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JMIR Res Protoc. 2017 Jun 22;6(6):e124. doi: 10.2196/resprot.6927.

An Interactive, Mobile-Based Tool for Personal Social Network Data Collection and Visualization Among a Geographically Isolated and Socioeconomically Disadvantaged Population: Early-Stage Feasibility Study With Qualitative User Feedback.

JMIR research protocols

Katherine S Eddens, Jesse M Fagan, Tom Collins

Affiliations

  1. Department of Health, Behavior & Society, College of Public Health, University of Kentucky, Lexington, KY, United States.
  2. Department of Management, Gatton College of Business and Economics, University of Kentucky, Lexington, KY, United States.
  3. University of Kentucky Rural Cancer Prevention Center, College of Public Health, University of Kentucky, Lexington, KY, United States.

PMID: 28642217 PMCID: PMC5500782 DOI: 10.2196/resprot.6927

Abstract

BACKGROUND: Personal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky.

OBJECTIVE: We explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky.

METHODS: A total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio.

RESULTS: OpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher's perspective, and hindering practicality in the field.

CONCLUSIONS: OpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations.

©Katherine S Eddens, Jesse M Fagan, Tom Collins. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.06.2017.

Keywords: Appalachia; cancer screening; diffusion of innovations; health disparities; low literacy; mobile surveys; personal networks; rural health; social network analysis; social networks; survey development; survey implementation

References

  1. J Med Internet Res. 2016 Aug 04;18(8):e216 - PubMed
  2. Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:5360-5371 - PubMed
  3. Am J Community Psychol. 1986 Oct;14(5):479-98 - PubMed
  4. Perspect Psychol Sci. 2015 Mar;10(2):227-37 - PubMed
  5. Annu Rev Public Health. 2007;28:69-93 - PubMed
  6. Science. 2012 Jul 6;337(6090):49-53 - PubMed
  7. Prev Chronic Dis. 2009 Jan;6(1):A34 - PubMed
  8. Sex Transm Dis. 2015 Nov;42(11):607-11 - PubMed
  9. Sex Transm Dis. 2006 Jul;33(7 Suppl):S23-31 - PubMed
  10. Nat Rev Neurol. 2016 Oct;12 (10 ):605-12 - PubMed
  11. AIDS Behav. 2012 Oct;16(7):2015-32 - PubMed
  12. Prev Chronic Dis. 2006 Oct;3(4):A124 - PubMed
  13. N Engl J Med. 2007 Jul 26;357(4):370-9 - PubMed
  14. JMIR Public Health Surveill. 2016 Jun 02;2(1):e28 - PubMed
  15. JMIR Ment Health. 2016 May 05;3(2):e16 - PubMed
  16. Prev Med. 2010 Jan-Feb;50(1-2):74-80 - PubMed
  17. Addict Sci Clin Pract. 2016 Mar 15;11(1):4 - PubMed
  18. Oncologist. 2011;16(8):1072-81 - PubMed
  19. Interact J Med Res. 2016 Jan 14;5(1):e3 - PubMed
  20. Field methods. 2011 Aug;23(3):287-206 - PubMed
  21. Am J Prev Med. 2009 May;36(5):452-7 - PubMed
  22. J Rural Health. 2004 Spring;20(2):181-7 - PubMed
  23. J Health Commun. 2009;14 Suppl 1:5-17 - PubMed
  24. Int J Qual Stud Health Well-being. 2016 Feb 22;11:30396 - PubMed
  25. Fam Community Health. 2012 Jan-Mar;35(1):31-43 - PubMed
  26. Science. 2009 Feb 13;323(5916):892-5 - PubMed
  27. JMIR Mhealth Uhealth. 2016 Jun 03;4(2):e46 - PubMed

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