Health Education Research Advance Access published online on November 19, 2007
Health Education Research, doi:10.1093/her/cym068
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Changing health behaviors to improve health outcomes after angioplasty: a randomized trial of net present value versus future value risk communication
1 Center for Complementary and Integrative Medicine, Weill Cornell Medical College, New York, NY 10065, USA
2 Department of Mathematics, Central Michigan University, Mount Pleasant, 48859
3 Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
4 Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, CA 94143, USA
5 Department of Medicine, Weill Cornell Medical College New York, New York, NY 10021, USA
6 Department of Health and Behavior Studies, Teachers College and Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027, USA
Correspondence to: * Correspondence to: M. E. Charlson. E-mail: mecharl{at}med.cornell.edu
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Abstract |
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Patients who have undergone angioplasty experience difficulty modifying at-risk behaviors for subsequent cardiac events. The purpose of this study was to test whether an innovative approach to framing of risk, based on ‘net present value’ economic theory, would be more effective in behavioral intervention than the standard ‘future value approach’ in reducing cardiovascular morbidity and mortality following angioplasty. At baseline, all patients completed a health assessment, recieved an individualized risk profile and selected risk factors for modification. The intervention randomized patients into two varying methods for illustrating positive effects of behavior change. For the experimental group, each selected risk factor was assigned a numeric biologic age (the net present value) that approximated the relative potential to improve current health status and quality of life when modifying that risk factor. In the control group, risk reduction was framed as the value of preventing future health problems. Ninety-four percent of patients completed 2-year follow-up. There was no difference between the rates of death, stroke, myocardial infarction, Class II–IV angina or severe ischemia (on non-invasive testing) between the net present value group and the future value group. Our results show that a net present risk communication intervention did not result in significant differences in health outcomes.
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Introduction |
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Patients who have undergone percutaneous transluminal coronary angioplasty (PTCA) must make behavioral changes, such as stopping smoking, increasing physical activity, decreasing cholesterol and reducing weight, in order to reduce the risk of subsequent cardiac events. Cessation of smoking, for example, has been shown to result in a lower recurrence rate, with less angina, fewer limitations of physical activity and increased survival [1–5]. Other lifestyle changes, such as taking lipid-lowering medicine, have been shown to reduce the recurrence of the disease. At least two studies have shown that intensive lipid-lowering therapy can result in regression of disease [6, 7].
Despite the risk of recurrence, most patients with coronary heart disease who have undergone PTCA do not make major changes in behaviors [8]. Because patients find it difficult to adopt and maintain behavioral changes after angioplasty [9, 10], we sought to investigate a novel approach to communicating risk in an effort to motivate patients to make changes in health behavior. The current approach to communicating risk information largely helps patients to understand how making behavioral changes will reduce ‘future risk’. Our intervention approach was designed to approach the communication of risk by framing the value of behavior modification in terms of the ‘net present value’ (what patients can gain now) of the change. Thus, our approach was designed to provide patients and their physicians with a personally relevant means by which they could prioritize lifestyle changes in terms of a tangible value associated with the change in relative risk associated with a change in behavior. This approach and its potential application to understanding risk communication in health behavior change have theoretical origins in net present value economic theory [11].
In economic theory, the concept of net present value involves taking the future value of a good or investment and relating it to what it is worth in present dollar terms by discounting based on expectations of interest rate, inflation and demand. For the study, this theoretical perspective was applied in communicating the benefit of changing health behavior in terms of reduced risk. To do so, the health effects of a potentially beneficial behavioral change were portrayed in present value or present value health terms that have been calculated within a short (3-year) time frame and for which the change in mortality can be clearly and understandably related to the change in risk at any given age. Thus, as Allegrante and Roizen [11] have pointed out: ‘... if smoking a pack {of cigarettes} a day increases the risk for the average 50-year-old Caucasian male to that of the average 58-year-old {counterpart}, net-present value of smoking today is 8 years of aging (or, in effect, a loss of 8 years)’. We have outlined in detail the theoretical framework and intervention methods underlying our approach to communicating future health risks in the PTCA patient population in a previously published paper [12].
This paper reports a randomized trial whose objective was to evaluate the efficacy of a behavioral intervention that communicated risk based on a net present value approach versus a standard ‘future value’ approach in reducing cardiovascular morbidity and mortality following angioplasty or stenting. In addition, we examined the impact of the intervention on stages of change operationalized from the Transtheoretical Model of Stages of Change [13, 14] and behavior-specific self-efficacy [15, 16].
