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Are biomarkers useful treatment aids for promoting health behavior change?
Are Biomarkers Useful Treatment Aids for Promoting Health Behavior Change? An Empirical Review Jennifer B. McClure, PhD Background: Nearly half of the leading causes of death in our society are attributable to behavioral risk factors. As such, it is critical that we continue to develop and refine effective interventions for health behavior change. Some researchers have suggested that using biomarkers to educate individuals about their health status and disease risk may be an effective strategy to promote behavior change. This tactic is also commonly employed by healthcare providers, but its empirical support is unclear. This article reviews the research literature to determine the effectiveness of using biomarker feedback to motivate and enable health behavior change. Potential limitations of this treatment strategy and issues requiring additional research are also discussed. Methods:
Articles were identified through PubMed (MEDLINE), PsychInfo, and the reference lists of pertinent manuscripts and book chapters.
Results:
Eight published, randomized trials were identified that met criteria for review. The results of this work were mixed, but suggest that biological information conveying harm exposure, disease risk, or impaired physical functioning may increase motivation to change. Subsequent behavior change is also affected by the availability and intensity of concomitant treatment.
Conclusions: Preliminary findings suggest that combining biomarkers with appropriate behavioral treatment may enhance health behavior change, but more research in this area is warranted. Medical Subject Headings (MeSH): behavior therapy, biological markers, health behavior, health promotion, motivation (Am J Prev Med 2002;22(3):200 –207) © 2002 American Journal of Preventive Medicine
Introduction
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early half of the leading causes of death in our society are attributable to modifiable, behavioral risk factors, such as lack of exercise, poor diet, substance abuse (e.g., tobacco and alcohol), and risky sex.1 Although sound, evidence-based behavioral interventions exist, their effectiveness can be improved. Interventions are needed that motivate individuals to adopt healthier lifestyles, provide the information and skills necessary to do this, and reinforce the maintenance of positive behavior changes. Providing feedback on individual’s biomarker status may be an effective strategy to increase motivation and promote behavior change.2– 4 In this context, biomarkers refer to biological indices of physical harm, disease, or increased disease risk (from either exposure to
From the Center for Health Studies, Group Health Cooperative, Seattle, Washington Address correspondence and reprint requests to: Jennifer McClure, PhD, Center for Health Studies, 1730 Minor Ave., Suite 1600, Seattle, WA 98101. E-mail: [email protected].
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harmful agents or by virtue of one’s genetic profile). For example, cholesterol level, carbon monoxide (CO) level (expired or carboxyhemoglobin), and cotinine level (a metabolite of nicotine) are all biomarkers of exposure to harmful substances that can increase one’s disease risk. Similarly, genetic screening can detect individuals at increased risk of disease. Diagnostic tests, such as blood pressure, pulmonary functioning tests, x-rays, magnetic resonance imaging, and computerized tomography scans, can be used to detect markers of existing physical harm or disease. The idea that knowledge of one’s harm exposure, disease risk, or physical damage may alter his or her intent to change is not new. It is a basic principle in many health behavior theories. The health belief model, health decision model, protection motivation theory, theory of reasoned action, and dual process model all suggest that behavior change is in part induced by one’s perceived susceptibility to disease and a desire to avoid this outcome.5 Thus, increasing awareness that one has some personal risk of harm, or has
Am J Prev Med 2002;22(3) 0749-3797/02/$–see front matter © 2002 American Journal of Preventive Medicine • Published by Elsevier Science Inc. PII S0749-3797(01)00425-1
already caused physical harm through unhealthy habits, may increase motivation for health behavior change. This assumption is also a basic treatment principle in motivational interviewing,6 a counseling technique used successfully to treat alcohol abuse,7,8 opiate abuse,9 compliance to diabetic regimens,10 and smoking.11 The goal of motivational interviewing is to motivate people to change, in part, by making them aware of the risks and consequences of their problematic behavior and, in so doing, to shift the cost-benefit ratio of engaging in the maladaptive behavior. This does not necessarily involve biologically based harm evidence, but the rationale is the same. Finally, using physical status to encourage the reduction of maladaptive behaviors or adoption of more healthful behaviors is a strategy used every day by healthcare providers. Physicians frequently link test results (e.g., cholesterol level, blood pressure, and glucose levels) and medical diagnoses with advice to make appropriate lifestyle changes. In short, it is commonly assumed that informing patients about their biomarkers of disease or disease risk can instigate behavior change. But is this assumption supported by the empirical evidence? If so, under what conditions is this type of feedback most effective? Does it depend on the type of health information employed, the type of behavior in question, or the use of concomitant intervention strategies? If there is no evidence of its effectiveness, why is it not effective? Do the data clearly rule out biomarker feedback as an effective strategy, or is there a lack of sufficient evidence to adequately address the question? To date, there has been no systematic evaluation of these questions. The utility of this practice has been tested, but this knowledge base has not been synthesized or reviewed. This is an important task for two reasons. First, the prevalence of behavior-related disease in our society makes it critical that we continue to improve our ability to promote more healthful behavior. To be effective, this work should be based on empirically validated techniques. Second, it is important to understand the limits of our current knowledge base in order to guide future research. The purpose of this paper is to examine the empirical evidence that informing individuals of their biomarker status is an effective means of increasing motivation to change or promoting behavior modification. Practical implications of this work and issues requiring additional research will be discussed.
Review Procedures Articles were identified through PubMed (MEDLINE), PsychInfo, and the reference lists of pertinent manuscripts and book chapters. PubMed searches were conducted for journal publications between 1966 and October 2001. PsychLit searches encompassed journal articles or book chapters
from 1900 through October 2001. Studies were sought if they included biomarker status feedback as a key component of a behavioral intervention and examined the effects of this intervention on (1) motivation, or intent, to change one’s health-related behaviors, or (2) change in one’s diet, physical activity, tobacco use, alcohol abuse/dependence, medical adherence, sexual practices, or utilization of preventive screening. Emphasis was placed on these behaviors because they represent the leading behavioral risk factors for disease in our society. Articles were selected based on the following criteria. First, only published randomized trials were reviewed, thereby allowing an objective analysis of the biomarkers’ impact on motivation and behavior. Without the methodologic rigor of a randomized controlled design, it is impossible to attribute observed changes to the treatment and not to confounding factors or artifact. Second, studies were included only if they either compared persons receiving biomarker feedback to those not receiving this type of intervention, or compared treatments in which the only difference was the frequency or intensity of the biomarker feedback. This made it easier to evaluate the independent effect of the biomarker treatment and compare the impact of different biomarker intensities. Third, behavior change had to be directly assessed, as opposed to only measuring biological proxies of behavior modification, such as a change in cholesterol level12,13 or infant birth weight.14 Biological proxies can reflect changes in the target behavior, but are also influenced by factors, such as medication use, regression to the mean in repeated measurements, or behaviors other than those under investigation. Fourth, only investigations involving adults were evaluated because children and adolescents’ health-related behaviors may be more dependent on external environmental pressures than their own internal control. Finally, studies were excluded if they met each of these criteria, but significantly co-varied the treatment intensity across groups, making it impossible to delineate the impact of the biomarker counseling from other concomitant treatment components. These criteria eliminated a number of studies.15–20
Literature Review Eight studies met all inclusion criteria for this review. A summary of each study, including the main outcomes of interest, overview of the treatment design, and review of key findings, is presented in Table 1. Because short-term and long-term treatment effects were published separately for one trial,3,21 a total of nine published reports are listed. The identified studies focused on three areas of behavior change: tobacco use, dietary change, and physical activity. They employed a variety of biomarkers, including expired CO, cotinine level, cholesterol level, the CYP2D6 genetic marker for lung cancer susceptibility, an index of physical fitness, and lung functioning assessed via spirometry and self-reported pulmonary symptoms. Most included only a single biomarker, but several combined more than one. Each study was examined for evidence that the biomarker intervention affected either motivation to change or actual behavior change. The results are presented below.
