Literature DB >> 27327066

Characterizing Social Networks and Communication Channels in a Web-Based Peer Support Intervention.

Jason E Owen1, Michaela Curran2, Erin O'Carroll Bantum3, Robert Hanneman2.   

Abstract

Web and mobile (mHealth) interventions have promise for improving health outcomes, but engagement and attrition may be reducing effect sizes. Because social networks can improve engagement, which is a key mechanism of action, understanding the structure and potential impact of social networks could be key to improving mHealth effects. This study (a) evaluates social network characteristics of four distinct communication channels (discussion board, chat, e-mail, and blog) in a large social networking intervention, (b) predicts membership in online communities, and (c) evaluates whether community membership impacts engagement. Participants were 299 cancer survivors with significant distress using the 12-week health-space.net intervention. Social networking attributes (e.g., density and clustering) were identified separately for each type of network communication (i.e., discussion board, blog, web mail, and chat). Each channel demonstrated high levels of clustering, and being a community member in one communication channel was associated with being in the same community in each of the other channels (φ = 0.56-0.89, ps < 0.05). Predictors of community membership differed across communication channels, suggesting that each channel reached distinct types of users. Finally, membership in a discussion board, chat, or blog community was strongly associated with time spent engaging with coping skills exercises (Ds = 1.08-1.84, ps < 0.001) and total time of intervention (Ds = 1.13-1.80, ps < 0.001). mHealth interventions that offer multiple channels for communication allow participants to expand the number of individuals with whom they are communicating, create opportunities for communicating with different individuals in distinct channels, and likely enhance overall engagement.

Entities:  

Mesh:

Year:  2016        PMID: 27327066      PMCID: PMC4931744          DOI: 10.1089/cyber.2015.0359

Source DB:  PubMed          Journal:  Cyberpsychol Behav Soc Netw        ISSN: 2152-2715


  32 in total

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2.  Randomized pilot of a self-guided internet coping group for women with early-stage breast cancer.

Authors:  Jason E Owen; Joshua C Klapow; David L Roth; John L Shuster; Jeff Bellis; Ron Meredith; Diane C Tucker
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3.  Social networks, social support mechanisms, and quality of life after breast cancer diagnosis.

Authors:  Candyce H Kroenke; Marilyn L Kwan; Alfred I Neugut; Isaac J Ergas; Jaime D Wright; Bette J Caan; Dawn Hershman; Lawrence H Kushi
Journal:  Breast Cancer Res Treat       Date:  2013-05-09       Impact factor: 4.872

4.  Impact of a patient-centered, computer-based health information/support system.

Authors:  D H Gustafson; R Hawkins; E Boberg; S Pingree; R E Serlin; F Graziano; C L Chan
Journal:  Am J Prev Med       Date:  1999-01       Impact factor: 5.043

5.  Chronic health conditions and internet behavioral interventions: a review of factors to enhance user engagement.

Authors:  Jane R Schubart; Heather L Stuckey; Ambika Ganeshamoorthy; Christopher N Sciamanna
Journal:  Comput Inform Nurs       Date:  2011-02       Impact factor: 1.985

6.  Measurement of depressive symptoms in cancer patients: evaluation of the Center for Epidemiological Studies Depression Scale (CES-D).

Authors:  D Hann; K Winter; P Jacobsen
Journal:  J Psychosom Res       Date:  1999-05       Impact factor: 3.006

7.  Longitudinal effects of social support and adaptive coping on the emotional well-being of survivors of localized prostate cancer.

Authors:  Eric S Zhou; Frank J Penedo; Natalie E Bustillo; Catherine Benedict; Mikal Rasheed; Suzanne Lechner; Mark Soloway; Bruce R Kava; Neil Schneiderman; Michael H Antoni
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Review 8.  Associations of social networks with cancer mortality: a meta-analysis.

Authors:  Martin Pinquart; Paul R Duberstein
Journal:  Crit Rev Oncol Hematol       Date:  2009-07-14       Impact factor: 6.312

9.  Effect of Internet peer-support groups on psychosocial adjustment to cancer: a randomised study.

Authors:  M T Høybye; S O Dalton; I Deltour; P E Bidstrup; K Frederiksen; C Johansen
Journal:  Br J Cancer       Date:  2010-04-27       Impact factor: 7.640

10.  The effect of psychosocial factors on breast cancer outcome: a systematic review.

Authors:  Matthew E Falagas; Effie A Zarkadoulia; Eleni N Ioannidou; George Peppas; Christos Christodoulou; Petros I Rafailidis
Journal:  Breast Cancer Res       Date:  2007       Impact factor: 6.466

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  6 in total

Review 1.  Social Media and Mobile Technology for Cancer Prevention and Treatment.

Authors:  Judith J Prochaska; Steven S Coughlin; Elizabeth J Lyons
Journal:  Am Soc Clin Oncol Educ Book       Date:  2017

2.  Development of the functional social network index for adolescent and young adult cancer survivors.

Authors:  I-Chan Huang; Conor M Jones; Tara M Brinkman; Melissa M Hudson; D Kumar Srivastava; Yuelin Li; Leslie L Robison; Kevin R Krull
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3.  Randomized Trial of a Social Networking Intervention for Cancer-Related Distress.

Authors:  Jason E Owen; Erin O'Carroll Bantum; Ian S Pagano; Annette Stanton
Journal:  Ann Behav Med       Date:  2017-10

4.  Online Platform to Assess Complex Social Relationships and Patient-Reported Outcomes Among Adolescent and Young Adult Cancer Survivors.

Authors:  Pragya G Poudel; Hailey E Bauer; D Kumar Srivastava; Kevin R Krull; Melissa M Hudson; Leslie L Robison; Zhaoming Wang; I-Chan Huang
Journal:  JCO Clin Cancer Inform       Date:  2021-08

Review 5.  The State of Digital Interventions for Demand Generation in Low- and Middle-Income Countries: Considerations, Emerging Approaches, and Research Gaps.

Authors:  Dustin G Gibson; Tigest Tamrat; Garrett Mehl
Journal:  Glob Health Sci Pract       Date:  2018-10-10

Review 6.  Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review.

Authors:  Linda C Smit; Jeroen Dikken; Marieke J Schuurmans; Niek J de Wit; Nienke Bleijenberg
Journal:  BMJ Open       Date:  2020-11-17       Impact factor: 2.692

  6 in total

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