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Human Genome Epidemiology, 2nd Edition$

Muin Khoury, Sara Bedrosian, Marta Gwinn, Julian Higgins, John Ioannidis, and Julian Little

Print publication date: 2009

Print ISBN-13: 9780195398441

Published to Oxford Scholarship Online: May 2010

DOI: 10.1093/acprof:oso/9780195398441.001.0001

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Role of social and behavioral research in assessing the utility of genetic information

Role of social and behavioral research in assessing the utility of genetic information

(p.497) 25 Role of social and behavioral research in assessing the utility of genetic information
Human Genome Epidemiology, 2nd Edition

Saskia C. Sanderson

Christopher H. Wade

Colleen M. McBride

Oxford University Press

Abstract and Keywords

This chapter describes and critiques the state of the science with respect to understanding how genetic information has been, and how genomic information might, in the future, be used to improve health by encouraging health behaviors that decrease chronic disease risk. To this end, it reviews studies in which the impact of providing genetic information has been evaluated as a means to influence behavioral outcomes. It then identifies gaps in the research and recommends new conceptual and methodological directions to accelerate this field of research. Lastly, it recommends roles that social and behavioral science can play in the next generation of research to consider the translation potential of genomic information for public health benefit.

Keywords:   genetic information, public health, health behavior, behavioral change, disease risk

The completion of the sequence of the human genome has led to the discovery of a growing number of genetic variants associated with common, complex diseases and traits. This improved understanding of the role that genetic variants play in common health conditions is anticipated to benefit public health through several routes. First, such research will shed light on mechanisms of disease and accelerate the deve lopment of new therapeutic interventions. Second, it will increase diagnostic precision and enable individualized therapies (e.g., pharmacogenomics). Third, and the focus for this chapter, is that new genomic information will allow for personalized risk prediction in ways that might motivate healthy individuals to engage in risk-reducing behavioral changes. For the purposes of this chapter, we are considering behavior change to include cancer screening, quitting smoking, improving diet, and increasing physical activity.

Common, complex diseases such as heart disease, cancer, and diabetes, as well as the precursors to these diseases such as obesity, hypertension, and hypercholesterolemia, represent a global health epidemic (1). This epidemic is attributed largely to population trends in poor diet (e.g., calorie-dense, nutrient-deficient foods) and phy sical inactivity (e.g., physical environments that discourage walking). Additionally, despite significant reductions in cigarette smoking in Western countries in the past few decades, many people still struggle unsuccessfully to quit, and a significant proportion of adolescents and young adults worldwide continue to start smoking. There are numerous evidence-based interventions to help individuals modify these health-harming behaviors. However, successfully producing long-term behavior changes and motivating individuals to avail themselves of behavior change interventions continues to be extremely challenging (2).

Throughout this chapter, we use the term “genetic information” to refer to general information about single gene variants or personalized information based on a genetic test result for a single gene variant. By contrast, we use the term “genomic information” to refer to general or personalized information that considers multiple genetic variants, gene–gene interactions or gene–environment interactions. We use (p.498) “social and behavioral research” to refer to the broad field of research concerned with applying theoretical models (e.g., Theory of Planned Behavior, Self-Regulation Theory, Protection Motivation Theory) to explain health behaviors and suggest behavior change strategies. Application of these models enables hypothesis-driven analyses of posited associations among social and psychological factors that can influence behavioral outcomes (3, 4). Social and behavioral research employs a range of measures and study designs that are suitable to varying degrees to address different research questions at different stages in the research process (5, 6).

Social and behavioral research has given a good deal of focus to the development and evaluation of disease risk communication approaches with the aim to motivate risk-reducing behavior changes. Social and behavioral theory suggests that the advantage of genomic information over other types of feedback (e.g., other biomarkers or behavioral risk assessments) is its highly personalized nature and its potential for greater motivational potency. Proponents of genomic information suggest that such feedback could be provided to healthy, asymptomatic individuals or populations and contribute substantially to primary prevention of common chronic diseases (7).

On the other hand, there is considerable skepticism being voiced about the potential of genomic information to motivate behavior change. Skeptics argue, for example, that the biology underlying these gene–disease risk estimates will be unclear or complex and that the low levels of risk conferred by common genetic risk variants (often ranging from 10% to 30% increased risk) for most common health conditions will result in confusion or, worse yet, create unnecessary concerns or provide false reassurances (8). However, relatively little social and behavioral research has been conducted to inform the debate about the potential utility of genomic information for motivating behavior change.