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Table 1. Summary of randomized trials evaluating effect of biomarker feedback on motivation and behavior change
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Follow-up period
Reference Sample
Biomarker(s)
Primary outcomes
Treatment design
427 smokers Lerman et al. (1997)3
CO level and genetic marker for lung cancer (CYP2D6)
Change in: (1) smokingrelated cognitions, (2) emotional distress, and (3) smoking behavior
Audrain 426 smokers et al. (1997)21
CO level and genetic marker for lung cancer (CYP2D6)
(1) Smoking abstinence, (2) quit attempts
2 months Group 1: standard counseling (1hour visit): Group 2: standard 1hour counseling ⫹ 10-minute motivational intervention with CO exposure feedback; Group 3: standard 1-hour counseling ⫹ CO feedback ⫹ 10-minute motivational counseling with feedback about susceptibility to lung cancer (CYP2D6 genotype) Same as above 1 year
Key results No difference in abstinence rates across groups. Combined counseling/ CO/CYP2D6 feedback produced greater effects on perceived risk, perceived benefits of quitting, and fear arousal than other treatments, but also resulted in greatest levels of post-treatment fear and depression. Combined counseling/CO/CYP2D6 group twice as likely to make a quit attempt, but no difference in abstinence rates across groups. No difference in depression scores across groups. 51% of feedback group and 44% of controls stated they intended to quit after the baseline intervention. Feedback group was twice as likely to quit sometime during the year (40% vs 16%, p ⬍ 0.05). CO-confirmed point prevalence abstinence at 1 year was 20% vs 6.7% (p ⫽ 0.06). Participants who were aware of their test result reported greater intent to change their diet (p ⫽ 0.02).
Risser 90 smokers CO level, and contacted spirometry, and Belcher during pulmonary (1990)22 routine symptoms medical visits
(1) Smoking cessation, (2) intent to quit smoking
Group 1: 1-hour counseling ⫹ selfhelp manual ⫹ setting a quit date ⫹ invitation for 9-session counseling program; Group 2: same ⫹ 10-minute motivational intervention with personalized health feedback
Aubin et 419 family al. practice (1998)23 clinic patients
Cholesterol level
(1) Intention to change fat intake, (2) dietary change
Godin et 200 solicited al. volunteers (1987)24
Physical fitness test (1) Intention to (index of exercise, cardiorespiratory (2) self-report functioning) exercise in last 3 months
Group 1: feedback on cholesterol ⫹ 3 months brochure on low-fat diet; completed questionnaire on intention to change behavior after receiving feedback; Group 2: same as above, but completed questionnaire before receiving feedback results 130 subjects completed the study and Group 1: physical-fitness evaluation; 2 weeks for Group 2: health hazard appraisal; “intentions” 112 included in final sample. Completing initial assessment without Group 3: physical-fitness evaluation and 3 being told of results did not affect and health hazard appraisal; months for initial intentions to exercise Group 4: control behavior between the groups undergoing testing, change but the control group was different from the combined treatment group. At 3 months, however, there were no differences in intentions between the groups and no significant effect on leisure-time exercise behavior during this period.
1, 4, and 12 months
(continued on next page)
Table 1. (continued) Reference Sample
Biomarker(s)
Primary outcomes
Treatment design
All participants informed of 137 participants with Cholesterol level (1) Cholesterol Gemson cholesterol level and risk group, borderline-high level, et al. provided brief dietary instruction, (2) weight, (1990)26 cholesterol given 1-page cholesterol (3) blood information sheet, and dietary pressure, brochure. Group 1: follow-up (4) smoking status, screening at 6 months only; (5) diet Group 2: follow-up screening at 2, 4, and 6 months
Bauman CO level 170 pregnant et al. women (80 (1983)27 smokers) contacted through public prenatal care visits
(1) Smoking reduction, (2) prevention of smoking initiation
Jamrozik 2110 smokers et al. contacted during (1984)28 routine medical visits
Smoking cessation
CO level
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Strychar 429 hospital workers Cholesterol level (1) Change in et al. cholesterol (1998)29 level, (2) self-reported dietary change
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CO, carbon monoxide.
Group 1: informed of CO level and informed of relation between smoking, CO, and consequences of smoking during pregnancy; participants then observed assessment of nonsmokers CO levels for comparison; Group 2: informed of the relation between CO, smoking and consequences during pregnancy, but were not informed of their CO level Group 1: control; Group 2: received advice to quit smoking ⫹ written self-help; Group 3: received advice to quit ⫹ self-help manual ⫹ feedback on CO level; Group 4: received advice to quit ⫹ written self-help ⫹ referral for in-clinic smoking cessation counselor All workers completed pre-test assessment of height, weight, cholesterol, tobacco use, alcohol use, and physical activity, and a 24hour dietary recall. All received a 20-minute nutrition education session, identified changes they could make over the next 3 months, and signed a behavioral contract. Participants in treatment group were told their cholesterol levels. Control participants were not
Follow-up period
Key results
2, 4, and 6 months Subjects reassessed at 2, 4, and 6 depending on months were significantly more treatment group likely to report “a lot” of dietary change during 6-month follow-up (24% vs 10.3%, p ⬍ 0.05). They were also more likely to report eating less red meat, cheese, butter, and fast food (p ⬍ 0.05). No group differences for cholesterol, weight, blood pressure, smoking status, or weekly exercise. 6 weeks There was no differences in smoking rates between groups.
1 year
Dietary questionnaire at 6 weeks and interview at 16 to 20 weeks
Reported abstinence rates at 1 year: 10.6% of control group, 15% of those receiving advice and selfhelp; 17.2% of group receiving CO feedback, and 13.2% of those receiving advice, self-help, and referral information. Biochemical confirmation suggested 24% 40% of all participants may have misreported smoking status. Among participants completing both assessments (n ⫽ 429), overall cholesterol levels decreased for all participants (p ⬍ 0.001) and all reportedly improved their diet. There was no difference in cholesterol between groups.
