A Multidimensional Integrative Medicine Intervention to Improve Cardiovascular Risk David Edelman, MD, MHS,1,2  Eugene Z. Oddone, MD, MHS,1,2 Richard S. Liebowitz, MD,1,3 William S. Yancy, Jr, MD, MHS,1,2 Maren K. Olsen, PhD,2,4 Amy S. Jeffreys, MStat,2  Samuel D. Moon, MD, MPH,3,5  Amy C. Harris, BA,2  Linda L. Smith,  PA,3 Ruth  E. Quillian-Wolever, PhD,3 Tracy W. Gaudet, MD,3,6

1Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC,  USA; 2Center for Health Services Research in Primary  Care, Durham VA Medical Center, Durham, NC,  USA; 3Center for Integrative Medicine, Duke University Medical Center, Durham, NC,  USA; 4Department of Biostatistics, Duke University Medical Center, Durham, NC,  USA; 5Department  of Community and Family Medicine, Duke University Medical Center, Durham, NC, USA; 6Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC,  USA.

BACKGROUND: Integrative medicine is an individualized, patient-cen- tered approach to  health, combining a whole-person model with evi- dence-based  medicine. Interventions based  in   integrative  medicine theory have not been tested as cardiovascular risk-reduction strate- gies. Our objective was to determine whether personalized health plan- ning  (PHP),   an  intervention based  on   the  theories and  principles underlying integrative medicine, reduces 10-year risk of coronary heart disease  (CHD).

METHODS: We  conducted a randomized, controlled trial among 154 outpatients age  45  or  over, with 1 or more known cardiovascular risk cardiovascular disease prevention is  a major goal  of al- most all  health care providers. Traditionally, prevention


has been targeted at individual risk factors (e.g.,  smoking ces- sation programs, hypertension clinical guidelines).1 However, patient-centered  strategies  hold promise in  risk reduction. Programs that allow  individuals to choose any of a number of their own  unhealthy behaviors to  reform can provide risk reduction for a broad spectrum of patients, with a wide  range of traditional risk factors (e.g.,  diabetes, hypertension) and risk 2,3 factors. Subjects were   enrolled from   primary care practices near an academic medical center, and the intervention was delivered at a university Center for Integrative Medicine. Following a health risk assessment, each subject in the intervention arm worked with a health coach and a medical provider to  construct a personalized health plan. The plan identified specific health behaviors important for each subject to modify; the choice of behaviors was driven both by cardiovascular risk reduction and the interests of each individual subject. The  coach then assisted each subject in implementing her/his health plan. Techniques used in  implementation included mindfulness meditation, relaxation training, stress  management, motivational techniques, and  health education and coaching. Subjects randomized to the comparison group received usual care (UC) without access to  the intervention. Our pri- mary outcome measure was 10-year risk of CHD,  as measured by  a standard Framingham risk score, and assessed at baseline, 5,  and 10 months. Differences between arms  were  assessed by  linear mixed effects modeling, with time and study arm as independent variables.

RESULTS: Baseline 10-year risk of CHD  was 11.1% for subjects randomized to UC  (n =77), and 9.3% for  subjects  randomized to  PHP (n =77). Over  10  months of the intervention, CHD  risk decreased to 9.8% for  UC subjects and 7.8% for  intervention subjects. Based on a linear mixed-effects model, there was a statistically significant difference in  the rate of risk improvement between the 2  arms (P =.04). In secondary analyses, subjects in  the PHP  arm were  found to  have increased days of  exercise per  week compared  with UC  (3.7   vs  2.4, P =.002), and subjects who  were  overweight on  entry into the study had greater weight loss in the PHP  arm compared with UC (P =.06).

CONCLUSIONS:  A multidimensional intervention based on  integrative medicine principles reduced risk of CHD,  possibly by increasing exercise  and improving weight loss.


KEY WORDS:  integrative medicine; randomized-controlled trial; cardi- ovascular risk reduction.

DOI:  10.1111/j.1525-1497.2006.0495.x

J GEN INTERN MED  2006; 21:728–734.




The  authors have no conflicts of interest to declare.

