目的：旨在試探步頻推估非最大走路運動強度的效果，並建立年輕成人步頻預測代謝當量的迴歸公式。方法：招募健康大學生男性19名與女性20名，參與者須先測量人體變項及22分鐘坐姿安靜休息攝氧量。再以平衡次序法進行時速4.8、5.6及6.4公里走路運動，每次測驗需間隔休息24小時，全程監測攝氧量及步頻。資料處理以線性迴歸分析推估步頻與代謝當量間的關係，再以多元迴歸分析將身高變項納入步頻推估代謝當量的迴歸公式，統計顯著水準訂為α=.05。結果：男、女迴歸公式顯示步頻推估代謝當量獲得的R2為.20與R2為.21，推估3與6 METs步頻為89與142步、96與149步。線性迴歸公式如下：男性METs = 0.0556x - 1.9206；女性METs = 0.057x - 2.4872。然而將步頻推估代謝當量的模型納入身高時，男、女性R2為.47與R2為.50，多元迴歸公式如下：男性METs = -12.871 + 4.859 × 身高（公尺）+ 0.076 × 步頻；女性METs = -4.460 + 1.041 × 身高（公尺）+ 0.059 × 步頻。結論：從結果顯示步頻解釋代謝當量的變異量約20至21%區間，然而納入身高提升預測代謝當量的效果的有限。

Purpose: To investigate the assessing effect of the submaximal walking exercise intensity by step rate. And establish the regression equation between step rate and METs of young adult. Methods: Recruited 19 male and 20 female healthy college students. First visiting were anthropometry and 22 min seated resting VO2 measurement. In second visiting, participants undertook 4.8, 5.6 and 6.4 km/hr walking exercise tests in counter-balance order. Each test separated by 24 hours. The VO2 and step rate were recorded during the tests. Linear regression analysis were used to predict the relationship between step rate and METs. In multiple regression analysis, the height was entered into step rate and METs regression model. Significance level was set at α =.05. Results: The R2 of predicting METs by step rate in male and female were .20 and .21, respectively. Linear regression equation were male: METs = 0.0556x - 1.9206; female: METs = 0.057x - 2.4872. However, when the height into the regression model yielded R2 .29 and .21 for male and female, respectively. Multiple regression equation were male: METs = -12.871 + 4.859 × height (m) + 0.076 × step rate; female: METs = -4.460 + 1.041 × height (m) + 0.059 × step rate. Conclusion: The results show that step rate explains the variation of METs about 20 to 21%. However, height only enhance the R2 slightly.

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