Validating the CalFit Smartphone Sensor in Two Epidemiological Investigations
在两项流行病学调查中验证 CalFit 智能手机传感器
基本信息
- 批准号:8619721
- 负责人:
- 金额:$ 9.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-15 至 2014-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectAir PollutionAreaBehaviorBicyclingBreathingCaliforniaCellular PhoneCitiesCollectionCommunitiesCommutingComplexDataDevicesDoseEcosystemEnrollmentEnvironmentEnvironmental EpidemiologyEnvironmental ExposureEpidemiologic StudiesEpidemiologyEuropeEuropeanEvaluationExerciseExposure toFundingGeographic LocationsGlosso-SterandrylGoldGovernmentGrowthHealthHome environmentIndividualInformation SystemsInvestigationLocationMeasurementMeasuresMetabolicModelingMonitorMoodsNitrogen DioxideObesityOzoneParticipantPatient Self-ReportPatternPerformancePhysical activityPollutionPopulationPopulation HeterogeneityPopulation StudyPositioning AttributePublic HealthReactionResearchResearch DesignResearch PersonnelRunningSamplingServicesSiteSourceSpainStudy SectionStudy SubjectSurveysSystemTechnologyTelephoneTestingTextTimeTransportationTravelUnited StatesValidationWalkingbasebody sensediariesinstrumentland usenovelobesity riskpollutantself reported behaviorsensorsocialsuburbtooltraffickingusabilityvalidation studies
项目摘要
DESCRIPTION (provided by applicant): PROJECT SUMMARY/ABSTRACT We propose to assess the validity, usability, and value of a novel cell phone-based personal exposure measurement system known as "CalFit" in two existing epidemiologic studies. The CalFit system runs on commercially available Android phones, and as currently configured, we can collect data on physical activity with accelerometry, geographic location through a global positioning system (GPS), air pollution, and self- reported environments, including mood, and behaviors with ecological momentary assessment (EMA) through interactive text messages. The CalFit system has already undergone numerous controlled validation studies and is ready for evaluation in large epidemiological studies. The combined measurement of physical activity, location and mobility, exposure to air pollution, and EMA represents a major advance in sensor technology. We will evaluate the instrument in two ongoing epidemiological investigations: (1) the Healthy PLACES study in Southern California, which is examining the impact of the built environment on physical activity and obesity in a Smart Growth community and five adjacent communities; and (2) in the Transportation, Air Pollution and physical ActivitieS (TAPAS) project in Barcelona, Spain, focused on determinants of active commuting by bicycling or walking. Both studies have enrolled subjects and are actively collecting accelerometry, GPS, or travel survey data. For Aim 1, we will validate the CalFit GPS and accelerometer by comparing the integrated sensor against current gold standard instruments (the Actigraph accelerometer and the Globalsat GPS). In Aim 2, we will demonstrate the added value of the EMA texting system for understanding self-reported behavior, mood, and social and physical contexts as antecedents, concomitants, and consequences of environmental exposures. For Aim 3, we will combine commercially available sensors with circuit boards developed by our team to measure nitrogen dioxide and ozone in the field. We will merge these data with GPS and accelerometry information to infer likely inhalations of air pollution. To demonstrate value added we will compare the inhalation estimates derived against those from assigning resolved spatial estimates of exposure to the home location. We will also deploy passive sensors for the two pollutants that yield an integrated 1-week sample. These will be compared with the real-time assessments to demonstrate the value added of having the locational and physical activity information merged with the real-time air pollution data, which will allow us to assess indoor and outdoor exposures, the likely inhalation, and the geographic context of the exposures. Cell phones are currently the most ubiquitous computing platform globally, and our research will demonstrate the viability of a cell phone as an exposure characterization tool that will have widespread utility for numerous epidemiological applications.
描述(由申请人提供):项目总结/摘要我们建议在两项现有的流行病学研究中评估一种新的基于手机的个人暴露测量系统(称为“CalFit”)的有效性、可用性和价值。CalFit系统在市售Android手机上运行,按照目前的配置,我们可以通过加速度计收集身体活动数据,通过全球定位系统(GPS)收集地理位置,空气污染,以及自我报告的环境,包括情绪,以及通过交互式短信进行生态瞬时评估(EMA)的行为。CalFit系统已经进行了大量的控制验证研究,并准备在大型流行病学研究中进行评估。对身体活动、位置和移动性、空气污染暴露和EMA的综合测量代表了传感器技术的重大进步。 我们将在两项正在进行的流行病学调查中评估该工具:(1)南加州的健康场所研究,该研究正在研究智能增长社区和五个相邻社区的建筑环境对身体活动和肥胖的影响;以及(2)在西班牙巴塞罗那的交通、空气污染和物理活动(TAPAS)项目中,重点关注骑自行车或步行的积极通勤的决定因素。这两项研究都招募了受试者,并正在积极收集加速度计、GPS或旅行调查数据。 对于目标1,我们将通过将集成传感器与当前的黄金标准仪器(Actigraph加速度计和Globalsat GPS)进行比较来验证CalFit GPS和加速度计。在目标2中,我们将展示EMA短信系统的附加价值,以了解自我报告的行为,情绪,社会和物理环境作为环境暴露的前因,伴随物和后果。对于目标3,我们将把联合收割机与我们团队开发的电路板结合起来,测量现场的二氧化氮和臭氧。我们将把这些数据与全球定位系统和加速度测量信息合并,以推断可能吸入的空气污染。为了证明附加值,我们将比较吸入估计值与分配家庭位置暴露的解析空间估计值。我们还将为这两种污染物部署被动传感器,以产生一个完整的1周样本。这些将与实时评估进行比较,以证明将位置和身体活动信息与实时空气污染数据合并的附加值,这将使我们能够评估室内和室外暴露,可能的吸入以及暴露的地理环境。 手机是目前全球最普遍的计算平台,我们的研究将证明手机作为暴露表征工具的可行性,这将对许多流行病学应用具有广泛的实用性。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Comparison of physical activity measures using mobile phone-based CalFit and Actigraph.
- DOI:10.2196/jmir.2470
- 发表时间:2013-06-13
- 期刊:
- 影响因子:7.4
- 作者:Donaire-Gonzalez D;de Nazelle A;Seto E;Mendez M;Nieuwenhuijsen MJ;Jerrett M
- 通讯作者:Jerrett M
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Michael LeoBrennan Jerrett其他文献
Michael LeoBrennan Jerrett的其他文献
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{{ truncateString('Michael LeoBrennan Jerrett', 18)}}的其他基金
Validating the CalFit Smartphone Sensor in Two Epidemiological Investigations
在两项流行病学调查中验证 CalFit 智能手机传感器
- 批准号:
8179576 - 财政年份:2011
- 资助金额:
$ 9.96万 - 项目类别:
Validating the CalFit Smartphone Sensor in Two Epidemiological Investigations
在两项流行病学调查中验证 CalFit 智能手机传感器
- 批准号:
8332310 - 财政年份:2011
- 资助金额:
$ 9.96万 - 项目类别:
Influence of Built Environments on the Development of Obesity During Childhood
建筑环境对儿童期肥胖发展的影响
- 批准号:
7685302 - 财政年份:
- 资助金额:
$ 9.96万 - 项目类别:
Influence of Built Environments on the Development of Obesity During Childhood
建筑环境对儿童期肥胖发展的影响
- 批准号:
7924758 - 财政年份:
- 资助金额:
$ 9.96万 - 项目类别:
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