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(Chest. 2004;126:608-613.)
© 2004 American College of Chest Physicians

Health-Care Costs and Exercise Capacity*

J. Peter Weiss, MD, MSc; Victor F. Froelicher, MD; Jonathan N. Myers, PhD and Paul A. Heidenreich, MD

* From the Division of Cardiovascular Medicine, Stanford University Medical Center and Veterans Affairs Health Care System, Palo Alto, CA.

Correspondence to: Victor Froelicher, MD, Cardiology Division (111C), VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304; e-mail: vicmd{at}aol.com


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Background: While the beneficial effect of exercise capacity on mortality is well-accepted, its effect on health-care costs remains uncertain. This study investigates the relationship between exercise capacity and health-care costs.

Methods: The Veterans Affairs Health Care System recently implemented a Decision Support System that provides data on patterns of care, patient outcomes, workload, and costs. Total inpatient and outpatient costs were derived from existing administrative and clinical data systems, were adjusted for relative value units, and were expressed in relative cost units. We used univariable and multivariable analyses to evaluate the 1-year total costs in the year following a standard exercise test. Costs were compared with exercise capacity estimated in metabolic equivalents (METs), other test results, and clinical variables for 881 consecutive patients who were referred for clinical reasons for treadmill testing at the Palo Alto Veterans Affairs Health Care System facility between October 1, 1998, and September 30, 2000.

Results: The patients had a mean age of 59 years, 95% were men, and 74% were white. Eight patients (< 1%) died during the year of follow-up. Exercise testing showed an average maximum heart rate of 138 beats/min, 8.2 METs, and a peak Borg scale of 17. In unadjusted analysis, costs were incrementally lower by an average of 5.4% per MET increase (p < 0.001). In a multivariable analysis adjusting for demographic variables, treadmill test performance and results, and clinical history, METs were found to be the most significant predictor of cost (F-statistic, 21.8; p < 0.001).

Conclusion: These findings are consistent with the hypothesis that exercise capacity is inversely associated with health-care costs.

Key Words: exercise capacity • exercise testing • health-care costs


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
The inverse relationship between mortality and exercise capacity has been well-established.12 Similarly, there has been general agreement that the health-care costs associated with physical inactivity are substantial and that improvement in fitness may have a favorable cost-efficacy.3456789101112 While the majority of these data have come from reviews and analytical models, there has not been a direct evaluation of the effect of measured exercise capacity on health-care costs. Based on this information, we performed an analysis of costs along with clinical and exercise test data from the Palo Alto Veterans Affairs Health Care System to evaluate the hypothesis that higher levels of exercise capacity would be associated with lower health-care costs, independent of age and other relevant clinical factors. This analysis was facilitated by the recent implementation of the Decision Support System (DSS) by the US Department of Veterans Affairs (VA), which provides data on patterns of care, patient outcomes, workload, and costs for specific patient care encounters. The demonstration of such an association would support the need for a prospective evaluation of this issue, and would provide further motivation for the prioritization of patient education and treatment directed toward increasing fitness until more definitive data are available.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Population
The population consisted of 881 consecutive patients who were referred for clinical reasons for a treadmill test at the Palo Alto VA Health Care System between October 1, 1998, and September 30, 2000, the period during which complete cost data were available. Patients already enrolled in research protocols were not considered in the analyses. Most of the patients were referred for the diagnosis of possible coronary artery disease or for the prognostic evaluation of known coronary artery disease.

Data Collection
Clinical Data: The exercise laboratory is affiliated with Stanford University and is staffed with rotating house officers and fellows. Tests were directly supervised by these physicians or by nurse practitioners, and all tests were read over by a senior investigator (V.F.F or J.M.). No imaging modality was performed in conjunction with exercise testing. A thorough clinical history, a list of medications, and cardiac risk factors were recorded prospectively at the time of testing using computerized forms.131415

Exercise Testing: Patients underwent symptom-limited treadmill testing using an individualized ramp treadmill protocol.1617 Heart rate targets were not used as an end point or to judge the adequacy of the test. Patients were placed in the supine position immediately after exercise.18 No medications were changed or therapy stopped prior to testing.

