scholarly journals Topic-Aware Physical Activity Propagation in a Health Social Network

2016 ◽  
Vol 31 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Nhathai Phan ◽  
Javid Ebrahimi ◽  
Dave Kil ◽  
Brigitte Piniewski ◽  
Dejing Dou
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Health Social Network

scholarly journals Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network

2016 ◽  
Vol 8 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Nhathai Phan ◽  
Javid Ebrahimi ◽  
David Kil ◽  
Brigitte Piniewski ◽  
Dejing Dou
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Temporal Dynamics ◽  
Health Social Network

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

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.

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.

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

Social Network Analysis in Child and Adolescent Physical Activity Research: A Systematic Literature Review

2020 ◽  
Vol 17 (2) ◽  
pp. 250-260 ◽  
Author(s):  
Tyler Prochnow ◽  
Haley Delgado ◽  
Megan S. Patterson ◽  
M. Renée Umstattd Meyer
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Children And Adolescents ◽  
Child And Adolescent ◽  
Adolescent Physical Activity ◽  
Final Sample ◽  
Network Composition ◽  
The Impact

Background: Regular physical activity (PA) has many benefits for children and adolescents, yet many do not meet PA recommendations. Social context is important for promoting or discouraging PA among children and adolescents. This review aimed to identify social network variables related to PA among children and adolescents. Methods: A systematic review of the literature was conducted in September 2018 using PsycINFO, MEDLINE, PubMed, and Web of Science. Included articles needed to (1) be focused on children (aged 5–11 y) or adolescents (aged 12–17 y), (2) include a measure of PA, (3) include a measure of egocentric or sociocentric social connection in which alters were nominated, and (4) perform an analysis between network data and PA. Results: A search of 11,824 articles was refined to a final sample of 29 articles. Social network themes and concepts such as homophily, centrality, and network composition were related to child and adolescent PA behavior across the literature. Conclusions: The impact of an individual’s social network is evident on their PA behaviors. More research is needed to examine why these networks form in relation to PA and how interventions can utilize social network analysis to more effectively promote PA, especially in underserved and minority populations.

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