A method for predicting peak work rate for cycle ergometer and treadmill ramp tests

Saengsuwan, Jiamjit; Nef, Tobias; Hunt, K.J.

doi:10.1111/cpf.12344

Cited by 4 publications

(5 citation statements)

References 28 publications

(39 reference statements)

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“…That equation includes three terms: one related to the cost of on‐the‐level running, which is linearly proportional to speed; one involving gravity, including both speed and slope; and the baseline (resting) cost. The modified work‐rate equation, augmented using the first of these terms, would then be

P false(t false) = m g v_{normalt} false(t false) sin false(normalθ (t) false) + k_{normalv} v_{normalt} false(t false),

where the constant of proportionality k v can be obtained using an estimate of the oxygen cost of the work done during level treadmill running (Saengsuwan et al ., ). The corresponding expression for the treadmill angle is [cf.…”

Section: Discussionmentioning

confidence: 97%

“…where the constant of proportionality k v can be obtained using an estimate of the oxygen cost of the work done during level treadmill running (Saengsuwan et al, 2016). The corresponding expression for the treadmill angle is [cf.…”

Section: Discussionmentioning

confidence: 99%

“…For both the CP and SP test protocols, work rate (power) was characterized as the rate of work done against gravity in moving the body mass up the treadmill slope using

P false(t false) = m g v_{normalt} false(t false) sin false(normalθ (t) false),

where m is body mass, g is gravitational field strength (9·81 N kg −1 ), v t is treadmill speed and θ is the treadmill angle (treadmill slope is related to θ as slope = tan θ · 100%). During the ramp phase of both protocols, the rate of work done against gravity increased linearly with time; the gradient of the ramp was set individually for each subject such that their predicted peak work rate would be reached in 10 min; predicted peak work rate was obtained using a methodology detailed elsewhere (Saengsuwan et al ., ).…”

Section: Methodsmentioning

confidence: 97%

“…where m is body mass, g is gravitational field strength (9Á81 N kg À1 ), v t is treadmill speed and h is the treadmill angle (treadmill slope is related to h as slope = tan h Á 100%). During the ramp phase of both protocols, the rate of work done against gravity increased linearly with time; the gradient of the ramp was set individually for each subject such that their predicted peak work rate would be reached in 10 min; predicted peak work rate was obtained using a methodology detailed elsewhere (Saengsuwan et al, 2016). For the CP protocol, to obtain the specified individual ramp work rate, speed and slope were preprogrammed to simultaneously increase in a nonlinear, equally smooth fashion according to the algorithm proposed by (Hunt, 2008;Jamieson et al, 2008), which is based upon the governing equation for the rate of work done against gravity, Eq.…”

Section: Ramp Test Protocolsmentioning

confidence: 99%

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A new method for self‐paced peak performance testing on a treadmill

Hunt

Anandakumaran

Loretz

et al. 2016

Clin Physio Funct Imaging

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Purpose Self-paced maximal testing methods may be able to exploit central mediation of function-limiting fatigue and therefore have potential to generate more valid estimates of peak oxygen uptake. The aim of this study was to investigate the feasibility of a new method for self-paced peak performance testing on treadmills and to compare peak and submaximal performance outcomes with those obtained using a non-self-paced ('computer-paced') method employing predetermined speed and slope profiles. Methods The proposed self-paced method is based upon automatic subject positioning using feedback control together with an exercise intensity which is driven by a predetermined, individualized work-rate ramp. Results Peak oxygen uptake was not significantly different for the computer-paced (CP) versus self-paced (SP) protocols: 4Á38 AE 0Á48 versus 4Á34 AE 0Á46 ml min À1 , P = 0Á42. Likewise, there were no significant differences in the other peak and submaximal cardiopulmonary parameters, viz. peak heart rate, peak respiratory exchange ratio and the first and second ventilatory thresholds. Ramp duration for CP was longer than for SP: 494Á5 AE 71Á1 versus 371Á3 AE 86Á0 s, P = 0Á00072. Concomitantly, the peak rate of work done against gravity was higher for CP: 264Á8 AE 40Á8 versus 203Á8 AE 53Á4 W, P = 0Á0021. Conclusions The self-paced approach was found to be feasible for estimation of the principal performance outcomes: the method was technically implementable, it was acceptable to the subjects and it showed good responsiveness. Further investigation of the self-paced method, with adjustment of the target ramp-phase duration or modification of the work-rate calculation equations, is warranted.

