Background: An increasing number of people are exploring their genetic predisposition to many diseases, allowing them to make healthcare decisions with improved knowledge. Objectives: The aim of this study was to identify factors that influence individuals to consider genetic testing utilising a modified health belief model (HBM). Method: The authors tested the modified HBM using a convenience sample of individuals from across the United States after a pilot study was used to test the validity and reliability of the constructs. Using SmartPLS, the researchers determined that the modified HBM explains the decision-making process used to determine what influences individuals to consider genetic testing. Results: Results suggested that perceived susceptibility, perceived benefits, cues to action, self-efficacy, e-health literacy and normative belief all play a role in an individual’s decision to test their genetics. Conclusion: By conducting genetic testing, individuals may benefit from knowing they are predisposed to certain cancers and other diseases. Yet, research results have indicated that most individuals are unaware of resources available online that will help them in understanding genetic test results and associated diseases. Implications: Since healthcare literacy is an issue reported by these individuals, health information management professionals are well qualified to support them in e-health literacy by assisting them to evaluate the trustworthiness of available resources, and to educate them about privacy rights regarding access to and protection of their genetic information.
AjzenI (1971) Attitudinal vs. normative messages: An investigation of the differential effects of persuasive communications on behavior. Sociometry34(2): 263.
2.
AjzenI (1985) From intentions to actions: a theory of planned behavior. In: KuhlJBeckmannJ (eds) Action Control. SSSP Springer Series in Social Psychology. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-69746-3_2, pp.11–39.
3.
Alzu’biAZhouLWatzlafV (2014) Personal genomic information management and personalized medicine: Challenges, current solutions, and roles of HIM professionals. Perspectives in Health Information Management 11(Spring): 1c.
4.
American Health Information Management Association (AHIMA) (2019) Career map. Available at: https://my.ahima.org/careermap (accessed 15 June 2024).
5.
American Health Information Management Association (AHIMA) Foundation (2022) 91 million* adult Americans struggle to access, understand, and use their health information. Available at: https://ahimafoundation.org/ (accessed 15 June 2024).
6.
AndersonJCGerbingDW (1988) Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin103(3): 411.
7.
BeckerMHDrachmanRHKirschtJP (1974) A new approach to explaining sick-role behavior in low-income populations. American Journal of Public Health64(3): 205–216.
8.
BennettSJ (1992) Perceived threats of individuals recovering from myocardial infarction. Heart & Lung: The Journal of Critical Care21(4): 322–326.
9.
BishopACBakerGRBoyleTA, et al. (2015) Using the health belief model to explain patient involvement in patient safety. Health Expectations18(6): 3019–3033.
10.
CarpenterCJ (2010) A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Communication25(8): 661–669.
11.
ChampionVL (1984) Instrument development for health belief model constructs. Advances in Nursing Science6: 73–85.
12.
Chin (1998) The partial least squares approach for structural equation modeling. In: MarcoulidesGA (ed.) Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates Publishers, pp.295–336.
13.
ChuangBKTsaiCHHsiehHL, et al. (2013) Applying health belief model to explore the adoption of telecare. In 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS), Niigata, Japan, 16–20 June. IEEE, pp. 269–272.
14.
CohenJ (1992) A power primer. Psychological Bulletin112(1): 155.
15.
DalyMBMontgomerySBinglerR, et al. (2016). Communicating genetic test results within the family: Is it lost in translation? A survey of relatives in the randomized six-step study. Familial cancer15: 697–706.
16.
DeVellisRF (2003) Scale Development: Theory and Applications. Thousand Oaks, CA: SAGE.
17.
DolanMWolterJNielsenC, et al. (2009) Consumer health informatics: Is there a role for HIM professionals?Perspectives in Health Information Management/AHIMA, American Health Information Management Association 6(Summer): 1a.
18.
DucharmeJ (2018) Millions of Americans could be identified using consumer genetic databases—even if they’ve never taken a DNA test. Time. Available at: https://time.com/5423170/dna-test-identify-millions/ (accessed 23 May 2023).
19.
FreydkinD (2008) ‘I have to... make this a positive’. USA Today, 07B-07B.
20.
FrostSMyersLBNewmanSP (2001) Genetic screening for Alzheimer’s disease: What factors predict intentions to take a test?Behavioral Medicine27(3): 101–109.
21.
GefenDRigdonEEStraubD (2011) An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly35(2): iii-A7.
22.
GoodingHCOrganistaKBurackJ, et al. (2006) Genetic susceptibility testing from a stress and coping perspective. Social Science & Medicine62(8): 1880–1890.
23.
HaefnerDPKirschtJP (1970) Motivational and behavioral effects of modifying health beliefs. Public Health Reports85(6): 478.
24.
