The Kinetics of the Work Capacity Above Critical Power

The critical power (CP) model includes two constants: the CP and the W′ [P = W′ / t) + CP]. The W′ is the finite work capacity available above CP. Power output above CP results in depletion of the W′; complete depletion of the W′ results in exhaustion. It is possible to model the charge and discharge of the W′ during intermittent exercise using a novel integrating model (the W′BAL model), and to generate a function describing a curvilinear relationship between time constants of reconstitution of the W′ in terms of the difference between recovery power and CP (DCP) (r2 = 0.77). The depletion of the W′ as predicted by the W′BAL model during intermittent exercise is linearly related to the rise in V ̇O_2 above exercise baseline (r2 = 0.82 – 0.96). During intermittent exercise, the W′BAL model is generally robust with respect to the length of work and recovery interval, yielding a mean under-prediction of the W′BAL of only -1.6 ±1.1 kJ. The amount of W′ remaining after a period of intermittent exercise correlates with the difference between the subject’s V ̇O_2 at that time (V ̇O_2START) and V ̇O_2PEAK (DVO2) (r = 0.79, p < 0.01). Moreover, the W′BAL model also performs well in the field, permitting accurate estimation of the point at which an athlete becomes exhausted during hard training or competition (mean W′BAL at exhaustion = 0.5 ± 1.3 kJ (95% CI = 0 – 0.9 kJ). The W′BAL model meets the mathematical criteria of an excellent diagnostic test for exhaustion (area under ROC curve = 0.91). 31P magnetic resonance spectroscopy during single leg extensor exercise revealed a correlation between the recovery of the W′BAL model and recovery of creatine phosphate ([PCr]) after a bout of exhaustive single leg extensor exercise (r = 0.99, p < 0.01). The W′BAL model also accurately predicted recovery of the W′ in this setting (r = 0.97, p < 0.05). However, a complete understanding of the relationship between the depletion and recovery of [PCr] and the depletion and recovery of the W′ remains elusive. Muscle carnosine content is curvilinearly related to the rate of W′BAL recovery, with higher muscle carnosine associated with faster recovery, with implications for muscle buffering capacity and calcium handling. The W′BAL model may be recast in the form of a differential equation, permitting definition of the time constant of recovery of the W′BAL in terms of the subject’s known W′ and the DCP. This permits the scaling of the model to different muscle groups or exercise modalities. Moreover, modifications to this mathematical form may help explain some of the variability noted in the model in earlier studies, suggesting novel avenues of research. However, the present formulation of the W′BAL model is mathematically robust and represents an important addition to the scientific armamentarium, which may aid the understanding the physiology of human performance

Triathlon

Triathlon consists of swimming, cycling, and running. Due to the large volume of training required, athlete injury may be the result of mechanical or physiological insult. Overuse injuries are common, as are traumatic injuries. However, athletes may also suffer physiological injury as a result of overwhelming the homeostatic mechanisms of the body. Many injuries can be avoided through appropriate planning in both the short and long term, termed periodization

The W\u27 balance model: Mathematical and methodological considerations

Since its publication in 2012, the W\u27 balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model\u27s intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W\u27 balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W\u27 during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W\u27 balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise

Utility of the W´BAL model in training program design for masters cyclists

The present study aims to determine the utility of integrating balance model (W´BAL-INT) in designing interval training programs as assessed by improvements in power output, critical power (CP), and W prime (W´) defined as the finite work capacity above CP. Fourteen male cyclists (age = 42 ± 7 yr, body mass = 69.6 ± 6.5 kg, height = 175 ± 5 cm, CP = 302 ± 32 W, relative CP = 4.35 ± 0.66 W·kg-1) were randomized into two training groups: Short-Medium-Long intervals (SML-INT; n = 7) or Long intervals (L-INT, n = 7) [training sessions separated by 72 h], along with 3-4 sessions of moderate intensity training per week, for 4 weeks. All sessions were designed to result in the complete depletion of the W´ as gauged by the W´BAL-INT. CP and W´ were assessed using the specified efforts (i.e., 12, 7 and 3 min) and calculated with the 2-parameter CP linear model. Training loads between the groups were compared using different metrics. CP improved in both the SML-INT and L-INT groups by 5 ± 4% and 6 ± 5% (p \u3c 0.001) respectively, without significant changes in W´. Mean maximal power over 3, 7 and 12 min increased significantly in the SML-INT group by 5%, 4% and 9%, (p \u3c 0.05) without significant changes in the L-INT group. There were no differences between groups in training zone distribution or training load using BikeScore and relative intensity, but there was significantly (p \u3c 0.05) higher TRIMPS for the Long-INT group. Therefore, W´BAL model may prove to be a useful tool for coaches to construct SML-INT training programs