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Purpose <p>Blood lactate accumulation (ΔBLC) during maximal short-term exercise is a crucial indicator of peak glycolytic activation in anaerobic performance assessment. However, the relationship between ΔBLC and sprint performance remains inconsistent, potentially due to variations in testing protocols and the use of absolute rather than relative performance metrics. This study investigated the relationship between ΔBLC and cycling sprint performance, hypothesizing normalization to body weight is essential for accurate metabolic performance evaluation.</p> Methods <p>Twenty-two trained male athletes performed a 10-s maximal isokinetic cycling sprint on an ergometer. Power output and cadence were continuously recorded to calculate peak power (<i>P</i><sub>peak</sub>), time to peak power (<i>t</i><sub><i>P</i>peak</sub>), mean power (<i>P</i><sub>mean</sub>), and power increase during the lactic phase (maxΔ<i>P</i>, Δ<i>P</i>). Capillary blood samples were collected pre-exercise and up to 12&#xa0;min post-exercise to determine pre-exercise (BLC<sub>pre</sub>) and maximal post-exercise blood lactate concentration (BLC<sub>max</sub>). ΔBLC was calculated as BLC<sub>max</sub>−BLC<sub>pre</sub>. Statistical analysis included Pearson correlations and stepwise multiple regression.</p> Results <p>ΔBLC exhibited significant correlations with body-weight-normalized maxΔ<i>P</i> (<i>r</i> = 0.78, <i>p</i> &lt; 0.001), <i>P</i><sub>mean</sub> (<i>r</i> = 0.70, <i>p</i> &lt; 0.001), and <i>P</i><sub>peak</sub> (<i>r</i> = 0.65, <i>p</i> &lt; 0.01). In contrast, no significant correlations were observed with absolute metrics (<i>p</i> &gt; 0.05). Stepwise regression analysis identified adjusted maxΔ<i>P</i> and <i>P</i><sub>mean</sub> as the strongest predictors of ΔBLC (adjusted <i>R</i><sup>2</sup> = 0.648, <i>p</i> &lt; 0.001).</p> Conclusion <p>Relative, body-weight-adjusted metrics, particularly maxΔ<i>P</i> and <i>P</i><sub>mean</sub>, are strongly associated with ΔBLC. The use of these relative metrics may enhance the precision of anaerobic performance assessment, facilitate more effective training monitoring, and improve talent identification processes in sports requiring high-intensity efforts.</p>

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Blood lactate accumulation during maximal cycling sprints and its relationship to sprint performance characteristics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月20日

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  • Ralf Haase,
  • Anna Katharina Dunst,
  • Nico Nitzsche

摘要

Purpose

Blood lactate accumulation (ΔBLC) during maximal short-term exercise is a crucial indicator of peak glycolytic activation in anaerobic performance assessment. However, the relationship between ΔBLC and sprint performance remains inconsistent, potentially due to variations in testing protocols and the use of absolute rather than relative performance metrics. This study investigated the relationship between ΔBLC and cycling sprint performance, hypothesizing normalization to body weight is essential for accurate metabolic performance evaluation.

Methods

Twenty-two trained male athletes performed a 10-s maximal isokinetic cycling sprint on an ergometer. Power output and cadence were continuously recorded to calculate peak power (Ppeak), time to peak power (tPpeak), mean power (Pmean), and power increase during the lactic phase (maxΔP, ΔP). Capillary blood samples were collected pre-exercise and up to 12 min post-exercise to determine pre-exercise (BLCpre) and maximal post-exercise blood lactate concentration (BLCmax). ΔBLC was calculated as BLCmax−BLCpre. Statistical analysis included Pearson correlations and stepwise multiple regression.

Results

ΔBLC exhibited significant correlations with body-weight-normalized maxΔP (r = 0.78, p < 0.001), Pmean (r = 0.70, p < 0.001), and Ppeak (r = 0.65, p < 0.01). In contrast, no significant correlations were observed with absolute metrics (p > 0.05). Stepwise regression analysis identified adjusted maxΔP and Pmean as the strongest predictors of ΔBLC (adjusted R2 = 0.648, p < 0.001).

Conclusion

Relative, body-weight-adjusted metrics, particularly maxΔP and Pmean, are strongly associated with ΔBLC. The use of these relative metrics may enhance the precision of anaerobic performance assessment, facilitate more effective training monitoring, and improve talent identification processes in sports requiring high-intensity efforts.