scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study

10.2196/12914 ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. e12914 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

Background Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. Objective The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. Methods We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). Results The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. Conclusions Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study (Preprint)

2018 ◽  
Author(s):  
Thabo J van Woudenberg ◽  
Bojan Simoski ◽  
Eric Fernandes de Mello Araújo ◽  
Kirsten E Bevelander ◽  
William J Burk ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Selection Criteria ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Network Characteristics ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention

BACKGROUND Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. OBJECTIVE The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention’s effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. METHODS We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention’s success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization). RESULTS The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. CONCLUSIONS Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions’ design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.

scholarly journals Promoting Participation in DCD: Physical Activity Levels and the Social Network

2020 ◽  
Vol 7 (2) ◽  
pp. 43-47
Author(s):  
Bert Steenbergen ◽  
Hidde Bekhuis ◽  
Femke van Abswoude
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
Developmental Coordination Disorder ◽  
Group Behavior ◽  
Systematic Research ◽  
Activity Levels ◽  
Active Lifestyle ◽  
The Social ◽  
Coordination Disorder

Abstract Purpose of Review Physical inactivity is a worldwide problem, also affecting children with motor problems, such as developmental coordination disorder. We try to understand what motivates children to start, continue, and stop having an active lifestyle and explore the role that the social network of the child can have to stimulate an active lifestyle. Recent Findings Social network theory is useful for understanding individual and group behavior related to physical activity. Social networks, ranging from peers and parents to teachers and medical professionals were shown to play an important role in bringing about sustainable behavioral change. Up to now, little systematic research has been done into how social networks can be used to keep children with developmental coordination disorder (DCD) physically active and motivated. Summary Future studies should more systematically examine and target the social network of the child with DCD. This social network can then be used to develop interventions for a sustained physical active lifestyle leading to increased participation in the society.

Reinforcement Effects of Social Network Intervention during Nutritional Supplementation in Frail Older Adults

Gerontology ◽  
2021 ◽  
pp. 1-13
Author(s):  
Chang-O Kim ◽  
Yunhui Jeong ◽  
Younjin Park ◽  
Jeong-Sook Bae ◽  
Yoonjeong Kwon ◽  
...  
Keyword(s):  
Older Adults ◽  
Social Network ◽  
Protein Supplementation ◽  
Control Group ◽  
Nutrition Program ◽  
Frail Older Adults ◽  
Social Network Intervention ◽  
The Social ◽  
Network Intervention ◽  
Post Hoc

<b><i>Introduction:</i></b> Chronic undernutrition and a homebound state are corelated and are both important components of frailty. However, whether social network intervention combined with protein supplementation is an effective strategy to prevent functional decline among frail older adults is unclear. <b><i>Methods:</i></b> 150 frail older adults participated in a 3-month, 3-armed, community-based clinical trial and were randomly assigned to one of 3 groups: high-protein supplementation (additional 27 g of protein/day), the Social Nutrition Program (additional 27 g of protein/day and social network intervention), or a control group. Those assigned to the Social Nutrition Program group received individual counseling from 1 dietitian and 1 social worker during 6 home visits and were encouraged to participate in 4 sessions of community-based cooking activities, the social kitchen program. Primary outcomes were changes in Physical Functioning (PF) and the Timed Up and Go (TUG) test and were assessed at 0 months (baseline), 1.5 months (interim), and 3, 6, and 9 months (postintervention). <b><i>Results:</i></b> Compared with the control group, participants in the Social Nutrition Program showed an average improvement of 2.2–3.0 s in the TUG test and this improvement persisted for 3 months after the end of the program (post hoc <i>p</i> ≤ 0.030). The Social Nutrition Program also increased PF by 1.3 points while the control group showed a 1.4 point reduction at the end of the program (post hoc <i>p</i> = 0.045). Improvement in PF and TUG results was primarily observed for the socially frail subgroup of older adults in the Social Nutrition Program group rather than the physically frail subgroup. Frequency of leaving home functioned as a mediator (<i>p</i> = 0.042) and explained 31.2% of the total effect of the Social Nutrition Program on PF change. <b><i>Conclusion:</i></b> Our results indicate that social network intervention combined with protein supplementation can improve both the magnitude and duration of functional status among frail older community-dwelling adults.

scholarly journals A randomized controlled trial testing a social network intervention to promote physical activity among adolescents

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Thabo J. van Woudenberg ◽  
Kirsten E. Bevelander ◽  
William J. Burk ◽  
Crystal R. Smit ◽  
Laura Buijs ◽  
...  
Keyword(s):  
Physical Activity ◽  
Randomized Controlled Trial ◽  
Social Network ◽  
Controlled Trial ◽  
Randomized Controlled ◽  
Social Network Intervention ◽  
Trial Testing ◽  
Network Intervention

Abstract P277: Exercise is Associated With Social Network Characteristics Among South Asian Immigrants

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Mitali Thanawala ◽  
Juned Siddique ◽  
Andrew Cooper ◽  
John A Schneider ◽  
Swapna Dave ◽  
...  
Keyword(s):  
Physical Activity ◽  
Social Networks ◽  
Social Network ◽  
South Asian ◽  
South Asians ◽  
Asian Immigrants ◽  
Network Member ◽  
P Value ◽  
South Asian Immigrants ◽  
Network Characteristics