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Patient eligibility criteria, screening and recruitment
This study protocol (#0302005991) was approved by the Institutional Review Board of Weill-Cornell Medical College on 6 July 1998 and has been reapproved every year since. The study was conducted from 1999 to 2003. All patients undergoing coronary artery catheterization who had at least single-vessel stenosis and revascularization with PTCA or coronary artery stenting were eligible for enrollment if they were (i) not enrolled in another trial designed to modify behavior, (ii) verbally fluent in English and (iii) able to provide informed consent within 1 week after the procedure. We identified and screened for potential eligibility a total of 2022 patients through a systematic, daily review of the cardiac catheterization schedule at the New York-Presbyterian Hospital—Weill-Cornell Medical College during an 18-month period. Patients were enrolled during hospitalization following PTCA or coronary artery stenting.
Various factors contributed to the ineligibility of screened patients (Fig. 1). In total, 38% of patients who were screened met eligibility requirements. In total 53% of eligible patients were enrolled. A total of 660 patients provided written informed consent and were randomized.
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Baseline demographic and clinical history
Patient demographic status was documented for age, sex, marital status, ethnicity, occupation, education and employment. The mean age of all patients was 62 years, and 27% were female. Twenty-one percent were African-American or Latino. Sixty-two percent were married. Forty-three percent were college graduates, and 16% had not completed high school. Forty-three percent were working full time and 36% were retired.
The duration and history of patients' symptoms and treatments were noted, as well as history of myocardial infarction, unstable angina, congestive heart failure, previous catheterization or coronary artery bypass grafting. Prior to angioplasty, 58% of patients had unstable angina and 25% had a history of myocardial infarction. The mean ejection fraction 50%. At baseline, 17% had undergone prior bypass surgery, while 36% had prior angioplasty/stenting. Overall, 57% had hypertension, 26% diabetes and 14% obesity.
The Charlson comorbidity scale [17] was used to evaluate the burden of comorbid disease. Baseline medications were also recorded, especially the use of aspirin, nitrates, beta adrenergic blockers, calcium channel blockers, digitalis, diuretics, angiotensin-converting enzyme inhibitors, other anti-hypertensive agents, insulin or oral hypoglycemic agents, sedative/hypnotics, anti-depressants and steroids. Height and weight were recorded, and body mass index was calculated. Total cholesterol, high density lipoprotein (HDL) and low density lipoprotein (LDL) levels were also recorded. Pertinent data from the angioplasty included the number of vessels treated, the number and type of stents, the ejection fraction and complications of the procedure, if any. Procedures were performed on an average of two vessels.
Patients completed a health assessment that evaluated 13 cardiac risk factors, including physical activity, smoking, diet and medications. The dietary questionnaire was based on the semi-quantitative food frequency questionnaire, a reproducible and valid measure of intake of many nutrients, which has been used longitudinally to assess the dietary habits of older adults and patients with chronic and cardiac disease [18–21]. The physical activity questionnaire comprised a modification of the Minnesota leisure time activity questionnaire [22]. The risk assessment provided all patients with their own individualized risk factor profile in which the behavior was highlighted for change, if an individual patient's status was outside normal or recommended ranges. The possible areas for cardiovascular risk behavior change can be broadly cast in five major domains:
- Physical activity: increase physical activity, increase aerobic exercise or increase strength training.
- Smoking: stop smoking (or continue not to smoke).
- Diet/weight: reduce weight, reduce red meat, reduce dietary intake of saturated fat and cholesterol, increase fiber-rich food, increase flavonoid-rich food, increase folic acid intake.
- Blood pressure, diabetes: control/reduce blood pressure, control diabetes.
- Medications: take beta blockers/reduce heart rate.
- Smoking: stop smoking (or continue not to smoke).
All patients received feedback about their status on the selected list of 13 cardiovascular risk factors. Recommendations for behaviors to target for change were based on pre-specified cutoffs [12]. Patients were then asked to choose two to three risk factors to target for lifestyle change. Although more risk factors could have been chosen, we decided that only two to three risk factors could be feasibly managed by patients. Both the patient and cardiologist received a copy of the risk profile and behavior change choices. All patients were then asked to consult with their cardiologist prior to implementing any new health behavior change activity.
Stage of change is a measure of readiness for initiating changes in each risk factor and included five possible stages: pre-contemplation, contemplation, preparation, action and maintenance. The stage of change model has been utilized in assessing behavior change with various health-related behaviors, including diet [23], weight [24], exercise [25] and changing more than one behavior at a time [26]. Researchers have evaluated rates of the stages of change and how they differently impact behavior change likelihood [27].
