consensus exists that lifestyle changes such as stopping smoking , higher levels of physical activity , and certain dietary patterns can lead to lower rates of cardiovascular disease ( cvd ) and other chronic diseases.1 , 2 , 3 , 4 , 5 current clinical guidelines in both the united states and europe include such changes in their recommendations for reducing the risk of cvd.1 , 6 , 7 for example , the 2013 american college of cardiology ( acc ) and american heart association ( aha ) guidelines to reduce cardiovascular risk give a strong , grade a recommendation for adults who need lowdensity lipoprotein and blood pressure ( bp ) lowering to follow a dietary pattern that emphasizes intake of vegetables , fruits , and whole grains and limit intake of sweets , sugarsweetened beverages , and red meats.1 
  clinical guidelines for providers and patients rely on evidence that includes data analysis of individual studies , data synthesis that pools results from many studies , and models that use data to project future risk .
an attractive feature of models and tools that predict risk is the ability to estimate the effectiveness of alternative strategies for different populations and subpopulations , and for individuals with specific characteristics .
in fact , the acc and aha recently published a new set of equations that can be used to estimate 10year and lifetime risk of a first hard atherosclerotic cvd event , or first occurrence of nonfatal myocardial infarction or death from coronary heart disease or fatal or nonfatal stroke.8 these equations , known as the pooled cohort equations , are based on data from diverse , communitybased cohorts that represent both black and white men and women in the united states
. full descriptions of the cvd risk equations and their development have been published elsewhere.8 , 9 
  with the introduction of the acc / aha guidelines and the accompanying debates on use of statin therapy , calls for more focus on personal lifestyle modification and behavior change have been made.10 , 11 , 12 there has additionally been growing interest in using data and analytic tools to develop personalized recommendations that consider an individual 's clinical risk profile.13 , 14 , 15 personalized recommendations utilizing comparative effectiveness research have been done previously using life expectancy gains based on us preventive services task force guidelines,16 , 17 , 18 with substantial gains in life expectancy shown with greater use of preventive services.19 
  the objective of this study is to estimate the risk reductions that can be expected from diverse lifestyle modifications for patients with specified clinical characteristics and establish the comparative effectiveness of these strategies , depending on individual risk profiles .
we calculated risk by inputting individual levels of key risk factors including age , bp , lipid levels , hypertension treatment , and smoking status into the pooled cohort equations and then using the best available evidence on the effects of different interventions on each of these risk factors to establish the comparative effectiveness of interventions for individuals depending on their profiles .
we considered the following lifestyle interventions : group therapy for stopping smoking , mediterranean diet ( a diet that emphasizes high intake of fruits , vegetables , whole grains , fatty fish , nuts , and olive oil),1 aerobic exercise ( specifically walking ) , and yoga ( a popular form of exercise focusing on the mind  body connection ) .
these interventions were chosen to encompass a range of strategies that may be part of a lifestyle modification regimen . for stopping smoking ,
already wellestablished as a main driver of cardiovascular risk , we chose group therapy counseling sessions rather than nicotine replacement therapy or medication to more closely align with the nonpharmacological nature of the other strategies .
a healthy diet and aerobic exercise are regarded as mainstays in prevention of cvd , with accumulating evidence on the impact of the mediterranean diet on cardiovascular risk.5 , 20 , 21 yoga , a form of physical activity that involves physical postures , breath work , and meditation or relaxation , is becoming more prevalent in the united states ; a 2012 national center for health statistics survey showing that 9.5% of adults ( 21  million ) used yoga in the previous 12  months.22 yoga is thought to reduce response to stress and affect the autonomic nervous system through relaxation techniques and breath control.23 , 24 three recent systematic reviews and metaanalyses suggest that yoga may significantly improve risk factors for cvd such as body weight , lipid profile , and bp.25 , 26 , 27 

this study uses the pooled cohort equations published by goff et  al to calculate 10year cvd risk separately for white and black individuals.8 we focused on the nondiabetic white and black populations .
risk factors in the equations are age , systolic bp , whether the patient is treated for hypertension or not , total cholesterol ( tc ) and highdensity lipoprotein cholesterol ( hdlc ) , current smoking status , and history of diabetes .
levels of risk factors were entered in the pooled cohort equations to calculate an individual 's absolute 10year atherosclerotic cvd risk .
the potential improvement in each risk factor that could be achieved through the 4 lifestyle management strategies  group therapy for smoking ( only for current smokers ) , the mediterranean dietary pattern , walking , and yoga  were obtained from published literature ( table  1 ) .
the risk factor effect sizes were then translated into reductions in 10year cvd risk for each intervention .
