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Social Network Interventions for Health Behaviours
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Social Network Interventions for Health Behaviours

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 9748

Special Issue Editors

Dr. Sabina B. Gesell

E-Mail Website
Guest Editor
Wake Forest School of Medicine, Wake Forest University, Winston-Salem, NC, USA
Interests: design and implementation of interventions that intentionally leverage social networks as a mechanism for spreading knowledge, norms, and behaviors to improve health; community health services; patient experience; design and implementation of complex health system interventions with stakeholder engagement
Dr. Kayla de la Haye

E-Mail Website
Guest Editor
Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Interests: application of social network analysis and systems science to health promotion and disease prevention; family and community social networks to promote healthy eating and prevent childhood obesity; role of social networks in group problem solving in families, teams, and coalitions
Dr. Katherine S. Eddens

E-Mail Website
Guest Editor
School of Public Health, Indiana University, Bloomington, IN, USA
Interests: role of networks in health and health communication inequities, particularly across the cancer prevention and control and survivorship continuum; applied health communication; technology development to improve network data collection methods; community-based research in rural and urban health inequities.
Dr. Thomas W. Valente

E-Mail Website
Guest Editor
Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Interests: social networks and health: models, methods, and applications; implementation of programs designed to prevent tobacco and substance abuse, unintended fertility, and STD/HIV infections; mapping community coalitions and collaborations to improve health care delivery and reduce healthcare disparities

Special Issue Information

Dear Colleagues,

Social networks can diffuse both positive and negative health behaviours through groups, and they can provide access to social support and social capital, or they can be a source of discrimination and social exclusion—all of which can impact health. Given the growing interest in the development and use of social-network-based interventions, we invite authors to submit original investigations, methods papers, or review papers that further our understanding of the effects of social networks and social influence on health behaviour throughout the lifespan.

Potential topics include, but are not limited to:

  • Interventions that leverage social networks to promote behaviour change (within real-world conditions and controlled trials);
  • Innovations in network data collection methods or tools, sampling approaches, incentives, intervention targets or topics, data analysis, network visualization, and the use of technology for network-based interventions;
  • Impact of social ties, social influences, and social structure on the adoption and maintenance of health behaviours (quantitative, qualitative, or mixed methods);
  • Development of new social networks to influence health;
  • Mechanisms by which social networks influence health behaviours;
  • Methods and analytic models to assess social processes and social network dynamics.

Dr. Sabina B. Gesell
Dr. Kayla de la Haye
Dr. Katherine S. Eddens
Dr. Thomas W. Valente
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Social network
  • Intervention
  • Implementation
  • Health behaviour
  • Health risk behaviours

Published Papers (4 papers)

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Research

22 pages, 758 KiB  
Article
Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions
by Kara Layne Johnson, Jennifer L. Walsh, Yuri A. Amirkhanian and Nicole Bohme Carnegie
Int. J. Environ. Res. Public Health 2021, 18(24), 13394; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182413394 - 20 Dec 2021
Cited by 1 | Viewed by 1471
Abstract
Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We [...] Read more.
Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM). Full article
(This article belongs to the Special Issue Social Network Interventions for Health Behaviours)
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16 pages, 617 KiB  
Article
The Structure of Social Support in Confidant Networks: Implications for Depression
by Liyuan Wang, Lindsay E. Young and Lynn C. Miller
Int. J. Environ. Res. Public Health 2021, 18(16), 8388; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18168388 - 08 Aug 2021
Cited by 4 | Viewed by 1862
Abstract
Social support differs for depressed and non-depressed individuals. However, the structural features of social supports, as represented via social networks and how they are related to depression, and its mitigation, are unclear. Here, we examine associations between personal support network structures and self-reports [...] Read more.
Social support differs for depressed and non-depressed individuals. However, the structural features of social supports, as represented via social networks and how they are related to depression, and its mitigation, are unclear. Here, we examine associations between personal support network structures and self-reports of depression and depression mitigation behaviors. Cross-sectional data were collected from participants (n = 1002 adults) recruited from a research volunteer website. Personal support networks were elicited by asking participants to nominate up to six people (i.e., confidants) that they talk to about interpersonal problems (e.g., unpleasant social encounters) and to indicate who knows whom among their confidants. Results show that the confidant networks of depressed and non-depressed participants did not differ in network size or in constraint—i.e., the degree to which confidants’ ties overlap with the ties of the participant. However, depressed participants’ confidants had significantly fewer average ties with one another (mean degree). Irrespective of depression diagnosis, lower network constraint and size predicted greater engagement in depression mitigation behaviors. That is, having relatively large confidant networks within which one can freely navigate one’s personal information can contribute to improvement in depressive outcomes. Implications are further discussed in the discussion section. Full article
(This article belongs to the Special Issue Social Network Interventions for Health Behaviours)
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19 pages, 983 KiB  
Article
The Co-Evolution of Network Structure and PrEP Adoption among a Large Cohort of PrEP Peer Leaders: Implications for Intervention Evaluation and Community Capacity-Building
by Lindsay E. Young and John A. Schneider
Int. J. Environ. Res. Public Health 2021, 18(11), 6051; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18116051 - 04 Jun 2021
Cited by 5 | Viewed by 2217
Abstract
Background: Peer leader interventions are effective strategies for promoting prevention behaviors in communities at risk for HIV, yet little is known about their effects on the social and behavioral dynamics of peer leaders themselves. Methods: Using data from PrEP Chicago, an RCT PrEP [...] Read more.
Background: Peer leader interventions are effective strategies for promoting prevention behaviors in communities at risk for HIV, yet little is known about their effects on the social and behavioral dynamics of peer leaders themselves. Methods: Using data from PrEP Chicago, an RCT PrEP for prevention intervention for young Black MSM (YBMSM), we apply stochastic actor-based models to longitudinally model the impact of study participation on the online friendship and PrEP adoption dynamics among a network of peer leaders (n = 174) and a network of control group counterparts (n = 166). Results: Peer leaders assigned to the same leadership training workshop were more likely to form new Facebook friendships with one another, whereas control participants assigned to the same attention control workshop were no more or less likely to form new friendships. Further, peer leaders with greater PrEP intentions and those living with HIV were more active in forming new friendships with other peer leaders, effects not found in the control network. PrEP adoption was not influenced by network dynamics in either group. Conclusions: The implications of these findings are discussed through the lens of community-capacity building and the role that peer leader interventions and the networks they engage can impact public health. Full article
(This article belongs to the Special Issue Social Network Interventions for Health Behaviours)
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11 pages, 530 KiB  
Article
An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places
by Andrew Pilny and C. Joseph Huber
Int. J. Environ. Res. Public Health 2021, 18(4), 1466; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18041466 - 04 Feb 2021
Cited by 2 | Viewed by 2251
Abstract
Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain [...] Read more.
Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing to the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one condition each. They were prompted to anonymously provide contacts and populated places visited from the past four days depending on what condition they were given. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely to recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to the development of innovative social network data collection methods for contract tracing survey instruments. Full article
(This article belongs to the Special Issue Social Network Interventions for Health Behaviours)
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