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

Ecological Momentary Assessment (EMA) (366) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1614) mHealth for Data Collection and Research (1110) Diabetes Self-Management (547) Glucose Tracking and Self-Monitoring of Blood Glucose (190) Machine Learning (1829)

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 (7) Tweetations (6) 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
  4. Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, Mondal S, Biswas U, Jana M, Bhattacharjee S. The biomedical applications of artificial intelligence: an overview of decades of research. Journal of Drug Targeting 2025;33(5):717 View

Books/Policy Documents

  1. Bourkhime H, Qarmiche N, Omari M, Charef N, Elghazi S, Tachfouti N, Fakir S, Berraho M, Otmani N. Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environment. View

Conference Proceedings

  1. K V, Sugumar R. 2023 International Conference on Network, Multimedia and Information Technology (NMITCON). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks View
  2. Schleicher M, Pryss R, Schobel J, Schlee W, Spiliopoulou M. 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS). Predicting User Engagement in mHealth Apps with Neighborhood-based Approaches View