Association Between Physical Activity Measurements and Key ...

Journal of Cardiac Failure Vol. 19 No. 9 2013

Association Between Physical Activity Measurements and Key Parameters of Cardiopulmonary Exercise Testing in Patients With Heart Failure VINICIUS Z.M. DA SILVA, PhD,1,2 ALEXANDRA C. LIMA, MD,1,2 FILLIPPE T. VARGAS, PT,1 LAWRENCE P. CAHALIN, PhD,3 ROSS ARENA, PhD,4 AND GERSON CIPRIANO JR, PhD1,2 Brasilia, Brazil; Miami, Florida; and Chicago, Illinois

ABSTRACT Background: A hallmark characteristic of heart failure (HF) is reduced physical activity (PA) patterns. The relationship between key cardiopulmonary exercise testing (CPX) variables and PA patterns has not been investigated. Therefore, we evaluated PA patterns in patients with ischemic HF and its relationship to peak oxygen consumption (VO2), the minute ventilation/carbon dioxide production (VE/VCO2) slope, and the oxygen uptake efficiency slope (OUES). Methods and Results: Sixteen patients with HF wore an accelerometer for six days to measure total steps/day as well as percentage of time at light, moderate, and vigorous PA. Symptom-limited CPX was performed on a treadmill using a ramping protocol. Total steps correlated with VO2 (r 5 0.64 P ! .05), the VE/VCO2 slope (r 5
0.72; P ! .05), and the OUES (0.63; P ! .05). The percentage of time at light-intensity PA correlated with the VE/VCO2 slope (r 5 0.58; P ! .05) and the OUES (r 5
0.51; P ! .05). The percentage of time at vigorous-intensity PA correlated with peak VO2 (r 5 0.55; P ! .05) and the VE/VCO2 slope (r 5
0.52; P ! .05). Conclusions: PA assessed by accelerometer is significantly associated with key CPX variables in patients with HF. (J Cardiac Fail 2013;19:635e640) Key Words: Heart failure, physical activity, exercise test.

Numerous studies demonstrate that patients with heart failure (HF) have lower levels of physical activity (PA).1e3 Moreover, studies have demonstrated that selfreported physical activity levels are associated with hospital admissions, clinical status, and mortality in chronic disease cohorts.4e6 Among the ways to assess PA patients with chronic disease, activity monitoring based on accelerometry has been reaffirmed as a very accurate method that

may provide valuable information for quantifying activity of daily living patterns.7 Physiologically, the level of PA is extremely correlated with aerobic performance,8 which has been described in different populations such as chronic obstructive pulmonary disease (COPD)9 and implantable cardioverter defibrillator.10 Cardiopulmonary exercise testing (CPX) is a highly reliable and valid approach to assessing aerobic performance. It is a well accepted assessment technique in the HF population,11 and American12 and European associations endorse its use. CPX is most often performed on a treadmill or cycle ergometer using ramping protocols, and the addition of ventilatory expired gas analysis to the standard exercise test enables measurement of oxygen consumption (VO2), carbon dioxide production (VCO2), and minute ventilation (VE) over time.13 Moreover, CPX provides a host of variables that are predictive of adverse events in HF patients, including peak VO2, the VE/VCO2 slope, and the oxygen uptake efficiency slope (OUES).12 However, there are potential patient difficulties associated with CPX,12 such as trepidation with the test itself, mouthpiece/mask intolerance, and effort dependency. In

From the 1Division of Physical Therapy, Ceilandia College, University of Brasilia, Brasilia, Brazil; 2Ergometry and Sports Medicine Department, Heart Institute of Distrito Federal, Brasılia, Brazil; 3Department of Physical Therapy, University of Miami, Miami, Florida and 4Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, Illinois. Manuscript received March 6, 2013; revised manuscript received July 20, 2013; revised manuscript accepted August 7, 2013. Reprint requests: Vinicius Z.M. da Silva, Division of Physical Therapy, University of Brasilia, SQS 215 Bloco A apto 305, 70294-010 Asa Sul, Brasılia, DF, Brazil. Tel: þ55 6133468374. E-mail: viniciusmaldaner@ gmail.com See page 639 for disclosure information. 1071-9164/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2013.08.002

