A Novel Method to Predict Carbohydrate and Energy Expenditure during Endurance Exercise Using Measures of Training Load
DOI:
https://doi.org/10.51224/SRXIV.438Keywords:
carbohydrate, energy expenditure, Cycling, kayak, RunningAbstract
Background: Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load (TL). However, there are limited methods to assess CHO utilization during training sessions.
Objectives: To 1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly-used, non-invasive measures of TL across sessions of varying duration and intensity, and 2) build and evaluate prediction models to estimate CHO utilization and EE with the same TL measures and easily-quantified individual factors.
Methods: This study was undertaken in two parts — a primary study, where participants performed four different laboratory-based cycle training sessions, and a validation study where different participants performed a single laboratory-based training session using one of three exercise modalities (cycling, running, or kayaking). The primary study included 15 cyclists (5 f; VdO2max, 52 ± 7 mL.kg-1.min-1), the validation study included 21 cyclists (7 f; VdO2max 53.5 ± 11.0 mL.kg-1.min-1), 20 runners (6 f; VdO2max 57.5 ± 7.2 mL.kg-1.min-1), and 17 kayakers (4 f; VdO2max 46.2 ± 4.1 mL.kg-1.min-1). Training sessions were quantified using six TL metrics: two using heart rate, three using power, and one using perceived exertion. CHO use and EE were determined separately for aerobic (gas exchange) and anaerobic (net lactate accumulation, body mass, and O2 lactate equivalent method) energy systems and summed. Repeated-measures correlations were used to examine relationships between TL and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using TL alongside measures of fitness and sex. Models were built in the primary study and tested in the validation study. Model performance is reported as the coefficient of determination (R2) and mean absolute error (MAE), with measures of calibration used for model evaluation and for sport-specific model re- calibration.
Results: Very-large to near-perfect within-subject correlations (r = 0.76–0.96) were evident between all TL metrics and both CHO utilization and EE. In the primary study, all models explained a large amount of variance (R2 = 0.77–0.96) and displayed good accuracy (MAE; CHO = 16–21 g [10–14%], EE = 53–82 kcal [7–11%]). In the validation study the MAE ranged from 17–50 g [15– 45%] for CHO models and 53–178 kcal [9–30%] for EE models. The calibrated MAE ranged from 8–20 g [7–18%] for CHO models and 36–72 kcal [6–12%] for EE models.
Conclusion: At the individual level, there are strong linear relationships between all measures of TL and both CHO utilization and EE during cycling. When combined with other measures of
fitness, EE and CHO utilization during cycling can be estimated accurately. These models can be applied in running and kayaking when used with a calibration adjustment.
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