three Cosmed Quark CPET units were used. HR was measured with the Cosmed torso belt (Cosmed srl, Rome, Italy). [La-]b was measured via enzymatic-amperometric electrochemical technique with Super GL2 analyser (Müller Gerätebau GmbH, Freital, Germany). The [La-]b analyser was regularly calibrated before each measurement series. The 40 m2 indoor, air-conditioned laboratory with 20–22°C temperature and 40–60% humidity, and 100 m ASL provided the same conditions for all BC and CPET. Each CPET began with a 5 min personalised warm-up (walk or easy jog with ‘conversational’ intensity for running, easy pedalling with ‘conversational’ intensity for cycling). Then after the preparation (about 5 min), the continuous progressive step test was conducted. Due to the population diversity (training status), the running test speed started from 7 to 12 km·hr–1 with a 1% treadmill incline. The choice of initial starting speed was determined by the interview and sports results achieved. For example, those running less than 60 min at a distance of 10 km started the test at 7 km/hr, while those running 10 km for less than 35 min started the test at an initial speed of 12 km/hr. The pace increased by 1 km·hr–1 every 2 min with no change in incline. The cycling test began at 60–150 W, depending on the athletes training status. The power increased by 20–30 W every 2 min. It was recommended to maintain a constant cadence of 80–90 (repetition·min–1) during the test. The tests were terminated due to exhaustion: volitional inability to continue the activity or/and VO2 and HR plateau with increasing load or/and observed disturbance of coordination in running or/and inability to maintain the set cadence. Due to the graded protocol used, the cycling power and running speed values have been calculated as a function of time to better reflect the actual level for the test moment being determined (Kuipers et al., 1985). Before the test, after every step, and 3 min after the termination of the effort technician took a 20 µL blood sample from a fingertip. Samples were collected during the test without interrupting the effort. The samples were taken from the initial puncture. The first blood drop was collected into the swab and the second blood drop was drawn for further analysis into the capillary. VO2max was recorded as the highest value (15 s intervals) before the termination of the test. HRmax was recorded as the highest value obtained at the end of the test, without averaging. The anaerobic threshold (AT) was established with the following criteria: (1) common start of VE/VO2 and VE/VCO2 curves, (2) end-tidal partial pressure of oxygen raised constantly with the end-tidal partial pressure of carbon dioxide (Beaver et al., 1986). The was established with the following criteria: (1) PetCO2 must decrease after reaching maximal amount, (2) the presence of fast nonlinear growth in VE (second deflection), (3) the VE/VCO2 ratio achieved minimum and started to rise, and (4) a nonlinear increase in VCO2 versus VO2 (lack of linearity) (Beaver et al., 1986). The [La-]b was estimated for AT and RCP in relation to power or speed (Wiecha et al., 2022). Data analysis Request a detailed protocol Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation). Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a custom-made script was used to generate the database in Excel (Python programming). Further, mean, SD, and 95% confidence intervals (CI) were calculated. The normality of the distribution of the data was examined using the Shapiro-Wilk test and intergroup differences were calculated using the Student’s t-test for independent variables. Three-step variable selection procedures based on random forests were applied using the R package VSURF in RStudio software (R Core Team, Vienna, Austria; version 3.6.4) (Genuer et al., 2016). For each level of measurement (AT, RCP) and their combination (AT+RCP), significant variables were identified separately. The first step was dedicated to eliminate irrelevant variables from the dataset. Second step aimed to select all variables related to the response for interpretation purposes. The third step refined the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purposes (Genuer et al., 2017). Each time for variables selection, the anthropometric variables as in Tables 1–2 and the CPET parameters given in Tables 3–4 from a specific level of measurement (AT; RCP) and their combinations