this retrospective study used data from the medical expenditure panel  survey ( meps ) , 2012 .
the meps , led by the agency for healthcare research and quality , is a set of largescale , national surveys about individuals and families and their medical providers and employers .
the household component ( hc ) of the meps collects data about health services used , their frequency and cost , charges , source of payment , income , employment , as well as ample data on insurance used by and available to us workers.23 the meps respondents are enrolled for 2  years of data collection , with a new panel beginning each year .
the sampling frame for the mepshc is drawn from respondents to the national health interview survey , and the design of the mepshc survey includes sampling weights , stratification , and clustering . the meps sampling weights incorporate adjustment for the complex sample design and reflect survey nonresponse and population totals from the current population survey.23 
  of all files in the hc of the survey , we used the fullyear consolidated data files and medical conditions files .
the fullyear consolidated data files contain most demographics at a person level , including information on resource utilization and costs , whereas the medical conditions files include each diagnosis a person has , which , after being transcribed verbatim at each survey , are translated into international classification of diseases , ninth edition , clinical modification ( icd9cm ) by professional coders .
because meps is comprised of publicly available , deidentified data files , this study was exempt of institutional review board approval.24 
  the initial sample consisted of 38  974 surveyed individuals .
we limited our study population to noninstitutionalized us adults 18  years of age ( excluding 11  154 individuals < 18  years of age ) .
individuals with body mass index ( bmi ) < 18.5  kg / m ( 532 ) were further excluded from the sample given that they often represent a sicker patient population . because of the subject matter of this study , individuals who , for the entire survey period , were either completely unable to walk or unable to walk up 10 steps ( 130 ) or pregnant at any point during the survey ( 919 ) were also excluded from all analyses .
individuals in the study sample were classified into pa based on their answers to the question ,  do you currently spend half hour or more in moderate to vigorous physical activity , at least five times a week ? 
the 2010 meps glossary ( valid for all subsequent surveys ) states ,  moderate physical activity causes only light sweating or a slight or moderate increase in breathing or heart rate and would include activities such as fast walking , raking leaves , mowing the lawn , or heavy cleaning .
vigorous physical activity causes heavy sweating or large increases in breathing or heart rate and would include activities such as running , race walking , lap swimming , aerobic classes , or fast bicycling.25 participants who answered  yes  to this question were categorized as 
an important point to be made is that most respondents who engaged in this level of pa closely resemble what most researchers refer to as  exercise .
 consequently , our categorization of nonoptimal pa individuals does not necessarily mean  sedentary ,  but that the 2008 physical activity guidelines advisory committee report recommendations for pa were not met.4 
  individuals in the study sample that had a diagnosis of coronary artery disease , stroke , heart failure , dysrhythmias , and/or peripheral artery disease ( ascertained by icd9cm codes : 410 , 413 , 414 , 433437 , 427 , 428 , 440 , 443 , and 447 , respectively ) were classified as having diagnosed cvd .
crfs were ascertained using selfreported questionnaires in the mepshc survey , where individuals with presence of 1 or more of : hypertension , diabetes mellitus , hypercholesterolemia , smoking , and/or obesity ( bmi 30  kg / m , a constructed variable using selfreported weight and height ) were included .
based on the presence of these individual risk factors , survey participants were profiled as  poor  ( 3 cardiovascular risk factors ) ,  average  ( 2 cardiovascular risk factors ) , or  optimal 
data for the calculation of this variable included expenditures from all payer groups and outofpocket spending , including information from hospitalizations , prescribed medications , outpatient visits ( hospital outpatient visits and officebased visits ) , emergency department ( ed ) visits , and other expenditures ( dental visits , vision aid , home health care , and other medical supplies ) . in a similar fashion
, resource utilization analysis assessed the total number of outpatient and ed visits , number of hospitalizations , and number of prescription medications purchases / refills each surveyed individual incurred .
