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

Ecological Momentary Assessment (EMA) (238) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1027) mHealth for Data Collection and Research (673) Diabetes Self-Management (367) Glucose Tracking and Self-Monitoring of Blood Glucose (126) Machine Learning (1001)

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 Zhang 1, 2 Author Orcid Image ;   Christopher Fonnesbeck 3 Author Orcid Image ;   Douglas C Schmidt 1, 2 Author Orcid Image ;   Jules White 1 Author Orcid Image ;   Samantha Kleinberg 4 Author Orcid Image ;   Shelagh A Mulvaney 5, 6, 7 Author Orcid Image
Article Authors Cited by (1) Tweetations (8) Metrics
Peng Zhang 1, 2 , PhD ;   Christopher Fonnesbeck 3 , PhD ;   Douglas C Schmidt 1, 2 , PhD ;   Jules White 1 , PhD ;   Samantha Kleinberg 4 , PhD ;   Shelagh A Mulvaney 5, 6, 7 , PhD

1 Department of Computer Science , School of Engineering , Vanderbilt University , Nashville , TN , US

2 Data Science Institute , Vanderbilt University , Nashville , TN , US

3 Department of Biostatistics , Vanderbilt University , Nashville , TN , US

4 Department of Computer Science , Stevens Institute of Technology , Hoboken , NJ , US

5 Department of Pediatrics , Vanderbilt University Medical Center , Nashville , TN , US

6 Department of Biomedical Informatics , Vanderbilt University Medical Center , Nashville , TN , US

7 School of Nursing , Vanderbilt University , Nashville , TN , US

Corresponding Author: