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Finally, it examines methodological issues in network interventions and proposed future lines of research. Decades of research demonstrate that behavioral interventions can be change health behaviors. Social environments have been well documented to have major influences on the adoption of health behaviors. Classic studies in social psychology suggest that social environments can be modified or constructed to have a greater impact on behavior as compared to personality, attitudes, and other individual-level factors.

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Behaviors or lack of support by significant others can present significant barriers to behavior change, and some notable randomized clinical trials of social support and network interventions have not lead to differences in behavior change. In this article, we examine evidence from social network and social influence approaches to behavior change to improve the impact, reach, and costs of health promotion interventions.

We also examine specific interpersonal relationships associated with health behaviors, mechanisms of both negative and positive social influences on individual-level and network-level behavior change, and psychological approaches used in developing network interventions. Social network interventions have been successfully used in international health programs to promote modern family planning methods and bullying reduction among adolescents. More recently, they have incorporated social media and mhealth components.

Network interventions often utilize existing social support, social exchange, and social influence processes such as modeling and verbal persuasion. They utilize the existing network structure to diffuse behavior change. Moreover, as network members often maintain relationships, networks interventions can help to sustain behavior change. Hence, for secondary prevention it can be helpful to involve and consider these network members in the support, care, and behavior changes processes.

Some network interventions focus on egocentric, or personal networks, which are defined as focal individuals and their social ties. For altering HIV risk behaviors, interventionists may focus on risk networks, such as their drug sharing and sexual partners. The smallest network is a dyad, which can also be the intervention focus. Sociometric network interventions often identify key individuals for training based on network structures, such as those with the greatest numbers of ties or influence potential. For example, a friendship structure within a school or classroom can be diagramed as a sociometric network by mapping the friendships, in the form of lines, among students.

They can be identified through social network structure. For example, network attributes such as centrality can be derived for each network member, and those with the highest scores targeted for training. Opinion leaders may also be identified through nominations and ethnographic observations. As with other network interventions, key factors to consider are the stability and structure of the network, frequency of interaction, and the credibility of the opinion leader for the specific topic.

An opinion leader who is credible about financial matters may lack credibility to promote safer sexual practices. Social network structural factors such as density i. To delineate the social influence processes within social network it is critical to adequately measure network attributes. Investigators have examined the reliability of network inventories and have developed methods to improve reliability.

Do networks inventories document key health-related individuals in the social environment and their attributes? Network inventory instruments usually first ask a set of questions to elicit names of specific network members and then a set of questions regarding their attributes. Name generating questions often ask for names of sources of social support, e. Name generators may also delineate networks based on behavioral interactions, shared venue attendance e.

In health domains, name generators may include specific health behaviors e. Once the list of names is generated, a set of attribute questions is asked about each network member. Attributional questions may include demographics, frequency of contact, duration of relationship, residential propinquity, role relation, health behaviors, perceived reciprocity of support, communication or support regarding health behaviors, as well as evaluative aspects of the relationship, e. Name generators and relationships are not mutually exclusive questions.

For a study on drug-related behavior, in delineating the drug network, it can be useful to use a name generator for listing which network members use drugs but then attributional questions about who uses with the respondent and the type, frequency, and mode of drug administration. From these questions, one can derive, for example, the number of daily heroin injectors in a network.

Table 1 presents a small sample from the universe of categorizations of network members. To examine sources of HIV transmission risk, the number of drug injectors who are also sex contacts can be identified. The infinite number of groupings, it highlights the importance in theory and health issues to determine which name generators and attributional questions are include in a network inventory.

Network inventories should be adapted for the study population, context, and the health topic. Formative qualitative research, empirical findings, and hypothesized associations help to delimit the name generating and attribute questions for a given study. As psychometric properties such as Cronbach's alpha are not usually derived from network inventories other measures of reliability test-retest and validity concurrent, discriminative, and predictive are well suited for assessing network instruments. Another key question in developing a network intervention, and hence should be assess, is the stability and frequency of interactions within the network.

If a network has high turnover it is unlikely that any network member will have adequate opportunity to promote effectively the target behavior.

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Similarly, if there is infrequent contact with network members there will be few opportunities to model and reinforce the behavior. In the analyses of personal network data, one can model turnover in and turnover out, which is the number of new individuals who enter the network and those who leave, and to identify factors associated with network turnover. Personal networks can be linked together to form larger sociometric networks.

Some study designs emphasize collecting of sociometric data with clear boundaries to ensure sampling of linked individuals, such as a classroom or by using a sampling strategy, which insures linkages between respondents. With sociometric analyses, a range of network structural characteristics can be calculated, such as centrality, or microstructural features such as cliques.

The National Longitudinal Study of Adolescent Health Add Health is an example of a survey that was designed to ensure that a subset of the sample formed a sociometric network to allow for network analyses of social influence. One major issue to improve network approaches to behavior change is identifying network members that have particular influence on a behavior of interest and thus would be most appropriate to train.