other variables included in the study were age , sex , health status , family income , race / ethnicity , employment , metropolitan statistical area , insurance type , education , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
categorical variables were classified as follows : 5 categories were used for age ( 1839 , 4054 , 5564 , 6574 , and 75 ) ; 3 categories for family income ( poor / near poor [ < 125% of the 2012 federal poverty level ] , low / middle income [ 125% to < 400% federal poverty level ] , and high income [ 400% federal poverty level ] ) ; 5 categories for race / ethnicity ( nonhispanic white , nonhispanic black , nonhispanic asian , nonhispanic other , and hispanic ) ; 3 categories for insurance type ( private , public only , and uninsured ) ; 5 categories for education ( less than high school , high school diploma , some college , college [ bachelor 's degree ] and masters , doctorate , or professional ) ; 4 categories for geographical region ( northeast , midwest , south , and west ) ; and 3 categories for modified charlson comorbidity index ( 0 , 1 , and 2 ) .
for comparison of demographic characteristics in our sample , chisquare tests were performed.26 because of the rightskewedness of expenditures data ( ie , most expenditures are seen in only a small proportion of the population ) , twopart models were utilized to study expenditures.27 twopart models are often used to model health care expenditures , and are the product of : ( 1 ) the probability that any given individual had any expenditures and ( 2 ) their mean expenditures.28 the first part of the model consists on a probabilistic regression model ( probit ) , which estimates the probability of zero versus positive expenditures .
contingent upon having a positive annual health care expenditure , a generalized linear model ( glm ) with gamma distribution and a logarithmiclink function estimates the average expenditure per capita28 , 29 ; we determined the distribution of the glm using the modified park test.30 for resource utilization , unadjusted and adjusted logistic regression models were utilized .
unadjusted means and proportions were calculated , adjusting for the survey design and sampling weight .
for multivariate analyses , variable selection for inclusion into the model was determined using a combination of the akaike information criterion and their relevance toward cost analysis .
all analyses were carried out using stata software ( version 13.1 ; statacorp lp , college station , tx ) .
total and marginal expenditures were estimated using the  margins  command after the twopart models.28 all analyses took into consideration the meps complex survey design .
this retrospective study used data from the medical expenditure panel  survey ( meps ) , 2012 .
the meps , led by the agency for healthcare research and quality , is a set of largescale , national surveys about individuals and families and their medical providers and employers .
the household component ( hc ) of the meps collects data about health services used , their frequency and cost , charges , source of payment , income , employment , as well as ample data on insurance used by and available to us workers.23 the meps respondents are enrolled for 2  years of data collection , with a new panel beginning each year .
the sampling frame for the mepshc is drawn from respondents to the national health interview survey , and the design of the mepshc survey includes sampling weights , stratification , and clustering . the meps sampling weights incorporate adjustment for the complex sample design and reflect survey nonresponse and population totals from the current population survey.23 
  of all files in the hc of the survey , we used the fullyear consolidated data files and medical conditions files .
the fullyear consolidated data files contain most demographics at a person level , including information on resource utilization and costs , whereas the medical conditions files include each diagnosis a person has , which , after being transcribed verbatim at each survey , are translated into international classification of diseases , ninth edition , clinical modification ( icd9cm ) by professional coders .
because meps is comprised of publicly available , deidentified data files , this study was exempt of institutional review board approval.24 

we limited our study population to noninstitutionalized us adults 18  years of age ( excluding 11  154 individuals < 18  years of age ) .
individuals with body mass index ( bmi ) < 18.5  kg / m ( 532 ) were further excluded from the sample given that they often represent a sicker patient population . because of the subject matter of this study , individuals who , for the entire survey period , were either completely unable to walk or unable to walk up 10 steps ( 130 ) or pregnant at any point during the survey ( 919 ) were also excluded from all analyses .
individuals in the study sample were classified into pa based on their answers to the question ,  do you currently spend half hour or more in moderate to vigorous physical activity , at least five times a week ? 
the 2010 meps glossary ( valid for all subsequent surveys ) states ,  moderate physical activity causes only light sweating or a slight or moderate increase in breathing or heart rate and would include activities such as fast walking , raking leaves , mowing the lawn , or heavy cleaning .
vigorous physical activity causes heavy sweating or large increases in breathing or heart rate and would include activities such as running , race walking , lap swimming , aerobic classes , or fast bicycling.25 participants who answered  yes  to this question were categorized as 
an important point to be made is that most respondents who engaged in this level of pa closely resemble what most researchers refer to as  exercise . 
consequently , our categorization of nonoptimal pa individuals does not necessarily mean  sedentary ,  but that the 2008 physical activity guidelines advisory committee report recommendations for pa were not met.4 
  individuals in the study sample that had a diagnosis of coronary artery disease , stroke , heart failure , dysrhythmias , and/or peripheral artery disease ( ascertained by icd9cm codes : 410 , 413 , 414 , 433437 , 427 , 428 , 440 , 443 , and 447 , respectively ) were classified as having diagnosed cvd .
