Background
Physical activity in childhood and adolescence is linked to numerous health benefits, such as lower cholesterol, blood pressure and BMI [
1]. People who are more physically active at a young age are also more active adults [
2]. Unfortunately, young people are not physically active enough and physical activity declines with age [
3,
4]. Nowadays, adolescents are even less physically active compared to previous generations [
5]. According to the World Health Organization (2011), adolescents should accumulate at least 60 min of moderate-to-vigorous physical activity (MVPA) every day. Yet, a worldwide majority (80%) of adolescents, aged 13 to 15 year old, are not meeting these guidelines [
6‐
8]. In the United States, for example, 93% of adolescents (12- to 15-year-olds) do not meet the recommended amount of physical activity [
9] and in the Netherlands (the country of the current study), 72% of the adolescents (12- to 17-year-olds) do not adhere to the norm of 60 min of MVPA per day [
10].
Physical activity of adolescents is found to be influenced by peers [
11,
12]. For example, studies have shown that adolescents are more active when they are together with peers [
13] and that adolescents are more often friends with others who are similar in terms of physical activity [
14]. In addition, some studies have used a social network framework to predict physical activity in youth. For example, a study by De La Haye, Robins, Mohr, and Wilson [
15] showed that adolescents (12- to 14-year-olds) selected friends based on the amount of self-reported MVPA, but also influenced the amount of physical activity of their friends. Similarly, Simpkins, Schaefer, Price and Vest [
16] found evidence for these so-called selection and influence effects, based on self-reported physical activity in adolescents (
M age = 15.97). Gesell, Tesdahl and Ruchman [
17] observed only friendship selection effects in children and adolescents (5- to 12-year-olds), based on physical activity measured by accelerometer. All together, these studies show the relationship between adolescents’ physical activity and the physical activity of friends and peers, and that it is plausible that physical activity can be influenced by their social network.
A social network framework can be used to design interventions for behaviors in which peer influence plays a crucial role [
18]. Social network interventions typically identify a small number of individuals within social networks, so-called
influence agents, and train these agents to promote specific behaviors within their networks. There are a number of ways in which influence agents are selected [
19]. Usually, influence agents are selected by choosing participants that are nominated most frequently by all members of the social network on one or more sociometric questions (e.g., regarding who they respect, want to be like or who are their friends; see [
20,
21]). Once the influence agents have been selected, they are approached and trained to promote the desired behavior in their network for intervention purposes. Previous research has shown promising results that influence agents can stimulate healthy behaviors, such as a healthy eating [
22] and water consumption [
23], or discourage unhealthy behaviors, such as smoking [
20,
21] and substance use [
24].
Despite the promising approach of using influence agents to promote health behavior, only two studies have tested a social network intervention to promote physical activity in adolescents [
25,
26]. Both studies were based on the ASSIST study framework [
20], in which influence agents are trained to promote or discourage behavior among their peers. Bell et al. [
25] selected the most nominated adolescents as influence agents and trained them in a two-day training session to promote healthy eating and physical activity at the same time. After a 10-week intervention period, no behavioral differences were observed between the control and intervention conditions. The authors suggested that it was too complicated for the influence agents to promote both health behaviors at the same time. The second study [
26] focused solely on physical activity of adolescent females. The most nominated female adolescents in each classroom were selected as influence agents. The influence agents received a three-day training program about physical activity and interpersonal communication skills. After the training, the influence agents were asked to informally diffuse messages about physical activity for a period of 10 weeks. Preliminary results suggest that this intervention was successful [
27]. That is, adolescent girls decreased less in MVPA compared to the control condition. These mixed findings show that more research is needed on social network interventions that promote physical activity.
Current study
This study extends research on social network interventions aimed at promoting adolescents’ physical activity by (a) using a different selection criterion to determine the influence agents, and (b) training the influence agents via smartphones.