Experimental and control intervention
The intervention theory and methods utilized in this study have been described in extensive detail in an earlier report [12]. Patients were randomized to one of two groups, which determined the specific type of feedback that they would receive: the experimental net present value (n = 329) or the control standard future value intervention (n = 331). The study biostatistician prepared a randomization schedule from random numbers prior to the enrollment of the first patient. The assignments were placed in sealed opaque numbered envelopes prior to the onset of the study. Randomization was stratified for diabetes, gender and minority status in order to achieve balancing of those factors. None of the demographic or clinical characteristics differed statistically between the two groups (Table I). Once adjusting for multiple comparisons among each of the characteristics, there was no significant difference for any of the patient characteristics at baseline.
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Patient education about each risk factor in an individualized profile was framed using either a future risk approach or a net present value approach, where each factor is weighted according to its relative risk. To help patients in both groups get started, we provided them with behavior change ‘tip sheets’ for each of the risks they selected to change, as well as referrals for behavior change programs and other community resources. The tip sheets provided practical advice about how to change behavior. For example, the physical activity tip sheet suggested parking a car at the far end of a parking lot in order to walk further to one's destination.
In both groups, stages of behavioral change [13] and behavior-specific self-efficacy [15, 16] for each of the behaviors of interest—increasing physical activity, stopping smoking, increasing or decreasing certain dietary elements, losing weight, controlling blood pressure and taking medications—were also assessed. The stage of change construct was assessed by a single-item rating of five stages (1 = pre-contemplation, 2 = contemplation, 3 = preparation, 4 = action and 5 = maintenance). We assessed stage of change for all patients at baseline and again at 12- and 24-month follow-ups for each of the chosen risk factor behaviors. To measure the discrete stage of change, we asked the individual whether they were seriously considering changing a risk factor behavior of interest within the next 6 months.
Similarly, behavior-specific self-efficacy was assessed with a single-item confidence rating on a scale of 1–10 (1 = not at all confident, 10 = very confident) for each chosen behavioral risk factors, at baseline and at 12- and 24-month follow-ups. For example, to measure self-efficacy with regard to overall physical activity, we asked: ‘How confident are you that you will be able to start doing more regular physical activity (starting with 10 min a day for at least 3 days a week; and gradually working up to longer periods for 4–5 days a week) within the next month?’
Net present value approach (experimental group)
The experimental group received feedback about their cardiovascular risk factors framed in a common metric—‘biologic age’. Biologic age was calculated with a research version of the RealAge® software, which was modified for use in this study. RealAge combines a person's historical data and habits to provide a posterior state of knowledge risk. In the modified version, at the outset an individual's biologic age is the same as his or her chronologic or calendar age. Patients were told their estimated biologic age (calculated using data from the baseline health risk assessment). The data for each risk factor were communicated in terms of the amount of ‘biologic age reduction’ one could achieve within 3 months and within 2 years, if each risk factor was changed. From this individual profile, patients were asked to choose two to three of these risk behaviors to begin changing ‘now’ because doing so ‘will decrease your biologic age’. For each risk factor, they were given a set of action steps, i.e. individualized guidelines for each risk factor that emphasized the present value of making a behavioral change.
Future value or standard approach (control group)
The control group received feedback about their cardiovascular risk factors in terms of deviation from normative values. From their individual profiles, patients were asked to choose two to three of these risk factors to begin changing now because doing so ‘will increase your lifespan’. For each risk factor, they were given a set of action steps, i.e. individualized guidelines for each risk factor that emphasized the future value of behavioral change.
Follow-up
For 2 years, both groups of patients were contacted and interviewed by telephone at 3-month intervals. The telephone contact provided motivational support to patients in both groups, using a specific script that was based loosely on the principles of motivational interviewing developed by Emmons and Rollnick [28], to assist patients in adopting and maintaining behavioral changes. In addition, each call included an updated assessment of interval clinical events and changes in cardiovascular risk profile, as well as an opportunity for the individual patient to change or add new risk factors.
The follow-up was designed to capture all relevant interval events, including behavioral change and new outcomes or cardiac procedures or related hospitalizations. In total, 88% of patients had their clinical status assessed at 3–6 months, 76% at 9–12 months, 63% at 15–18 months and 93% at 24 months. In total, 86% of patients had detailed risk factor profiles updated at 3–6 months, 71% at 9–12 months, 53% at 15–18 months and 47% at 21–24 months. There was no difference in follow-up rates between the net present value and future value groups. Seventy-six percent of the patients completed four or more follow-up assessments.