were visible. Table 1 Basic anthropometric characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDAge (years)36.235.6–36.98.4535.935.5–36.38.0535.534.9–36.28.14Height (cm)180.0179.6–180.56.04179.4179.1–179.76.13179.7179.2–180.26.61BM (kg)77.777.0–78.49.3577.777.3–78.19.2977.977.1–78.610.1BMI (kg·m–2)23.923.8–24.12.4324.124.0–24.22.4124.123.9–24.32.56BF (%)15.415.1–15.74.5515.515.3–15.74.5215.415.1–15.84.55FM (kg)12.211.9–12.64.6812.312.1–12.54.6512.311.9–12.74.92FFM (kg)65.565.0–66.06.4365.465.1–65.76.3165.665.1–66.16.86 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 2 Basic anthropometric characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDAge (years)37.336.6–38.09.1337.135.9–38.49.5037.636.5–38.88.46Height (cm)179.9179.4–180.46.27180.1179.2–181.06.96180.2179.4–181.06.13BM (kg)78.878.1–79.69.8079.177.7–80.510.479.878.4–81.310.9BMI (kg·m–2)24.324.1–24.62.6324.424.0–24.72.8024.624.2–25.02.96BF (%)16.415.7–17.14.9916.115.7–16.54.8116.215.5–16.84.87FM (kg)13.312.6–14.15.6613.012.6–13.45.2713.312.5–14.05.85FFM (kg)65.864.9–66.66.2565.865.4–66.36.0666.665.7–67.46.58 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 3 Cardiopulmonary exercise testing (CPET) characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)38.438.1–38.85.0138.538.3–38.74.8838.137.7–38.55.16RERAT0.870.86–0.870.040.870.86–0.870.040.870.86–0.870.04HRAT (beats·min–1)151.5150.8–152.310.3151.0150.6–151.510.8152.0151.2–152.810.8VEAT (L·min–1)79.178.1–80.012.278.377.8–78.912.077.276.3–78.212.0SPEEDAT (km·h–1)11.010.9–11.11.4511.011.0–11.11.3610.910.8–11.01.42LAAT (mmol·L–1)2.082.02–2.140.631.801.76–1.830.622.352.27–2.420.72rVO2RCP (mL·min–1·kg–1)47.547.0–48.05.8847.747.4–48.06.1547.346.8–47.86.16RERRCP1.001.00–1.000.041.001.00–1.000.041.001.00–1.000.03HRRCP (beats·min–1)173.4172.7–174.19.21173.2172.8–173.69.30174.3173.5–175.09.50VERCP (L·min–1)114.7113.5–116.015.9113.9113.1–114.616.7112.7111.4–114.016.2SPEEDRCP (km·h–1)14.013.9–14.11.7714.114.0–14.11.7013.913.8–14.11.75LARCP (mmol·L–1)4.724.63–4.821.044.404.34–4.451.044.814.69–4.931.14rVO2max (mL·min–1·kg–1)53.853.3–54.36.6754.354.0–54.66.9553.853.3–54.37.09 CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; SPEEDAT, velocity at anaerobic threshold; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; SPEEDRCP, velocity at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; rVO2max, maximal oxygen uptake relative to body mass. Table 4 Cardiopulmonary exercise testing (CPET) characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)33.032.5–33.45.8433.232.4–33.95.6833.732.9–34.55.89RERAT0.870.87–0.870.040.870.87–0.880.040.870.87–0.880.04HRAT CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; power at anaerobic threshold relative to body mass; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; power at respiratory compensation point relative to body mass; rVO2max, maximal oxygen uptake relative to body mass. selection variables were in the further only selected parameters were into multiple linear regression The data for MLR model building were randomly into that is testing, validation and of the a only significant were in the Derived are by the of mean and mean analysis was used to the model’s precision and accuracy during validation and tests to the fulfilment of MLR test the of in MLR test assessment between and test of Each model was examined the and any have not been 2 package in RStudio (R Core Team, Vienna, Austria; version version for and software version were used in was considered as the Results Somatic measurements and CPET results data of the runners models for testing, and validation are in Table while cyclists are in Table The runners of and for testing, and validation the cyclists and differences between of runners and cyclists were in BMI and between testing in all between validation only in CPET results for runners models are in Table 3 and for cyclists in Table Runners in the cohort achieved relative to body mass VO2max of in testing group and in validation group cyclists mean was and for testing, and validation to body mass oxygen uptake at anaerobic threshold in runners for ± ± and ± of in testing, and validation it was ± ± and ± of rVO2max, relative to body mass oxygen uptake at respiratory compensation point in runners for ± ± and ± of for testing, and validation while in cyclists for ± ± and ± of rVO2max, There were no significant differences in values between testing, and validation the