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Original Investigation |

A Comparison of Live Counseling With a Web-Based Lifestyle and Medication Intervention to Reduce Coronary Heart Disease Risk A Randomized Clinical Trial FREE

Thomas C. Keyserling, MD, MPH1,2; Stacey L. Sheridan, MD, MPH1,2,3; Lindy B. Draeger, MPH2; Eric A. Finkelstein, PhD, MA, MHA4; Ziya Gizlice, PhD2; Eliza Kruger, MHE4; Larry F. Johnston, MA2; Philip D. Sloane, MD3,5; Carmen Samuel-Hodge, PhD, RD2,6; Kelly R. Evenson, PhD, MS7; Myron D. Gross, PhD8; Katrina E. Donahue, MD, MPH3,5; Michael P. Pignone, MD, MPH1,3; Maihan B. Vu, DrPH, MPH2; Erika A. Steinbacher, MD9; Bryan J. Weiner, PhD10; Shrikant I. Bangdiwala, PhD2,11; Alice S. Ammerman, DrPH, RD2,5,6
[+] Author Affiliations
1Division of General Medicine and Clinical Epidemiology, School of Medicine, University of North Carolina, Chapel Hill
2Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill
3Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill
4Health Services and Systems Research Program, Duke–National University of Singapore Graduate Medical School, Singapore
5Department of Family Medicine, School of Medicine, University of North Carolina, Chapel Hill
6Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
7Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
8Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis
9Cabarrus Family Medicine, Kannapolis, North Carolina
10Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
11Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
JAMA Intern Med. 2014;174(7):1144-1157. doi:10.1001/jamainternmed.2014.1984.
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Published online

Importance  Most primary care clinicians lack the skills and resources to offer effective lifestyle and medication (L&M) counseling to reduce coronary heart disease (CHD) risk. Thus, effective and feasible CHD prevention programs are needed for typical practice settings.

Objective  To assess the effectiveness, acceptability, and cost-effectiveness of a combined L&M intervention to reduce CHD risk offered in counselor-delivered and web-based formats.

Design, Setting, and Participants  A comparative effectiveness trial in 5 diverse family medicine practices in North Carolina. Participants were established patients, aged 35 to 79 years, with no known cardiovascular disease, and at moderate to high risk for CHD (10-year Framingham Risk Score [FRS], ≥10%).

Interventions  Participants were randomized to counselor-delivered or web-based format, each including 4 intensive and 3 maintenance sessions. After randomization, both formats used a web-based decision aid showing potential CHD risk reduction associated with L&M risk-reducing strategies. Participants chose the risk-reducing strategies they wished to follow.

Main Outcomes and Measures  The primary outcome was within-group change in FRS at 4-month follow-up. Other measures included standardized assessments of blood pressure, blood lipid levels, lifestyle behaviors, and medication adherence. Acceptability and cost-effectiveness were also assessed. Outcomes were assessed at 4 and 12 months.

Results  Of 2274 screened patients, 385 were randomized (192 counselor; 193 web): mean age, 62 years; 24% African American; and mean FRS, 16.9%. Follow-up at 4 and 12 months included 91% and 87% of the randomized participants, respectively. There was a sustained reduction in FRS at both 4 months (primary outcome) and 12 months for both counselor-based (−2.3% [95% CI, −3.0% to −1.6%] and −1.9% [95% CI, −2.8% to −1.1%], respectively) and web-based groups (−1.5% [95% CI, −2.2% to −0.9%] and −1.7% [95% CI, −2.6% to −0.8%] respectively). At 4 months, the adjusted difference in FRS between groups was −1.0% (95% CI, −1.8% to −0.1%) (P = .03), and at 12 months, it was −0.6% (95% CI, −1.7% to 0.5%) (P = .30). The 12-month costs from the payer perspective were $207 and $110 per person for the counselor- and web-based interventions, respectively.

Conclusions and Relevance  Both intervention formats reduced CHD risk through 12-month follow-up. The web format was less expensive.

Trial Registration  clinicaltrials.gov Identifier: NCT01245686

Figures in this Article

A healthy lifestyle1,2 and appropriate medications35 can substantially reduce the risk for coronary heart disease (CHD), yet getting patients to change their lifestyle and initiate and adhere to risk-reducing medication regimens can be difficult to achieve in clinical practice. In particular, most primary care clinicians lack the skills6,7 and resources8 to offer effective lifestyle and medication (L&M) counseling to reduce CHD risk. Thus, to improve CHD prevention in primary care practices, where half of Americans are seen annually,9 clinicians need access to effective and feasible CHD prevention programs that could be implemented in their practice settings.

While many primary care–based programs to reduce CHD risk have been previously tested, these programs have limitations.10,11 Most have not jointly addressed lifestyle change and medication optimization, and few have taken a patient-centered approach that informs patients about the relative merits of strategies to reduce CHD risk and encourages them to select their preferred risk-reducing strategies. Furthermore, few have been evaluated in comparative effectiveness studies12,13 that: (1) compare clinically relevant implementation strategies; (2) include a diverse population of participants; (3) include a heterogeneous selection of practices; and (4) collect data on a broad range of outcomes.

Given increasing evidence that supports the effectiveness of web-based interventions,14,15 we developed a combined L&M intervention to reduce CHD risk and tested it in 2 formats: counselor-delivered and web-based. While the counselor intervention provides human interaction and offers the potential for a higher degree of tailoring, the web intervention has greater reach, offers flexibility to patients in the timing and delivery of the intervention, and minimizes clinic staff demands and costs.16 Herein we report the results of a comparative effectiveness trial conducted to assess the effectiveness, acceptability, and cost-effectiveness of the intervention when offered in alternative formats.