Primary outcomes
Patients completed their final evaluation for major post-operative clinical outcomes at 
24 months (±8 weeks). The primary outcome was freedom from death, myocardial infarction, stroke, Class II–IV angina or severe asymptomatic ischemia at 24 months. The time course of freedom from death, myocardial infarction, stroke, Class II–IV angina or severe asymptomatic ischemia was also documented, with the primary end point being the time to first event. Revascularization was not included as an end point because indications vary widely.
End point review was conducted by a cardiologist who was blinded to randomization group and included:
- Mortality: all deaths from any cause.
- Myocardial infarction: new persistent Q waves of >0.03 msec and >1 mV in depth were required in two contiguous leads on a standard 12-lead electrocardiogram in the absence of a new conduction abnormality or a marked change in the QRS axis.
- Angina: the absence of angina was defined as freedom from Class II–V anginal symptoms.
- Severe ischemia on non-invasive testing: non-invasive testing was not routinely performed on patients enrolled in this trial. Whether or not patients had non-invasive testing was decided entirely by the patient's own physician. This end point was designed to capture those patients who were without anginal symptoms, but who were found on non-invasive testing to have sufficiently severe ischemia to warrant revascularization.
- Stroke: this was defined as the occurrence of a new major focal neurologic deficit, which persisted >24 hours.
- Myocardial infarction: new persistent Q waves of >0.03 msec and >1 mV in depth were required in two contiguous leads on a standard 12-lead electrocardiogram in the absence of a new conduction abnormality or a marked change in the QRS axis.
Power and data analysis
We have estimated the occurrence of the principal outcome defined above (mortality, myocardial infarction, stroke, Class II–IV angina or severe ischemia) is 30%. (Total event rates after angioplasty range from 30—to 45%, and the event rates after stents are somewhat lower; 30% is taken as the event rate in the control arm.) Hence, the expected outcome rate in the control arm, Pc, is taken to be 0.30. A drop in the incidence of any of the adverse outcomes to a level of 0.20 would constitute a clinically important difference between the two management strategies. Therefore, 
(where = Pc – Pe) is taken to be 0.10. Based on the above values of Pc, and a two-sided alpha of 0.05, with a ß error of 0.2 for 80% power, the sample size is estimated to be a total of 590 patients or 295 in each group.
Sample size was based on 80% power for the occurrence of any one of the following outcomes: death, myocardial infarction, stroke, Class II–IV angina or severe ischemia on non-invasive testing. The level for testing the significance of the principal outcome was set at P = 0.05. All data were entered into an integrated series of databases and then transported to SAS for windows v.8.02 (Cary, SC) for statistical analyses. After 24 months of follow-up, we analyzed the data for main effects between groups, efficacy and safety according to the intention-to-treat principle.
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Risk factors at baseline
Overall, there were no statistically significant differences between randomization groups in the specific risk factors chosen for change, as described below. Patients had an average of six risk factors recommended for behavioral change at baseline. Patients in both groups were asked to identify risk factors they wished to select for change, and, on average, they chose three risk factors for change. Table II shows the percentage of patients at baseline for whom a risk factor was recommended for change and the percentage of patients who chose a risk factor for change, if recommended. Increasing physical activity was recommended for change in 85% of patients and reducing cholesterol was recommended in 86%; quitting smoking was recommended in the 25% of patients who were current smokers. The most common risk factors chosen at baseline for change were overall physical activity (66%), smoking (62%) and weight loss (59%).
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Table II shows the proportion of patients whose stage of change was preparation as well as ratings of perceived self-efficacy. Most patients who chose to change a behavior were at the preparation stage for change. Patients had high self-efficacy or confidence in their ability to modify health behaviors immediately after angioplasty, with average self-efficacy scores of between 7 and 9. For either stage of change or self-efficacy, there were no statistically significant differences between randomization groups.
At baseline, patient biologic ages were calculated, with biologic age being an identical mean of 68 years in both groups. On average, the biologic age of patients participating in the study was 6 years older than their actual chronologic age. Only patients in the net present value group were given feedback in terms of biologic age.
