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題名:特定族群專用之能量消耗預估方程式設計與應用-以運動員與非運動員為例
作者:張淳皓
作者(外文):CHANG, CHUN-HAO
校院名稱:國立體育大學
系所名稱:運動科學研究所
指導教授:何金山
學位類別:博士
出版日期:2019
主題關鍵詞:能量消耗穿戴裝置心率運動員energy expenditurewearable devicesheart rateathletes
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本研究主要目的在於改良與設計適用於不同體能素質族群的穿戴感測裝置之能量消耗預測方程式。方法:在計畫中,招募了120名成年受測者,依體能狀況分為四組(非耐力型運動員、耐力型運動員、運動習慣者、坐式生活者),每組30人且男性與女性各半數。本研究採用運動心肺功能監測系統作為標準量測,同步與 ActiGraph GT9X-Link 加速規,以及心率監測器進行5種速度(4.80、6.42、8.04、9.66 與 11.28公里/小時)之跑步機測驗。為了瞭解四個族群在每一個速度測驗下的能量消耗差異,使用單因子變異數分析 (one-way ANOVA),並使用 Bonferroni 進行試後比較。為了探討兩個量測系統(標準量測,CMEE 與加速規,GT9X-EE)在測驗中的量測差異,使用成對樣本t檢定 (Paired t tests) 進行分析,並計算Cohen's d 效應量 (effect size,ES) 與絕對平均百分比誤差 (Mean Absolute Percentage Error,MAPE)。線性迴歸 (Linear regression) 被使用來修正能量消耗預測模型。估算能量消耗的可靠性更進一步利用效標分析:皮爾森相關 (Pearson coefficient of determination),和信賴度:組內相關係數 (Intraclass correlation coefficient,ICC) 進行評定。結果:在相同運動強度下,四組之間在標準量測的結果 (CMEE) 達到顯著性差異 (p < .001),反而四組的加速規輸出結果 (GT9X-EE) 皆未達到顯著性差異 (p > .05),且GT9X-EE顯著低於CMEE (p < .05)。透過心律儲備 (heart rate reserve, HRR) 參數以多元線性回歸修正傳統方程式,相較於加速規輸出結果,HRR 顯示出最高的決定系數 (R2) (SG : 0.851、EHG : 0.869、NEG : 0.863、EG : 0.864) 和ICC (SG : 0.919、EHG : 0.930、NEG : 0.927、EG : 0.927)。結論:加速規的向量參數結合HRR參數的能量消耗預測公式,使得不同體能特性的受試者族群,顯著性的提升了預測能量消耗準確性,特別是對於非耐力型運動選手的能量消耗估算有大幅的提升。
The purpose of this study is to design an energy expenditure (EE) formulas of wearable device applies to different groups. In this study, we plan to improve and design the new energy consumption prediction formulas of wearable devices which can be suitable for different physical qualities. One hundred and twenty adult subjects were divided into four groups (sedentary, SG; exercise habit, EHG; non-endurance, NEG; endurance, EG) according to physical fitness status, with 30 in each group (half male and female). This project were using the Cardiopulmonary Exercise Testing System as a criterion measurement (CM), and with the ActiGraph GT9X accelerometer and heart rate monitor to measure 5 speeds (4.80, 6.42, 8.04, 9.66, & 11.28 km/h) treadmill test. Using one-way ANOVA with Bonferroni post hoc method to understand the difference in EE of the four groups at each speed test. Using Paired t test to investigate the difference between the two measurement systems (CMEE and GT9X-EE) in the test, and calculate Cohen's d effect Size (ES) and Mean Absolute Percentage Error (MAPE). Linear regression was used to modify the EE prediction model. Estimating the reliability of EE is further evaluated by the criterion analysis: Pearson coefficient of determination, and intraclass correlation coefficient (ICC). At the same exercise intensity, statistically significant measurement results of CMEE between the four groups were observed (p < 0.000), but there was no apparent difference in the GT9X-EE outcomes (p > 0.05), which were lower than those of CMEE (p < 0.05). Through the integration of vector magnitudes with HRR parameters to correct the traditional formula with multiple linear regression, the HRR showed the highest coefficient of determination (R2) (SG : .851, EHG : .869, NEG : .863, EG : .864) and the ICC (SG : .919, EHG : .930, NEG : .927, EG : .927) compared with accelerometer outputs. The EE predictive equation integrating the accelerometer VM and HRR parameters dramatically improved the accuracy of EE prediction in subject population of different physical characteristics, and especially a significant improvement in the EE estimation of the non-endurance athlete group.
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