Impact of Biomarker Feedback on Stated Motivation to Change Three studies assessed participants’ stated motivation to change after receiving biomarker feedback. Risser and Belcher22 contacted smokers during routine medical visits. Participants were randomly assigned to receive brief counseling plus self-help materials and an invitation to join a more comprehensive counseling program or the same intervention plus feedback on one’s CO level, lung functioning (assessed via spirometry), and self-reported pulmonary symptoms. After the baseline intervention, slightly more participants who received their CO level and spirometry results reported an intent to quit smoking (51% vs 44%). Aubin et al.23 assessed intent to modify dietary behavior among 419 family practice clinic patients. All patients participated in a cholesterol screening, but intent to make dietary changes was assessed prior to receiving the test results for one group and after receiving the results for another. Not surprisingly, patients informed of their cholesterol level reported greater intent to change their diet than those who did not receive this information (p ⬍0.05). Finally, Godin et al.24 examined the impact of three different interventions on intent to increase exercise. Participants were recruited from the community and randomly assigned to receive a physical fitness test (Canadian Home Fitness Test), health hazard appraisal, a combination of the fitness test and health hazard appraisal, or no treatment (control group). The physical fitness test estimated cardiorespiratory fitness based on post-exercise heart rates. In contrast, the health hazard appraisal was used to predict participants’ probability of dying from a potentially preventable cause in the next 10 years based on their age, gender, medical history, and health risk. After each intervention, researchers assessed the probability that participants would engage in one or more physical activities on a regular basis during the next 3 months. Behavioral intent was assessed immediately after testing for half of participants in each group, after 3 weeks for the remaining half of participants, and at 3-month follow-up for all participants. Subjects assessed immediately following testing were unaware of their test results, but those assessed at 2 weeks were aware of their results. As with Aubin et al.,23 motivation was unaffected when participants were not aware of their test results; but after receiving the test results, participants who took the fitness test and those in the combined intervention both reported significantly greater intent to increase their physical activity than controls. Curiously, results of the fitness test alone had a greater effect than the more intensive combined intervention (p ⬍0.01 and p ⬍0.05, respectively), but by the 3-month follow-up, only the combined group still endorsed greater intent to engage in physical activity compared to controls (p ⬍0.05). The 204
health hazard risk appraisal alone had no impact on behavioral intent at any assessment point. In sum, biomarker feedback was associated with greater reported motivation to change. This is important since intent is necessary for behavior modification to occur, but it is not sufficient. Intent predicts only about 30% of the variance in behavior change.25 Even with good intentions, people may fail to act on them, or they may act but not have the requisite skills and resources needed to be successful. Thus, intent alone cannot ensure behavior change. Furthermore, it is not necessarily the best indicator of motivation. Since behavior change is, at least in part, dependent on altered motivation, a better index may be behavior change itself. That is, if there is evidence of attempted change, successful or not, it can be assumed that motivation was impacted.
Impact of Biomarker Feedback on Behavior Change Eight randomized trials, including those mentioned above, have evaluated the impact of biomarker feedback on behavior change, although one published the short-term and long-term treatment effects separately.3,21 The findings from this body of work are mixed. In some cases participants who received biological information about their harm exposure, disease risk, or physical functioning were more likely to report relevant health behavior modifications than those who did not receive this feedback,21,22,26 while most reports failed to present significant findings.3,24,27–29 A comparison of the study designs offers insight into the reason for these conflicting results. In general, studies that failed to find a significant behavior effect used only a single biomarker (CO level, cholesterol, or an index of physical fitness)24,27–29 and provided feedback on a single occasion.3,27–29 Studies that reported significant results used a single biomarker assessed on multiple occasions26 or multiple biomarkers assessed on a single occasion.22,24 Moreover, the information conveyed was particularly relevant to participants’ health. For example, Gemson et al.26 randomized individuals with borderline-high cholesterol to receive multiple cholesterol screenings or not. Participants screened at baseline, 2, 4, and 6 months were more likely to report dietary changes at 6-month follow-up (p ⬍0.05) than those screened only at baseline and 6 months. Audrain et al.21 combined multiple biomarkers in a single screening. Smokers were assigned to one of three treatment conditions: (1) 1 hour of standard smoking cessation counseling, (2) standard counseling plus a 10-minute motivational intervention incorporating feedback on smokers’ CO level, or (3) each of these conditions plus a 10-minute motivational intervention
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including feedback on smokers’ genetic susceptibility to lung cancer based on their CYP2D6 genotype. The CYP2D6 enzyme is thought to be responsible for metabolism of tobacco carcinogens. Based on this marker, people can be categorized as extensive metabolizers (90% of the population) or poor metabolizers (10% of the population). Extensive metabolizers have a two- to three-fold greater risk of lung cancer.30 Because the authors were interested in the effect of being told that one is genetically more susceptible to cancer, poor metabolizers were excluded from the analyses. After 2 months, the combined counseling/CO/genotype intervention had a greater impact on perceived risk and perceived benefits of quitting smoking than the other treatments.3 After 1 year, there were no differences in abstinence rates, but participants in the more comprehensive counseling/CO/genotype treatment were twice as likely to have tried to quit smoking than those in the standard counseling group. There was no significant difference in quit attempts between the standard counseling and counseling-plus-CO-feedback groups.21 In other words, CO feedback did not enhance the effect of the standard smoking cessation counseling, but when this was combined with genetic risk information, smokers were apparently more motivated to try to quit smoking. Risser and Belcher22 found a similar result when they combined feedback on CO level, spirometry results, and self-reported pulmonary symptoms with smoking cessation counseling. Smokers receiving the biomarkerenhanced treatment were twice as likely to make a quit attempt (p ⬍0.05) as those receiving only the standard counseling. Thus, three of eight trials found evidence of behavior change, which in turn implies an effect on motivation. Each “successful” trial either combined multiple biomarkers or assessed a single marker multiple times. In addition, the feedback in each study was particularly relevant to participants’ health. Each time subjects in the Gemson trial26 had their cholesterol screened, they were reminded of their elevated cardiovascular disease risk. In the Risser and Belcher22 study, participants were recruited during medical visits. It is not clear how many patients had abnormal lung functioning or reported significant pulmonary symptoms, but the risk of pulmonary disease seems high. Most patients were heavy smokers, had smoked on average more than 35 years, and had an average of five active medical conditions. All smokers in Audrain et al.21 were advised they had a high genetic risk for lung cancer. In short, in each case the biomarker feedback was particularly relevant to the participants’ health and probably caused a shift in their perceived disease susceptibility, thereby increasing their motivation to make lifestyle changes. This supposition is consistent with most prominent theories of health behavior change and Lerman
et al.’s3 finding that smokers informed of their genetic risk reported greater perceived disease risk. Another critical element appears to be the concomitant treatment offered in these studies. Each “successful” trial offered in-person counseling and relevant treatment materials. In Gemson et al.’s trial,26 participants met face-to-face with a nurse who provided brief dietary instruction and gave individuals written information on cholesterol and a copy of “The American Heart Association Diet.” Both smoking cessation trials provided cessation counseling, skills training, and selfhelp materials.21,22 But in each study, this treatment was not effective when offered without the biomarker feedback. Studies that failed to find evidence of behavior change provided minimal or no concomitant treatment. In summary, this evidence suggests that biomarker feedback can increase motivation for behavior change, which in turn may increase utilization of available behavioral treatment. Whether or not subsequent behavior change occurs, however, may depend on the adequacy of the behavioral treatment offered.
