Sage Journals: Discover world-class research

Abstract

Background

Risk calculators are popular websites that provide individualized disease risk assessments to the public. Little is known about their effect on risk perceptions and health behavior.

Objective

This study sought to test whether risk calculator features—namely, personalized estimates of one’s disease risk and feedback about the effects of risk-mitigating behaviors—improve risk perceptions and motivate healthy behavior.

Design

A web-based experimental study using simple randomization was conducted to compare the effects of 3 prediabetes risk communication websites.

Setting

The study was conducted in the context of ongoing health promotion activities sponsored by a university’s human resources office.

Patients

Participants were adult university employees.

Intervention

The control website presented nonindividualized risk information. The personalized noninteractive website presented individualized risk calculations. The personalized interactive website presented individualized risk calculations and feedback about the effects of hypothetical risk-mitigating behaviors.

Measurements

Pre- and postintervention risk perceptions were measured in absolute and relative terms. Health behavior was measured by assessing participant interest in follow-up preventive health services.

Results

On average, risk perceptions decreased by 2%. There was no general effect of personalization or interactivity in aligning subjective risk perceptions with objective risk calculations or in increasing healthy behaviors. However, participants who previously overestimated their risk reduced their perceptions by 16%. This was a significantly larger change than the 2% increase by participants who underestimated their risk.

Limitations

Results may not generalize to different populations, different diseases, or longer-term outcomes.

Conclusions

Compared to nonpersonalized information, individualized risk calculators had little positive effect on prediabetes risk perception accuracy or health behavior. Risk perception accuracy was improved in people who receive relatively “good news” about risk rather than “bad news.”

Keywords

health literacy risk communication or risk perception consumer informatics randomized trial methodology

Get full access to this article

View all access options for this article.

References

American Diabetes Association. New risk management tool developed by the American Diabetes Association: diabetes PHD. Available from: URL: http://professional.diabetes.org/UserFiles/File/Scientific%20Sessions/2004/DIABETES%20PHD%20wnews letterhead.doc

Weinstein

Atwood

Puleo

Fletcher

Colditz

Emmons

. Colon cancer: Risk perceptions and risk communication. J Health Commun. 2004;9(1):53–65.

Petty

Cacioppo

. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York, NY: Springer- Verlag; 1986.

Brewer

Weinstein

Cuite

Herrington

. Risk perceptions and their relation to risk behavior. Ann Behav Med. 2004;27(2):125–30.

Edwards

Hood

Matthews

. The effectiveness of one-to-one risk communication interventions in health care: a systematic review. Med Decis Making. 2000;20:290–7.

Witte

Allen

. A meta-analysis of fear appeals: implications for effective public health campaigns. Health Educ Behav. 2000;27(5):591–615.

Lipkus

Samsa

Rimer

. General performance on a numeracy scale among highly educated samples. Med Decis Making. 2001;21(1):37–44.

Armstrong

Schwartz

Fitzgerald

Putt

Ubel

. Effect of framing as gain versus loss on understanding and hypothetical treatment choices: survival and mortality curves. Med Decis Making. 2002;22(1):76–83.

Malenka

Baron

Johansen

Wahrenberger

Ross

. The framing effect of relative and absolute risk. J Gen Intern Med. 1993;8(10):543–8.

10.

McNeil

Pauker

Sox

Tversky

. On the elicitation of preferences for alternative therapies. N Engl J Med. 1982;306(21):1259–62.

11.

Akl

Oxman

Herrin

. Using alternative statistical formats for presenting risks and risk reductions. Cochrane Database Syst Rev. 2011;3.

12.

Lord

Ross

Lepper

. Biased assimilation and attitude polarization: the effects of prior theories on subsequently considered evidence. J Pers Soc Psychol. 1979;37(11):2098–109.

13.

Kunda

. The case for motivated reasoning. Psychol Bull. 1990;108(3):480–98.

14.

Han

PKJ

Klein

WMP

Lehman

Masset

Lee

Freedman

. Laypersons’ responses to the communication of uncertainty regarding cancer risk estimates. Med Decis Making. 2009;29:391–403.

15.

Klein

Kunda

. Maintaining self-serving social comparisons: biased reconstruction of one’s past behaviors. Pers Soc Psychol Bull. 1993;19(6):732–9.

16.