Address correspondence and  requests for  reprints to  Dr.  Edelman: HSR&D  (152)  Durham VA Medical Center, 508 Fulton St.,  Durham,  NC

Strategies  should work by effecting favorable changes in patient behaviors, which would then  lead to  improved cardiovascular risk by modifying a wide  number of risk factors and improving control of several risk conditions.

Integrative medicine is  based on  specific principles, including use of patient-centered, individualized therapeutic approaches and mind-body techniques (e.g.,   meditation, hypnosis).8,9 Evidence from  randomized, controlled trials sup- ports the use of individualized strategies and mind-body techniques  in   depression,  chronic  pain,  anxiety,  and  other diseases in  which symptom management is  the primary goal of therapy.10–13 However, randomized, controlled trials of in- dividual techniques utilized in  integrative medicine are either lacking14  or  conflicting in  their results (e.g.,  transcendental meditation to  treat hypertension).15–17  No  trial has demonstrated reduction in cardiovascular risk because of an over- arching integrative medicine approach.

Our primary objective was to test the effect  of personalized health planning (PHP),  an Integrative Medicine intervention, on  cardiovascular risk reduction in  a population with heterogeneous cardiovascular risks.


We conducted a randomized, controlled trial of our PHP intervention at the Center for Integrative Medicine at Duke University  Medical Center  (DUMC). The  DUMC  Institutional  Review Board approved all  study protocols and all  subjects provided written informed consent.


We required subjects to  be  age  45  or  over, to have a primary care provider, and to report 1 or more of the following risk factors for cardiovascular disease: diabetes, hypertension, dyslpidemia, smoking, or  body mass index (BMI)425 kg/m2. We excluded subjects with active cardiovascular disease, defined as a history of myocardial infarction (MI), congestive heart failure, or cerebrovascular accident (CVA). We also excluded subjects with terminal illness, a history of psychosis, or no access to  a telephone. Pregnant women were   excluded  because  of complexity in  interpreting longitudinal anthropometric measurements.


After obtaining informed consent and gathering baseline data, we randomized participants to either the PHP  intervention, or to  a comparison group. We  balanced treatment arm assignments by using randomization block sizes of 20  subjects. The PHP  intervention is  summarized in  Table 1.  The  comparison group received a mailed report including their health risk assessment and baseline blood test results. They  were  then returned to their usual care, with no  access to the PHP  intervention.

Personalized Health Planning. The  PHP intervention was delivered predominantly by a health coach, a Masters-level professional trained in  activating techniques to  assist patients in setting and achieving health goals.18–22   A manual standardized  the intervention. The  intervention proceeded through 2 phases: risk education, and development and execution of a Personalized Health Plan.

Table 1.  Outline of the Personalized Health Planning (PHP) Inter- vention

Personal risk education

Visits with integrative medicine provider at baseline and 5 mo

Know  your number

  1. Personalized health plan

Training on  an integrative model of health

Simultaneously consider multiple domains of health behavior

Set  personal behavioral goals

Behavioral goal-setting

Prioritize 1 to 3 goals as primary behaviors to change

New behavioral goals added once previous goals are maintained

III. Techniques

Small group sessions

7–11 subjects per  group

Mind-body approaches: Mindfulness meditation Progressive muscle relaxation Yoga

Guided visualization

Stress management

Other lifestyle approaches: Goal  setting

Risk prevention

Continuing health self-education

Communication skills


Physical activity

Complementary and alternative approaches

Creating behavior change

Relapse prevention

Individual coaching sessions

20–30 min biweekly phone sessions

Clarifying priorities

Reinforce mind-body and lifestyle skills learned in group

Enhancing motivation

        Two individual meetings with a nutritionist                                                            


The PHP intervention was standardized by using detailed manuals and formal training for intervention personnel.