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P false(t false) = m g v_{normalt} false(t false) sin false(normalθ (t) false) + k_{normalv} v_{normalt} false(t false),

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

“…For both the CP and SP test protocols, work rate (power) was characterized as the rate of work done against gravity in moving the body mass up the treadmill slope using

P false(t false) = m g v_{normalt} false(t false) sin false(normalθ (t) false),

Section: Methodsmentioning

confidence: 97%

Section: Ramp Test Protocolsmentioning

confidence: 99%

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A new method for self‐paced peak performance testing on a treadmill

Hunt

Anandakumaran

Loretz

et al. 2016

Clin Physio Funct Imaging

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“…In general, the fact that the participant is not familiar with the type of exercise impairs the result during the exercise test, and the calculated work load is often too low [27]. The test-retest reliability and repeatability of incremental testing using the DLP has not been evaluated, and the predicted peak work rate during the incremental test was adapted from a method developed for a cycle ergometer [28]. As a complement to the present assessment of the DLP for exercise training, future work should include the evaluation of test-retest reliability of cardiopulmonary exercise testing using the DLP, the establishment of a method to predict peak work rate during incremental tests, and the investigation of physiological adaptations to long term training programmes for healthy and patient populations.…”

Section: Discussionmentioning

confidence: 99%

Technical feasibility of constant-load and high-intensity interval training for cardiopulmonary conditioning using a re-engineered dynamic leg press

2019

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Background: Leg-press devices are one of the most widely used training tools for musculoskeletal strengthening of the lower-limbs, and have demonstrated important cardiopulmonary benefits for healthy and patient populations. Further engineering development was done on a dynamic leg-press for work-rate estimation by integrating force and motion sensors, power calculation and a visual feedback system for volitional work-rate control. This study aimed to assess the feasibility of the enhanced dynamic leg press for cardiopulmonary exercise training in constant-load training and high-intensity interval training. Five healthy participants aged 31.0 ± 3.9 years (mean ± standard deviation) performed two cardiopulmonary training sessions: constant-load training and high-intensity interval training. Participants carried out the training sessions at a work rate that corresponds to their first ventilatory threshold for constant-load training, and their second ventilatory threshold for high-intensity interval training. Results: All participants tolerated both training protocols, and could complete the training sessions with no complications. Substantial cardiopulmonary responses were observed. The difference between mean oxygen uptake and target oxygen uptake was 0.07 ± 0.34 L/min (103 ±17%) during constant-load training, and 0.35 ± 0.66 L/min (113 ±27%) during high-intensity interval training. The difference between mean heart rate and target heart rate was −7 ± 19 bpm (94 ±15%) during constant-load training, and 4.2 ± 16 bpm (103 ±12%) during high-intensity interval training. Conclusions: The enhanced dynamic leg press was found to be feasible for cardiopulmonary exercise training, and for exercise prescription for different training programmes based on the ventilatory thresholds.

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Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features

Wenzel,

Liebig,

Swoboda

et al. 2024

Eur J Appl Physiol

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Purpose Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ($$\dot{V}$$ V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict $$\dot{V}$$ V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for $$\dot{V}$$ V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of $$\dot{V}$$ V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion Machine learning models predict $$\dot{V}$$ V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number DRKS00031401 (6 March 2023, retrospectively registered).

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A method for predicting peak work rate for cycle ergometer and treadmill ramp tests

Cited by 4 publications

References 28 publications

A new method for self‐paced peak performance testing on a treadmill

A new method for self‐paced peak performance testing on a treadmill

Technical feasibility of constant-load and high-intensity interval training for cardiopulmonary conditioning using a re-engineered dynamic leg press

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features

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