Visual ST-segment depression was measured at the J junction and was corrected for preexercise ST-segment depression while standing. The ST slope was measured over the following 60 ms and was classified as upsloping, horizontal or downsloping. The ST response considered was the most horizontal or downsloping ST-segment depression in any lead except the aVR during exercise or recovery. An abnormal response was defined as ≥ 1 mm of horizontal or downsloping ST-segment depression. Ventricular tachycardia was defined as a run of three or more consecutive premature ventricular contractions (PVCs), and frequent PVCs were defined as previously described.1920 BP was measured manually. Estimated metabolic equivalents (METs) were calculated from treadmill speed and grade. No test was classified as being indeterminate.21

Clinical and exercise test data were entered into a unique collection and reporting program that automatically generates a report for distribution within the VA clinical database. This program relies on a set of carefully defined clinical and exercise variables that are also stored in a relational database. We used this database to provide the clinical and exercise data for analysis and as the parent database used to query DSS for health-care costs.

DSS
The DSS is a set of programs that uses relational databases to provide data on the costs, patterns of care, patient outcomes, and workload details of specific patient care encounters within the VA health-care systems. Central to this system is the Veterans Health Information Systems and Technology Architecture with which the VA records clinical data and documents health-care encounters. This system includes modules that record data from laboratory, pharmacy, radiology, surgery, and other departments, includes information from the abstract of the hospital discharge, and records outpatient visits, including codes for the type of clinic visited, procedures, and diagnoses.

DSS cost data, including both direct and indirect costs, is extracted from the VA payroll and general ledger. Departmental costs come from the estimates, payroll, and general ledger data, and overhead costs to patient care departments use a step-down method, with direct costs or the number of square feet of occupied space used as the basis of the distribution. The cost of each intermediate product, such as a chest radiograph or a unit of blood is also included. Relative value units (RVUs) are assigned to each product based on an estimate of the relative costs of the local resources. The department cost per RVU is calculated and multiplied by the RVUs assigned to the intermediate product to determine its cost. The data from each patient encounter includes the number of intermediate products used, their cost, and the total cost of that encounter. An inpatient encounter is a hospital stay. An outpatient encounter is defined as a clinic visit, with a residual category for all other services provided on a single day. Costs are given in units roughly equivalent to dollars but require adjustment for local differences in cost component values.

The list of patients who had undergone treadmill tests in the time window to take advantage of the DSS implementation in late 1998 was submitted to the Austin VA Automation Center. The output generated by the center, which provided inpatient and outpatient relative costs without further detail, was merged with our treadmill database. The total cost for each patient was estimated for 1 year following the treadmill test based on a time-weighted average of the costs during the year of the test and the following year.

Follow-up
The California Death Statistical Master File was used to match all of the patients, using name and social security number. The index is updated yearly, and current information was used. Death status was determined as of July 2000.

Statistical Analysis
Patient characteristics were listed as the percentage of the total or the mean ± SD. Unadjusted and adjusted analyses were performed using simple and multiple linear least squares regression with the outcome variable of cost logarithmically transformed to better approximate normality. Adjustment variables included age, gender, treadmill test performance and results, and clinical history (ie, all of the variables listed in the tables and figures). Analysis of variance was used to estimate the predictive values of each variable entered into the regression. Analysis was performed using statistical software packages (S-PLUS; Mathsoft; Seattle, WA; and NCSS 2001; Number Cruncher Statistical Systems; Kaysville, UT).


