일상생활 신체활동 측정을 위한 스마트폰기반 검사도구의수렴타당도검증
Convergent Validity Evidence of Smartphone-based Physical Activity Measures in Free-living
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초록
이 연구의 목적은 일상생활의 신체활동을 측정하기 위해 빈도 높게 이용되고 있는 스마트폰기반 내장형과 손목밴드형 신체활동 검사도구의 측정오차를 검증하고 수렴타당도 증거를 확보하는데 있다 . 성인 남녀 45 명 ( 연령 : 51.67±7.33) 을 대상으로 앉기 , 서기 , 일상적인 걷기 , 계단오르내리기 (4 층 ), 오르막과 내리막 걷기 , 평지 걷기 , 달리기 , 물건옮기기 (2.4kg), 물걸레질하기 등 일상생활을 수행하였다 . Galaxy Note2 S-health 와 Fitbit Charge 의정확성을 검증하기 위해 비교검사로서 Actigraph GT3X+ 와 Omron Pedometer 를 이용하였다 . 검사도구에서 얻어진 측정값의 평균치 차이검증을 위해 Independent t-test, Repeated Measures ANOVA, Dependent t-test 를 적용(α=.05) 하고 Type I error 를 통제하기 위해 Bonferroni Correction Technique 을 적용하였다 . 연구 결과에 의하면 , 스마트폰기반 신체활동 검사도구의 보행수는 비교검사도구인 보행계수계와 비교해볼 때 1~8% 과대평가 되는것으로 확인되었다 . 또한 , 스마트폰기반 내장형 검사도구가 손목밴드형 검사도구보다 낮은 오차율을 보였다 . 한편 , 가속도계에서 얻은 보행수가 상대적으로 높은 오차율을 보였다 . 추후 연구에서 보다 정확한 신체활동량추정과 활동패턴 분석을 위해 머신러닝이나 딥러닝과 같이 진화된 기법이 적용되어야 할 것이다.
Abstract
The purpose of this study was to examine the convergent validity of smartphone-based physical activity measures in free-living. A total of 45 participants (age: 51.67±7.33years) performed a series of physical activities in free-living including sitting, standing, daily walking, stair up and down, walking up and down hills, running, moving boxes, mopping, etc. Smartphone-based physical activity measures, such as Galaxy Note2 S-health and Fitbit Charge were employed as well as Actigraph GT3X+ and Omron Pedometer as reference measures. To examine statistical differences of physical activity values between genders and among the devices, independent t-tests, Repeated Measures ANOVA, dependent t-tests were applied (α=.05) adjusted by Bonferroni Correction Technique when it was needed. There were the least errors in the smartphone-based application (S-health) compared to the wearable device (Fitbit Charger), which were 1% to 8% error rates based on the pedometer. Surprisingly, the walking steps from accelerometer (Actigraph GT3X+) showed the highest error rates among the devices. Consequently, the smartphone-based physical activity measures can be applied to assess an amount of physical activity in free-living. On the other hand, more efforts are required to improve the accuracy of measuring physical activity levels and patterns by applying advanced scientific approaches, such as machine learning and deep learning in the near future.
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