Overall patterns of behavior change
At baseline, patients chose an average of 2.8 risk factors for change. This increased to a total of 5.1 over the course of the 2-year follow-up interval. Table III shows the percentage of risk factors chosen for change at any follow-up compared with those chosen at baseline for both groups. Also shown is the percent of patients who achieved action or maintenance on any behavior at any time point compared with action or maintenance at the last assessment for the behavior. Over the period of follow-up, patients in the net present value group reached action on 1.5 risk factors, identical to the rates of action in the future value group. On average, patients in both groups were able to maintain action at the last assessment on 1.1 risk factors, most often physical activity, weight reduction or lowering cholesterol. Overall, 37% of patients made no health behavior changes, 34% changed one health behavior and 29% changed two or more health behaviors.
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Main trial outcomes
In total, we obtained major trial outcomes at the 2-year follow-up on 622 of the 660 patients (94%) enrolled in the study (Fig. 1). Of the 622 patients who completed a 2-year follow-up, 6 patients had died before the end of the study. Thirty-eight patients were lost to follow-up at 24 months, though six of these patients had outcomes prior to loss to follow-up.
At the 2-year follow-up, there was no difference in the principal outcome of the trial or in the combined rates of death, stroke, myocardial infarction, Class II–IV angina and severe ischemia on non-invasive testing by randomization group. The difference in outcome rate between the two groups was not statistically significant, assessed with a test for difference in proportions P = 0.23; with a 95% confidence interval for the difference of the net present value percentage minus the future value percentage of –13 to 2.9%. Using logistic regression to control for baseline and demographic variables such as sex, age, comorbidity score and clinical status (ejection fraction, etc.) did not change the results: there was no difference between the net present value and future value groups. This was not unexpected as the two groups were so closely matched (Table I). Table IV shows that the rates of each outcome were similar in both groups, and the overall rate of combined outcomes was 39.1% in the net present value group and 34.2% in the future value group.
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When we analyzed all patients regardless of randomization group, the greater the number of health behaviors successfully changed, the less likely it was that an adverse event would occur for an individual patient.
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Discussion |
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Cardiac rehabilitation following coronary revascularization seeks to assist patients who have undergone PTCA with necessary lifestyle changes. Recent American and European studies [29–34] of cardiac rehabilitation that includes comprehensive lifestyle modification have shown promise in reducing and even reversing coronary artery disease. Early studies by Ornish et al. [29] and Haskell et al. [30]demonstrated that a program of intensive multifactor risk reduction reduced luminal narrowing in coronary arteries of men and women with coronary artery disease. In addition, those who made changes in lifestyle and took lipid-lowering medication over a 4-year period were found to have decreased rates of hospitalization for cardiac-related clinical events [30]. In a later study, Ornish et al. [31] showed that an intensive lifestyle modification program of dietary fat restriction, stress management, exercise, smoking cessation and group psychosocial support over a 5-year period could reduce coronary atherosclerosis without the use of lipid-lowering drugs. Koertge et al. [32] demonstrated that a multicomponent lifestyle change program for diet, exercise, stress management and social support can produce significant improvements in diet, exercise and stress management practices, which can be maintained over a 12-month period. In another study, Pasquali et al. [33] evaluated the impact of cardiac rehabilitation on functional outcomes among patients who had been revascularized and found significant improvements in physical functioning and secondary preventive behaviors at 6 months, particularly among men; similar findings have been reported in a 2003 study by the Norwegian Vestfold Heartcare Study Group [34]. While such studies show the promise of cardiac rehabilitation, most patients with coronary heart disease do not easily make necessary lifestyle changes following angioplasty, and maintaining such changes in the long term still remains challenging. Recent surveys conducted by EUROASPIRE I and II Group [35] show that lifestyle change among people with coronary artery disease is a concern. Moreover, McDonald et al. [36] have reported that even when effective in the long term, behavioral interventions designed to improve adherence with medication often involve complex and intensive combinations of information and various other behavioral techniques.
The objective of this randomized trial was to evaluate whether feedback of individualized risk profiles framed as the opportunity to reduce one's biologic age was more effective after 2 years in reducing mortality and major cardiovascular morbidity among patients who had just undergone PTCA or coronary artery stenting, as compared with the standard risk reduction approach. The novel theoretical concept underlying this behavior change intervention was framing risk reduction as the opportunity to reduce one's present biologic age (net present value approach) contrasted with the standard motivation to reduce risk, which was framed as one's opportunity to reduce future risk (future value approach).