runners and cyclists between runners and cyclists results were all significant Prediction models based on AT and RCP Full of MLR prediction models for cyclists are in Table for runners in Table The models prediction performance is as with and for cyclists from for somatic parameters to for RCP equations. For runners from for to for AT and equations. for cyclists models was the for RCP and the highest for For from for AT and to for equation. observed for cyclists was the for RCP in the validation group and the highest for while in runners the for AT and and the highest for The performance of prediction is in Figure Figure 2 Download asset Open asset of prediction for Abbreviations: maximal oxygen anaerobic threshold; RCP, respiratory compensation point; All values are in performance for running while the performance for cycling equations. performance of the prediction model for for for AT and for somatic-only equation. Table 5 VO2max prediction for cyclists. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass power at anaerobic threshold relative to body mass rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass VERCP, pulmonary ventilation at respiratory compensation point BF, body fat BM, body mass Table VO2max prediction for runners. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass SPEEDAT, velocity at anaerobic threshold FFM, fat mass VEAT, pulmonary ventilation at anaerobic threshold HRAT, heart rate at anaerobic threshold BF, body fat rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass SPEEDRCP, velocity at respiratory compensation point Models validation of each model for cyclists is in Table while for runners in Table the performance of our prediction was to that observed in the

which unique insights can be gained from the investigations of the physiology of the growing child? Are there pragmatic outcomes to research in children's exercise science that bear specific utility to this age group or to their future life? What importance do exercise responses in youth bear to our

ESAR feet are prosthetic feet with carbon fiber parts that store mechanical energy while standing and release it during propulsion. It is believed to reduce the metabolic energy needed for walking, and to promote the economy of walking. However, there is little scientific evidence to support this claim. This study aimed to compare the energy storage properties of two prosthetic feet made of carbon fiber using the P-Walk, G-Walk, and Podium devices developed for gait analysis, which is a systematic examination of human movement, enabling phasing, estimation of musculoskeletal performance, and determination of kinematic and motor parameters. The amount of energy was calculated for each of the feet using the load deflection test, and the results showed that the new artificial foot with an energy of 6.186 joules showed a great improvement in the results of the tests compared to the old artificial foot with an energy of 3.403 joules. The Podium device tests showed a significant improvement in walking patterns and pressure distribution after using a new foot. The pressure distribution became almost equal on both sides, and the angular deviation of COP decreased from -7 to 1.3 degrees. Ground reaction force vector tilt results also improved, with a body angle of 0 degrees and inclination varying slightly depending on the tibiofemoral angle for males. P-Walk results reveal left-sided static test pressure distribution, exposing amputees to osteoarthritis risk and revealing lack of confidence in prosthetic foot. After use the new prosthetic foot, amputees press more on right foot, indicating balance restoration. The G-Walk device shows the effectiveness of both healthy left and prosthetic foot when walking on an amputated right leg when use the new prosthetic foot. The amputated side's performance is similar to a healthy limb, with minimal difference and within normal limits. Walking cadence and speed values are within normal ranges, while stride length and step length are outside normal ranges for both sides. Obliquity results show a small difference in pelvic angles due to weak pelvic muscles, but these are close to standard values for prosthetic foot use. The amputee's opinions about the evaluation of the new prosthetic foot were good when using the T-score by 61.0 with a rate of 86.4%. It was a significant improvement compared to the old foot with an evaluation of 53.6 by 63.9%.

