Dataset Information

Development and validation of the online social support for smokers scale.

ABSTRACT: Social networks play an important role in smoking. Provision of social support during cessation is a cornerstone of treatment. Online social networks for cessation are ubiquitous and represent a promising modality for smokers to receive and provide the support necessary for cessation. There are no existing measures specific to online social support for smoking cessation.The objective was to develop a measure of social support to be used in online smoking cessation treatment research.Initial items for the Online Social Support for Smokers Scale (OS4) were based on existing theory and scales delineated in various taxonomies. Preliminary field analysis (N = 73) was conducted on 23 initial items to optimize the scale. Further development was conducted on a refined 15-item scale in the context of a large randomized trial of Internet and telephone cessation treatment with follow-ups at 3, 6, 12, and 18 months. In all, 1326 participants were randomized to an enhanced Internet arm that included a large online social network; psychometric analyses employed 3-month follow-up data from those reporting use of the enhanced Internet intervention at least once (n = 873). Items were subjected to a factor analysis, and the internal consistency reliability of the scale was examined along with construct and criterion validity. Other measures used in the study included demographics, nicotine dependence, partner support for cessation, general social support, social integration, stress, depression, health status, online community use, Internet use behaviors, intervention satisfaction, and 30-day point prevalence abstinence.The final 12-item OS4 scale demonstrated high internal consistency reliability (Cronbach alphas .86-.89) across demographic and smoking strata of interest. The OS4 also demonstrated good construct and criterion validity, with the directionality of the observed associations providing support for most a priori hypotheses. Significant Pearson correlations were observed between the OS4 and the Partner Interaction Questionnaire (PIQ) Positive subscale (? = .24, P < .001). As hypothesized, participants with the highest OS4 scores were more likely to have actively participated in the enhanced Internet community and to have high levels of satisfaction with the enhanced Internet intervention. In logistic regression analyses, the OS4 was highly predictive of 30-day point-prevalence abstinence at 6, 12, and 18 months (all P values <.001). The odds of abstinence at 6 months rose by 48% for each standard unit increase in online social support (95% confidence interval [CI] 1.17-1.71), dropping only slightly to 37% at 12 and 18 months (95% CI 1.17-1.59).The OS4 is a brief, reliable, and valid instrument for measuring online social support for smoking cessation. Results should be replicated and extended, but this study suggests the OS4 can be used to advance theory, understand mechanisms, and potentially help to improve the tailoring of Internet-based smoking cessation treatments. It can also inspire development of similar measures for other online health-related intervention research.Clinicaltrials.gov #NCT00282009; http://clinicaltrials.gov/ct2/show/NCT00282009 (Archived by WebCite at http://www.webcitation.org/60XNj3xM6).

SUBMITTER: Graham AL

PROVIDER: S-EPMC3222172 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Development and validation of the online social support for smokers scale.

Graham Amanda L AL Papandonatos George D GD Kang Hakmook H Moreno Jose L JL Abrams David B DB

Journal of medical Internet research 20110928 3

<h4>Background</h4>Social networks play an important role in smoking. Provision of social support during cessation is a cornerstone of treatment. Online social networks for cessation are ubiquitous and represent a promising modality for smokers to receive and provide the support necessary for cessation. There are no existing measures specific to online social support for smoking cessation.<h4>Objective</h4>The objective was to develop a measure of social support to be used in online smoking cess ...[more]

