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      Associations between self-referral and health behavior responses to genetic risk information

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          Abstract

          Background

          Studies examining whether genetic risk information about common, complex diseases can motivate individuals to improve health behaviors and advance planning have shown mixed results. Examining the influence of different study recruitment strategies may help reconcile inconsistencies.

          Methods

          Secondary analyses were conducted on data from the REVEAL study, a series of randomized clinical trials examining the impact of genetic susceptibility testing for Alzheimer’s disease (AD). We tested whether self-referred participants (SRPs) were more likely than actively recruited participants (ARPs) to report health behavior and advance planning changes after AD risk and APOE genotype disclosure.

          Results

          Of 795 participants with known recruitment status, 546 (69%) were self-referred and 249 (31%) had been actively recruited. SRPs were younger, less likely to identify as African American, had higher household incomes, and were more attentive to AD than ARPs (all P < 0.01). They also dropped out of the study before genetic risk disclosure less frequently (26% versus 41%, P < 0.001). Cohorts did not differ in their likelihood of reporting a change to at least one health behavior 6 weeks and 12 months after genetic risk disclosure, nor in intentions to change at least one behavior in the future. However, interaction effects were observed where ε4-positive SRPs were more likely than ε4-negative SRPs to report changes specifically to mental activities (38% vs 19%, p < 0.001) and diets (21% vs 12%, p = 0.016) six weeks post-disclosure, whereas differences between ε4-positive and ε4-negative ARPs were not evident for mental activities (15% vs 21%, p = 0.413) or diets (8% versus 16%, P = 0.190). Similarly, ε4-positive participants were more likely than ε4-negative participants to report intentions to change long-term care insurance among SRPs (20% vs 5%, p < 0.001), but not ARPs (5% versus 9%, P = 0.365).

          Conclusions

          Individuals who proactively seek AD genetic risk assessment are more likely to undergo testing and use results to inform behavior changes than those who respond to genetic testing offers. These results demonstrate how the behavioral impact of genetic risk information may vary according to the models by which services are provided, and suggest that how participants are recruited into translational genomics research can influence findings.

          Trial registration

          ClinicalTrials.gov NCT00089882 and NCT00462917

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13073-014-0124-0) contains supplementary material, which is available to authorized users.

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          Most cited references41

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          The Modified Mini-Mental State (3MS) examination.

          E Teng, H Chui (1987)
          The Mini-Mental State (MMS) examination is a widely used screening test for dementia. The Modified Mini-Mental State (3MS) incorporates four added test items, more graded scoring, and some other minor changes. These modifications are designed to sample a broader variety of cognitive functions, cover a wider range of difficulty levels, and enhance the reliability and the validity of the scores. The 3MS retains the brevity, ease of administration, and objective scoring of the MMS but broadens the range of scores from 0-30 to 0-100. Greater sensitivities of the 3MS over the MMS are demonstrated with the pentagon item drawn by 249 patients. A summary form for administration and scoring that can generate both the MMS and the 3MS scores is provided so that the examiner can maintain continuity with existing data and can obtain a more informative assessment.
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            A comparison of inclusive and restrictive strategies in modern missing data procedures.

            Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.
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              A Language and Environment for Statistical Computing

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                Author and article information

                Contributors
                kchristensen@genetics.med.harvard.edu
                jscottr@umich.edu
                bzikmund@umich.edu
                skardia@umich.edu
                colleen.marie.mcbride@emory.edu
                linnenbringere@wudosis.wustl.edu
                rcgreen@genetics.med.harvard.edu
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                31 January 2015
                31 January 2015
                2015
                : 7
                : 1
                : 10
                Affiliations
                [ ]Division of Genetics, Brigham and Women’s Hospital and Harvard Medical School, EC Alumnae Building, Suite 301, 41 Avenue Louis Pasteur, Boston, MA 02115 USA
                [ ]Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
                [ ]Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
                [ ]Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Atlanta, GA 30322 USA
                [ ]Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO 63110 USA
                [ ]Division of Genetics, Brigham and Women’s Hospital/Harvard Medical School/Partners Personalized Medicine, Boston, MA 02115 USA
                Article
                124
                10.1186/s13073-014-0124-0
                4311425
                25642295
                a28856f5-874f-4816-a2a4-cc9db9b91e97
                © Christensen et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 August 2014
                : 11 December 2014
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Molecular medicine
                Molecular medicine

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