Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum.

BACKGROUND: Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain. OBJECTIVE: This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions. METHODS: We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis. RESULTS: In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (ß=-.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (ß=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users. CONCLUSIONS: Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission.

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here, we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-importantly-the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Mixbiotic society measures: Assessment of community well-going as living system.

Social isolation and fragmentation represent global challenges, with the former stemming from a lack of interaction and the latter from exclusive mobs-both rooted in communication issues. Addressing these challenges, the philosophical realm introduces the concept of the "mixbiotic society." In this framework, individuals with diverse freedoms and values mix together in physical proximity with diverse mingling, recognizing their respective "fundamental incapacities" and uniting in solidarity. This study aims to provide novel measures to balance freedom and solidarity, specifically the intermediate between isolation and mobbing, within a mixbiotic society. To achieve this, we introduce simplified measures to evaluate dynamic communication patterns. These measures complement traditional social network analysis of static structures and conventional entropy-based assessments of dynamic patterns. Our specific hypothesis posits that the measures corresponding to four distinct phases are established by representing communication patterns as multidimensional vectors. These measures include the mean of Euclidean distance to quantify "mobism" for fragmentation, the relative distance change for "atomism" indicating isolation, and a composite measure derived from multiplying the mean and variance of cosine similarity for "mixism," reflecting the well-going state of a mixbiotic society. Additionally, nearly negligible measures correspond to "nihilism." Through the evaluation of seven real-society datasets (high school, primary school, workplace, village, conference, online community, and email), we demonstrate the utility of the "mixism" measure in assessing freedom and solidarity in society. These measures can be employed to typify communities on a radar chart and a communication trajectory graph. The superiority of the measures lies in their ability to evaluate dynamic patterns, ease of calculation, and easily interpretable meanings compared to conventional analyses. As a future development, alongside additional validation using diverse datasets, the mixbiotic society measures will be employed to analyze social issues and applied in the fields of digital democracy and platform cooperativism.

The impact of social network embeddedness, TPB, and danwei system on residents' participation intention in old neighborhood regeneration.

Residents' participation is crucial for sustainable old neighborhood regeneration. Residents' intentions to participate in the regeneration are low in China, resulting in unsustainable neighborhood development problems. Therefore, it is essential to investigate the drivers of residents' participation. Residents' behavioral mechanisms are deeply embedded in their social networks, especially in acquaintance social networks typical of old neighborhoods in China. By constructing a mediated moderation model based on the Theory of Planned Behavior (TPB) and danwei system and taking the old neighborhood regeneration in Xi'an as a case study, this paper investigates the paths and mechanisms of the social network embeddedness, TPB, and danwei system on residents' participation intention. The results show that social network embeddedness can improve residents' intention to participate in regeneration through both direct and indirect pathways, and its indirect effect is much larger than the direct effect. Attitude, subjective norm, and perceived behavioral control significantly and positively mediated the contribution of social network embeddedness on participation intention. However, the specific mediating effect of attitude is significantly smaller than the other two. The danwei system has a significant moderating effect in the first stage of the relationship between social network embeddedness and participation intention. These imply that the extrinsic social factor of social network embeddedness is internalized as a driving force for convergence and motivation of residents' participation intentions mainly through the normative pressure and exchange of experiences between acquaintances and that the danwei system reinforces this extrinsic-intrinsic facilitation process through the danwei's "physical presence." These findings provide revelatory countermeasures for exploring the factors that motivate residents to participate actively in neighborhood regeneration and encourage their involvement at the intervention level.

[Teenage pregnancy: when digital social networks support becoming a mother]. / Grossesse à l'adolescence : quand les réseaux sociaux numériques viennent supporter le devenir mère.

The onset of pregnancy during adolescence brings with it upheavals for the body, identity, family and society. This particular context can make some teenagers psychopathologically fragile, while others adapt perfectly. When these mothers-to-be receive support, their adjustment to pregnancy seems to be facilitated. At present, it seems easy for teenagers to maintain contacts and thus benefit from a form of support on digital social networks. Recently discussed in the literature, the latter could represent a source of social support in this type of pregnancy, but also of support for the identity process of becoming a mother.