Cull

Anderson

Campbell

Steel

. The impact of genetic counselling about breast cancer risk on women’s risk perceptions and levels of distress. Br J Cancer. 1999;79(3/4):501–8.

17.

Gurmankin

Domchek

Stopfer

Fels

Armstrong

. Patients’ resistance to risk information in genetic counseling for BRCA1/2. Arch Intern Med. 2005;165(5):523–9.

18.

Senay

Kaphingst

. Anchoring-and-adjustment bias in communication of disease risk. Med Decis Making. 2009;29:193–201.

19.

Slovic

. Perceptions of risk. Science. 1987;236:280–5.

20.

Edwards

AGK

Evans

Dundon

Haigh

Hood

Elwyn

. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2006;4.

21.

Sheridan

Viera

Krantz

. The effect of giving global coronary risk information to adults: a systematic review. Arch Intern Med. 2010;170(3):230–9.

22.

Emmons

Wong

Puleo

Weinstein

Fletcher

Colditz

. Tailored computer-based cancer risk communication: correcting colorectal cancer risk perception. J Health Commun. 2004;9(2):127–41.

23.

Harle

Downs

Padman

Nagin

. The Impact of Web- Based Risk Calculators on Health Risk Perceptions and Information Processing. Pittsburgh, PA: Carnegie Mellon University, Heinz School; 2008.

24.

American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2007;30(Suppl 1):S42–7.

25.

Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population: National Health and Nutrition Examination Survey 1999–2002. Diabetes Care. 2006;29(6):1263–8.

26.

American Diabetes Association. Diabetes basics: prediabetes FAQs. Available from: URL: http://www.diabetes.org/diabetes- basics/prevention/pre-diabetes/pre-diabetes-faqs.html

27.

Harle

Padman

Downs

. Design, implementation, and preliminary evaluation of a web-based health risk calculator. In: Forty- second Annual Hawaii International Conference on Systems Sciences [CD-ROM]. Big Island, HI: Computer Society Press; 2009.

28.

Centers for Disease Control and Prevention. National Center for Health Statistics (NCHS), National Health and Nutrition Examination Survey Data. Hyattsville, MD: US Department of Health and Human Services; 2007.

29.

Bish

Sutton

Golombok

. Predicting uptake of a routine cervical smear test: a comparison of the health belief model and the theory of planned behavior. Psychol Health. 2000;15(1):35–50.

30.

Conner M, Norman P. Comparing the health belief model and the theory of planned behavior in health screening. In: Rutter DR, Quine L, eds. Social Psychology and Health: European Perspectives. Brookfield, VT: Avebury/Ashgate; 1994:1–24.

31.

Weinstein

Rothman

. Commentary: revitalizing research on health behavior theories. Health Educ Res. 2005;20(3):294–7.

32.

American Diabetes Association. Americans are afraid of that? Available from: URL: http://www.diabetes.org/for-media/2008/americans-are-afraid-of-that.html

33.

Harle

Padman

Downs

. The impact of web-based diabetes risk calculators on information processing and risk perceptions. In: Proceedings of the 2008 American Medical Informatics Association Symposium, Washington, DC.

34.

Fox

. Online Health Search 2006. Washington, DC: Pew Internet & American Life Project; 2006.

35.

Ogden

Fryar

Carroll

Flegal

. Mean body weight, height, and body mass index, United States 1960–2002. Adv Data Vital Health Stat. 2004;347.

36.

Valdez

Yoon

Liu

Khoury

. Family history and prevalence of diabetes in the US population. Diabetes Care. 2007;30:2517–22.

37.

Ostchega

Yoon

Hughes

Louis

. Hypertension awareness, treatment and control—continued disparities in adults: United States 2005–2006. NCHS Data Brief. 2008;3.

38.

Walker

Mertz

Kalten

Flynn

. Risk perceptions for developing diabetes. Diabetes Care. 2003;26:2543–8.

39.

Han

PKJ

Klein

WMP

Lehman

Killam

Massett

Freedman

. Communication of uncertainty regarding individualized cancer risk estimates. Med Decis Making. 2011;31(2):354–66.

Effectiveness of Personalized and Interactive Health Risk Calculators

Abstract

Background

Objective

Design

Setting

Patients

Intervention

Measurements

Results

Limitations

Conclusions

Keywords

Get full access to this article

References