A study by Davey and colleagues 29 found that the size of drug use network per say was not associated with subsequent entry into drug abuse treatment; however, other network characteristics did predict drug treatment entry. Having a greater number of network members who were currently in drug treatment predicted entry, the number of current cocaine users was negatively associated with treatment entry, and the number of heroin users in the network had no association with drug treatment entry.

One interpretation of this finding is that since there are not effective pharmacological treatments for cocaine addiction, those network members who use cocaine would not find treatment effective and hence less likely to encourage others to seek drug treatment. The relationship between network factors and health outcomes may also differ by gender. Knowlton and colleagues found that for HIV seropositive men, having a female partner and reciprocity of social support were positively associated with adherence to HIV medications.

For women, however, the presence of emotional support from a sexual partner in the social network was negatively associated with medication adherence. Socioeconomic status may also moderate the link to the relationship between social network factors on health. However, knowing associations from observational studies does not always provide information on the best relationships to target for interventions. For example, with medication adherence is it better to target friends, current sexual partners, or certain family members?

It is likely that factors to consider in choosing network members may include the health condition, target behavior, social and economic status, gender and other social roles, as well as resources and support functions and frequency of interaction with social network members. In order to target network members for intervention, network inventories need to collect the most relevant information within the minimal amount of time. In the fields of psychology and mental health, social support has been viewed as one of the major social influences on health behaviors.

Social networks can be conceptualized as the specific sources of social support. The exchange of social support is the major basis of developing and maintaining social relationships. Although social support has been typically found to benefit health, interventions to enhance social support for promoting health outcomes have not always shown consistent positive effects on health outcomes.

The specificity of social relationships that can be delineated by social network analyses can help to provide a more nuanced analysis of social support and may help to explain seemingly contradictory findings. There has been a consensus in the literature of five major types of social support: emotional, financial or material, informational, instrumental, and socialization. Networks can be conceptualized as sources and pathways of tangible and intangible support. For marginalized populations, who have limited individual-level resources, support network members are important sources of basic resources, such as food or housing, and information on where to obtain such resources.

Emotional support has been frequently found to have a main effect on depressive symptoms, but in low resourced communities, material support, as compared to emotional support, may be more strongly associated with psychological well-being. Support provided by network members may not always fit the need. Support provision may also have unintended consequences.

From social network analyses, it is possible to ascertain if individuals who provide material and emotional support are also the source of conflict as well as the level of reciprocity among network members.

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The same network members who provide material support and are sources of conflict may simultaneously impede and aid certain health outcomes. The literature on informal caregiving has examined attributes of network relationships that may impede and foster the provision of social support. One notable factor is reciprocity. The support may lead to conflict and greater stress in the relationship due to recipient and supporter both trying to assert control over the same health behaviors.

It is also possible that for some health behaviors perceived social support is important, whereas for other health-related behaviors, such as medical adherence, enacted support is more important. Interventions may change enacted support without changing perceived support or change perceived support without changing enacted support. Moreover, support from weak ties may enhance well-being, but they may not sustain behavior change.

One of the advantages of many measures of social support is that they are relatively brief, but they do not usually indicate who provides what type of support and the potentially problematic aspect of the support provided. There are a range of mechanisms that can be employed in social network interventions to foster behaviors change. Network members can reward or punish others for engaging in the behavior.

Based on social cognitive theory, network members can model behaviors, which may increase self-efficacy and response efficacy. Observational studies have found that social norms are clustered within social networks and that these norms influence health and academic behaviors. Network members can influence social norms by engaging in descriptive norms or endorsing a behavior injunctive norms. Network members may also change communication norms so that it becomes more socially acceptable to talk about certain health related topics such as safer sex or cancer screening and prevention.

Network members who are trained to discuss the importance of prostate and breast examinations may change the acceptability of communication norms. Talking about and modeling health behaviors can make social norms for engaging and endorsing these behaviors more salient, appear to be more prevalent and acceptable in the network, and hence influence behaviors.

A peer educator who talks to friends about cancer screening may alter social norms to not only make the topic socially acceptable, but it may also increase the perception that cancer screening is normative as well as serve as a cue to act and heighten the perceived risk of cancer. Once a network incorporates a certain behavior it can become part of the group's social identity and hence the group will work to maintain the behavior to keep its identity. Networks members who promote certain health behaviors can also help to define a situation.

For example, a network member may comment that eating a certain food is unhealthy where previously the behavior was not viewed from the perspective of health. One of the major approaches to promoting behavior change within social networks is by network members talking about and encouraging certain health behaviors. Trainings in communication skills are necessary to initiate and maintain conversations about health behaviors. There is also the necessity of training individuals in communication skills to reward verbally others who engage in the behavior, present information or model behaviors that are credible, and discuss health behaviors in a manner that does not elicit reactance or impugn self-efficacy.

Telling network members that smoking is bad for their health may not be the best approach for a peer educator to initiate a conversation. Such a statement may be perceived as judgmental and not lead to additional conversations about smoking. One of the goals of such conversations is to heighten social norms. A discussion of the need to reduce second hand smoke exposure to children may heighten norms regarding children's well-being. The effectiveness of communications to change health behaviors has been linked to dose.

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