Use of emerging technologies to assess differences in outdoor physical activity in St. Louis, Missouri
Use of emerging technologies to assess differences in outdoor physical activity in St. Louis, Missouri
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DOI：
10.3389/fpubh.2014.00041
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发表时间：
2014-01-01
影响因子：
5.2
通讯作者：
Hipp, James A.
中科院分区：
文献类型：
--
作者：
Adlakha, Deepti;Budd, Elizabeth L.;Hipp, James A.
简介：大量证据表明，定期的体育活动（PA）是预防不同社会经济地位（SES）和种族人群肥胖的有效策略。在公园进行的PA的比例，以及这是如何不同的邻近SES还没有得到彻底的调查。本项目分析了在线公共网络数据源，以评估在户外PA的差异由附近SES在圣路易斯，MO，USA.Methods：首先，运行和步行路线的网站MapMyRun.com的用户提交的下载。该网站使参与者能够在在线数据库中计划，绘制，记录和分享他们的锻炼路线和户外活动，如跑步，散步和徒步旅行。接下来，使用地理信息系统直观地说明了路线。此后，使用公园的数据和2010年密苏里州人口普查贫困数据，运行和步行路线穿越低SES社区，并穿越公园在低SES社区的几率进行了检查比较穿越高SES社区和高SES parks.Results：结果表明，大多数运行和步行路线发生在或至少穿越公园的几率。然而，这一发现并不成立时，比较低的SES社区，以较高的SES社区在圣路易斯。在低社会经济地位社区的公园跑步的几率比在高社会经济地位社区的公园跑步的几率低54%（OR D 0.46，CI D 0.17-1.23）。在低社会经济地位社区的公园散步的几率比在高社会经济地位社区的公园散步的几率低17%（OR = 0.83，CI = 0.26-2.61）。结论：本研究的新方法包括使用廉价的、不显眼的和公开的网络数据源来研究公园的PA和社区社会经济地位的差异。像MapMyRun.com这样的新兴技术在增强跨大的地理和时间设置的用户定义的PA的跟踪方面具有显著的优势。
Introduction: Abundant evidence shows that regular physical activity (PA) is an effective strategy for preventing obesity in people of diverse socioeconomic status (SES) and racial groups. The proportion of PA performed in parks and how this differs by proximate neighborhood SES has not been thoroughly investigated. The present project analyzes online public web data feeds to assess differences in outdoor PA by neighborhood SES in St. Louis, MO, USA.Methods: First, running and walking routes submitted by users of the website MapMyRun.com were downloaded. The website enables participants to plan, map, record, and share their exercise routes and outdoor activities like runs, walks, and hikes in an online database. Next, the routes were visually illustrated using geographic information systems. Thereafter, using park data and 2010 Missouri census poverty data, the odds of running and walking routes traversing a low-SES neighborhood, and traversing a park in a low-SES neighborhood were examined in comparison to the odds of routes traversing higher-SES neighborhoods and higher-SES parks.Results: Results show that a majority of running and walking routes occur in or at least traverse through a park. However, this finding does not hold when comparing low-SES neighborhoods to higher-SES neighborhoods in St. Louis. The odds of running in a park in a low-SES neighborhoodwere54% lower than running in a park in a higher-SES neighborhood (OR D 0.46, CI D 0.17-1.23). The odds of walking in a park in a low-SES neighborhood were 17% lower thanwalking in a park in a higher-SES neighborhood (OR = 0.83, CI = 0.26-2.61).Conclusion: The novel methods of this study include the use of inexpensive, unobtrusive, and publicly available web data feeds to examine PA in parks and differences by neighborhood SES. Emerging technologies like MapMyRun.com present significant advantages to enhance tracking of user-defined PA across large geographic and temporal settings.