Social network interventions for health behaviour change: a systematic review

Hunter, Ruth F.; Badham, Jennifer; Valente, Tom; Clarke, Mike; Kee, Frank

doi:10.1016/s0140-6736(17)32982-3

Cited by 25 publications

(19 citation statements)

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“…Examples of sociocentric studies conducted in resource-limited settings are rare (31) and require careful consideration of ethical issues such as consent for data collection and processing (32). One way to circumvent the resource requirements of sociocentric network data collection is by using existing network datasets, such as online social networks or phone records (33,34). While such methods can be extremely powerful, they rarely provide the specific kinds of detail required to evaluate HIV treatment and prevention programs.…”

Section: Social Network Data Collectionmentioning

confidence: 99%

Using social networks to understand and overcome implementation barriers in the global HIV response

Harling¹,

Tsai²

2019

Preprint

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Despite the development of several efficacious HIV prevention and treatment methods in the past decades, HIV continues to spread globally. Uptake of interventions is non-randomly distributed across populations, and such inequality is socially patterned both statically (due to homophily) and dynamically (due to social selection and influence). Social network analysis (SNA) methods, including egocentric, sociocentric, and respondent-driven sampling, provide tools to measure mostat-risk populations, to understand how epidemics spread, and to evaluate intervention take-up. SNAinformed designs can improve intervention effectiveness by reaching otherwise inaccessible populations and improve efficiency by maximizing spillovers to at-risk but susceptible individuals through social ties; they thus have the potential to be both more effective and less unequal in their effects than SNA-naïve approaches. While SNA-informed designs are often resource-intensive, they are uniquely able to help reach those most in need of HIV prevention and treatment interventions.Increased collection of social network data during both research and implementation work would provide important information to improve the roll-out of existing studies in the present and to inform the design of more data-efficient, SNA-informed interventions in the future.

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Section: Social Network Data Collectionmentioning

confidence: 99%

Using social networks to understand and overcome implementation barriers in the global HIV response

Harling¹,

Tsai²

2019

Preprint

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“…exploration, adoption, implementation and sustainment) in an effort to develop multi-sector partnerships involving community leaders, organizations, practitioners and researchers, and improve the implementation process (18). A recent review by Hunter et al identified 39 published public health interventions that targeted or altered social network phenomena in efforts to promote health behaviour change (19). These interventions employed several types of network intervention strategies, such as network segmentation or alteration, and the review found strong evidence for their short-term and long-term effectiveness (19).…”

Section: Introductionmentioning

confidence: 99%

Applications of social network analysis to obesity: a systematic review

Zhang

2018

Obesity Reviews

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People's health behaviours and outcomes can be profoundly shaped by the social networks they are embedded in. Based on graph theory, social network analysis is a research framework for the study of social interactions and the structure of these interactions among social actors. A literature search was conducted in PubMed and Web of Science for articles published until August 2017 that applied social network analysis to examine obesity and social networks. Eight studies (three cross-sectional and five longitudinal) conducted in the US (n = 6) and Australia (n = 2) were identified. Seven focused on adolescents' and one on adults' friendship networks. They examined structural features of these networks that were associated with obesity, including degree distribution, popularity, modularity maximization and K-clique percolation. All three cross-sectional studies that used exponential random graph models found individuals with similar body weight status and/or weight-related behaviour were more likely to share a network tie than individuals with dissimilar traits. Three longitudinal studies using stochastic actor-based models found friendship network characteristics influenced change in individuals' body weight status and/or weight-related behaviour over time. Future research should focus on diverse populations and types of social networks and identifying the mechanisms by which social networks influence obesity to inform network-based interventions.

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“…Four broad approaches have been described (Valente 2012;Hunter et al 2017). The first two approaches identify people to promote or participate in the intervention based on their network properties.…”

Section: Introductionmentioning

confidence: 99%

“…The evidence from real world behaviour change interventions is limited. A recent systematic review found strong evidence of behaviour change with interventions that select popular individuals to lead peer education activities (Hunter et al 2017). However, in almost all studies reviewed, the control intervention for comparison does not involve peer led interventions; that is, the effect arising from use of popular leaders cannot be separated from the effect of peer led delivery.…”

Section: Introductionmentioning

confidence: 99%

“…We frame our analysis with school based interventions, which are popular for diverse public health behaviours (Jepson et al 2010;Kriemler et al 2011;Hale et al 2014;Das et al 2016) and also as a setting for network interventions (Hunter et al 2017). The only network of friendship nominations in existing simulation studies was collected in a prison setting, with the other networks including a large online messaging service, networks constructed with common algorithms, and observed interactions between adults (Aral et al 2013;Badham et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness variation in simulated school-based network interventions

2019

Self Cite

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Background: Previous simulation studies have found that starting with high degree seeds leads to faster and more complete diffusion over networks. However, there are few studies and none have used networks that are relevant to a school setting. Methods: We construct 17 networks from friendship nominations in schools and simulate diffusion from a seed group of 15% of the students. That seed group is constructed with seven different approaches (referred to as interventions). The effectiveness of the intervention is measured by the proportion of simulated students reached and the time taken. Results: Seed groups comprising popular students are effective compared to other interventions across a range of measures and simulated contagions. As operationalised, selecting persuasive students is also effective for many simulation scenarios. However, this intervention is not strictly comparable with the others tested. Conclusions: Consistent with previous simulation studies, using popular students as a seed group is a robust approach to optimising network interventions in schools. In addition, researchers should consider supplementing the seed group with influential students.

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Social network interventions for health behaviour change: a systematic review

Cited by 25 publications

References 0 publications

Using social networks to understand and overcome implementation barriers in the global HIV response

Using social networks to understand and overcome implementation barriers in the global HIV response

Applications of social network analysis to obesity: a systematic review

Effectiveness variation in simulated school-based network interventions

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