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
Patient characteristics are shown in Table 1 . The patients had a mean age of 59 years, 95% were men, and 74% were white. A relatively high proportion of patients had one or more significant risk factors for coronary heart disease (CHD), including history of smoking (68%), hypertension (52%), family history of CHD (22%), and diabetes (14%). However, a notably smaller proportion had a history of documented cardiovascular disease. Exercise testing (Table 2 ) showed an average maximum heart rate of 138 beats/min and an average peak exercise capacity of 8.2 METs. Approximately 50% of patients had an abnormal test result that was suggestive of ischemic heart disease (ST depression, 43%; angina as reason for stopping, 3%; exercise-induced frequent PVCs or ventricular tachycardia, 5%). However, the overall mortality rate of the population was low, with only eight patients (< 1%) dead at 1 year of follow-up.


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  		Table 1.. Baseline Patient Characteristics*

 

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  		Table 2.. Exercise Test-Related Patient Characteristics*

 
In unadjusted analysis, costs were incrementally lower by an average of 5.4% per MET increase (p < 0.001). This result is illustrated in Figure 1 with the patient population divided into five groups based on peak exercise capacity. The first group (< 5 METs) was selected to represent a clearly deconditioned patient segment. The subsequent categories were selected at basic intervals of METs and to maintain roughly equivalent patient numbers in each group.



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  		Figure 1.. The 1-year cost by exercise test performance given in METs. In unadjusted analysis, costs were incrementally lower by an average of 5.4% per MET increase (p < 0.001). The data shown are the median with 25th and 75th percentiles.

 
Separate analyses of inpatient and outpatient costs revealed similar results. The effect of peak exercise capacity on outpatient costs closely resembled the overall result with an estimated cost decrease of 5.7% per MET increase (p < 0.001). Due to the fact that inpatient costs were incurred in only 318 patients (36%), the result for this subgroup was not statistically significant (p = 0.13). Nevertheless, the effect of exercise capacity on inpatient costs also trended similarly to the overall result, with costs decreasing an average of 4.9% per MET increase. Similarly, the exclusion of patients who died within the year of follow-up had no appreciable effect on the results, with an estimated cost decrease of 5.6% per MET increase (p < 0.001).

Multivariable analysis further demonstrated the importance of peak METs in predicting subsequent 1-year costs. Figure 2 illustrates the multivariable analysis for the variables in the model found to be significantly predictive of health-care costs. After adjustment for demographic data, treadmill test performance and results, and clinical history, the peak MET was found to be the most significant predictor of cost (F-statistic, 21.8; p < 0.001). It is also noteworthy that peak MET was the only variable included in the model that was significantly associated with lower costs at 1 year (point estimate of effect, –0.30; 95% confidence interval (CI), –0.43 to –0.17), while strong predictors of increased costs included male gender (point estimate of effect, 0.45; 95% CI, 0.10 to 0.81), history of CHF or cardiomyopathy (point estimate of effect, 0.29; 95% CI, –0.03 to 0.62), and abnormal ST depression (point estimate of effect, 0.23; 95% CI, 0.03 to 0.42).



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  		Figure 2.. The relative importance of predictors, as derived from the age-adjusted multivariable regression model for 1-year cost. Costs are given in units roughly equivalent to dollars but require adjustment for local differences in cost component values. Predictors with values to the right of the solid line predicted increased costs. The number of METs achieved during exercise testing was a highly significant predictor of reduced cost (F-statistic, 21.8; p < 0.001). ETT = exercise treadmill testing; CHF = congestive heart failure.

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
Our results demonstrate that exercise capacity is a strong predictor of 1-year total health-care costs in patients who were referred for exercise testing for clinical reasons. Previous publications on this subject have largely been literature reviews or based on mathematical modeling. Our study is unique in that it provides information on a real-life clinical population with available detailed clinical and cost data. Furthermore, while previous studies have involved generally healthy populations, our study demonstrates that exercise capacity is a strong predictor of cost in a clinically referred population, including both patients with and without documented cardiovascular disease. The demographics and test results in this sample of veterans are similar to those of the larger population they are derived from, and in which we reported a 12% lower annual mortality per MET increase in exercise capacity.1 As such, this study demonstrates that measured exercise capacity is a powerful measure of health, not only in terms of mortality but also in terms of health-care resource utilization.