The ongoing debate raises important questions and testable hypotheses about the mechanisms through which genomic information might be more motivational than existing risk feedback approaches. For example, can genomic risk information improve upon state-of-the-art risk communications by personalizing risk in different or better ways than other risk indicators (e.g., blood pressure, cholesterol level, or family history) to motivate adoption of healthy behaviors? And can personalized genomic information inspire risk reduction above that achieved with current intervention approaches, given that the disease risk conferred by individual, common gene variants is modest? Indeed, rigorous social and behavioral research is needed to address these and other questions in order to evaluate whether and how genomic information can be translated into public health benefit through improved communication strategies and behavior change interventions.

In this chapter, we describe and critique the state of the science with respect to understanding how genetic information has been, and how genomic information might in the future be used to improve health by encouraging health behaviors that decrease chronic disease risk. To this end, we review studies in which the impact of providing genetic information has been evaluated as a means to influence (p.499) behavioral outcomes. We then identify gaps in the research and recommend new conceptual and methodological directions to accelerate this field of research. Lastly, we recom mend roles that social and behavioral science can play in the next generation of research to consider the translation potential of genomic information for public health benefit. We have organized the chapter to address three broad questions: Does the available evidence indicate that genetic information motivates behavior change? What research gaps can social and behavioral science fill with respect to gauging the utility of genomic information to change behavior? What future roles should social and behavioral research play in the evaluation of the utility of genomic information to improve public health?

In selecting the content for this chapter we have made the following decisions and assumptions. First, we excluded studies that explore treatment matching based on post hoc comparisons of the relative efficacy of pharmacological treatments by genotype (see for example References 9 and 10). These studies have not provided genetic information to individuals to influence their behaviors. Additionally, we assume it to be unlikely that genetic or genomic information will stand alone as a risk communication and behavior change strategy. Instead, we anticipate that such information will be combined with other risk factors such as gender, family history, and behavior and that these amalgamated risk assessments will be provided in the context of multicomponent complex interventions—that is, those that are “made up of various interconnecting parts” (11). Examples of complex interventions include programs to prevent heart disease and health promotion interventions directed at individuals to support dietary change (11). To limit the scope of the chapter, we have not included discussion of the opportunities and challenges of family history-based risk assessments but direct the reader to other thorough reviews on this subject (12–17). Lastly, with the rapid pace of genome-wide association studies (GWAS), we assume that clinically valid genetic or genomic “markers” of disease risk will be forthcoming and that individually these markers will have relatively modest associations with disease risk.

Does the Available Evidence Indicate that Genetic Information Motivates Behavior Change?

The best answer to this first question that can be distilled from the literature is that there is not yet enough evidence to determine whether genomic information can motivate behavior change. To date, most of the work in this area has focused on whether feedback of mutation carrier status for hereditary breast, ovarian, and colon cancers influences behaviors such as cancer screening. A less developed but emerging area of research is exploring how lifestyle behaviors such as smoking, unhealthy diet, and physical inactivity are affected by personalized genetic information relating to common health conditions that have more complex etiologies, such as heart disease and lung cancer. This latter area of research has focused primarily on genetic information as a tool to motivate smoking cessation, but is starting to (p.500) move toward examining other behavior changes, such as making improvements in dietary habits. We will briefly describe the evidence regarding whether personal genomic information influences each of these behaviors (cancer screening, smoking cessation, diet, and exercise).

Cancer Screening

Several comprehensive reviews have been published summarizing the evidence regarding the impact of genetic information on behavioral outcomes such as cancer screening (9, 18–20). As reviewed in these articles, early studies of the impact of genetic information on cancer screening focused primarily on the impact of providing individuals with personal test results indicating the presence or absence of BRCA1 and BRCA2 gene mutations. While rare, these mutations are strongly associated with increased risk (35–85% increased lifetime risk) of hereditary breast and ovarian cancer (HBOC).

The most recent review (20) included 32 studies on HBOC, hereditary nonpolyposis colorectal cancer (HNPCC), or both HBOC and HNPCC. Most of these studies were conducted in tertiary care cancer centers with populations at high family history-based risk of, or already diagnosed with, cancer. The findings from these studies generally indicate that, after receiving genetic test results, individuals informed that they are carriers of a BRCA1/2 mutation are significantly more likely than noncarriers to have a mammogram (21, 22) and to undergo appropriate ovarian cancer screening (23) in the recommended time interval. Similarly, individuals informed that they carry an HNPCC-related gene mutation are more likely than noncarriers to have a colonoscopy in the recommended time interval (24–27). In most cases, the between-group differences are accounted for by maintenance of already high rates of screening among mutation carriers, and appropriately decreased rates of screening among noncarriers (27).

These studies have generally provided participants with intensive genetic counseling sessions (an hour or more in duration) conducted by certified genetic counselors both before and after delivery of personal test results. The sample sizes for these studies have been quite small, and descriptive study designs have predominated and rarely include randomization of participants to different genetic information delivery formats.