Cross-lagged panel analysis of the relationship between social networking sites use (SNSU) and sleep problems among university students.

BACKGROUND: Sleep remains a cornerstone for sociopsychological well-being, but it is in decline, especially among today's youth. Simultaneously, engagement with social media is escalating. Research has identified a link between social networking sites use and sleep problems; however, the nature and direction of the relations remain obscure. Therefore, it is imperative to pursue longitudinal research to elucidate this correlation and guide suitable intervention practices. The present study aimed to examine the reciprocal relationship between social networking sites use and sleep problems. METHODS: By adopting a three-stage cross-lagged design across four time points, assessment results from 194 university students were gathered at four-week intervals. RESULTS: The findings indicate that (1) Social networking sites use was significantly greater in females than in males at all four time points, while sleep problems were significantly greater in females than in males at Time 3 and Time 4. (2) Sleep problems at the second time point serve as a positive predictor of subsequent social networking sites use at the third time point. (3) Social networking sites use at the initial time point could marginally significantly predict sleep problems at the fourth time point. CONCLUSIONS: This study elucidates the dynamic relationship between social networking sites use and sleep problems across an academic term, suggesting the need for temporally tailored interventions.

Peer Influence on Motivation to Use Pre-Exposure Prophylaxis Among Latino Sexual Minority Men in Miami, Florida: A Network Autocorrelation Model.

Despite the availability of pre-exposure prophylaxis (PrEP), Latino sexual minority men (LSMM) continue to experience disparities in PrEP uptake and subsequently, HIV vulnerability. Social network norms are an underutilized solution to increase PrEP uptake. We used a peer influence model (network autocorrelation model) to examine the role of social network descriptive norms (i.e., actual behaviors) surrounding PrEP use. A total of 11 sociocentric networks of 13 friends (n = 143 LSMM) were recruited into our study from 2018 to 2019 in South Florida. Most participants were in PrEP pre-contemplation (n = 44), and almost one-third of our sample were using PrEP (n = 38). Three network autocorrelation models were estimated using an empirically informed Bayesian analysis. We found a positive association between participants' Motivational PrEP Cascade (MPC) position and their network members' (friends') cascade position based on three different measures of connection even when accounting for PrEP knowledge: friendship (ρ = 0.22; 95% CIa = 0.01-0.42), emotional closeness (ρ = 0.24; 95% CI = 0.03-0.44), and frequency of interaction (ρ = 0.22; CI = 0.03-0.42). Our findings highlight that an individual's progress in the MPC may be influenced by their network members' progress in the MPC, suggesting that LSMM using PrEP may serve as role models to their peers for PrEP use due to descriptive norms. Our findings further suggest that PrEP interventions for LSMM along the MPC can be implemented at the social network level.

Analysis of user trends in digital health communities using big data mining.

Camoni, the largest digital health community in Israel, involves thousands of patients in the decision-making process concerning their illness and treatment. This approach reflects the recent global shift towards digital tools that combine professional information with social networking capabilities to enable problem-solving, emotional support, and knowledge sharing. Digital health communities serve as an invaluable resource for individuals seeking to learn more about their health, connect with others with shared experiences, and receive encouragement. Our research investigates user trends in digital health communities using the Camoni platform as a case study. To this end, we compile a comprehensive database of 12 years of site activity and conduct a large-scale analysis to identify and assess significant trends in user behavior. We observe several significant trends concerning different genders engagement and note a narrowing of gaps between men and women users' participation and publication volume. Furthermore, we find that younger users have become increasingly active on the platform over time. We also uncover unique gender-specific behavior patterns that we attempt to characterize and explain. Our findings suggest that the rise of digital health communities has accelerated in recent years, reflecting the public's growing preference to take a more active role in their medical care.

Antecedents of entrepreneurial networking behavior and its impact on business performance - a systematic literature review.

