Validity and Reliability of Hydraulic-Analogy Bioenergetic Models in Sprint Roller Skiing

Individualized physiology-based digital twin model for sports performance prediction: a reinterpretation of the Margaria-Morton model

Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible work i.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation and a reinterpretation (including a physiological meaning of parameters) of the three-component Margaria-Morton model were performed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings was possible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin was therefore proposed and applied for national-level cyclists. The twins thus created were validated by comparison with field performance, experimental observations, and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Considering the literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate and phosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodology has a wide range of applications, including prediction and optimization of the performance of individually modeled athletes.

Development and validation of dynamic bioenergetic model for intermittent ergometer cycling

PURPOSE

The aim of this study was to develop and validate a bioenergetic model describing the dynamic behavior of the alactic, lactic, and aerobic metabolic energy supply systems as well as different sources of the total metabolic energy demand.

METHODS

The bioenergetic supply model consisted of terms for the alactic, lactic, and aerobic system metabolic rates while the demand model consisted of terms for the corresponding metabolic rates of principal cycling work, pulmonary ventilation, and accumulated metabolites. The bioenergetic model was formulated as a system of differential equations and model parameters were estimated by a non-linear grey-box approach, utilizing power output and aerobic metabolic rate (MRae) data from fourteen cyclists performing an experimental trial (P2) on a cycle ergometer. Validity was assessed by comparing model simulation and measurements on a similar follow-up experimental trial (P3).

RESULTS

The root mean square error between modelled and measured MRae was 61.9 ± 7.9 W and 79.2 ± 30.5 W for P2 and P3, respectively. The corresponding mean absolute percentage error was 8.6 ± 1.5% and 10.6 ± 3.3% for P2 and P3, respectively.

CONCLUSION

The validation of the model showed excellent overall agreement between measured and modeled MRae during intermittent cycling by well-trained male cyclist. However, the standard deviation was 38.5% of the average root mean square error for P3, indicating not as good reliability.

The effect of exercise hyperpnea on gross efficiency and anaerobic capacity estimates during a 3-min cycle time trial

This study aimed to analyze the effect of exercise-induced hyperpnea on gross efficiency (GE) and anaerobic capacity estimates during a self-paced 3-min supramaximal cycle time trial (TT). Fourteen highly trained male cyclists performed 7 × 4-min submaximal stages, a 6-min passive rest, a 3-min TT, a 5-min passive rest, and a 6-min submaximal stage. Three models were based on the 7 × 4-min linear regression extrapolation method, using 1) the conventional model (7-Y_LIN); 2) the same 7-Y_LIN model but correcting for the additional ventilatory cost (i.e., hyperpnea) (7-Y_LIN-V-cor); and 3) accounting for linearly declining GE during the TT (7-Y_LIN-D). The other three models were based on GE from the last submaximal stage, using the conventional model (GE_LAST) and the same modifications as described for 7+Y_LIN, i.e., 1) GE_LAST, 2) GE_LAST-V-cor, and 3) GE_LAST-D. The GE_LAST model generated 18% higher values of anaerobic capacity than the 7-Y_LIN model (P < 0.05). During the TT, the hyperpnea-corrected model (i.e., 7-Y_LIN-V-cor or GE_LAST-V-cor) generated, compared with the respective conventional model (i.e., 7-Y_LIN or GE_LAST), ∼0.7 percentage points lower GE and ∼11% higher anaerobic capacity (all, P < 0.05). The post-TT GE was 1.9 percentage points lower (P < 0.001) and the 7-Y_LIN-D or GE_LAST-D model generated, compared with the respective conventional model, a lower GE (∼1.0 percentage points) and ∼17% higher anaerobic capacity during the TT (all, P < 0.05). In conclusion, the correction for a declining GE due to hyperpnea during a supramaximal TT resulted in an increased required total metabolic rate and anaerobic energy expenditure compared with the conventional models.NEW & NOTEWORTHY This study demonstrates that GE declines during a 3-min supramaximal cycle TT, which is possibly related to the hyperpneic response during supramaximal exercise. The finding from this study also provides novel insight into how the increased ventilatory energy cost from exercise-induced hyperpnea contributes to decreased GE, increased required total metabolic rate, and increased anaerobic energy expenditure during supramaximal exercise. Therefore, conventional linear models for estimating anaerobic capacity are likely to generate underestimated values.

Physiological Responses and Performance During a 3-Minute Cycle Time Trial: Standard Paced Versus All-Out Paced

PURPOSE

To compare performance and physiological responses between a standard-paced 3-minute time trial (TTSP, ie, pacing based on normal intention) and a consistently all-out-paced 3-minute time trial (TTAOP).

METHODS

Sixteen well-trained male cyclists completed the TTSP and TTAOP, on separate days of testing, on a cycling ergometer with power output and respiratory variables measured. Time trials were preceded by 7 × 4-minute submaximal stages of increasing intensity with the linear relationship between power output and metabolic rate used to estimate the contribution from aerobic and anaerobic energy resources. The time course of anaerobic and aerobic contributions to power output was analyzed using statistical parametric mapping.

RESULTS

Mean power output was not different between the 2 pacing strategies (TTSP = 417 [43] W, TTAOP = 423 [41] W; P = 0.158). TTAOP resulted in higher peak power output (P < .001), mean ventilation rate (P < .001), mean heart rate (P = .044), peak accumulated anaerobically attributable work (P = .026), post-time-trial blood lactate concentration (P = .035), and rating of perceived exertion (P = .036). Statistical parametric mapping revealed a higher anaerobic contribution to power output during the first ∼30 seconds and a lower contribution between ∼90 and 170 seconds for TTAOP than TTSP. The aerobic contribution to power output was higher between ∼55 and 75 seconds for TTAOP.

CONCLUSIONS

Although there was no significant difference in performance (ie, mean power output) between the 2 pacing strategies, differences were found in the distribution of anaerobically and aerobically attributable power output. This implies that athletes can pace a 3-minute maximal effort very differently but achieve the same result.