Online Diabetes Community Research Articles

Information Seeking and Receiving of Older Adults with Diabetes in the Online Health Community: An Information Need Contextualization Perspective

ABSTRACT Online health communities (OHCs) are important online health communication channels for older adults with diabetes to access health information. When seeking health information, they often disclose a variety of contextual information (e.g., socio-economic situations) in their questions. Selective contextual information disclosure is a type of communication strategy for users in OHCs to elicit replies from others. In this study, we adopted text analysis to investigate what contextual information older adults with diabetes disclose to articulate their information needs and used the fixed-effect Poisson model to examine the relationships between different types of contextual information disclosure and informational support receipt. Our analyses were based on a dataset of 4,505 questions and corresponding replies from an online diabetes community. The results showed that cognitive information is the most frequently disclosed contextual information, while older adults tend to disclose demographic information in their questions less. Providing demographic and situational details in questions can enhance informational support receiving, resulting in an increased number of informational supports. However, disclosing cognitive, affective, informational channels, or support information does not significantly affect the informational support receiving. These findings can contribute to extending our existing understanding of information seekers’ communication strategies in OHCs.

Getting better? Examining the effects of social support in OHCs on users’ emotional improvement

Online health communities (OHCs) have emerged as dynamic platforms facilitating patient communication and knowledge exchange on health matters. While extant research underscores the emotional benefits accrued from engagement in OHCs, the precise quantification of how social support within these communities influences users’ emotional states remains an intricate challenge. In light of this, the primary objective of this study is to explore the influential mechanisms underlying the received social support on the emotional improvement of users in OHCs, while concurrently investigating the potential moderating effects posed by individuals’ health conditions and levels of community involvement. Leveraging authentic user interaction data harvested from an online diabetes community, this study employed deep learning models and natural language processing methods to gauge the magnitude of received social support and the variation of users’ emotional states. Subsequently, a multiple linear regression model was formulated to evaluate the effects of both informational and emotional support on users' emotional improvement. Empirical results reaffirm the pivotal role of both informational and emotional support in enhancing users’ emotional states, with emotional support emerging as particularly influential. Notably, prolonged disease duration amplifies the beneficent impact of social support on emotional enhancement. The research outcomes bear implications for ameliorating negative emotional experiences and effectively managing chronic diseases.

55-LB: “Older patients manifest it differently”—HCP Perspectives of People Aging with Type 1 Diabetes

In the last decade, people with type 1 diabetes (T1D) are living longer due to advancements in diabetes technology. However, there is little research on treating this population. This study examined health care professionals’ (HCPs) experiences in treating older adults with T1D to identify clinical and practical differences. Participants were recruited from T1D Exchange (an online diabetes community) and 11 HCPs (average time in practice: 21.5 years) were interviewed. Interview questions focused on their clinical experiences, challenges that older people experienced, and needs of this aging population that might be different from other age groups. The interviews were recorded, transcribed, independently coded by 2 coders and thematically analyzed. Themes included treatment recommendations for older adults with T1D, diabetes education, post-severe hypoglycemic event recommendations, fear of hypoglycemia, insurance, and polypharmacy. As older adults with diabetes continue to live longer, new recommendations for this age group should be considered. Specifically, treatment recommendations amongst older adults differ in glycemic target goals and HbA1c range. These recommendations are made to combat any severe hypoglycemic episodes prone to this population. There is a greater degree of neurocognitive decline which poses a greater risk for life threatening emergencies, not only in older adults but also older adults with T1D. It is the HCPs duty to prepare this population for the risks of developing certain complications associated with T1D as well as the possible dangers if the more emergent situations are not addressed promptly. Disclosure K. Uddin: None. H. L. Stuckey: Consultant; Eli Lilly and Company. A. Hughes: None.

Investigating the experiences of low-carbohydrate diets for people living with Type 2 Diabetes: A thematic analysis.

