The current study compared peer nominated networks with more unobtrusive measures of peer connections: Online communication networks and proximity networks based on smartphones’ Bluetooth signals measuring peer proximity. The three networks were compared in coverage, stability, overlap, and criterion validity. Two samples were derived from the MyMovez project: a longitudinal sample of 444 adolescents and a cross-sectional sample of 774 adolescents. Participants received a research smartphone for one or three weeks. On this smartphone, participants received peer nomination questions. In addition, the smartphone scanned for other smartphones via Bluetooth signal every 15 minutes of the day. In the second sample, participants could chat with peers on the research smartphone. The results show that nominated networks provided data for the most participants compared to the other two networks, but in these networks participants had the lowest number of connections with peers. The overlap between the three networks was rather small, indicating that the networks measured different type of connections. The communication and proximity network seem promising unobtrusive measures of peer connections and are less of a burden to the participant compared to a nominated network. However, the communication and proximity networks should not be used as direct substitutes for sociometric nominations.
There is a need to stimulate physical activity among adolescents, but unfortunately, they are hard to reach with traditional mass media interventions. Given the popularity and the networked structure of social media, social network intervention seems to be a promising alternative. In social network interventions, a small group of individuals (influence agents) is selected to promote health behaviors within their social network. This study investigates whether a social network intervention is more effective to promote physical activity, compared to a mass media intervention and no intervention. Adolescents (N = 446; Mage = 11.35, SDage = 1.34; 47% male) were randomly allocated by classroom (N = 26) to one of three conditions: social network intervention, mass media intervention, or control condition. In the social network intervention, 15% of the participants (based on peer nominations) was approached to become an influence agent, who then created several vlogs about physical activity. During the intervention period, participants were able to view the vlogs on a smartphone. In the mass media intervention, participants were exposed to vlogs made by unfamiliar peers (i.e., the vlogs of the social network intervention). The control condition did not receive vlogs about physical activity. All participants received a research smartphone to complete questionnaires and a wrist-worn accelerometer to measure physical activity. There were no differences between the social network intervention and the control condition in the short-term, and an unexpected increase in the control condition compared to the social network intervention in the long-term. No differences between the social network intervention and mass media intervention were observed either. Exploratory analyses suggest that the social network intervention increased the perceived social norm toward physical activity and responses to the vlogs were more positive in the social network intervention than in the mass media intervention. The current study does not provide evidence that a social network intervention is more effective in increasing physical activity in adolescents compared to a mass media or no intervention. However, exploratory results suggest that the social network intervention has a positive effect on the perceived descriptive norm and the responses towards the vlogs. These first results warrant further research to investigate the role of the perceived social norms and the added benefit of using influence agents for social network interventions.
Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviours, including physical activity. However, designing successful social network interventions is a considerable research challenge. For example, it is unclear which criteria should be used to select influence agents that serve as successful promoters of the targeted health behaviour. Investigating this question through field experiments is a time consuming and infeasible process. Fortunately, advancements in computer science enable us to simulate these complex processes. In this work, we rely on social network analysis and agent-based simulations in order to better understand and capitalize on the complex interplay of social networks and health behaviours. More specifically, we investigate which criteria for selecting influence agents can be expected to produce the most successful social network health interventions.The aim is to use a computational model to simulate different selection criteria for social network interventions and to 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 will rely on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes thanothers based on network characteristics.We used a previously validated agent-based model to understand how physical activity spreads in social networks and who is influencing the spread of behaviour. Based on the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions ranging in selection criteria for the influence agents (i.e. in-degree, betweenness and closeness centrality and random influence agents) and a control condition (i.e. no intervention condition). 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 (i.e. network density and network centralization).The one-year simulations showed that the social network interventions were more effective compared to the control condition, β = .30, t(100) = 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, β = .46, t(100) = 3.86, P < .001. Also, the closeness centrality condition outperformed the betweenness centrality condition, β = .59, t(100) = 2.02, P = .046. The anticipated interaction effects of the network characteristics were not observed. Social network interventions 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 modelling 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.
Journal of Medical Internet Research,
The current study examined the effectiveness of a social network intervention to promote physical activity among adolescents. Social network interventions utilize peer influence to change behavior by identifying the most influential individuals within social networks (i.e., influence agents), and training them to promote the target behavior. A total of 190 adolescents were randomly allocated to either the intervention or control condition. In the intervention condition, the most influential adolescents in each classroom were trained to promote physical activity among their classmates. Participants received a research smartphone to complete questionnaires and an accelerometer to measure physical activity at baseline, and during the intervention one month later. However, no intervention effect was observed.This was one of the first studies to test whether physical activity in adolescents could be promoted via influence agents, and the first social network intervention to use smartphones to do so.
BMC Public Health,