In-vivo Design Feedback and Perceived Utility of a Genetically-informed Smoking Risk Tool Among Current Smokers in the Community

Overview

Journal BMC Med Genomics

Publisher Biomed Central

Specialty Genetics

Date 2021 May 27

PMID 34039360

Citations 1

Authors

Jessica L Bourdon

Amelia Dorsey

Maia Zalik

Amanda Pietka

Patricia Salyer

Michael J Bray

Laura J Bierut

Alex T Ramsey

Affiliations

Soon will be listed here.

Abstract

Background: The use of genetically-informed personalized risk information for behavioral disorders, namely smoking and smoking-related behaviors, is a promising yet understudied area. The Genetics and Smoking Risk Profile, or RiskProfile, leverages genetic and environmental information to communicate one's risk for smoking-related diseases. Although prior studies have examined attitudes toward genetic results, little research has investigated these perceptions through a lens of in-vivo testing; that is, user-centered design feedback in response to personalized genetic results being returned contemporaneously. This qualitative study engaged current smokers in usability testing of the RiskProfile within the context of concurrently receiving this personalized, genetically-informed smoking cessation intervention.

Methods: Eighty-nine participants who were current smokers responded to open-ended interview questions on perceptions of smoking-related genetic information and the content and format of the RiskProfile intervention that they had received moments before. Data were analyzed via the conventional content analysis approach in which themes were allowed to emerge throughout the analysis.

Results: Participants were able to reference and offer design input on specific elements of the RiskProfile. Overall, current smokers perceived the RiskProfile to have high potential utility. Constructive feedback that current smokers offered about the tool centered around suggested improvements to optimize its usability and technical content.

Conclusions: The detailed and constructive feedback from participants highlights that in-vivo feedback offers a useful design approach that addresses concerns of rigor and relevance when returning genetic results. This unique method demonstrated perceived utility and constructive design feedback for the RiskProfile among current smokers and can play an important role in optimizing the design and implementation of personalized genetic risk interventions moving forward.

Citing Articles

Feasibility of precision smoking treatment in a low-income community setting: results of a pilot randomized controlled trial in The Southern Community Cohort Study.

Lee S, Senft Everson N, Sanderson M, Selove R, Blot W, King S Addict Sci Clin Pract. 2024; 19(1):16.

PMID: 38491559 PMC: 10941447. DOI: 10.1186/s13722-024-00441-1.

References

Klein W, McBride C, Allen C, Arredondo E, Bloss C, Kaphingst K . Optimal Integration of Behavioral Medicine into Clinical Genetics and Genomics. Am J Hum Genet. 2019; 104(2):193-196. PMC: 6369539. DOI: 10.1016/j.ajhg.2019.01.011. View

Norman P, Brain K . An application of an extended health belief model to the prediction of breast self-examination among women with a family history of breast cancer. Br J Health Psychol. 2005; 10(Pt 1):1-16. DOI: 10.1348/135910704X24752. View

Costain G, Esplen M, Toner B, Hodgkinson K, Bassett A . Evaluating genetic counseling for family members of individuals with schizophrenia in the molecular age. Schizophr Bull. 2012; 40(1):88-99. PMC: 3885286. DOI: 10.1093/schbul/sbs124. View

Jeibouei S, Akbari M, Kalbasi A, Aref A, Ajoudanian M, Rezvani A . Personalized medicine in breast cancer: pharmacogenomics approaches. Pharmgenomics Pers Med. 2019; 12:59-73. PMC: 6549747. DOI: 10.2147/PGPM.S167886. View

Waltz M, Meagher K, Henderson G, Goddard K, Muessig K, Berg J . Assessing the implications of positive genomic screening results. Per Med. 2020; 17(2):101-109. PMC: 7147673. DOI: 10.2217/pme-2019-0067. View

Hamilton S . The promise of psychiatric pharmacogenomics. Biol Psychiatry. 2014; 77(1):29-35. DOI: 10.1016/j.biopsych.2014.09.009. View

