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가속도계 추정공식과 강도기준치(Cut-point)의 신체활동량 추정오차검증

Error Rates of Prediction Equations and Cut-points of Actigraph GT3X+

이효|이미영|최지엽
한국체육측정평가학회 (Korean Society For Measurement And Evaluation In Physical Education And Sports Science)|2016.04|한국체육측정평가학회지 (The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science)|제18권 제1호|pp. 17-29|DOI 10.21797/ksme.2016.18.1.002

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키워드

정확성Validity신체활동측정오차Physical Activity LevelsMeasurement Errors

초록

신체활동을 보다 정확하게 측정하고자 하는 노력은 지속적이면서 급속적으로 증가하였다. 이로 인해 다양한 신체활동량 추정공식이 개발되고 추정오차를 줄이기 위한 결정요인들이 밝혀졌지만, 추정공식과 Cut-point에 대한 오차검증은 부족한 실정이다. 따라서, 이 연구에서는 장년을 대상으로 다양한 신체활동추정식의 추정오차를 검증하고 개발된Cut-point가 정확하게 신체활동 강도를 분류하는지를 검증하였다. 연구참여자는 평균 50대의 남자 10명(50.30±10.65 세), 여자 38명(53.29±7.36세)을 대상으로 트레드밀, 대표적인 일상생활, 일련의 신체활동상황에서 신체활동량을 준거검사와 가속도계(Actigraph GT3X+)를 이용하여 측정하였다. 준거검사와 가속도계를 이용하여 추정된 신체활동량(MET) 점수 간의 차이는 절대오차와 상대오차를 계산하고 추가적으로 Paired t-test를 적용하여 통계적으로 유의한차이가 있는지 검증하였다. 신체활동강도분류에 대한 정확성 검증은 각 강도의 비율(%)을 계산하였다. 이 연구에서 얻어진 결과를 살펴보면 다양한 공식가운데 Freedson_1998 공식이 트레드밀이나 일상생활의 신체활동추정에 있어 오차가 상대적으로 낮았다. 또한, Troiano_2008 Cut-point가 신체활동강도를 보다 정확하게 분류한 반면 Freedson VM Cut-point는 낮은 강도활동 추정시 높은 오차를 보였다. 추후 연구에서 신체활동량 추정오차를 줄이기 위해 공식개발뿐만 아니라 인공지능망(Artificial Neural Networks)이나 딥러닝(deep learning) 적용 등 새로운 접근이 필요할 것으로 예상된다.

Abstract

The purposes of this study was to examine error rates of existing prediction equations and cut-points of Actigraph GT3X+. Ten healthy males (50.30±10.65yrs) and thirty eight females (53.29±7.36yrs) without any chronic disease were participated in this study. The participants were wearing Actigraph and K4b2 during all experiment and performed three selected types of settings in treadmill, daily physical activities, and free-living. Epoch of Actigraph was set at 1 second, and K4b2were collected breath-by-breath data. Those data were converted to MET values with 60second epoch. Obtained MET values from the criterion measure and Actigrph accelerometer were compared to calculated median, mean, and standard deviations as well as Mean Absolute and Relative Percent Error. Paired t-test was applied to examine the statistical differences of the MET values. To determine the accuracy of the categorization of the physical activity intensity, percent (%) of the correct assignments were calculated. All statistical analysis was calculated by SPSS 20.0 and Stata 12 with α<.05. As a result of the study, overeall Freedson (1998) equation was most accurately estimating MET values comparing to other equations. Troiano (2008) cut-point was precisely classified the intensity of the physical activity among the various cut-points, but not Freedson VM (2011). Advanced technology to estimate physical activity levels should be applied to reduce the error rate of predicting physical activity levels in the near future.

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