Risk Education. We performed a baseline assessment of cardiovascular risk using Know  Your  Number, a proprietary tool designed to educate patients about their risks for disease and illustrate the possibilities for  improving risk. Using the patient’s health  information (blood pressure,  smoking status, frequency of  exercise, etc.), Know   Your   Number provides a graphic display to  demonstrate both disease risks that the patient currently has, and what the risk could be  if healthier behaviors were  adopted or individual diseases (e.g.,  hypertension) more tightly controlled. Assessment  was performed at baseline, 5, and 10  months later. An integrative medicine physician or  physician’s assistant  provided one-on-one feedback to subjects on  the baseline and 5-month assessments

Health Planning. At baseline, each subject was teamed with a coach. Over  the first 7 weeks of the intervention, participants learned about the integrative model of health, and explored healthier  behavioral changes.  After   this exploration phase, participants  prioritized 1  to 3  goals as primary behaviors to change. The remainder of the intervention was spent changing the  specified behaviors  chosen  during  the  first  7   weeks through education, skill building, and coaching strategies.

The  core of the intervention involved health coaches sup- porting subjects in the use of specific techniques for maintaining  focus on  their commitment to  healthier behaviors. These techniques  included: the  following mindfulness  meditation; yoga; relaxation training;  communication  skills, specifically with medical providers and important support people; and exploration of values. Coaches also provided patient education on  the topics of  nutrition, physical activity, and continuing personal medical education (i.e.,  how  to learn more about specific  medical conditions), as well  as creating and maintaining behavior change, and the integration of complementary and alternative approaches.

Subjects interacted with the coach during group meetings, and individual telephone sessions. The  coach led  group meetings. Each group had 7  to 11  participants. Groups had 28

2-hour meetings over the 10 months of the intervention, weekly for the first 4 months, biweekly for months 5 through 9, and then once at the conclusion of the intervention. The  groups allowed a context for education, teaching integrative medicine skills (e.g.,  meditation, relaxation strategies, healthy cooking, strength training), and support among members of the group.

Participants also had 20  to 30  minute phone sessions with their coach every   2  weeks throughout  the intervention. The coaches used these calls to reinforce the techniques taught at group meetings. During health planning, phone sessions al- lowed participants to obtain guidance in clarifying their priorities and setting realistic goals. In later sessions, the individual coaching focused on enhancing motivation to reach and main- tain goals, and on  support in  locating resources. The  individual sessions also allowed subjects to add new  behavioral goals once previous goals were   being maintained.  The  content  of these phone calls was individualized, but in general focused on the subjects’ successes with or  barriers to  achieving the objectives set in  their health plan. Subjects wanting further assistance  from   the  coaches  were   allowed  brief  contact  by telephone or  e-mail between scheduled phone calls. Partici- pants also had 2  chances to  meet individually with a nutritionist to obtain individualized support and recommendations for improvements in eating behaviour.

We measured the intensity of the intervention for each sub- ject  by logging both group meeting attendance and completed individual coaching sessions. Subjects also kept a daily log of the number of minutes spent in meditation.

Primary Outcome  Measure

The primary outcome measure was the Framingham risk score (FRS),   a validated estimate, derived from   the Framingham Cohort, of the risk (measured as a proportion) of having non- fatal myocardial infarction or cardiac death over  10  years.23,24

FRS  is often used to measure  the efficacy of short-term cardiovascular interventions that  have the potential to  affect a number of  cardiovascular risk factors.25  We  used a version of  FRS   that  required only   the input of  age, gender,  blood pressure, diabetes status,  smoking status,  and lipid data.26

A research assistant  blinded to  treatment arm assignment measured the data required to  calculate FRS  at baseline, 5, and 10  months; these time points were   chosen to  measure initial impact  and  intermediate-term  sustainability  of  the intervention.

Secondary Outcome Measures

The  same blinded research assistant measured all  secondary outcomes at baseline, 5, and 10  months. Secondary outcomes fell into 2 categories: biological and behavioural.

Biological Outcome  Measures. Biological outcome measures included body mass index (calculated as weight in kg/(height in m)2); waist circumference; blood pressure; and fasting lipid profile. Blood pressure  was measured by an electronic cuff  on the right arm after resting in the seated position for 5 minutes. Blood pressure was analyzed both as a continuous variable, and as a categorical variable (in or out of control). Blood pres- sure was defined as in control if a subject without diabetes had both systolic blood pressure (SBP)o140 mmHg and diastolic blood pressure (DBP)o90 mmHg. For  a subject with diabetes, blood pressure control was defined as SBPo130 mmHg and DBPo80 mmHg. Fasting serum lipids were  sent to a reference lab   for  standardized  measurement  (LabCorp, Durham,  NC). Waist circumference was measured  twice at each measure- ment interval, and the 2 results were  averaged.

