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

Background Mobile 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. Objective In 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. Methods A 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. Results The 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. Conclusions The 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.

[1]  Tiffany C. Veinot,et al.  "Bacon Bacon Bacon": Food-Related Tweets and Sentiment in Metro Detroit , 2018, ICWSM.

[2]  T. Wadden,et al.  Reprint: 2013 AHA/ACC Guideline on Lifestyle Management to Reduce Cardiovascular Risk. , 2014, Journal of the American Pharmacists Association : JAPhA.

[3]  Megan F. Liu,et al.  Effects of self-management on chronic kidney disease: A meta-analysis. , 2017, International journal of nursing studies.

[4]  David Ogilvie,et al.  Use of global positioning systems to study physical activity and the environment: a systematic review. , 2011, American journal of preventive medicine.

[5]  Kevin G. Stanley,et al.  Ethical implications of location and accelerometer measurement in health research studies with mobile sensing devices. , 2017, Social science & medicine.

[6]  W. Brown,et al.  Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry , 2016, Journal of medical Internet research.

[7]  M. LeFevre,et al.  Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force Recommendation Statement. , 2014, Annals of internal medicine.

[8]  Mark W. Horner,et al.  Time-geographic density estimation for home range analysis , 2011, Ann. GIS.

[9]  Harvey J. Miller,et al.  What about people in geographic information science? , 2003, Comput. Environ. Urban Syst..

[10]  Mei Po Kwan Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility , 2013 .

[11]  Y. Kestens,et al.  Using experienced activity spaces to measure foodscape exposure. , 2010, Health and Place.

[12]  J. Palmer,et al.  Socioeconomic status and incidence of type 2 diabetes: results from the Black Women's Health Study. , 2010, American journal of epidemiology.

[13]  Scott Duncan,et al.  Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings , 2014, Front. Public Health.

[14]  Christopher N. Morrison,et al.  Brief report: Using global positioning system (GPS) enabled cell phones to examine adolescent travel patterns and time in proximity to alcohol outlets. , 2016, Journal of adolescence.

[15]  Basile Chaix,et al.  Geographic life environments and coronary heart disease: a literature review, theoretical contributions, methodological updates, and a research agenda. , 2009, Annual review of public health.

[16]  W. Gesler,et al.  International Journal of Health Geographics a Suite of Methods for Representing Activity Space in a Healthcare Accessibility Study , 2022 .

[17]  Johnny Saldaña,et al.  The Coding Manual for Qualitative Researchers , 2009 .

[18]  Xiaoqin He,et al.  Diabetes self-management education reduces risk of all-cause mortality in type 2 diabetes patients: a systematic review and meta-analysis , 2017, Endocrine.

[19]  K. Axhausen,et al.  Activity spaces: Measures of social exclusion? , 2003 .

[20]  Anna C. Porter,et al.  Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. , 2015, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[21]  Mark Ellis,et al.  Work Together, Live Apart? Geographies of Racial and Ethnic Segregation at Home and at Work , 2004 .

[22]  Bryan J Boruff,et al.  Using GPS technology to (re)-examine operational definitions of ‘neighbourhood’ in place-based health research , 2012, International Journal of Health Geographics.

[23]  Adam Drewnowski,et al.  Participant Experience Using GPS Devices in a Food Environment and Nutrition Study , 2016 .

[24]  Nongjian Tao,et al.  Novel monitor paradigm for real-time exposure assessment , 2011, Journal of Exposure Science and Environmental Epidemiology.

[25]  Jim P Stimpson,et al.  Neighborhood deprivation and health risk behaviors in NHANES III. , 2007, American journal of health behavior.

[26]  R. Golledge,et al.  Spatial Behavior: A Geographic Perspective , 1996 .

[27]  John S Brownstein,et al.  No place to hide--reverse identification of patients from published maps. , 2006, The New England journal of medicine.

[28]  M. Duncan,et al.  Portable global positioning system receivers: static validity and environmental conditions. , 2013, American journal of preventive medicine.

[29]  Shannon N Zenk,et al.  Feasibility of using global positioning systems (GPS) with diverse urban adults: before and after data on perceived acceptability, barriers, and ease of use. , 2012, Journal of physical activity & health.

[30]  Anne R. Pebley,et al.  Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared , 2014, Demography.

