Background. Internet-based cancer risk assessment tools might serve as a strategy for translating epidemiological risk prediction research into public health practice. Understanding how such tools affect key social-cognitive precursors of behavior change is crucial for leveraging their potential into effective interventions. Purpose. To test the effects of a publicly available, Internet-based, breast cancer risk assessment tool on social-cognitive precursors of physical activity. Methods. Women (N = 132) aged 40–78 with no personal cancer history indicated their perceived risk of breast cancer and were randomly assigned to receive personalized (www.yourdiseaserisk.wustl.edu) or nonpersonalized breast cancer risk information. Immediately thereafter, breast cancer risk perceptions and physical activity-related behavioral intentions, self-efficacy, and response efficacy were assessed. Results. Personalized information elicited higher intentions, self-efficacy, and response efficacy than nonpersonalized information, P values < 0.05. Self-efficacy and response efficacy mediated the effect of personalizing information on intentions. Women who received personalized information corrected their inaccurate risk perceptions to some extent, P values < 0.05, but few fully accepted the information. Conclusion. Internet-based risk assessment tools can produce beneficial effects on important social-cognitive precursors of behavior change, but lingering skepticism, possibly due to defensive processing, needs to be addressed before the effects can be maximized.
American Cancer Society. Cancer Facts & Figures 2015. Atlanta (GA): American Cancer Society;2015.
2.
GailMHBrintonLAByarDPCorleDKGreenSBSchairerC. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879–86.
3.
ColditzGAHankinsonSE. The Nurses’ Health Study: lifestyle and health among women. Nat Rev Cancer. 2005;5(5):388–96.
4.
FreedmanANGraubardBIRaoSRMcCaskill-StevensWBallard-BarbashRGailMH. How many US women are eligible to use tamoxifen for breast cancer chemoprevention? How many women would benefit?Am J Oncol Rev. 2004;3(1):47.
5.
GraubardBIFreedmanANGailMH. Five-year and lifetime risk of breast cancer among U.S.subpopulations: implications for magnetic resonance imaging screening. Cancer Epidemiol Biomarkers Prev. 2010;19(10):2430–6.
6.
HelmesAWCulverJOBowenDJ. Results of a randomized study of telephone versus in-person breast cancer risk counseling. Patient Educ Couns. 2006;64(1–3):96–103.
7.
EdwardsAGNaikGAhmedHElwynGJPicklesTHoodK. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2013;(2):CD001865.
8.
EdwardsAUnigweSElwynGHoodK. Effects of communicating individual risks in screening programmes: Cochrane systematic review. BMJ. 2003;327:703.
9.
MooreSCLeeIMWeiderpassECampbellPTSampsonJNKitaharaCM. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA Intern Med. 2016;176(6):816–25.
10.
KreuterMWStrecherVJ. Changing inaccurate perceptions of health risk: results from a clinical trial. Health Psychol. 1995;14(1):56–63.
11.
WeinsteinNDAtwoodKPuleoEFletcherRColditzGEmmonsKM. Colon cancer: risk perceptions and risk communication. J Health Commun. 2004;9:53–65.
12.
CroyleRTLermanC. Risk communication in genetic testing for cancer susceptibility. J Natl Cancer Inst Monogr. 1999;25:59–66.
13.
LipkusIMSamsaGRimerBK. General performance on a numeracy scale among highly educated samples. Med Decis Making. 2001;21(1):37–44.
14.
Zikmund-FisherBJ. The right tool is what they need, not what we have: a taxonomy of appropriate levels of precision in patient risk communication. Med Care Res Rev. 2013;70(1 suppl):37S–49S.
15.
WindschitlPDMartinRFlugstadAR. Context and the interpretation of likelihood information: the role of intergroup comparisons on perceived vulnerability. J Pers Soc Psychol. 2002;82(5):742–55.
16.
DrielingRLMaJThiyagarajanSStaffordRS. An Internet-based osteoporotic fracture risk program, effect on knowledge, attitudes, and behaviors. J Womens Health (Larchmt). 2011;20(12):1895–907.
17.
PriceHCGriffinSJHolmanRR. Impact of personalized cardiovascular disease risk estimates on physical activity—a randomized controlled trial. Diabet Med. 2011;28(3):363–72.
18.
WelschenLMBotSDKostensePJDekkerJMTimmermansDRvan der WeijdenT. Effects of cardiovascular disease risk communication for patients with type 2 diabetes on risk perception in a randomized controlled trial: the @RISK study. Diabetes Care. 2012;35(12):2485–92.
19.
WatersEASullivanHWNelsonWHesseBW. What is my cancer risk? Identifying how Internet-based cancer risk calculators convey individualized risk estimates to the public. J Med Internet Res. 2009;11(3):e33.
20.
HolmbergCWatersEAWhitehouseKDalyMMcCaskill-StevensW. My lived experiences are more important than your probabilities: the role of individualized risk estimates for decision making about participation in the Study of Tamoxifen and Raloxifene (STAR). Med Decis Making. 2015;35(8):1010–22.
21.
ConnerMNormanP, eds. Predicting Health Behaviour. Buckingham (UK)/Philadelphia (PA): Open University Press;1995.
22.
National Cancer Institute. Health Information National Trends Survey (HINTS): National Cancer Institute; 2017. Available from: URL: http://hints.cancer.gov/
23.
SchwarzerR. Social-cognitive factors in changing health-related behavior. Curr Dir Psychol Sci. 2001;10(2):47–51.
24.
SheeranPHarrisPREptonT. Does heightening risk appraisals change people’s intentions and behavior? A meta-analysis of experimental studies. Psychol Bull. 2014;140(2):511–43.
25.