Introduction Commercially available dynamic response (DR) prosthetic feet have unique ankle designs, which provide different degrees of ankle motion. Differences in ankle motion between the prosthetic and anatomical foot creates an asymmetry in the locomotor system and affects its functions of shock absorption, progression, and stabilization. Ankle motion during gait can be assessed by quantifying the center of pressure (CoP) movement during the stance period of gait. Although previous studies have compared gait parameters between DR and non-DR feet, no studies have examined difference in CoP trajectories among common designs of DR feet. Methods LP-Variflex, Talux, and Variflex prosthetic feet were used as test feet to represent the common DR feet, that is, low-profile (LP), multi-axial (MX), and J-shaped (JS) ankle designs, respectively. Five subjects with unilateral transtibial amputation (TTA), six subjects with unilateral transfemoral amputation (TFA), and three healthy controls participated in the study. Subjects were prosthetic users for at least 18 months and were classified as K-level 3 or K-level 4 functional level. Each subject received standardized functional prosthetic gait training and accommodation with each test foot. For data collection, subjects walked on level ground using standardized shoes and CoP data were collected using in-sole sensors. The testing order of feet was randomized. The A-P and M-L location of CoP was determined at the following gait phases: initial contact (2% of gait cycle), early midstance (15% of gait cycle), terminal stance (35% of gait cycle), preswing (50% of gait cycle), and before toe-off (60% of gait cycle). The maximum A-P and M-L CoP excursion was also calculated. The A-P displacement of the CoP was normalized to the foot length, and the M-L displacement was normalized to the foot width. Analysis of variance was used to calculate differences in CoP between test feet, and a comparison-wise alpha of P &lt; 0.1 was considered as significant. Results In the TTA group, there was a significant difference between feet in A-P CoP location during early midstance (15% gait cycle). For the MX foot, the CoP was located at 21.3% ± 3.1% of the foot length, which was significantly greater than the LP and JS feet (15.6% ± 2% and 14.1% ± 3.4% foot length, respectively). During the period from terminal stance to toe-off (35%–60% gait cycle), the split keel design of JS and LP feet had greater M-L movement compared with that of the MX foot. In the TFA group, there were no differences in CoP trajectory between test feet. The maximum A-P excursion for all prosthetic feet was significantly greater than control subjects (80.4% ± 0.5% and 69.1% ± 2.7% foot length, respectively). The maximum M-L excursion for all prosthetic feet was significantly lesser than control subjects (14% ± 3.7% and 26.2% ± 3.7% foot width, respectively). Conclusions Differences in ankle design among different DR prosthetic feet can influence the asymmetry and functions of the locomotor system. In the TTA group, the MX foot resulted in a faster transition from initial contact to midstance, compared with the JS and LP feet. The split keel design of JS and LP feet closely mimicked the motion of the anatomical foot in late-stance period and provided controlled weight transfer from the prosthetic limb to the intact limb. In the TFA group, ankle design did not have a significant effect on CoP movement. All prosthetic feet had greater A-P CoP excursion and lower M-L excursion compared with the anatomical foot. Additional studies investigating the effects of DR foot/ankle designs on distinct functions of the locomotor system are needed to guide prosthetic foot prescription.

L'aptitude physique: étude comparée chez l'homme et la femme

PO374 Comparison of Effects of a Comprehensive Cardiac Rehabilitation Program on Aerobic Capacity in Patients With Low Versus Normal Ejection Fraction In India

This study aimed to compare changes in maximal oxygen uptake (VO2max) in response to two regimens of chronic interval training. Twenty healthy sedentary women (mean ± SD age and VO2max = 23.0 ± 5.7 years and 30.1 ± 4.4 mL kg(-1) min(-1), respectively) were randomized to complete 12 weeks of one of two interval training regimes, while an additional seven women served as controls. Training was performed 3 days week(-1) on a cycle ergometer and consisted of 6-10 bouts of 1 min duration at lower (60-80 % W max = LO, n = 10) or more intense (80-90 % W max = HI, n = 10) workloads separated by a brief recovery. Every 3 weeks, measures of VO2max and W max were repeated to assign new training intensities. Changes in blood pressure and body composition were also examined. Data revealed significant (p < 0.001) improvements in VO2max in LO (22.3 ± 6.9 %) and HI (21.9 ± 11.6 %) that were similar (p > 0.05) between groups. Approximately 60 % of the increase in VO2max in HI was observed in the initial 3 weeks, compared to only 20 % in LO. No change (p > 0.05) in body weight or body composition was revealed in response to training. Results demonstrate that a relatively prolonged regimen of moderate or more intense interval training induces similar improvements in cardiorespiratory fitness, although HI induced greater increases in VO2max early on in training than LO. Completion of more intense interval training may be an effective means to expedite increases in VO2max soon after initiation of exercise training.