Low-Carbohydrate Diets (LCDs) are becoming increasingly popular to manage Type 2 diabetes mellitus (T2DM). However, there is a need to identify people with T2DM's understanding of LCDs, their reasons for engaging in such a diet, how they sustain it and any challenges they face. This study aimed to assess the experience of adhering to a LCD within a sample of individuals with T2DM. Ten participants with T2DM were recruited using a self-selecting sampling method from an online diabetes community that promotes LCDs. Participants completed one-to-one digitally recorded semi-structured interviews, which were later transcribed verbatim and data subjected to Thematic Analysis. Five core themes and twelve subthemes were developed during the analysis: (1) Lack of professional guidance; (2) Fear of complications & long-term medication use; (3) Dietary control as motivation; (4) Positive health outcomes; and (5) Social support. The findings are discussed with reference to a psychological model of behaviour, COM-B. Participants reported gaining knowledge and skills to increase their Capability to engage in LCDs, Motivation to manage diabetes outcomes influenced adherence. However, challenges were reported with the Opportunity to engage in behaviour, mainly influenced by social support. Health professionals and significant others may benefit from resources to help build knowledge and understanding and assist with maintaining a LCD long-term.

Influence of social support on individual health knowledge adoption in online diabetes communities: the mediating role between cognition and emotion

PurposeTo address the problem of how to guide and promote health knowledge adoption, based on online diabetes communities, this study explores the impact mechanism of social support on users' individual health knowledge adoption to provide insights for online diabetes community management and personal health management.Design/methodology/approachIntegrating the theories of cognitive-affective personality system (CAPS) and social support, this study constructs a theoretical model, collects data through a questionnaire and uses a structural equation model to analyse 356 data.FindingsThe results show that: (1) Considering the online diabetes communities, it is reasonable to divide social support into emotional, information, network and respect support. (2) Social support affects individual health knowledge adoption through the intermediaries of knowledge argument quality, knowledge source credibility and positive emotions. (3) The order of the mediating effect of cognitive and emotional factors between social support and health knowledge adoption is knowledge argument quality > knowledge source credibility > positive emotions and rationality > sensibility. (4) Users pay more attention to the source credibility of professional health knowledge than that of experiential health knowledge.Originality/valueThis research expands the application scope of CAPS and opens the “black box” of the impact of social support on individual health knowledge adoption behaviour. Simultaneously, the dimensions of social support and the mediating effect between social support and the two types of health knowledge are discussed.

What users seek and share in online diabetes communities: examining similarities and differences in expressions and themes

PurposeThis paper aims to investigate user health information seeking and sharing patterns and content in an online diabetes community and explore the similarities and differences in the ways and themes they expressed.Design/methodology/approachMultiple methods are applied to analyze the expressions and themes that users seek and share based on large-scale text data in an online diabetes community. First, a text classifier using deep learning method is performed based on the expression category this study developed. Second, statistical and social network analyses are used to measure the popularity and compare differences between expressions. Third, topic modeling, manual coding and similarity analysis are used to mining topics and thematic similarity between seeking and sharing threads.FindingsThere are four different ways users seek and share in online health communities (OHCs) including informational seeking, situational seeking, objective information sharing and experiential information sharing. The results indicate that threads with self-disclosure could receive more replies and attract more users to contribute. This study also examines the 10 topics that were discussed for information seeking and 14 topics for information sharing. They shared three discussion themes: self-management, medication and symptoms. Information about symptoms can be largely matched between seeking and sharing threads while there is less overlap in self-management and medication categories.Originality/valueBeing different from previous studies that mainly describe one type of health information behavior, this paper analyzes user health information seeking and sharing behaviors in OHCs and investigates whether there is a correspondence or discrepancy between expressions and information users spontaneously seek and share in OHCs.

From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare

In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majority of a community’s users (∼90%) remain inactive (lurk), simply observing contributions made by intermittent (∼9%) and heavy (∼1%) contributors. Our research examines increases and decreases of types of user engagement in online communities, characterizing user engagement based on trace user activity or lack of activity. Some lurkers later become workers (i.e., engaged in the community), but some will not. Differentiating lurkers who can be engaged from those who cannot enables managers to anticipate and proactively direct their resources toward the users who are most likely to become or remain workers (i.e., heavy contributors), thereby promoting community welfare. Our research, based on analysis of 533,714 posts from an online diabetes community, can thus guide managerial interventions to increase online community welfare.

From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare

In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majority of a community’s users (90%) remain inactive (lurk), simply observing contributions made by intermittent (9%) and heavy (1%) contributors. Our research examines increases and decreases of types of user engagement in online communities using Hidden Markov Models. These models characterize latent states of user engagement from trace user activity or lack of activity. The resulting framework then differentiates lurkers who can later become workers'' (i.e., engaged with the community) from those who will not. Differentiating lurkers who can be engaged from those who cannot enables managers to anticipate and proactively direct their resources towards the users who are most likely to become or remain workers (i.e., heavy contributors), thereby promoting community welfare. Analysis of 533,714 posts from an online diabetes community shows that incorporating latent user engagement variables can significantly improve the accuracy of welfare prediction models and guide managerial interventions. Application of our framework to five additional communities of various contexts demonstrates its generalizability.

