Social Interaction and Support in a Type 2 Diabetes Computer-Mediated Environment

Abstract

Problem:Type 2 diabetes (T2D) is increasing in incidence and prevalence in the United States, and T2D remains the 7th leading cause of death. Consistent T2D self-management delays the onset of co-morbidities associated with this disease; however rates of self-management in adults living with T2D remain sub-optimal. The literature indicates that frequent and sustained social interaction between an individual living with T2D, and other peers living with T2D and a healthcare provider, helps sustain critical self-management behaviors. Yet, an individual living with T2D may experience challenges in obtaining this helpful interaction and support due to temporal, geographical, or physical limitations. Computer mediated environments (CMEs; programs via the Internet) facilitate the exchange of T2D-specific information and support, and may ameliorate limitations to obtaining support and information. CMEs facilitate the creation of an online community in which individuals can choose their participation and the amount of personal information they reveal to other individuals. These personal narratives facilitate the exchange of support and reinforcement as individuals reveal personal challenges and successes encountered during daily T2D self-management. Yet, interventions via CMEs aimed at increasing self-management in individuals living with T2D, have shown mixed results for sustained behavior change. One way to improve disease-specific CMEs is to understand how individuals interact with others in these CMEs. This study used Strong/Weak Ties Theory and Social Penetration Theory to describe the frequency and content of social interaction and support in a CME. In Strong/Weak Ties Theory, tie strength is defined as time in which the relationship can develop and occur, the intensity of emotions within that relationship, the breadth and depth of intimacy, and whether the relationship is reciprocal and mutual. Social Penetration Theory purports that the perceived value of a relationship influences the breadth (number of topics discussed) and depth (degree of intimacy and personalization of discussed topics) of a relationship. This study defined social support using the four types of support commonly identified in the literature: emotional, instrumental, appraisal, and informational. Therefore, the overall purpose of this study was to develop knowledge to advance the science of social relationships in CMEs, with a focus on characterizing social interaction and social support as they relate to self-management in a chronic disease. In particular, knowledge was developed about how social interaction and social support among adults living with T2D was exchanged in a CME. Materials:This study was a secondary analysis of qualitative data collected from the Second Life Impacts Diabetes Education & Self-Management (SLIDES) study. The SLIDES study provided self-management knowledge and support via a CME to individuals living with T2D. These data included recorded synchronous (i.e., real-time) and asynchronous (i.e., delay present) conversations among study participants (n = 20) and diabetes educators and study investigators (n = 4). Diabetes educators offered two education sessions and one support session per week, and these sessions were opportunities for participants to ask questions, learn about T2D, and interact with other participants who had T2D. During the study, all conversational data (i.e., synchronous conversations and text-chat; asynchronous emails and discussion board posts) among study participants, diabetes educators, and study staff within the CME were recorded to MP3 files and stored on a secure server at the University.The conversational data used in the analysis was prepared over a period of several months; this attention to detail enabled a rich description of social interaction and support in a CME. The researcher listened to the MP3 files and then transcribed several files (n = 164) in order to gain knowledge of the SLIDES CME. A professional transcription service transcribed the remaining MP3 files (n = 697). The researcher cleaned all transcribed conversations by simultaneously listening to the MP3 files (n = 861) and reading the transcribed text to verify accuracy. Then, a systematic approach was used to link each spoken word to a SLIDES participant by voice recognition or reference to names to determine which participants contributed to, and how much each participant engaged in, a conversation. Once the researcher reviewed all MP3 files and transcribed text for accuracy, the Microsoft Word document (n = 1,537 pages) was imported into Atlas.ti to support coding and analysis.Methods: The primary study used a qualitative study design to comprehensively describe conversational data from participants who engaged in a CME that focused on T2D. Qualitative content analysis guided the description of social interaction and social support. The researcher developed inductive and deductive codes to describe social interaction and social support based upon the guiding framework. A research team comprised of a PhD candidate and two research experts reviewed all emergent codes and themes during first and second level coding. Results:Findings from the primary study describe social interaction and social support among individuals who interacted in a CME. Four characteristics of a social interaction in a CME developed: (1) communication techniques, or how participants interact in real-time communication in a CME; (2) expressions of self-management, or the content of participants’ self-management discussions; (3) depth of conversation, or intensity of personal information shared; and (4) breadth of conversation, or the number of topics discussed among participants in a conversation. The findings illustrate that the four types of social support are present in a CME: (1) instrumental support, or the exchange T2D-specific tangible goods in the CME; (2) appraisal support, or the exchange of affirmational support for T2D self-management behaviors; (3) informational support, or the exchange of T2D-specific information; and (4) emotional support, or the exchange of empathy among participants in a conversation. Additionally, the findings illustrate the elicitation behaviors (i.e., prompts that individuals used to obtain support) and the support responses (i.e., the support provided) to further describe social support. A social interaction in a CME is a multidimensional construct because the communication medium (i.e., the CME) influences the communication techniques individuals’ use when talking amongst themselves to exchange information and support. The findings indicated that participants will reveal personal information about themselves in a CME, and most of the topics discussed are salient to T2D. The primary study indicated that strong/weak ties between individuals exist in CME relationships, and these ties develop over time with the sharing of personal information. The ties developed among (1) diabetes educators and individuals living with T2D, and (2) individuals living with T2D. The findings from this study indicate that social interaction and social support are exchanged among peers and providers in a CME. Conclusion:Computer-mediated environments enable persons living with T2D to obtain valuable interaction and support at times and locations that are convenient. Type 2 diabetes self-management is person-specific and dynamic, which highlights the need for social support that is personalized and timely. One way participants received personalized support and information was through interactions with the diabetes educators and other participants. The interactions in a CME mirrored those in a face-to-face environment as participants asked questions of each other, responded to other participants, talked about personal challenges, and provided information. This study began to characterize the social interactions among persons living with T2D who interacted in a CME. Future research on the characterization of social interactions in a CME should focus on analyzing social interactions in a larger, more diverse sample that includes men and women of diverse ages, races, ethnicities, education levels, and income levels.