Purpose: The purpose of this paper is to review the existing research on the antecedents of entrepreneurial networking behavior namely motivational factors and personality factors and its impact on business performance. Design/methodology/approach: This study employs a systematic review methodology, adhering to PRISMA guidelines.. Using the SCOPUS database, the search involved Boolean operators to narrow down relevant articles published in English between 2000 and 2024. Following a meticulous screening process, 32 articles were selected for review after removing duplicates and excluding non-English articles. Methodological quality assessment ensuring focused research questions, precise subject selection methods, representative samples, and reliable measurement instruments was carried out. Findings: The results demonstrate that entrepreneurs who are self-reliant and extroverted have fewer partners, however, these traits positively impact networking activities. The Big Five personality traits predict business creation and success of ventures. Desire for financial gain, risk-taking propensity, self-confidence, and the need for achievement influences networking behavior. Implications: The insights given in this paper can be used for establishing valuable connections, investing in resources, and preparing effective strategies for businesses. Policymakers who aim to promote entrepreneurial behavior among experienced individuals must emphasize the motivations for starting a business and the role of personalities in harnessing and leveraging individual entrepreneurial expertise. Originality: The existing literature on antecedents of entrepreneurial networking Behavior and the relationship of these antecedents together with business performance is limited. Further, this review article also offers avenues for future researchers.

The temporal dynamics of group interactions in higher-order social networks.

Representing social systems as networks, starting from the interactions between individuals, sheds light on the mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most social interactions occur among groups of individuals, requiring higher-order network representations. Despite the recent interest in higher-order networks, little is known about the mechanisms that govern the formation and evolution of groups, and how people move between groups. Here, we leverage empirical data on social interactions among children and university students to study their temporal dynamics at both individual and group levels, characterising how individuals navigate groups and how groups form and disaggregate. We find robust patterns across contexts and propose a dynamical model that closely reproduces empirical observations. These results represent a further step in understanding social systems, and open up research directions to study the impact of group dynamics on dynamical processes that evolve on top of them.

Social networks and posttraumatic stress symptoms five to ten years after the Fukushima nuclear power plant disaster.

Since the 2011 Fukushima nuclear power plant accident, multiple social network disruptions have been reported among the community in Fukushima, while posttraumatic stress symptoms among the residents have persisted. In this study, we aimed to explore the influence of time and social networks on the recovery of posttraumatic stress symptoms based on longitudinal data from community residents in Fukushima, following up five to ten years after the nuclear power plant accident. We conducted five questionnaire surveys quasi-annually, the targets of which were randomly sampled 4900 non-evacuee community residents. In this study, the data of 1809 respondents who participated in at least one survey were used (36.9% of the initial target). Setting posttraumatic stress symptoms as the outcome, we examined the interaction between time and social network size using a mixed model, adjusting for sociodemographic characteristics and disaster-related events. Their interaction was statistically significant, and the posttraumatic stress symptoms of those with small social networks persisted, while those with larger social networks recovered. Maintaining and promoting social networks may contribute to mental health recovery after a nuclear disaster.

Epidemic modeling for misinformation spread in digital networks through a social intelligence approach.

Online digital networks, including social networks, have significantly impacted individuals' personal and professional lives. Aside from exchanging news and topics of interest, digital networks play an essential role in the diffusion of information, which frequently significantly impacts worldwide societies. In this paper, we present a new mathematical epidemic model for digital networks that considers the sentiment of solitary misinformation in the networks and characteristics of human intelligence that play an important role in judging and spreading misinformation inside the networks. Our mathematical analysis has proved the existence and validity of the system in a real-time environment. Considering the real-world data, our simulation predicts how the misinformation could spread among different global communities and when an intervention mechanism should have to be carried out by the policyholders. Our simulation using the model proves that effective intervention mechanisms by isolating the fake news can effectively control the spread of misinformation among larger populations. The model can analyze the emotional and social intelligence of groups frequently subjected to disinformation and disseminating fake news.

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

Structure of higher-order interactions in social-ecological networks through Q-analysis of their neighbourhood and clique complex.

This paper studies higher-order interactions in social-ecological networks, which formally represent interactions within the social and ecological units of an ecosystem. Many real-world social ecosystems exhibit not only pairwise interactions but also higher-order interactions among their units. Therefore, the conventional graph-theoretic description of networks falls short of capturing these higher-order interactions due to the inherent limitations of the graph definition. In this work, a mathematical framework for capturing the higher-order interactions of a social-ecological system has been given by incorporating notions from combinatorial algebraic topology. In order to achieve this, two different simplicial complexes, the clique and the neighbourhood complex, have been constructed from a pairwise social-ecological network. As a case study, the Q-analysis and a structural study of the interactions in the rural agricultural system of southern Madagascar have been done at various structural levels denoted by q. The results obtained by calculating all the structural vectors for both simplicial complexes, along with exciting results about the participation of facets of the clique complex at different q-levels, have been discussed. This work also establishes significant theorems concerning the dimension of the neighbourhood complex and clique complex obtained from the parent pairwise network.

