Literature DB >> 28269969

Factors Contributing to Dropping-out in an Online Health Community: Static and Longitudinal Analyses.

Shaodian Zhang1, Noémie Elhadad1.   

Abstract

Dropping-out, which refers to when an individual abandons an intervention, is common in Internet-based studies as well as in online health communities. Community facilitators and health researchers are interested in this phenomenon because it usually indicates dissatisfaction towards the community and/or its failure to deliver expected benefits. In this study, we propose a method to identify dropout members from a large public online breast cancer community. We then study quantitatively what longitudinal factors of participation are correlated with dropping-out. Our experimental results suggest that dropout members discuss diagnosis- and treatment-related topics more than other topics. Furthermore, in the time before withdrawing from the community, dropout members tend to initiate more discussions but do not receive adequate response from the other members. We also discuss implications of our results and challenges in dropout-member identification. This study contributes to further understanding community participation and opens up a number of future research questions.

Entities:  

Mesh:

Year:  2017        PMID: 28269969      PMCID: PMC5333218     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  26 in total

Review 1.  Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions.

Authors:  Gunther Eysenbach; John Powell; Marina Englesakis; Carlos Rizo; Anita Stern
Journal:  BMJ       Date:  2004-05-15

2.  What is the role of online support from the perspective of facilitators of face-to-face support groups? A multi-method study of the use of breast cancer online communities.

Authors:  Jacqueline L Bender; Joel Katz; Lorraine E Ferris; Alejandro R Jadad
Journal:  Patient Educ Couns       Date:  2013-08-06

3.  Expression and reception of treatment information in breast cancer support groups: how health self-efficacy moderates effects on emotional well-being.

Authors:  Kang Namkoong; Dhavan V Shah; Jeong Yeob Han; Sojung Claire Kim; Woohyun Yoo; David Fan; Fiona M McTavish; David H Gustafson
Journal:  Patient Educ Couns       Date:  2010-11-01

4.  Does sustained participation in an online health community affect sentiment?

Authors:  Shaodian Zhang; Erin Bantum; Jason Owen; Noémie Elhadad
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

5.  Finding influential users of online health communities: a new metric based on sentiment influence.

Authors:  Kang Zhao; John Yen; Greta Greer; Baojun Qiu; Prasenjit Mitra; Kenneth Portier
Journal:  J Am Med Inform Assoc       Date:  2014-01-21       Impact factor: 4.497

6.  Who talks? The social psychology of illness support groups.

Authors:  K P Davison; J W Pennebaker; S S Dickerson
Journal:  Am Psychol       Date:  2000-02

7.  Online interaction. Effects of storytelling in an internet breast cancer support group.

Authors:  Mette Terp Høybye; Christoffer Johansen; Tine Tjørnhøj-Thomsen
Journal:  Psychooncology       Date:  2005-03       Impact factor: 3.894

8.  Social networking in online support groups for health: how online social networking benefits patients.

Authors:  Jae Eun Chung
Journal:  J Health Commun       Date:  2013-04-04

9.  Patients optimizing epilepsy management via an online community: the POEM Study.

Authors:  John D Hixson; Deborah Barnes; Karen Parko; Tracy Durgin; Stephanie Van Bebber; Arianne Graham; Paul Wicks
Journal:  Neurology       Date:  2015-06-17       Impact factor: 9.910

10.  Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

Authors:  Yi-Chia Wang; Robert E Kraut; John M Levine
Journal:  J Med Internet Res       Date:  2015-04-20       Impact factor: 5.428

View more
  2 in total

1.  To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App.

Authors:  Haitham Maaieh; Niranjan Bidargaddi; Daniel Almirall; Susan Murphy; Inbal Nahum-Shani; Michael Kovalcik; Timothy Pituch; Victor Strecher
Journal:  JMIR Mhealth Uhealth       Date:  2018-11-29       Impact factor: 4.773

2.  Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial.

Authors:  Timothy NeCamp; Srijan Sen; Elena Frank; Maureen A Walton; Edward L Ionides; Yu Fang; Ambuj Tewari; Zhenke Wu
Journal:  J Med Internet Res       Date:  2020-03-31       Impact factor: 5.428

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.