Social Network Analysis to Delineate Interaction Patterns That Predict Weight Loss Performance

2014 ◽  
Author(s):  
Taridzo Chomutare ◽  
Anna Xu ◽  
M. Sriram Iyengar
Keyword(s):  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Interaction Patterns ◽  
Predict Weight Loss

New Technologies, New Horizons

2011 ◽  
pp. 23-41
Author(s):  
Hiller A. Spires ◽  
Meixun Zheng ◽  
Manning Pruden
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Graduate Students ◽  
Network Analysis ◽  
New Technologies ◽  
Interaction Patterns ◽  
Teaching With Technology ◽  
Graduate Course ◽  
Pedagogical Content ◽  
New Horizons

The purpose of this chapter is to present graduate students’ views of their Technological Pedagogical Content Knowledge (TPACK) development. These graduate students are also teachers. Data was collected using a mixed method approach founded on the TPACK Framework and social network analysis. Koehler and Mishra (2006) claim that effective teaching with technology requires TPACK, or an ability to integrate content, pedagogy and technology flexibly during the act of teaching. As part of a graduate course on new literacies and media, participants were required to design and implement lessons that incorporated a range of technologies, produce written reflections about their experiences, and engage in online interactions with participants in the class. Qualitative results from participants’ written reflections revealed four themes relative to TPACK. Additionally, a social network analysis demonstrated a positive relationship between participants’ views on their TPACK development and their interaction patterns within the online learning environment. This study shows that the TPACK framework can be a useful tool, giving educators a productive way to think about technology integration as they navigate the rapid changes prompted by emerging technologies.

scholarly journals Social Network Analysis to Examine Interaction Patterns in Knowledge Building Communities

2010 ◽  
Vol 36 (1) ◽  
Author(s):  
Donald N. Philip
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Building ◽  
Group Processes ◽  
Interaction Patterns ◽  
Face To Face ◽  
Grade 5 ◽  
Teachers And Students ◽  
Building Community

This paper describes use of social network analysis to examine student interaction patterns in a Grade 5/6 Knowledge Building class. The analysis included face-to-face interactions and interactions in the Knowledge Forum® Knowledge Building environment. It is argued that sociogram data are useful to reveal group processes; in sociological terms, the community lies in the connections among the group. A classroom of unconnected individuals is unlikely to form as a Knowledge Building community; data analyses reported in this study show promise in understanding the dynamics of Knowledge Building in a consistent and measurable way. The strength of the work is not in particular patterns demonstrated but in new forms of assessment and their potential to inform work as it proceeds. The research reported shows that teachers and students are finding social network analysis useful and that through their engagement research-practitioner-engineer teams are better positioned to develop tools to advance Knowledge Building pedagogy.

scholarly journals Passing Network Analysis of Positional Attack Formations in Handball

2019 ◽  
Vol 70 (1) ◽  
pp. 209-221 ◽  
Author(s):  
Florian Korte ◽  
Martin Lames
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Suitable Method ◽  
Interaction Patterns ◽  
Optimal Position ◽  
Key Players ◽  
Sport Teams ◽  
The Individual ◽  
General Balance

Abstract The aim of this study was to characterize handball from a social network analysis perspective by analyzing 22 professional matches from the 2018 European Men's Handball Championship. Social network analysis has proven successful in the study of sports dynamics to investigate the interaction patterns of sport teams and the individual involvement of players. In handball, passing is crucial to establish an optimal position for throwing the ball into the goal of the opponent team. Moreover, different tactical formations are played during a game, often induced by two-minute suspensions or the addition of an offensive player replacing the goalkeeper as allowed by the International Handball Federation since 2016. Therefore, studying the interaction patterns of handball teams considering the different playing positions under various attack formations contributes to the tactical understanding of the sport. Degree and flow centrality as well as density and centralization values were computed. As a result, quantification of the contribution of individual players to the overall organization was achieved alongside the general balance in interplay. We identified the backcourt as the key players to structure interplay across tactical formations. While attack units without a goalkeeper were played longer, they were either more intensively structured around back positions (7 vs. 6) or spread out (5 + 1 vs. 6). We also found significant differences in the involvement of wing players across formations. The additional pivot in the 7 vs. 6 formation was mostly used to create space for back players and was less involved in interplay. Social network analysis turned out as a suitable method to govern and quantify team dynamics in handball.

