Stanford Prevention Research Center, Department of Medicine, School of Medicine, Stanford University, 1070 Arastradero Road, Suite 100, Palo Alto, CA, 94304, USA.
Department of Health Research and Policy, School of Medicine, Stanford University, Palo Alto, USA.
Int J Health Geogr. 2018 Jun 5;17(1):17. doi: 10.1186/s12942-018-0140-1.
Identifying elements of one's environment-observable and unobservable-that contribute to chronic stress including the perception of comfort and discomfort associated with different settings, presents many methodological and analytical challenges. However, it also presents an opportunity to engage the public in collecting and analyzing their own geospatial and biometric data to increase community member understanding of their local environments and activate potential environmental improvements. In this first-generation project, we developed a methodology to integrate geospatial technology with biometric sensing within a previously developed, evidence-based "citizen science" protocol, called "Our Voice." Participants used a smartphone/tablet-based application, called the Discovery Tool (DT), to collect photos and audio narratives about elements of the built environment that contributed to or detracted from their well-being. A wrist-worn sensor (Empatica E4) was used to collect time-stamped data, including 3-axis accelerometry, skin temperature, blood volume pressure, heart rate, heartbeat inter-beat interval, and electrodermal activity (EDA). Open-source R packages were employed to automatically organize, clean, geocode, and visualize the biometric data.
In total, 14 adults (8 women, 6 men) were successfully recruited to participate in the investigation. Participants recorded 174 images and 124 audio files with the DT. Among captured images with a participant-determined positive or negative rating (n = 131), over half were positive (58.8%, n = 77). Within-participant positive/negative rating ratios were similar, with most participants rating 53.0% of their images as positive (SD 21.4%). Significant spatial clusters of positive and negative photos were identified using the Getis-Ord Gi* local statistic, and significant associations between participant EDA and distance to DT photos, and street and land use characteristics were also observed with linear mixed models. Interactive data maps allowed participants to (1) reflect on data collected during the neighborhood walk, (2) see how EDA levels changed over the course of the walk in relation to objective neighborhood features (using basemap and DT app photos), and (3) compare their data to other participants along the same route.
Participants identified a variety of social and environmental features that contributed to or detracted from their well-being. This initial investigation sets the stage for further research combining qualitative and quantitative data capture and interpretation to identify objective and perceived elements of the built environment influence our embodied experience in different settings. It provides a systematic process for simultaneously collecting multiple kinds of data, and lays a foundation for future statistical and spatial analyses in addition to more in-depth interpretation of how these responses vary within and between individuals.
识别环境要素——可观察和不可观察的——有助于慢性压力，包括与不同环境相关的舒适和不适的感知，这提出了许多方法学和分析上的挑战。然而，它也为公众参与收集和分析自己的地理空间和生物特征数据提供了机会，以增加社区成员对其当地环境的了解，并激发潜在的环境改善。在这个第一代项目中，我们开发了一种将地理空间技术与生物特征感应相结合的方法，该方法整合在一个先前开发的、基于证据的“公民科学”协议中，称为“我们的声音”。参与者使用基于智能手机/平板电脑的应用程序，称为发现工具 (DT)，收集有关对其幸福感有贡献或有影响的建筑环境元素的照片和音频叙述。一个腕戴式传感器 (Empatica E4) 用于收集时间戳数据，包括三轴加速度计、皮肤温度、血压、心率、心跳间隔和皮肤电活动 (EDA)。开源 R 包用于自动组织、清理、地理编码和可视化生物特征数据。
共有 14 名成年人 (8 名女性，6 名男性) 成功被招募参与调查。参与者使用 DT 记录了 174 张图像和 124 个音频文件。在参与者确定为正面或负面的图像中 (n = 131)，超过一半是正面的 (58.8%，n = 77)。参与者的正面/负面评分比例相似，大多数参与者将 53.0%的图像评为正面 (SD 21.4%)。使用 Getis-Ord Gi* 局部统计量识别出了正、负照片的显著空间聚类，还观察到了参与者 EDA 与 DT 照片之间的距离以及街道和土地利用特征之间的线性混合模型显著关联。交互式数据地图允许参与者 (1) 反思在社区步行过程中收集的数据，(2) 看到 EDA 水平在步行过程中如何相对于客观的邻里特征而变化 (使用 basemap 和 DT 应用程序照片)，以及 (3) 将他们的数据与同一路线上的其他参与者进行比较。
参与者确定了许多社会和环境特征，这些特征对他们的幸福感有促进作用或阻碍作用。这项初步研究为进一步研究奠定了基础，该研究结合了定性和定量数据的捕获和解释，以确定建筑环境的客观和感知要素如何影响我们在不同环境中的体验。它提供了一个同时收集多种数据的系统过程，并为未来的统计和空间分析奠定了基础，此外还深入解释了这些反应在个体内部和个体之间的变化。
