Conceptual framework of psychographic research in the field of prevention of non-communicable diseases

October 21, 2020
937
Specialities :
Resume

Aim — to describe the conceptual framework of the joint research by Shupyk National Medical Academy of Postgraduate Education and Uzhhorod National University focused on the application of psychographic segmentation of the target audience on the factors of non-communicable diseases.

Object and methods. Bibliosemantic, biostatistical, sociological methods, and the method of expert assessments were used. Reports and questionnaires of three international cross-sectional studies on the attitude of Ukrainian citizens to certain aspects of health, available World Health Organization guidelines for the last 15 years on communication, prevention of non-communicable diseases, and promotion of a healthy lifestyle were analyzed. A Questionnaire has been developed to study the factors of occurrence and measures for preventing of non-communicable diseases (to be filled in by the public and medical doctors).

Results. The developed Questionnaire for the Study of Factors and Prevention of Noncommunicable Diseases provides answers to questions in 14 areas, such as understanding the problem, a person’s willingness to change behavior, take specific measures for maintaining health, assessing levels of personal engagement or barriers.

Conclusion. The implementation of the survey and the analysis of respondents’ answers were designed to determine the structure of the population’s beliefs about non-communicable diseases prevention measures, the main factors, population segments, and determinants of readiness to choose healthy behavior.

References:

  • Balashov K.V. (2020) Public health and culture: points of contact. III International scientific-practical conference «Modern science: problems and innovations». Stockholm, June 1–3, p. 74–79.
  • State Statistics Service of Ukraine, Ukrainian Center for Social Reforms, PJSC «Statinformconsulting» and oth. (2012) Ukraine. Multi-indicator cluster survey of households (https://ucsr.kiev.ua/publications/Ukraine_MICS_Final_Report_UKR(1)2.pdf).
  • Paniotto V., Kharchenko N. (2017) Survey methods. Kyiv, «Kyiv-Mohyla Academy» Publishing House, 342 p.
  • Allara E., Angelini P., Gorini G. et al. (2019) Effects of a prevention program on multiple health-compromising behaviours in adolescence: a cluster randomized controlled trial. Prev. Med., 124: 1–10. doi: 10.1016/j.ypmed.2019.04.001.
  • Balku E., Tóth G., Nárai E. et al. (2017) Methodology for identification of healthstyles for developing effective behavior change interventions. J. Public Health, 25: 387–400.
  • Betsch C., Böhm R. (2016) Cultural Diversity Calls for Culture — Sensitive Health Communication. Med. Decis. Making, 36(7): 795–797. doi: 10.1177/0272989X16663482.
  • Büchter R.B., Betsch C., Ehrlich M. et al. (2019) Communicating Uncertainty From Limitations in Quality of Evidence to the Public in Written Health Information: Protocol for a Web-Based Randomized Controlled Trial. JMIR Res. Protoc., 8(5): e13425. doi: 10.2196/13425.
  • DeBord D.G., Carreón T., Lentz Th.J. et al. (2016) Use of the «Exposome» in the Practice of Epidemiology: A Primer on -Omic Technologies. Am. J. Epidemiol., 184(4): 302–314. doi: 10.1093/aje/kwv325.
  • Diez-Roux A.V. (2000) Multilevel Analysis In Public Health Research. Annu. Rev. Public Health, 21: 1710192. doi: 10.1146/annurev.publhealth.21.1.171.
  • Engl E., Smittenaar P., Sgaier S.K. (2019) Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide (https://gatesopenresearch.org/articles/3-1503).
  • Gan G., Ma Ch., Wu J. (2007) Data Clustering: Theory, Algorithms, and Applications. DOI: 10.1137/1.9780898718348.
  • Cheng H., Kotler Ph., Lee N. (2011) Social Marketing for Public Health: Global Trends and Success Stories. Jones & Bartlett Learning, 422 p.
  • Liao M., Li Y., Kianifard F. et al. (2016) Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis. BMC Nephrol., 17: 25. doi: 10.1186/s12882-016-0238-2.
  • Maibach E.W., Maxfield A., Ladin K., Slater M. (1996) Translating Health Psychology into Effective Health Communication: The American Healthstyles Audience Segmentation Project. J. Health Psychol., 1(3): 261–277. https://doi.org/10.1177/135910539600100302.
  • Nárai E. (2009) Healthstyle segmentation, attitudes to health (http://pszichologia.phd.elte.hu/vedesek/2010/Theses_Erzsebet_Narai.pdf).
  • Navarro F.H. (1990) Profiles of Attitudes Toward Healthcare: Psychographic Segmentation. DOI: 10.13140/RG.2.1.3420.9522.
  • Rappaport S. (2011) Implications of the exposome for exposure science. J. Expo. Sci. Environ. Epidemiol., 21: 5–9.
  • Slater M.D., Flora J.A. (1991) Health Lifestyles: Audience Segmentation Analysis for Public Health Interventions. Health Education Quarterly, 221–233.
  • Slater M.D., Kelly K.J., Thackeray R. (2006) Segmentation on a Shoestring: Health Audience Segmentation in Limited-Budget and Local Social Marketing Interventions. Health Promot. Pract., 7(2): 170–173.
  • Vuik S.I., Mayer E., Darz A. (2016) A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population. Population Health Metrics, 44(2016): https://pophealthmetrics.biomedcentral.com/articles/10.1186/s12963-016-0115-z.
  • WHO (2011) From Burden to «Best Buys»: Reducing the Economic Impact of Non-Communicable Diseases in Low- and Middle-Income Countries (https://www.who.int/nmh/publications/best_buys_summary/en/).
  • WHO (2013) Global action Plan for the prevention and control of NCDs 2013–2020 (https://www.who.int/nmh/events/ncd_action_plan/en/).
  • WHO (2018) Global action plan on physical activity 2018–2030: more active people for a healthier world (https://www.who.int/ncds/prevention/physical-activity/global-action-plan-2018-2030/en/).