Research Limitations and Issues for Future Study Although the available data indicate that biological evidence of disease or risk may alter people’s motivation and help promote health behavior change, it is premature to draw definitive conclusions about the utility of this treatment strategy. This work is limited in several ways. First, the scope of the work to date is limited. Less than a dozen randomized trials have been conducted, only a small number of biomarkers have been used, and only three behavioral outcomes have been examined—smoking cessation, dietary change, and increased physical activity. The effectiveness of biomarker feedback needs to be tested among a broader array of health behaviors and with other biomarkers. The work to date is also limited by the research methodology used. More confidence can be placed in these studies’ results than if they were observational or quasi-experimental, but the findings are weakened by their reliance on retrospective self-report to assess behavior change. This practice poses several potential methodologic problems that must be taken into consideration. First, retrospective recall is inherently biased by forgetting and other cognitive schema that alter memory.31 Moreover, self-report can be biased by the demand characteristics of the assessment situation.32 Participants in the biomarker-based treatments may have reported more behavior changes, not because changes were made, but because they assumed that this was expected of them. Minimal or no-treatment control participants may have felt less demand to report positive treatment effects. Finally, most of the studies lacked Am J Prev Med 2002;22(3)
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sensitive assessments. Participants were asked questions such as, “Have you changed your diet since the screening?”26 or “Since your counseling session 12 months ago, have you tried to quit smoking?”21 These are important questions, but do not adequately assess the full behavioral repertoires of interest. A single diet question does not specify what types of dietary changes were made or how consistently they were maintained. And just because smokers did not report quitting in the past year does not mean that they did not alter their smoking behavior. They could have significantly reduced tobacco use. Before it can validly be concluded that behavioral changes did or did not occur, all relevant behaviors should be assessed in a methodologically stringent manner. Assessing discrete behavior at frequent intervals over time would provide a more valid representation of the biomarkers’ effects. It would also allow better assessment of the potential mechanisms by which these changes occur. For instance, biomarker feedback may increase motivation to change by increasing individuals’ perceived disease susceptibility. However, only one study actually measured treatment impact on perceived disease risk, and this was only assessed once.21 In future research it would be important to assess this effect over time and to evaluate the association between perceived disease risk and sustained motivation to change or maintenance of behavior modifications. Future research should also take into account the potential negative effects of biomarker feedback. Informing individuals that they have done physical damage or are at increased disease risk can cause psychological distress and adverse behavior changes.33–36 Moreover, it may actually decrease motivation to change or lessen one’s belief in his or her ability to alter their behavior. These issues are particularly important to consider when conveying evidence of immutable genetic risk25 or when persons perceive their risk to be uncontrollable. In a recent survey of parents whose children tested positive for familial hypercholesterolemia, those who interpreted the test to mean their children had elevated cholesterol viewed the condition as controllable and less threatening. When the test results were viewed as a genetic problem, however, parents considered the results uncontrollable and more threatening.37 The same reaction could occur to other types of biomarkers if individuals do not perceive that changing their behavior will alter the markers’ status. This is an important concern that will need to be evaluated for each type of biomarker, as each conveys different risk information. If future research supports an association between a biomarker and negative psychological or behavior effects, it would have profound implications for the viability of that marker’s use. Future work should also explore the effectiveness of different biomarker types. Are indicators of physical damage or of disease risk more effective? Does this 206
effect depend on the health of the population of interest? If biomarker feedback increases motivation by altering perceived vulnerability to disease, then one would expect its effectiveness to vary based on these factors. Finally, all of the studies reviewed evaluated the impact of a single person’s biomarker status on his or her behavior. Depending on the behavior in question, one could also provide relevant biological feedback on others’ health. For example, children’s CO and cotinine levels may motivate parents to quit smoking, at least around their children, since these markers indicate risk to the children’s health caused by the parents’ smoking. Research has yet to find that evidence of a child’s harm exposure significantly modifies parental smoking behavior,38,39 but this is an intriguing issue and warrants further well-controlled study.
Summary and Conclusions This paper reviewed the empirical data to determine if providing individuals with evidence of biological harm or disease risk increases motivation for risk reduction or promotes health behavior change. Less than a dozen well-controlled, randomized studies have addressed this issue. Their results suggest that biological information conveying harm exposure, disease risk, or impaired physical functioning may increase motivation to change, particularly when there is evidence of physical damage or significant personal risk. Behavior change may also occur, depending on the intensity of the concomitant treatment. These results are encouraging, but additional research is needed. Future work should continue to evaluate the effectiveness of this treatment strategy and attempt to delineate the optimal conditions under which biomarker feedback can promote behavior change. I would like to thank the anonymous reviewers and Susan J. Curry, PhD, for their thoughtful comments on this work. Funding for the preparation of this manuscript was provided by the Robert Wood Johnson Foundation (grant #037933) and the National Cancer Institute (1K07 CA84603-01).
