Network, Network, Network: The fact that our interactions with others influence many of our decisions has led to research on characterizing the networks to which we belong and, more recently, on interventions that can change networks thereby changing behavior. This can have a variety of purposes, including promoting information flow through an organization or finding vulnerable points in bioterrorist networks. Valente (p. 49 ) reviews a variety of strategies for affecting networks.

This article reviews 10 techniques used to identify opinion leaders to promote behavior change. Opinion leaders can act as gatekeepers for interventions, help change social norms, and accelerate behavior change. Few studies document the manner in which opinion leaders are identified, recruited, and trained to promote health. The authors categorize close to 200 studies that have studied or used opinion leaders to promote behavior change into 10 different methods. They present the advantages and disadvantages of the 10 opinion leader identification methods and provide sample instruments for each. Factors that might influence programs to select one or another method are then discussed, and the article closes with a discussion of combining and comparing methods.

There has been a growing interest in understanding the effects of social networks on health-related behaviour, with a particular backdrop being the emerging prominence of complexity or systems science in public health. Social network interventions specifically use or alter the characteristics of social networks to generate, accelerate, or maintain health behaviours. We conducted a systematic review and meta-analysis to investigate health behaviour outcomes of social network interventions. We searched eight databases and two trial registries from 1990 to May 28, 2019, for English-language reports of randomised controlled trials (RCTs) and before-and-after studies investigating social network interventions for health behaviours and outcomes. Trials that did not specifically use social networks or that did not include a comparator group were excluded. We screened studies and extracted data from published reports independently. The primary outcome of health behaviours or outcomes at ≤6 months was assessed by random-effects meta-analysis. Secondary outcomes included those measures at >6–12 months and >12 months. This study is registered with the International Prospective Register of Systematic Reviews, PROSPERO: CRD42015023541. We identified 26,503 reports; after exclusion, 37 studies, conducted between 1996 and 2018 from 11 countries, were eligible for analysis, with a total of 53,891 participants (mean age 32.4 years [SD 12.7]; 45.5% females). A range of study designs were included: 27 used RCT/cluster RCT designs, and 10 used other study designs. Eligible studies addressed a variety of health outcomes, in particular sexual health and substance use. Social network interventions showed a significant intervention effect compared with comparator groups for sexual health outcomes. The pooled odds ratio (OR) was 1.46 (95% confidence interval [CI] 1.01–2.11; I² = 76%) for sexual health outcomes at ≤6 months and OR 1.51 (95% CI 1.27–1.81; I² = 40%) for sexual health outcomes at >6–12 months. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in haemoglobin A1c (HbA1c), and smoking cessation. Because of clinical and measurement heterogeneity, it was not appropriate to pool data on these other behaviours in a meta-analysis. For sexual health outcomes, prespecified subgroup analyses were significant for intervention approach (p < 0.001), mean age of participants (p = 0.002), and intervention length (p = 0.05). Overall, 22 of the 37 studies demonstrated a high risk of bias, as measured by the Cochrane Risk of Bias tool. The main study limitations identified were the inclusion of studies of variable quality; difficulty in isolating the effects of specific social network intervention components on health outcomes, as interventions included other active components; and reliance on self-reported outcomes, which have inherent recall and desirability biases. Our findings suggest that social network interventions can be effective in the short term (6 months) for sexual health outcomes. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in HbA1c, and smoking cessation.

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.

Objectives. Our study tested the effectiveness of network methods for identifying opinion leaders and for constructing groups. Methods. Three conditions—random, teacher, and network—were randomly assigned to 84 6th-grade classrooms within 16 schools. Pre- and postcurriculum data on mediators of tobacco use were collected from 1961 students. Peer leaders in the network condition were identified by student nominations, and those leaders were matched with the students who nominated them. Results. Students in the network condition relative to the random condition liked the prevention program more and had improved attitudes (β = −0.06; P < .01), improved self-efficacy (β = −0.10; P < .001), and decreased intention to smoke (adjusted odds ratio [OR] = 0.46; 95% confidence interval [CI] = 0.38, 0.55). Conclusions. The network method was the most effective way to structure the program. Future programs may refine this technique and use it in other settings.