PMID: 21955465

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Project description:BackgroundThe role of online health communities (OHCs) in patient empowerment is growing and has been increasingly studied in recent years. Research has focused primarily on individualistic conception of patients' empowerment, with much less attention paid to the role of OHCs in the development of patients' collective empowerment. Although OHCs have immense potential for empowerment that goes beyond the individual, the concept and scale of collective empowerment in OHCs have not yet been developed or validated.ObjectiveThis study aimed to develop an instrument for measuring collective empowerment in online health communities (CE-OHC) and to test its quality by investigating its factorial structure, reliability, construct validity, and predictive validity.MethodsThe CE-OHC scale was developed according to a strict methodology for developing valid and reliable scales. An initial set of 20 items was first tested in the pilot study conducted in 2016 using a sample of 280 registered users of Slovenia's largest OHC. A refined version with 11 items was tested in the main study conducted in 2018 on a random sample of 30,000 registered users of the same OHC. The final sample comprised 784 users. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to investigate the factorial structure, discriminant validity, and convergent validity of the scale. Cronbach alpha coefficient was used to determine the CE-OHC scale's internal consistency. To establish the predictive validity, ordinary least squares regression was performed to test the role of CE-OHC in users' civic participation.ResultsThe EFA resulted in a two-factor solution, and the two factors-knowledge of resources and resource mobilization for collective action-together explain 63.8% of the variance. The second-order CFA demonstrated a good fit to the data (root mean square error of approximation=0.07) and the scale had a good internal consistency (alpha=.86). Although evidence of the scale's convergent validity was partially provided, discriminant validity of the scale remained unconfirmed. Overall, CE-OHC was confirmed to be a predictor of users' civic participation, but the influence was somewhat weak and inconsistent across two subscales.ConclusionsThe proposed CE-OHC scale is a reliable and relatively valid instrument and serves as a good baseline to advance the measurement of collective empowerment in OHC contexts. This is the first scale developed for this purpose, and future research should focus on the development of a clear nomological network of the collective empowerment construct in relation to the OHC settings.

Project description:BackgroundIn recent years, people with mental health problems are increasingly using online social networks to receive social support. For example, in online depression communities, patients can share their experiences, exchange valuable information, and receive emotional support to help them cope with their disease. Therefore, it is critical to understand how patients with depression develop online social support networks to exchange informational and emotional support.ObjectiveOur aim in this study was to investigate which user attributes have significant effects on the formation of informational and emotional support networks in online depression communities and to further examine whether there is an association between the two social networks.MethodsWe used social network theory and constructed exponential random graph models to help understand the informational and emotional support networks in online depression communities. A total of 74,986 original posts were retrieved from 1077 members in an online depression community in China from April 2003 to September 2017 and the available data were extracted. An informational support network of 1077 participant nodes and 6557 arcs and an emotional support network of 1077 participant nodes and 6430 arcs were constructed to examine the endogenous (purely structural) effects and exogenous (actor-relation) effects on each support network separately, as well as the cross-network effects between the two networks.ResultsWe found significant effects of two important structural features, reciprocity and transitivity, on the formation of both the informational support network (r=3.6247, P<.001, and r=1.6232, P<.001, respectively) and the emotional support network (r=4.4111, P<.001, and r=0.0177, P<.001, respectively). The results also showed significant effects of some individual factors on the formation of the two networks. No significant effects of homophily were found for gender (r=0.0783, P=.20, and r=0.1122, P=.25, respectively) in the informational or emotional support networks. There was no tendency for users who had great influence (r=0.3253, P=.05) or wrote more posts (r=0.3896, P=.07) or newcomers (r=-0.0452, P=.66) to form informational support ties more easily. However, users who spent more time online (r=0.6680, P<.001) or provided more replies to other posts (r=0.5026, P<.001) were more likely to form informational support ties. Users who had a big influence (r=0.8325, P<.001), spent more time online (r=0.5839, P<.001), wrote more posts (r=2.4025, P<.001), or provided more replies to other posts (r=0.2259, P<.001) were more likely to form emotional support ties, and newcomers (r=-0.4224, P<.001) were less likely than old-timers to receive emotional support. In addition, we found that there was a significant entrainment effect (r=0.7834, P<.001) and a nonsignificant exchange effect (r=-0.2757, P=.32) between the two networks.ConclusionsThis study makes several important theoretical contributions to the research on online depression communities and has important practical implications for the managers of online depression communities and the users involved in these communities.

Project description:BACKGROUND:While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. OBJECTIVE:The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. METHODS:We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. RESULTS:The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. CONCLUSIONS:We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Dataset Information

Development and validation of the online social support for smokers scale.

Publications

Development and validation of the online social support for smokers scale.

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