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

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

Books/Policy Documents

  1. Keusch F, Struminskaya B, Kreuter F, Weichbold M. Big Data Meets Survey Science. View