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Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data

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Abstract

This study aimed (i) to determine whether people could be differentiated on the basis of their sun protection belief profiles and individual characteristics and (ii) explore the use of a crowdsourcing web service for the assessment of sun protection beliefs. A sample of 500 adults completed an online survey of sun protection belief items using Amazon Mechanical Turk. A two-phased cluster analysis (i.e., hierarchical and non-hierarchical K-means) was utilized to determine clusters of sun protection barriers and facilitators. Results yielded three distinct clusters of sun protection barriers and three distinct clusters of sun protection facilitators. Significant associations between gender, age, sun sensitivity, and cluster membership were identified. Results also showed an association between barrier and facilitator cluster membership. The results of this study provided a potential alternative approach to developing future sun protection promotion initiatives in the population. Findings add to our knowledge regarding individuals who support, oppose, or are ambivalent toward sun protection and inform intervention research by identifying distinct subtypes that may best benefit from (or have a higher need for) skin cancer prevention efforts.

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References

  1. American Cancer Society (2015) Cancer facts & figures 2015. American Cancer Society, Atlanta

    Google Scholar 

  2. Coups EJ, Manne SL, Heckman CJ (2008) Multiple skin cancer risk behaviors in the U.S. population. Am J Prev Med 34:87–93

    Article  PubMed  Google Scholar 

  3. U.S. Department of Health and Human Services (2014) The surgeon general’s call to action to prevent skin cancer. U.S. Department of Health and Human Services, Office of the Surgeon General, Washington, DC

    Google Scholar 

  4. de Troya-Martin M, Delgado-Sanchez N, Blazquez-Sanchez N, Ortega-Tudela G, Toribio-Montero JC, Jabalera-Mesa ML, Rivas-Ruiz F (2014) Skin cancer prevention campaign aimed at beachgoers on the Costa del Sol (southern Spain). Int J Dermatol 53:526–553

    Article  Google Scholar 

  5. Dudley DA, Winslade MJ, Wright BJ, Cotton WG, McIver JL, Jackson KS (2015) Rationale and study protocol to evaluate the SunSmart policy intervention: a cluster randomised controlled trial of a primary school-based health promotion program. BMC Public Health. doi:10.1186/s12889-015-1371-8

    PubMed  PubMed Central  Google Scholar 

  6. Glanz K, Escoffery C, Elliott T, Nehl EJ (2014) Randomized trial of two dissemination strategies for a skin cancer prevention program in aquatic settings. Am J Public Health. doi:10.2105/AJPH.2014.302224

    PubMed  Google Scholar 

  7. Kearney GD, Xu X, Balanay JAG, Becker AJ (2014) Sun safety among farmers and farmworkers: a review. J Agromed 19:53–65

    Article  Google Scholar 

  8. Nahar VK, Ford MA, Hallam JS, Bass MA, Vice MA (2013) Sociodemographic and psychological correlates of sun protection behaviors among outdoor workers: a review. J Skin Cancer. doi:10.1155/2013/453174

    PubMed  PubMed Central  Google Scholar 

  9. Bowen DJ, Burke W, Hay JL, Meischke H, Harris JN (2014) Effects of web-based intervention on risk reduction behaviors in melanoma survivors. J Cancer Surviv. doi:10.1007/s11764-014-0412-0

    PubMed  PubMed Central  Google Scholar 

  10. Huang C, Yan S, Ren J, Xiang L, Hu Y, Kang K, Seite S (2013) A quantitative assessment of the effects of formal sun protection education on photosensitive patients. Photodermatol Photoimmunol Photomed 29:261–265

    Article  PubMed  Google Scholar 

  11. Glanz K, Volpicelli K, Jepson C, Ming ME, Schuchter LM, Armstrong K (2015) Effects of tailored risk communications for skin cancer prevention and detection: the PennSCAPE randomized trial. Cancer Epidemiol Biomarkers Prev 24:415–421

    Article  CAS  PubMed  Google Scholar 

  12. Manne SL, Jacobsen PB, Ming ME, Winkel G, Dessureault S, Lessin SR (2010) Tailored versus generic interventions for skin cancer risk reduction for family members of melanoma patients. Health Psychol 29:583–593