Smoking Cessation

To date, 13 studies have explored the impact of genetic information on motivation to quit smoking or smoking cessation. Figure 25.1 illustrates that the studies spanned over a decade in their execution (1997–2008), a time of rapid developments in genetics research and of considerable change in public awareness of genetic testing. As Figure 25.1 shows, four of these studies were randomized controlled trials (RCTs) where the effects of actual genetic test result feedback on cessation outcomes were compared with a nontested control condition (28–31). All four studies tested individuals for a single genetic variant believed to be associated with increased (p.501)

 							              Role of social and behavioral research in assessing the utility of genetic information

Figure 25.1 Studies examining the impact of genetic information on smoking cessation and related outcomes.

susceptibility to lung cancer and used this as the basis for their personal risk feedback, although three different genetic variants were used (CYP2D6, GSTM1, and L-myc). In addition, the studies varied greatly in their approach.

The four RCT studies differed in the intensity of interpersonal support provided to participants, which has been associated positively with successful smoking cessation. For example, Lerman and colleagues (28) provided smokers with a 60-minute comprehensive face-to-face quit smoking consultation. Two-thirds of participants were then randomized to receive an additional 10-minute motivational discussion based on either results of breath samples analyzed for carbon monoxide, or personal genetic test results based on CYP2D6 genotyping. Those identified as “extensive metabolizers” based on CYP2D6 testing were told that they were more susceptible to lung cancer than poor metabolizers.

McBride and colleagues (29) randomly assigned participants to either a standard- of-care self-help smoking cessation intervention alone or in combination with GSTM1 genetic testing and telephone counseling. Smokers who underwent genetic testing for the GSTM1 gene received an in-person explanation from a health educator about the GSTM1 gene prior to testing and a personalized GSTM1 genetic test result booklet along with four follow-up counseling calls from a health educator. Use of the GSTM1 gene for feedback enabled comparisons between those who received higher than average genetic risk feedback (about one-third of the participants in this study) and those who received “not at higher” risk feedback.

Ito and colleagues (30) randomly assigned participants (a third of whom already had cancer) to either a control condition in which no smoking cessation intervention was delivered, or a genetic information condition in which participants received a (p.502) blood test, a face-to-face 10-minute explanation about the effects of the L-myc polymorphism on cancer risk due to smoking and an L-myc genetic test result by mail. Participants who had the LL genotype received a “non-risky” test result and participants who had the LS/SS genotypes received a “risky” test result. The participants received no other assistance with smoking cessation.

Most recently, Sanderson and colleagues (31) provided all participating smokers with a brief 20-minute in-person smoking cessation intervention. Two-thirds of the participants were subsequently randomized to an in-person explanation of lung cancer risk associated with the GSTM1 genotype using a 17-page illustrated guide to GSTM1, smoking, and lung cancer, were offered genetic testing for GSTM1 using a cheek-cell DNA swab, and received a copy of the GSTM1 information booklet to take home. These participants received their GSTM1 test results in person two weeks later, and received another copy of the GSTM1 booklet that had their personal test result marked in it. The Ito and Sanderson study designs (30, 31) had the advantage over previous trials of enabling pair-wise comparisons of cessation rates and associated cognitive and affective outcomes between a nontested control condition, a “risky/higher-risk” genetic test result condition, and a “non-risky/lower-risk” genetic test result condition.

Cessation rates for these randomized trials were measured at different time points with inconsistent outcomes. Lerman and colleagues (28) found no effect of personal genetic test result feedback on smoking cessation rates at a 2-month or a 1-year follow-up. McBride and colleagues (29) reported a significantly greater cessation rate in the genetic testing group compared to the nontested comparison group at 6-month follow-up, but the independent contribution of genetic risk feedback could not be disentangled from the known positive effects of telephone counseling. Within the genetic testing group, cessation rates did not differ between those who received “high” versus “not high” risk results. Ito and colleagues (30) found a significant difference in cessation rates at one of the follow-ups within the subgroup of participants who had not had cancer: 8% of the no-intervention control group and 9% of the group receiving the “non-risky genotype” feedback who had quit smoking, compared to 21% of the group receiving the “risky genotype” feedback had quit smoking, at the 9-month follow-up. Sanderson and colleagues (31) found significantly higher cessation rates in the “higher-risk” group than control group at 1-week follow-up, but no difference at a 2-month follow-up (although note that this study was considerably smaller than the others and so was underpowered to detect a difference at this later follow-up).

An observational study (32) recruited smokers to participate in genetic testing for alpha-1 antitrypsin deficiency (AATD), a condition exacerbated by smoking that can lead to early-onset emphysema and hepatic impairment. This intervention was conducted almost entirely by mail with no face-to-face contact. Smokers who took the genetic test and were subsequently informed that their test results indicated they were severely AAT deficient were no more likely to have quit smoking at 3-month follow-up than those informed that their test results indicated they had normal AAT levels.