Study Overview

All participants provided written informed consent, and the study was approved and monitored by the institutional review board of the University of North Carolina at Chapel Hill. We conducted this study at 5 diverse family medicine practices in central North Carolina using data collected between January 31, 2011, and November 26, 2012. Our primary intent was to determine the comparative effectiveness of the 2 intervention formats on reducing CHD risk, as assessed by the Framingham Risk Score (FRS).17 Participants were randomized to receive individually tailored counseling interventions similar in contact time and educational content but different in format (Figure 1). Study outcomes were assessed at 4 and 12 months. Details of the study design, study practices, participant enrollment, and intervention components are described elsewhere.17

Place holder to copy figure label and caption
Figure 1.
Study Flow Diagram

MI indicates myocardial infarction; CHD, coronary heart disease.aAt baseline there were 55 smokers in the counselor intervention and 58 smokers in the web-based intervention.bIncludes intent to start or increase regimen of blood pressure or cholesterol medication or start taking aspirin.

Graphic Jump Location
Participants, Enrollment, and Randomization

Participants were established patients (ie, had at least 1 office visit in the last 2 years), aged between 35 and 79 years, with no known cardiovascular disease (CVD), who were at moderate to high risk for CHD (≥10% 10-year risk of angina, myocardial infarction, or CHD death) based on their FRS. Participants were identified by chart reviews of patients scheduled for routine office visits and by referrals from clinicians and self-referrals based on word-of-mouth or in response to waiting-room flyers. As an initial eligibility screen, the FRS was calculated using risk factors assessed by chart review (age, blood pressure, total cholesterol level, high-density lipoprotein cholesterol [HDL-C] level, diabetes, smoking, aspirin use, and left ventricular hypertrophy).17 Diabetes was included in the FRS and was not considered a CVD equivalent. Because aspirin was not accounted for by the Framingham risk equation, we modeled its effect on CHD risk using a 23% risk reduction for men and 0% reduction for women.3 Those with an FRS of 10% or higher were further evaluated by their primary care clinicians, who (1) determined if the patient should be excluded for a variety of previously described17 medical conditions and (2) approved participation in the overall and physical activity component of the study.

Patients screened as eligible attended an enrollment visit during which study staff obtained written informed consent, confirmed inclusion criteria, screened participants for potential bleeding risk associated with aspirin use, reassessed smoking status, assessed blood pressure using a standard protocol, and obtained a blood sample for study laboratory assessments. Participants’ FRSes were recalculated based on this standardized assessment, and if the recalculated FRS was 10% or higher, the participants were contacted for the baseline telephone survey. Those completing this survey were invited to the first intervention visit, where they were randomized as previously described.17

Interventions

Both intervention formats began with a web-based decision aid, followed by the counseling program. As described elsewhere,17 the intervention was based on previously developed and tested L&M interventions revised to be consistent with the latest evidence on CHD risk reduction.

Decision Aid

The decision aid (1) calculated participants’ 10-year FRS, (2) educated participants about their CHD risk factors and the pros and cons of risk-reducing strategies, and (3) showed participants how much their CHD risk might be reduced by 1 or more of the following: changes in diet, increased physical activity, smoking cessation, initiation of aspirin (for men only), or initiation or intensification of treatment with statins or hypertension medication. The following risk-reduction estimates were used: 20% to 40% for diet,1,1721 10% to 20% for physical activity,22,23 50% for smoking cessation, and 20% to 30% for type of medication (statins, blood pressure medication, and aspirin for men).24 For women who indicated an interest in aspirin, the decision aid provided information on stroke risk and the potential reduction in stroke with aspirin of 23%. Participants navigated the decision aid with the assistance of the health counselor and were encouraged to choose the risk-reducing strategies they wished to focus on as part of this program.

Counseling

Both formats included 7 counseling sessions: 4 during a 4-month intensive phase (each about 45-60 minutes long at 1-month intervals) followed by 3 during an 8-month maintenance phase (each about 15-30 minutes long at 2-month intervals). Counseling was tailored to choice of risk-reducing strategy: diet, physical activity, medications, or any combination. To standardize counseling, the sequence, educational content, and tailoring of the counselor and web formats were the same. Specifically, both formats used the same set of questions to assess baseline habits and barriers. Additionally, counseling sessions included identical educational content (including graphics) that were presented in a 3-ring binder for counselor format and on a sequence of web pages for web format. Finally, the counselor and interactive web program used the same process to select tailored goals and list first steps. For the counselor format, these goals were checked on a sheet; for the web format, they were printed.

Dietary counseling focused on improving carbohydrate and fat quality; physical activity counseling focused on walking 7500 steps or 30 minutes on 5 days each week; and medication counseling focused on understanding medication instructions, planning ahead for refills, and partnering with clinician to make good decisions about medications to reduce CHD risk. All participants received a cookbook, a pedometer for self-monitoring, and a guide with information on local resources promoting healthy eating and physical activity. The initial visit was conducted at the clinic, where the counselor could assist participants with the web program if needed. Subsequent visits were conducted at the clinic or remotely (by phone for the counseling arm or by computer for the web arm). Counseling was conducted by trained health counselors, as previously described.17 Requests for medication regimen initiation or intensification were routed to participants’ clinicians for approval.

Outcomes and Measures

Study measures addressed effectiveness, acceptability, and cost-effectiveness and were assessed by trained research staff at participating practices and by phone. The primary effectiveness measure was within-group change in FRS at 4-month follow-up. The FRS was calculated using a well-validated Framingham risk equation25 with input of relevant risk factor data measured in a standardized fashion and baseline age used for follow-up assessments. Prespecified secondary effectiveness outcomes included between-group changes in FRS and change in dietary intake, physical activity, smoking, medication adherence, blood pressure, blood lipid levels, and health-related quality of life. In addition, an analysis of moderators of outcomes was also planned.

