Holli H Seitz1, Laura Gibson2, Christine Skubisz3, Heather Forquer4, Susan Mello5, Marilyn M Schapira6, Katrina Armstrong7, Joseph N Cappella8. 1. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: hollihseitz@gmail.com. 2. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: lgibson@asc.upenn.edu. 3. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: christine_skubisz@emerson.edu. 4. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: heather.forquer@gmail.com. 5. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: s.mello@northeastern.edu. 6. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA; Center for Health Equity Research and Promotion, Crescenz VA Medical Center, Philadelphia, PA 19104, USA. Electronic address: mschap@mail.med.upenn.edu. 7. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA. Electronic address: karmstrong6@mgh.harvard.edu. 8. Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA, 19104, USA. Electronic address: jcappella@asc.upenn.edu.
Abstract
OBJECTIVE: This experiment tested the effects of an individualized risk-based online mammography decision intervention. The intervention employs exemplification theory and the Elaboration Likelihood Model of persuasion to improve the match between breast cancer risk and mammography intentions. METHODS:2918 women ages 35-49 were stratified into two levels of 10-year breast cancer risk (<1.5%; ≥1.5%) then randomly assigned to one of eight conditions: two comparison conditions and six risk-based intervention conditions that varied according to a 2 (amount of content: brief vs. extended) x 3 (format: expository vs. untailored exemplar [example case] vs. tailored exemplar) design. Outcomes included mammography intentions and accuracy of perceived breast cancer risk. RESULTS: Risk-based intervention conditions improved the match between objective risk estimates and perceived risk, especially for high-numeracy women with a 10-year breast cancer risk ≤1.5%. For women with a risk≤1.5%, exemplars improved accuracy of perceived risk and all risk-based interventions increased intentions to wait until age 50 to screen. CONCLUSION: A risk-based mammography intervention improved accuracy of perceived risk and the match between objective risk estimates and mammography intentions. PRACTICE IMPLICATIONS: Interventions could be applied in online or clinical settings to help women understand risk and make mammography decisions.
RCT Entities:
OBJECTIVE: This experiment tested the effects of an individualized risk-based online mammography decision intervention. The intervention employs exemplification theory and the Elaboration Likelihood Model of persuasion to improve the match between breast cancer risk and mammography intentions. METHODS: 2918 women ages 35-49 were stratified into two levels of 10-year breast cancer risk (<1.5%; ≥1.5%) then randomly assigned to one of eight conditions: two comparison conditions and six risk-based intervention conditions that varied according to a 2 (amount of content: brief vs. extended) x 3 (format: expository vs. untailored exemplar [example case] vs. tailored exemplar) design. Outcomes included mammography intentions and accuracy of perceived breast cancer risk. RESULTS: Risk-based intervention conditions improved the match between objective risk estimates and perceived risk, especially for high-numeracy women with a 10-year breast cancer risk ≤1.5%. For women with a risk≤1.5%, exemplars improved accuracy of perceived risk and all risk-based interventions increased intentions to wait until age 50 to screen. CONCLUSION: A risk-based mammography intervention improved accuracy of perceived risk and the match between objective risk estimates and mammography intentions. PRACTICE IMPLICATIONS: Interventions could be applied in online or clinical settings to help women understand risk and make mammography decisions.
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