effectiveness of lifestyle interventions on reducing cvd risk through changes in risk factors cvd indicates cardiovascular disease ; hdl , highdensity lipoprotein . * probability of quitting smoking with group therapy compared to 5% probability of quitting without intervention .
the relative proportion of quitting ( relative risk ) is 1.98 for group therapy vs control ( 95% ci : 1.602.46).28 
  intervention effect sizes were obtained from metaanalyses of randomized controlled trials ( rcts ) .
published literature was searched and metaanalyses were selected if they included rcts using intentiontotreat analysis , contained summary estimates of all relevant risk factors within a single report , and compared the treatment to a control group of no / minimal intervention or usual care .
we used an alternate set of estimates in a sensitivity analysis . for current smokers , group therapy for smoking cessation consisted of about 6 to 8 scheduled meetings led by professional facilitators for at least 6  months that included information , advice , and encouragement for quitting.28 for mediterranean diet , data were taken from a metaanalysis of rcts in which patients followed a diet rich in fruits and vegetables , low in red meat , and moderate in fat from nuts and oils for at least 6  months with a followup of 2  years.29 evidence on change in risk factors from walking was obtained from a metaanalysis on pedometer use as a means to increase physical activity.30 specifically , participants were given a pedometer and encouraged to increase their daily steps ( either with or without a daily step goal ) .
this intervention resulted in an additional 2491 steps per day , roughly equivalent to 1 mile of walking .
walking was chosen to represent aerobic exercise as a lowimpact form of aerobic activity that is accessible and suitable for most individuals .
data on yoga came from the most recent metaanalysis of rcts evaluating a variety of movementbased yoga styles including hatha , vinyasa , and ashtanga and excluding breathing or meditationonly styles.27 yoga was practiced 3 to 4 times per week on average , and the mean duration of the yoga interventions and followup was 18  weeks .
calculations for risk factor changes as a result of lifestyle interventions were based on an intentiontotreat approach .
the intervention effect estimates from table  1 were added to the initial risk factor level for an individual with specific clinical characteristics to generate a new risk factor level .
these new levels were then entered in the pooled cohort equations to obtain a new cvd risk .
first we calculated the cvd risk difference between a smoker and nonsmoker , with all other risk factors unchanged to get the pure effect of group therapy on cvd risk .
then we multiplied this number by the probability a smoker would successfully quit with group therapy versus no group therapy to get the intentiontotreat estimate ( see data s1 for details on calculations ) .
we calculated the absolute risk reduction of 10year cvd risk across the 4 different interventions for the average patient in each risk subcategory .
basecase results were displayed using heatmaps , using color shading to represent gradation of risk across subgroups and strategies .
we included 6 risk factor profiles of hypothetical patients with varying levels of systolic bp , tc , and hdlc with different combinations of smoking and hypertension treatment status to represent individuals with a wide range of cvd risk , extending from 1% to nearly 30% .
the 6 profiles were created for both white and black patients for a total of 12 different profiles .
for each profile , we estimated the 10year cvd risk in the absence of a lifestyle management intervention and with the addition of each of the 4 interventions .
we conducted several analyses to explore the impact of parameter uncertainty and the impact of alternative assumptions .
first , we conducted a deterministic sensitivity analysis using the published 95% ci for the effect measure for each intervention .
worse case  scenario analysis using the smallest favorable change for all risk factors and a  best case  scenario analysis using the largest favorable change for all risk factors .
second , we conducted an analysis that explored the impact on cvd risk for an individual smoker who successfully quits .
in contrast to the primary  intentiontotreat  analysis explained above , this secondary analysis is more akin to a per protocol type of analysis .
for example , a provider who is considering options to recommend to a patient will discuss the relative benefits of those options based on evidence from study results analyzed by intentiontotreat .
however , a patient might be interested in acquiring information for the  benefit  they would obtain if they successfully stopped smoking .
therefore , we conducted an analysis in which we assumed a patient successfully quits and refer to this second analysis as  successful smoking cessation .
 third , we conducted a probabilistic sensitivity analysis to determine how often a strategy would be considered optimal for the 12 hypothetical patients .
this analysis was done for both the basecase analysis and the analysis with successful smoking cessation . in each analysis
, 1000 random draws were performed in which we simultaneously varied the parameters in table  1the effect size of the interventions for each risk factor and the probability of successfully quitting smoking with group therapy  according to its probability density function .
intervention parameters were drawn from a normal distribution and the probability of quitting smoking drawn from a beta distribution .
probability of successfully quitting was designated as 100% for the individual patient who successfully quits smoking .
the basecase intervention effect estimates were used as means and the 95% cis that were derived from published literature were used to inform the spread of the distribution .
fourth , we used an alternative set of intervention effect estimates from published literature for the 12 hypothetical patient profiles to see if the ranking was sensitive to our choice of metaanalysis .