(p.503) All of the above studies evaluated genetic risk information based on a single common gene variant. This risk communication approach grossly oversimplifies what is a complex etiology involving multiple genes and multiple facets of smoking topo graphy. Hamajima and colleagues (33, 34) therefore made initial steps toward using more complex genomic information, by providing smokers with information and feedback about cancer susceptibility based on three common variants in genes that code for enzymes involved in the detoxification of carcinogens: GSTM1, GSTT1, and NQO1. Participants who received personal information that they had more high-risk gene variants were more likely to quit smoking than those who received personal information that they had fewer high-risk variants (4% of those with 0 or 1 genotypes with no enzyme activity quit smoking, compared to 17% of those with 2 or 3 genotypes with no enzyme activity).

In a recent observational study (35), relatives of patients with late-stage lung cancer were offered a web-based decision aid and GSTM1 genetic testing. Results indicated that smokers receiving “higher-risk” genetic test results were no more likely than those receiving “lower-risk” genetic test results to take up offered free smoking cessation services (35). However, participants in the study were highly motivated to quit smoking prior to seeking genetic testing.

It is difficult to draw conclusions from these studies due to the multiple methodological differences between the studies. Moreover, these studies offer little insight into the immediate emotional and cognitive responses participants may have had to genetic feedback. For several of the studies, it is not clear how individuals responded to the feedback, only that such feedback did not prompt changes in their success at quitting smoking (29, 32). More in-depth information about immediate responses to genetic feedback could be informative in guiding the development of alternative approaches to be tested in future RCTs. The studies suggest future directions for research and provide the groundwork for RCTs that, for example, compare different types of genetic information content and delivery. Such preliminary research often is essential to establish the probable active ingredients of complex interventions (11).

The “experimental analog” method that uses hypothetical genetic testing scenarios rather than real genetic testing, may have a number of advantages for early phase formative research. Currently, the use of real genetic testing is costly, and time-consuming (return of feedback can take 3 months or more). Experimental analog methods allow researchers to: anticipate reactions to genetic tests that are not yet a clinical reality, have greater experimental control than is possible in a clinical situation to reduce differences between experimental conditions, and allow researchers to assign equal numbers of participants to each genetic test result condition (36). In experimental analog studies generally, participants are asked to imagine themselves in a situation and to respond as if they had experienced the events described (37).

Four studies have used experimental analog methodology to evaluate the potential effect of genetic information on psychological antecedents of smoking cessation such as perceived personal control or ability to quit smoking and/or motivation to quit smoking (36–39). Wright and colleagues (38) asked smokers to imagine they had (p.504) received a high-risk genetic test result for nicotine addiction. They found that smokers who received the high-risk test results were more likely to choose a pharmacological intervention over their own willpower than those in a control condition, a finding the authors suggested might be indicative of lower perceived personal control also referred to as “genetic fatalism.” Moreover, in a second study (36), smokers who received similar genetic test results for heart disease risk were no more motivated to quit smoking than those who did not receive personal genetic test results. In the third experimental analog study (37), smokers receiving Crohn's disease risk assessments were no more motivated to quit smoking when the risk assessment included genetic information than when it was based on family history and smoking status alone. In contrast, Sanderson and Michie (39) found that smokers who imagined receiving a high-risk genetic test result for heart disease risk reported greater intention to quit smoking than those whose personal results indicated a low-risk genetic test result or a high-risk result based on an oxidative stress test. Additionally, Sanderson and Wardle (40) included questions in a mailed survey to explore whether genetic information about different diseases might be more or less motivational to smokers. When imagining receiving a high-risk test result, smokers who considered a hypothetical scenario about personal cancer genetic information did not differ in motivation to quit smoking from those who considered a scenario about personal heart disease genetic information (40).

While it is difficult to draw any firm conclusions about the impact of genetic information on smoking cessation and related outcomes, the research to date has suggested a range of methodological approaches and research questions that can be applied and explored in future research.

Diet and Exercise

Only five studies have examined the impact of genetic testing on lifestyle behaviors other than smoking, such as eating a healthy diet and exercising (41–45). As with the research on smoking cessation, the studies exploring the effects of genetic information on motivation to improve diet and to be physically active have varied in behavioral outcomes, disease or trait phenotypes tested, timing of follow-ups, and control conditions.

Two studies have explored genetic information with respect to its potential to motivate or demotivate weight loss. Harvey-Berino and colleagues (41) evaluated genetic risk information based on a variant in the beta-3-adrenergic receptor (beta-3-AR) gene, which was believed to negatively influence weight loss and energy expenditure. In this small pilot study conducted with 30 obese women who were participating in a weight loss program, women who were told they had the adverse variant showed no differences over time in reported confidence in their ability to lose weight compared to those who were told they did not have the adverse variant. Frosch and colleagues (44) used an experimental analog design in which participants were randomly assigned to review one of four hypothetical vignettes in a 2×2 experimental design (genetic versus hormone test, and increased versus average risk of obesity). There was no effect of test type on the primary outcome, intention to eat a healthy diet.