crfs were ascertained using selfreported questionnaires in the mepshc survey , where individuals with presence of 1 or more of : hypertension , diabetes mellitus , hypercholesterolemia , smoking , and/or obesity ( bmi 30  kg / m , a constructed variable using selfreported weight and height ) were included . based on the presence of these individual risk factors ,
survey participants were profiled as  poor  ( 3 cardiovascular risk factors ) ,  average  ( 2 cardiovascular risk factors ) , or  optimal  ( 01 cardiovascular risk factors ) .
data for the calculation of this variable included expenditures from all payer groups and outofpocket spending , including information from hospitalizations , prescribed medications , outpatient visits ( hospital outpatient visits and officebased visits ) , emergency department ( ed ) visits , and other expenditures ( dental visits , vision aid , home health care , and other medical supplies ) . in a similar fashion
, resource utilization analysis assessed the total number of outpatient and ed visits , number of hospitalizations , and number of prescription medications purchases / refills each surveyed individual incurred .
other variables included in the study were age , sex , health status , family income , race / ethnicity , employment , metropolitan statistical area , insurance type , education , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
categorical variables were classified as follows : 5 categories were used for age ( 1839 , 4054 , 5564 , 6574 , and 75 ) ; 3 categories for family income ( poor / near poor [ < 125% of the 2012 federal poverty level ] , low / middle income [ 125% to < 400% federal poverty level ] , and high income [ 400% federal poverty level ] ) ; 5 categories for race / ethnicity ( nonhispanic white , nonhispanic black , nonhispanic asian , nonhispanic other , and hispanic ) ; 3 categories for insurance type ( private , public only , and uninsured ) ; 5 categories for education ( less than high school , high school diploma , some college , college [ bachelor 's degree ] and masters , doctorate , or professional ) ; 4 categories for geographical region ( northeast , midwest , south , and west ) ; and 3 categories for modified charlson comorbidity index ( 0 , 1 , and 2 ) .
individuals in the study sample were classified into pa based on their answers to the question ,  do you currently spend half hour or more in moderate to vigorous physical activity , at least five times a week ? 
the 2010 meps glossary ( valid for all subsequent surveys ) states ,  moderate physical activity causes only light sweating or a slight or moderate increase in breathing or heart rate and would include activities such as fast walking , raking leaves , mowing the lawn , or heavy cleaning .
vigorous physical activity causes heavy sweating or large increases in breathing or heart rate and would include activities such as running , race walking , lap swimming , aerobic classes , or fast bicycling.25 participants who answered  yes  to this question were categorized as 
an important point to be made is that most respondents who engaged in this level of pa closely resemble what most researchers refer to as  exercise . 
consequently , our categorization of nonoptimal pa individuals does not necessarily mean  sedentary ,  but that the 2008 physical activity guidelines advisory committee report recommendations for pa were not met.4 

individuals in the study sample that had a diagnosis of coronary artery disease , stroke , heart failure , dysrhythmias , and/or peripheral artery disease ( ascertained by icd9cm codes : 410 , 413 , 414 , 433437 , 427 , 428 , 440 , 443 , and 447 , respectively ) were classified as having diagnosed cvd .
crfs were ascertained using selfreported questionnaires in the mepshc survey , where individuals with presence of 1 or more of : hypertension , diabetes mellitus , hypercholesterolemia , smoking , and/or obesity ( bmi 30  kg / m , a constructed variable using selfreported weight and height ) were included .
based on the presence of these individual risk factors , survey participants were profiled as  poor  ( 3 cardiovascular risk factors ) ,  average  ( 2 cardiovascular risk factors ) , or  optimal  ( 01 cardiovascular risk factors ) .
data for the calculation of this variable included expenditures from all payer groups and outofpocket spending , including information from hospitalizations , prescribed medications , outpatient visits ( hospital outpatient visits and officebased visits ) , emergency department ( ed ) visits , and other expenditures ( dental visits , vision aid , home health care , and other medical supplies ) . in a similar fashion ,
resource utilization analysis assessed the total number of outpatient and ed visits , number of hospitalizations , and number of prescription medications purchases / refills each surveyed individual incurred .
