School of Nursing, The University of Auckland, Auckland 1023, New Zealand.
School of Sport and Recreation, Auckland University of Technology, Auckland 0627, New Zealand.
Int J Environ Res Public Health. 2019 Mar 12;16(5):897. doi: 10.3390/ijerph16050897.
Compositional data techniques are an emerging method in physical activity research. These techniques account for the complexities of, and interrelationships between, behaviours that occur throughout a day (e.g., physical activity, sitting, and sleep). The field of health geography research is also developing rapidly. Novel spatial techniques and data visualisation approaches are increasingly being recognised for their utility in understanding health from a socio-ecological perspective. Linking compositional data approaches with geospatial datasets can yield insights into the role of environments in promoting or hindering the health implications of the daily time-use composition of behaviours. The 7-day behaviour data used in this study were derived from accelerometer data for 882 Auckland school children and linked to weight status and neighbourhood deprivation. We developed novel geospatial visualisation techniques to explore activity composition over a day and generated new insights into links between environments and child health behaviours and outcomes. Visualisation strategies that integrate compositional activities, time of day, weight status, and neighbourhood deprivation information were devised. They include a ringmap overview, small-multiple ringmaps, and individual and aggregated time⁻activity diagrams. Simultaneous visualisation of geospatial and compositional behaviour data can be useful for triangulating data from diverse disciplines, making sense of complex issues, and for effective knowledge translation.
成分数据技术是体力活动研究中的一种新兴方法。这些技术可以解释一天中发生的行为(例如体力活动、久坐和睡眠)之间的复杂性和相互关系。健康地理研究领域也在迅速发展。新颖的空间技术和数据可视化方法越来越被认为有助于从社会生态角度理解健康。将成分数据方法与地理空间数据集联系起来,可以深入了解环境在促进或阻碍行为日常时间利用成分对健康的影响方面所起的作用。本研究使用的 7 天行为数据来自奥克兰 882 名学童的加速度计数据,并与体重状况和社区贫困程度相关联。我们开发了新颖的地理空间可视化技术来探索一天中的活动构成,并深入了解环境与儿童健康行为和结果之间的联系。设计了集成成分活动、一天中的时间、体重状况和社区贫困信息的可视化策略,包括环形图概述、小倍数环形图、个体和聚合时间-活动图。同时可视化地理空间和成分行为数据对于从不同学科收集数据、理解复杂问题以及进行有效的知识转化都非常有用。