In addition to the concept of communicating risk using net present value (biologic age), our intervention incorporated intervention concepts from well-established models of behavior change, including those of individualized feedback [37, 38], stages of change [13, 14] and self-efficacy [15, 16]. We also scheduled frequent telephone follow-up by trained research interventionists who provided a minimal level of motivational support to all patients throughout the study. Our overall goal in the experimental arm of the trial was to convey to patients the immediate health impact of specific behaviors on their biologic age as well as the immediate impact of changing them. We hypothesized that armed with the value of how much biologic age reduction one could achieve by, for example, stopping smoking (a difficult behavior, but one that yields greater immediate reduction in biologic age than, for example, taking folate), the pattern in the choice of behaviors to change would be different in the two groups. Although patients most often chose physical activity, weight reduction or lowering cholesterol as the behaviors of interest for change, there was no difference in pattern of choice in the two groups. Moreover, there was no difference in the main trial outcomes at the end of the study. For example, the confidence intervals calculated for the difference in net present and future value showed that this difference would probably not be different by >13% and that net present value could not be better than future value by >2.9%, which is only a negligible improvement.
Why were no differences in pattern of choice for health behavior change or health outcome observed at the end of the trial? There are several plausible reasons—both theoretical and methodological—why the intervention failed to produce the hypothesized changes. First, patients in both arms of the trial received multiple telephone contacts throughout a 2-year follow-up period during which they were interviewed about their health and given motivational support. As a consequence, the attention to the control group may have diluted any potential differential impact of the presentation of biologic age in the experimental net present value group.
Second, our use of an understandable and personally relevant metric—biologic age—was intended to be consistent with a rational model of risk communication. We believed that the value of communicating risk in terms of biologic age was in revealing to the patient the increment of reduction in biologic age that could be gained by changing each individual risk factor. However, we did not assess our study participants' belief in the concept prior to the study, so it is possible that patients in the intervention group simply did not believe in the concept of biologic age reduction or that they could successfully reduce their biologic age by changing their health behaviors (the outcome expectancy). Moreover, many people do not understand concepts of probabilistic thinking, survival or the distinctions between harmful events occurring within populations and those within individuals or absolute versus relative risk [39, 40]. For example, when attempting to communicate the potential reduction in risk for coronary artery disease that can be attributed to taking lipid-lowering drugs, the difference between reporting relative or absolute risk is likely to influence the perception of risk by the patient [41].
Third, consistent with the Transtheoretical Model of Stages of Change, telling people that they can reduce their biologic age by changing their behavior constitutes an intervention largely aimed at raising risk awareness and increasing positive outcome expectancies. However, in the context of most theories of health behavior change, risk awareness and outcome expectancies are typically specified as predictors of the non-action phases or behavioral intentions. Thus, because of the ‘intention-behavior-gap’ that has been observed in previous research [42], targeting such cognitive factors alone is not sufficient for achieving behavior change.
Fourth, had we assessed potential mediating variables such as risk awareness and outcome expectancies, both of which theoretically might have been influenced by our intervention, we may have been able to gain additional insight into why our intervention did not work and why [43]. For example, it is possible that our intervention was not effective in influencing critical mediating variables or that our intervention ‘did’ influence important mediators that we did not assess, but, although doing so, did not make any difference in the main outcomes.
Finally, our study was not only aimed at reducing those behavioral risk factors that were potentially modifiable but also showing an improvement in clinical outcomes. Even though patients may reasonably be expected to initiate behavior changes, maintaining such changes over a 2-year period of time is difficult without an intensive behavioral intervention that was beyond our interventional attempt to motivate patients through a novel risk communication message. It is possible that we might have observed the hypothesized difference between the two groups had we designed the intervention for a shorter time period. Moreover, the dose-response rate of health behavior change and clinical outcomes needs to be considered. Although people might be able to increase their physical activity from 0 to 1 time a week, this might not be sufficient to influence the clinical outcomes we studied.
While these results are disappointing, identifying an effective means of motivating and supporting behavioral changes in patients with coronary artery disease who have undergone PTCA remains a challenge. Although the communication of risk to such patients is of great interest, communicating risk alone may not be sufficient to stimulate the necessary behavior change in the face of treatment that produces such dramatic results in the relief of symptoms. Because patients undergoing PTCA and stenting do get dramatic symptomatic relief it is not surprising that patients—and the doctors who perform such procedures—view the treatment as a veritable magic bullet. In light of this, perhaps the interventional cardiologist needs to reinforce both the importance and urgency of behavioral change rather than telling the patient that the problem ‘has been fixed’. To do otherwise may leave the patient with a false sense of cure for the underlying causes of a disease that invariably requires persistence at changing behavior if the benefits of PTCA are to be maintained over time.