other variables included in the study were age , sex , health status , family income , race / ethnicity , employment , metropolitan statistical area , insurance type , education , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
categorical variables were classified as follows : 5 categories were used for age ( 1839 , 4054 , 5564 , 6574 , and 75 ) ; 3 categories for family income ( poor / near poor [ < 125% of the 2012 federal poverty level ] , low / middle income [ 125% to < 400% federal poverty level ] , and high income [ 400% federal poverty level ] ) ; 5 categories for race / ethnicity ( nonhispanic white , nonhispanic black , nonhispanic asian , nonhispanic other , and hispanic ) ; 3 categories for insurance type ( private , public only , and uninsured ) ; 5 categories for education ( less than high school , high school diploma , some college , college [ bachelor 's degree ] and masters , doctorate , or professional ) ; 4 categories for geographical region ( northeast , midwest , south , and west ) ; and 3 categories for modified charlson comorbidity index ( 0 , 1 , and 2 ) .
for comparison of demographic characteristics in our sample , chisquare tests were performed.26 because of the rightskewedness of expenditures data ( ie , most expenditures are seen in only a small proportion of the population ) , twopart models were utilized to study expenditures.27 twopart models are often used to model health care expenditures , and are the product of : ( 1 ) the probability that any given individual had any expenditures and ( 2 ) their mean expenditures.28 the first part of the model consists on a probabilistic regression model ( probit ) , which estimates the probability of zero versus positive expenditures .
contingent upon having a positive annual health care expenditure , a generalized linear model ( glm ) with gamma distribution and a logarithmiclink function estimates the average expenditure per capita28 , 29 ; we determined the distribution of the glm using the modified park test.30 for resource utilization , unadjusted and adjusted logistic regression models were utilized .
unadjusted means and proportions were calculated , adjusting for the survey design and sampling weight . for multivariate analyses , variable selection for inclusion into the model
was determined using a combination of the akaike information criterion and their relevance toward cost analysis .
all analyses were carried out using stata software ( version 13.1 ; statacorp lp , college station , tx ) .
total and marginal expenditures were estimated using the  margins  command after the twopart models.28 all analyses took into consideration the meps complex survey design .
the final study population consisted of 26  239 participants 18  years of age ( 47.617  years ; 51.5% female ) , which translates to 223.7  million us adults ; demographic information is presented in table  1 .
overall , 1896 ( 9% ) had a cvd diagnosis , representing 19.4  million of the noninstitutionalized adult population in the united states .
fortynine percent of those without cvd and 32% of those with cvd reported engaging in moderatevigorous pa . irrespective of cvd status , participants with optimal pa were less likely to have underlying cvd risk factors , as well as reporting better health status , higher socioeconomic and education strata , and lower prevalence of comorbid conditions ( table  1 ) .
sample characteristics from the medical expenditure panel  survey 2012 , stratified by physical activity and cvd status crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ged , general education development ; pa , physical activity .
cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
univariate and multivariate models estimating average per capita health care expenditures are presented in table  2 .
presence of cvd was independently associated with higher health care expenditures when compared to individuals without cvd . despite this , presence of optimal pa
was associated with lower health care expenditures across cvd status and crf spectrum . among those without cvd , those with optimal crf and pa had a mean annual expenditure of $ 2328 ( 95% ci , 1932 , 2726 ) , compared to $ 5475 ( 95% ci , 4668 , 6283 ) of those with poor crf and pa ( table  2 , model 1 ) . after adjusting for other covariates ( demographics , socioeconomic status , insurance type , and comorbidities ) , these marked differences remained ( table  2 , model 3 ) .
moreover , when comparing the marginal expenditures of optimal pa versus nonoptimal pa , the impact of pa on health care expenditures was highest for those with established cvd , followed by participants without cvd but underlying poor crf profile ( table  2 ) .
average expenditures of physical activity per capita , by cvd status and crf category cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
model 2 : adjusted for age , sex , family income , and race / ethnicity .