(p.505) Hicken and Tucker (42) evaluated the effect of genetic risk feedback about a fictitious disease called Asch syndrome on intentions to adopt risk-reducing behaviors, including reducing dietary fat, and consuming soy products. Participants were informed that they had a positive family history for Asch syndrome, and were then randomly assigned to one of three experimental conditions: increased risk (30–40%) based on “positive family history” alone; increased risk (30–40%) based on “positive family history” plus a “positive genetic test result”; or average risk (10–12%) conferred by a “positive family history” plus a “negative genetic test result.” Participants who were told that their increased risk was based on a genetic test were no more likely to report intending to engage in any of the recommended behaviors than those who were informed that their increased risk was based on family history alone.

Marteau and colleagues (43) evaluated whether genetic information negatively influenced adherence to cholesterol-lowering medication, diet, physical activity, and smoking cessation. Participants with familial hypercholesterolemia (a hereditary form of heart disease) and their relatives were randomized to one of two groups: routine clinical diagnosis or routine clinical diagnosis plus genetic testing. Results indicated no support for the supposition that genetic confirmation of the condition was associated with lowered personal control, nor were there any differences on any of the behavioral outcomes.

Roberts and colleagues (45, 46) randomly assigned first-degree relatives of Alzheimer patients to receive either individualized numerical risk assessment based on family history and gender alone (control group) or an individualized numerical risk assessment based on family history, gender, and APOE genotype (intervention group). Control participants were given lifetime risk estimates of 18% through 29%, and intervention participants received estimates of 13% through 57%. Although participants were informed that there were no proven preventive measures for Alzheimer disease, those receiving the higher-risk ɛ 4-positive result were significantly more likely than both ɛ 4-negative participants (52% versus 24%) and control participants (52% versus 30%) to self-report at least one of three health behavior changes (diet, exercise, or medications and/or vitamins). Use of medications or vitamins, and adding vitamin E specifically, were the most commonly reported behavior changes amongst ɛ 4-positive participants.

The differences between these studies limit the conclusions that can be drawn. However, as in the case of smoking cessation, genetic feedback, even for conditions such as Alzheimer disease, does not appear to demoralize individuals. However, these results also suggest that such feedback may not consistently be a motivator for behavior change.

Gaps in the Research

With only a few years having passed since the completion of the Human Genome Project, it is not surprising that there are significant gaps in public health applications of genomics and related social and behavioral research. In this section, we (p.506) outline four particularly noteworthy and interrelated gaps. Attending to these gaps now could advance the social and behavioral research field significantly, and in turn improve the chances that genomic discoveries will result in public health benefit.

Few Studies Have Examined the Psychological Impact of Genomic Information About Common, Complex Diseases and Traits

Social and behavioral translational research in the genetics field has to date been heavily influenced by early discoveries of gene variants that independently confer very high lifetime risks of familial cancer syndromes. This has had several effects on the emerging research agenda to evaluate the potential of genomic information related to common complex diseases and traits. First, the majority of studies directed to common complex diseases have continued in the tradition of evaluating genetic information based on single genetic variants, rather than multiple genetic variants or genomic information. This clearly belies the genetic, behavioral, and environmental complexity of these conditions. Additionally, this means that risk messages have been based on odds ratios of 1.2–1.5, with little understanding of how these lower probabilities, as compared to Mendelian-inherited conditions, might influence the motivational potency of these messages.

Second, the Mendelian inheritance paradigm has influenced selection of psychological (cognitive, affective, and behavioral) outcomes that have been assessed. Research to understand the impact of genomic information on social and behavioral outcomes has fallen largely under the aegis of the Ethical, Legal, and Social Implications (ELSI) research, which has to date focused more on the potential harms than benefits of developments in genetics. Thus, rather than focusing on affective and behavioral outcomes suggested by behavior change theories, the research has emphasized the potential negative implications, for instance, measures of traumatic distress such as the impact of events scale. While concerns about genomics must be taken seriously, even highly predictive genetic tests (e.g., BRCA1/2 and APOE) have not been shown to lead to any sustained adverse emotional outcomes such as depression and worry (45, 47, 48). Despite this, there remains a general perception that the potential for personal genomic information to lead to adverse outcomes is high. Consequently, research that explores possible benefits of personal genomic information, such as the potential to lead to much-needed improvements in lifestyle behaviors, has lagged behind that focusing on potential harms.