Hollands G, French D, Griffin S, Prevost A, Sutton S, King S . The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ. 2016; 352:i1102. PMC: 4793156. DOI: 10.1136/bmj.i1102. View

Kaphingst K, McBride C, Wade C, Hensley Alford S, Brody L, Baxevanis A . Consumers' use of web-based information and their decisions about multiplex genetic susceptibility testing. J Med Internet Res. 2010; 12(3):e41. PMC: 2956320. DOI: 10.2196/jmir.1587. View

Roberts J, Gornick M, Carere D, Uhlmann W, Ruffin M, Green R . Direct-to-Consumer Genetic Testing: User Motivations, Decision Making, and Perceived Utility of Results. Public Health Genomics. 2017; 20(1):36-45. DOI: 10.1159/000455006. View

10.

Visscher P, Wray N, Zhang Q, Sklar P, McCarthy M, Brown M . 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017; 101(1):5-22. PMC: 5501872. DOI: 10.1016/j.ajhg.2017.06.005. View

11.

Hartz S, Olfson E, Culverhouse R, Cavazos-Rehg P, Chen L, DuBois J . Return of individual genetic results in a high-risk sample: enthusiasm and positive behavioral change. Genet Med. 2014; 17(5):374-9. PMC: 4344933. DOI: 10.1038/gim.2014.110. View

12.

McBride C, Graves K, Kaphingst K, Allen C, Wang C, Arredondo E . Behavioral and social scientists' reflections on genomics: a systematic evaluation within the Society of Behavioral Medicine. Transl Behav Med. 2019; 9(6):1012-1019. PMC: 7184899. DOI: 10.1093/tbm/ibz044. View

13.

Collins F, Varmus H . A new initiative on precision medicine. N Engl J Med. 2015; 372(9):793-5. PMC: 5101938. DOI: 10.1056/NEJMp1500523. View

14.

Stewart K, Wesselius A, Schreurs M, Schols A, Zeegers M . Behavioural changes, sharing behaviour and psychological responses after receiving direct-to-consumer genetic test results: a systematic review and meta-analysis. J Community Genet. 2017; 9(1):1-18. PMC: 5752648. DOI: 10.1007/s12687-017-0310-z. View

15.

Cornetta K, Brown C . Balancing personalized medicine and personalized care. Acad Med. 2013; 88(3):309-13. PMC: 3584182. DOI: 10.1097/ACM.0b013e3182806345. View

16.

Ramsey A, Chen L, Hartz S, Saccone N, Fisher S, Proctor E . Toward the implementation of genomic applications for smoking cessation and smoking-related diseases. Transl Behav Med. 2018; 8(1):7-17. PMC: 6065540. DOI: 10.1093/tbm/ibx060. View

17.

Shahin M, Johnson J . Clopidogrel and warfarin pharmacogenetic tests: what is the evidence for use in clinical practice?. Curr Opin Cardiol. 2013; 28(3):305-14. PMC: 3731766. DOI: 10.1097/HCO.0b013e32835f0bbc. View

18.

Meiser B, Guo X, Putt S, Fullerton J, Schofield P, Mitchell P . Psychosocial implications of living with familial risk of a psychiatric disorder and attitudes to psychiatric genetic testing: A systematic review of the literature. Am J Med Genet B Neuropsychiatr Genet. 2020; 183(5):277-288. DOI: 10.1002/ajmg.b.32786. View

19.

Khoury M, Iademarco M, Riley W . Precision Public Health for the Era of Precision Medicine. Am J Prev Med. 2015; 50(3):398-401. PMC: 4915347. DOI: 10.1016/j.amepre.2015.08.031. View

20.

Chen L, Kaphingst K, Tseng T, Zhao S . How are lung cancer risk perceptions and cigarette smoking related?-testing an accuracy hypothesis. Transl Cancer Res. 2017; 5(Suppl 5):S964-S971. PMC: 5685517. DOI: 10.21037/tcr.2016.10.75. View