Weight, blood pressure, and levels of total cholesterol, HDL-C, directly measured low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c, high-sensitivity C-reactive protein, alanine aminotransferase, creatinine, and plasma carotenoids26 were assessed at baseline, 4 months, and 12 months, as previously described.17 At the first counseling visit, numeracy,27 literacy,28 and medication adherence29 were assessed using validated instruments. At follow-up visits, aspirin use was assessed by serum thromboxane level and smoking by the NicAlert urine test (Nymox Pharmaceutical Corporation), as previously described.17

The following measures were assessed by telephone at baseline and in person at 4- and 12-month follow-up: medication use, fruit and vegetable intake,30 dietary fat quality,31 physical activity,32,33 and quality of life (SF-12 [Quality Metric Inc]). Medication regimen adherence29 and acceptability of the interventions were assessed in person at 4- and 12-month follow-up.

Process measures were collected at intervention sessions by the counselor or the web program. Participants were advised to wear an Omron HJ-720ITC pedometer (Omron Healthcare) during the week before study measurement visits. Steps were assessed by averaging at least 3 days of 500 steps/d or more during the week prior to the visit. Assessment of costs for the cost-effective analysis are described in the eAppendix in the Supplement.

Sample Size

Sample size was based on the hypothesis that both interventions would reduce the FRS by at least 1.5 percentage points (absolute risk reduction of 1.5%). Using a 1-sided test, a standard deviation of 3.1 units,24 an α value of 0.05, and an expected 10% attrition, a sample of 225 participants in each arm would provide greater than 99% power to detect a within-group reduction in FRS of 1.5 percentage points. This sample size would additionally provide 85% power to detect a 0.9 percentage point difference in FRS between the counselor and web arms (2-sided test).

Analysis

We summarized baseline sample characteristics using descriptive statistics and compared groups using χ2 and t tests. The primary outcome analysis was conducted using an intention-to-treat approach with a paired t test (1-sided) for changes in FRS within each intervention arm. Additionally, for the primary outcome, we used multiple approaches for imputing missing data including last observations carried forward and multiple imputation methods.17

Secondary outcomes were examined using paired t tests or McNemar tests for within-group comparisons (2-sided tests). Additional analyses were conducted to compare the mean changes in FRS and other outcomes between arms using a simple t test and a multivariable analysis of covariance model (ANCOVA) adjusting for the baseline value of the outcome, practice, and additional variables deemed relevant to behavior change a priori (age, race, educational achievement, and body mass index [BMI, calculated as weight in kilograms divided by height in meters squared]) or that differed between intervention groups at baseline (P < .10). In addition, we conducted longitudinal analyses with FRS data from all 3 time points using generalized linear mixed models that included time, study groups, and time by study group interaction as fixed and participants as random effects along with site and the full set of covariates as fixed effects. To assess potential moderators of change in FRS, we used linear regression models that included the baseline FRS, the potential moderator of interest, and study arm by potential moderator interaction term.

For cost-effectiveness, we assessed the incremental cost-effectiveness ratio (ICER) of each intervention from the payer, participant, and societal perspectives, as described in the eAppendix in the Supplement. We calculate the ICER per 1 absolute percentage point reduction in CHD risk and per quality-adjusted life year (QALY) gained at 12 months. We calculate QALY gained in 1 year by converting SF-12 scores into a health-related quality-of-life weight using a well-defined algorithm.34 Because our analysis considers only a 1-year time horizon, this weight is equivalent to QALYs saved over this time period. We then report incremental cost-effectiveness per QALY gained and compare these ratios to common thresholds of cost-effectiveness. All analyses were conducted using SAS software, version 9.3 (SAS Institute Inc) and Stata, version 12 (StataCorp LP) with P ≤ .05 considered significant.

Enrollment and Baseline Characteristics of Participants

As depicted in Figure 1, of 2274 patients eligible to be screened for the study, 633 agreed to participate. Of these, 114 were ineligible because their FRS calculated using standardized measures was less than 10%; 111 took part in another intervention for those with known CVD, as described elsewhere17; 23 were lost to follow-up or declined participation; and 385 participants took part in this study.

Table 1 lists baseline characteristics of the study participants. The mean age was 62 years; 24% were African American; 32% were employed full time; and 88% had health insurance. Overall, the sample was at high risk for CHD: 86% had current or previous high blood pressure; 85% had current or previous high blood cholesterol levels; 61% had diabetes; and the mean FRS was 16.9%. Also, two-thirds of participants reported that they were comfortable or very comfortable using a computer.

Table Graphic Jump LocationTable 1.  Baseline Participant Characteristicsa
Participants Choice of Risk-Reducing Strategies, Intervention Participation, and Follow-up Rates

As illustrated in Figure 1, after viewing the decision aid, 366 participants (95%) elected to work on improving their diet; 256 (66%) chose to work on increasing their physical activity; 71 (18%) decided to work on smoking cessation; and 142 (37%) chose to start or increase regimens of blood pressure or cholesterol medication or start taking aspirin. Follow-up rates at 4 and 12 months were 91% and 87%, respectively. Those who did not return for follow-up at 4 months were more likely to be white, younger, and walk fewer minutes each week; those who did not return for follow-up at 12 months were more likely to consume fewer fruits and vegetables and be less adherent to medication regimens (P < .05 for all comparisons).

