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

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (792) Ecological Momentary Assessment (EMA) (297) mHealth for Data Collection and Research (917) Development and Evaluation of Research Methods, Instruments and Tools (439)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41412, first published .
Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study

Authors of this article:

Aja Murray1 Author Orcid Image ;   Anastasia Ushakova2 Author Orcid Image ;   Xinxin Zhu1 Author Orcid Image ;   Yi Yang1 Author Orcid Image ;   Zhuoni Xiao1 Author Orcid Image ;   Ruth Brown3 Author Orcid Image ;   Lydia Speyer4 Author Orcid Image ;   Denis Ribeaud5 Author Orcid Image ;   Manuel Eisner5, 6 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Murray A, Xie T, Power L, Condon L. Recruitment and retention of adolescents for an ecological momentary assessment measurement burst mental health study: The MHIM engagement strategy. Health Expectations 2024;27(3) View
  2. Xiao Z, Robertson S, Long E, Flaig R, Kirby L, Romaniuk L, Murray A, Whalley H. Loneliness in the Digital World: protocol for a co-produced ecological momentary assessment study in adolescents. BMJ Open 2024;14(6):e087374 View