Good Intentions, Bad Outcomes: The Effects of Mismatches between Social Support and Health Outcomes in an Online Weight Loss Community

Yan, Lu

doi:10.1111/poms.12793

Cited by 61 publications

(74 citation statements)

References 65 publications

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“…In obesity and weight‐loss studies, social support has been associated with adherence to diet and exercise and recognized as a critical factor in individuals’ health behaviors (Khaylis et al. , Yan ). Research on perceived support has found consistent evidence that it promotes health and buffers the impact of stress, thus improving health outcomes (Wills and Shinar ).…”

Section: Background and Related Theoriesmentioning

confidence: 99%

The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight‐Loss Outcomes

Yan

2020

Production and Operations Management

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Social media‐based online communities are becoming increasingly popular for various social interactions, including those for healthcare and health‐related activities. The benefits from these activities, however, are constrained by how a platform is designed, as a platform's design defines what activities can be done and how individuals can engage and interact on the platform. In this study, we focus on weight‐loss communities and social tools that facilitate social communication and establish a variety of relationships between users. In particular, we examine the effectiveness of one‐way and two‐way social relationships on individuals’ weight‐loss management. Drawing from theories of social support, social reciprocity, and social indebtedness, we use two‐way friendship relationships to proxy perceived support and one‐way commenting relationships to proxy received support and conjecture that they work through different pathways. We find, through empirical analyses, that both types of social relationships as well as self‐monitoring are effective in promoting weight loss, but perceived and received support have different impacts. Whereas both perceived and received support are positively related to weight‐loss outcomes, the effect of received support is found to be higher than that of perceived support and the difference is statistically significant. Moreover, we find that received support is positively associated with self‐monitoring behaviors, whereas perceived support is not. These findings provide insights for platform providers to improve the social design aspect of online services and for healthcare practitioners in their efforts to advise individuals on weight self‐management. Our results also can be used to design and implement more effective online interventions.

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Section: Background and Related Theoriesmentioning

confidence: 99%

The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight‐Loss Outcomes

Yan

2020

Production and Operations Management

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“…In the next step, we conducted sentiment analysis (Alias‐i ) to extract the opinion embedded in the text. Following approaches used in prior literature (Yan , Yan and Tan ), the sentiment value returned in this study is within [0, 1]. When the value for textual review is closer to 1, it implies that this comment is more positive, while closer to 0 has a more negative implication.…”

Section: Data and Variablesmentioning

confidence: 98%

Shared Minds: How Patients Use Collaborative Information Sharing via Social Media Platforms

Yan

Tan

et al. 2019

Production and Operations Management

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D espite our understanding that social media and online healthcare communities can help to eliminate health information asymmetry and improve patients' self-care engagement, we have yet to understand what happens when patients have access to others' health data and how patients' access to these shared experiences and opinions influence their health knowledge and perceived treatment outcome. In this study, we apply social information processing theory and incorporate (1) uncertainty of a treatment, (2) information exposure, and (3) credibility of the information source into patients' information evaluation function to assess how patients utilize shared health information and experiences. An empirical model, which combines various aspects of patients' firsthand experiences about treatments into a single construct, yields empirical evidence that patients' perceived treatment outcome is prone to social influence from other patients' shared experiences. By disaggregating the sources of social influence, we find that social influence created by generalized others in the community outweighs that by familiar others of one's intimate social group. In addition, we find that other factors, such as positive sentiment in comments and patients' prior experiences, also affect patients' perceived treatment outcome. Based on our findings, implications for health promotion and health behaviors are presented.

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“…Notably, different studies reported that online social support by non-professionals might not always result in beneficial self-management and health outcomes. For example, this occurs when participation in online communities leads to mismatches between needed and received support, or when support hinders health improvements for patients with high self-efficacy [47].…”

Section: Social Support and Technology-supported Diabetes Selfmanagementmentioning

confidence: 99%

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Brew‐Sam

Chib

Rossmann

2020

BMC Med Inform Decis Mak

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Background: Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus nonprofessional (family/friends) support on mobile app use for diabetes self-management. Methods: This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N = 21, Study 1) and an online survey (N = 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus nonprofessionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decisionmaking, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory. Results: The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physicianpatient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use. Conclusion: The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literature. In particular, the use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and may decrease when such support is high (e.g., no perceived need to use technology).

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Good Intentions, Bad Outcomes: The Effects of Mismatches between Social Support and Health Outcomes in an Online Weight Loss Community

Cited by 61 publications

References 65 publications

The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight‐Loss Outcomes

The Kindness of Commenters: An Empirical Study of the Effectiveness of Perceived and Received Support for Weight‐Loss Outcomes

Shared Minds: How Patients Use Collaborative Information Sharing via Social Media Platforms

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

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