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

mHealth for Data Collection and Research (1202) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1000) Mobile Health (mhealth) (3476) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (138) Epidemiology (196) Methodological Innovations (37) New Methods (1137)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28857, first published .
Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study

Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study

Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study

Authors of this article:

Anna L Beukenhorst1, 2 Author Orcid Image ;   Jamie C Sergeant1, 3 Author Orcid Image ;   David M Schultz4, 5 Author Orcid Image ;   John McBeth1 Author Orcid Image ;   Belay B Yimer1 Author Orcid Image ;   Will G Dixon1, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations (9) Metrics

Journals

  1. Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296 View
  2. Beukenhorst A, Burke K, Scheier Z, Miller T, Paganoni S, Keegan M, Collins E, Connaghan K, Tay A, Chan J, Berry J, Onnela J. Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies. JMIR mHealth and uHealth 2022;10(2):e31877 View
  3. Carnes N, Kolaja C, Lewis C, Castañeda S, Rull R, Bukowinski A, Carey F, Geronimo-Hara T, Hall C, Harbertson J, Ignacio D, Jacobson I, LeardMann C, Perez V, Piega A, Rivera A, Salvatier R, Sharifian N, Speigle S, Trone D, Villalobos J, Zhu K. Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study. BMC Medical Research Methodology 2025;25(1) View