Third, concerns about the possibly exceptional nature of genomic information also have influenced selection of target populations. For example, much of the research has targeted adult patient populations already known to have or be at very high risk of disease. Asymptomatic general-public populations have rarely been the focus of this research. However, the preventive potential of genomic risk information suggests that such healthy individuals may be the most appropriate targets of this research. This also raises the thorny issue of the appropriate age to introduce the possibility of genetic testing. Expert panels have recommended against genetic testing of minors to assess susceptibility for adult-onset conditions (49) although (p.507) some have argued that it is unethical to deny the option of testing when it may be beneficial (50). Given that early adoption of preventive behaviors might have the greatest benefit to health for minors (51), the question of at which age it is appropriate to start genetic testing requires greater exploration.

Methodological Rigor Has Been Limited

The research to date has had notable methodological weaknesses. Here we focus on three that raise questions about the veracity of the research findings: (i) small, highly self-selected samples; (ii) over-reliance on self-reported outcomes; and (iii) inadequately or inappropriately timed follow-up assessments.

Populations studied. Study samples have over-represented females, whites, highly educated individuals with health insurance, and those who have access to medical care. Moreover, the majority of studies have had sample sizes of less than 100, with most underpowered to evaluate behavioral outcomes. Study recruitment has been conducted in settings serving predominantly high-risk populations. Few studies have recruited from the general population or primary care settings where health promotion and disease prevention efforts typically take place. Base rates of screening behaviors and other health behaviors of high-risk populations such as those with familial cancer syndromes are not likely to be comparable to those found among general populations. Moreover, general population groups are likely to have lower genetic literacy (52) and lower awareness of the availability of genetic testing than high-risk populations. The general lack of population-based recruitment approaches that would enable comparison of characteristics of those who do and do not seek genetic testing makes it difficult to evaluate the external validity of study findings. Additionally, attrition rates are rarely described, raising additional questions about external validity. These limitations make it hard to draw inferences from the current research about the motivational potential of genomic information.

Over reliance on self-reported outcomes. Almost all of the studies to date have relied on self-reported behavioral outcomes, such as individuals telling survey interviewers after participating in an intervention whether they have or have not changed the target behavior. Few of the smoking cessation studies used gold standard biochemical validation of abstinence such as cotinine assays. Studies relying on hypothetical vignettes about genetic information necessarily have relied on self-reports of motivation and intention to change behavior. The hypothetical nature of these studies and the social desirability of reporting motivation to behave in a healthier manner might explain in part the higher rates of favorable effects of genetic information on motivation and intentions in these studies, which have less often been found in studies using actual genetic testing.

Timing of follow-up assessments. The studies to date have varied greatly in the time points at which follow-up data have been collected. Additionally, many of the studies have timed follow-up assessments at 3-, 6-, and 12-month follow-ups, the standard follow-up points for behavior change interventions. While this timing makes (p.508) good conceptual sense for assessing standard behavioral outcomes (e.g., smoking cessation, improvements in physical activity), it is less well suited to tap into more immediate cognitive and motivational changes that might accompany consideration of genetic testing and interpretation of test results, and precede behavioral changes. A case in point is that the few randomized controlled trials to date included standard follow-up points for assessing behavioral outcomes. These trials not only found no benefit of genetic information for behavioral outcomes but also provided few clues for developing the next generation of genetic information for testing. Immediate and early cognitive and emotional responses to genetic information in the hours, days, and weeks following genetic testing are needed to gain insight into how they influence behavioral outcomes further downstream. The frequency of assessment also must be considered carefully so as not to encourage response bias that could result from too frequent or too closely timed assessments.

Research is Too Narrowly Focused on Perceived Risk and Fatalism

Perceived disease risk has been considered almost exclusively as the mediating cognitive mechanism through which genomic information might influence behavior. However, although people who feel threatened are somewhat more likely to take action and change their behavior than people who do not feel threatened, simply raising an individual's perception of personal risk is not always sufficient in and of itself to directly motivate behavior change (53). Other important cognitions and emotions associated with behavior include: “perceived response-efficacy” or confidence that the recommended behavior can reduce the threat (53, 54), “perceived self-efficacy” or confidence in ability to change the behavior (55, 56), beliefs about the causes and consequences of the threat (57), and self-esteem (58).

To date, when these other cognitions have been addressed, research questions have usually been posed in a negative frame, asking whether genetic information might induce feelings of fatalism, lack of personal control, or reduced self- efficacy. However, self-esteem (positive self-image or feelings of self-worth) could be increased by providing individuals with information indicating that their tendency to “eat in the absence of hunger” (59) is influenced by dopamine gene variants, not simply a “lack of willpower.” Whether genomic information can be used to enhance positive feelings of self-worth, improve positive self-image and reduce stigma, guilt, and self-blame has yet to be explored. Clearly, the cognitive and emotional pathways through which genomic information might influence behavioral outcomes are far more complex than the impact on perceived disease risk alone.