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National Heart, Lung, and Blood Institute (R01 HL62161091).
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Conflict of interest statement |
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None declared.
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Acknowledgements |
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A research version of the RealAge software was made available to the investigators, without charge, by RealAge. We thank Paula McKinley and Candace Young for their contributions in the early phases of the study and the anonymous reviewers for their insightful comments and useful suggestions for revision.
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References |
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1. Cavender JB, Rogers WJ, Fisher LD, et al. Effects of smoking on survival and morbidity in patients randomized to medical or surgical therapy in the Coronary Artery Surgery Study (CASS): 10-year follow-up. CASS Investigators. J Am Coll Cardiol (1992) 20:287–94.[Abstract]
2. Galan KM, Deligonul U, Kern MJ, et al. Increased frequency of restenosis in patients continuing to smoke cigarettes after percutaneous transluminal coronary angioplasty. Am J Cardiol (1988) 61:260–3.[CrossRef][Web of Science][Medline]
3. Myler RK, Shaw RE, Stertzer SH, et al. Recurrence after coronary angioplasty. Cathet Cardiovasc Diagn (1987) 13:77–86.[CrossRef][Web of Science][Medline]
4. Ramanathan KB, Vander Zwaag R, Maddock V, et al. Interactive effects of age and other risk factors on long-term survival after coronary artery surgery. J Am Coll Cardiol (1990) 15:1493–9.[Abstract]
5. Reis GJ, Kuntz RE, Silverman DI, et al. Effects of serum lipid levels on restenosis after coronary angioplasty. Am J Cardiol (1991) 68:1431–5.[CrossRef][Web of Science][Medline]
6. Watts GF, Lewis B, Brunt JN, et al. Effects on coronary artery disease of lipid-lowering diet, or diet plus cholestyramine, in the St Thomas’ Atherosclerosis Regression Study (STARS). Lancet (1992) 339:563–9.[CrossRef][Web of Science][Medline]
7. Brown G, Albers JJ, Fisher LD, et al. Regression of coronary artery disease as a result of intensive lipid-lowering therapy in mean with high levels of apolipoprotein B. N Engl J Med (1990) 323:1289–98.[Abstract]
8. McKenna KT, Mass F, McEniery PT. Coronary risk factor status after percutaneous transluminal coronary angioplasty. Heart Lung (1995) 24:207–12.[CrossRef][Web of Science][Medline]
9. Gaw BL. Motivation to change life-style following PTCA. Dimens Crit Care Nurs (1992) 11:68–74.[Medline]
10. Gulanick M, Bliley A, Perino B, et al. Recovery patterns and lifestyle changes after coronary angioplasty: the patient's perspective. Heart Lung (1998) 27:253–62.[CrossRef][Web of Science][Medline]
11. Allegrante JP, Roizen MF. Can net-present value economic theory be used to explain and change health-related behaviors? Health Educ Res (1988) 13:i–iv.
12. Charlson ME, Allegrante JP, McKinley PS, et al. Improving health behaviors and outcomes after angioplasty: using economic theory to inform intervention. Health Educ Res (2002) 17:606–18.
13. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol (1992) 47:1102–14.[CrossRef][Medline]
14. Prochaska J, Redding C, Evers K. The transtheorectical model and states of change. In: Health Behavior and Health Education (1997) New York, NY: Jossey Bass.
15. Bandura A. Social Learning Theory (1977) Englewood Cliffs, NJ: Prentice-Hall.
16. Strecher V, DeVellis B, Becker M, et al. The role of self-efficacy in achieving health behavior change. Health Educ Q (1986) 13:73–91.[Web of Science][Medline]
17. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis (1987) 40:373–83.[CrossRef][Web of Science][Medline]
18. Rimm EB, Giovannucci EL, Stampfer MJ, et al. Reproducibility and validity of an expended self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol (1992) 135:1114–26. discussion 1127–36.
19. Liu S, Stampfer M, Hu F, et al. Whole-grain consumption and risk of coronary heart disease: results from the Nurses’ Health Study. Am J Clin Nutr (1999) 70:412–9.