To compare the maximum oxygen uptake V0(2max) of elite endurance athletes and to explain why the body mass exponent, necessary to render V0(2max) independent of body mass, appears to be greater than 0.67. Study 1: V0(2max) of 174 international sportsmen and women was assessed. Athletes were recruited from seven sports (middle- and long-distance runners, heavyweight and lightweight rowers, triathletes, and squash and badminton players). Study 2: calf and thigh leg muscle masses were estimated in 106 male and 30 female athletes from 11 sports. Differences in V0(2max) and leg muscle masses between "sports" and "sex" were analyzed independent of body mass by using allometric log-linear ANCOVA. Heavyweight rowers had the greatest V0(2max) when expressed in L.min but long-distance runners had the highest V0(2max)in mL.kg.min. However, the ANCOVA identified no difference in "mass independent" V0(2max) between the five "pure" endurance sports (runners, rowers, and triathletes) (P > 0.05) with the two racket sports being significantly lower. The body mass covariate exponent was inflated, estimated as 0.94. The results from study 2 estimated calf and thigh leg muscle masses to increase in proportion to body mass, and, respectively. After having controlled for differences in body mass, V0(2max) did not differ between pure endurance sports (P > 0.05). Assuming that athletes' thigh muscle mass increases in proportion to body mass as observed in study 2, a similar disproportional increase in V0(2max) would be anticipated, providing a plausible explanation for the inflated mass exponent associated with V0(2max) identified in this and other studies.

BackgroundMaximal aerobic speed (MAS) is a useful parameter to assess aerobic capacity and estimate training intensity in middle- and long-distance runners. However, whether middle- and long-distance runners reach different levels of MAS compared to other endurance athletes with similar V̇O2max has not been previously studied. Therefore, we aimed to compare V̇O2max, MAS and spatiotemporal parameters between sub-elite middle- and long-distance runners (n = 6) and endurance non-runners (n = 6). In addition, we aimed to compare the maximal blood lactate concentration [BLa] experienced by participants after conducting these tests.MethodsTelemetric portable respiratory gas analysis, contact and flight time, and stride length and rate were measured using a 5-m contact platform during an incremental test at a synthetic athletics track. V̇O2, heart rate, respiratory quotient values in any 15 s average period during the test were measured. [BLa] was analyzed after the test . Running spatiotemporal parameters were recorded at the last two steps of each 400 m lap. A coefficient of variation (%CV) was calculated for each spatiotemporal variable in each participant from 8 km h−1 onwards.ResultsWhereas runners reported faster MAS (21.0 vs. 18.2 km h−1) than non-runners (p = 0.0001, ES = 3.0), no differences were found for V̇O2max and maximum blood lactate concentration during the running tests (p > 0.05). While significant increases in flight time and stride length and frequency (p < 0.001, 0.52 ≤ }{}{eta }_{p}^{2} ≤ 0.8) were observed throughout the tests, decreases in contact time (p < 0.001, }{}{eta }_{p}^{2}=0.9) were reported. Runners displayed a greater %CV (p = 0.015) in stride length than non-runners. We conclude that middle- and long-distance runners can achieve a faster MAS compared to non-running endurance athletes despite exhibiting a similar V̇O2max. This superior performance may be associated to a greater mechanical efficiency. Overall, runners displayed a greater ability to modify stride length to achieve fast speeds, which may be related to a more mechanically efficient pattern of spatiotemporal parameters than non-runners.