    Article  PubMed  PubMed Central  Google Scholar 

  13. Robinson JK, Guevara Y, Gaber R, Clayman ML, Kwasny MJ, Friedewald JJ, Gordon EJ (2014) Efficacy of a sun protection workbook for kidney transplant recipients: a randomized controlled trial of a culturally sensitive educational intervention. Am J Transplant 14:2821–2829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Amazon Web Services (2014) Amazon Mechanical Turk: requester UI guide.http://awsdocs.s3.amazonaws.com/MechTurk/latest/amt-ui.pdf

  15. Santiago-Rivas, Marimer, Betty Wang, and Lina Jandorf. 2014. Sun protection beliefs among Hispanics in the US. Journal of Skin Cancer. doi: 10.1155/2014/161960

  16. Fitzpatrick TB (1988) The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 124:869–871

    Article  CAS  PubMed  Google Scholar 

  17. Harber P, Gondy L (2015) Assessing work-asthma interaction with Amazon Mechanical Turk. J Occup Environ Med 57:381–385

    Article  PubMed  Google Scholar 

  18. Carter, Rebecca R., Analisa DiFeo, Kath Bogie, Guo-Qiang, and Jiayang Sun. (2014) Crowdsourcing awareness: exploration of the ovarian cancer knowledge gap through Amazon Mechanical Turk. PLoS One 9. doi: 10.1371/journal.pone.0085508

  19. Gardner RM, Brown DL, Boice R (2012) Using Amazon’s Mechanical Turk website to measure accuracy of body size estimation and body dissatisfaction. Body Image 9:532–534

    Article  PubMed  Google Scholar 

  20. Kiefner-Burmeister AE, Hoffmann DA, Meers MR, Koball AM, Musher-Eizenman DR (2014) Food consumption by young children: a function of parental feeding goals and practices. Appetite 74:6–11

    Article  PubMed  Google Scholar 

  21. Reidy, D. E., Kathryn A. Brookmeyer, Brittany Gentile, Danielle S. Berke, and Amos Zeichner. (2015) Gender role discrepancy stress, high-risk sexual behavior, and sexually transmitted disease. Arch Sex Behav 7. 10.1007/s10508-014-0413-0

  22. Rand DG (2012) The promise of Mechanical Turk: how online labor markets can help theorists run behavioral experiments. J Theor Biol 299:172–179

    Article  PubMed  Google Scholar 

  23. SAS Institute Inc (2008) SAS/STAT® 9.2 user’s guide. SAS Institute Inc., Cary

    Google Scholar 

  24. Clatworthy J, Buick D, Hankins M, Weinman J, Horne R (2005) The use and reporting of cluster analysis in health psychology: a review. Br J Health Psychol 10:329–358

    Article  PubMed  Google Scholar 

  25. Babbin SF, Velicer WF, Paiva AL, Brick LAD, Redding CA (2015) Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use. Addict Behav 40:57–65

    Article  PubMed  Google Scholar 

  26. Ahmed, Nasar U., Kelly Winter, Ahmed N. Albatineh, and Gilian Haber. (2012) Clustering very low-income, insured women’s mammography screening barriers into potentially functional subgroups. Women’s Health Issues 22. doi: 10.1016/j.whi.2012.02.001

  27. Santiago-Rivas M, Velicer WF, Redding CA, Prochaska JO, Paiva AL (2013) Outcomes of cluster profiles within stages of change for sun protection behavior. Psychol Health Med 18:471–481

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nahar VK, Ford MA, Boyas JF, Brodell RT, Hutcheson A, Davis RE, Kim RB, Bass MA, Biviji-Sharma R (2014) Skin cancer preventative behaviors in state park workers: a pilot study. Environ Health Prev Med 19:467–474

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by a grant received by the Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai (National Cancer Institute at the National Institutes of Health R25-CA081137).

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Authors and Affiliations

  1. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, Medical Institute, 1425 Madison Ave, Suite 3-50, One Gustave L. Levy Place, Box 1130, New York, NY, 10029, USA

    Marimer Santiago-Rivas, Julie B. Schnur & Lina Jandorf

Authors
  1. Marimer Santiago-Rivas
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  2. Julie B. Schnur
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  3. Lina Jandorf
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Correspondence to Marimer Santiago-Rivas.

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Santiago-Rivas, M., Schnur, J.B. & Jandorf, L. Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data. J Canc Educ 31, 673–678 (2016). https://doi.org/10.1007/s13187-015-0882-4

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  • DOI: https://doi.org/10.1007/s13187-015-0882-4

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