Background

Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions.

Objectives

We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load 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 (five female; maximal oxygen consumption [\(\dot{V}\)O₂max], 51.9 ± 7.2 mL/kg/min), the validation study included 21 cyclists (seven female; \(\dot{V}\)O₂max 53.5 ± 11.0 mL/kg/min), 20 runners (six female; \(\dot{V}\)O₂max 57.5 ± 7.2 mL/kg/min), and 18 kayakers (five female; \(\dot{V}\)O₂max 45.6 ± 4.8 mL/kg/min). Training sessions were quantified using six training load metrics: two using heart rate, three using power, and one using perceived exertion. Carbohydrate 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 training load and both CHO utilization and EE. General estimating equations were used to model CHO utilization and EE, using training load 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, 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 training load 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 (mean absolute error; CHO = 16–21 g [10–14%], EE = 53–82 kcal [7–11%]). In the validation study, the mean absolute error ranged from 16–50 g [15–45%] for CHO models to 53–182 kcal [9–31%] for EE models. The calibrated mean absolute error ranged from 9–20 g [8–18%] for CHO models to 36–72 kcal [6–12%] for EE models.

Conclusions

At the individual level, there are strong linear relationships between all measures of training load 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.

中文翻译：

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一种使用训练负荷测量来预测耐力运动期间碳水化合物和能量消耗的新方法

 背景

运动营养指南建议碳水化合物 （CHO） 摄入量因运动员而异，并根据训练负荷的变化进行调整。但是，在培训课程期间评估 CHO 利用率的方法有限。

 目标

我们的目标是 （1） 量化 CHO 与运动期间总体能量消耗 （EE） 之间的双变量关系，以及在不同持续时间和强度的训练中常用的非侵入性训练负荷测量，以及 （2） 构建和评估预测模型，以使用相同的训练负荷测量和易于量化的个体因素来估计 CHO 利用率和 EE。

 方法

这项研究分两部分进行：一项初步研究，参与者进行了四次不同的基于实验室的自行车训练，以及一项验证研究，不同的参与者使用三种锻炼方式（骑自行车、跑步或皮划艇）中的一种进行一次基于实验室的训练。主要研究包括 15 名骑自行车的人（5 名女性;最大耗氧量 [\（\dot{V}\）O₂max]，51.9 ± 7.2 mL/kg/min），验证研究包括 21 名骑自行车的人（7 名女性;\（\dot{V}\）O₂最大 53.5 ± 11.0 mL/kg/min），20 名跑步者（6 名女性;\（\dot{V}\）O₂max 57.5 ± 7.2 mL/kg/min）和 18 名皮划艇运动员（5 名女性;\（\dot{V}\）O₂最大 45.6 ± 4.8 mL/kg/min）。使用 6 个训练负荷指标量化训练会话：2 个使用心率，3 个使用功率，1 个使用感知用力。分别确定有氧（气体交换）和厌氧（净乳酸积累、体重和 O₂ 乳酸当量法）能量系统的碳水化合物使用和 EE 并求和。重复测量相关性用于检查训练负荷与 CHO 利用率和 EE 之间的关系。一般估计方程用于对 CHO 利用率和 EE 进行建模，使用训练负荷以及健康和性别测量。模型是在主要研究中构建的，并在验证研究中进行了测试。模型性能报告为决定系数 （R²） 和平均绝对误差，校准测量用于模型评估和特定运动的模型重新校准。

 结果

所有训练负荷指标与 CHO 利用率和 EE 之间的受试者内相关性非常大到近乎完美 （r = 0.76–0.96） 很明显。在主要研究中，所有模型都解释了大量的方差 （R² = 0.77–0.96） 并显示出良好的准确性 （平均绝对误差;CHO = 16-21 克 [10-14%]，EE = 53-82 大卡 [7-11%]）。在验证研究中，平均绝对误差范围从 CHO 模型的 16-50 g [15-45%] 到 EE 模型的 53-182 kcal [9-31%]。校准的平均绝对误差范围为 CHO 型号的 9-20 g [8-18%] 到 EE 型号的 36-72 kcal [6-12%]。

 结论

在个人层面，训练负荷的所有测量与骑行期间的 CHO 利用率和 EE 之间存在很强的线性关系。当与其他健康测量相结合时，可以准确估计骑行期间的 EE 和 CHO 利用率。当与校准调整一起使用时，这些模型可应用于跑步和皮划艇。