不同年齡組別選手在24小時超級馬拉松中的配速策略與表現分析

Pacing strategies and performance analysis of runners from different age groups in a 24-hour ultramarathon

緒論：超級馬拉松（超馬）是一種跑步距離超過42.195公里或比賽時間超過6小時的極限耐力運動。隨著參賽人數增加，如何制定有效的配速策略以提升表現成為關注焦點。雖已有研究指出穩定配速與成績有關，但針對不同年齡層在24小時超馬中的配速變化與表現仍少有系統探討。本研究旨在分析年齡對24小時超馬選手配速穩定性與成績之影響。方法：本研究分析2016至2022年東吳國際24小時超馬賽事180位選手（男122人、女58人），依年齡分為七組。使用晶片記錄每圈時間並計算每小時平均速度與速度變異率以評估配速穩定性。統計方法包括單因子變異數分析比較不同年齡組的成績、平均速度與速度變異率差異，重複測量變異數分析比較每小時速度變化，並以皮爾森相關分析探討不同年齡組速度變異率與表現之關聯。結果：各年齡組皆呈現反J型配速趨勢：初期速度下降，中期穩定，末期回升。35-39歲組成績最佳（212.96±30.39 km）、平均速度最高（9.71±1.09 km/h），且速度變異率最低（12.29%），顯示配速最穩定。≥60歲組成績與速度最低，速度變異率最高。速度變異率與平均速度（r = - .534）及成績（r = - .690）呈顯著負相關（p < .001），以18-34歲與35-39歲組相關性最強。結論：年齡顯著影響24小時超馬選手之配速策略與比賽表現，35-54歲表現最佳且配速穩定性較高；18-34歲與≥60歲組配速波動較大。穩定配速與佳績密切相關，建議跑者與教練依年齡制定個別化配速策略，以提升表現。

Introduction: Ultramarathons are extreme endurance events involving distances over 42.195 km or durations exceeding 6 hours. With growing participation, optimal pacing strategies have gained attention. While previous studies suggest stable pacing is linked to better performance, limited research has explored how age influences pacing and outcomes in 24-hour ultramarathons. This study aims to examine the relationship between age, pacing stability, and performance. Methods: We analyzed data from 180 finishers (122 men, 58 women) in the Soochow International 24-hour Ultramarathon (2016-2022), divided into seven age groups. Each runner wore an RFID chip that recorded lap times. Hourly average speed and the coefficient of variation (CV) of speed were calculated to assess pacing stability. Statistical analyses included one-way ANOVA for group differences in total distance, mean speed, and CV; repeated measures ANOVA for hourly pacing changes; and Pearson correlation to explore relationships among age, CV, and performance. Results: All age groups followed a reverse-J pacing trend: early speed decline, mid-race stability, and endrace rebound. The 35-39 year group had the best performance (212.96±30.39 km), highest average speed (9.71±1.09 km/h), and lowest CV (12.29%). The≥60-year group had the lowest distance and speed, with the highest CV. CV was significantly negatively correlated with average speed (r = - .534) and total distance (r = - .690, p < .001), with the 18-34 and 35-39 year groups showing the strongest correlations. Conclusion: Age significantly affects pacing and performance in 24-hour ultramarathons. Runners aged 35-54 demonstrated better performance and pacing stability, while younger (18-34) and older (≥60) groups showed greater pacing variability. Stable pacing was strongly associated with improved outcomes. These findings suggest that age-specific pacing strategies may enhance endurance performance and provide practical insights for athletes and coaches.