Dataset Information

Educational gradient in social network changes at disease diagnosis.

ABSTRACT: This study examines the educational gradient in social network changes following the diagnosis of new chronic diseases. Using a representative sample of the US older population, the author shows that the network size and amount of health-related discussion with social network members increased only for older adults with a bachelor's degree. Tie-level analyses reveal that such increase is from the activation of health discussion with the existing social network members, but not from the changes in discussion partners. These results suggest that heterogeneous temporal dynamics in social networks may be one mechanism that shapes diverging health trajectories in later life.

SUBMITTER: Joo WT

PROVIDER: S-EPMC10039803 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Educational gradient in social network changes at disease diagnosis.

Joo Won-Tak WT

Social science & medicine (1982) 20221223

This study examines the educational gradient in social network changes following the diagnosis of new chronic diseases. Using a representative sample of the US older population, the author shows that the network size and amount of health-related discussion with social network members increased only for older adults with a bachelor's degree. Tie-level analyses reveal that such increase is from the activation of health discussion with the existing social network members, but not from the changes i ...[more]

PMID: 36586184

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Project description:BackgroundSocial networking sites (SNSs) such as Facebook have the potential to enhance online public health interventions, in part, as they provide social exposure and reinforcement.ObjectiveThe objective of the study was to evaluate whether social exposure provided by SNSs enhances the effects of online public health interventions.MethodsAs a sample intervention, we developed Food Hero, an online platform for nutritional education in which players feed a virtual character according to their own nutritional needs and complete a set of virtual sport challenges. The platform was developed in 2 versions: a "private version" in which a user can see only his or her own score, and a "social version" in which a user can see other players' scores, including preexisting Facebook friends. We assessed changes in participants' nutritional knowledge using 4 quiz scores and 3 menu-assembly scores. Monitoring feeding and exercising attempts assessed engagement with the platform.ResultsThe 2 versions of the platform were randomly assigned between a study group (30 members receiving the social version) and a control group (33 members, private version). The study group's performance on the quizzes gradually increased over time, relative to that of the control group, becoming significantly higher by the fourth quiz (P=.02). Furthermore, the study group's menu-assembly scores improved over time compared to the first score, whereas the control group's performance deteriorated. Study group members spent an average of 3:40 minutes assembling each menu compared to 2:50 minutes in the control group, and performed an average of 1.58 daily sport challenges, compared to 1.21 in the control group (P=.03).ConclusionsThis work focused on isolating the SNSs' social effects in order to help guide future online interventions. Our results indicate that the social exposure provided by SNSs is associated with increased engagement and learning in an online nutritional educational platform.

Project description:BackgroundDiagnostic delay in rare disease (RD) is common, occasionally lasting up to more than 20 years. In attempting to reduce it, diagnostic support tools have been studied extensively. However, social platforms have not yet been used for systematic diagnostic support. This paper illustrates the development and prototypic application of a social network using scientifically developed questions to match individuals without a diagnosis.ObjectiveThe study aimed to outline, create, and evaluate a prototype tool (a social network platform named RarePairs), helping patients with undiagnosed RDs to find individuals with similar symptoms. The prototype includes a matching algorithm, bringing together individuals with similar disease burden in the lead-up to diagnosis.MethodsWe divided our project into 4 phases. In phase 1, we used known data and findings in the literature to understand and specify the context of use. In phase 2, we specified the user requirements. In phase 3, we designed a prototype based on the results of phases 1 and 2, as well as incorporating a state-of-the-art questionnaire with 53 items for recognizing an RD. Lastly, we evaluated this prototype with a data set of 973 questionnaires from individuals suffering from different RDs using 24 distance calculating methods.ResultsBased on a step-by-step construction process, the digital patient platform prototype, RarePairs, was developed. In order to match individuals with similar experiences, it uses answer patterns generated by a specifically designed questionnaire (Q53). A total of 973 questionnaires answered by patients with RDs were used to construct and test an artificial intelligence (AI) algorithm like the k-nearest neighbor search. With this, we found matches for every single one of the 973 records. The cross-validation of those matches showed that the algorithm outperforms random matching significantly. Statistically, for every data set the algorithm found at least one other record (match) with the same diagnosis.ConclusionsDiagnostic delay is torturous for patients without a diagnosis. Shortening the delay is important for both doctors and patients. Diagnostic support using AI can be promoted differently. The prototype of the social media platform RarePairs might be a low-threshold patient platform, and proved suitable to match and connect different individuals with comparable symptoms. This exchange promoted through RarePairs might be used to speed up the diagnostic process. Further studies include its evaluation in a prospective setting and implementation of RarePairs as a mobile phone app.

Dataset Information

Educational gradient in social network changes at disease diagnosis.

Publications

Educational gradient in social network changes at disease diagnosis.

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