Behavioral Outcome  Measures. Behavioral outcome measures included exercise frequency, measured as self-reported days per  week of exercising at least 30  minutes; smoking status; and readiness to increase exercise and to lose weight, each measured by a single validated question.27,28


We used intention-to-treat principles for  all  comparisons be- tween study arms; all  subjects were  analyzed as part of the arm to which they were  randomized. For our primary analysis, we fit linear mixed effects models with treatment arm, time of measurement, and a treatment arm by  time of measurement interaction term as the only  independent variables. We  log- transformed FRS because of skewed distribution and used log FRS  as the dependent variable in  the primary analysis. Secondary outcomes were   analyzed using  linear mixed-effects modeling for continuous variables and generalized estimating equations for  dichotomous variables (e.g.,  proportion of subjects with blood pressure  in  control); again, treatment  arm, time of measurement, and a treatment arm by  time of measurement  interaction term were   the only   variables in  these models. In  our primary analyses, all  data from   all  enrolled patients were  used. To assess the impact of differential drop- outs on  our findings, we  also performed the FRS  outcome analysis carrying forward the last measured observation for all  subjects who  dropped out (‘‘last-observation-carried- forward’’), as well as using only  those subjects who  completed the study (‘‘completers’’).29  Analyses were  performed using the SAS analysis system (Version 9.0, SAS, Cary, NC) and S-PLUS analysis software (Version 6.1, Insightful Corp., Seattle, WA).


Subject Enrollment and  Demographics

The  flowchart for  enrollment and randomization is  shown in Figure 1. We randomized 77 subjects to each arm. In the usual care (UC) arm, 66  subjects (86%)  were  retained through the entire study; desire to receive the intervention was the primary reason for  dropout in  the UC arm. In  the PHP  arm, 56  (73%) were  retained; time requirement of the intervention was the primary reason for dropout in the PHP  arm.

Baseline subject information is  summarized in  Table 2. Briefly, the  population  was  80%   female;  two-thirds  were college graduates;   over   half    had  family incomes  of   over $60,000 per   year; and  three-fourths  of  the  subjects  were white. Subjects were   at moderate cardiovascular risk, with an average risk of developing coronary disease over the next 10 years (measured by baseline FRS)  of approximately 10%.

FIGURE 1. Flowchart for subject enrollment.



Table 2.  Baseline Subject Characteristics












  Baseline 5 mos 10 mos









  UC PHP P-value  


Age—mean (SD) 53.4 (4.8) 52.2 (5.2) .15  
Female gender 79% 82% .68  
Education—completed 64.9% 70.1% .49 2
Marital status—married 57.1% 55.8% .87  
Race       1.8
White 75.3% 77.9% .26  
African American 23.4% 16.9%    

Family income

1.3% 5.2%   1.6
o$39,999 15.6% 15.6% .50
$40,000 to $59,999 24.7% 32.5%  
4$60,000 59.7% 50.7%  


Cardiovascular risk factors


BMI—Mean (SD) 34.1 (7.7) 33.3 (7.8) .54


LDL cholesterol                   137.1 (35.6)        132.4 (35.1)             .41

Hypertension                       40.3%              35.1%                 .51

Diabetes                               15.6%              15.6%               1.00

Smoking                               11.7%                9.1%                 .60

10-y risk of CHD                            11.1%                      9.3%                      .03                   


UC,  usual care; PHP,  personalized health plan; CHD,  coronary heart disease; LDL, low density lipid;  BMI, body mass index.


Adherence to the Intervention

Intervention subjects attended 61%  of group sessions; the me- dian number of  sessions attended was 20  (of 28).   Subjects completed 63%  of scheduled individual phone sessions. The median percentage of calls completed by  individual subjects was 74%.