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636 Journal of Cardiac Failure Vol. 19 No. 9 September 2013 truth, the application of CPX in the HF population is limited to a subset of patients who are being considered for more aggressive management (eg, left ventricular device implantation or heart transplantation). Therefore, measurements capturing PA patterns, and presumably CPX performance, which can be easily obtained in a broader proportion of the HF population, may be useful in reflecting disease severity and the level of functional limitation. To our knowledge, the relationship between PA patterns and key CPX variables in HF patients has not been evaluated. Therefore, the primary aim of the present study was to evaluate PA patterns and its relationship to peak VO2, the VE/VCO2 slope, and the OUES in patients with ischemic HF. Methods Subject Characteristics This cross-sectional study consisted of 16 male subjects diagnosed with ischemic HF. All subjects were on stable pharmacologic management before initiation of the study and were being treated by the same cardiologist. All study participants were recruited from Cardiology Institute of Distrito Federal, Brazil. Inclusion criteria consisted of a diagnosis of HF14 and evidence of systolic left ventricular dysfunction (left ventricular ejection fraction [LVEF] #35%) by echocardiography.15 Any subject with a previous diagnosis of moderate to severe chronic obstructive pulmonary disease based on absence of respiratory symptoms history and hospital admission related to respiratory disorder16 or who were unable to walk without assistance were excluded from this study. All subjects provided informed written consent, and the study was approved by the Ethics Committee of the Heart Institute of Distrito Federal.

input into spreadsheet software (Excel; Microsoft Corp, Bellevue, Washington) to calculate the VE/VCO2 slope via least-squares linear regression (y 5 mx þ b; m 5 slope).19 The OUES, which represents the relation between VO2 and VE during the incremental exercise test, was calculated by logarithm expression of ventilation, in which OUES is defined as the regression slope a in VO2 5 a  logVE þ b. A higher OUES represents greater efficiency of VO2, whereas a low OUES represents a greater VE in relation to VO2 and ventilatory inefficiency during exertion.20 Physical Activity Patterns. The subjects wore an accelerometer (GT3X; Actigraph, Pensacola, Florida) on a belt over their right hip for 7 days after CPX. This device is a triaxial accelerometer that measures vertical, anteroposterior, and mediolateral acceleration. The device also filters the data to detect only movements that occur within a given frequency (0.25e2.5 Hz) so as to detect human body movement and reject other forms of movement, such as vibration. The acceleration data is integrated over user-specified time intervals, called epochs, providing a number of counts. The more activity a person does, the greater the number of counts recorded by the device. From the determination of counts, time spent time in sedentary or light-intensity (0e99 counts/min), moderate-intensity (100-2,220 count/min), and vigorous-intensity (O2,220 counts/ min) for each individual can be estimated. The software package for the accelerometer also has provides information on total steps per day and percentage of time active. Echocardiography. LVEF and LV mass were assessed by 2-dimensional echocardiography. Standard M-mode and 2-dimensional echocardiography as well as Doppler flow measurements were performed with the use of a Sonos 5500 device (Hewlette Packard, Andover, Massachusetts) and after $15 minutes of rest, according to the recommendations of the American Society of Echocardiography.21

Measurements and Procedures

Statistical Analysis

Cardiopulmonary Exercise Testing (CPX). Each patient performed a symptom-limited CPX on a treadmill (T2100; General Electric, Waukesha, Wisconsin), with an increase of 0.5 metabolic equivalents (METs)/min.17 The aim was to achieve peak exercise in z10 minutes. If the test duration was O12 minutes or !8 minutes, the test was repeated the next day with an appropriate titration in progressive work rate. Ventilatory expired gas analysis was obtained with the use of a metabolic cart (K4; Cosmed, Milan, Italy). The oxygen and carbon dioxide sensors were calibrated before each test using gases with known oxygen, nitrogen, and carbon dioxide concentrations. The flow sensor was also calibrated before each test with the use of a 3-L syringe. Monitoring consisted of continuous 12-lead electrocardiography (Cardiosoft; General Electric, Waukesha, Wisconsin), manual blood pressure measurements (Tycos Welch Allyn, Skaneateles Falls, New York) at every stage, and heart rate recordings at every stage via the electrocardiogram. Test termination criteria consisted of patient request, ventricular tachycardia, $2 mm horizontal or down-sloping ST-segment depression or a drop in systolic blood pressure of $20 mm Hg during exercise. The same qualified physician conducted each test. Oxygen consumption (L/min and mL kg
1 min
1), VCO2 (L/ min), and VE (L/min) were collected continuously throughout the exercise test. Peak VO2 was expressed as the highest 30-second average value obtained during the last stage of CPX. Peak respiratory exchange ratio18 was the highest 30-second average value obtained during the last stage of the CPX. Ten-second averaged VE and VCO2 data, obtained from the initiation of exercise to peak, were

An a priori analysis for this study revealed that 14 subjects were needed to achieve 80% power (a 5 0.05; b 5 0.20). All analyses were carried out with a statistical software package (Graphpad Prism v 5; Graphpad Software, La Jolla, California). Values for continuous variables were represented as mean 6 SD. Pearson correlation coefficient was used to evaluate the relationship between CPX and echocardiographic variables and PA patterns. A P value of !.05 was considered to be statistically significant for all correlations evaluated.

Results Baseline and CPX characteristics are listed in Table 1. All subjects were in New York Heart Association functional class II or III and presented with a history of hypertension and dyslipidemia. Pharmacologic management was consistent with current clinical standards. Cardiopulmonary exercise testing results demonstrated functional limitations/ abnormalities consistent with the HF population. Average daily physical activity data are presented in Table 2. The accelerometer recordings were available for a mean of 6 days, which is enough time for an accurate PA pattern characterization.22 The subjects spent 67% of their time in lowintensity activities and the average time spent in vigorous intensity activities was very low (4.7%).

