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

Ecological Momentary Assessment (EMA) (295) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1309) mHealth for Data Collection and Research (914) Diabetes Self-Management (463) Glucose Tracking and Self-Monitoring of Blood Glucose (150) Machine Learning (1385)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21959, first published .
Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Authors of this article:

Peng Zhang1, 2 Author Orcid Image ;   Christopher Fonnesbeck3 Author Orcid Image ;   Douglas C Schmidt1, 2 Author Orcid Image ;   Jules White1 Author Orcid Image ;   Samantha Kleinberg4 Author Orcid Image ;   Shelagh A Mulvaney5, 6, 7 Author Orcid Image
Article Authors Cited by (4) Tweetations (8) Metrics

Journals

  1. Michalek D, Onengut-Gumuscu S, Repaske D, Rich S. Precision Medicine in Type 1 Diabetes. Journal of the Indian Institute of Science 2023;103(1):335 View
  2. van Dalen M, Snijders A, Dietvorst E, Bracké K, Nijhof S, Keijsers L, Hillegers M, Legerstee J. Applications of the experience sampling method (ESM) in paediatric healthcare: a systematic review. Pediatric Research 2024;95(4):887 View
  3. Khalifa M, Albadawy M. Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management. Computer Methods and Programs in Biomedicine Update 2024;5:100141 View