Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health
Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health
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DOI：
10.1186/s12942-018-0140-1
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发表时间：
2018-06-05
影响因子：
4.9
通讯作者：
King, Abby C.
中科院分区：
文献类型：
--
作者：
Chrisinger, Benjamin W.;King, Abby C.
背景资料：识别一个人的环境因素，可观察到的和不可观察的，有助于慢性压力，包括舒适和不适的感觉与不同的设置，提出了许多方法和分析的挑战。然而，它也提供了一个机会，让公众参与收集和分析他们自己的地理空间和生物特征数据，以增加社区成员对其当地环境的了解，并激活潜在的环境改善。在这个第一代项目中，我们开发了一种方法，将地理空间技术与生物识别传感结合在以前开发的基于证据的“公民科学”协议中，称为“我们的声音”。“参与者使用一个基于智能手机/平板电脑的应用程序，称为发现工具（DT），收集有关建筑环境元素的照片和音频叙述，这些元素有助于或阻碍他们的福祉。使用腕戴式传感器（Empatica E4）收集时间戳数据，包括3轴加速度计、皮肤温度、血容量压、心率、心跳间间期和皮肤电活动（EDA）。开源的R软件包被用来自动组织，清洁，地理编码，和可视化的生物特征data.Results：在总数，14名成年人（8名女性，6名男性）被成功招募参加调查。参与者使用DT记录了174张图像和124个音频文件。在参与者确定的正面或负面评级（n = 131）的捕获图像中，超过一半是正面的（58.8%，n = 77）。参与者内的积极/消极评级比率相似，大多数参与者将53.0%的图像评为积极（SD 21.4%）。显着的空间集群的积极和消极的照片被确定使用Getis-Ord Gi* 本地统计，和参与者EDA和距离DT照片，街道和土地利用特征之间的显着关联也观察到线性混合模型。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）查看EDA水平如何在步行过程中与客观的社区特征相关（使用底图和DT应用程序照片），以及（3）将他们的数据与沿着的其他参与者的数据进行比较。参与者确定了各种有助于或损害其福祉的社会和环境特征。这项初步调查为进一步的研究奠定了基础，结合定性和定量数据采集和解释，以确定建筑环境的客观和感知要素，影响我们在不同环境中的具体体验。它为同时收集多种数据提供了一个系统的过程，并为未来的统计和空间分析奠定了基础，此外还对这些反应如何在个人内部和个人之间发生变化进行了更深入的解释。
Background: 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.Results: 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.Conclusions: 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.