Publication:

老人自行車運動具較高抗B型肝炎病毒表面抗體表現活性

背景:交錯式模式的生物阻抗分析法是利用人體可用最長電流路徑的生物阻抗值來進行體組成分析估測。本研究目的為驗證該模式與最為常用的手對腳模式用於估測人體去脂肪質量(FFM)的差異性。 Methods: 232位女性與264位男性分別參與本實驗，應用雙能X光吸收儀(DXA)為體組成成分測量參考，受測者在分別測量其站立式交錯模式阻抗值(左手至右腳, ZCR) 、手對腳模式阻抗值(右手至右腳,ZHF)，配合其身體測量參數，依不同性別與模式分別以線性迴歸分析建立去脂肪質量估測方程式。 Result: DXA測得男性的FFM與手對腳、交錯式模式測得的FFM(分別以FFMmHF、FFMmCR表示)，其線性迴歸分析的相關係數與SEE(Standard error of estimate)分別為0.91、3.34 kg與0.91、3.48 kg，Bland-Altman plots的Limits of agreement (LOA)分別為-6.78至6.78 kg與-7.06至7.06kg，Paired t-test 檢驗結果均未達顯著差異(P> 0.05)。DXA女性受測者的FFM與手對腳、交錯式模式測得的FFM(分別以FFMfHF、FFMfCR表示)，其線性迴歸分析的相關係數與SEE分別為0.85、2.96 kg與0.86、2.92 kg，Bland-Altman plots的LOA分別為-5.91至5.91 kg與-5.84至5.84kg，亦未達顯著差異(P > 0.05)。 Conclusion：在亞洲人以最長電流路徑所測得的交錯阻抗直的生物阻抗分析，用於人體的去脂肪質量的估測，與現有單側的手對腳測量模式的生物阻抗分析，兩者的估測結果無顯著差異，表示交錯模式的生物阻抗分析亦可用於人體的體組成估測。

Background: This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations 8 for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network 9 (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual 10 X-ray absorptiometry (DXA) as reference method. 11 Methods: A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression 12 equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for 13 predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The 14 FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model 15 (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to 16 validate than accuracy of the predictive models. 17 Results: The results showed the significant predictors were impedance, gender, age, height and weight in 18 developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of 19 estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as 20 the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and 21 relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with 22 linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between 23 FFMLR and FFMDXA. 24 Conclusion: When compared the performance of developed prediction equations for estimating reference FFMDXA, 25 the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated 26 ANN model is more suitable for estimating FFM.