we have presented the table of alternative estimates in table  s1 . for smoking cessation , we used another nonpharmaceutical intervention , physician advice for smoking cessation , to mimic the behavior change necessary to quit smoking.31 another metaanalysis on the mediterranean dietary pattern for rcts longer than 3  months from the cochrane database was selected for this sensitivity analysis32 ; however , only tc was pooled and reported and thus we varied only this parameter . to more broadly represent exercise as an intervention , we widened this intervention category to include more kinds of aerobic exercise including running , jogging , and cycling . for this
, we used a recent metaanalysis of rcts of lipid measurements ( tc and hdl)33 supplemented with a metaanalysis of the effect of aerobic exercise on systolic bp.34 both analyses used similar definitions of aerobic exercise , had similar patient populations , and both had median durations of 12  weeks ( range 23  weeks to 2  years ) .
lastly , we conducted a sensitivity analysis with the general framingham risk score to examine if and how the relative rankings of the interventions would change with this commonly used risk algorithm.35 this framingham risk score closely matches the pooled cohort equations used in the base case , assessing all the same cardiovascular disease events plus cardiac failure .
this study does not constitute human subjects research and thus did not require institutional review board approval .
published literature was searched and metaanalyses were selected if they included rcts using intentiontotreat analysis , contained summary estimates of all relevant risk factors within a single report , and compared the treatment to a control group of no / minimal intervention or usual care .
we used an alternate set of estimates in a sensitivity analysis . for current smokers , group therapy for smoking cessation consisted of about 6 to 8 scheduled meetings led by professional facilitators for at least 6  months that included information , advice , and encouragement for quitting.28 for mediterranean diet
, data were taken from a metaanalysis of rcts in which patients followed a diet rich in fruits and vegetables , low in red meat , and moderate in fat from nuts and oils for at least 6  months with a followup of 2  years.29 evidence on change in risk factors from walking was obtained from a metaanalysis on pedometer use as a means to increase physical activity.30 specifically , participants were given a pedometer and encouraged to increase their daily steps ( either with or without a daily step goal ) .
this intervention resulted in an additional 2491 steps per day , roughly equivalent to 1 mile of walking .
walking was chosen to represent aerobic exercise as a lowimpact form of aerobic activity that is accessible and suitable for most individuals .
data on yoga came from the most recent metaanalysis of rcts evaluating a variety of movementbased yoga styles including hatha , vinyasa , and ashtanga and excluding breathing or meditationonly styles.27 yoga was practiced 3 to 4 times per week on average , and the mean duration of the yoga interventions and followup was 18  weeks .
calculations for risk factor changes as a result of lifestyle interventions were based on an intentiontotreat approach .
the intervention effect estimates from table  1 were added to the initial risk factor level for an individual with specific clinical characteristics to generate a new risk factor level .
these new levels were then entered in the pooled cohort equations to obtain a new cvd risk .
first we calculated the cvd risk difference between a smoker and nonsmoker , with all other risk factors unchanged to get the pure effect of group therapy on cvd risk .
then we multiplied this number by the probability a smoker would successfully quit with group therapy versus no group therapy to get the intentiontotreat estimate ( see data s1 for details on calculations ) .
we calculated the absolute risk reduction of 10year cvd risk across the 4 different interventions for the average patient in each risk subcategory .
basecase results were displayed using heatmaps , using color shading to represent gradation of risk across subgroups and strategies .
we included 6 risk factor profiles of hypothetical patients with varying levels of systolic bp , tc , and hdlc with different combinations of smoking and hypertension treatment status to represent individuals with a wide range of cvd risk , extending from 1% to nearly 30% .
the 6 profiles were created for both white and black patients for a total of 12 different profiles . for each profile , we estimated the 10year cvd risk in the absence of a lifestyle management intervention and with the addition of each of the 4 interventions .
we conducted several analyses to explore the impact of parameter uncertainty and the impact of alternative assumptions .
first , we conducted a deterministic sensitivity analysis using the published 95% ci for the effect measure for each intervention .
worse case  scenario analysis using the smallest favorable change for all risk factors and a  best case  scenario analysis using the largest favorable change for all risk factors .
second , we conducted an analysis that explored the impact on cvd risk for an individual smoker who successfully quits .
in contrast to the primary  intentiontotreat  analysis explained above , this secondary analysis is more akin to a per protocol type of analysis .
for example , a provider who is considering options to recommend to a patient will discuss the relative benefits of those options based on evidence from study results analyzed by intentiontotreat .
however , a patient might be interested in acquiring information for the  benefit  they would obtain if they successfully stopped smoking . therefore , we conducted an analysis in which we assumed a patient successfully quits and refer to this second analysis as 
 third , we conducted a probabilistic sensitivity analysis to determine how often a strategy would be considered optimal for the 12 hypothetical patients .