Study Outcomes

Change in study outcomes from baseline to follow-up, by treatment arm, are listed in Table 2 and Table 3. For the FRS, there was a statistically significant and sustained reduction at both 4 months (primary outcome) and 12 months for participants in both study groups. For the counselor group, the change was −2.3% and −1.9% at 4 and 12 months, respectively. For the web group, it was −1.5% and −1.7%, respectively. When values of no change and multiple imputations methods were used to impute missing FRS scores, results did not change appreciably.

Table Graphic Jump LocationTable 2.  Change From Baseline by Study Group at 4-Month Follow-upa
Table Graphic Jump LocationTable 3.  Change From Baseline by Study Group at 12-Month Follow-upa

In both groups, all components of the FRS changed in the direction of decreased risk, and most changes were statistically significant and maintained from 4- to 12-month follow-up. Likewise, most changes in diet and physical activity were in the direction of decreased risk and sustained over time. Moreover, there were substantial increases in appropriate use of and adherence with medication regimens to reduce CHD risk. Other statistically significant outcomes of note include slight weight loss at 12 months, a reduction in A1c level in the counselor group, and a sustained improvement in the physical component measure of quality of life in both groups.

Self-reported results for tobacco cessation and aspirin use at follow-up were confirmed by biomarkers. Of 23 smokers who reported cessation, 18 (78%) were confirmed by urine cotinine testing, and of 415 participants who reported aspirin use, 311 (75%) had serum thromboxane levels consistent with aspirin use.

The differences in study outcomes between treatment arms are listed in Table 4 and Table 5. At 4-month follow-up, the adjusted change (standard error [SE]) in FRS was −2.4% (0.3) for counselor and −1.4% (0.3) for web, for a difference of −1.0% (95% CI, −1.8% to −0.1%) (P = .03). At 12-month follow-up, the adjusted change (SE) in FRS was −2.1% (0.4) for counselor and −1.5% (0.4) for web, for a difference of −0.6% (95% CI, −1.7% to 0.5%) (P = .30). When change in FRS was assessed by longitudinal analysis, there was no significant time-by-group interaction (P = .27), and within- and between-group comparisons were similar to analyses at each time point.

Table Graphic Jump LocationTable 4.  Crude and Adjusted Differences in Continuous Variables for Change in Outcome Between Study Groupsa at 4 and 12 Monthsb
Table Graphic Jump LocationTable 5.  Crude Differencesa in Categorical Variablesb for Change in Outcome Between Study Groups at 4 and 12 Monthsc
Subgroup Analysis

Figure 2 shows the change in FRS at 4 and 12-month follow-up stratified on selected baseline variables. Assessing change in FRS by subgroups, without regard to treatment arm, we found that the intervention was significantly more effective at 4 and 12 months among younger participants (P = .05 and P < .001, respectively). In addition, at 4-month follow-up, the intervention was more effective among men (P = .04), those without diabetes (P = .02), and those choosing L&M (P = .01). We noted little difference in the effectiveness of the counselor-delivered vs web-based interventions when change in FRS was assessed by treatment arm and subgroups. At 4-month follow-up, there were a larger improvement in FRS among participants with diabetes in the counselor group (P = .03 for interaction).

Place holder to copy figure label and caption
Figure 2.
Change in Framingham Risk Score (FRS)

Stratified on selected baseline variables, shown by treatment arm at 4- and 12-month follow-up.aAt 4-month follow-up, P ≤ .05 for comparison of FRS between subgroups with web and counselor groups combined.bAt 12-month follow-up, P ≤ .05 for same comparison.

Graphic Jump Location
Adverse Outcomes

There were no reported adverse effects related to dietary change or increased physical activity. Deaths due to CHD and newly diagnosed CHD are noted in Figure 1. There were no other deaths during follow-up. In addition, there was no material change in ALT or creatinine levels from baseline to follow-up.

Acceptability

Both counselor and web formats were well received. At 4-month follow-up, among 177 counselor participants completing the acceptability survey, 137 (77%) strongly agreed and 36 (20%) agreed that they would recommend this program to others. Similarly, among 173 web participants, 128 (74%) strongly agreed and 42 (24%) agreed with this statement. At 12-month follow-up, among 170 counselor and 166 web participants completing the survey, 166 (98%) counselor and 161 (97%) web participants would recommend or strongly recommend this program to others.

Cost-effectiveness

At 12 months, the costs (SE) per participant from the payer perspective were $207 ($3.40) and $110 ($3.50) for the counselor and web interventions respectively (P < .001). From the payer perspective, the incremental cost-effectiveness ratio for the less expensive web intervention, compared with no intervention, was $73 per percentage-point reduction in CHD risk and $2973 per QALY gained, which is considered very cost-effective based on common benchmarks.35 Additional results are reported in the eAppendix in the Supplement.

Sensitivity Analysis

A limited sensitivity analysis was conducted (eTable 4 in the Supplement) to assess change in 10-year risk for CHD as calculated with the Adult Treatment Panel III risk calculator36 (which calculates myocardial infarction and CHD death) and the Framingham risk calculator used for this study25 without including a term for aspirin. Overall, results were similar, with significant reductions in estimated CHD risk in both groups at 4 and 12 months.

In this comparative effectiveness trial, a combined L&M intervention lowered predicted 10-year CHD risk within each treatment arm (from baseline to follow-up) at both 4 and 12 months. This risk reduction was achieved by improvements in lifestyle or medication use, or both, and mediated through improvements in blood pressure, blood lipid levels, cigarette smoking, and aspirin use. The intervention was highly acceptable to participants, and the web format was cost-effective based on established benchmarks.