[31]  Andy P. Jones,et al.  How can GPS technology help us better understand exposure to the food environment? A systematic review , 2016, SSM - population health.

[32]  D. DeMets,et al.  Management of patients with atrial fibrillation (compilation of 2006 ACCF/AHA/ESC and 2011 ACCF/AHA/HRS recommendations): a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines. , 2013, Circulation.

[33]  Pierre Chauvin,et al.  Investigating the effects of medical density on health-seeking behaviours using a multiscale approach to residential and activity spaces: Results from a prospective cohort study in the Paris metropolitan area, France , 2012, International Journal of Health Geographics.

[34]  Paul D. Smith,et al.  Urban activity spaces: Illustrations and application of a conceptual model for integrating the time and space dimensions , 1998 .

[35]  Chuen Seng Tan,et al.  Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions , 2017, JMIR mHealth and uHealth.

[36]  Eric A Whitsel,et al.  Neighborhood disparities in incident hospitalized myocardial infarction in four U.S. communities: the ARIC surveillance study. , 2009, Annals of epidemiology.

[37]  S. Jilcott,et al.  Food venue choice, consumer food environment, but not food venue availability within daily travel patterns are associated with dietary intake among adults, Lexington Kentucky 2011 , 2013, Nutrition Journal.

[38]  S. Friedman,et al.  Feasibility and Acceptability of Global Positioning System (GPS) Methods to Study the Spatial Contexts of Substance Use and Sexual Risk Behaviors among Young Men Who Have Sex with Men in New York City: A P18 Cohort Sub-Study , 2016, PloS one.

[39]  A. D. Diez Roux,et al.  Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic study of Atherosclerosis. , 2009, Archives of internal medicine.

[40]  Marc Schlossberg,et al.  Comparing Transit-Oriented Development Sites by Walkability Indicators , 2004 .

[41]  D. Cohen,et al.  Non-residential neighborhood exposures suppress neighborhood effects on self-rated health. , 2007, Social science & medicine.

[42]  Y. Jang,et al.  Standards of Medical Care in Diabetes-2010 by the American Diabetes Association: Prevention and Management of Cardiovascular Disease , 2010 .

[43]  JoEllen Wilbur,et al.  Activity space environment and dietary and physical activity behaviors: a pilot study. , 2011, Health & place.

[44]  Konrad Paul Kording,et al.  Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study , 2015, Journal of medical Internet research.

[45]  4. Lifestyle Management: Standards of Medical Care in Diabetes—2018 , 2017, Diabetes Care.

[46]  F. Wahle,et al.  Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild , 2016, JMIR mHealth and uHealth.

[47]  Lauren B Sherar,et al.  Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review , 2015, Journal of medical Internet research.

[48]  Cindy Shearer,et al.  Measuring food availability and accessibility among adolescents: Moving beyond the neighbourhood boundary. , 2015, Social science & medicine.

[49]  James Tung,et al.  User Acceptance of Wrist-Worn Activity Trackers Among Community-Dwelling Older Adults: Mixed Method Study , 2017, JMIR mHealth and uHealth.

[50]  I. Hertz-Picciotto,et al.  Development of Time-location Weighted Spatial Measures Using Global Positioning System Data , 2013, Environmental health and toxicology.

[51]  Tiffany C. Veinot,et al.  User acceptance of location-tracking technologies in health research: Implications for study design and data quality , 2018, J. Biomed. Informatics.

[52]  Iris N. Gomez-Lopez,et al.  Using Social Media to Identify Sources of Healthy Food in Urban Neighborhoods , 2017, Journal of Urban Health.

[53]  A. El-geneidy,et al.  Beyond the Quarter Mile: Re-Examining Travel Distances by Active Transportation , 2010 .

[54]  D. Kleinbaum,et al.  Neighborhood poverty and racial differences in ESRD incidence. , 2008, Journal of the American Society of Nephrology : JASN.

[55]  Mirco Musolesi,et al.  Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis , 2015, UbiComp.

[56]  Jing Chen,et al.  The Metabolic Syndrome and Chronic Kidney Disease in U.S. Adults , 2004, Annals of Internal Medicine.

[57]  M. McNarry,et al.  Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review , 2016, JMIR mHealth and uHealth.

[58]  Oscar Mayora-Ibarra,et al.  Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients , 2014, AH.

[59]  Harshvardhan Vathsangam,et al.  Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study , 2017, JMIR mHealth and uHealth.