Come for Information, Stay for Support: Harnessing the Power of Online Health Communities for Social Connectedness during the COVID-19 Pandemic.
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ABSTRACT:
SUBMITTER: Green BM
PROVIDER: S-EPMC8656945 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Social determinants of public health have gained increasing attention. This paper studied whether social support from online communities related to maternal mental health. We focused on online maternity communities that group users with a similar prenatal status to facilitate their exchange of personal experiences and knowledge about maternal caring during pregnancy. Such online maternity communities are getting increasingly popular and can be found across countries and societies. We invited users-currently pregnant and gave birth within 1 year at the time of the study-from one such community in China to participate in a survey. The survey measured their perceived social support (PSS) exclusively from the peer group in the online community, their mental health and newborns' birth outcomes (N = 500). Users reported high score in PSS from the online peer group which was comparable to the ones from family, significant other and friends in other studies. We used linear regression models to examine the effects of PSS on mental health and birth outcomes. We found that a one-point increase in the PSS score was associated with a 0.19-point (p < 0.1) decrease in the prenatal depression and a 0.26-point (p < 0.01) decrease in the postnatal depression, which was equivalent to 3% and 4.5% of the average respectively. Moreover, a one-point increase in the PSS score was associated with a 14.49-gram increase in a newborn's weight (p < 0.01).
| S-EPMC10092248 | biostudies-literature
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.
| S-EPMC7819780 | biostudies-literature
Project description:BackgroundOnline health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts.ObjectiveThis study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC.MethodsWe collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn.ResultsUsers' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC.ConclusionsDetecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.
| S-EPMC5422656 | biostudies-literature
Project description:ObjectiveDizziness is a common reason patients present to doctors, but effective diagnostic tests and treatments for dizziness are underused. The internet is a way to disseminate medical information and is emerging as an intervention platform. The objective of this study was to describe internet searches for dizziness terms to assess the possible consumer demand for internet-based dizziness diagnostic and treatment tools.Study design/methodsGoogle AdWords and Google Insights for Search were used for keyword search data on the following generic terms: vertigo, dizzy, dizziness, lightheaded, and lightheadedness. Data collected included keyword ideas (i.e., additional keywords identified by Google as being related search terms), global and US only monthly search frequencies, as well as trends in top searches related to dizziness terms from 2004 to 2012. Keywords suggestive of benign paroxysmal positional vertigo (BPPV) or BPPV processes were identified.ResultsOf the five generic dizziness terms, vertigo had the most global searches per month (1.83 million) and lightheadedness had the least (90,500). Four BPPV-specific terms had more than 100,000 global searches per month. Three BPPV terms ("positional vertigo," "benign vertigo," and "benign positional vertigo") have been in the list of top searches related to vertigo every quarter since 2004.ConclusionSubstantial demand exists for dizziness information via the internet. Future studies should seek to better characterize the population seeking this information. The magnitude of this potential demand suggests that validated and tested diagnostic and treatment tools could contribute to healthcare efficiencies and patient outcomes.
| S-EPMC3997034 | biostudies-literature
Project description:BackgroundFood insecurity is a major global public health issue. Social capital has been identified as central to maintaining food security across a wide range of low- and middle-income country contexts, but few studies have examined this relationship through sociocentric network analysis.ObjectiveWe investigated relationships between household- and community-level social connectedness, household food security, and household income; and tested the hypothesis that social connectedness modified the relationship between income and food security.MethodsA cross-sectional census with an embedded questionnaire to capture social relationships was conducted among eleven peri-urban communities. Community connectedness was related to study outcomes of food security and per-capita income through regression models.ResultsOf 1520 households identified, 1383 were interviewed (91.0%) and 1272 (83.9%) provided complete data. Households in the youngest communities had the most total contacts, and the highest proportion of contacts outside of the community. Household income was also associated with more outside-community contacts (0.05 more contacts per standard deviation increase in income, p<0.001).Less food secure households reported more contacts nearby (0.24 increase in household food insecurity access scale (HFIAS) for each additional contact, p<0.001). After adjusting for household-level socioeconomic status, membership in an older, larger, and better-connected community, with a greater proportion of residents engaged in rural livelihood strategies, was associated with greater food security (-0.92 decrease in HFIAS for each one-unit increase in community mean degree, p=0.008). There was no evidence that social connectedness modified the relationship between income and food security such that lower-income households benefited more from community membership than higher-income households.ConclusionsAlthough households reported networks that spanned rural villages and urban centers, contacts within the community, with whom food was regularly shared, were most important to maintaining food security. Interventions that build within-community connectedness in peri-urban settings may increase food security.
| S-EPMC5976826 | biostudies-literature
Project description:Social isolation has been linked to numerous health risks, including depression and mortality. Parents raising children in low-income and under-resourced communities are at an increased risk for experiencing social isolation and its negative effects. Social connectedness (SC), one's sense of belongingness and connection to other people, or a community, has been linked to reduced social isolation and improved health outcomes in the general population, yet little is known about the impact SC has on parents with low incomes. This integrative review aims to describe the current state of the science surrounding SC in parents with low incomes, summarize how SC is being defined and measured, evaluate the quality of the science, and identify gaps in the literature to guide future research. Five electronic databases were searched, yielding 15 articles for inclusion. Empirical studies meeting the following criteria were included: population focused on parents who have low incomes or live in low-income communities and have dependent children, outcomes were parent-centered, SC was a study variable or a qualitative finding, and publication date was before March 2021. Findings emphasize SC as a promising construct that may be protective in the health and well-being of parents and children living in low-income communities. However, a lack of consensus on definitions and measures of SC makes it difficult to build a strong science base for understanding these potential benefits. Future research should focus on understanding the mechanisms by which SC works to benefit parents and their children.
| S-EPMC9292156 | biostudies-literature
Project description:ObjectiveThis pilot study evaluated the feasibility and efficacy of two methods of delivering a cognitive behaviorally informed Internet intervention for depression for adults 65 years and older.MethodsForty-seven participants were enrolled and assigned to receive one of two versions of the Internet intervention, either delivered individually (III) or with peer support (II+PS), or to a wait list control group (WLC). Primary outcomes included change in depressive symptoms from baseline to post-intervention (week 8), site use, self-reported usability, and coach time. Secondary outcomes included measures of social support and isolation and anxiety.ResultsFollow-up data were provided by 85.1% (40 of 47) of enrolled participants. There were significant differences in depression change across groups (F(2,37) = 3.81, p = 0.03). Greater reductions in depressive symptoms were found for the III (p = 0.02) and II+PS (p = 0.03) compared with WLC, and significantly less coach time was required in the II+PS (p = 0.003).ConclusionsThese results highlight the potential of cognitive-behaviorally informed Internet interventions for older adults with depression, and indicate that peer-supported programs are both acceptable and equivalent to individually delivered Internet interventions. Including peer support may be a viable and potentially more cost-effective option for disseminating online treatments for depression for older adults.
| S-EPMC5600661 | biostudies-literature
Project description:Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
| S-EPMC4015991 | biostudies-literature
Project description:ObjectiveInformational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.MethodsA multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.ResultsThe results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.ConclusionsThis study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.
| S-EPMC9941603 | biostudies-literature
Project description:BackgroundWhile 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.ObjectiveThe 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.
| S-EPMC5559652 | biostudies-literature