Evidence map›Paper›PMID 27327066›Full record

ArticleCyberpsychology, behavior and social networking2016

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

Jason E Owen, Michaela Curran, Erin O'Carroll Bantum, Robert Hanneman

Open access · greenAbstract read
In one paragraph

Article in Cyberpsychology, behavior and social networking, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Randomized Trial of a Social Networking Intervention for Cancer-Related Distress.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2017
    Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Social Media and Mobile Technology for Cancer Prevention and Treatment.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Jason E Owen1 Veterans Affairs Palo Alto Health Care System, Dissemination & Training Division, National Center for Posttraumatic Stress Disorder , Menlo Park, California.
Michaela Curran2 Department of Sociology, University of California , Riverside, California.
Erin O'Carroll Bantum3 Cancer Research Center, University of Hawai'i , Honolulu, Hawaii.
Robert Hanneman2 Department of Sociology, University of California , Riverside, California.
University of California, Riverside · US

Funding

Impact of Social Networking on Dose and Effects of Cancer Survivorship TrialsR21CA166029 · NCI · PALO ALTO VETERANS INSTIT FOR RESEARCH · PI BANTUM, ERIN O'CARROLL, OWEN, JASON E · 2013 to 2015
$351k
NCI NIH HHS R21 CA166029
6 · The paper itself

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.

Indexed as

Social BehaviorSocial NetworkingSocial SupportAdultBloggingCommunicationCounselingElectronic MailFemaleHumansInternetMaleMiddle AgedNeoplasmsResidence CharacteristicsSurvivors

Identifiers

PMID27327066
PMCPMC4931744
OpenAlexW2468164286

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.