this analysis was done for both the basecase analysis and the analysis with successful smoking cessation . in each analysis
, 1000 random draws were performed in which we simultaneously varied the parameters in table  1the effect size of the interventions for each risk factor and the probability of successfully quitting smoking with group therapy  according to its probability density function .
intervention parameters were drawn from a normal distribution and the probability of quitting smoking drawn from a beta distribution .
probability of successfully quitting was designated as 100% for the individual patient who successfully quits smoking .
the basecase intervention effect estimates were used as means and the 95% cis that were derived from published literature were used to inform the spread of the distribution .
fourth , we used an alternative set of intervention effect estimates from published literature for the 12 hypothetical patient profiles to see if the ranking was sensitive to our choice of metaanalysis .
we have presented the table of alternative estimates in table  s1 . for smoking cessation , we used another nonpharmaceutical intervention , physician advice for smoking cessation , to mimic the behavior change necessary to quit smoking.31 another metaanalysis on the mediterranean dietary pattern for rcts longer than 3  months from the cochrane database was selected for this sensitivity analysis32 ; however , only tc was pooled and reported and thus we varied only this parameter . to more
broadly represent exercise as an intervention , we widened this intervention category to include more kinds of aerobic exercise including running , jogging , and cycling . for this
, we used a recent metaanalysis of rcts of lipid measurements ( tc and hdl)33 supplemented with a metaanalysis of the effect of aerobic exercise on systolic bp.34 both analyses used similar definitions of aerobic exercise , had similar patient populations , and both had median durations of 12  weeks ( range 23  weeks to 2  years ) .
lastly , we conducted a sensitivity analysis with the general framingham risk score to examine if and how the relative rankings of the interventions would change with this commonly used risk algorithm.35 this framingham risk score closely matches the pooled cohort equations used in the base case , assessing all the same cardiovascular disease events plus cardiac failure .
this study does not constitute human subjects research and thus did not require institutional review board approval .
figures  1a through 1h and 2a through 2h display heatmaps of risk reductions from the 4 interventions for all hypothetical groups of patients , dichotomized by race , sex , smoking status , and treatment for hypertension .
axes represent the covariates  age , systolic bp , tc , and hdl  in the pooled cohort equations . in the heatmaps ,
the colors vary from red ( smallest risk reduction ) to green ( moderate risk reduction ) to blue ( largest risk reduction ) .
differences among the strategies for an individual patient can be observed by comparing the same cell across the 4 different interventions
. risk reduction by intervention for ( a ) white males , not treated for hypertension , nonsmoker ; ( b ) white males , not treated for hypertension , smoker ; ( c ) white males , treated for hypertension , nonsmoker ; ( d ) white males , treated for hypertension , smoker ; ( e ) white females , not treated for hypertension , nonsmoker ; ( f ) white females , not treated for hypertension , smoker ; ( g ) white females , treated for hypertension , nonsmoker ; ( h ) white females , treated for hypertension , smoker .
reductions range from smallest ( red ) to moderate ( green ) to largest ( blue ) .
hdl indicates highdensity lipoprotein cholesterol ; sbp indicates systolic blood pressure . a , risk reduction by intervention for black males , not treated for hypertension , nonsmoker ; ( b ) black males , not treated for hypertension , smoker ; ( c ) black males , treated for hypertension , nonsmoker ; ( d ) black males , treated for hypertension , smoker ; ( e ) black females , not treated for hypertension , nonsmoker ; ( f ) black females , not treated for hypertension , smoker ; ( g ) black females , treated for hypertension , nonsmoker ; ( h ) black females , treated for hypertension , smoker .
reductions range from smallest ( red ) to moderate ( green ) to largest ( blue ) .
hdl indicates highdensity lipoprotein cholesterol ; sbp indicates systolic blood pressure . in general across all profiles displayed in the heatmaps ,
the lowest risk reductions were with group therapy for smoking ( red ) and the highest reductions with yoga and walking exercise ( blue ) .
although the results are heterogeneous across individual patients , the largest reductions on average were seen with yoga .
the average risk reduction for all patients was 2.6% for yoga , compared to 1.9% for walking , 1.5% for mediterranean diet , and 0.4% for smoking cessation counseling . for the highestrisk individuals who may experience the largest absolute reductions ,
walking typically fared second ( max reduction 11.4% ) , followed by mediterranean diet ( max reduction 9.2% ) and then group therapy for smoking if an individual was a current smoker ( max reduction 1.6% ) . for some  white individuals with moderately low tc and high levels of hdlcholesterol and elderly black women 
table  2 shows the risk reduction achieved through the various lifestyle modifications for the risk factor profiles representing 12 hypothetical individual patients .
