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Social Physique Anxiety is defined as an emotional response that reflects individuals’ concerns regarding the way their body may be observed or judged by others.
To explore the relationship between physical activity and social physique anxiety.
A literature review haw been made through pubmed database.
Social Physique Anxiety is negatively related to participation in physical activity and commitment to exercise. Studies examining the relationship between motivation and social physique anxiety have shown that external motivations, such as improving muscle tone and body attractiveness, are directly linked to social physique anxiety. In addition, social physique anxiety is negatively related to self-efficacy. Individuals who believe that they will be judged by others to be ineffective are less likely to be engaged in physical activity programs. Social Physique Anxiety has been linked to negative effects on mental health such as low self-esteem, smoking and eating disorders.
Given all the negative effects of social physique anxiety, as it is responsible for a wide range of health-related behaviors, it is important to identify physical activity-related motivational mechanisms in order to reduce the impact of social physique anxiety.
Motivation is an important indicator of predicting an adult’s commitment to exercise so it is important to explore the reasons that may lead a person to participate in physical activity programs.
To investigate the socio-demographic and psychological parameters that motivate adults to participate in exercise programs and athletic activities.
245 adults, being engaged in physical activity programs were given a questionnaire to collect information on socio-demographic characteristics, possible previous problems with body weight, type of exercise, frequency and main reason for their participation in exercise programs, as well as the somatometric characteristics of the participants.
It is noteworthy that participants’ motive for exercise was pleasure (for 46.1% of the participants), championship (for 20.8% of the participants), health reasons (for 18.4% of the participants), weight loss (for 7.8% of the participants) and improvement of physical appearance (for 6.9% of the participants). A greater percentage of male compared to female participants were engaged to exercise due to championship reasons, while more women than men exercised to a statistically significant extent in order to improve their appearance and for health reasons.
Understanding the main factors that make individuals being engaged to physical activity may help health professionals to implement educational and counseling intervention programs regarding the positive effects of exercise on individuals’ mental and emotional health. Physical activity contributes to the improvement of their quality of life, which may be the most important issue for mental and public health.
Chapter 2 is methodological, offering a primer on multimodal network analysis. It proceeds by quickly reviewing 1-mode network analysis, paying special attention to summarizing several measures of network centrality and how they relate to power. Often, relational data that are 2-mode or multimodal are “projected” into one of the node sets. Ties are then defined by their shared relations to the second-mode nodes so that 1-mode measures of centrality and algorithms for community detection can be employed. We discuss the loss of information on structure and agency that projection entails and argue that, in many cases, projection is neither helpful nor necessary. We then proceed to detailed discussions of methods for 2-mode and 3-mode network analysis, from first principles of matrix algebra to centrality measures and core-periphery analysis; faction analysis and community detection; as well as structural/regular equivalence and blockmodeling. We conclude with a brief introduction to recent advances in statistical network modelling that facilitate inferences about multimodal networks.
Chapter 1 lays out the cornerstones of our argument. We highlight how power is multidimensional and is related to network position. We review several works on field theory, contest arenas, and social spaces to highlight how analysts can theorize political action in multimodal settings. Then we explain why communities are a key concept for theory and research in political networks: how they can be identified, how they are created, and what effects they have on individual-, community-, and systemic-level outcomes. Last, we show that while some researchers have studied multimodal social networks (particularly 2-mode networks), few have conducted systematic treatments of multimodal political networks.
Chapter 3 tackles a major theme applied throughout successive chapters: agency. We begin with an overview of how agency, leadership, and entrepreneurship have been identified using network analysis. We present political entrepreneurship and leadership as network constructs and identify political influence as often operating across multiple modes. We demonstrate these arguments with three applications: a unimodal case of EU competition policy, a 2-mode case analyzing interests in US labor policy, and a multimodal case inferring agency at multiple levels in global fisheries governance.
Chapter 5 identifies the participation and roles of individuals in civil society. We argue that concentrating only on individuals would be more taxing and less meaningful than a multimodal analysis of interactions between individuals and associations in collective action fields. Individuals’ overlapping memberships allow organizations to monitor their environment, allocate resources, communicate, ally with others, and define the boundaries of their actions. At the same time, organizations enable individuals to meet similar others, strengthen their collective identity, share their skills and experiences, deal with threats, explore opportunities, and develop individual identities. We demonstrate how to use data on individual participation from the European Values Survey to conduct a relational, comparative analysis of the structure of political communities. Although multiple membership data are often employed to classify organizational types, here we investigate the structure and roles of the actors involved using projection and structurally equivalent blockmodeling. We examine networks of individuals and organizations in Italy, the UK, and Germany in 1990 and 2008 for a rich, comparative design that reveals the different profiles of political communities in those three countries.
