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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Feb 26, 2020
Date Accepted: Jun 4, 2020

The final, peer-reviewed published version of this preprint can be found here:

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

Myneni S, Pavia K, Lewis B, Wang J

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

JMIR Med Inform 2020;8(6):e18441

DOI: 10.2196/18441

PMID: 32602843

PMCID: 7367515

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Diabetes self-management in the age of social media: Large-scale analysis of peer interactions using semi-automated methods

  • Sahiti Myneni; 
  • Kristi Pavia; 
  • Brittney Lewis; 
  • Jing Wang

ABSTRACT

Background:

Online communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.

Objective:

In this paper, we characterize peer interactions in an online community for chronic disease management. Our objective is to 1) identify key communication themes and 2) study their prevalence in online social interactions.

Methods:

The American Diabetes Association Online community is an online social network for diabetes self-management. We analyzed 80,481 randomly selected de-identified peer-to-peer messages from 1,212 members, posted between June 1, 2012 and May 30, 2019. Our mixed-methods approach comprised qualitative coding and automated text analysis to identify, visualize, and analyze content-specific communication patterns underlying diabetes self-management.

Results:

Qualitative analysis revealed that “Social support” was the most prevalent theme (84.9%), followed by “readiness to change” (18.8%), “teachable moments” (14.7%), “pharmacotherapy” (13.7%), and “progress” (13.3%). The support vector machine classifier resulted in reasonable accuracy with recall=0.76 and precision= 0.78, and allowed us to extend our thematic codes to the entire dataset.

Conclusions:

Modeling health-related communication through high throughput methods can enable the identification of specific content related to sustainable chronic disease management, which facilitates targeted health promotion.


 Citation

Please cite as:

Myneni S, Pavia K, Lewis B, Wang J

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

JMIR Med Inform 2020;8(6):e18441

DOI: 10.2196/18441

PMID: 32602843

PMCID: 7367515

Per the author's request the PDF is not available.