Evidence map›Paper›PMID 30104185›Full record

ArticleJMIR mHealth and uHealth2018

Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study.

Robert Goodspeed, Xiang Yan, Jean Hardy, V G Vinod Vydiswaran, Veronica J Berrocal, Philippa Clarke, Daniel M Romero, Iris N Gomez-Lopez, Tiffany Veinot

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
6.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Robert GoodspeedUrban and Regional Planning Program, Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-4858-8387
Xiang YanUrban and Regional Planning Program, Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-8619-0065
Jean HardySchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-3877-8208
V G Vinod VydiswaranSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-3122-1936
Veronica J BerrocalDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-0304-7202
Philippa ClarkeDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-4925-2968
Daniel M RomeroSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-8351-3463
Iris N Gomez-LopezInstitute for Social Research, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-1791-8478
Tiffany VeinotSchool of Information, University of Michigan, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-1200-9515
University of Michigan · US

Funding

Strategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3M
6 · The paper itself

Abstract

backgroundMobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices.

objectiveIn this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways-a global positioning system (GPS) watch and a mobile phone with Google's Location History feature enabled.

methodsA total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone-based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices' trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces.

resultsThe watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation.

conclusionsThe most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities.

Indexed as

data accuracyenvironment and public healthmobile phonespatial behaviorurban population

Identifiers

PMID30104185
PMCPMC6111146
OpenAlexW2809528641

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.