model 3 : adjusted for variables in model 2 plus insurance type , geographical region , and modified charlson comorbidity index ( charlson comorbidity index without cardiovascular components ) .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; pa , physical activity . weighted and adjusted estimates by specific expenditure category ( health care utilization expenditures : hospitalizations , prescription medications , ed visits , outpatient visits , and other ) according to crf profile and cvd status are presented in figure  1 . among those with cvd
, the highest expenditures pertained to hospitalizations ( with expenditures of $ 5644 and $ 4233 for nonoptimal pa and optimal pa , respectively ) , followed by prescription medications and outpatient visits . among those without cvd ,
the major part of costs among noncvd individuals was attributed to outpatient visits , with those with poor crf spending an annual average of $ 2019 and $ 1918 ( nonoptimal pa and optimal pa , respectively ) , compared to $ 1319 and $ 1215 of those with optimal crf .
the highest differences were noted in prescription medications , with those with a poor crf profile and nonoptimal pa spending $ 400 more than their optimal pa counterparts .
the lowest expenditures were noted among those without cvd , with optimal crf profile and optimal pa , with an average of $ 810 on hospitalizations , $ 587 on prescription medications , and $ 1215 on outpatient visits ( figure  1 ) .
weighted , adjusted mean health care expenditures by category , among optimal and nonoptimal pa , with and without cvd , further subclassified by crf profile .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ed , emergency department .
other = dental , vision aid , home healthcare , medical devices , others ; pa , physical activity .
cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
table  3 summarizes health care utilization rates across pa , crf , and cvd categories .
overall , optimal pa was associated with significantly less use of health care resources . among surveyed participants with cvd , those with optimal versus nonoptimal pa were less likely to have an ed visit ( 24% vs 31% ) or any hospitalization ( 21% vs 27% ) .
the lowest rate of ed visits ( 9.1% ) and hospitalizations ( 2.6% ) was noted among those reporting optimal pa without a cvd diagnosis and presence of optimal crf profile . after adjusting for key covariates , among individuals without cvd , those with optimal pa had lower odds of being hospitalized ( optimal crf : odds ratio [ or ] , 0.75 ; 95% ci [ 0.60 , 0.93 ] ; average crf : or , 0.70 ; 95% ci [ 0.51 , 0.95 ] ; poor crf : or , 0.65 ; 95% ci [ 0.45 , 0.93 ] ) , lower odds of having an outpatient visit ( optimal crf : or , 0.88 ; 95% ci [ 0.80 , 0.97 ] , average crf : or , 0.67 ; 95% ci [ 0.55 , 0.82 ] , poor crf : or , 0.69 ; 95% ci [ 0.51 , 0.94 ] ) , and lower odds of purchasing / refilling a prescription medication ( optimal crf : or , 0.83 ; 95% ci [ 0.75 , 0.92 ] , average crf : or , 0.73 ; 95% ci [ 0.60 , 0.89 ] , poor crf : or , 0.65 ; 95% ci [ 0.48 , 0.88 ] ) .
( at or above the 75th percentile for usage at each particular healthcare resource ; figure  2a and 2b ) .
health care resource utilization physical activity categories , by cvd status and crf category cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ed , emergency department ; pa , physical activity .
crf indicates cardiovascular risk factor profile ; ed , emergency department ; pa , physical activity .
color scheme : blue bars , univariate model ; red bars ; multivariate model . cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
b , odds ratios for  high  ( 75% percentile ) healthcare utilization of optimal pa versus nonoptimal pa .
color scheme : blue bars , univariate model ; red bars , multivariate model . cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
note :  high hospitalizations  equaled 1 hospitalization , given the right shift of utilization .
the final study population consisted of 26  239 participants 18  years of age ( 47.617  years ; 51.5% female ) , which translates to 223.7  million us adults ; demographic information is presented in table  1 .
overall , 1896 ( 9% ) had a cvd diagnosis , representing 19.4  million of the noninstitutionalized adult population in the united states .
fortynine percent of those without cvd and 32% of those with cvd reported engaging in moderatevigorous pa . irrespective of cvd status , participants with optimal pa were less likely to have underlying cvd risk factors , as well as reporting better health status , higher socioeconomic and education strata , and lower prevalence of comorbid conditions ( table  1 ) .
sample characteristics from the medical expenditure panel  survey 2012 , stratified by physical activity and cvd status crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ged , general education development ; pa , physical activity .
cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
univariate and multivariate models estimating average per capita health care expenditures are presented in table  2 .
