@Article{信息:doi 10.2196 / /移动医疗。7167，作者=“麦迪逊，拉尔夫和杰明，Luke和mondero, Javier和Bolger, Linda和Belton, Sarahjane和Issartel, John和Marsh, Samantha和Direito, Artur和Solenhill, Madeleine和Zhao，金峰和Exeter, Daniel John和Vathsangam, Harshvardhan和Rawstorn, Jonathan Charles”，标题=“使用Movn智能手机应用程序量化人类运动:验证与现场研究”，期刊=“JMIR移动健康Uhealth”，年=“2017”，月=“8”，日=“17”，卷=“5”，号=“8”，页=“e122”，关键词=“远程医疗;智能手机;验证研究;地理信息系统;运动;身体活动;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。包括数十亿数字痕迹的大数据为科学家提供了一个新的视角，可以细致地检查PA，并使我们能够跟踪人们的地理编码运动模式，以确定他们与环境的互动。 Objective: The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data. Methods: The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement. Results: A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). 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. ", issn="2291-5222", doi="10.2196/mhealth.7167", url="http://mhealth.www.mybigtv.com/2017/8/e122/", url="https://doi.org/10.2196/mhealth.7167", url="http://www.ncbi.nlm.nih.gov/pubmed/28818819" }