TY - JOUR AU - Maddison, Ralph AU - Gemming, Luke AU - Monedero, Javier AU - Bolger, Linda AU - Belton, Sarahjane AU - Issartel, Johann AU - Marsh, Samantha AU - Direito, Artur AU - Solenhill, Madeleine AU - Zhao, Jinfeng AU - Exeter, Daniel John AU - Vathsangam, Harshvardhan AU - Rawstorn, Jonathan Charles PY - 2017 DA - 2017/08/17 TI -使用Movn智能手机应用程序量化人体运动:验证和实地研究JO - JMIR Mhealth Uhealth SP - e122 VL - 5 IS - 8 KW -远程医疗KW -智能手机KW -验证研究KW -地理信息系统KW -运动KW -身体活动KW -人类AB -背景:使用嵌入式智能手机传感器提供了测量身体活动(PA)和人体运动的机会。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。在基于实验室的跑步机运动中，Movn的EE与量热法相当(偏差=0.36[- 0.07至0.78]kcal/min, t82=1.66, P= 0.10)，但与ActiGraph加速度计相比高估了(偏差=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. SN - 2291-5222 UR - http://mhealth.www.mybigtv.com/2017/8/e122/ UR - https://doi.org/10.2196/mhealth.7167 UR - http://www.ncbi.nlm.nih.gov/pubmed/28818819 DO - 10.2196/mhealth.7167 ID - info:doi/10.2196/mhealth.7167 ER -