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

Formative Studies and eHealth/mHealth Development (524) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Diabetes Self-Management (484) Visualization and Design for Diabetes Data (11)

Published on in Vol 8, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13517, first published .
Enhancing Diabetes Self-Management Through Collection and Visualization of Data From Multiple Mobile Health Technologies: Protocol for a Development and Feasibility Trial

Enhancing Diabetes Self-Management Through Collection and Visualization of Data From Multiple Mobile Health Technologies: Protocol for a Development and Feasibility Trial

Enhancing Diabetes Self-Management Through Collection and Visualization of Data From Multiple Mobile Health Technologies: Protocol for a Development and Feasibility Trial

Authors of this article:

Ryan J Shaw1, 2 Author Orcid Image ;   Angel Barnes1 Author Orcid Image ;   Dori Steinberg1 Author Orcid Image ;   Jacqueline Vaughn1 Author Orcid Image ;   Anna Diane1 Author Orcid Image ;   Erica Levine1 Author Orcid Image ;   Allison Vorderstrasse3 Author Orcid Image ;   Matthew J Crowley4, 5 Author Orcid Image ;   Eleanor Wood6 Author Orcid Image ;   Daniel Hatch1 Author Orcid Image ;   Allison Lewinski4 Author Orcid Image ;   Meilin Jiang7 Author Orcid Image ;   Janee Stevenson1 Author Orcid Image ;   Qing Yang1 Author Orcid Image
Article Authors Cited by (8) Tweetations (1) Metrics

Journals

  1. Yang Q, Hatch D, Crowley M, Lewinski A, Vaughn J, Steinberg D, Vorderstrasse A, Jiang M, Shaw R. Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis. JMIR mHealth and uHealth 2020;8(6):e17730 View
  2. Shaw R, Yang Q, Barnes A, Hatch D, Crowley M, Vorderstrasse A, Vaughn J, Diane A, Lewinski A, Jiang M, Stevenson J, Steinberg D. Self-monitoring diabetes with multiple mobile health devices. Journal of the American Medical Informatics Association 2020;27(5):667 View
  3. Cole C, Powers S, Tomko R, Froeliger B, Valafar H. Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology. JMIR Formative Research 2021;5(2):e20464 View
  4. Lewinski A, Vaughn J, Diane A, Barnes A, Crowley M, Steinberg D, Stevenson J, Yang Q, Vorderstrasse A, Hatch D, Jiang M, Shaw R. Perceptions of Using Multiple Mobile Health Devices to Support Self‐Management Among Adults With Type 2 Diabetes: A Qualitative Descriptive Study. Journal of Nursing Scholarship 2021;53(5):643 View
  5. Huhn S, Axt M, Gunga H, Maggioni M, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR mHealth and uHealth 2022;10(1):e34384 View
  6. Tolentino D, Ali S, Jang S, Kettaneh C, Smith J. Type 2 Diabetes Self-Management Interventions Among Asian Americans in the United States: A Scoping Review. Health Equity 2022;6(1):750 View
  7. Yang Q, Jiang M, Li C, Luo S, Crowley M, Shaw R. Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach. BMC Medical Research Methodology 2024;24(1) View