10year cvd risk ( % ) for different risk factor profiles with and without lifestyle interventions with 95% credible intervals bc indicates basecase ; cvd , cardiovascular disease ; hdlc , highdensity lipoprotein cholesterol ; med , mediterranean ; n / a , not applicable ; sbp , systolic blood pressure ; sc , successful cessation ; tc , total cholesterol . numbers with * represent the most effective cvd risk reduction strategy in the basecase intentiontotreat analysis . numbers with   represent the most effective strategy assuming successful cessation of smoking . for both the bestcase and worsecase scenarios , trends were similar to those found in the base case , with the largest risk reductions achieved with yoga for all 12 profiles .
results of our secondary sensitivity analyses assuming successful smoking cessation are shown in column  sc  in table  2 with optimal strategies indicated with .
if the patient is a smoker and successfully quits , then this lifestyle modification achieves the largest reductions in risk .
tables  3 and 4 show the results from the probabilistic sensitivity analysis based on 1000 random draws for the base case and successful smoking cessation analyses .
the highestranking strategy was yoga in over 88% of the simulations in the base case , with walking being the second most frequent optimal strategy . with successful cessation of smoking ( ie , complete smoking cessation ) ,
percentage of simulations that the strategy was ranked first ( % ) in the base case , by racespecific risk profile n / a indicates not applicable .
percentage of simulations that the strategy was ranked first ( % ) in the secondary analysis assuming successful cessation of smoking , by racespecific risk profile figure  s1 shows the comparison of the basecase estimates and alternative estimates for each hypothetical profile .
as shown in the figure , the optimal strategy for each profile does not change , nor do the rankings among the interventions .
in fact , some absolute risk reductions are lower with the alternate estimates compared to the base case .
when the framingham algorithm is used ( see figure  s2 ) , the relative rankings among the interventions also remain the same .
the absolute 10year risk using the framingham risk equations is higher in some cases , as the framingham risk score includes both populations in a single score and also predicts risk of heart failure in addition to the outcomes predicted in the pooled cohort equations .
in general across all profiles displayed in the heatmaps , the lowest risk reductions were with group therapy for smoking ( red ) and the highest reductions with yoga and walking exercise ( blue ) .
although the results are heterogeneous across individual patients , the largest reductions on average were seen with yoga .
the average risk reduction for all patients was 2.6% for yoga , compared to 1.9% for walking , 1.5% for mediterranean diet , and 0.4% for smoking cessation counseling . for the highestrisk individuals who may experience the largest absolute reductions , the maximum reduction achieved with yoga was 16.7% .
walking typically fared second ( max reduction 11.4% ) , followed by mediterranean diet ( max reduction 9.2% ) and then group therapy for smoking if an individual was a current smoker ( max reduction 1.6% ) .
for some  white individuals with moderately low tc and high levels of hdlcholesterol and elderly black women 
table  2 shows the risk reduction achieved through the various lifestyle modifications for the risk factor profiles representing 12 hypothetical individual patients .
10year cvd risk ( % ) for different risk factor profiles with and without lifestyle interventions with 95% credible intervals bc indicates basecase ; cvd , cardiovascular disease ; hdlc , highdensity lipoprotein cholesterol ; med , mediterranean ; n / a , not applicable ; sbp , systolic blood pressure ; sc , successful cessation ; tc , total cholesterol . numbers with * represent the most effective cvd risk reduction strategy in the basecase intentiontotreat analysis .
for both the bestcase and worsecase scenarios , trends were similar to those found in the base case , with the largest risk reductions achieved with yoga for all 12 profiles .
results of our secondary sensitivity analyses assuming successful smoking cessation are shown in column  sc  in table  2 with optimal strategies indicated with .
if the patient is a smoker and successfully quits , then this lifestyle modification achieves the largest reductions in risk .
tables  3 and 4 show the results from the probabilistic sensitivity analysis based on 1000 random draws for the base case and successful smoking cessation analyses .
the highestranking strategy was yoga in over 88% of the simulations in the base case , with walking being the second most frequent optimal strategy . with successful cessation of smoking ( ie , complete smoking cessation ) ,
percentage of simulations that the strategy was ranked first ( % ) in the base case , by racespecific risk profile n / a indicates not applicable .
percentage of simulations that the strategy was ranked first ( % ) in the secondary analysis assuming successful cessation of smoking , by racespecific risk profile
figure  s1 shows the comparison of the basecase estimates and alternative estimates for each hypothetical profile .
as shown in the figure , the optimal strategy for each profile does not change , nor do the rankings among the interventions .
in fact , some absolute risk reductions are lower with the alternate estimates compared to the base case .
