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      Social Network Type and Long-Term Condition Management Support: A Cross-Sectional Study in Six European Countries

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          Abstract

          Background

          Network types and characteristics have been linked to the capacity of inter-personal environments to mobilise and share resources. The aim of this paper is to examine personal network types in relation to long-term condition management in order to identify the properties of network types most likely to provide support for those with a long-term condition.

          Method

          A cross-sectional observational survey of people with type 2 diabetes using interviews and questionnaires was conducted between April and October 2013 in six European countries: Greece, Spain, Bulgaria, Norway, United Kingdom, and Netherlands. 1862 people with predominantly lower socio-economic status were recruited from each country. We used k-means clustering analysis to derive the network types, and one-way analysis of variance and multivariate logistic regression analysis to explore the relationship between network type socio-economic characteristics, self-management monitoring and skills, well-being, and network member work.

          Results

          Five network types of people with long-term conditions were identified: restricted, minimal family, family, weak ties, and diverse. Restricted network types represented those with the poorest self-management skills and were associated with limited support from social network members. Restricted networks were associated with poor indicators across self-management capacity, network support, and well-being. Diverse networks were associated with more enhanced self-management skills amongst those with a long-term condition and high level of emotional support. It was the three network types which had a large number of network members (diverse, weak ties, and family) where healthcare utilisation was most likely to correspond to existing health needs.

          Discussion

          Our findings suggest that type of increased social involvement is linked to greater self-management capacity and potentially lower formal health care costs indicating that diverse networks constitute the optimal network type as a policy in terms of the design of LTCM interventions and building support for people with LTCs.

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          Most cited references13

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          From social integration to health: Durkheim in the new millennium.

          It is widely recognized that social relationships and affiliation have powerful effects on physical and mental health. When investigators write about the impact of social relationships on health, many terms are used loosely and interchangeably including social networks, social ties and social integration. The aim of this paper is to clarify these terms using a single framework. We discuss: (1) theoretical orientations from diverse disciplines which we believe are fundamental to advancing research in this area; (2) a set of definitions accompanied by major assessment tools; and (3) an overarching model which integrates multilevel phenomena. Theoretical orientations that we draw upon were developed by Durkheim whose work on social integration and suicide are seminal and John Bowlby, a psychiatrist who developed attachment theory in relation to child development and contemporary social network theorists. We present a conceptual model of how social networks impact health. We envision a cascading causal process beginning with the macro-social to psychobiological processes that are dynamically linked together to form the processes by which social integration effects health. We start by embedding social networks in a larger social and cultural context in which upstream forces are seen to condition network structure. Serious consideration of the larger macro-social context in which networks form and are sustained has been lacking in all but a small number of studies and is almost completely absent in studies of social network influences on health. We then move downstream to understand the influences network structure and function have on social and interpersonal behavior. We argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social influence; (3) on social engagement and attachment; and (4) access to resources and material goods.
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            The influence of social support on chronic illness self-management: a review and directions for research.

            A review of the empirical literature examining the relationship between social support and chronic illness self-management identified 29 articles, of which 22 were quantitative and 7 were qualitative. The majority of research in this area concerns diabetes self-management, with a few studies examining asthma, heart disease, and epilepsy management. Taken together, these studies provide evidence for a modest positive relationship between social support and chronic illness self-management, especially for diabetes. Dietary behavior appears to be particularly susceptible to social influences. In addition, social network members have potentially important negative influences on self-management There is a need to elucidate the underlying mechanisms by which support influences self-management and to examine whether this relationship varies by illness, type of support, and behavior. There is also a need to understand how the social environment may influence self-management in ways other than the provision of social support
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              Social network type and subjective well-being in a national sample of older Americans.

              The study considers the social networks of older Americans, a population for whom there have been few studies of social network type. It also examines associations between network types and well-being indicators: loneliness, anxiety, and happiness. A subsample of persons aged 65 years and older from the first wave of the National Social Life, Health, and Aging Project was employed (N = 1,462). We applied K-means cluster analysis to derive social network types using 7 criterion variables. In the multivariate stage, the well-being outcomes were regressed on the network type construct and on background and health characteristics by means of logistic regression. Five social network types were derived: "diverse," "friend," "congregant," "family," and "restricted." Social network type was found to be associated with each of the well-being indicators after adjusting for demographic and health confounders. Respondents embedded in network types characterized by greater social capital tended to exhibit better well-being in terms of less loneliness, less anxiety, and greater happiness. Knowledge about differing network types should make gerontological practitioners more aware of the varying interpersonal milieus in which older people function. Adopting network type assessment as an integral part of intake procedures and tracing network shifts over time can serve as a basis for risk assessment as well as a means for determining the efficacy of interventions.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 August 2016
                2016
                : 11
                : 8
                : e0161027
                Affiliations
                [1 ]NIHR Collaboration for Leadership in Applied Health Research (CLAHRC) Wessex, Health Sciences, University of Southampton, Highfield Campus, Southampton, United Kingdom
                [2 ]University Hospital Heidelberg, Department of General Practice and Health Services Research, Im Neuenheimer Feld, Marsilius Arkaden, Turm West, Heidelberg, Germany
                [3 ]Aarhus University, Department of Public Health, Bartholins Alee 2, 8000, Aarhus C, Denmark
                National Institute of Health, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: IV AR AK.

                • Data curation: DC JK IV MW.

                • Formal analysis: DC RO IV.

                • Funding acquisition: AR AK IV MW.

                • Investigation: AR AK IV MW MP JK.

                • Methodology: DC IV MW JK.

                • Project administration: AR AK IV.

                • Software: DC IV.

                • Supervision: AR.

                • Validation: DC MW JK IV AR AK.

                • Visualization: DC IV RO.

                • Writing - original draft: IV.

                • Writing - review & editing: AR AK MW JK MP RO DC IV.

                Author information
                http://orcid.org/0000-0002-2206-8247
                Article
                PONE-D-16-17816
                10.1371/journal.pone.0161027
                4990169
                27536988
                878d71c3-4055-4734-9b91-e9d0c4209457
                © 2016 Vassilev et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 May 2016
                : 28 July 2016
                Page count
                Figures: 0, Tables: 4, Pages: 15
                Funding
                Funded by: European Commission Framework Programme 7
                Funded by: National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) Wessex
                This research has been funded by the NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) Wessex and EU FP7 Collaborative Research Grant—EU-WISE. CLAHRC Wessex is a partnership between Wessex NHS organisations and partners and the University of Southampton and is part of the National Institute for Health Research. EU-WISE is an integrated project under the 7th Framework Programme of the European Commission. The views expressed in this article are those of the authors and not necessarily those of the NIHR.
                Categories
                Research Article
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Social Sciences
                Sociology
                Human Families
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Research and Analysis Methods
                Database and Informatics Methods
                Health Informatics
                People and Places
                Geographical Locations
                Europe
                Norway
                People and Places
                Geographical Locations
                Europe
                Netherlands
                Medicine and Health Sciences
                Public and Occupational Health
                Behavioral and Social Aspects of Health
                Custom metadata
                All relevant data are within the paper.

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