Literature DB >> 26194777

Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data.

Marimer Santiago-Rivas1, Julie B Schnur2, Lina Jandorf2.   

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.

Entities:  

Keywords:  Health behaviors; Health beliefs; Internet; Skin cancer prevention

Mesh:

Substances:

Year:  2016        PMID: 26194777      PMCID: PMC4723289          DOI: 10.1007/s13187-015-0882-4

Source DB:  PubMed          Journal:  J Cancer Educ        ISSN: 0885-8195            Impact factor:   2.037


  23 in total

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Authors:  J K Robinson; Y Guevara; R Gaber; M L Clayman; M J Kwasny; J J Friedewald; E J Gordon
Journal:  Am J Transplant       Date:  2014-11-13       Impact factor: 8.086

2.  Using Amazon's Mechanical Turk website to measure accuracy of body size estimation and body dissatisfaction.

Authors:  Rick M Gardner; Dana L Brown; Russell Boice
Journal:  Body Image       Date:  2012-07-24

3.  Assessing work-asthma interaction with Amazon Mechanical Turk.

Authors:  Philip Harber; Gondy Leroy
Journal:  J Occup Environ Med       Date:  2015-04       Impact factor: 2.162

4.  A quantitative assessment of the effects of formal sun protection education on photosensitive patients.

Authors:  Chunyun Huang; Shuxian Yan; Jie Ren; Leihong Xiang; Yue Hu; Kefei Kang; Sophie Seite
Journal:  Photodermatol Photoimmunol Photomed       Date:  2013-10       Impact factor: 3.135

5.  Skin cancer preventative behaviors in state park workers: a pilot study.

Authors:  Vinayak K Nahar; M Allison Ford; Javier F Boyas; Robert T Brodell; Amanda Hutcheson; Robert E Davis; Kim R Beason; Martha A Bass; Rizwana Biviji-Sharma
Journal:  Environ Health Prev Med       Date:  2014-10-01       Impact factor: 3.674

6.  Multiple skin cancer risk behaviors in the U.S. population.

Authors:  Elliot J Coups; Sharon L Manne; Carolyn J Heckman
Journal:  Am J Prev Med       Date:  2008-02       Impact factor: 5.043

7.  Outcomes of cluster profiles within stages of change for sun protection behavior.

Authors:  Marimer Santiago-Rivas; Wayne F Velicer; Colleen A Redding; James O Prochaska; Andrea L Paiva
Journal:  Psychol Health Med       Date:  2013-01-24       Impact factor: 2.423

8.  Food consumption by young children: a function of parental feeding goals and practices.

Authors:  Allison E Kiefner-Burmeister; Debra A Hoffmann; Molly R Meers; Afton M Koball; Dara R Musher-Eizenman
Journal:  Appetite       Date:  2013-11-23       Impact factor: 3.868

9.  Rationale and study protocol to evaluate the SunSmart policy intervention: a cluster randomised controlled trial of a primary school-based health promotion program.

Authors:  Dean A Dudley; Matthew J Winslade; Bradley J Wright; Wayne G Cotton; Jackie L McIver; Kirsten S Jackson
Journal:  BMC Public Health       Date:  2015-01-31       Impact factor: 3.295

10.  Crowdsourcing awareness: exploration of the ovarian cancer knowledge gap through Amazon Mechanical Turk.

Authors:  Rebecca R Carter; Analisa DiFeo; Kath Bogie; Guo-Qiang Zhang; Jiayang Sun
Journal:  PLoS One       Date:  2014-01-22       Impact factor: 3.240

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  5 in total

1.  Crowdsourced Health Data: Comparability to a US National Survey, 2013-2015.

Authors:  Veronica Yank; Sanjhavi Agarwal; Pooja Loftus; Steven Asch; David Rehkopf
Journal:  Am J Public Health       Date:  2017-06-22       Impact factor: 9.308

Review 2.  Comparing Amazon's Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature.

Authors:  Karoline Mortensen; Taylor L Hughes
Journal:  J Gen Intern Med       Date:  2018-01-04       Impact factor: 5.128

3.  Home Cooking Quality Assessment Tool Validation Using Community Science and Crowdsourcing Approaches.

Authors:  Margaret Raber; Nalini Ranjit; Larkin L Strong; Karen Basen-Engquist
Journal:  J Nutr Educ Behav       Date:  2022-01-06       Impact factor: 2.822

4.  Mapping of Crowdsourcing in Health: Systematic Review.

Authors:  Perrine Créquit; Ghizlène Mansouri; Mehdi Benchoufi; Alexandre Vivot; Philippe Ravaud
Journal:  J Med Internet Res       Date:  2018-05-15       Impact factor: 5.428

Review 5.  The application of crowdsourcing approaches to cancer research: a systematic review.

Authors:  Young Ji Lee; Janet A Arida; Heidi S Donovan
Journal:  Cancer Med       Date:  2017-09-29       Impact factor: 4.452

  5 in total

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