First, this study used closeness centrality as the selection criterion to determine the influence agents. In previous social network interventions, influence agents have been selected by identifying participants in the network who received the most nominations on one or more sociometric questions. This selection criterion is referred to as
indegree centrality. In most cases, the participants with the highest indegree centrality are the most popular individuals within a classroom. However, this might impair the effectiveness of the intervention, because popularity could be a detrimental characteristic of influence agents [
28]. For example, Valente argued that popular adolescents often depend on the social norms of the network to remain popular, and therefore may be reluctant to change their behavior or perform the role of an influence agent. As a solution, Borgatti [
29,
30] reasoned that when an intervention aims to promote health behavior, one should select the influence agents based on
closeness centrality. Based on this criterion, the influence agents are those in a classroom who are closely connected to all other classmates. More specifically, closeness refers to how many relationship ties are needed to link an individual to all others in a social network. Closeness centrality is calculated by taking the sum of the length of the shortest paths between each participant and all the classmates. People who have a small average path length, need fewer intermediaries to reach all members of a network. Therefore, it takes less time (i.e., fewer interactions) for the intervention message to reach the entire classroom [
30]. For this reason, the current intervention selected the influence agents based on closeness centrality.
Second, this study used smartphones to train the influence agents. Typically, influence agents are trained using repeated face-to-face meetings with trained experts. Delivering the training via smartphones increased the feasibility of social network interventions because it is a low-cost and less time-consuming method [
31]. For example, the influence agents can be trained at any location and time without having to miss part of their school curriculum. In addition, the use of smartphones fits adolescents’ lifestyle and the training of influence agents can be done covertly without raising suspicion of their peers, because they do not have to leave the classroom to attend the training.
The aim of this study was to test the effectiveness of a social network intervention that promotes physical activity in adolescents, based on these two extensions. We hypothesized that adolescents who are exposed to the social network intervention would be more physically active than adolescents who are not exposed to the social network intervention.
Discussion
This study was one of the first to test the effectiveness of a social network intervention to promote physical activity among adolescents. In addition, the study selected the influence agents based on their closeness centrality within the social networks, and used an innovative approach to train the influence agents via smartphones. Contrary to our expectation, we did not find an effect of the social network intervention on the physical activity of adolescents.
The findings are not in line with previous social network interventions promoting other types of health behaviors than physical activity [
20,
21,
23,
24]. In these studies, social network interventions have shown promising results to promote a variety of health behaviors. When focusing on physical activity, our findings are not in line with Sebire et al. [
27] who was successful in promoting physical activity in adolescent females via a social network intervention. However, our study shows similar results as Bell et al. [
31], who observed no social network intervention effect on dietary intake or physical activity. Their main recommendation was that the training should be relatively simple and the intervention message should be easy to pass on. Bell et al. advised focussing the intervention on one health behavior at a time. Our study followed this advice and focused only on physical activity. However, this did not increase the effectiveness of the social network intervention. In our view, there are two plausible explanations for the discrepancy between our study and the previously discussed social network interventions.
One explanation for our finding is that we adjusted the existing social network interventions to a smartphone environment to increase feasibility and make the intervention more fun and suitable for large-scale deployment. This study was the first to incorporate smartphones in a social network intervention. Influence agents were approached and trained via the research app in the
Wearable Lab. This is a less personal approach compared to previous social network intervention studies in which the influence agents met face-to-face with their trainers and other influence agents [
20,
21,
23,
31]. Also, because of the smartphone-based training, the instructions took less time compared to previous studies. It might have resulted in less commitment and team effort to perform their task. Although the influence agents indicated at the end of the intervention that they liked their role, it was unclear whether they completely understood the training and were motivated to be influence agents. In order to decrease the psychological distance between the researchers and the influence agents, we added a photograph of the researcher who gave the instruction in the school to the training and contacted the influence agents personally via the smartphone after completing the training. A more personal approach has been successfully used by Smith et al. [
50] in a smartphone obesity prevention trial to promote physical activity for boys with an increased risk of obesity. Apart from three interactive seminars at school focusing on increasing physical activity and decreasing screen-time, participants used a smartphone application to receive feedback and to keep in touch with the researchers. Future research could adapt this to social network interventions by combination between personal contact (e.g., at the start of or during the intervention) and contact via the smartphone (e.g., during the intervention), and test whether this approach is a feasible tool for training and a way to keep in contact with the influence agents.
