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目的：针对运动员训练水平难以在重大赛事中稳定转化为巅峰表现的难题，在系统分析赛前心率变异性(heart rate variability,HRV)与比赛成绩内在关联的基础上，构建并验证个体化预测模型。方法：采用前瞻性纵向设计，于2023年3月—2025年8月，对2名世界顶尖女子链球运动员进行了系统追踪，采集包括17场重要比赛在内的比赛成绩与赛前0～3 d的34项HRV数据。基于比赛相对成绩的上三分位数，将竞技表现划分为良好、一般两组。通过独立样本t检验或Mann-Whitney U检验筛选出具有显著组间差异的HRV关键指标，采用留一法交叉验证(leave-one-out cross validation,LOOCV)构建个体化预测模型，并进一步进行皮尔逊相关分析和多元线性回归分析。在2025年世界田径锦标赛上对模型进行应用验证。结果：运动员的敏感预测指标存在显著个体差异。运动员A竞技表现良好组的低频功率指标[LF/%、LF(n.u.)]和交感-副交感平衡指数(LF/HF)显著低于一般组，而高频功率指标[HF(n.u.)]则显著高于一般组(P<0.05)，同时低频功率指标LF(log)和LF/%均与比赛相对成绩之间呈显著负相关(P<0.05),LF/ms2和LF/%组成的指标组合与比赛相对成绩(Y)构成线性预测模型Y=1.055-1.435×10-5×LF/ms2-0.001×LF/%。将运动员A在2025年世界田径锦标赛的数据代入以上模型进行验证，结果显示，模型预测的竞技状态与运动员在2025年世界田径锦标赛的实际表现高度一致，预测准确率为97.8%。运动员B竞技表现良好组的高频功率指标(HF/Hz)和非线性特征的线性量化指标(DFAα2)显著低于一般组(P<0.05)，同时DFAα2与比赛相对成绩之间呈显著负相关(P<0.05)，并且DFAα2单个指标与比赛相对成绩(Y)构成线性预测模型Y=1.203-0.495×DFAα2。结论：赛前HRV可作为预测精英女子链球运动员竞技状态的有效工具，但敏感指标具有高度个体化特征，提示在备战重大赛事时，应建立基于个体基线的个性化动态数字化监控体系。研究结果表明，运动实践中不宜局限于对通用指标的单一探索，而应在揭示指标共性规律的基础上，进一步为运动员构建个体化的多维度生理监控模型。
Abstract：Objective: Addressing the challenge of athletes struggling to consistently convert their training levels into peak performance during major competitions,this study aimed to develop and validate individualized predictive models by systematically analyzing the intrinsic relationship between pre-competition heart rate variability(HRV) and competition performance. Methods: Employing a prospective longitudinal design from March 2023 to August 2025, two world-class female hammer throwers were systematically monitored. Data collected included performance results from 17 major competitions and 34 HRV metrics within 0-3 d prior to each competition. Based on the upper tertile of relative competition performance, athletic performance was categorized into Good and Normal groups. Key HRV indicators with significant inter-group differences were identified using independent samples t-tests or Mann-Whitney U tests. Individualized predictive models were subsequently developed using leave-one-out cross-validation(LOOCV), with further exploration of linear relationships conducted through Pearson correlation and multiple linear regression analyses. The models were then prospectively validated at the 2025 World Athletics Championships in Tokyo. Results: Significant individual differences were observed in the sensitive predictive indicators between the two athletes. For Athlete A, the low-frequency power indices [LF/%, LF(n.u.)] and the sympathovagal balance index(LF/HF ratio) were significantly lower in the Good group, whereas the high-frequency power index [HF(n.u.)] was significantly higher(P<0.05). Concurrently, LF(log) and LF/% both demonstrated a significant negative correlation with relative competition performance(P<0.05). A linear predictive model was established in which a combination of LF/ms2 and LF/% predicted relative performance(Y), formulated as Y=1.055-1.435×10-5×LF/ms2-0.001×LF/%. Validation using Athlete A's data from the 2025 World Championships revealed that the model's predicted competitive state was highly consistent with her actual performance, achieving a predictive accuracy of 97.8%. For Athlete B, both the high-frequency power index(HF/Hz) and a linear quantifier of non-linear features(DFAα2 were significantly lower in her Good group(P<0.05). DFAα2 also exhibited a significant negative correlation with relative performance(P<0.05), and a linear model was established in which DFAα2 served as a single predictor for relative performance(Y), formulated as Y=1.203-0.495×DFAα2. Conclusions: Pre-competition HRV serves as an effective tool for predicting the competitive state of elite female hammer throwers. However, the highly individualized nature of sensitive indicators suggests the necessity of establishing personalized, dynamic and digital monitoring systems grounded in individual baselines during the preparation for major competitions. The findings also indicate that sports practice should not be confined to the singular pursuit of universal indicators. Instead, while uncovering common principles, a greater emphasis should be placed on developing individualized, multi-dimensional physiological monitoring models for athletes.
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基本信息:
DOI：10.16470/j.csst.2026013
中图分类号:G824.4
引用信息:
[1]卢璐,叶奎刚,于冰,等.基于长期纵向心率变异性数据的优秀女子链球运动员比赛表现预测模型构建研究[J].中国体育科技,2026,62(03):3-16.DOI:10.16470/j.csst.2026013.
基金信息:
中国田径协会科技服务工作（TJBZ-FW-20230309）
2026-03-15
2026-03-15