presence of cvd was independently associated with higher health care expenditures when compared to individuals without cvd . despite this , presence of optimal pa
was associated with lower health care expenditures across cvd status and crf spectrum . among those without cvd , those with optimal crf and pa had a mean annual expenditure of $ 2328 ( 95% ci , 1932 , 2726 ) , compared to $ 5475 ( 95% ci , 4668 , 6283 ) of those with poor crf and pa ( table  2 , model 1 ) . after adjusting for other covariates ( demographics , socioeconomic status , insurance type , and comorbidities ) , these marked differences remained ( table  2 , model 3 ) .
moreover , when comparing the marginal expenditures of optimal pa versus nonoptimal pa , the impact of pa on health care expenditures was highest for those with established cvd , followed by participants without cvd but underlying poor crf profile ( table  2 ) .
average expenditures of physical activity per capita , by cvd status and crf category cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
model 2 : adjusted for age , sex , family income , and race / ethnicity .
model 3 : adjusted for variables in model 2 plus insurance type , geographical region , and modified charlson comorbidity index ( charlson comorbidity index without cardiovascular components ) .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; pa , physical activity . weighted and adjusted estimates by specific expenditure category ( health care utilization expenditures : hospitalizations , prescription medications , ed visits , outpatient visits , and other ) according to crf profile and cvd status are presented in figure  1 . among those with cvd
, the highest expenditures pertained to hospitalizations ( with expenditures of $ 5644 and $ 4233 for nonoptimal pa and optimal pa , respectively ) , followed by prescription medications and outpatient visits . among those without cvd ,
the major part of costs among noncvd individuals was attributed to outpatient visits , with those with poor crf spending an annual average of $ 2019 and $ 1918 ( nonoptimal pa and optimal pa , respectively ) , compared to $ 1319 and $ 1215 of those with optimal crf .
the highest differences were noted in prescription medications , with those with a poor crf profile and nonoptimal pa spending $ 400 more than their optimal pa counterparts .
the lowest expenditures were noted among those without cvd , with optimal crf profile and optimal pa , with an average of $ 810 on hospitalizations , $ 587 on prescription medications , and $ 1215 on outpatient visits ( figure  1 ) .
weighted , adjusted mean health care expenditures by category , among optimal and nonoptimal pa , with and without cvd , further subclassified by crf profile .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ed , emergency department .
other = dental , vision aid , home healthcare , medical devices , others ; pa , physical activity . cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
table  3 summarizes health care utilization rates across pa , crf , and cvd categories .
overall , optimal pa was associated with significantly less use of health care resources . among surveyed participants with cvd , those with optimal versus nonoptimal pa were less likely to have an ed visit ( 24% vs 31% ) or any hospitalization ( 21% vs 27% ) .
the lowest rate of ed visits ( 9.1% ) and hospitalizations ( 2.6% ) was noted among those reporting optimal pa without a cvd diagnosis and presence of optimal crf profile . after adjusting for key covariates , among individuals without cvd , those with optimal pa had lower odds of being hospitalized ( optimal crf : odds ratio [ or ] , 0.75 ; 95% ci [ 0.60 , 0.93 ] ; average crf : or , 0.70 ; 95% ci [ 0.51 , 0.95 ] ; poor crf : or , 0.65 ; 95% ci [ 0.45 , 0.93 ] ) , lower odds of having an outpatient visit ( optimal crf : or , 0.88 ; 95% ci [ 0.80 , 0.97 ] , average crf : or , 0.67 ; 95% ci [ 0.55 , 0.82 ] , poor crf : or , 0.69 ; 95% ci [ 0.51 , 0.94 ] ) , and lower odds of purchasing / refilling a prescription medication ( optimal crf : or , 0.83 ; 95% ci [ 0.75 , 0.92 ] , average crf : or , 0.73 ; 95% ci [ 0.60 , 0.89 ] , poor crf : or , 0.65 ; 95% ci [ 0.48 , 0.88 ] ) .