Cardiovascular Risk

Our primary hypothesis was that the PHP  intervention would provide greater reduction  in  10-year cardiovascular risk as measured by FRS  than UC. These results are shown in Figure

  1. The Mean FRS improved in both arms, from  11.1% to 9.8% in the UC arm (a 12%  relative decline) and from  9.3% to 7.8% in  the PHP  arm (a 16%  relative decline). In  a linear mixed-ef- fects model using log-transformed FRS  as the outcome, FRS improved more (compared with baseline) for  subjects in  the PHP arm than in the UC arm at both 5 and 10 months (P =.006 at 5  months and P =.04 at 10  months). These findings were similar in  the last-observation-carried-forward analysis (15% relative decline vs  7%  relative decline at 10  months, P =.03) and in  the completers’ analysis (18%  relative decline vs  7% relative decline at 10  months, P =.05).

To better understand how  the intervention affected cardiovascular risk, we examined separate behavioral changes. Fol- lowing a model that assumes that the PHP intervention would lead to  behavioral changes that, in  turn,  would lead to  im- provement in specific cardiovascular risk factors and thus reduce overall cardiovascular risk, we  assessed  differences in changes between the intervention and UC  for  2  categories  of precursor to cardiovascular risk: risk behaviors and risk conditions.

Risk Behavior Analyses. We focused our analysis on  2 behaviors: diet and exercise (smoking was not addressed owing to the small number of smokers in  the study (n =16)).  We  first evaluated subjects’ readiness to  lose  weight, and to  increase

FIGURE 2. Changes in log-transformed Framingham risk score, personalized health planning intervention versus usual care.


physical activity (Fig.  3).  Subjects in  both arms showed substantial improvement over  baseline. Personalized health plan subjects had greater increases in readiness to increase phys- ical  activity than did  UC  subjects (P =.02) and greater readiness to lose  weight (P =.06). Figure 3 shows the differences in exercise and weight between the 2 arms. Subjects in both arms showed increased days of exercise and reduced weight com- pared with baseline. Days of  exercise were   significantly in- creased in the PHP arm compared with the increase in the UC arm (3.7  vs  2.4 days, P =.002), and there was greater weight loss  for   intervention subjects  (BMI  reduction  1.2  vs   0.6, P =.11). The  weight reduction  was approximately 2  pounds greater in the PHP  arm.

Risk Condition Analyses. We next assessed the effects of our intervention on  2 risk conditions prevalent in  our population: hypertension and hyperlipidemia (Fig. 4). Over  10  months, we observed an overall (both PHP and UC) 8 mmHg decline in SBP, and a decline of 7 mg/dL in low density lipid (LDL)-cholesterol. The  difference in the change in LDL-cholesterol between base- line  and 5 months was statistically significant between the 2 arms  (P =.02),  but  the  change between baseline and  10 months  was not statistically significant (P =.25). Similarly, there was a difference in  the improvement in  BP  control be- tween the 2  arms at 5  months but no  such difference at 10 months compared with baseline (P =.06 at 5 months, P =.34 at 10  months).



We found that PHP, a multidimensional intervention based on Integrative Medicine principles and using a relationship-cen- tered, mind-body approach in  supporting behavior change, improved cardiovascular risk compared with usual care. The magnitude of the relative improvement in  FRS  at 10  months was modest (16%  in  the intervention arm vs 12%  in  the UC arm). This remains statistically significant in  part because of small standard  deviations in  FRS,  but also in  part owing to failure of randomization. The UC arm would have been expect- ed,  in  the absence of any intervention  effect, to improve more than the intervention arm because of regression to the mean, because they had a much higher mean FRS  at baseline. This






















Baseline           5 mos          10 mos



























Baseline           5 mos          10 mos



3                                                                                     34

UC                                                                                        UC

2                                                                                     33



1                                                                                     32




0                                                                                     31

Baseline           5 mos          10 mos                                  Baseline           5 mos          10 mos


FIGURE 3. Changes in risk behaviors, personalized health planning intervention versus usual care.