HanifFReadJCGoodacreJA, et al. (2009) The role of quality tools in assessing reliability of the internet for health information. Informatics for Health and Social Care34(4): 231–243.
HoyleP (2019) Health information is central to changes in health care: A clinician’s view. Health Information Management Journal48(1): 48–51.
27.
JanzNKBeckerMH (1984) The health belief model: A decade later. Health Education & Behavior11(1): 1.
28.
JonesCLJensenJDScherrCL, et al. (2015) The health belief model as an explanatory framework in communication research: Exploring parallel, serial, and moderated mediation. Health Communication30(6): 566–576.
29.
JorvandRSadeghiradKHaeri MehriziAA, et al. (2019) Determinants of daily exercises among employees with overweight or obesity: The application of health belief model. Journal of Research & Health9(5): 387–393.
30.
KamalMMEl-BorgyMDWahbaMSI (2017) Application of health belief model for hygienic behavior of mothers of hospitalized children in Alexandria. Bulletin of High Institute of Public Health47: 13–21.
31.
KenneallyC (2015). The invisible history of the human race: How DNA and history shape our identities and our futures. Penguin Books.
32.
KhorsandiBKhakbazanZMahmoodzadehHA, et al. (2020) Self-efficacy of the first-degree relatives of patients with breast cancer in the prevention of cancer: Using the health belief model. Journal of Cancer Education35: 977–982.
33.
KirschtJP (1974) The health belief model and illness behavior. Health Education & Behavior2(4): 387–408.
34.
KleidermanEKnoppersBMFernandezCV, et al. (2014) Returning incidental findings from genetic research to children: views of parents of children affected by rare diseases. Journal of Medical Ethics40(10): 691–696.
35.
LiedeACaiMCrouterTF, et al. (2018) Risk-reducing mastectomy rates in the US: a closer examination of the Angelina Jolie effect. Breast cancer research and treatment171: 435–442.
36.
Mehdi HazavehieSMLotfinikZMoeiniB, et al. (2018) The effect of education based on health belief model on postpartum care in pregnant women. Journal of Research & Health8(4): 382–391.
NormanCDSkinnerHA (2006) eHEALS: The eHealth literacy scale. Journal of Medical Internet Research8(4): e27.
39.
OrjiRVassilevaJMandrykR (2012) Towards an effective health interventions design: An extension of the health belief model. Online Journal of Public Health Informatics4(3): e61050.
40.
PrictorMRychkovaM (2023) Recording our genes: Stakeholder views on genetic test results in networked electronic medical records. Health Information Management Journal52(3): 194–203.
41.
RaphaelJVermaSHewittP, et al. (2016) The impact of Angelina Jolie (AJ)’s story on genetic referral and testing at an academic cancer centre in Canada. Journal of Genetic Counseling25: 1309–1316.
Rodriguez-RinconDParkinsonSHockingL, et al. (2022). Assessing the Impact of Developments in Genetic Testing on Insurers’ Risk Exposure. Rand Health Quarterly 9(4).
44.
RosenstockIM (1960) What research in motivation suggests for public health. American Journal of Public Health and the Nations Health50(3_Pt_1): 295–302.
45.
RosenstockIM (1966) Why people use health services. The Milbank Memorial Fund Quarterly44(3): 94–127.
46.
RosenstockIM (1974) The health belief model and preventive health behavior. Health Education & Behavior2(4): 354.
47.
RosenstockIMDerryberryMCarrigerBK. (1959) Why people fail to seek poliomyelitis vaccination. Public Health Reports74(2): 98.
48.
RosenstockIMKirschtJP (1974) Practice implications. Health Education & Behavior2(4): 470–473.
49.
RosenstockIMStrecherVJBeckerMH (1988) Social learning theory and the health belief model. Health Education Quarterly15(2): 175–183.
50.
SoleymanianANiknamiSHajizadehE, et al. (2014) Development and validation of a health belief model based instrument for measuring factors influencing exercise behaviors to prevent osteoporosis in pre-menopausal women (HOPE). BMC Musculoskeletal Disorders15: 61–61.
51.
SoltaniKTavafianSS (2016) Using the health belief model regarding HIV/AIDS prevention among female high school students. Journal of Research & Health6(1): 161–166.
SulatJSPrabandariYSSanusiR, et al. (2018) The validity of health belief model variables in predicting behavioral change. Health Education118(6): 499–512.
54.
TavakolMDennickR (2011) Making sense of Cronbach’s alpha. International Journal of Medical Education2: 53.
55.
VinziVETrincheraLAmatoS. (2010) “PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement.” Handbook of partial least squares: Concepts, methods and applications. New York, NY: Springer, pp. 47–82.
56.
ZimmermanRSVernbergD (1994) Models of preventive health behavior: Comparison, critique, and meta-analysis. Advances in Medical Sociology4: 45–67.
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.