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nary symptom results to smoking cessation counseling: a randomized trial. J Gen Intern Med 1990;5:16 –22. Aubin M, Godin G, Vezina L, Maziade J, Desharnais R. Hypercholesterolemia screening: Does knowledge of blood cholesterol level affect dietary fat intake? Can Fam Physician 1998;44:1289 –97. Godin G, Desharnais R, Jobin J, Cook J. The impact of physical fitness and health–age appraisal upon exercise intentions and behavior. J Behav Med 1987;10:241–50. Marteau TM, Lerman C. Genetic risk and behavioural change. BMJ 2001;322:1056 –59. Gemson DH, Sloan RP, Messeri P, Goldberg IJ. A public health model for cardiovascular risk reduction. Impact of cholesterol screening with brief nonphysician counseling. Arch Intern Med 1990;150:985–9. Bauman KE, Bryan ES, Dent CW, Koch GG. The influence of observing carbon monoxide level on cigarette smoking by public prenatal patients. Am J Public Health 1983;73:1089 –91. Jamrozik K, Vessey M, Fowler G, Wald N, Parker G, Van Vunakis H. Controlled trial of three different antismoking interventions in general practice. BMJ 1984;288:1499 –503. Strychar IM, Champagne F, Ghadirian P, Bonin A, Jenicek M, Lasater TM. Impact of receiving blood cholesterol test results on dietary change. Am J Prev Med 1998;14:103–10. Amos CI, Caporaso NE, Weston A. Host factors in lung cancer risk: a review of the interdisciplinary studies. Cancer Epidemiol Biomarkers Prev 1992; 1:505–13. Shiffman S, Stone A. Introduction to the Special Section: ecological momentary assessment in health psychology. Health Psychol 1998;17:3–5. Neale JM, Liebert RM. Science and behavior: an introduction to the methods of research, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1986. Bloom JR, Monterossa S. Hypertension labeling and sense of well-being. Am J Public Health 1981;71:1228 –32. Haynes RB, Sackett DL, Taylor DW, Gibson ES, Johnson AL. Increased absenteeism from work after detection and labeling of hypertensive patients. N Engl J Med 1978;299:741– 4. Lawson K, Wiggins S, Green T, Adam S, Bloch M, Hayden MR. Adverse psychological events occurring in the first year after predictive testing for Huntington’s disease. The Canadian Collaborative Study Predictive Testing. J Med Genet 1996;33:856 – 62. Macdonald LA, Sackett DL, Haynes RB, Taylor DW. Labeling in hypertension: a review of the behavioral and psychological consequences. J Chronic Dis 1984;37:933– 42. Senior V, Marteau TMPTJ. Will genetic testing for predisposition for disease result in fatalism? A qualitative study of parents’ responses to neonatal screening for familial hypercholesterolaemia. Soc Sci Med 1999; 48:1857– 69. Chilmonczyk BA, Palomaki GE, Knight GJ, Williams J, Haddow JE. An unsuccessful cotinine-assisted intervention strategy to reduce environmental tobacco smoke exposure during infancy. Am J Dis Child 1992;146:357– 60. McIntosh NA, Clark NM, Howatt WF. Reducing tobacco smoke in the environment of the child with asthma: a cotinine-assisted, minimal-contact intervention. J Asthma 1994;31:453– 62.
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Articles were identified through PubMed (MEDLINE), PsychInfo, and the reference lists of pertinent manuscripts and book chapters.
Results:
Eight published, randomized trials were identified that met criteria for review. The results of this work were mixed, but suggest that biological information conveying harm exposure, disease risk, or impaired physical functioning may increase motivation to change. Subsequent behavior change is also affected by the availability and intensity of concomitant treatment.
Conclusions: Preliminary findings suggest that combining biomarkers with appropriate behavioral treatment may enhance health behavior change, but more research in this area is warranted. Medical Subject Headings (MeSH): behavior therapy, biological markers, health behavior, health promotion, motivation (Am J Prev Med 2002;22(3):200 –207) © 2002 American Journal of Preventive Medicine
Introduction
N
early half of the leading causes of death in our society are attributable to modifiable, behavioral risk factors, such as lack of exercise, poor diet, substance abuse (e.g., tobacco and alcohol), and risky sex.1 Although sound, evidence-based behavioral interventions exist, their effectiveness can be improved. Interventions are needed that motivate individuals to adopt healthier lifestyles, provide the information and skills necessary to do this, and reinforce the maintenance of positive behavior changes. Providing feedback on individual’s biomarker status may be an effective strategy to increase motivation and promote behavior change.2– 4 In this context, biomarkers refer to biological indices of physical harm, disease, or increased disease risk (from either exposure to
From the Center for Health Studies, Group Health Cooperative, Seattle, Washington Address correspondence and reprint requests to: Jennifer McClure, PhD, Center for Health Studies, 1730 Minor Ave., Suite 1600, Seattle, WA 98101. E-mail: [email protected].
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harmful agents or by virtue of one’s genetic profile). For example, cholesterol level, carbon monoxide (CO) level (expired or carboxyhemoglobin), and cotinine level (a metabolite of nicotine) are all biomarkers of exposure to harmful substances that can increase one’s disease risk. Similarly, genetic screening can detect individuals at increased risk of disease. Diagnostic tests, such as blood pressure, pulmonary functioning tests, x-rays, magnetic resonance imaging, and computerized tomography scans, can be used to detect markers of existing physical harm or disease. The idea that knowledge of one’s harm exposure, disease risk, or physical damage may alter his or her intent to change is not new. It is a basic principle in many health behavior theories. The health belief model, health decision model, protection motivation theory, theory of reasoned action, and dual process model all suggest that behavior change is in part induced by one’s perceived susceptibility to disease and a desire to avoid this outcome.5 Thus, increasing awareness that one has some personal risk of harm, or has
Am J Prev Med 2002;22(3) 0749-3797/02/$–see front matter © 2002 American Journal of Preventive Medicine • Published by Elsevier Science Inc. PII S0749-3797(01)00425-1
already caused physical harm through unhealthy habits, may increase motivation for health behavior change. This assumption is also a basic treatment principle in motivational interviewing,6 a counseling technique used successfully to treat alcohol abuse,7,8 opiate abuse,9 compliance to diabetic regimens,10 and smoking.11 The goal of motivational interviewing is to motivate people to change, in part, by making them aware of the risks and consequences of their problematic behavior and, in so doing, to shift the cost-benefit ratio of engaging in the maladaptive behavior. This does not necessarily involve biologically based harm evidence, but the rationale is the same. Finally, using physical status to encourage the reduction of maladaptive behaviors or adoption of more healthful behaviors is a strategy used every day by healthcare providers. Physicians frequently link test results (e.g., cholesterol level, blood pressure, and glucose levels) and medical diagnoses with advice to make appropriate lifestyle changes. In short, it is commonly assumed that informing patients about their biomarkers of disease or disease risk can instigate behavior change. But is this assumption supported by the empirical evidence? If so, under what conditions is this type of feedback most effective? Does it depend on the type of health information employed, the type of behavior in question, or the use of concomitant intervention strategies? If there is no evidence of its effectiveness, why is it not effective? Do the data clearly rule out biomarker feedback as an effective strategy, or is there a lack of sufficient evidence to adequately address the question? To date, there has been no systematic evaluation of these questions. The utility of this practice has been tested, but this knowledge base has not been synthesized or reviewed. This is an important task for two reasons. First, the prevalence of behavior-related disease in our society makes it critical that we continue to improve our ability to promote more healthful behavior. To be effective, this work should be based on empirically validated techniques. Second, it is important to understand the limits of our current knowledge base in order to guide future research. The purpose of this paper is to examine the empirical evidence that informing individuals of their biomarker status is an effective means of increasing motivation to change or promoting behavior modification. Practical implications of this work and issues requiring additional research will be discussed.