These findings reinforce increasing evidence suggesting that web-based interventions can have an important role in clinical practice.14,37,38 In this study, the web-based intervention was equally effective to the counselor-delivered intervention at 12-month follow-up. This suggests that web interventions could be used to fill important gaps in counselor availability and, where counselors are available, allow counselors to focus their efforts on harder-to-change behaviors, such as refractory lifestyle behaviors.37 Web interventions might also be used to reach populations who have limited access to the clinic.

This study has several limitations. It was designed as a comparative effectiveness trial, without a no-intervention control group. Thus, observed changes might in part reflect regression to the mean (though baseline screening included 2 sequential assessments of FRS), secular trends, or other factors. Though nonintervention factors may account for some of the observed change, we believe much of the change was intervention effect: the components of the current intervention have previously been compared with no-intervention control groups and have been shown to be effective.17 In a previous trial of a similar web-delivered medication intervention,24 the additional reduction in FRS between intervention and control groups at 3-month follow-up was 1.1 percentage points overall and 1.4 percentage points among a prespecified subgroup of participants with a 10-year predicted risk higher than 10%. In a previous trial of a similar counselor-delivered dietary intervention,39 there was a substantial increase in fruit and vegetable intake, confirmed by measurement of blood carotenoids.

Additional limitations include many secondary outcomes that were self-reported behaviors, which may be exaggerated due to social-desirability reporting bias; however, we did measure biomarker change for fruit and vegetable intake, aspirin use, and smoking cessation. Also, we present many comparisons in our secondary analysis, and some P values may be significant by chance. Our follow-up interval was 12 months, and the intervention effects may attenuate over time. Furthermore, our achieved sample size was somewhat less than our goal, decreasing the power to detect between-group differences. The generalizability of our findings may be limited to established, older patients who are at high risk for CHD. Finally, as lifestyle change may have beneficial effects on CHD risk independent of traditional risk factors,1,40 calculated change in FRS may underestimate intervention benefit.

In conclusion, the combined L&M intervention tested in alternative formats yielded a substantial and sustained reduction in predicted 10-year CHD risk. Risk reduction was similar in both intervention formats at 12-month follow-up, though the web was less expensive to implement. Future research should assess the implementation and maintenance of high-quality evidence-based interventions in a broad selection of clinical settings. In addition, the lifestyle component of the interventions could be used, and should be studied, in nonclinical health promotion settings.

Corresponding Author: Thomas C. Keyserling, MD, MPH, 5039 Old Clinic Bldg, CB 7110, University of North Carolina, Chapel Hill, NC 27599 (thomas_keyserling@med.unc.edu).

Accepted for Publication: March 30, 2014.

Published Online: May 26, 2014. doi:10.1001/jamainternmed.2014.1984.

Author Contributions: Drs Keyserling and Sheridan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Keyserling, Sheridan, Draeger, Finkelstein, Gizlice, Sloane, Evenson, Gross, Steinbacher, Ammerman.

Acquisition, analysis, or interpretation of data: Keyserling, Sheridan, Draeger, Finkelstein, Kruger, Johnston, Sloane, Samuel-Hodge, Gross, Donahue, Pignone, Vu, Weiner, Bangdiwala.

Drafting of the manuscript: Keyserling, Sheridan, Finkelstein, Gizlice, Kruger, Sloane, Ammerman.

Critical revision of the manuscript for important intellectual content: Keyserling, Sheridan, Draeger, Finkelstein, Gizlice, Johnston, Sloane, Vu, Samuel-Hodge, Evenson, Gross, Donahue, Pignone, Steinbacher, Weiner, Bangdiwala, Ammerman.

Statistical analysis: Finkelstein, Gizlice, Kruger, Vu, Bangdiwala.

Obtained funding: Keyserling, Sheridan, Gizlice, Samuel-Hodge, Gross, Ammerman.

Administrative, technical, or material support: Keyserling, Draeger, Johnston, Gross, Donahue, Steinbacher, Ammerman.

Study supervision: Keyserling, Sheridan, Draeger, Samuel-Hodge, Steinbacher.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was supported by the US Centers for Disease Control and Prevention (CDC), American Recovery and Reinvestment Act of 2009, Cooperative Agreement No. 1U48DP002658, and also supported in part by CDC cooperative agreement U48/DP001944 to the University of North Carolina Center for Health Promotion and Disease Prevention (a CDC Prevention Research Center) and by National Institutes of Health grant P30DK056350 to the University of North Carolina at Chapel Hill Nutrition Obesity Research Center.

Role of the Sponsors: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank the staff of the participating practices in the North Carolina Family Medicine Research Network (Cabarrus Family Medical, Kannapolis; Caswell Family Medical Center, Yanceyville; Dayspring Family Medicine, Eden; Durham Family Practice, Durham; and Moncure Community Health Center, Moncure). We also thank the health counselors who delivered interventions at these sites (Kim Grimm, BA, Beth Jenks, MS, Taimur Khan, MIA, Lauren Martin, MSW, and Sara Lindsley, MEd); these health counselors were compensated for their service only in the normal course of their employment. We thank Russell Tracy, PhD, and Elaine Cornell, BA, at the Laboratory for Clinical Biochemistry Research at the University of Vermont, for performing serum thromboxane assays and who were compensated for their efforts only in the normal course of their employment. And finally, we thank the study participants, whose willing participation made this study possible.

Correction: This article was corrected on May 28, 2014, to fix a missing word in the title.