Another explanation for our findings involves our approach to use closeness centrality as a means of identifying the influence agents. Previous social network interventions have exclusively used indegree centrality to identify influence agents [
20,
21,
23,
27,
31]. Based on the idea that individuals who receive the most nominations would be reluctant to change behavior because they want to remain popular [
28], we opted to use closeness centrality because these individuals were expected to have more influence within the entire network when it comes to the promotion of health behavior [
30]. A possible consequence is that the influence agents in our study were closely connected to all the other classmates, but were not effective in persuading others because they did not have a high status. Future research should further investigate the selection of the influence agents by systematically evaluating the effectiveness of influence agents identified by these (and other) selection criteria. By doing so, the generalizability of the diffusion mechanism of the health campaign will become more clear.
Limitations
Innovative studies go along with a number of limitations and several limitations should be discussed in interpreting the results. First, active parental consent was required for participants to be included in this study due to ethical and legal considerations. As a result, there were some students in each classroom that did not participate, which may have influenced the identification of the influence agents in the social network. That is, the adolescents who did not participate did not provide nominations nor could they be nominated by participants. It also remains unclear whether non-participants differed in their physical activity compared to the participants. It could be that the non-participants did not want to participate because of their sedentary lifestyle. To reduce this potential confound, however, classes with a high percentage of non-participants (participation lower than 60%) were excluded.
Second, only one large school was approached to participate in order to reduce potential differences between the classes in the control condition and in the intervention condition. This may have had an effect on the external validity. Future research should include multiple schools to examine whether differences occur between different locations or school types, and make the results more generalizable.
Third, compared to other social network studies, the intervention period was rather short. In previous studies, the intervention period lasted for multiple weeks. A longer intervention period enables more opportunities for the influence agents to perform their role and influence the behavior of the rest of the class. Due to time constraints of the participating school and limited availability of the research material, the intervention period in this study was only one week. Future research should consider using a longer intervention period than a week to provide more time for the influence agents to promote the health behavior among their classmates.
Conclusion
Despite these limitations, this study advanced the field of social network interventions in three ways. First, the present study was the first social network intervention that used a ‘greedy search algorithm’ to identify influence agents based on closeness centrality. Although we did not directly compare influence agents identified using a different criterion, our study extended social network theory by using an alternative selection criterion that reflects the main tenets of social network theory. Our study provides implications for future research to build on this extended way of thinking about the role of influence agents.
Second, this study was the first that used smartphones to train the influence agents in a social network intervention to promote physical activity in adolescents. Evaluations showed that research using smartphones is a feasible research tool to not only collect various types of data, but also to train and keep in touch with the influence agents. Nevertheless, maintaining personal contact with influence agents is still an important aspect to consider.
Third, the present study used a sophisticated analytic procedure utilizing multilevel analyses and multiple imputations, to adjust for the nested structure of the data and to include individuals with missing values. This procedure provided a more stringent test of the intervention effect by accounting for variance in physical activity due to daily fluctuations in activity levels and to individual differences.
In this study, we did not observe an effect of the social network intervention on the physical activity of adolescents. However, given that social network interventions in physical activity (as well as other health behaviors) are relatively underutilized and understudied, we encourage continued research applying social network interventions among adolescents to promote health behaviors and advance behavioral health science.
Acknowledgements
The author express their deepest appreciation to Koen Clevis, the research manager at the participating primary school, for his efforts and hard work to before-, during-, and after the data collection. In addition, the authors would like to acknowledge with much appreciation the advice provided by Ben Pelzer on multilevel modeling.