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Supplementary files for Network Science

Additional file 1.

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH". Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona. Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation research

The importance of social environment in preventing smoking: RSiena analysis. Analysis of the co-evolution of network and smoking susceptibility using Stochastic Actor Orientated Models. (PDF 108 kb)

Additional file 2: Table S2. Accelerometer data for participating schools. Table S3. Proportion of nominated friends in teams. Figure S1. MVPA (minutes) at baseline and 6 months: relationship with friendship density in team. Figure S2. Change in minutes of MVPA from baseline to 6 months: relationship with friendship density in team.

Supplementary Figures S3 to S8 are available as a download. (PDF 345 kb)

Background: While Patient and Public Involvement (PPI) is encouraged throughout the research process, engagement is typically limited to intervention design and post-analysis stages. There are few approaches to participatory data analyses within complex health interventions. Methods: Using qualitative data from a feasibility RCT, this proof of concept study tests the value of a new approach to participatory data analysis called Participatory Theme Elicitation (PTE). Forty excerpts were given to eight members of a youth advisory PPI panel to sort into piles based on their perception of related thematic content. Using algorithms to detect communities in networks, excerpts were then assigned to a thematic cluster that combined the panel members' perspectives. Network analysis techniques were also used to identify key excerpts in each grouping that were then further explored qualitatively. Results: While PTE analysis was, for the most part, consistent with the researcher led analysis, young people also identified new emerging thematic content. Conclusions: PTE appears promising for encouraging user led identification of themes arising from qualitative data collected during complex interventions. Further work is required to validate and extend this method.

The agent-based social simulation component of the TELL ME project (WP4) developed prototype software to assist communications planners to understand the complex relationships between communication, personal protective behaviour and epidemic spread. Using the simulation, planners can enter different potential communications plans, and see their simulated effect on attitudes, behaviour and the consequent effect on an influenza epidemic.The model and the software to run the model are both freely available (see section 2.2.1 for instructions on how to obtain the relevant files). This report provides the documentation for the prototype software. The major component is the user guide (Section 2). This provides instructions on how to set up the software, some training scenarios to become familiar with the model operation and use, and details about the model controls and output.The model contains many parameters. Default values and their source are described at Section 3. These are unlikely to be suitable for all countries, and may also need to be changed as new research is conducted. Instructions for how to customise these values are also included (see section 3.5).The final technical reference contains two parts. The first is a guide for advanced users who wish to run multiple simulations and analyse the results (section 4.1). The second is to orient programmers who wish to adapt or extend the simulation model (section 4.2). This material is not suitable for general users.

Epidemiological models are used to inform health policy on issues such as target vaccination levels, comparing quarantine options and estimating the eventual size of an epidemic. Models that incorporate some elements of the social network structure are used for diseases where close contact is required for transmission. The motivation of this research is to extend epidemic models to include the relationship with a broader set of relevant real world network properties. The impact of degree distribution by itself is reasonably well understood, but studies with assortativity or clustering are limited and none examine their interaction. To evaluate the impact of these properties, I simulate epidemics on networks with a range of property values. However, a suitable algorithm to generate the networks is not available in the literature. There are thus two research aspects: generating networks with relevant properties, and estimating the impact of social network structure on epidemic behaviour. Firstly, I introduce a flexible network generation algorithm that can independently control degree distribution, clustering coefficient and degree assortativity. Results show that the algorithm is able to generate networks with properties that are close to those targeted. Secondly, I fit models that account for the relationship between network properties and epidemic behaviour. Using results from a large number of epidemic simulations over networks with a range of properties, regression models are fitted to estimate the separate and joint effect of the identified social network properties on the probability of an epidemic occurring and the basic reproduction ratio. The latter is a key epidemic parameter that represents the number of people infected by a typical initial infected person in a population. Results show that social network properties have a significant influence on epidemic behaviour within the property space investigated. Ignoring the differences between social networks can lead to substantial errors when estimating the basic reproduction ratio from an epidemic and then applying the estimate to a different social network. In turn, these errors could lead to failure in public health programs that rely on such estimates.

