使用Movn智能手机应用程序量化人体运动卡塔尔世界杯8强波胆分析验证和实地研究%A Maddison,Ralph %A Gemming,Luke %A Monedero,Javier %A Bolger,Linda %A Belton,Sarahjane %A Issartel,Johann %A Marsh,Samantha %A Direito,Artur %A Solenhill,Madeleine %A Zhao, jineng %A Exeter,Daniel John %A Vathsangam,Harshvardhan %A Rawstorn,Jonathan Charles %+迪肯大学运动与营养科学学院体育活动与营养研究所，Burwood Highway 221号，Burwood, 3125，澳大利亚，61 3924 68461，jonathan.rawstorn@deakin.edu.au远程医疗智能手机验证研究地理信息系统运动身体活动人类背景:嵌入式智能手机传感器的使用为测量身体活动(PA)和人体运动提供了机会。大数据——包括数十亿的数字痕迹——为科学家们提供了一种新的视角，以细粒度的细节检查PA，并允许我们跟踪人们的地理编码运动模式，以确定他们与环境的相互作用。目的:本研究的目的是检验Movn智能手机应用程序(移动分析)收集PA和人体运动数据的有效性。方法:与间接量热法(标准参考)和PA研究中常用的独立加速度计(GT1m, ActiGraph Corp，收敛参考)相比，在实验室和自由生活环境中评估了Movn智能手机应用程序估算能量消耗(EE)的标准和收敛效度。一项支持性交叉验证研究评估了在不同智能手机设备上收集的活动数据的一致性。将全球定位系统(GPS)和加速度计数据与地理信息软件相结合，以验证人体运动地理空间分析的可行性。结果:共有21名参与者参与了线性回归分析，从Movn活动计数估计EE(估计的标准误差[SEE]=1.94 kcal/min)。在独立样本(N=42, SEE=1.10 kcal/min)中进行交叉验证。 During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [−0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps. Conclusions: The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics. %M 28818819 %R 10.2196/mhealth.7167 %U http://mhealth.www.mybigtv.com/2017/8/e122/ %U https://doi.org/10.2196/mhealth.7167 %U http://www.ncbi.nlm.nih.gov/pubmed/28818819