Methods ARTICLE

Front. Public Health, 11 March 2014 | doi: 10.3389/fpubh.2014.00019

Using MapMyFitness to place physical activity into neighborhood context

imageJana A. Hirsch1*, imagePeter James2,3, imageJamaica R. M. Robinson1, imageKyler M. Eastman4, imageKevin D. Conley5, imageKelly R. Evenson6 and imageFrancine Laden2,3,7
  • 1Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, Ann Arbor, MI, USA
  • 2Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
  • 3Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
  • 4MapMyFitness, Inc., Austin, TX, USA
  • 5Department of Electrical Engineering, Stanford University, Stanford, CA, USA
  • 6Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA
  • 7Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

It is difficult to obtain detailed information on the context of physical activity at large geographic scales, such as the entire United States, as well as over long periods of time, such as over years. MapMyFitness is a suite of interactive tools for individuals to track their workouts online or using global positioning system in their phones or other wireless trackers. This method article discusses the use of physical activity data tracked using MapMyFitness to examine patterns over space and time. An overview of MapMyFitness, including data tracked, user information, and geographic scope, is explored. We illustrate the utility of MapMyFitness data using tracked physical activity by users in Winston-Salem, NC, USA between 2006 and 2013. Types of physical activities tracked are described, as well as the percent of activities occurring in parks. Strengths of MapMyFitness data include objective data collection, low participant burden, extensive geographic scale, and longitudinal series. Limitations include generalizability, behavioral change as the result of technology use, and potential ethical considerations. MapMyFitness is a powerful tool to investigate patterns of physical activity across large geographic and temporal scales.

Keywords: physical activity, GPS, quantified self, big data, recreation, parks, MapMyFitness, MapMyRun

Citation: Hirsch JA, James P, Robinson JRM, Eastman KM, Conley KD, Evenson KR and Laden F (2014) Using MapMyFitness to place physical activity into neighborhood context. Front. Public Health 2:19. doi: 10.3389/fpubh.2014.00019

Received: 09 January 2014; Accepted: 20 February 2014;
Published online: 11 March 2014.

Edited by:

James Aaron Hipp, Washington University in St. Louis, USA

Reviewed by:

Deepti Adlakha, Washington University in St. Louis, USA
Sonia Sequeira, Centers for Disease Control, USA
Cheryl Kelly, University of Colorado Colorado Springs, USA

Copyright: © 2014 Hirsch, James, Robinson, Eastman, Conley, Evenson and Laden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jana A. Hirsch, Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA e-mail: jahirsch@umich.edu

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