Review Procedures Articles were identified through PubMed (MEDLINE), PsychInfo, and the reference lists of pertinent manuscripts and book chapters. PubMed searches were conducted for journal publications between 1966 and October 2001. PsychLit searches encompassed journal articles or book chapters
from 1900 through October 2001. Studies were sought if they included biomarker status feedback as a key component of a behavioral intervention and examined the effects of this intervention on (1) motivation, or intent, to change one’s health-related behaviors, or (2) change in one’s diet, physical activity, tobacco use, alcohol abuse/dependence, medical adherence, sexual practices, or utilization of preventive screening. Emphasis was placed on these behaviors because they represent the leading behavioral risk factors for disease in our society. Articles were selected based on the following criteria. First, only published randomized trials were reviewed, thereby allowing an objective analysis of the biomarkers’ impact on motivation and behavior. Without the methodologic rigor of a randomized controlled design, it is impossible to attribute observed changes to the treatment and not to confounding factors or artifact. Second, studies were included only if they either compared persons receiving biomarker feedback to those not receiving this type of intervention, or compared treatments in which the only difference was the frequency or intensity of the biomarker feedback. This made it easier to evaluate the independent effect of the biomarker treatment and compare the impact of different biomarker intensities. Third, behavior change had to be directly assessed, as opposed to only measuring biological proxies of behavior modification, such as a change in cholesterol level12,13 or infant birth weight.14 Biological proxies can reflect changes in the target behavior, but are also influenced by factors, such as medication use, regression to the mean in repeated measurements, or behaviors other than those under investigation. Fourth, only investigations involving adults were evaluated because children and adolescents’ health-related behaviors may be more dependent on external environmental pressures than their own internal control. Finally, studies were excluded if they met each of these criteria, but significantly co-varied the treatment intensity across groups, making it impossible to delineate the impact of the biomarker counseling from other concomitant treatment components. These criteria eliminated a number of studies.15–20
Literature Review Eight studies met all inclusion criteria for this review. A summary of each study, including the main outcomes of interest, overview of the treatment design, and review of key findings, is presented in Table 1. Because short-term and long-term treatment effects were published separately for one trial,3,21 a total of nine published reports are listed. The identified studies focused on three areas of behavior change: tobacco use, dietary change, and physical activity. They employed a variety of biomarkers, including expired CO, cotinine level, cholesterol level, the CYP2D6 genetic marker for lung cancer susceptibility, an index of physical fitness, and lung functioning assessed via spirometry and self-reported pulmonary symptoms. Most included only a single biomarker, but several combined more than one. Each study was examined for evidence that the biomarker intervention affected either motivation to change or actual behavior change. The results are presented below.
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Table 1. Summary of randomized trials evaluating effect of biomarker feedback on motivation and behavior change
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Follow-up period
Reference Sample
Biomarker(s)
Primary outcomes
Treatment design
427 smokers Lerman et al. (1997)3
CO level and genetic marker for lung cancer (CYP2D6)
Change in: (1) smokingrelated cognitions, (2) emotional distress, and (3) smoking behavior
Audrain 426 smokers et al. (1997)21
CO level and genetic marker for lung cancer (CYP2D6)
(1) Smoking abstinence, (2) quit attempts
2 months Group 1: standard counseling (1hour visit): Group 2: standard 1hour counseling ⫹ 10-minute motivational intervention with CO exposure feedback; Group 3: standard 1-hour counseling ⫹ CO feedback ⫹ 10-minute motivational counseling with feedback about susceptibility to lung cancer (CYP2D6 genotype) Same as above 1 year
Key results No difference in abstinence rates across groups. Combined counseling/ CO/CYP2D6 feedback produced greater effects on perceived risk, perceived benefits of quitting, and fear arousal than other treatments, but also resulted in greatest levels of post-treatment fear and depression. Combined counseling/CO/CYP2D6 group twice as likely to make a quit attempt, but no difference in abstinence rates across groups. No difference in depression scores across groups. 51% of feedback group and 44% of controls stated they intended to quit after the baseline intervention. Feedback group was twice as likely to quit sometime during the year (40% vs 16%, p ⬍ 0.05). CO-confirmed point prevalence abstinence at 1 year was 20% vs 6.7% (p ⫽ 0.06). Participants who were aware of their test result reported greater intent to change their diet (p ⫽ 0.02).
Risser 90 smokers CO level, and contacted spirometry, and Belcher during pulmonary (1990)22 routine symptoms medical visits
(1) Smoking cessation, (2) intent to quit smoking
Group 1: 1-hour counseling ⫹ selfhelp manual ⫹ setting a quit date ⫹ invitation for 9-session counseling program; Group 2: same ⫹ 10-minute motivational intervention with personalized health feedback
Aubin et 419 family al. practice (1998)23 clinic patients
Cholesterol level
(1) Intention to change fat intake, (2) dietary change
Godin et 200 solicited al. volunteers (1987)24
Physical fitness test (1) Intention to (index of exercise, cardiorespiratory (2) self-report functioning) exercise in last 3 months
Group 1: feedback on cholesterol ⫹ 3 months brochure on low-fat diet; completed questionnaire on intention to change behavior after receiving feedback; Group 2: same as above, but completed questionnaire before receiving feedback results 130 subjects completed the study and Group 1: physical-fitness evaluation; 2 weeks for Group 2: health hazard appraisal; “intentions” 112 included in final sample. Completing initial assessment without Group 3: physical-fitness evaluation and 3 being told of results did not affect and health hazard appraisal; months for initial intentions to exercise Group 4: control behavior between the groups undergoing testing, change but the control group was different from the combined treatment group. At 3 months, however, there were no differences in intentions between the groups and no significant effect on leisure-time exercise behavior during this period.
1, 4, and 12 months
(continued on next page)
Table 1. (continued) Reference Sample
Biomarker(s)
Primary outcomes
Treatment design
All participants informed of 137 participants with Cholesterol level (1) Cholesterol Gemson cholesterol level and risk group, borderline-high level, et al. provided brief dietary instruction, (2) weight, (1990)26 cholesterol given 1-page cholesterol (3) blood information sheet, and dietary pressure, brochure. Group 1: follow-up (4) smoking status, screening at 6 months only; (5) diet Group 2: follow-up screening at 2, 4, and 6 months
Bauman CO level 170 pregnant et al. women (80 (1983)27 smokers) contacted through public prenatal care visits
(1) Smoking reduction, (2) prevention of smoking initiation
Jamrozik 2110 smokers et al. contacted during (1984)28 routine medical visits
Smoking cessation
CO level
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Strychar 429 hospital workers Cholesterol level (1) Change in et al. cholesterol (1998)29 level, (2) self-reported dietary change
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CO, carbon monoxide.