TikTok as a teaching and learning method for nursing students: A quasi-experimental study.

BACKGROUND: Social networks have emerged as powerful tools in education, offering new opportunities for interactive learning. TikTok has gained popularity among young people and has potential for use as a teaching tool in health disciplines. OBJECTIVE: To evaluate the effectiveness of an educational program based on a multimodal intervention using the TikTok platform to improve theoretical-practical knowledge about diabetic debut and vaccination schedules and satisfaction in nursing students. DESIGN: A quasi-experimental study design. SETTINGS: Faculty of Nursing, University of Cadiz, Spain. PARTICIPANTS: 74 third-year undergraduate university nursing students. METHODS: According to Kolb's experiential learning model, a multimodal educational intervention was carried out during the academic year 2022/2023, with 12 h of training. In these sessions, theoretical content on the vaccination calendar and diabetic debut was developed with written case studies. In the last session, 19 working groups were formed and each group was given a practical case study to solve through role-playing and creating a video in TikTok. A questionnaire to measure individual knowledge and a rubric questionnaire were used to evaluate the simulations in TikTok platform. RESULTS: A significant improvement (z = 6.843, p < 0.001) was observed in the mean scores (9.93 ± 1.65) of the posttest of the knowledge questionnaire, used to evaluate abstract conceptualisation abilities. Additionally, a mean score of 10.42 (SD = 1.42) of the items on the rubric questionnaire, designed to assess active experimentation and reflective observation abilities, were correctly performed. It stands out that 97.1 % of the students positively valued the elements of teaching innovation implemented, while 82.9 % showed a high level of satisfaction with their activity. CONCLUSION: A multimodal educational intervention using TikTok platform proved to be effective in improving the acquisition of theoretical-practical knowledge in the university students included in the study. Therefore, this type of experiential training is recommended for the training of future health professionals.

Social capital mediates knowledge gaps in informing sexual and reproductive health behaviours across Africa.

Advancing sexual and reproductive health is essential for promoting human rights and women's empowerment, and combating the HIV/AIDS epidemic. A large body of literature across the social sciences emphasizes the importance of social capital, generated through the strength of social networks, for shaping health behaviours. However, large-scale measurement of social capital and social networks remains elusive, especially in the context of low-income countries. Here we delve into the role of social capital dynamics, and in particular social connectedness across communities as measured through Facebook friendship links, in shaping knowledge diffusion and behaviour related to sexual and reproductive health in 495 regions across 33 countries in Africa. Our findings demonstrate that regions with higher levels of social connectedness are more similar in their knowledge about contraception and HIV testing, as well as their adoption of these behaviours. We further observe that the influence of social connectedness becomes stronger when the knowledge gaps between regions are larger. In other words, regions are more similar in behaviours, despite knowledge gaps, when they are socially connected. These insights carry significant policy implications, especially for the design and targeting of public health campaigns. We highlight that social connectedness can serve both as a driver and an obstacle in behaviour formation, underscoring the importance of understanding its influence on health-related outcomes.

Social network of families involved in child neglect: building a multidimensional perspective. / Rede social de famílias envolvidas na negligência contra crianças e adolescentes: construindo um olhar multidimensional.

Neglect is one of the most frequently reported forms of violence against children and adolescents, although it has rarely been explored in national studies. In this light, the present study aimed to analyze the personal social network of families involved in negligence against children and adolescents. This work takes a qualitative approach, anchored in the Paradigm of Complexity, conducted with twenty families involved in negligence against children and adolescents in a municipality in the countryside of the state of São Paulo, Brazil. Data collection took place through minimal maps of the personal social network and semi-structured interviews in January 2021. The networks were limited, with little to no interaction among the different services and sectors, and were predominantly homogeneous. Because they have many weakened ties, they are relatively unsupportive, pointing out difficulties in access to work, education, and health. Due to the characteristics of the network, the complexity of the phenomenon of neglect was identified, in which elements condition and perpetuate experiences of absence and fragility. Interprofessional and intersectoral views and actions are requested and recommended.