Chapter 4 analyzes public policy networks, especially in relation to policymaking events. We begin by reviewing key concepts in this field – policy communities, policy events, and event public networks – before presenting a restricted 2-mode perspective on policy communities. Our application is to the US labor policy domain, analyzed with concepts and methods introduced in the preceding chapters: core/periphery models and optimal modularity community analysis. We next extend the application to a less-restricted 3-mode network of private-sector organizations’ interests in events, government organizations’ interests in events, and direct communication ties between (but not within) the private and government organizations. A multidimensional scaling analysis of this 3-mode structure reveals how homogenous and relatively tightly structured this policy field is. By preserving complete multimodal network information, the results both support previous research on event publics and yield a more nuanced understanding of the structural contexts within which policy communities attend to their interests.
Chapter 8 investigates legislative influence. We analyze US senators’ voting on bills in the 112th Congress and campaign contributions senators received from PACs. Optimal modularity analysis identifies communities by maximizing densities of ties within communities. It finds great polarization between Republican and Democratic senators, their campaign financiers, and their legislative voting agendas. The core-periphery model finds that the core and peripheral communities are both heterogeneous mixtures of senatorial partisan affiliations, funding sources, and voting decisions. The affiliated graph model allows some entities to belong to more than one community and others to none. On balance, the AGM result provide a more plausible and nuanced depiction of the complex nexus between political money and legislative voting. Each community contains almost all the senators of one political party, their PAC funders, and their preferred legislative bills. But, both communities exhibit heterogenous mixtures of entities due to a substantial dual-community component comprising subsets of the three entities. The AGM approach strongly supports a research hypothesis that US legislative communities are divided into two bipartisan camps. However, a subgroup of entities belonging to both communities has the structural potential to play a power brokerage, or go-between, role.
Chapter 7 examines nations trading and fighting. It begins by reviewing networks-related research in three fields: world systems, world polity, and international relations. We proposed two hypotheses from these fields: the trade–conflict hypothesis that there is an inverse relationship between trade and conflict; and the democratic peace hypothesis that democratic states are less likely to engage in militarized disputes. We investigate both hypotheses using data collected by the Correlates of War project from 2001 to 2010. Analysis of a 2-mode network of bilateral trade ties and memberships in intergovernmental organizations identifies four communities. A 2-mode network of diplomatic exchanges and memberships in military alliances also finds four communities. To test the hypotheses, we use Quadratic Assignment Procedure to regress militarized interstate disputes (MIDs) between dyads on trading communities, alliance communities, and types of governmental regimes. Nations belonging to the same international trade community were more likely to engage in MIDs. Democratic states were not less likely to fight one another, nor were authoritarian regimes more likely to experience MIDs. But, conflicts were very much more likely to erupt between democratic and authoritarian states.
Chapter 9 concludes this volume with a brief reflection on the future of multimodal political network analysis. We also offer suggestions about the benefits and research designs of a set of future projects that would apply the multimodal political network analyses theories and methods illustrated throughout the volume.
Chapter 6 extends beyond the preceding chapter and explores collective action fields. It begins by reviewing some limitations with the previous approach: its granularity is limited to organizational types and not particular associations, and it does not incorporate the role of events in the political process in tandem for individuals and organizations. Our example illustrates how to overcome such limitations where data allow it. Focusing on civil society actors in one British city, Bristol, we explore the networks linking citizens’ associations, their core members, and local public events of both a contentious and non-contentious kind. We treat those networks from two different perspectives: first as a “restricted” 3-mode network in which ties only occur between elements that are logically proximate to each other (in our case, individuals participating in organizations that themselves promote or support specific events); then as a “general” 3-mode network that additionally allows for ties across all different modes (in our case, this means including individuals’ direct participation in events). We show that again, where data allow, multimodal political network analysis offers a fruitful avenue to the analysis of political settings.
Research on social networks has become a significant area of investigation in the social sciences, and social network concepts and tools are widely employed across many subfields within the field. This volume introduces political theorists and researchers to new theoretical, methodological, and substantive tools for extending political network research into new realms and revitalizing established domains. The authors synthesize new understandings of multimodal political networks, consisting of two or more types of social entities - voters, politicians, parties, events, organizations, nations - and the complex relations between them. They discuss ways to theorize about multimodal connections, methods for measuring and analyzing multimodal datasets, and how the results can reveal new insights into political structures and action. Several empirical applications demonstrate in great detail how multimodal analysts can detect and visualize political communities consisting of diverse social entities.