76-OR: In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems

Background: Thousands with type 1 diabetes are estimated to be using open source Artificial Pancreas Systems (APS) with commercially available insulin pumps, continuous glucose monitors (CGM), and an open source control algorithm to process glucose readings and adjust insulin delivery. OpenAPS and similar do-it-yourself (DIY) closed loop systems gained considerable interest in the online diabetes community. Many using DIY closed loop systems have chosen to donate their data to a shared, anonymized data repository called the “OpenAPS Data Commons.” The present study evaluated glycemic control and glycemic variability of CGM readings of 80 DIY closed loop users. Methods: We analyzed 19251 days (53 years) of CGM readings with a mean duration of 134 days per patient (min. 3 days, max. 917 days) after the patient started looping. Results: Mean glucose was 137 ± 20mg/dl and estimated glycated hemogloblin A1c (eA1c) was 6.40 ± 0.70%. Time in target range (70-180mg/dL) was 77.5 ± 10.5%, 4.3% of CGM readings were below 70mg/dL, 1.3% were below 54mg/dL, 18.2% were above 180mg/dL, and 4.1% of CGM readings were above 250mg/dL, respectively. A total of 6474 hypoglycemic events (CGM reading < 54mg/dL) was observed (daytime: 5004 [73.9%]; nighttime: 1765 [26.1%]), which corresponds to 0.34 hypoglycemic events per day. The mean duration of each hypoglycemia event was 65.4 ± 41.4 minutes, and 1484 events were prolonged (duration > 120 minutes; daytime: 1043 [76.30%]; nighttime: 324 [23.7%]). Coefficient of variation (CV) was 35.5 ± 5.9% (daytime: 35.4%; nighttime: 33.9%) and mean of daily differences (MODD) was 50.1 ± 13.5 mg/dL. Conclusion: Open source AP systems show potential to support stable glycemic control in people with T1D. This is the largest descriptive analysis of open source APS data to date. The results are promising, but open source APS should be investigated in additional detail before a conclusion about their safety and efficacy can be drawn. Disclosure A. Melmer: None. T. Züger: None. D.M. Lewis: Consultant; Self; Diabeloop SA, Roche Diabetes Care. S.M. Leibrand: Consultant; Self; Diabeloop SA, Roche Diabetes Care. M. Laimer: None.

String-based Multinomial Naïve Bayes for Emotion Detection among Facebook Diabetes Community

This paper determined the emotions among the online diabetes community using the string-based Multinomial Naïve Bayes algorithm. Facebook posts from official diabetes support groups were crawled, for a total of 15 000 pre-processed posts. Of these, 800 were manually annotated by human experts. The posts were first classified according to Plutchik’s wheel of emotions, comprising of eight dominant emotions: anger, sadness, fear, joy, surprised, trust, anticipation and disgust using the NRC Emotion Lexicon (Emolex). The emotion classifications were then refined using string-based Multinomial Naïve Bayes algorithm, with results indicating a 6.3% improvement (i.e. 82% vs. 75.7% for average F-score) when compared to the Emolex-approach, and other machine learning algorithms, namely, Naïve Bayes and Multinomial Naïve Bayes. The higher accuracy in emotion classification reflects the feasibility of our approach. Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community.

Does Sharing Your Emotion Make You Feel Better? An Empirical Investigation on the Association Between Sharing Emotions on a Virtual Mood Wall and the Relief of Patients' Negative Emotions.

Background:Emotional management is very important for patients with chronic diseases, such as diabetes. Since these patients may experience strong negative emotions, it is highly desirable to develop an information technology solution for the relief of negative emotions. Recently, online health communities introduced a new design element called the virtual mood wall, which allows patients to express their emotions. We are interested in determining the association between the use of virtual mood wall and the relief of patients' negative emotions.Objective:The main purpose of this work is to evaluate whether sharing one's emotions on the virtual mood wall is associated with the relief of the patient's negative emotions.Methods:We used a secondary econometric analysis using transaction data from 2013 obtained from a leading online diabetes community in China, Sweet Home. A total of 572 patients were included. We compared the change in negative emotion from 1 month before to 1 month after the patient's initial post on the virtual mood wall. Multivariate regression and propensity score matching were used to estimate the effect of the virtual mood wall on the relief of patients' negative emotions.Results:Posting on the virtual mood wall is significantly associated with the decrease of patients' negative emotions (p < 0.001). The control variables included the initial negative emotion (p < 0.001), the summer (p < 0.05) and autumn (p < 0.05) season, and the volume of homepage visitors (p < 0.05), all of which are significantly associated with the relief of negative emotions.Conclusions:This study empirically validated the positive association between the online sharing of emotions and the relief of negative emotions. We demonstrated that information technology provided a channel for sharing emotions. The virtual mood wall is an effective design element to help patients with chronic disease to recover from their negative emotions.