The Social and Behavioral Research Agenda Has Not Been Guided by Consensus Priorities or Strategic Planning

There is currently no systematic planning effort underway to understand whether and how genomic information might best be applied to address public health (p.509) priorities related to multifactorial, complex conditions (e.g., heart disease, type 2 diabetes, asthma, and obesity) that are influenced by multiple genetic, behavioral, and environmental factors (60). As a result, the “tail has wagged the dog,” with most genomic information research focusing on the latest genetic variant to accrue sufficient evidence base for an association with a disease outcome. Study outcomes have focused on disease risk, the genetic risk variants used to indicate risk have differed between studies even when studying the same disease, and study designs and outcome measures have varied widely. Additionally, the literature is largely dominated by descriptive studies of genetic test uptake, and the cognitive and emotional responses to genetic test results measured have usually been negative, such as depression, anxiety, worry, and fatalism.

Roles for Social and Behavioral Research

Based on the gaps outlined above, we recommend four roles for future social and behavioral research to increase understanding of the utility of genomic information to improve public health through behavior change means.

ROLE 1: Building a bridge between basic science, medicine, and public health

Dr. Elias Zerhouni, former Director of the NIH, suggests that we are in a time of “revolutionary and rapid changes in science” (http://nihrecord.od.nih.gov/newsletters/03_16_2004/story01.htm) and that currently researchers are not organized optimally for tackling the complexity and scale of biological problems. He suggests that “multidisciplinary research teams of the future” are needed to address contemporary health problems. Public health genomics is one such new field concerned with the responsible and effective translation of genome-based knowledge and technologies for the benefit of population health (61). Researchers from all backgrounds will increasingly need to be prepared and able to talk across disciplinary boundaries, and to arrive at mutual understandings of methodologies and conceptual models that can be used to translate basic science most effectively into public health benefit. More programs in public health genomics such as the flagship interdepartmental undergraduate and graduate training programs at the University of Michigan (see http://www.sph.umich.edu/genetics/) and the University of Washington (see http://depts.washington.edu/phgen) are needed to train scientists in translation and the conduct of high-quality research into the potential utility of genomic information.

Additionally, it will be essential that the genetic literacy of medical students and the frontline primary care providers of the future be improved far beyond what it is today. It may also be useful for there to be a paradigm shift within the genetic counseling training programs to incorporate preventive health education and behavior change counseling.

(p.510) ROLE 2: Expanding research emphasis to include both distal disease phenotypes and more proximal intermediate phenotypes

Evaluation of the utility of genetic information has almost completely focused on information based on genetic variations associated with distal disease phenotypes such as heart disease and cancer. However, genetic variants are now starting to be associated with more proximal intermediate phenotypes such as obesity (62), satiety (63), airway reactivity (64), and physiological response to exercise (65). Each of these traits or biological processes plays a role in not one but many diseases. Social and behavioral research approaches are needed to evaluate the utility of providing individuals with personal genomic information about these emerging intermediate phenotypes. This research will require a focus not only on the potential cognitive, affective, and behavioral outcomes of providing individuals with this information, but also how to communicate the information effectively and appropriately for the general population as well as different subgroups. Whether personal genomic information about variants associated with intermediate phenotypes, such as appetite or responses to physical activity, is more, less, or equally motivating compared to personal genomic information about heart disease or cancer risk is an important question that also remains to be answered.

ROLE 3: Priority setting and planning horizon-scanning research agendas that consider future technologies

The rapid progression and decreasing cost of genetic technologies and GWAS have had a profound effect on the pace at which the genetic risk factors for diseases and traits are being identified (66). Computational advances will enable increasingly comprehensive and accurate “genomic risk portraits” of individuals based on proteomic, transcriptomic, lifestyles, and environmental factors. The resultant risk algorithms and personal risk messages will be much more complex than those based on traditional risk factors or single genetic variants in isolation.

Social and behavioral research provides useful theoretical frameworks for advancing hypothesis-driven research to understand how these increasing levels of personalization might best be communicated to target audiences, how such information might influence behavioral outcomes, and how this information might improve upon current public health interventions. A few of the research questions that need to be addressed include: Can genomic information be integrated with existing widely available risk prediction and communication tools (e.g., www.yourdiseaserisk.harvard.edu) to increase their efficacy for motivating behavior change? Can individuals make sense of these complex risk profiles? How should messages be framed when information appears to contradict itself, for example, when an individual has both risk-reducing and risk-conferring gene variants for the same disease or trait? Given the wide range of research questions that will be generated by these new and emerging technologies, priority setting based on what has been learned previously (p.511) about the dissemination of technological innovations related to public health will be essential.