These findings are in strong agreement with and extend the findings of several previously published studies that utilized a variety of nonprospective methodologies. In both retrospective database analysis as well as extensive literature review, the association between body habitus and physical activity habits on outcomes including morbidity or mortality has been clearly established.12 Literature review also has been used to evaluate the economic implications of this association. Colditz6 searched the MEDLINE database for studies reporting the economic costs of obesity or inactivity, along with the cost of illness. The direct costs of lack of physical activity were found to be approximately $24 billion or 2.4% of US health-care expenditures. The combined direct costs of inactivity and obesity were estimated to account for some 9.4% of the national health-care expenditures.

Several other studies have attempted to estimate the economic impact of exercise through projections made from the relative risk of diseases associated with obesity and lack of fitness. A Canadian study7 estimated the direct health-care costs attributable to physical inactivity, the number of lives lost prematurely each year, and the effect that a reduction of 10% in inactivity levels could have on reducing direct health-care costs. Their computations suggested that approximately $2.1 billion, or 2.5% of the total direct health-care costs in Canada, were attributable to physical inactivity in 1999. Furthermore, they estimated that approximately 21,000 lives were lost prematurely in 1995 because of inactivity and that a 10% reduction in the prevalence of physical inactivity could potentially reduce direct health-care expenditures by $150 million a year. Nicholl et al3 used relative risk estimates to project the incidence of hospital admissions, mortality, and associated health-care costs that could be prevented if the whole population exercised. The results showed that in younger adults (ages 15 to 44 years) the average annual medical care costs per person that might be incurred as a result of full participation in sports and exercise (approximately $60) exceed the costs that might be avoided by the disease-prevention effects of exercise (< $10). However, in older adults (ie, ≥ 45 years of age) the estimated costs avoided (> $60) greatly outweigh the costs that would be incurred (< $20). A similar result has been found in a study of a Dutch population.4

Mathematical models also have been used to address this important issue. In one report, cost-effectiveness analysis was used to estimate the health and economic implications of exercise in preventing CHD.5 The investigators created two hypothetical cohorts (one with exercise and the other without exercise) of 1,000 35-year-old men who were followed for 30 years to observe differences in the number of CHD events, life expectancy, and quality-adjusted life expectancy. They estimated that exercising regularly results in 78 fewer CHD events and 1,138 quality-adjusted life years gained over the 30-year study period. Under their base case assumptions, exercise did not produce economic savings. However, the cost per quality-adjusted life year gained of $11,313 is favorable when compared with other preventive or therapeutic interventions for CHD. Similarly, Jones and Eaton10 evaluated the potential cost-benefit implications of walking in the prevention of CHD by modeling hypothetical cohorts of sedentary men and women aged 35 to 74 years. At a relative risk of 1.9 for heart disease associated with sedentary behavior, it was estimated that $5.6 billion would be saved annually if 10% of adults began a regular walking program. Dunnagan et al9 utilized a hurdle model to examine health survey and health insurance costs data by stage of exercise participation. They found that employees in the maintenance stage (regular exercisers) of exercise adoption had lower costs and a lower probability of being classified in the high-cost group than did employees in the other stages of change for exercise participation.

The potential costs and benefits of exercise-training programs on large population cohorts also have been evaluated. Using the cardiovascular disease life expectancy model, Lowensteyn et al11 estimated the life expectancy of average Canadians who were 35 to 74 years old. They then modeled the impacts of exercise training on cardiovascular risk factors, exercise adherence patterns, and the costs of supervised and unsupervised exercise programs to calculate cost-effectiveness ratios. The cost-effectiveness of an unsupervised exercise program was < $12,000 per year of life saved (YOLS) for all individuals, while the cost-effectiveness of a supervised exercise program was < $15,000 per YOLS for persons with CHD and < $45,000 per YOLS for those without CHD. A British National Health Service study8 estimated the likely costs, health benefits, and consequences that might result from a program of regular exercise that was made available to 10,000 people over the age of 65 years. Providing twice-weekly exercise classes for 10,000 participants was estimated to cost approximately $1.6 million per year, but would prevent 76 deaths and 230 in-patient episodes, avoiding annual health-care costs of approximately $1.2 million. Assuming the mean expectation of life after 65 to be 10 years, the program would cost about $600 per life-year saved.