Group 1: informed of CO level and informed of relation between smoking, CO, and consequences of smoking during pregnancy; participants then observed assessment of nonsmokers CO levels for comparison; Group 2: informed of the relation between CO, smoking and consequences during pregnancy, but were not informed of their CO level Group 1: control; Group 2: received advice to quit smoking ⫹ written self-help; Group 3: received advice to quit ⫹ self-help manual ⫹ feedback on CO level; Group 4: received advice to quit ⫹ written self-help ⫹ referral for in-clinic smoking cessation counselor All workers completed pre-test assessment of height, weight, cholesterol, tobacco use, alcohol use, and physical activity, and a 24hour dietary recall. All received a 20-minute nutrition education session, identified changes they could make over the next 3 months, and signed a behavioral contract. Participants in treatment group were told their cholesterol levels. Control participants were not
Follow-up period
Key results
2, 4, and 6 months Subjects reassessed at 2, 4, and 6 depending on months were significantly more treatment group likely to report “a lot” of dietary change during 6-month follow-up (24% vs 10.3%, p ⬍ 0.05). They were also more likely to report eating less red meat, cheese, butter, and fast food (p ⬍ 0.05). No group differences for cholesterol, weight, blood pressure, smoking status, or weekly exercise. 6 weeks There was no differences in smoking rates between groups.
1 year
Dietary questionnaire at 6 weeks and interview at 16 to 20 weeks
Reported abstinence rates at 1 year: 10.6% of control group, 15% of those receiving advice and selfhelp; 17.2% of group receiving CO feedback, and 13.2% of those receiving advice, self-help, and referral information. Biochemical confirmation suggested 24% 40% of all participants may have misreported smoking status. Among participants completing both assessments (n ⫽ 429), overall cholesterol levels decreased for all participants (p ⬍ 0.001) and all reportedly improved their diet. There was no difference in cholesterol between groups.
Impact of Biomarker Feedback on Stated Motivation to Change Three studies assessed participants’ stated motivation to change after receiving biomarker feedback. Risser and Belcher22 contacted smokers during routine medical visits. Participants were randomly assigned to receive brief counseling plus self-help materials and an invitation to join a more comprehensive counseling program or the same intervention plus feedback on one’s CO level, lung functioning (assessed via spirometry), and self-reported pulmonary symptoms. After the baseline intervention, slightly more participants who received their CO level and spirometry results reported an intent to quit smoking (51% vs 44%). Aubin et al.23 assessed intent to modify dietary behavior among 419 family practice clinic patients. All patients participated in a cholesterol screening, but intent to make dietary changes was assessed prior to receiving the test results for one group and after receiving the results for another. Not surprisingly, patients informed of their cholesterol level reported greater intent to change their diet than those who did not receive this information (p ⬍0.05). Finally, Godin et al.24 examined the impact of three different interventions on intent to increase exercise. Participants were recruited from the community and randomly assigned to receive a physical fitness test (Canadian Home Fitness Test), health hazard appraisal, a combination of the fitness test and health hazard appraisal, or no treatment (control group). The physical fitness test estimated cardiorespiratory fitness based on post-exercise heart rates. In contrast, the health hazard appraisal was used to predict participants’ probability of dying from a potentially preventable cause in the next 10 years based on their age, gender, medical history, and health risk. After each intervention, researchers assessed the probability that participants would engage in one or more physical activities on a regular basis during the next 3 months. Behavioral intent was assessed immediately after testing for half of participants in each group, after 3 weeks for the remaining half of participants, and at 3-month follow-up for all participants. Subjects assessed immediately following testing were unaware of their test results, but those assessed at 2 weeks were aware of their results. As with Aubin et al.,23 motivation was unaffected when participants were not aware of their test results; but after receiving the test results, participants who took the fitness test and those in the combined intervention both reported significantly greater intent to increase their physical activity than controls. Curiously, results of the fitness test alone had a greater effect than the more intensive combined intervention (p ⬍0.01 and p ⬍0.05, respectively), but by the 3-month follow-up, only the combined group still endorsed greater intent to engage in physical activity compared to controls (p ⬍0.05). The 204
health hazard risk appraisal alone had no impact on behavioral intent at any assessment point. In sum, biomarker feedback was associated with greater reported motivation to change. This is important since intent is necessary for behavior modification to occur, but it is not sufficient. Intent predicts only about 30% of the variance in behavior change.25 Even with good intentions, people may fail to act on them, or they may act but not have the requisite skills and resources needed to be successful. Thus, intent alone cannot ensure behavior change. Furthermore, it is not necessarily the best indicator of motivation. Since behavior change is, at least in part, dependent on altered motivation, a better index may be behavior change itself. That is, if there is evidence of attempted change, successful or not, it can be assumed that motivation was impacted.
Impact of Biomarker Feedback on Behavior Change Eight randomized trials, including those mentioned above, have evaluated the impact of biomarker feedback on behavior change, although one published the short-term and long-term treatment effects separately.3,21 The findings from this body of work are mixed. In some cases participants who received biological information about their harm exposure, disease risk, or physical functioning were more likely to report relevant health behavior modifications than those who did not receive this feedback,21,22,26 while most reports failed to present significant findings.3,24,27–29 A comparison of the study designs offers insight into the reason for these conflicting results. In general, studies that failed to find a significant behavior effect used only a single biomarker (CO level, cholesterol, or an index of physical fitness)24,27–29 and provided feedback on a single occasion.3,27–29 Studies that reported significant results used a single biomarker assessed on multiple occasions26 or multiple biomarkers assessed on a single occasion.22,24 Moreover, the information conveyed was particularly relevant to participants’ health. For example, Gemson et al.26 randomized individuals with borderline-high cholesterol to receive multiple cholesterol screenings or not. Participants screened at baseline, 2, 4, and 6 months were more likely to report dietary changes at 6-month follow-up (p ⬍0.05) than those screened only at baseline and 6 months. Audrain et al.21 combined multiple biomarkers in a single screening. Smokers were assigned to one of three treatment conditions: (1) 1 hour of standard smoking cessation counseling, (2) standard counseling plus a 10-minute motivational intervention incorporating feedback on smokers’ CO level, or (3) each of these conditions plus a 10-minute motivational intervention
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including feedback on smokers’ genetic susceptibility to lung cancer based on their CYP2D6 genotype. The CYP2D6 enzyme is thought to be responsible for metabolism of tobacco carcinogens. Based on this marker, people can be categorized as extensive metabolizers (90% of the population) or poor metabolizers (10% of the population). Extensive metabolizers have a two- to three-fold greater risk of lung cancer.30 Because the authors were interested in the effect of being told that one is genetically more susceptible to cancer, poor metabolizers were excluded from the analyses. After 2 months, the combined counseling/CO/genotype intervention had a greater impact on perceived risk and perceived benefits of quitting smoking than the other treatments.3 After 1 year, there were no differences in abstinence rates, but participants in the more comprehensive counseling/CO/genotype treatment were twice as likely to have tried to quit smoking than those in the standard counseling group. There was no significant difference in quit attempts between the standard counseling and counseling-plus-CO-feedback groups.21 In other words, CO feedback did not enhance the effect of the standard smoking cessation counseling, but when this was combined with genetic risk information, smokers were apparently more motivated to try to quit smoking. Risser and Belcher22 found a similar result when they combined feedback on CO level, spirometry results, and self-reported pulmonary symptoms with smoking cessation counseling. Smokers receiving the biomarkerenhanced treatment were twice as likely to make a quit attempt (p ⬍0.05) as those receiving only the standard counseling. Thus, three of eight trials found evidence of behavior change, which in turn implies an effect on motivation. Each “successful” trial either combined multiple biomarkers or assessed a single marker multiple times. In addition, the feedback in each study was particularly relevant to participants’ health. Each time subjects in the Gemson trial26 had their cholesterol screened, they were reminded of their elevated cardiovascular disease risk. In the Risser and Belcher22 study, participants were recruited during medical visits. It is not clear how many patients had abnormal lung functioning or reported significant pulmonary symptoms, but the risk of pulmonary disease seems high. Most patients were heavy smokers, had smoked on average more than 35 years, and had an average of five active medical conditions. All smokers in Audrain et al.21 were advised they had a high genetic risk for lung cancer. In short, in each case the biomarker feedback was particularly relevant to the participants’ health and probably caused a shift in their perceived disease susceptibility, thereby increasing their motivation to make lifestyle changes. This supposition is consistent with most prominent theories of health behavior change and Lerman
et al.’s3 finding that smokers informed of their genetic risk reported greater perceived disease risk. Another critical element appears to be the concomitant treatment offered in these studies. Each “successful” trial offered in-person counseling and relevant treatment materials. In Gemson et al.’s trial,26 participants met face-to-face with a nurse who provided brief dietary instruction and gave individuals written information on cholesterol and a copy of “The American Heart Association Diet.” Both smoking cessation trials provided cessation counseling, skills training, and selfhelp materials.21,22 But in each study, this treatment was not effective when offered without the biomarker feedback. Studies that failed to find evidence of behavior change provided minimal or no concomitant treatment. In summary, this evidence suggests that biomarker feedback can increase motivation for behavior change, which in turn may increase utilization of available behavioral treatment. Whether or not subsequent behavior change occurs, however, may depend on the adequacy of the behavioral treatment offered.
