Social Support in Online Health Communities

2018 ◽  
Author(s):  
Srikanth Parameswaran ◽  
Rajiv Kishore
Keyword(s):  
Social Support ◽  
Online Health Communities ◽  
Health Communities

scholarly journals Antecedents and consequences of the perceived usefulness of smoking cessation online health communities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chenglong Li ◽  
Hongxiu Li ◽  
Reima Suomi
Keyword(s):  
Social Support ◽  
Smoking Cessation ◽  
Knowledge Sharing ◽  
Emotional Support ◽  
Perceived Usefulness ◽  
Informational Support ◽  
Continuance Intention ◽  
Content Type ◽  
Online Health Communities ◽  
Health Communities

PurposeAn empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).Design/methodology/approachTo validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.FindingsThe empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.Originality/valueThe study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

Do online health communities enhance patient–physician relationship? An assessment of the impact of social support and patient empowerment

2017 ◽  
Vol 31 (3) ◽  
pp. 154-162 ◽  
Author(s):  
Anne-Francoise Audrain-Pontevia ◽  
Loick Menvielle
Keyword(s):  
Social Support ◽  
Structural Equation ◽  
Medical Information ◽  
Patient Empowerment ◽  
Online Health Communities ◽  
User Empowerment ◽  
Computer Mediated ◽  
Physician Relationship ◽  
Health Communities ◽  
The Impact

The diffusion of the Web 2.0 has made it possible for patients to exchange on online health communities, defined as computer-mediated communities dedicated to health topics, wherein members can build relationships with other members. It is now acknowledged that online health communities provide users not only with medical information but also with social support with no time or geographical boundaries. However, in spite of their considerable interest, there is still a paucity of research as to how online health communities alter the patient–physician relationship. This research aims at filling this gap and examines how online health communities, while providing users with computer-mediated social support and empowerment, impact the patient–physician relationship. Six hypotheses are proposed and tested. A survey was developed and 328 responses were collected from online patient groups in Canada in 2016. The data were analysed using structural equation modelling. All but one hypothesis are validated. The results show that user computer-mediated social support positively influences user empowerment and participation during the consultation, which in turn determines user commitment to the relationship with the physician. Importantly and contrary to our expectations, user empowerment is found to be significantly but negatively related to user commitment with the physician.

User recommendation in online health communities using adapted matrix factorization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hangzhou Yang ◽  
Huiying Gao
Keyword(s):  
Social Support ◽  
Matrix Factorization ◽  
User Behavior ◽  
Social Information ◽  
Healthcare Outcomes ◽  
Community Members ◽  
Content Type ◽  
Online Health Communities ◽  
Health Communities ◽  
User Recommendation

PurposeOnline health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.Design/methodology/approachThis study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.FindingsThe experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.Practical implicationsThis study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.Originality/valueThis study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.

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