Past studies on the efficacy of participation in online cancer support groups have primarily focused on the role of expression in the accrual of health benefits for participants. Unfortunately, few steps have been taken to determine whether this observed effect arises solely from the internal mental processes underlying the act of expressing or, perhaps, owes something to a nuanced, multidimensional understanding of expression that includes reception of responses to what is expressed. To test for the multilayered effect, we attend to one of the key concepts in the online support community scholarship: empathy. Our findings suggest that it is a combination of empathy expression and reception that is crucial to attaining optimal benefits for cancer patients. Further, our finding supports the buffering hypothesis that empathic expression provides a salutary effect for patients who experienced a higher degree of concern associated with their cancer diagnosis and follow-up treatments.

Objective To better understand the process and effect of social support exchanges within computer-mediated social support (CMSS) groups for breast cancer patients, this study examines 1) the dynamic interplay between emotional support giving and receiving and 2) the relative effects of emotional support giving and receiving on patients’ psychosocial health outcomes. Methods Data was collected from 177 patients who participated in online cancer support groups within the Comprehensive Health Enhancement Support System (CHESS) during the 4-month intervention. Data included 1) pretest and/or posttest survey scores of demographic, disease-related, and psychosocial factors, 2) automatically collected CHESS usage data, and 3) computer-aided content analysis of social support messages posts. Results Hierarchical regression analyses revealed that those who receive higher levels of support from others have fewer breast cancer-related concerns (β= −.15, p<.05), while those who give higher levels of support to others reframe their own problems in a positive light and adopt more positive strategies for coping (β= .16, p<.05). In addition to these positive effects, partial correlation analysis indicated that these two supportive behaviors are reciprocal. Conclusions We concluded that supportive exchanges of receiving and giving play positive, but different, roles in predicting psychosocial health outcomes. Moreover, emotional support giving and receiving tend to reinforce each other. Our findings help practitioners, health care providers, and health system designers make sense of diverse social support processes among cancer patients participating within CMSS groups.

There is increasing importance for scholars to distinguish the effects of expression from reception to understand the processes involved in producing psychosocial benefits. This study shows that emotional support is more than something cancer patients receive; it is part of an active, complex process that can be facilitated by social media.

The U.S. criminal justice system refers more people to substance abuse treatment than any other system. Low treatment completion rates and high relapse rates among addicted offenders highlight the need for better substance use disorder treatment and recovery tools. Mobile health applications (apps) may fill that need by providing continuous support. In this pilot test, 30 participants in a Massachusetts drug court program used A-CHESS, a mobile app for recovery support and relapse prevention, over a four-month period. Over the course of the study period, participants opened A-CHESS on average of 62% of the days that they had the app. Social networking tools were the most utilized services. The study results suggest that drug court participants will make regular use of a recovery support app.This pilot study sought to find out if addicted offenders in a drug court program would use a mobile application to support and manage their recovery.

Background: Social support has been linked to many therapeutic benefits (e.g., treatment retention, reduced posttreatment relapse) for individuals with alcohol use disorder. However, the positive impacts of social support have not been well-understood in the context of alcohol-impaired driving. This article examines the role of social support in motivating those with histories of DWI arrest to reduce alcohol use by testing three major models of social support: the Main-Effects model, the Buffering model, and the Optimal Matching model. Method: One hundred and nineteen participants with histories of DWI arrest were recruited from a correctional treatment facility (n = 59) and the local community (n = 60). Participants completed interviews to assess alcohol consumption, psychiatric/physical conditions, and psychosocial factors associated with drinking behavior (e.g., social support, alcohol-related problems, motivation to change). Hierarchical regression analyses were conducted to test the three models. Additionally, the relative magnitude of the effects of general and recovery-specific social support was compared based on the Cumming’s (2004) approach of statistical inference of confidence intervals. Results: Overall social support was positively associated with some motivation to change (i.e., importance of change, confidence in change) among alcohol-impaired drivers, supporting the Main-Effects model. However, the impact of overall social support on motivation to change was not moderated by alcohol-related problems of individuals arrested for DWI, which did not confirm the Buffering model. Lastly, recovery-specific social support, rather than general social support, contributed to increasing motivation to reduce alcohol use, which supported the Optimal Matching model. Conclusion: These findings highlight the benefits of social support (i.e., increased motivation to change alcohol use) for alcohol-impaired drivers. Regardless of the severity of alcohol-related problems of alcohol-impaired drivers, social support had direct positive impacts on motivation to change. In particular, the results underscore that social support can be more effective when it is matched to the recovery effort of individuals, which is consistent with the Optimal Matching model.

This study investigated the role of breast cancer survivors in a computer-mediated social support (CMSS) group for women with breast cancer. Applying a computer-aided content analytic method, the present study examined the differences in support provision between survivors and newly diagnosed patients. This study further investigated the impacts of survivor-provided social support on psychosocial adjustment of newly diagnosed patients. The results revealed that, compared with newly diagnosed patients, breast cancer survivors provided more emotional and informational support. Receiving emotional support from survivors contributed to an improvement in the quality of life and the depression of patients. The effects of survivor-provided informational support were not significant.

Increasingly, individuals with alcohol use disorder (AUD) seek and provide support for relapse prevention in text-based online environments such as discussion forums. This paper investigates whether language use within a peer-to-peer discussion forum can predict future relapse among individuals treated for AUD. A total of 104 AUD sufferers who had completed residential treatment participated in a mobile phone-based relapse-prevention program, where they communicated via an online forum over the course of a year. We extracted patterns of language use on the forum within the first four months on study using Linguistic Inquiry and Word Count (LIWC), a dictionary-based text analysis program. Participants reported their incidence of risky drinking via a survey at 4, 8, and 12 months. A logistic regression model was built to predict the likelihood that individuals would engage in risky drinking within a year based on their language use, while controlling for baseline characteristics and rates of utilizing the mobile system. Results show that all baseline characteristics and system use factors explained just 13% of the variance in relapse, whereas a small number of linguistic cues, including swearing and cognitive mechanism words, accounted for an additional 32% of the total 45% of variance in relapse explained by the model. Effective models for predicting relapse are needed. Messages exchanged on AUD forums could provide an unobtrusive and cost-effective window into the future health outcomes of AUD sufferers, and their psychological underpinnings. As online communication expands, models that leverage user-submitted text toward predicting relapse will be increasingly scalable and actionable.

This article presents an innovative methodology to study computer-mediated communication (CMC), which allows analysis of the multi-layered effects of online expression and reception. The methodology is demonstrated by combining the following three data sets collected from a widely tested eHealth system, the Comprehensive Health Enhancement Support System (CHESS): 1) a flexible and precise computer-aided content analysis; 2) a record of individual message posting and reading; and 3) longitudinal survey data. Further, this article discusses how the resulting data can be applied to online social network analysis and demonstrates how to construct two distinct types of online social networks – open and targeted communication networks – for different types of content embedded in social networks.