20. Liu S, Lee I, Ajani U, et al. Intake of vegetables rich in carotenoids and risk of coronary heart disease in men: the Physicians’ Health Study. Int Epidemiol Assoc (2001) 30:130–5.[CrossRef]
21. Sesso H, Liu S, Gaziano J, et al. Dietary lycopene, tomato-based food products and cardiovascular disease in women. J Nutr (2003) 133:2336–41.
22. Taylor HL, Jacobs DR, Schucker B, et al. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis (1997) 31:741–55.[CrossRef]
23. Ni Mhurchu C, Margetts B, Speller V. Applying the stages-of-change model to dietary change. Nutr Rev (1997) 55:10–6.[Web of Science][Medline]
24. Jeffery R, French S, Rothman A. Stage of change as a predictor of success in weight control in adult women. Health Psychol (1999) 18:543–6.[CrossRef][Web of Science][Medline]
25. Costakis C, Dunnagan T, Haynes G. The relationship between the stages of exercise adoption and other health behaviors. Am J Health Promot (1999) 14:22–30.[Web of Science][Medline]
26. Boyle R, O'Connor P, Pronk N, et al. Stages of change for physical activity, diet, and smoking among HMO members with chronic conditions. Am J Health Promot (1998) 12:170–5.[Web of Science][Medline]
27. Nigg C, Burbank P, Padula C, et al. Stages of change across ten health risk behaviors for older adults. Gerontologist (1999) 39:473–82.[Abstract]
28. Emmons KM, Rollnick S. Motivational interviewing in health care setting. Opportunities and limitations. Am J Prev Med (2001) 20:68–74.[CrossRef][Web of Science][Medline]
29. Ornish D, Brown SE, Scherwitz LW, et al. Can lifestyle changes reverse coronary heart disease? The Lifestyle Heart Trial. Lancet (1990) 336:129–33.[CrossRef][Web of Science][Medline]
30. Haskell WL, Alderman EL, Fair JM, et al. Effects of intensive multiple risk factor reduction on coronary atherosclerosis and clinical cardiac events in men and women with coronary artery disease. The Stanford Coronary Risk Intervention Project (SCRIP). Circulation (1994) 89:975–90.
31. Ornish D, Scherwitz L, Billings J, et al. Intensive lifestyle changes for reversal of coronary heart disease. J Am Med Assoc (1998) 280:2001–7.
32. Koertge J, Weidner G, Elliott-Eller M, et al. Improvement in medical risk factors and quality of life in woman and men with coronary artery disease in the Multicenter Lifestyle Demonstration Project. Am J Cardiol (2003) 91:1316–22.[CrossRef][Web of Science][Medline]
33. Pasquali S, Alexander K, Coombs L, et al. Effect of cardiac rehabilitation on functional outcomes after coronary revascularization. Am Heart J (2003) 145:445–51.[CrossRef][Web of Science][Medline]
34. Group VHS. Influence on lifestyle measures and five-year coronary risk by a comprehensive lifestyle intervention programme in patients with coronary heart disease. Eur J Cardiovasc Prev Rehabil (2003) 10:429–37.[CrossRef][Medline]
35. Group, E.I.a.I. European Action on Secondary Prevention by Intervention to Reduce Events. Clinical reality of coronary prevention guidelines: a comparison of EUROSPIREI and II in nine countries. Lancet (2001) 357:995–1001.[CrossRef][Web of Science][Medline]
36. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: scientific review. J Am Med Assoc (2002) 288:2868–79.
37. Champion V, Foster J, Menon U. Tailoring interventions for health behavior change in breast cancer screening. Cancer Pract (1997) 5:283–8.[Web of Science][Medline]
38. Prochaska J, DiClemente C, Velicer W, et al. Standardized, individualized, interactive, and personalized self-help programs for smoking cessation. Health Psychol (1993) 12:399–405.[CrossRef][Web of Science][Medline]
39. Alasewski A, Horlick-Jones T. How can doctors communicate information about risk more effectively? Br Med J (2003) 327:729–31.
40. Alasewski A. A person-centered approach to communicating risk. PLoS Med (2005) 2:e41.[CrossRef][Medline]
41. Skolbekken J. Communicating the risk reduction achieved by cholesterol reducing drugs. Br Med J (1998) 316:1956–8.
42. Sheeran P. Intention-behavior Relations. A Conceptual and Empirical Review (2002) Chichester, England. 1–36.
43. Michie S, Abraham C. Interventions to change health behaviors: evidence based or evidence-inspired? Psychol Health (2004) 19:29–49.[CrossRef][Web of Science]
Received on April 21, 2006; accepted on September 4, 2007
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