Detecting clinically related content in online patient posts

Patients with chronic health conditions use online health communities to seek support and information to help manage their condition. For clinically related topics, patients can benefit from getting opinions from clinical experts, and many are concerned about misinformation and biased information being spread online. However, a large volume of community posts makes it challenging for moderators and clinical experts, if there are any, to provide necessary information. Automatically identifying forum posts that need validated clinical resources can help online health communities efficiently manage content exchange. This automation can also assist patients in need of clinical expertise by getting proper help. We present our results on testing text classification models that efficiently and accurately identify community posts containing clinical topics. We annotated 1817 posts comprised of 4966 sentences of an existing online diabetes community. We found that our classifier performed the best (F-measure: 0.83, Precision: 0.79, Recall:0.86) when using Naïve Bayes algorithm, unigrams, bigrams, trigrams, and MetaMap Symantic Types. Training took 5 s. The classification process took a fraction of 1 s. We applied our classifier to another online diabetes community, and the results were: F-measure: 0.63, Precision: 0.57, Recall: 0.71. Our results show our model is feasible to scale to other forums on identifying posts containing clinical topic with common errors properly addressed.

Answers to Health Questions: Internet Search Results Versus Online Health Community Responses.

BackgroundAbout 6 million people search for health information on the Internet each day in the United States. Both patients and caregivers search for information about prescribed courses of treatments, unanswered questions after a visit to their providers, or diet and exercise regimens. Past literature has indicated potential challenges around quality in health information available on the Internet. However, diverse information exists on the Internet—ranging from government-initiated webpages to personal blog pages. Yet we do not fully understand the strengths and weaknesses of different types of information available on the Internet.ObjectiveThe objective of this research was to investigate the strengths and challenges of various types of health information available online and to suggest what information sources best fit various question types.MethodsWe collected questions posted to and the responses they received from an online diabetes community and classified them according to Rothwell’s classification of question types (fact, policy, or value questions). We selected 60 questions (20 each of fact, policy, and value) and the replies the questions received from the community. We then searched for responses to the same questions using a search engine and recorded theResultsCommunity responses answered more questions than did search results overall. Search results were most effective in answering value questions and least effective in answering policy questions. Community responses answered questions across question types at an equivalent rate, but most answered policy questions and the least answered fact questions. Value questions were most answered by community responses, but some of these answers provided by the community were incorrect. Fact question search results were the most clinically valid.ConclusionsThe Internet is a prevalent source of health information for people. The information quality people encounter online can have a large impact on them. We present what kinds of questions people ask online and the advantages and disadvantages of various information sources in getting answers to those questions. This study contributes to addressing people’s online health information needs.

An Innovative Approach to Informing Research: Gathering Perspectives on Diabetes Care Challenges From an Online Patient Community.