Secondary analyses to determine a possible mechanism of action for  the intervention fit  our model in  that there was a broad range of modest improvements in risks and conditions. While  exercise and weight change are not used in  calculating FRS,  these behaviors might have translated,  indirectly, into improvement in  FRS.  However, for  a number of risk factors, including weight loss, blood pressure  control, and LDL-cho- lesterol, PHP subjects had greater improvement than UC subjects. While  the effects were  all favorable, suggesting a positive impact on these parameters for the intervention, the differenc- es  failed to achieve statistical significance, probably for 3 rea- sons. First, allowing patients to choose their own  focus for the intervention dilutes our ability to measure the effect  of the intervention on any single condition. Second, with no cardiovascular  risk  condition common to   our entire population, a relatively smaller number of patients will have to modify a single  risk factor, leading to  less visible changes in  specific risk factors. Third, the unexpected improvement shown by the UC arm led to decrease between arm differences in many risk factors. It is worth noting that the arms had a statistically significant difference in change in FRS despite this improvement by the UC arm. It is also important to note that our measured risk factors are not the only  possible mechanisms for improvement in cardiovascular risk; for example, although we did  not make medication changes in  the intervention, it is possible that we activated patients to take their medication or talk to their doc- tors about further medication, and that these changes caused part of the improvement.

Our study has a number of limitations. First, our subjects were  predominantly female, educated, and had family incomes well above median. It is not clear as to whether the success of our intervention can be  translated to a more heterogeneous population. Also,   the  clinical heterogeneity of  our subjects left    us   without  adequate  statistical   power  to    measure clinically relevant effects on  individual risk factors. The  limited  time frame of  our follow-up does not permit us to  draw inference about the sustainability of this intervention beyond the year in  which subjects were  studied, and were  part of the intervention. Additionally, our intervention is multifaceted; we cannot determine the relative efficacy of components. Regarding  our readiness to  change data, expectation bias may have led  intervention patients to be  more likely  to assert readiness to  change compared with control patients.  Finally, the improvements seen in  our UC  arm suggest that our subjects may have been highly motivated to change their behavior. It is unclear whether our findings generalize to a less engaged population.

Another limitation relates to subject drop-outs. Drop-outs may not have occurred randomly, as many were  due to lack of satisfaction with the arm to which they were  randomized (both control and intervention). The results from  the completer analysis suggest that this issue may not have been critical.

Integrative Medicine incorporates  the principles both of whole-person approaches  to  medicine and  evidence-based medicine. This must  also include investigation designed to provide evidence of the clinical efficacy of these models. Our study provides the first evidence that these necessarily multi- dimensional approaches can be  efficacious in cardiovascular risk reduction. In order to show the effectiveness of Integrative Medicine and translate  its principles into accepted practice, future research in Integrative Medicine should attempt to evaluate specific components of complex and multifaceted interventions for their relative efficacy and cost-effectiveness, and should identify target populations most likely  to  benefit from these interventions. Nevertheless, our study provides proof of the concept that PHP  can reduce, at least in  the short term, risk of  morbidity from   what is  conventionally considered  a physical disease.


We  would like to acknowledge our  health coaches: Jeanne Gresko, MS, Kerry Little, MA, Jessica Psujek,  MS, and Andrea Shaw, PhD. We would also like to acknowledge our  nutritionist Greg Hottinger, MPH, RD, our  mindfulness consultant Jeff  Brantley, MD, and Karen Gray for her  administrative support. Finally, we would like to acknowledge Cheryl Richardson for consulting on the intervention and her  coaching of the coaches, and Ralph Snyderman,  MD,  for  help actualizing the concept  of PHP.

This work was funded by a grant from the Center for Medicare and Medicaid Services. Dr. Yancy is a VA Health Services

Research and Development Career Awardee. All authors had full access to all the data in the study and take responsibility for  the integrity of  the data and the accuracy of  the data analysis


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Yoga and Hypertension: A systemic review

Anupama Tyagi , Marc Cohen

Alternative Therapies: Mar/April 2014 Vol. 20.2

(This article is full of tables which do not translate well into the word press form; if you are interested I'll be glad to send to you.)