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Ebrahim  S, Beswick  A, Burke  M, Davey Smith  G.  Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev. 2006;(4):CD001561.
PubMed
Goldstein  MG, Whitlock  EP, DePue  J; Planning Committee of the Addressing Multiple Behavioral Risk Factors in Primary Care Project.  Multiple behavioral risk factor interventions in primary care. Summary of research evidence. Am J Prev Med. 2004;27(2)(suppl):61-79.
PubMed   |  Link to Article
Slutsky  JR, Clancy  CM.  Patient-centered comparative effectiveness research: essential for high-quality care. Arch Intern Med. 2010;170(5):403-404.
PubMed   |  Link to Article
Tunis  SR, Stryer  DB, Clancy  CM.  Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003;290(12):1624-1632.
PubMed   |  Link to Article
Portnoy  DB, Scott-Sheldon  LA, Johnson  BT, Carey  MP.  Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988-2007. Prev Med. 2008;47(1):3-16.
PubMed   |  Link to Article
Ritterband  LM, Tate  DF.  The science of internet interventions: introduction. Ann Behav Med. 2009;38(1):1-3.
PubMed   |  Link to Article
Noell  J, Glasgow  RE.  Interactive technology applications for behavioral counseling: issues and opportunities for health care settings. Am J Prev Med. 1999;17(4):269-274.
PubMed   |  Link to Article
Sheridan  SL, Draeger  LB, Pignone  MP,  et al.  Designing and implementing a comparative effectiveness study of two strategies for delivering high quality CHD prevention: methods and participant characteristics for the Heart to Health study. Contemp Clin Trials. 2013;36(2):394-405.
PubMed   |  Link to Article
Esposito  K, Maiorino  MI, Di Palo  C, Giugliano  D; Campanian Postprandial Hyperglycemia Study Group.  Adherence to a Mediterranean diet and glycaemic control in Type 2 diabetes mellitus. Diabet Med. 2009;26(9):900-907.
PubMed   |  Link to Article
Mozaffarian  D.  Effects of dietary fats versus carbohydrates on coronary heart disease: a review of the evidence. Curr Atheroscler Rep. 2005;7(6):435-445.
PubMed   |  Link to Article
Oh  K, Hu  FB, Manson  JE, Stampfer  MJ, Willett  WC.  Dietary fat intake and risk of coronary heart disease in women: 20 years of follow-up of the nurses’ health study. Am J Epidemiol. 2005;161(7):672-679.
PubMed   |  Link to Article
Pereira  MA, O’Reilly  E, Augustsson  K,  et al.  Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Intern Med. 2004;164(4):370-376.
PubMed   |  Link to Article
Hamer  M, Chida  Y.  Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9-13.
PubMed   |  Link to Article
Zheng  H, Orsini  N, Amin  J, Wolk  A, Nguyen  VT, Ehrlich  F.  Quantifying the dose-response of walking in reducing coronary heart disease risk: meta-analysis. Eur J Epidemiol. 2009;24(4):181-192.
PubMed   |  Link to Article
Sheridan  SL, Draeger  LB, Pignone  MP,  et al.  A randomized trial of an intervention to improve use and adherence to effective coronary heart disease prevention strategies. BMC Health Serv Res. 2011;11:331.
PubMed   |  Link to Article
Anderson  KM, Odell  PM, Wilson  PW, Kannel  WB.  Cardiovascular disease risk profiles. Am Heart J. 1991;121(1, pt 2):293-298.
PubMed   |  Link to Article
Jilcott  SB, Keyserling  TC, Samuel-Hodge  CD, Johnston  LF, Gross  MD, Ammerman  AS.  Validation of a brief dietary assessment to guide counseling for cardiovascular disease risk reduction in an underserved population. J Am Diet Assoc. 2007;107(2):246-255.
PubMed   |  Link to Article
Schwartz  LM, Woloshin  S, Black  WC, Welch  HG.  The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med. 1997;127(11):966-972.
PubMed   |  Link to Article
Davis  TC, Long  SW, Jackson  RH,  et al.  Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med. 1993;25(6):391-395.
PubMed
Morisky  DE, Ang  A, Krousel-Wood  M, Ward  HJ.  Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). 2008;10(5):348-354.
PubMed   |  Link to Article
Block  G, Gillespie  C, Rosenbaum  EH, Jenson  C.  A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med. 2000;18(4):284-288.
PubMed   |  Link to Article
Kraschnewski  JL, Gold  AD, Gizlice  Z,  et al.  Development and evaluation of a brief questionnaire to assess dietary fat quality in low-income overweight women in the southern United States. J Nutr Educ Behav. 2013;45(4):355-361.
PubMed   |  Link to Article
Giles-Corti  B, Timperio  A, Cutt  H,  et al.  Development of a reliable measure of walking within and outside the local neighborhood: RESIDE’s Neighborhood Physical Activity Questionnaire. Prev Med. 2006;42(6):455-459.
PubMed   |  Link to Article
Jones  SA, Evenson  KR, Johnston  LF,  et al.  Psychometric properties of the modified RESIDE physical activity questionnaire among low-income overweight women [published online January 1, 2014]. J Sci Med Sport. doi:10.1016/j.jsams.2013.12.007.
PubMed
Brazier  JE, Roberts  J.  The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851-859.
PubMed   |  Link to Article
Murray  CJ, Evans  DB, Acharya  A, Baltussen  RM.  Development of WHO guidelines on generalized cost-effectiveness analysis. Health Econ. 2000;9(3):235-251.
PubMed   |  Link to Article
National Heart, Lung, and Blood Institute of the National Institutes of Health. Risk assessment tool for estimating your 10-year risk of having a heart attack.http://cvdrisk.nhlbi.nih.gov/calculator.asp. Accessed March 16, 2014.
Glasgow  RE, Bull  SS, Piette  JD, Steiner  JF.  Interactive behavior change technology. A partial solution to the competing demands of primary care. Am J Prev Med. 2004;27(2)(suppl):80-87.
PubMed   |  Link to Article
Linn  AJ, Vervloet  M, van Dijk  L, Smit  EG, Van Weert  JC.  Effects of eHealth interventions on medication adherence: a systematic review of the literature. J Med Internet Res. 2011;13(4):e103.
PubMed   |  Link to Article
Keyserling  TC, Samuel Hodge  CD, Jilcott  SB,  et al.  Randomized trial of a clinic-based, community-supported, lifestyle intervention to improve physical activity and diet: the North Carolina enhanced WISEWOMAN project. Prev Med. 2008;46(6):499-510.
PubMed   |  Link to Article
de Lorgeril  M, Renaud  S, Mamelle  N,  et al.  Mediterranean alpha-linolenic acid-rich diet in secondary prevention of coronary heart disease. Lancet. 1994;343(8911):1454-1459.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Study Flow Diagram