( at or above the 75th percentile for usage at each particular healthcare resource ; figure  2a and 2b ) .
health care resource utilization physical activity categories , by cvd status and crf category cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
crf indicates cardiovascular risk factor ; cvd , cardiovascular disease ; ed , emergency department ; pa , physical activity .
crf indicates cardiovascular risk factor profile ; ed , emergency department ; pa , physical activity .
color scheme : blue bars , univariate model ; red bars ; multivariate model . cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
b , odds ratios for  high  ( 75% percentile ) healthcare utilization of optimal pa versus nonoptimal pa .
color scheme : blue bars , univariate model ; red bars , multivariate model . cardiovascular risk factors : hypertension , diabetes mellitus , hypercholesterolemia , obesity , and smoking .
noncvd : adjusted for age , sex , family income , race / ethnicity , insurance type , geographical region , and modified charlson comorbidity index ( without cardiovascular components ) .
note :  high hospitalizations  equaled 1 hospitalization , given the right shift of utilization .
past reports have described the relationship between some level of pa and costs13 , 17 , 19 , 20 , 22 , 31 however , none have recently shown this on the basis of cvd,16 with a focus on crfs .
we found lower health care expenditures and resource utilization associated with moderatevigorous pa , regardless of cvd status .
additionally , we found significantly lower health care expenditures of pa individuals by each individual crf profile .
our results are consistent with most past reports and are an important addition to the understanding of health care expenditures relating to pa .
first , this study adds to current literature by providing current national estimates of health care savings among those who engage in moderatevigorous pa .
second , this project not only ascertains the differences between cvd and noncvd populations , but also brings a special focus on noncvd individuals and their crf profile to demonstrate that efforts concerning prevention are still much needed .
third , to the authors knowledge , this is the first attempt to describe both costs and their relating resource utilization estimates regarding pa in a single study .
it has become widely accepted that pa leads to better health outcomes and enhances the prevention of diseases like cvd , diabetes mellitus , cancer , hypertension , obesity , depression , and osteoporosis.32 consequently , many studies have assessed pa 's economic impact .
some have reported no differences in savings related to pa ; chevan et  al . found no shortterm savings related to pa,13 whereas martinson et  al . found no differences for those in their forties , but significant lower shortterm charges in those physically active aged 50 and above , compared to those inactive.15 others have found the opposite to be true .
reported savings of more than 50% when comparing cvd versus noncvd individuals from a nationally representative sample.16 from our results , we estimate that pa results in a 20% reduction in health care expenditure among those with cvd and in a 50% reduction when comparing those with cvd versus noncvd and having poor crf ( the most affected individuals in the noncvd categorization ; table  2 , model 3 ) .
studied modifiable health risks and shortterm health care  related charges , and found that those that engaged in pa had a 4.7% reduction in median charges when compared to those not engaged in pa .
additionally , they reported that in a general population of 60  years of age , without diabetes mellitus , or congenital heart disease , highrisk individuals ( bmi 25  kg / m , current smokers , and no pa ) had a consistent 49% increase in mean annual charges compared to lowrisk individuals.14 likewise , even with a slightly different classification of risk factors , our results show health care savings of more than 50% when comparing poor versus optimal crf , regardless of pa ( table  2 , model 3 ) .
furthermore , our results show relative savings of 16.7% and 13% when comparing optimal pa versus nonoptimal pa among those with poor and optimal crf , respectively .
reported that inactive adults spent $ 920 more than their active equivalents.12 although carlson et  al . did not focus on cvd , our results seem to be in line with their findings , with $723 as the difference between optimal pa versus nonoptimal pa in the multivariate model , in the general population . another recent study analyzed costs related to pa using the cooper center longitudinal study ( ccls).17 they found that expenditures in later life ( 65  years of age ) were greatly diminished if cardiorespiratory fitness was achieved in midlife years ( around 50  years of age )
. our results are not readily comparable with theirs given that they only studied medicare and medicaidcovered individuals .
moreover , individuals from the ccls tend to be healthier than the general us population.33 notwithstanding , their conclusions and ours support the same concept : pa needs to be promoted and/or improved , with data to bolster thirdparty payers into action for a lessdevastating economic future for the nation , especially given that chronic disease prevalence is on the rise.34 , 35 from the health care resource utilization point of view , a canadian study reported an inverse association with pa and utilization among individuals 65  years and older.36 our results too show a tendency of diminishing resource utilization when comparing optimal pa versus nonoptimal pa , especially when comparing cvd and noncvd individuals .
estimated that by the year 2025 , most growth in outpatient visits will be reflected primarily in the field of cardiology , which , along with vascular surgery , will have the highest projected growth in demand.37 consequently , several initiatives have been set up with a goal to help mitigate the increase in noncommunicable diseases , such as the american heart association 's ( aha ) 2020 strategic goals,34 health 2020,38 or the  2525 
strategy.39 unsurprisingly , pa is an integral part of all these efforts , and yet the current prevalence of pa engagement remains below 50% for noncvd and below 35% for cvd individuals , according to our results , including the advent of recent reports favoring interventions among middleaged individuals,40 or even identifying the benefits of simpler strategies , such as walking , in the prevention of cvd.41 
  several limitations of this study should be noted .
