Evidence map›Paper›PMID 26839162›Full record

ArticleJournal of medical Internet research2016

In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

Sahiti Myneni, Nathan Cobb, Trevor Cohen

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
13.9field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.

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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

3 authors at 3 institutions in 1 country.

Sahiti MyneniSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States. sahiti.myneni@uth.tmc.edu.ORCID http://orcid.org/0000-0002-9211-1626
Georgetown University · USThe University of Texas Health Science Center · USThe University of Texas Health Science Center at Houston · US

Funding

Content-based social network analysis methods for data-driven health promotionR21LM012271 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MYNENI, SAHITI · 2015 to 2016
$379k
NLM NIH HHS 1R21LM012271-01NLM NIH HHS R21 LM012271
6 · The paper itself

Abstract

backgroundResearch studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual's efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era.

objectiveThe objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms.

methodsIn this paper, we describe grounded theory-based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual's efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy.

resultsWe identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include "sleepiness," "pledge," "patch," "spouse," and "slip." Examples of themes include "traditions," "social support," "obstacles," "relapse," and "cravings." Results indicate that themes consisting of member-generated strategies such as "virtual bonfires" and "pledges" were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member-generated communication content supports sociocognitive constructs from more than one behavior change model, unlike the majority of the existing theory-driven interventions.

conclusionsWith the onset of mobile phones and ubiquitous Internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by health consumers in real time. This study offers methodological insights for qualitative investigations that examine the various kinds of behavioral constructs prevalent in the messages exchanged among users of online communities. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like online health communities. Pragmatically, it sets the stage for real-time, data-driven sociobehavioral interventions promoting healthy lifestyle modifications by allowing us to understand the emergent user needs to sustain a desired behavior change.

Indexed as

Health BehaviorCommunicationHumansInternetPeer GroupSmoking CessationSocial MediaSocial SupportTelemedicinebehavior changeonline social mediasmoking cessationweb interventions

Identifiers

PMID26839162
PMCPMC4756252
OpenAlexW2287695441

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.