Research Limitations and Issues for Future Study Although the available data indicate that biological evidence of disease or risk may alter people’s motivation and help promote health behavior change, it is premature to draw definitive conclusions about the utility of this treatment strategy. This work is limited in several ways. First, the scope of the work to date is limited. Less than a dozen randomized trials have been conducted, only a small number of biomarkers have been used, and only three behavioral outcomes have been examined—smoking cessation, dietary change, and increased physical activity. The effectiveness of biomarker feedback needs to be tested among a broader array of health behaviors and with other biomarkers. The work to date is also limited by the research methodology used. More confidence can be placed in these studies’ results than if they were observational or quasi-experimental, but the findings are weakened by their reliance on retrospective self-report to assess behavior change. This practice poses several potential methodologic problems that must be taken into consideration. First, retrospective recall is inherently biased by forgetting and other cognitive schema that alter memory.31 Moreover, self-report can be biased by the demand characteristics of the assessment situation.32 Participants in the biomarker-based treatments may have reported more behavior changes, not because changes were made, but because they assumed that this was expected of them. Minimal or no-treatment control participants may have felt less demand to report positive treatment effects. Finally, most of the studies lacked Am J Prev Med 2002;22(3)
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sensitive assessments. Participants were asked questions such as, “Have you changed your diet since the screening?”26 or “Since your counseling session 12 months ago, have you tried to quit smoking?”21 These are important questions, but do not adequately assess the full behavioral repertoires of interest. A single diet question does not specify what types of dietary changes were made or how consistently they were maintained. And just because smokers did not report quitting in the past year does not mean that they did not alter their smoking behavior. They could have significantly reduced tobacco use. Before it can validly be concluded that behavioral changes did or did not occur, all relevant behaviors should be assessed in a methodologically stringent manner. Assessing discrete behavior at frequent intervals over time would provide a more valid representation of the biomarkers’ effects. It would also allow better assessment of the potential mechanisms by which these changes occur. For instance, biomarker feedback may increase motivation to change by increasing individuals’ perceived disease susceptibility. However, only one study actually measured treatment impact on perceived disease risk, and this was only assessed once.21 In future research it would be important to assess this effect over time and to evaluate the association between perceived disease risk and sustained motivation to change or maintenance of behavior modifications. Future research should also take into account the potential negative effects of biomarker feedback. Informing individuals that they have done physical damage or are at increased disease risk can cause psychological distress and adverse behavior changes.33–36 Moreover, it may actually decrease motivation to change or lessen one’s belief in his or her ability to alter their behavior. These issues are particularly important to consider when conveying evidence of immutable genetic risk25 or when persons perceive their risk to be uncontrollable. In a recent survey of parents whose children tested positive for familial hypercholesterolemia, those who interpreted the test to mean their children had elevated cholesterol viewed the condition as controllable and less threatening. When the test results were viewed as a genetic problem, however, parents considered the results uncontrollable and more threatening.37 The same reaction could occur to other types of biomarkers if individuals do not perceive that changing their behavior will alter the markers’ status. This is an important concern that will need to be evaluated for each type of biomarker, as each conveys different risk information. If future research supports an association between a biomarker and negative psychological or behavior effects, it would have profound implications for the viability of that marker’s use. Future work should also explore the effectiveness of different biomarker types. Are indicators of physical damage or of disease risk more effective? Does this 206
effect depend on the health of the population of interest? If biomarker feedback increases motivation by altering perceived vulnerability to disease, then one would expect its effectiveness to vary based on these factors. Finally, all of the studies reviewed evaluated the impact of a single person’s biomarker status on his or her behavior. Depending on the behavior in question, one could also provide relevant biological feedback on others’ health. For example, children’s CO and cotinine levels may motivate parents to quit smoking, at least around their children, since these markers indicate risk to the children’s health caused by the parents’ smoking. Research has yet to find that evidence of a child’s harm exposure significantly modifies parental smoking behavior,38,39 but this is an intriguing issue and warrants further well-controlled study.
Summary and Conclusions This paper reviewed the empirical data to determine if providing individuals with evidence of biological harm or disease risk increases motivation for risk reduction or promotes health behavior change. Less than a dozen well-controlled, randomized studies have addressed this issue. Their results suggest that biological information conveying harm exposure, disease risk, or impaired physical functioning may increase motivation to change, particularly when there is evidence of physical damage or significant personal risk. Behavior change may also occur, depending on the intensity of the concomitant treatment. These results are encouraging, but additional research is needed. Future work should continue to evaluate the effectiveness of this treatment strategy and attempt to delineate the optimal conditions under which biomarker feedback can promote behavior change. I would like to thank the anonymous reviewers and Susan J. Curry, PhD, for their thoughtful comments on this work. Funding for the preparation of this manuscript was provided by the Robert Wood Johnson Foundation (grant #037933) and the National Cancer Institute (1K07 CA84603-01).
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