Only two studies have systematically investigated the effects of caffeine during exercise in children (Barta, Acta Paediatr.,1982; Turley, Med. Sci. Sports Exerc., 2005). Both studies used relatively high levels of caffeine (4.0 and 5.0 mg caffeine/kg body mass), respectively. No study has used lower doses to determine their effects on physiological responses to submaximal exercise in young children. PURPOSE: Investigate the effects of low-moderate doses of caffeine on metabolic and cardiovascular responses to exercise in young children. METHODS: Twelve 7–9 year old children (6 girls & 6 boys) voluntarily participated in a double-blind, counter-balanced study design. Children received either a placebo (drink only-PL), 1.0 mg/kg body mass, (CAF-1), and 3 mg/kg body mass (CAF-3) of anhydrous caffeine (mixed in cherry flavored Sprite®) on three separate days. Following a 60 minute absorption period, children rode at 25 W and then 60% VO2 max for 8 minutes each on an electronically braked cycle ergometer at 60–80 RPM while heart rate (HR), oxygen consumption, and respiratory exchange ratio (RER) were monitored continuously, and blood pressure (BP) was measured every two minutes. RESULTS: HR was significantly (p < 0.05) higher in PL vs CAF-1 and CAF-3 at both 25 W(125±12, 119±11, 120±10bpm) and 60% VO2max(159±14, 154±14, 154±12 bpm), respectively. There were no differences in systolic BP (SBP-mmHg) at 25 W (120±10 vs 120±8) or 60% VO2 max (140±11 vs 139±12), and diastolic BP (DBP-mmHg) at 25 W (64±4 vs 66±5) or 60% VO2 max (65±6 vs 65±6) in PL vs. CAF-1, respectively. There was a trend for lower SBP at 25 W (120±10 vs 126±11-p=0.06) and 60% VO2max (140±11 vs 143±13-p=0.20), and no difference in DBP at 25 W (64±4vs 67±7-p=0.21) and 60% VO2 max (65±6 vs 67±5-p=0.23) between PL vs. CAF-3, respectively. VO2 was the same at both 25 W (0.63±0.07, 0.64±0.09, 0.65±0.11 l•min) and 60%VO2max(0.99±0.19, 1.00±0.21, 1.00±0.22 l•min) in PL, CAF-1 and CAF-3, respectively. RER was also the same at both 25 W (0.94±0.03, 0.92±0.04, 0.95±0.05) and 60% VO2max (0.97±0.03, 0.95±0.04, 0.97±0.03) in PL, CAF-1 and CAF-3, respectively. CONCLUSION: In young children, low (1 mg/kg body mass) doses of caffeine do not have an effect on cardiovascular or metabolic responses to submaximal exercise. Moderate (3 mg/kg body mass) doses of caffeine have no metabolic effects in young children, but there is a trend for elevated blood pressure at this dose.

This study was chiefly concerned with the analysis of physical work capacity of rural adolescents. The number of subjects, aged from 9 to 18, were 192 for male and 173 for female respectively, but a few subjects could not undergo all the items of the test because of their temporary physical disorders. Analyzing the data from each of the measurements with respect to physique, pulmonary function, and aerobic work capacity, the following results were obtained: 1. Body height and weight were significantly or insignificantly less than the national mean values, but the weight to height in each age and sex was almost equal to the standardized weight to height. 2. At different ages the boys had a little more vital capacity till 13 years of age, but later through the ages of 14 and 15 years the remarkable sex differences appeared to be 87% and 74%. After 16 years the absolute values for boys showed slight increase, but for girls showed hardly any fluctuations. The one-second timed vital capacities also showed the same inclinations as in the case of the vital capacities. Percentages of expired air qualified by the one-second time interval ranged from 85 to 90% in regardless with age and sex. For female subjects maximal breathing capacities decreased gradually after 15 years. 3. For boys the average values of maximal oxygen uptake increased gradually from 1.54 l per minute for 10 year old boys to 2.08 l for the boys of 13 years, and increased sharply to 3.00 l for the boys aged 15 years, when the maximal oxygen uptake reached the plateau. The average values of the girls up to the age of 13 years were 3 to 7% lower than those of the boys of the same age, and as there was no increase later the average values of the girls aged 14 and 15 years were 20 to 28% lower respectively. 4. The highest average values of maximal oxygen uptakes per kg body weight were 58.l ml at the age of 15 years for the boys and 49.6 ml for the girls aged 13 years respectively. And thereafter the average values gradually decreased for both sexes. Pronounced difference between the sexes suddenly appeared at the age of 14 years, and that is, the percentages of the girls to the boys decreased fr0m 88.4% at 13 years to 79.8% at 14 years. 