A randomized controlled trial testing a social network intervention to promote physical activity among adolescents

Woudenberg, Thabo J. van; Bevelander, Kirsten E.; Burk, William J.; Smit, Crystal R.; Buijs, Laura; Buijzen, Moniek

doi:10.1186/s12889-018-5451-4

Cited by 51 publications

(71 citation statements)

References 49 publications

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“…In addition, HIV is still a common and anxious concern of MSM in China. There should be more intervention strategies [66,67] in the HIV transmitted infections among the MSM population, such as identifying and training the most influential individuals (or opinion leaders) [68] within the MSM interacting network, or facilitating peer influence [69] to promote target health behaviors.…”

Section: Discussionmentioning

confidence: 99%

Network Evolution of a Large Online MSM Dating Community: 2005–2018

Liu

2019

IJERPH

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Due to multiple sexual partners and low rates of condom use, the HIV infection rate among MSM (men who have sex with men) is much higher than that of the general population. In order to analyze the characteristics of online activities of MSM, and to understand the evolution of their social networks, in this study we collect a comprehensive dataset, covering the period from January 2005 to June 2018, from the largest Chinese online community, Baidu Tieba. We build an online dating network for MSM-related individuals in the gay-bar community, and analyze the network from static and dynamic aspects. It is found that there is a strong homophily regarding the cities where users reside when developing interactions with others, and that most network measurements tend to be stable at the later stages of evolution, while the size of the largest community fluctuates. This is an indication that the network is formed of rapidly flexible interactions which changes quickly. In comparison with studies on heterosexual networks, we find that the MSM dating network shows differences in many aspects, such as the positive degree-degree correlation and high clustering coefficient, suggesting different thinking and measures should be taken in the policy making of public health management towards the MSM population.

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Section: Discussionmentioning

confidence: 99%

Network Evolution of a Large Online MSM Dating Community: 2005–2018

Liu

2019

IJERPH

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“…The average physical activity per wave was calculated by taking the average steps per day of at least 3 days of valid data. If participants had less than 3 days of valid data per wave, daily step count was imputed with the same strategy as in the study by van Woudenberg et al [7], by using single multilevel (predictive mean matching) imputation [37]. Missing data were imputed based on other physical activity data of the same participant, day of the week, measurement period, sex, and age.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the A Stop Smoking In Schools Trial study trained influence agents to encourage peers not to smoke in secondary schools [4]. Other studies have trained influence agents to stimulate peers to increase health behaviors, such as drinking more water [5] or being more physically active [6,7].…”

Section: Introductionmentioning

confidence: 99%

Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study

Woudenberg¹,

Simoski²,

Araújo³

et al. 2019

J Med Internet Res

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Background Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions. Objective The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and 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 relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics. Methods We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). 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 (ie, network density and network centralization). Results The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=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 (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed. Conclusions Social network intervention 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 modeling 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.

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“…Self-efficacy was measured by two items (van der Horst et al, 2007): 'Do you think you are able to be more physically active?' and 'Does being more physically active seem difficult to you?'.…”

Section: Self-efficacymentioning

confidence: 99%

Testing a Social Network Intervention Using Vlogs to Promote Physical Activity Among Adolescents: A Randomized Controlled Trial

et al. 2020

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There is a need to stimulate physical activity among adolescents, but unfortunately, they are hard to reach with traditional mass media interventions. A promising alternative is to carry out social network interventions. In social network interventions, a small group of individuals (influence agents) is selected to promote health-related 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; M age = 11.35, SD age = 1.34; 47% male) were randomly allocated by classroom (N = 26, in 11 schools) 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 created vlogs about physical activity that were shown during the intervention. In the mass media intervention, participants were exposed to vlogs made by unfamiliar peers (i.e., 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 wristworn accelerometer to measure physical activity. The trial was registered a priori in the Dutch Trial Registry (NTR6903). There were no differences in objectively measured physical activity between this social network intervention and the control condition in the short-term, but there was 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. The current study does not provide evidence that this social network intervention is effective in increasing physical activity in adolescents. Exploratory analyses suggest that this 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. These initial results warrant further research to investigate the role of the social norms and the added benefit of using influence agents for social network interventions. Clinical Trial Registration: https://www.trialregister.nl/, identifier NTR6903.

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A randomized controlled trial testing a social network intervention to promote physical activity among adolescents

Cited by 51 publications

References 49 publications

Network Evolution of a Large Online MSM Dating Community: 2005–2018

Network Evolution of a Large Online MSM Dating Community: 2005–2018

Identifying Influence Agents That Promote Physical Activity Through the Simulation of Social Network Interventions: Agent-Based Modeling Study

Testing a Social Network Intervention Using Vlogs to Promote Physical Activity Among Adolescents: A Randomized Controlled Trial

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