MI indicates myocardial infarction; CHD, coronary heart disease.aAt baseline there were 55 smokers in the counselor intervention and 58 smokers in the web-based intervention.bIncludes intent to start or increase regimen of blood pressure or cholesterol medication or start taking aspirin.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Change in Framingham Risk Score (FRS)

Stratified on selected baseline variables, shown by treatment arm at 4- and 12-month follow-up.aAt 4-month follow-up, P ≤ .05 for comparison of FRS between subgroups with web and counselor groups combined.bAt 12-month follow-up, P ≤ .05 for same comparison.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Baseline Participant Characteristicsa
Table Graphic Jump LocationTable 2.  Change From Baseline by Study Group at 4-Month Follow-upa
Table Graphic Jump LocationTable 3.  Change From Baseline by Study Group at 12-Month Follow-upa
Table Graphic Jump LocationTable 4.  Crude and Adjusted Differences in Continuous Variables for Change in Outcome Between Study Groupsa at 4 and 12 Monthsb
Table Graphic Jump LocationTable 5.  Crude Differencesa in Categorical Variablesb for Change in Outcome Between Study Groups at 4 and 12 Monthsc

References

Estruch  R, Ros  E, Salas-Salvadó  J,  et al; PREDIMED Study Investigators.  Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med. 2013;368(14):1279-1290.
PubMed   |  Link to Article
Mozaffarian  D, Appel  LJ, Van Horn  L.  Components of a cardioprotective diet: new insights. Circulation. 2011;123(24):2870-2891.
PubMed   |  Link to Article
Baigent  C, Blackwell  L, Collins  R,  et al; Antithrombotic Trialists’ (ATT) Collaboration.  Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials. Lancet. 2009;373(9678):1849-1860.
PubMed   |  Link to Article
Baigent  C, Blackwell  L, Emberson  J,  et al; Cholesterol Treatment Trialists’ (CTT) Collaboration.  Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376(9753):1670-1681.
PubMed   |  Link to Article
Gueyffier  F, Froment  A, Gouton  M.  New meta-analysis of treatment trials of hypertension: improving the estimate of therapeutic benefit. J Hum Hypertens. 1996;10(1):1-8.
PubMed
Ammerman  AS, DeVellis  RF, Carey  TS,  et al.  Physician-based diet counseling for cholesterol reduction: current practices, determinants, and strategies for improvement. Prev Med. 1993;22(1):96-109.
PubMed   |  Link to Article
Kushner  RF.  Barriers to providing nutrition counseling by physicians: a survey of primary care practitioners. Prev Med. 1995;24(6):546-552.
PubMed   |  Link to Article
Fineberg  HV.  The paradox of disease prevention: celebrated in principle, resisted in practice. JAMA. 2013;310(1):85-90.
PubMed   |  Link to Article
Centers for Disease Control and Prevention. Ambulatory care use and physician visits.http://www.cdc.gov/Nchs/fastats/docvisit.htm. Accessed December 17, 2013.
Ebrahim  S, Beswick  A, Burke  M, Davey Smith  G.  Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev. 2006;(4):CD001561.
PubMed
Goldstein  MG, Whitlock  EP, DePue  J; Planning Committee of the Addressing Multiple Behavioral Risk Factors in Primary Care Project.  Multiple behavioral risk factor interventions in primary care. Summary of research evidence. Am J Prev Med. 2004;27(2)(suppl):61-79.
PubMed   |  Link to Article
Slutsky  JR, Clancy  CM.  Patient-centered comparative effectiveness research: essential for high-quality care. Arch Intern Med. 2010;170(5):403-404.
PubMed   |  Link to Article
Tunis  SR, Stryer  DB, Clancy  CM.  Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003;290(12):1624-1632.
PubMed   |  Link to Article
Portnoy  DB, Scott-Sheldon  LA, Johnson  BT, Carey  MP.  Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988-2007. Prev Med. 2008;47(1):3-16.
PubMed   |  Link to Article
Ritterband  LM, Tate  DF.  The science of internet interventions: introduction. Ann Behav Med. 2009;38(1):1-3.
PubMed   |  Link to Article
Noell  J, Glasgow  RE.  Interactive technology applications for behavioral counseling: issues and opportunities for health care settings. Am J Prev Med. 1999;17(4):269-274.
PubMed   |  Link to Article
Sheridan  SL, Draeger  LB, Pignone  MP,  et al.  Designing and implementing a comparative effectiveness study of two strategies for delivering high quality CHD prevention: methods and participant characteristics for the Heart to Health study. Contemp Clin Trials. 2013;36(2):394-405.
PubMed   |  Link to Article
Esposito  K, Maiorino  MI, Di Palo  C, Giugliano  D; Campanian Postprandial Hyperglycemia Study Group.  Adherence to a Mediterranean diet and glycaemic control in Type 2 diabetes mellitus. Diabet Med. 2009;26(9):900-907.
PubMed   |  Link to Article
Mozaffarian  D.  Effects of dietary fats versus carbohydrates on coronary heart disease: a review of the evidence. Curr Atheroscler Rep. 2005;7(6):435-445.