While the above models provide useful and interesting data, few analyses have utilized actual population and cost data. Pratt et al12 performed a cross-sectional stratified analysis of the 1987 National Medical Expenditures Survey. For those without physical limitations, the average annual direct medical costs were $1,019 for those who were regularly physically active and $1,349 for those who reported being inactive. Medical care use was also lower for physically active people than for inactive people. The mean net annual benefit of physical activity was $330 per person. The authors estimated that increasing participation in regular physical activity among the > 88 million inactive Americans over the age of 15 years could reduce annual national medical costs by as much as $77 billion. Similarly, Luepker et al22 reported an analysis of 5-year Medicare costs in 3,393 women and 2,495 men aged ≥ 65 years who were categorized as to activity level based on the National Heart, Lung, and Blood Institute Cardiovascular Health Study. When placed in quartiles based on the number of kilocalories expended per week, healthy nonsedentary seniors in the two least active quartiles averaged $4,300 less in 5-year costs than did healthy sedentary seniors. Savings were improved to $6,180 in the most active quartile. These studies were based on a questionnaire of exercise habits rather than on the results of an exercise test.

The present analysis corroborates and extends the findings of these previously performed studies. While our study utilized a limited well-defined patient population and is smaller than some of those mentioned above, it embodies several characteristics that we think make it uniquely informative. Foremost among these characteristics is our use of measured exercise capacity, which is a more accurate measurement of fitness than activity status as assessed by even the most sophisticated questionnaire. In addition, the present study represents an unselected population of people who were referred for exercise testing within the VA system. As such, the generalization of these findings to other patient groups must be undertaken with caution. However, we think that the present study represents a useful sample of a relatively high-risk subset within the general population.

Our study is limited in that we did not have access to detailed data on clinical events during the follow-up period or on costs that may have been incurred outside of the VA system. While it may be assumed that the majority of the patient population was referred for exercise testing due to either known or suspected cardiovascular disease, we are unable at this time to give further information on subsequent diagnostic and therapeutic interventions that may have occurred. It should be noted, however, that exercise capacity was shown to be a more important predictor of cost in our model than even an abnormal exercise test result, which presumably would have been a major driver of subsequent cardiovascular diagnostic and therapeutic interventions. Also, since the model is adjusted for baseline disease status, it appears that fitness is associated with lower costs independent of comorbid disease.

It would be informative to provide information about potentially significant sources of cost as well as other details of clinical care during the follow-up period. These issues may be addressed in future work as more detailed follow-up data become available. Nevertheless, we do not think that this detracts from our main point that exercise capacity was found to be the most significant predictor of overall cost.


   Conclusion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
We think that the present study contributes to the growing body of evidence that supports the association between exercise capacity and both improved survival and lessened health-care costs. It is our hope that this study will serve to inspire further work, including the possibility of causal investigations. Furthermore, as the strength of association between exercise capacity and both outcomes and health-care costs grows, so too do the potential public heath implications. While the present study does not specifically address the implementation of exercise-training programs, the preponderance of evidence clearly points to the potential for improvement in both the physical and economic health of our society that might be achieved through low-risk and relatively inexpensive interventions that are targeted at improving physical fitness.