when the framingham algorithm is used ( see figure  s2 ) , the relative rankings among the interventions also remain the same . the absolute 10year risk using the framingham risk equations is higher in some cases , as the framingham risk score includes both populations in a single score and also predicts risk of heart failure in addition to the outcomes predicted in the pooled cohort equations .
we assessed the comparative effectiveness of 4 lifestyle management strategies on 10year cvd risk . in the basecase intentiontotreat analysis
, yoga resulted in the largest risk reduction out of the 4 interventions for nonsmokers and smokers alike . walking as a form of aerobic exercise generally placed after yoga , followed by mediterranean diet . in both the deterministic and probabilistic sensitivity analyses , the same trend in the base case held for our 12 hypothetical patients ( yoga , walking , diet , and group therapy for smoking ) . in the basecase analysis ,
the impact of group therapy for stopping smoking on cardiac health was based on an intentiontotreat calculation , in order to be consistent across the 4 interventions ; thus , only those who successfully quit realize reduced cvd risk . with the probability of successfully quitting being low , the benefits were correspondingly minimal . however , in a secondary analysis we demonstrated that for a current smoker , successful smoking cessation is the most effective of all lifestyle changes .
our results are consistent with others who assessed the effect of cvd prevention . a simulation model by kahn et  al that focuses specifically on cvd interventions showed that the greatest benefits in life expectancy would be achieved through the treatment of hypertension and elevated lowdensity lipoproteinc.36 on a population level , the large , multinational interheart case 
control study found that abnormal lipids was the most important risk factor with respect to population attributable risk for acute myocardial infarction across all age groups.37 accordingly , our study shows that yoga , which provides the largest improvements in systolic bp and lipid levels , also provides the largest reduction in cvd risk . in the analysis by taksler et 
al , among us preventive services task force recommendations for preventive care , tobacco cessation , diabetes control , weight loss , and bp reduction were consistently ranked among the top guidelines for increases in life expectancy across patients.18 tobacco cessation ranked high in their study as well as the interheart study , which demonstrated that the odds of an acute myocardial infarction almost tripled for a current smoker versus a nonsmoker.37 our finding that successful smoking cessation is the most effective of all lifestyle changes for current smokers is consistent with these studies . while risk assessment was based on recently published pooled cohort equations that allowed uptodate , accurate , racespecific estimation , there are several limitations of this study .
first , we focused changes in risk factors translated into cvd risk in the nondiabetic population .
although we evaluate diverse sets of patient characteristics , results may not generalize to different populations .
the acc / aha report did not include any measures of adiposity in their algorithm , although the lifestyle interventions studied could impact those measures , further altering cvd risk .
second , insufficient data precluded subgroupspecific pooled estimates of interventions on risk factors as well as any projections of future changes in disease outcomes .
future longterm studies could determine the effects of these interventions on cvd outcomes rather than on risk factors and model additional changes in risk factors and other disease processes using more complex microsimulation models .
third , we chose to use the new acc / aha pooled cohort equations to calculate 10year atherosclerotic cvd risk defined as a composite end point of first occurrence of nonfatal myocardial infarction , death from coronary heart disease , or fatal or nonfatal stroke .
other risk calculators  the framingham heart study coronary heart disease calculator,38 the euroscore,39 and the adult treatment panel  ( atp ) iii40 to name a few  are also used to calculate risk scores for cardiovascular outcomes . while criticism over use of the acc / aha risk equations exists,41 , 42 , 43 our model focuses on a change in risk among strategies , so it would not be affected by choice of risk algorithm .
our parameters are based on the best available published data , namely , metaanalyses of controlled clinical trials . for example , data on the effectiveness of yoga on risk factors were obtained from a metaanalysis of rcts of moderate quality and generally small sample size.27 other studies have , however , corroborated the results and found improvements of similar magnitude.25 , 26 additionally , estimates for mediterranean diet were obtained from a metaanalysis of rcts involving overweight and obese patients , who in some cases have access to additional advice and counseling29 ; the actual benefit may differ based on individual characteristics and participation . as with all models ,
estimation is subject to the data available and results should be interpreted recognizing the limitations of the metaanalyses used .
next , we recognize that patients have preferences for certain approaches and doctors may have prescribing preferences , including taking and prescribing medication , respectively . we have presented a rank order of strategies that do not include taking any pills or medication . as such , nonadherence with lifestyle change and other health behaviors , including pilltaking ,
our calculations are based on intentiontotreat rates from the clinical trials , which incorporate nonadherence .
personalized medicine may , in fact , aid adherence to lifestyle changes.44 nonetheless , realworld adherence and associated risk improvement may differ from what we have presented .
it is possible that patients may try multiple interventions simultaneously ( eg , smoking cessation therapy and walking ) and that these interventions would reduce cvd risk in a synergistic fashion .