PubMed   |  Link to Article
Oh  K, Hu  FB, Manson  JE, Stampfer  MJ, Willett  WC.  Dietary fat intake and risk of coronary heart disease in women: 20 years of follow-up of the nurses’ health study. Am J Epidemiol. 2005;161(7):672-679.
PubMed   |  Link to Article
Pereira  MA, O’Reilly  E, Augustsson  K,  et al.  Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Intern Med. 2004;164(4):370-376.
PubMed   |  Link to Article
Hamer  M, Chida  Y.  Active commuting and cardiovascular risk: a meta-analytic review. Prev Med. 2008;46(1):9-13.
PubMed   |  Link to Article
Zheng  H, Orsini  N, Amin  J, Wolk  A, Nguyen  VT, Ehrlich  F.  Quantifying the dose-response of walking in reducing coronary heart disease risk: meta-analysis. Eur J Epidemiol. 2009;24(4):181-192.
PubMed   |  Link to Article
Sheridan  SL, Draeger  LB, Pignone  MP,  et al.  A randomized trial of an intervention to improve use and adherence to effective coronary heart disease prevention strategies. BMC Health Serv Res. 2011;11:331.
PubMed   |  Link to Article
Anderson  KM, Odell  PM, Wilson  PW, Kannel  WB.  Cardiovascular disease risk profiles. Am Heart J. 1991;121(1, pt 2):293-298.
PubMed   |  Link to Article
Jilcott  SB, Keyserling  TC, Samuel-Hodge  CD, Johnston  LF, Gross  MD, Ammerman  AS.  Validation of a brief dietary assessment to guide counseling for cardiovascular disease risk reduction in an underserved population. J Am Diet Assoc. 2007;107(2):246-255.
PubMed   |  Link to Article
Schwartz  LM, Woloshin  S, Black  WC, Welch  HG.  The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med. 1997;127(11):966-972.
PubMed   |  Link to Article
Davis  TC, Long  SW, Jackson  RH,  et al.  Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med. 1993;25(6):391-395.
PubMed
Morisky  DE, Ang  A, Krousel-Wood  M, Ward  HJ.  Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). 2008;10(5):348-354.
PubMed   |  Link to Article
Block  G, Gillespie  C, Rosenbaum  EH, Jenson  C.  A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med. 2000;18(4):284-288.
PubMed   |  Link to Article
Kraschnewski  JL, Gold  AD, Gizlice  Z,  et al.  Development and evaluation of a brief questionnaire to assess dietary fat quality in low-income overweight women in the southern United States. J Nutr Educ Behav. 2013;45(4):355-361.
PubMed   |  Link to Article
Giles-Corti  B, Timperio  A, Cutt  H,  et al.  Development of a reliable measure of walking within and outside the local neighborhood: RESIDE’s Neighborhood Physical Activity Questionnaire. Prev Med. 2006;42(6):455-459.
PubMed   |  Link to Article
Jones  SA, Evenson  KR, Johnston  LF,  et al.  Psychometric properties of the modified RESIDE physical activity questionnaire among low-income overweight women [published online January 1, 2014]. J Sci Med Sport. doi:10.1016/j.jsams.2013.12.007.
PubMed
Brazier  JE, Roberts  J.  The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851-859.
PubMed   |  Link to Article
Murray  CJ, Evans  DB, Acharya  A, Baltussen  RM.  Development of WHO guidelines on generalized cost-effectiveness analysis. Health Econ. 2000;9(3):235-251.
PubMed   |  Link to Article
National Heart, Lung, and Blood Institute of the National Institutes of Health. Risk assessment tool for estimating your 10-year risk of having a heart attack.http://cvdrisk.nhlbi.nih.gov/calculator.asp. Accessed March 16, 2014.
Glasgow  RE, Bull  SS, Piette  JD, Steiner  JF.  Interactive behavior change technology. A partial solution to the competing demands of primary care. Am J Prev Med. 2004;27(2)(suppl):80-87.
PubMed   |  Link to Article
Linn  AJ, Vervloet  M, van Dijk  L, Smit  EG, Van Weert  JC.  Effects of eHealth interventions on medication adherence: a systematic review of the literature. J Med Internet Res. 2011;13(4):e103.
PubMed   |  Link to Article
Keyserling  TC, Samuel Hodge  CD, Jilcott  SB,  et al.  Randomized trial of a clinic-based, community-supported, lifestyle intervention to improve physical activity and diet: the North Carolina enhanced WISEWOMAN project. Prev Med. 2008;46(6):499-510.
PubMed   |  Link to Article
de Lorgeril  M, Renaud  S, Mamelle  N,  et al.  Mediterranean alpha-linolenic acid-rich diet in secondary prevention of coronary heart disease. Lancet. 1994;343(8911):1454-1459.
PubMed   |  Link to Article

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Multimedia

Supplement.

eAppendix. Cost and QALY Analysis

eReferences

eTable 1. Cost Summary for 12 Month Intervention

eTable 2. Imputed QALY, SF Physical and SF Mental Score Unadjusted Results at 12 Month Follow-Up

eTable 3. Incremental Cost-Effectiveness Ratios (ICERs) at 12 Month Follow-up

eTable 4. Sensitivity Analysis of Change in 10-Year Risk for CHD as Assessed by ATP III and FRS Total Events Equations Without a Term for Aspirin

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