Funder: Research Trainees Coordinating Centre; doi: http://dx.doi.org/10.13039/501100000659, Background: Inactive lifestyles are becoming the norm and creative approaches to encourage adolescents to be more physically active are needed. Little is known about how gamification techniques can be used in physical activity interventions for young people. Such approaches may stimulate interest and encourage physical activity behaviour. The study investigated the feasibility of implementing and evaluating a physical activity intervention for adolescents which included gamification techniques within schools. We tested recruitment and retention strategies for schools and participants, the use of proposed outcome measures, and explored intervention acceptability. Methods: This school-based feasibility study of a randomised cluster trial recruited adolescents aged 12–14 years (n = 224) from five schools (three intervention; two control) in Belfast, Northern Ireland. The 22-week intervention (The StepSmart Challenge) informed by self-determination theory and incorporating gamification strategies involved a school-based pedometer competition. Outcomes, measured at baseline, and post-intervention (at 22 weeks post-baseline and 52 weeks post-baseline) included daily minutes of moderate to vigorous physical activity (MVPA) (measured using ActiGraph accelerometer), mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale), social support for physical activity, time preference (for delayed and larger rewards or immediate and smaller rewards), pro-social behaviour (Strengths and Difficulties Questionnaire (SDQ)) and the influence of social networks. The intervention’s acceptability was explored in focus groups. Results: We invited 14 schools to participate; eight showed interest in participating. We recruited the first five who responded; all five completed the trial. Of the 236 pupils invited, 224 participated (94.9%): 84.8% (190/224) provided valid MVPA (minutes/day) at baseline and 57.2% (123/215) at 52 weeks. All other outcomes were well completed apart from the SDQ (65% at baseline). Qualitative data highlighted that participants and teachers found The StepSmart Challenge to be an acceptable intervention. Conclusions: The level of interest and high recruitment and retention rates provide support for the feasibility of this trial. The intervention, incorporating gamification strategies and the recruitment methods, using parental opt-out procedures, were acceptable to participants and teachers. Teachers also suggested that the implementation of The StepSmart Challenge could be embedded in a lifelong learning approach to health within the school curriculum. As young people’s lives become more intertwined with technology, the use of innovative gamified interventions could be one approach to engage and motivate health behavioural change in this population. Trial registration: NCT02455986 (date of registration: 28 May 2015).

Additional file 2: Table S2. Accelerometer data for participating schools. Table S3. Proportion of nominated friends in teams. Figure S1. MVPA (minutes) at baseline and 6 months: relationship with friendship density in team. Figure S2. Change in minutes of MVPA from baseline to 6 months: relationship with friendship density in team.

BACKGROUND: Inactive lifestyles are becoming the norm and creative approaches to encourage adolescents to be more physically active are needed. Little is known about how gamification techniques can be used in physical activity interventions for young people. Such approaches may stimulate interest and encourage physical activity behaviour. The study investigated the feasibility of implementing and evaluating a physical activity intervention for adolescents which included gamification techniques within schools. We tested recruitment and retention strategies for schools and participants, the use of proposed outcome measures, and explored intervention acceptability. METHODS: This school-based feasibility study of a randomised cluster trial recruited adolescents aged 12-14 years (n = 224) from five schools (three intervention; two control) in Belfast, Northern Ireland. The 22-week intervention (The StepSmart Challenge) informed by self-determination theory and incorporating gamification strategies involved a school-based pedometer competition. Outcomes, measured at baseline, and post-intervention (at 22 weeks post-baseline and 52 weeks post-baseline) included daily minutes of moderate to vigorous physical activity (MVPA) (measured using ActiGraph accelerometer), mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale), social support for physical activity, time preference (for delayed and larger rewards or immediate and smaller rewards), pro-social behaviour (Strengths and Difficulties Questionnaire (SDQ)) and the influence of social networks. The intervention's acceptability was explored in focus groups. RESULTS: We invited 14 schools to participate; eight showed interest in participating. We recruited the first five who responded; all five completed the trial. Of the 236 pupils invited, 224 participated (94.9%): 84.8% (190/224) provided valid MVPA (minutes/day) at baseline and 57.2% (123/215) at 52 weeks. All other outcomes were well completed apart from the SDQ (65% at baseline). Qualitative data highlighted that participants and teachers found The StepSmart Challenge to be an acceptable intervention. CONCLUSIONS: The level of interest and high recruitment and retention rates provide support for the feasibility of this trial. The intervention, incorporating gamification strategies and the recruitment methods, using parental opt-out procedures, were acceptable to participants and teachers. Teachers also suggested that the implementation of The StepSmart Challenge could be embedded in a lifelong learning approach to health within the school curriculum. As young people's lives become more intertwined with technology, the use of innovative gamified interventions could be one approach to engage and motivate health behavioural change in this population. TRIAL REGISTRATION: NCT02455986 (date of registration: 28 May 2015).