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Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time

Overview of attention for article published in Frontiers in Public Health, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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133 Mendeley
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2 CiteULike
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Article details
Title
Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
Published in
Frontiers in Public Health, January 2014
DOI 10.3389/fpubh.2014.00002
Pubmed ID
Authors
Abstract

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the "LifeLog." A graphic interface tool, "LifeLog View," enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 <1%
United Kingdom 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 129 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 32 24%
Researcher 22 17%
Student > Master 20 15%
Student > Doctoral Student 8 6%
Student > Bachelor 7 5%
Other 20 15%
Unknown 24 18%
Readers by discipline
Readers by discipline Count As %
Social Sciences 28 21%
Medicine and Dentistry 17 13%
Sports and Recreations 8 6%
Computer Science 6 5%
Psychology 6 5%
Other 29 22%
Unknown 39 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 May 2020.
All research outputs
#6,031,291
of 32,942,026 outputs
Outputs from Frontiers in Public Health
#3,155
of 22,129 outputs
Outputs of similar age
#58,896
of 362,746 outputs
Outputs of similar age from Frontiers in Public Health
#8
of 18 outputs
Altmetric has tracked 32,942,026 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,129 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 362,746 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.