furthermore , walking was chosen as a comparator intervention in this study as it is considered lowimpact and widely accessible .
more vigorous exercise such as jogging may confer even greater cardiovascular and fitness benefits and may change the rank order of strategies .
studies on personalized medicine for preventive care have found that some of the greatest gains in life expectancy come from services targeting cvd.18 , 19 , 36 accurate , personalized recommendations regarding cardiovascular risk reduction can lead to better health and economic outcomes , reducing healthcare and patient time costs , and increasing quality of life and productivity .
use of risk estimators has been shown to have significant , modest effects on prescribing preventive therapies and on risk factor levels.9 , 35 , 45 , 46 
  based on this model and available data , yoga to reduce cvd risk can be considered by physicians and patients as a preventivecare strategy alongside smoking cessation , diet , and exercise . the potential benefit of yoga in lowering lipids , bp , and body weight warrant further exploration of its health effects and dose  response relationship in future clinical trials .
the information presented in this study has the potential to help inform clinicians and patients on which activities could be more effective at reducing longterm cvd risk based on their personal risk and preferences for interventions .
these findings can be included in a decision aid used in the context of shared decisionmaking between clinician and patient .
additionally , the lifestyle management strategies analyzed in this study require little to no equipment , no expensive medication , and can be performed at home or at the convenience of patients .
further research can examine whether these lowcost strategies are costeffective compared to other cvd management strategies and demonstrate whether tailored therapies do improve patient adherence and health outcomes .
while risk assessment was based on recently published pooled cohort equations that allowed uptodate , accurate , racespecific estimation , there are several limitations of this study .
first , we focused changes in risk factors translated into cvd risk in the nondiabetic population .
although we evaluate diverse sets of patient characteristics , results may not generalize to different populations .
the acc / aha report did not include any measures of adiposity in their algorithm , although the lifestyle interventions studied could impact those measures , further altering cvd risk .
second , insufficient data precluded subgroupspecific pooled estimates of interventions on risk factors as well as any projections of future changes in disease outcomes .
future longterm studies could determine the effects of these interventions on cvd outcomes rather than on risk factors and model additional changes in risk factors and other disease processes using more complex microsimulation models .
third , we chose to use the new acc / aha pooled cohort equations to calculate 10year atherosclerotic cvd risk defined as a composite end point of first occurrence of nonfatal myocardial infarction , death from coronary heart disease , or fatal or nonfatal stroke .
other risk calculators  the framingham heart study coronary heart disease calculator,38 the euroscore,39 and the adult treatment panel  ( atp ) iii40 to name a few  are also used to calculate risk scores for cardiovascular outcomes . while criticism over use of the acc / aha risk equations exists,41 , 42 , 43 our model focuses on a change in risk among strategies , so it would not be affected by choice of risk algorithm .
our parameters are based on the best available published data , namely , metaanalyses of controlled clinical trials .
for example , data on the effectiveness of yoga on risk factors were obtained from a metaanalysis of rcts of moderate quality and generally small sample size.27 other studies have , however , corroborated the results and found improvements of similar magnitude.25 , 26 additionally , estimates for mediterranean diet were obtained from a metaanalysis of rcts involving overweight and obese patients , who in some cases have access to additional advice and counseling29 ; the actual benefit may differ based on individual characteristics and participation . as with all models ,
estimation is subject to the data available and results should be interpreted recognizing the limitations of the metaanalyses used .
next , we recognize that patients have preferences for certain approaches and doctors may have prescribing preferences , including taking and prescribing medication , respectively . we have presented a rank order of strategies that do not include taking any pills or medication . as such , nonadherence with lifestyle change and other health behaviors , including pilltaking ,
our calculations are based on intentiontotreat rates from the clinical trials , which incorporate nonadherence .
personalized medicine may , in fact , aid adherence to lifestyle changes.44 nonetheless , realworld adherence and associated risk improvement may differ from what we have presented .
it is possible that patients may try multiple interventions simultaneously ( eg , smoking cessation therapy and walking ) and that these interventions would reduce cvd risk in a synergistic fashion .
furthermore , walking was chosen as a comparator intervention in this study as it is considered lowimpact and widely accessible .
more vigorous exercise such as jogging may confer even greater cardiovascular and fitness benefits and may change the rank order of strategies .
studies on personalized medicine for preventive care have found that some of the greatest gains in life expectancy come from services targeting cvd.18 , 19 , 36 accurate , personalized recommendations regarding cardiovascular risk reduction can lead to better health and economic outcomes , reducing healthcare and patient time costs , and increasing quality of life and productivity .
