Type 1 Diabetes Mellitus Virtual Patient Network as a Peer Support Community: Protocol for Social Network Analysis and Content Analysis

Nancy Wu; Anne-Sophie Brazeau; Meranda Nakhla; Deborah Chan; Deborah Da Costa; Geetha Mukerji; Sonia Butalia; Daniele Pacaud; Mélanie Henderson; Constadina Panagiotopoulos; Elham Rahme; Kaberi Dasgupta

doi:10.2196/18714

Type 1 Diabetes Mellitus Virtual Patient Network as a Peer Support Community: Protocol for Social Network Analysis and Content Analysis

JMIR Res Protoc. 2020 Aug 31;9(8):e18714. doi: 10.2196/18714.

Authors

Affiliations

¹ Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
² School of Human Nutrition, McGill University, Montreal, QC, Canada.
³ Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
⁴ Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁵ Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
⁶ Department of Pediatrics, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada.
⁷ Division of Endocrinology and Diabetes, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
⁸ Division of Endocrinology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁹ Department of Medicine, McGill University, Montreal, QC, Canada.

PMID: 32865502
PMCID: PMC7490683
DOI: 10.2196/18714

Abstract

Background: Type 1 Diabetes Mellitus Virtual Patient Network (T1DM-VPN) is a private Facebook group for youths with type 1 diabetes mellitus (T1DM) in Canada intended to facilitate peer-to-peer support. It was built on the finding that stigma is prevalent among youth with T1DM and impedes self-management.

Objective: We aim to determine if T1DM-VPN provides support as intended and to ascertain what type of members provide support. Specifically, we will (1) identify text consistent with any one of 5 social support categories, (2) describe the network by visualizing its structure and reporting basic engagement statistics, and (3) determine whether being a designated peer leader is related to a member's centrality (ie, importance in the network) and how frequently they offer social support.

Methods: We will manually extract interaction data from the Facebook group (posts, comments, likes/reactions, seen) generated from June 21, 2017 (addition of first member), to March 1, 2020. Two researchers will independently code posts and comments according to an existing framework of 5 social support categories-informational, emotional, esteem, network, and tangible-with an additional framework for nonsocial support categories. We will calculate how frequently each code is used. We will also report basic engagement statistics (eg, number of posts made per person-month) and generate a visualization of the network. We will identify stable time intervals in the history of T1DM-VPN by modeling monthly membership growth as a Poisson process. Within each interval, each member's centrality will be calculated and standardized to that of the most central member. We will use a centrality formula that considers both breadth and depth of connections (centrality = 0.8 × total No. of connections + 0.2 × total No. of interactions). Finally, we will construct multivariate linear regression models to assess whether peer leader status predicts member centrality and the frequency of offering social support. Other variables considered for inclusion in the models are gender and age at diagnosis.

Results: T1DM-VPN was launched in June 2017. As of March 1, 2020, it has 196 patient-members. This research protocol received ethics approval from the McGill University Health Centre Research Ethics Board on May 20, 2020. Baseline information about each group member was collected upon addition into the group, and collection of interaction data is ongoing as of May 2020.

Conclusions: This content analysis and social network analysis study of a virtual patient network applies epidemiological methods to account for dynamic growth and activity. The results will allow for an understanding of the topics of importance to youth with T1DM and how a virtual patient network evolves over time. This work is intended to serve as a foundation for future action to help youth improve their experience of living with diabetes.

International registered report identifier (irrid): PRR1-10.2196/18714.

Keywords: content analysis; social media; social network analysis; type 1 diabetes; youth.

©Nancy Wu, Anne-Sophie Brazeau, Meranda Nakhla, Deborah Chan, Deborah Da Costa, Geetha Mukerji, Sonia Butalia, Daniele Pacaud, Mélanie Henderson, Constadina Panagiotopoulos, Elham Rahme, Kaberi Dasgupta. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 31.08.2020.