scholarly journals Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

10.2196/24690 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24690
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Web Based ◽  
Network Characteristics ◽  
Using Data

Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Using Social Network Analysis to Understand the Effects of a Social Media-based Weight Loss Intervention Targeting Low-SES Adults (Preprint)

2020 ◽  
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Network Characteristics ◽  
Using Data ◽  
The Relationship

BACKGROUND Obesity is a known risk factor for cardiovascular disease (CVD) risk factors including hypertension and type II diabetes. Although numerous weight-loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from online platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. OBJECTIVE The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social-media based weight loss intervention. METHODS This study performed secondary analysis using data from a pilot study that delivered a dietary and physical activity intervention to a group of low-SES participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period, and linked participants’ network characteristics (e.g. in-degree, out-degree and network constraint) to participants’ changes in theoretical mediators (i.e. dietary knowledge, perceived social support, self-efficacy) and weight loss using regression analysis. This study also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. RESULTS 47 participants from two waves completed the study and were included in the analysis. We found that participants creating posts, comments and reactions predicted weight-loss (β=-.94, P=.042); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009); the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight-loss (Indirect effect=-.89, P=.017). CONCLUSIONS Our analyses using data from this pilot study have linked participants’ network characteristics with changes in several important study outcomes of interest, such as self-efficacy, social support and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which online behavioral interventions affects participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Association of Social Network Analysis with Mental Models in Emergency Management Teams

2019 ◽  
Author(s):  
Bjørn Sætrevik ◽  
Line Solheim Kvamme
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Management ◽  
Mental Models ◽  
Shared Mental Models ◽  
Management Teams ◽  
Interaction Patterns ◽  
Team Members ◽  
The Impact

Social network analysis is a preferred approach to examine the impact of social processes and mechanisms on team performance, but it can be challenging to measure these dynamics in applied settings. Our aim was to test whether the understanding of the task at hand was more accurate and more shared for teams with more evenly distributed interaction patterns. We pre-registered a novel approach for measuring social networks from sparse reporting of ranked interactions. Our sample was eleven emergency management teams that performed a scenario training exercise, where we asked factual questions about the ongoing task during performance, and retrospective questions about who were the most important communication and collaboration partners. We quantified shared mental models as the extent to which a team member showed the same understanding as the rest of their team, and quantified situation awareness as the extent to which team members showed the same knowledge as their team leader. We calculated which team members where most central to the network, and which networks had more evenly distributed networks. Our findings support the pre-registered hypotheses that more interconnected teams are associated with more accurate and more shared mental models, while the individual’s position in the network was not associated with MM.

Interactive Governance Between and Within Governmental Levels and Functions: A Social Network Analysis of China's Case Against COVID-19

2021 ◽  
pp. 027507402110595
Author(s):  
Dongmin Yao ◽  
Jing Li ◽  
Yijing Chen ◽  
Qiunan Gao ◽  
Wenhong Yan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Type I ◽  
Interaction Patterns ◽  
Health Crisis ◽  
Interactive Governance ◽  
Governance Systems ◽  
Multi Level ◽  
Definition Of

COVID-19 has created long-lasting yet unprecedented challenges worldwide. In addition to scientific efforts, political efforts and public administration are also crucial to contain the disease. Therefore, understanding how multi-level governance systems respond to this public health crisis is vital to combat COVID-19. This study focuses on China and applies social network analysis to illustrate interactive governance between and within levels and functions of government, confirming and extending the existing Type I and Type II definition of multi-level governance theory. We characterize four interaction patterns—vertical, inter-functional, intra-functional, and hybrid—with the dominant pattern differing across governmental functions and evolving as the pandemic progressed. Empirical results reveal that financial departments of different levels of government interact through the vertical pattern. At the same time, intra-functional interaction also exists in provincial financial departments. The supervision departments typically adopt the inter-functional pattern at all levels. At the cross-level and cross-function aspects, the hybrid interaction pattern prevails in the medical function and plays a fair part in the security, welfare, and economic function. This study is one of the first to summarize the interaction patterns in a multi-level setting, providing practical implications for which pattern should be applied to which governmental levels/functions under what pandemic condition.

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