use of risk estimators has been shown to have significant , modest effects on prescribing preventive therapies and on risk factor levels.9 , 35 , 45 , 46 
  based on this model and available data , yoga to reduce cvd risk can be considered by physicians and patients as a preventivecare strategy alongside smoking cessation , diet , and exercise . the potential benefit of yoga in lowering lipids , bp , and body weight warrant further exploration of its health effects and dose  response relationship in future clinical trials .
the information presented in this study has the potential to help inform clinicians and patients on which activities could be more effective at reducing longterm cvd risk based on their personal risk and preferences for interventions . these findings can be included in a decision aid used in the context of shared decisionmaking between clinician and patient .
additionally , the lifestyle management strategies analyzed in this study require little to no equipment , no expensive medication , and can be performed at home or at the convenience of patients .
further research can examine whether these lowcost strategies are costeffective compared to other cvd management strategies and demonstrate whether tailored therapies do improve patient adherence and health outcomes .
this study reports the comparative effectiveness of several forms of lifestyle modifications , from the commonly advised smoking cessation , diet , and exercise strategies to a more contemporary form of exercise , yoga , on 10year cvd risk . for a current smoker
smoking cessation is , however , difficult to achieve and group therapy for stopping smoking has only a small probability of success . from an intentiontotreat perspective ,
if yoga is as effective as reported in currently published metaanalyses , then yoga could be considered among the strongest lifestyle interventions for reducing cvd risk . with more individualized estimates and recommendations
, providers can better predict cvd risk and manage prevention more effectively , particularly among different ethnic groups who may be at higher risk of cvd and can benefit from early cvd risk discussion.47 patients themselves can become more informed and involved in their own health and lifestyle choices .
tailored recommendations for managing cvd risk have the potential to inform practice , improve care , and reduce healthcare expenditures .
our analysis supports the findings of several largescale studies that cvd risk can be modified by lifestyle changes .
additional research can take into account adherence to management strategies as well as investigate the effects of multiple interventions .
as the interventions we have included are relatively low cost , a priority for future research is to analyze the effect of these management strategies on quality of life , costs ( ie , affordability ) , and costeffectiveness ( ie , value for money ) .
support for this research was provided by a grant from the harvard global health institute , cambridge , ma .
alternative estimates for effectiveness of lifestyle interventions on reducing cvd risk through changes in risk factors 
 figure  s1 .
sensitivity analysis with alternate metaanalyses , shown by hypothetical profile ( yaxes represent 10year cvd risk , xaxes represent interventions ) .
note that the optimal intervention with the lowest 10year risk with the alternate estimates ( red and purple bars ) remains the same as that in the base case ( blue and green bars ) for each profile and race . 

sensitivity analysis with framingham risk equations , shown by hypothetical profile ( yaxes represent 10year cvd risk , xaxes represent interventions ) .
note that the optimal intervention with the lowest 10year risk with the framingham equations ( red bars ) remains the same as that in the base case ( blue and green bars ) for each profile .
 backgroundevidence shows that healthy diet , exercise , smoking interventions , and stress reduction reduce cardiovascular disease risk . 
 we aimed to compare the effectiveness of these lifestyle interventions for individual risk profiles and determine their rank order in reducing 10year cardiovascular disease risk.methods and resultswe computed risks using the american college of cardiology / american heart association pooled cohort equations for a variety of individual profiles . 
 using published literature on risk factor reductions through diverse lifestyle interventions  group therapy for stopping smoking , mediterranean diet , aerobic exercise ( walking ) , and yoga  we calculated the risk reduction through each of these interventions to determine the strategy associated with the maximum benefit for each profile . 
 sensitivity analyses were conducted to test the robustness of the results . in the basecase analysis , 
 yoga was associated with the largest 10year cardiovascular disease risk reductions ( maximum absolute reduction 16.7% for the highestrisk individuals ) . walking generally ranked second ( max 11.4% ) , followed by mediterranean diet ( max 9.2% ) , and group therapy for smoking ( max 1.6% ) . 
 if the individual was a current smoker and successfully quit smoking ( ie , achieved complete smoking cessation ) , then stopping smoking yielded the largest reduction . 
 probabilistic and 1way sensitivity analysis confirmed the demonstrated trend.conclusionsthis study reports the comparative effectiveness of several forms of lifestyle modifications and found smoking cessation and yoga to be the most effective forms of cardiovascular disease prevention . 
 future research should focus on patient adherence to personalized therapies , costeffectiveness of these strategies , and the potential for enhanced benefit when  interventions are performed simultaneously rather than as single measures . 
Introduction
Methods
Interventions
Statistical Analysis
Sensitivity Analysis
Results
Base Case
Sensitivity Analysis
Probabilistic Sensitivity Analysis
Alternative Estimates
Discussion
Limitations
Clinical Implications and Policy Relevance
Conclusions
Sources of Funding
Disclosures
Supporting information