first , because of the limited assessment of pa degree in the meps questionnaires , we were only able to dichotomize pa levels as those engaging versus not in selfreported moderatevigorous activity , 30  minutes , 5  days / week ( optimal vs nonoptimal ) .
this limitation precludes the opportunity to robustly describe the interplay of reported pa levels across a wider spectrum with cvd status as well as burden of modifiable crf on the overall health care expenditures .
given that the nonoptimal pa category likely included individuals with minimal pa , it likely resulted in moreconservative estimated differences of medical expenditures between the 2 groups .
furthermore , because of the nature of selfreported pa , there is a risk for misclassification , though likely random . as a result
it is important to note that selfreported pa has been shown to have moderate validity in other national surveys.42 second , as with any observational study , residual confounding is a possibility .
even though efforts were made to prevent this by controlling for most important variables , there is potential for unmeasured characteristics , which could affect our study outcomes .
third , the prevalence of cvd in this study is lower than past national estimates ( 9% vs 36%).1 this is because the diagnosis of cvd in our study did not consider hypertension and instead was included in the spectrum of crf profile assessment .
fourth , because cvd and modifiable crf were selfreported , underestimation of the true national prevalence is likely , as has been previously described , especially with chronic conditions.43 as a result , the estimates in our study are likely to be conservative . because of lack of information on dietary habits , we were not able to account for this important modifiable risk factor .
in addition , because of reliance on selfreported risk factors and lack of information on clinical values ( eg , blood pressure ) , we were unable to estimate the prevalence of the aha defined  ideal cv health status  in our study .
fifth , other analyses have found that meps data tend to underestimate total medical expenditures.44 , 45 , 46 this limitation would lead to a likely underestimation of the actual cost associated with increasing burden of modifiable crf as well estimating savings from primordial prevention strategies . in conclusion , from a national representative population , we provide strong evidence of the association between moderatevigorous pa and significantly lower health care expenditures and resource utilization , irrespective of cvd status and/or crf burden .
these robust estimates for potential health care savings strongly support the aha 's strategic goals for optimizing pa levels as a mean to favorably impact the increasing burden of cvd and associated costs .
dr nasir is on the advisory board for quest diagnostic and is a consultant for regeneron .
 backgroundphysical activity ( pa ) has an established favorable impact on cardiovascular disease ( cvd ) outcomes and quality of life . in this study 
 , we aimed to estimate the economic effect of moderatevigorous pa on medical expenditures and utilization from a nationally representative cohort with and without cvd.methods and resultsthe 2012 medical expenditure panel  survey data were analyzed . 
 our study population was limited to noninstitutionalized us adults 18  years of age . 
 variables of interest included cvd ( coronary artery disease , stroke , heart failure , dysrhythmias , or peripheral artery disease ) and cardiovascular modifiable risk factors ( crfs ; hypertension , diabetes mellitus , hypercholesterolemia , smoking , and/or obesity ) . 
 twopart econometric models were utilized to study cost data ; a generalized linear model with gamma distribution and link log was used to assess expenditures per capita . 
 the final study sample included 26  239 surveyed individuals . 
 overall , 47% engaged in moderatevigorous pa 30  minutes , 5  days / week , translating to 111.5  million adults in the united states stratifying by cvd status ; 32% reported moderatevigorous pa among those with cvd versus 49% without cvd . 
 generally , participants reporting moderatevigorous pa incurred significantly lower health care expenditures and resource utilization , displaying a stepwise lower total annual health care expenditure as moving from cvd to noncvd ( and each crf category).conclusionsmoderatevigorous pa 30  minutes , 5  days / week is associated with significantly lower health care spending and resource utilization among individuals with and without established cvd . 
Methods
Study Design and Sampling
Participants
Study Variables
Physical activity
Cardiovascular disease and average cardiovascular modifiable risk factor profile
Expenditures and resource utilization
Covariates
Statistical Analysis
Results
Population Characteristics
Healthcare Expenditures
Healthcare Resource Utilization
Discussion
Disclosures