   Footnotes
 
Abbreviations: CHD = coronary heart disease; CI = confidence interval; DSS = Decision Support System; MET = metabolic euqivalent; PVC = premature ventricular contraction; RVU = relative value unit; VA = US Department of Veterans Affairs; YOLS = year of life saved

Received for publication February 18, 2004. Accepted for publication February 19, 2004.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

  1. Myers, J, Prakash, M, Froelicher, VF, et al (2002) Exercise capacity and mortality among men referred for exercise testing. N Engl J Med 346,793-801[Abstract/Free Full Text]
  2. Blair, SN, Brodney, S Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Med Sci Sports Exerc 1999;31(suppl),S646-S662[CrossRef]
  3. Nicholl, JP, Coleman, P, Brazier, JE Health and healthcare costs and benefits of exercise. Pharmacoeconomics 1994;5,109-122[ISI][Medline]
  4. Reijnen J, Velthuijsen JW. Economic aspects of health through sport. Paper presented at: International conference on sports: an economic force in Europe; November 20–22, 1989; Lilleshall, UK; 76–90
  5. Hatziandreu, EI, Koplan, JP, Weinstein, MC, et al A cost-effectiveness analysis of exercise as a health promotion activity. Am J Public Health 1988;78,1417-1421[Abstract/Free Full Text]
  6. Colditz, GA Economic costs of obesity and inactivity. Med Sci Sports Exerc 1999;31(suppl),S663-S667[CrossRef]
  7. Katzmarzyk, PT, Gledhill, N, Shephard, RJ The economic burden of physical inactivity in Canada. Can Med Assoc J 2000;163,1435-1440[Abstract/Free Full Text]
  8. Munro, J, Brazier, J, Davey, R, et al Physical activity for the over-65s: could it be a cost-effective exercise for the NHS? J Public Health Med 1997;19,397-402[Abstract/Free Full Text]
  9. Dunnagan, T, Haynes, G, Smith, V The relationship between the stages of change for exercise and health insurance costs. Am J Health Behav 2001;25,447-459[ISI][Medline]
  10. Jones, TF, Eaton, CB Cost-benefit analysis of walking to prevent coronary heart disease. Arch Fam Med 1994;3,703-710[Abstract]
  11. Lowensteyn, I, Coupal, L, Zowall, H, et al The cost-effectiveness of exercise training for the primary and secondary prevention of cardiovascular disease. J Cardiopulm Rehabil 2000;20,147-55[CrossRef][Medline]
  12. Pratt, M, Macera, CA, Wang, G Higher direct medical costs associated with physical inactivity. Physician Sportsmed 2000;28,63-70
  13. Ustin, J, Umann, T, Froelicher, V Data management: a better approach. Physician Comput 1994;12,30-33
  14. Froelicher, VF, Myers, J Research as part of clinical practice: use of Windows-based relational data bases. Veterans Health System J 1998;2,17-19
  15. Shue, P, Froelicher, V Extra: an expert system for exercise test reporting. J Noninvasive Testing 1998;II-4,21-27
  16. Myers, J, Buchanan, N, Walsh, D, et al A comparison of the ramp versus standard exercise protocols. J Am Coll Cardiol 1991;17,1334-1342[Abstract]
  17. Myers, J, Do, D, Herbert, W, et al A nomogram to predict exercise capacity from a specific activity questionnaire and clinical data. Am J Cardiol 1994;73,591-596[CrossRef][ISI][Medline]
  18. Lachterman, B, Lehmann, KG, Abrahamson, D, et al "Recovery only" ST-segment depression and the predictive accuracy of the exercise test. Ann Intern Med 1990;112,11-16[ISI][Medline]
  19. Yang, JC, Wesley, RC, Froelicher, VF Ventricular tachycardia during routine treadmill testing: risk and prognosis. Arch Intern Med 1991;151,349-353[Abstract]
  20. Partington, S, Myers, J, Cho, S, et al Prevalence and prognostic value of exercise-induced ventricular arrhythmias. Am Heart J 2003;145,139-146[CrossRef][ISI][Medline]
  21. Reid, M, Lachs, M, Feinstein, A Use of methodological standards in diagnostic test research. JAMA 1995;274,645-651[Abstract]
  22. Luepker, R, Lumley, T, Jollis, J, et al Medicare costs of physical inactivity in older adults [abstract]. Circulation 2002;106(suppl),3509



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