Objective: Low physical activity increases cardiovascular disease (CVD) risk. Social context, operationalized through social networks, has been shown to drive health behaviors. This study examined the association between personal social networks and moderate-to-vigorous leisure-time physical activity (LTPA) among South Asian (Indian, Pakistani, Bangladeshi, Sri Lankan, Nepalese) immigrants, a group with high CVD rates. Methods: This study used cross-sectional data from an ancillary study of social networks (2014-2017) in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study cohort. Participants, free from CVD at baseline and living in the San Francisco Bay-area, CA and Chicago, IL, were administered a detailed social networks questionnaire and physical activity questionnaire adapted from the Cross-Cultural Activity Participation Study. Participants reported on the exercise behaviors of each social network member and if they exercised with the network member. Network members who exercised with a participant were categorized as exercise partners. Moderate-vigorous LTPA was calculated as Metabolic Equivalent of Task (MET) minutes per week. Sex-stratified, linear regression models were used to examine associations between social network characteristics and MET-min/week of LTPA, independent of age, marital status, and network size. The effect of having an exercise partner in the network, above simply having network members who exercised, was tested using a partial F-test to compare nested models. Results: Among the 700 participants, this analysis only included the 89% who reported any LTPA (n=623, 43% female). These individuals reported a median of 1335 MET-min/week of LTPA (IQR=735-2212 MET-min/week) and had an average of 4 network members (SD +/- 1). The proportion of network members who exercised was 0.89, and the proportion of exercise partners was 0.28. Exercise partners were most commonly spouses (56%) and friends (20%). Among South Asian men who exercised, having a social network member who exercised instead of having a non-exercising network member, significantly increased LTPA by 310 MET-min/wk (95% CI=152-470). For men, having a social network member who was an exercise partner instead of a non-exercising network member, was associated with an additional 520 MET-min/wk of LTPA (95% CI= 344-696). The effect on LTPA of having an exercise partner in the network was significantly greater than the effect of simply having a network member who exercised (p-value < 0.001). Results were similar for women, but not statistically significant (p-value=0.05). Conclusions: Among South Asian immigrants, having an exercise partner in one’s personal social network was associated with significantly more LTPA. Social network support, in the form of an exercise partner, may be an effective component of interventions to promote LTPA in South Asians.

scholarly journals Study protocol for ‘The Project About Loneliness and Social networks (PALS)’: a pragmatic, randomised trial comparing a facilitated social network intervention (Genie) with a wait-list control for lonely and socially isolated people

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028718 ◽  
Author(s):  
Rebecca Band ◽  
Sean Ewings ◽  
Tara Cheetham-Blake ◽  
Jaimie Ellis ◽  
Katie Breheny ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Setting ◽  
Well Being ◽  
Control Group ◽  
Wait List ◽  
Wait List Control ◽  
Social Network Intervention ◽  
Network Intervention ◽  
Socially Isolated

IntroductionLoneliness and social isolation have been identified as significant public health concerns, but improving relationships and increasing social participation may improve health outcomes and quality of life. The aim of the Project About Loneliness and Social networks (PALS) study is to assess the effectiveness and cost-effectiveness of a guided social network intervention within a community setting among individuals experiencing loneliness and isolation and to understand implementation of Generating Engagement in Network Involvement (Genie) in the context of different organisations.Methods and analysisThe PALS trial will be a pragmatic, randomised controlled trial comparing participants receiving the Genie intervention to a wait-list control group. Eligible participants will be recruited from organisations working within a community setting: any adult identified as socially isolated or at-risk of loneliness and living in the community will be eligible. Genie will be delivered by trained facilitators recruited from community organisations. The primary outcome will be the difference in the SF-12 Mental Health composite scale score at 6-month follow-up between the intervention and control group using a mixed effects model (accounting for clustering within facilitators and organisation). Secondary outcomes will be loneliness, social isolation, well-being, physical health and engagement with new activities. The economic evaluation will use a cost-utility approach, and adopt a public sector perspective to include health-related resource use and costs incurred by other public services. Exploratory analysis will use a societal perspective, and explore broader measures of benefit (capability well-being). A qualitative process evaluation will explore organisational and environmental arrangements, as well as stakeholder and participant experiences of the study to understand the factors likely to influence future sustainability, implementation and scalability of using a social network intervention within this context.Ethics and disseminationThis study has received NHS ethical approval (REC reference: 18/SC/0245). The findings from PALS will be disseminated widely through peer-reviewed publications, conferences and workshops in collaboration with our community partners.Trial registration numberISRCTN19193075

Gossip and Reputation in Social Networks

2019 ◽  
pp. 434-457
Author(s):  
Lea Ellwardt
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Sharing ◽  
Future Research ◽  
Meso Level ◽  
Network Characteristics ◽  
Local Structures ◽  
The Social ◽  
Micro Level ◽  
Constraint Structure

This contribution suggests and defends two propositions. First, gossip and reputation are coevolving relational phenomena, which conceptually overlap and empirically reinforce one another. Second, this coevolution is shaped by the opportunity and constraint structure of the social networks (e.g., organizations) in which these phenomena are typically embedded. The chapter presents theorizing, measures, and empirical findings on information sharing along three analytical levels. At the macro level, global structures describe overall network characteristics, such as density; at the meso level, local structures concern triadic configurations, like transitivity and clustering; at the micro level, individual structures cover ego-centric measures, including actor centrality and betweenness. The overview closes with three major suggestions for future research avenues on the study of gossip and reputation from a social network perspective.

scholarly journals Deep Learning Exploration of Agent-Based Social Network Model Parameters

2021 ◽  
Vol 4 ◽  
Author(s):  
Yohsuke Murase ◽  
Hang-Hyun Jo ◽  
János Török ◽  
János Kertész ◽  
Kimmo Kaski
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model ◽  
Network Models ◽  
Agent Based Modeling ◽  
Training Data ◽  
Model Parameters ◽  
Social Phenomena ◽  
Agent Based ◽  
Input Parameters

Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding these characteristics of social networks is the primary goal of their research as they constitute scaffolds for various emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing individuals, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This generalized weighted social network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previous works. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and used the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling.

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