This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (888) mHealth for Data Collection and Research (955) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (111) Fitness Trackers and Smart Pedometers/Accelerometers (561) Ecological Momentary Assessment (EMA) (309)

Published on in Vol 6, No 8 (2018): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9771, first published .
Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study

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

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

Authors of this article:

Robert Goodspeed1 Author Orcid Image ;   Xiang Yan1 Author Orcid Image ;   Jean Hardy2 Author Orcid Image ;   VG Vinod Vydiswaran2, 3 Author Orcid Image ;   Veronica J Berrocal4 Author Orcid Image ;   Philippa Clarke5, 6 Author Orcid Image ;   Daniel M Romero2 Author Orcid Image ;   Iris N Gomez-Lopez6 Author Orcid Image ;   Tiffany Veinot2, 7 Author Orcid Image
Article Authors Cited by (16) Tweetations (16) Metrics

Journals

  1. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  2. Zhou J, Hou Q, Dong W. Spatial Characteristics of Population Activities in Suburban Villages Based on Cellphone Signaling Analysis. Sustainability 2019;11(7):2159 View
  3. Piau A, Wild K, Mattek N, Kaye J. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. Journal of Medical Internet Research 2019;21(8):e12785 View
  4. Uddin M, Amirul Islam F. Psychometric evaluation of the modified 19-item Bengali version of WHOQOL scale using Rasch analysis: a cross-sectional study of a rural district in Bangladesh. BMC Psychology 2020;8(1) View
  5. Uddin M, Islam F. Psychometric evaluation of the modified Kessler seven-item version (K7) for measuring psychological distress using Rasch analysis: a cross-sectional study in a rural district of Bangladesh. BMJ Open 2020;10(2):e034523 View
  6. Gluck S, Andrawos A, Summers M, Lange J, Chapman M, Finnis M, Deane A. The use of smartphone-derived location data to evaluate participation following critical illness: A pilot observational cohort study. Australian Critical Care 2022;35(3):225 View
  7. Beukenhorst A, Sergeant J, Schultz D, McBeth J, Yimer B, Dixon W. Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study. JMIR mHealth and uHealth 2021;9(11):e28857 View
  8. Keusch F, Wenz A, Conrad F. Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors. Computers in Human Behavior 2022;127:107054 View
  9. Trappmann M, Haas G, Malich S, Keusch F, Bähr S, Kreuter F, Schwarz S. Augmenting Survey Data with Digital Trace Data: Is There a Threat to Panel Retention?. Journal of Survey Statistics and Methodology 2023;11(3):541 View
  10. Sun Y, Liu C, Zhang C, Cristina Manresa-Yee C. Mobile Technology and Studies on Transport Behavior: A Literature Analysis, Integrated Research Model, and Future Research Agenda. Mobile Information Systems 2021;2021:1 View
  11. Keusch F, Conrad F. Using Smartphones to Capture and Combine Self-Reports and Passively Measured Behavior in Social Research. Journal of Survey Statistics and Methodology 2022;10(4):863 View
  12. Keusch F, Bähr S, Haas G, Kreuter F, Trappmann M, Eckman S. Non-Participation in Smartphone Data Collection Using Research Apps. Journal of the Royal Statistical Society Series A: Statistics in Society 2022;185(Supplement_2):S225 View
  13. Fancello G, Vallée J, Sueur C, van Lenthe F, Kestens Y, Montanari A, Chaix B. Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population. Environment International 2023;178:108095 View
  14. Bear Don't Walk O, Paullada A, Everhart A, Casanova-Perez R, Cohen T, Veinot T. Opportunities for incorporating intersectionality into biomedical informatics. Journal of Biomedical Informatics 2024;154:104653 View

Books/Policy Documents

  1. Keusch F, Struminskaya B, Kreuter F, Weichbold M. Big Data Meets Survey Science. View
  2. Zafeiridi E, Qirtas M, Bantry White E, Pesch D. Bridging the Gap Between AI and Reality. View