5. In the 9 to 12 years age group the correlations between static dimensions such as body height, weight, body surface area, and vital capacity and functional dimensions such as maximal respiratory volume, maximal oxygen uptakes, and maximal breathing capacity were considerably high for the sexes. The boys in the 12 to 15 years age group had higher correlations than in the younger age group. The correlations for the girls were lower than in the younger age group because in puberty static dimensions did not accompany so functional dimensions as before. The boys in the 15 to 18 years age group showed lower correlations than in the previous age group. This may be partly due to the development of functional dimensions, though not strictly, independent upon static dimensions and partly due to the greater individual differences in the both dimensions. On the other hand the correlations for the girls were much less than in the previous two age groups. This is on the ground that most parts of development of static dimensions appeared as characteristic developments of female and functional dimensions concerning aerobic work capacity were inclined to cease in these ages. 6. The performance of the five-minute run develops up to the age of 15 years for the boys and thereafter no noticeable developments were found. The 13 year old girls showed the best performance and later the girls rather decreased the endurance performance. 7. The correlations of the five-minute run and maximal oxygen uptake decreased with advanced age group for the sexes.

A prototype of a single-axis prosthetic foot is proposed as an alternative prosthetic foot for patients with disabilities to perform daily activities. Three leading positions within 0-64% of the gait cycle are heel strike, midstance, and toe-off positions used as boundary conditions. The Finite Element Method can analyze a model to determine the values of deformation, stress, and strain that occur in detail. This research is conducted to fabricate an optimal prosthetic foot product or prototype and determine its strength to support the weight of the user's body and provide comfort and safety before being fabricated using 3D printing and resin infusion methods. The fabrication of the prosthetic foot prototype used Fusion 360 software and continued with simulation testing using ABAQUS 6.14 software. The prototype model was designed and evaluated to obtain strength and feasibility before being fabricated with an Anycubic Photon Mono X type DLP 3D printing machine and resin infusion method. The results obtained in this study show that the prototype of a single-axis prosthetic foot can support the human body's weight with loading level P3 or 60 kg, level P4 or 80 kg, and level P5 or 100 kg according to ISO 10328. The mass of the prototype or single-axis prosthetic foot product is 657 grams, according to the criteria, not more than 1.7% of the user's total body weight, with a hardness value of 55.5 HA, and can withstand 50,000 cycles in fatigue testing.

Ankle injury is one of the most common conditions in athletics and military activities. Strength asymmetry (SA) and imbalance may represent a risk factor for injury, but past investigations have produced ambiguous conclusions. Perhaps one explanation for this ambiguity is the fact that these authors used univariate models to predict injury. To evaluate the predictive utility of SA and imbalance calculations for ankle injury in univariate and multivariate prediction models. Prospective cohort study. Laboratory. A total of 140 male US Air Force Special Forces. Baseline testing consisted of body composition, isometric strength, and aerobic and anaerobic capacity. A clinician conducted medical chart reviews 365 days posttesting to document the incidence of ankle injury. Strength asymmetries were calculated based on the equations most prevalent in the literature along with known physiological predictors of injury in the military: age, height, weight, body composition, and aerobic capacity. Simple logistic regression was conducted using each predictor, and backward stepwise logistic regression was conducted with each equation method and the physiological predictors entered initially into the model. Strength asymmetry or imbalance or both, as a univariate predictor, was not able to predict ankle injury 365 days posttesting. Body mass (P = .01) and body mass index (P = .01) significantly predicted ankle injury. Strength asymmetry or imbalance or both significantly predicted ankle injury when considered with body mass (P = .002-.008). As a univariate predictor, SA did not predict ankle injury. However, SA contributed significantly to predicting ankle injury in a multivariate model using body mass. Interpreting SA and imbalance in the presence of other physiological variables can help elucidate the risk of ankle injury.