BackgroundFunding agencies and researchers increasingly recognize the importance of patient stakeholder engagement in research. Despite calls for greater patient engagement, few studies have engaged a broad-based online community of patient stakeholders in the early stages of the research development process.ObjectiveThe objective of our study was to inform a research priority-setting agenda by using a Web-based survey to gather perceptions of important and difficult aspects of diabetes care from patient members of a social networking site-based community.MethodsInvitations to participate in a Web-based survey were sent by email to members of the PatientsLikeMe online diabetes community. The survey asked both quantitative and qualitative questions addressing individuals’ level of difficulty with diabetes care, provider communication, medication management, diet and exercise, and relationships with others. Qualitative responses were analyzed using content analysis.ResultsOf 6219 PatientsLikeMe members with diabetes who were sent survey invitations, 1044 (16.79%) opened the invitation and 320 (5.15% of 6219; 30.65% of 1044) completed the survey within 23 days. Of the 320 respondents, 33 (10.3%) reported having Type 1 diabetes; 107 (33.4%), Type 2 diabetes and taking insulin; and 180 (56.3%), Type 2 diabetes and taking oral agents or controlling their diabetes with lifestyle modifications. Compared to 2005-2010 National Health and Nutrition Examination Survey data for individuals with diabetes, our respondents were younger (mean age 55.8 years, SD 9.9 vs 59.4 years, SE 0.5); less likely to be male (111/320, 34.6% vs 48.4%); and less likely to be a racial or ethnic minority (40/312, 12.8% vs 37.5%). Of 29 potential challenges in diabetes care, 19 were categorized as difficult by 20% or more of respondents. Both quantitative and qualitative results indicated that top patient challenges were lifestyle concerns (diet, physical activity, weight, and stress) and interpersonal concerns (trying not to be a burden to others, getting support from family/friends). In our quantitative analysis, similar concerns were expressed across patient subgroups.ConclusionsLifestyle and interpersonal factors were particularly challenging for our online sample of adults with Type 1 or Type 2 diabetes. Our study demonstrates the innovative use of social networking sites and online communities to gather rapid, meaningful, and relevant patient perspectives that can be used to inform the development of research agendas.

Text classification for assisting moderators in online health communities

ObjectivesPatients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators’ help. MethodsWe employed a binary classifier on WebMD’s online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. ResultsUsing sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients’ posts. DiscussionWe showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members’ needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges. ConclusionSocial media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators’ expertise in these large-scale, social media environments.

Collaborative Help in Chronic Disease Management: Supporting Individualized Problems.

Coping with chronic illness disease is a long and lonely journey, because the burden of managing the illness on a daily basis is placed upon the patients themselves. In this paper, we present our findings for how diabetes patient support groups help one another find individualized strategies for managing diabetes. Through field observations of face-to-face diabetes support groups, content analysis of an online diabetes community, and interviews, we found several help interactions that are critical in helping patients in finding individualized solutions. Those are: (1) patients operationalize their experiences to easily contextualize and share executable strategies; (2) operationalization has to be done within the larger context of sharing illness trajectories; and (3) the support groups develop common understanding towards diabetes management. We further discuss how our findings translate into design implications for supporting chronic illness patients in online community settings.

‘Oh dear, should I really be saying that on here?’: Issues of identity and authority in an online diabetes community

We explore peer-to-peer discussions which took place in a UK-based diabetes 'Virtual Clinic' online community. In particular, we seek to understand the rhetorical nature and content of exchanges over a period of six months from the community's inception. Data were captured weekly and analysis based on thematic discourse analysis. Two key issues emerged regarding how the community shaped the nature of the discussion forum. First, the identity of the forum was established, and boundaries drawn about what was, and was not, acceptable. Second, participants sought to present themselves as reliable and authoritative sources of information. Internet discussion communities are shaped in important ways early on by the community of users, including how the character and focus of discussion is formed, and how both information and users can be constructed as authoritative and reliable.

Sharing Data for Public Health Research by Members of an International Online Diabetes Social Network

BackgroundSurveillance and response to diabetes may be accelerated through engaging online diabetes social networks (SNs) in consented research. We tested the willingness of an online diabetes community to share data for public health research by providing members with a privacy-preserving social networking software application for rapid temporal-geographic surveillance of glycemic control.Methods and FindingsSN-mediated collection of cross-sectional, member-reported data from an international online diabetes SN entered into a software applicaction we made available in a “Facebook-like” environment to enable reporting, charting and optional sharing of recent hemoglobin A1c values through a geographic display. Self-enrollment by 17% (n = 1,136) of n = 6,500 active members representing 32 countries and 50 US states. Data were current with 83.1% of most recent A1c values reported obtained within the past 90 days. Sharing was high with 81.4% of users permitting data donation to the community display. 34.1% of users also displayed their A1cs on their SN profile page. Users selecting the most permissive sharing options had a lower average A1c (6.8%) than users not sharing with the community (7.1%, p = .038). 95% of users permitted re-contact. Unadjusted aggregate A1c reported by US users closely resembled aggregate 2007–2008 NHANES estimates (respectively, 6.9% and 6.9%, p = 0.85).ConclusionsSuccess within an early adopter community demonstrates that online SNs may comprise efficient platforms for bidirectional communication with and data acquisition from disease populations. Advancing this model for cohort and translational science and for use as a complementary surveillance approach will require understanding of inherent selection and publication (sharing) biases in the data and a technology model that supports autonomy, anonymity and privacy.