A negligência constitui uma das violências contra crianças e adolescentes mais notificadas, entretanto ainda pouco explorada em estudos nacionais. O objetivo deste estudo foi analisar a rede pessoal social de famílias envolvidas na negligência contra crianças e adolescentes. Estudo qualitativo, ancorado no paradigma da complexidade, realizado junto a 20 familiares envolvidos na negligência contra crianças e adolescentes em município do interior de São Paulo, Brasil. A coleta de dados se deu por mapas mínimos da rede pessoal social e entrevistas semiestruturadas em janeiro de 2021. As redes se apresentaram reduzidas, com pouca ou nenhuma articulação entre os diferentes serviços e setores e predominantemente homogêneas. Por terem muitos vínculos fragilizados, mostrou-se pouco sustentadora, apontando dificuldades no acesso a trabalho, educação e saúde. Pelas características da rede, identificou-se a complexidade do fenômeno da negligência, em que elementos condicionam e perpetuam vivências de ausências e fragilidades. Olhares e ações interprofissionais e intersetoriais são pleiteadas e recomendadas.

Impact of social network composition on cognitive decline: Digital Dementia Registry Bavaria (digiDEM Bayern).

BACKGROUND: Currently, there is no curative treatment for dementia. The implementation of preventive measures is of great importance. Therefore, it is necessary to identify and address individual and modifiable risk factors. Social isolation, defined through social networks, is a factor that may influence the onset and progression of the disease. The networks of older people are mostly composed of either family or friends. The aim of this study is to examine the influence of social isolation and network composition on cognition over the course of 12 months in people with cognitive impairment. METHODS: Data basis is the multicentre, prospective, longitudinal register study 'Digital Dementia Registery Bavaria-digiDEM Bayern'. The degree of social isolation was assessed using the Lubben Social Network Scale- Revised (LSNS-R) and the degree of cognitive impairment using the Mini Mental State Examination (MMSE), conducted at baseline and after 12 months. Data were analysed using pre-post ANCOVA, adjusted for baseline MMSE, age, gender, education, living situation and Barthel-Index. RESULTS: 106 subjects (78.9 ± 8.2 years; 66% female) were included in the analysis. The mean MMSE score at baseline was 24.3 (SD = 3.6). Within the friendship subscore, risk for social isolation was highly prevalent (42.5%). Though, there was no difference between individuals with higher/ lower risk of social isolation within the friendship-network after adjusting for common risk factors in cognitive decline over time, F (1,98) = .046, p = .831, partial η2 = .000. CONCLUSION: The results of this study showed that the risk of social isolation from friends is very high among people with cognitive impairment. However, social isolation does not appear to have a bearing influence on the course of cognition. Nevertheless, it is important for people with cognitive impairment to promote and maintain close social contacts with friends.

Impact of community structure on the spread of epidemics on time-varying multiplex networks.

Community structure plays a crucial role in realistic networks and different communities can be created by groups of interest and activity events, and exploring the impact of community properties on collective dynamics is an active topic in the field of network science. Here, we propose a new coupled model with different time scales for online social networks and offline epidemic spreading networks, in which community structure is added into online social networks to investigate its role in the interacting dynamics between information diffusion and epidemic spreading. We obtain the analytical equations of epidemic threshold by MMC (Microscopic Markov Chain) method and conduct a large quantities of numerical simulations using Monte Carlo simulations in order to verify the accuracy of the MMC method, and more valuable insights are also obtained. The results indicate that an increase in the probability of the mobility of an individual can delay the spread of epidemic-related information in the network, as well as delaying the time of the peak of the infection density in the network. However, an increase in the contact ability of mobile individuals produces a facilitating effect on the spread of epidemics. Finally, it is also found that the stronger the acceptance of an individual to information coming from a different community, the lower the infection density in the network, which suggests that it has an inhibitory effect on the disease spreading.