New planning mechanisms need to be developed to begin these conversations. The CDC's Office of Public Health Genomics has played a critical role to date in increasing attention to these important issues. The American Public Health Association (APHA) and the Society of Behavioral Medicine (SBM) are examples of additional leading organizations that also could play a central role in developing forums for setting priorities for social and behavioral research on the utility of genomic information. Integrating genomics into the scientific programs and publications of these influential organizations will be useful in directing research to understand the potential translation of genomic information into public health benefit.

ROLE 4: Implementing appropriately phased programs of applied research

The increasing recognition of the complex biopsychosocial nature of health means that interventions designed to improve health will likely increase in complexity as well. Social and behavioral research into changing behavior suggests that complex, multicomponent, sustained interventions are most successful in promoting enduring behavior change. However, arriving at the optimal combinations of intervention components that are efficacious, cost-effective and not overly burdensome for individuals will continue to require a good deal of research. Campbell and colleagues (11, 67) speak to this eloquently in calling for a more systematic, phased approach to research to understand and shape improvements in complex health promotion interventions. As Campbell and others show, there are several phases to the research process that can be conducted linearly or simultaneously. Figure 25.2 illustrates that this type of phased framework is useful in guiding structured research programs to assess how best to integrate new genomic discoveries into existing public health interventions.

As shown in Figure 25.2, the first step, the Preclinical or Theory Phase (11, 67), is to explore relevant models and theories to suggest mechanisms by which genomic information might have beneficial impact on motivation, emotions, cognitions, and ultimately behavior change. For example, Marteau and Weinman (68) adapted Leventhal's common sense model of the self-regulation of health and illness to suggest hypotheses about whether and how individual beliefs about disease causation may influence psychological responses to genomic information (68).

In the Phase I “Modeling” step, interviews and surveys, as well as focus groups, experimental analog (hypothetical scenarios) studies, case studies, and observational studies are conducted to define the components of the intervention and to provide evidence that the underlying mechanisms through which genomic information exerts its effects, as well as how it interacts with other components of the intervention, can be predicted. It is important to highlight that this Phase I research is viewed not as being conducted in isolation, but rather, where appropriate, as part of comprehensive research programs in which the results are directly used to inform the design of subsequent studies using actual genetic tests.


 							              Role of social and behavioral research in assessing the utility of genetic information

Figure 25.2 Adaptation of the MRC framework (11) to illustrate the sequential phases of developing complex interventions that incorporate genomic information to improve health.

In Phase II, the information gathered in Phase I is used to develop the optimum intervention and study design, and one or more small exploratory RCTs are conducted to describe a feasible protocol for comparing an intervention incorporating genomic information using real genetic testing with an appropriate alternative. When is it appropriate to move from hypothetical scenario methods to studies that use actual genetic testing and feedback of real personal genomic information? The planning strategies laid out (see Role 3 above) should be helpful in addressing this critical question. Additionally, Phase II trials can be useful as an intermediate step between Phase I hypothetical studies and large fully powered Phase III trials. Phase II trials are useful in the development and evaluation of different delivery formats, appropriate genomic feedback information materials, and determining appropriate comparison conditions. A Phase II methodological approach also enables increased methodological rigor allowing greater internal validity to consider and refine best practices for delivering interventions and considering the individual contributions of components of these interventions.

In Phase III, a theoretically defensible and adequately controlled RCT with appropriate statistical power is conducted comparing a fully defined complex intervention that incorporates a genomic information component with an appropriate alternative. Large Phase III trials might be considered once enough Phase I and II data demonstrate the safety and optimal interventions on which to base a large-scale trial.

Finally, the purpose of Phase IV is to examine the implementation of the intervention into practice, paying particular attention to the rate of uptake, the stability (p.513) of the intervention, any broadening of subject groups, and the possible existence of adverse effects (11, 67). As suggested more generally by Campbell and colleagues, implementing appropriately phased programs of applied research in this way will give researchers and funding bodies reasonable confidence that appropriately designed and relevant studies are being proposed, which examine the potential utility of genomic information as part of complex interventions to improve health (11).

Concluding Comment: What Is the Role of Social and Behavioral Research in Assessing Utility of Genetic Information?

In this chapter, we have provided an overview of the social and behavioral research that has been conducted to date assessing the potential utility of genetic information, pointed out some of the gaps in this research, and made some recommendations about how to move the field forward. We suggest that social and behavioral science has multiple roles to play in assessing the utility of genetic information, including research agenda setting, collaborating, training, and matching appropriate methodologies and conceptual models to important questions. All these activities are essential and needed now if we are to begin to amass the empirical evidence needed to inform whether and how genomics can be translated and incorporated into multicomponent behavior change interventions to produce benefit for the health of all.

Note: No statement in this article should be construed as an official position of the National Human Genome Research Institute, NIH, or the Department of Health and Human Services.


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