Physical Activity Contagion and Homophily in an Adaptive Social Network Model

2018 ◽  
pp. 87-98 ◽  
Author(s):  
Marit van Dijk ◽  
Jan Treur
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Network Model

scholarly journals Methods of Measuring Influence of Bank Customer Using Social Network Model

2015 ◽  
Vol 05 (04) ◽  
pp. 155-160 ◽  
Author(s):  
Huaqing Mao ◽  
Xinyuan Jin ◽  
Li Zhu
Keyword(s):  
Social Network ◽  
Network Model

scholarly journals Social Networks and Physical Activity in Senior Housing: A Pilot Feasibility Study

2019 ◽  
Vol 29 (Suppl 1) ◽  
pp. 201-208
Author(s):  
Noah J. Webster ◽  
Toni C. Antonucci ◽  
Neil B. Alexander
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Social Network ◽  
Social Relations ◽  
Low Income ◽  
Strategic Partnership ◽  
Multiple Chronic Conditions ◽  
Activity Levels ◽  
Senior Housing ◽  
Physical Activity Interventions

Objective: Only 16% of people aged >65 years engage in recommended levels of physical activity, putting a vast major­ity at risk for multiple chronic conditions including heart disease. Physical activity is even lower among older adults with fewer economic resources. Research is needed to develop context-specific approaches to pair with physical activity interventions to increase effectiveness. In this pilot study, we examine social ties and physical activity levels of older adults living in a US Depart­ment of Housing and Urban Development subsidized senior housing community to test feasibility of a social network-based approach to physical activity interventions. This study is grounded in Social Contagion Theory and the Convoy Model of Social Relations, which argue health and health-related behaviors are facilitated through network ties.Methods: Data were collected through face-to-face interviews conducted over the course of three months (September- November 2018) with 46 residents living in a low-income senior housing community in southeast Michigan. Residents were asked about physical activity, people they know in the community, and their close social network composition.Results: Residents reported knowing, on average, six other residents and approxi­mately 28% of those in their close networks were also residents. Sociocentric network analysis identified two socially engaged (known by seven or more other residents) physically active residents, whereas ego-centric analysis identified four (60% or more of their network comprised residents).Conclusions: This study demonstrates potential feasibility of a strategic partnership that involves pairing social resources with physical activity interventions in afford­able senior housing. Multiple approaches, which need to be evaluated, exist to identify socially engaged residents.Ethn Dis. 2019;29(Suppl 1): 201-208; doi:10.18865/ ed.29.S1.201.

Social Network Analysis of Childhood and Youth Physical Activity

2012 ◽  
Vol 43 (6) ◽  
pp. 636-642 ◽  
Author(s):  
Kyle Macdonald-Wallis ◽  
Russell Jago ◽  
Jonathan A.C. Sterne
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Childhood And Youth ◽  
Youth Physical Activity

Achieving Efficient Team Cooperation Based on the Passing Social Network Model

2021 ◽  
Author(s):  
Zonglin Li ◽  
Ao Zhang ◽  
Haiyang Cheng
Keyword(s):  
Social Network ◽  
Network Model ◽  
Team Cooperation

Associations of Social Network Characteristics With Gait Speed in Older Women From Physical Activity Community Groups

2020 ◽  
pp. 1-8
Author(s):  
Carlos M. Arango-Paternina ◽  
Jhon F. Ramírez-Villada ◽  
Annie A. Tibaduiza-Romero ◽  
Leonardo Rodríguez-Perdomo
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Gait Speed ◽  
Cross Sectional Study ◽  
Adult Women ◽  
Community Groups ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Speed Test ◽  
The Social

The identification of social network factors associated with gait speed may offer different perspectives for improving community and clinical interventions for older adults. The objective of this study was to explore the associations of the social network of friends with gait speed. This was a cross-sectional study conducted in a sample of 128 older adult women recruited in community groups of physical activity. Clinical screening, social network questions, body composition evaluation, and gait speed test were applied to the participants. Logistic regression models were used to analyze associations between characteristics of the social network of friends and high gait speed. Findings indicated that social isolation was not associated with high gait speed and that popularity and proportion of friends with high gait speed were significantly associated with high gait speed. Findings suggested that there was a relationship between social network factors and the ability to perform high gait speed.

Autonomy-oriented proximity mobile social network modeling in smart city for emergency rescue

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhao Chunxiao ◽  
Guo Junjie
Keyword(s):  
Social Network ◽  
Network Model ◽  
Smart City ◽  
Disaster Relief ◽  
Network Modeling ◽  
Mobile Social Network ◽  
Emergency Rescue ◽  
Routing Strategy ◽  
Social Network Modeling ◽  
Post Disaster

Nearest neighbor mobile social network means that movers approaching in position communicate through their social sensors, which is called Proximity Mobile Social Network. Proximity Mobile Social Network can provide more social and business opportunities for users. To carry out disaster relief work in post-disaster environment, we need to collect incident information during the search process and report to the sink in time. Proximity Mobile Social Network provides flexible systems for emergency handling and disaster relief. Therefore, how to find a better data forwarding and routing strategy is the key problem of post-disaster rescue, and the research of user mobility model is the basis of the above problems. This article presents an Autonomy-Oriented Proximity Mobile Social Network modeling for emergency rescue in smart city, which simulates the network operating environment. First, we verify the performance of Autonomy-Oriented Proximity Mobile Social Network model in terms of self-organization, scale-free, aggregation, and community structure. Then, the rescue efficiency is discussed through the coverage of mobile sensors. Finally, performance of the routing strategy based on Autonomy-Oriented Proximity Mobile Social Network model is analyzed, and the effectiveness of the method is proved.

Factors affecting BIM application in China: a social network model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuangliang Tai ◽  
Yao Zhang ◽  
Ting Li
Keyword(s):  
Social Network ◽  
Network Model ◽  
Information Modeling ◽  
Information Communication ◽  
Key Factors ◽  
Content Type ◽  
Factors Affecting ◽  
Building Information ◽  
Management Modes ◽  
Definition Of

Purpose The purpose of this paper is to promote the application of building information modeling (BIM) in China’s construction industry, key factors and their relationships are explored. Design/methodology/approach Based on a literature review, 28 factors were extracted and their relationships (239 in total) obtained using the Delphi method. A social network model of the factors was constructed and factors were analyzed using social network analysis (SNA). Findings The top 10 key factors and their relationships were obtained using SNA. Among the top 10 critical factors, six were source factors. They were: training for the application of BIM, guidance from experts, proper management modes, efficient BIM teams, specifications and demonstrations and standards for building information communication. The other four factors included as follows: a willingness to accept BIM, knowledge of its value and benefits, the definition of its benefits and the availability of IT software and hardware. These were mediating factors that could further the influence of the source factors. Originality/value The results provide useful information for public agencies and professionals to understand the immediate and mediating influences of the factors on the application of BIM. Solutions and future efforts for different participants are presented to promote the application of BIM-based on the key factors and their relationships.

scholarly journals LINKS BETWEEN SOCIAL NETWORK CHARACTERISTICS AND PHYSICAL ACTIVITY IN SENIOR HOUSING

2018 ◽  
Vol 2 (suppl_1) ◽  
pp. 274-274
Author(s):  
N Webster ◽  
N Alexander ◽  
T Antonucci
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Senior Housing ◽  
Network Characteristics
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