SheeranPMakiAMontanaroEAvishai-YitshakABryanAKleinWM. The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: a meta-analysis. Health Psychol. 2016;35(11):1178–88.
26.
HarkinBWebbTLChangBPPrestwichAConnerMKellarI. Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychol Bull. 2016;142(2):198–229.
27.
GollwitzerPMSheeranP. Implementation intentions and goal achievement: a meta-analysis of effects and processes. Adv Exp Soc Psychol. 2006;38:69–119.
28.
JacksonKMAikenLS. Evaluation of a multicomponent appearance-based sun-protective intervention for young women: uncovering the mechanisms of program efficacy. Health Psychol. 2006;25(1):34–46.
29.
KypriKMcAnallyHM. Randomized controlled trial of a web-based primary care intervention for multiple health risk behaviors. Prev Med. 2005;41(3–4):761–6.
EmmonsKMKoch-WeserSAtwoodKConboyLRuddRColditzG. A qualitative evaluation of the Harvard Cancer Risk Index. J Health Commun. 1999;4:181–93.
32.
EmmonsKMWongMPuleoEWeinsteinNFletcherRColditzG. Tailored computer-based cancer risk communication: correcting colorectal cancer risk perception. J Health Commun. 2004;9:127–41.
33.
ColditzGA. Your Cancer Risk [replaced by Your Disease Risk]; 2000. Available from: URL: www.yourdiseaserisk.wustl.edu
34.
WatersEAColditzGADartH. Your Disease Risk: The Culmination of 17 Years of Transdisciplinary Research. Invited webinar presented to the National Cancer Institute; March 22, 2012.
35.
WangCMillerSMEglestonBLHayJLWeinbergDS. Beliefs about the causes of breast and colorectal cancer among women in the general population. Cancer Causes Control. 2010;21(1):99–107.
36.
Centers for Disease Control and Prevention. Adult participation in aerobic and muscle-strengthening physical activities—United States, 2011. MMWR Morb Mortal Wkly Rep. 2013;62(17):326–30.
37.
WebbTLSheeranP. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol Bull. 2006;132(2):249–68.
38.
MilneSSheeranPOrbellS. Prediction and intervention in health-related behavior: a meta-analytic review of protection motivation theory. J Appl Soc Psychol. 2000;30(1):106–43.
39.
StokolsD. Social ecology and behavioral medicine: implications for training, practice and policy. Behav Med. 2000;26:129–38.
40.
ColditzGARosnerB. Cumulative risk of breast cancer to age 70 years according to risk factor status: data from the Nurses’ Health Study. Am J Epidemiol. 2000;152(10):950–64.
41.
WillisGB. Cognitive Interviewing: A Tool for Improving Questionnaire Design. Thousand Oaks (CA): Sage Publications; 2004.
42.
WeinsteinNDSandmanPM. The precaution adoption process model. In: GlanzKRimerBKLewisFM, eds. Health Behavior and Health Education: Theory, Research, and Practice. 3rd ed.San Francisco (CA): John Wiley and Sons; 2002. p 121–43.
43.
ConnerMSparksP. The theory of planned behaviour and health behaviours. In: ConnerMNormanP, eds. Predicting Health Behaviour. Philadelphia (PA): Open University Press; 1995. p 121–62.
44.
LorigKStewartARitterPGonzálezVLaurentDLynchJ. Outcome Measures for Health Education and Other Health Care Interventions. Thousand Oaks (CA): Sage;1996.
45.
SchuzBSniehottaFFWiedemannASeemannR. Adherence to a daily flossing regimen in university students: effects of planning when, where, how and what to do in the face of barriers. J Clin Periodontol. 2006;33(9):612–9.
HayesAFScharkowM. The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: does method really matter?Psychol Sci. 2013;24(10):1918–27.
48.
HaasJSBaerHJEibensteinerKKlingerEVSt HubertSGettyG. A cluster randomized trial of a personalized multi-condition risk assessment in primary care. Am J Prev Med. 2017;52(1):100–5.
49.
HarleCADownsJSPadmanR. Effectiveness of personalized and interactive health risk calculators: a randomized trial. Med Decis Making. 2012;32(4):594–605.
50.
de BruinMSheeranPKokGHiemstraAPrinsJMHospersHJ. Self-regulatory processes mediate the intention-behavior relation for adherence and exercise behaviors. Health Psychol. 2012;31(6):695–703.
51.
HanPKHootsmansNNeilsonMRoyBKungelTGutheilC. The value of personalised risk information: a qualitative study of the perceptions of patients with prostate cancer. BMJ Open. 2013;3(9):e003226.
52.
SchererLDUbelPAMcClureJGreeneSMAlfordSHHoltzmanL. Belief in numbers: when and why women disbelieve tailored breast cancer risk statistics. Patient Educ Couns. 2013;92(2):253–9.
53.
ShepperdJAKleinWMWatersEAWeinsteinND. Taking stock of unrealistic optimism. Perspect Psychol Sci. 2013;8(4):395–411.
54.
KundaZ. The case for motivated reasoning. Psychol Bull. 1990;108(3):480–98.
55.
ShepperdJANovellCAO’NeillSCDochertySLSandersonSCMcBrideCM. Contemplating genetic feedback regarding lung cancer susceptibility. Ann Behav Med. 2014;47(3):395–403.
56.
CroyleRTSunY-CLouieDH. Psychological minimization of cholesterol test results: moderators of appraisal in college students and community residents. Health Psychol. 1993;12(6):503–7.
57.
LipkusIMHollandsJG. The visual communication of risk. J Natl Cancer Inst Monogr. 1999;25(1):149–63.
58.
CookTDCampbellDT. Quasi-Experimentation: Design & Analysis Issues for Field Settings. Boston (MA): Houghton Mifflin; 1979.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