Physical Activity and CPX in HF Table 1. General Characteristics of Population (Ischemic Heart Failure; n 5 16) Variable Age (y) BMI (kg/m2) NYHA II III Risk factors Hypertension Diabetes mellitus Dyslipidemia Smokers Medications ACEI Beta-blocker Furosemide Digoxin Statin CPX % HR max reached Peak VO2 (mL kg
1 min
1) VE/VCO2 slope OUES Peak RER Echocardiography LVEF (%) LV mass (g) Smokers PFT (n 5 4) FEV1 (% pred) FEV1/FVC (% pred)

59 6 7.21 26.92 6 3.88 6 (37%) 10 (63%) 16 6 16 4

(100%) (37%) (100%) (25%)

12 16 12 4 16

(75%) (100%) (75%) (25%) (100%)

79.92 6 11.75 18.41 6 6.40 39.50 6 7.38 1.81 6 0.55 1.11 6 0.16 28.79 6 4.34 247.6 6 23.8 91 6 21 73 6 9

BMI, body mass index; NYHA, New York Heart Association functional class; ACEI, angiotensin-converting enzyme antagonist; CPX, cardiopulmonary exercise testing; % HR max, percentage of maximum heart rate (based on age) reached in cardiopulmonary testing; VO2, peak oxygen comsumption; VE/VCO2 slope, relationship between ventilation and dioxid carbon production during the exercise; OUES, oxygen uptake efficiency slope; LVEF, left ventricular ejection fraction; LV, left ventricular; RER, respiratory exchange ratio; PFT, pulmonary function test; FEV1, forced expiratory volume at one second; FVC, Forced vital capacity.

Among the correlations evaluated, we found significant correlations between PA patterns and key CPX key variables (r 5 0.48e0.67; P ! .05), with the strongest correlation between total steps and the VE/VCO2 slope (r 5
0.79; P ! .05). Only the VE/VCO2 slope showed significant correlations with all the PA measurements (Table 3; Fig 1). We did not find significant correlations between total steps and peak oxygen pulse (r 5 0.25; P O .05), LVEF (r 5 0.15; P O .05), or LV mass (r 5 0.19; P O .05). Discussion

Table 2. Daily Physical Activity Patterns in Heart Failure Patients Variable Total steps Percentage Percentage Percentage Percentage

of of of of

time time time time

active in light-intensity activities in moderate-intensity activities in vigorous-intensity activities

da Silva et al

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key CPX variables in patients with HF. The novel findings of this study are as follows: 1) Individuals with ischemic HF showed lower levels of PA compared with the expected values and spent most of their time in low-intensity activities; and 2) the PA patterns were significantly associated with key CPX key variables. A hallmark characteristic in HF patients is severely reduced PA levels, often coinciding with accentuated exertional dyspnea and exercise intolerance.23 Increasing PA, through a structured exercise program, has clearly been shown to improve functional capacity, quality of life, and clinical outcomes in this patient population.24,25 Therefore, measurement of PA levels can be an important clinical measurement and serve as the basis for a primary therapeutic target in these patients (ie, prescribe an exercise program in all stable patients). Given our findings, it would appear that most patients with HF would benefit from a PA intervention. This approach embraces a growing enthusiasm for exercise to be considered as an important clinical intervention, as illustrated by the American College of Sports Medicine Exercise Is Medicine campaign.26 In the present study, we used an accelerometer device to directly measure PA patterns, rather than use a questionnaire to subjectively estimate PA. Earlier research suggests that self-reported PA measurements are less accurate in assessing light- to moderate-intensity activities.27 This is an important consideration in this patient population, which spends a larger percentage of their day engaging in lowto moderate-intensity activities. Therefore, to accurately assess PA in the clinical and research settings, use of an accelerometer seems to be preferable. Current guidelines have demonstrated the clinical importance of engaging in greater amounts of at least moderate activities to achieve health benefits.28 In our study, HF patients spent 32% of their PA in medium- or high-intensity activities (28% medium, 4% high intensities), which was similar to results from Nguyen et al10 and Jehn et al29 who measured activity levels via similar devices in the similar population. Previous studies in patients with COPD have demonstrated that lower-intensity activities and total steps/day may be related to hospitalization30 and decreased skeletal muscle mass.31

Table 3. Correlations Between PA Patterns With Key CPX Variables in Individuals With Heart Failure (n 5 16)

To our knowledge, this is the first study to assess PA patterns using an accelerometer and examine correlations with

1. 2. 3. 4. 5.



9,229 35.94 66.48 28.75 4.32

6 6 6 6 6

3,017 4.26 3.24 2.82 1.101

Peak VO2

VE/VCO2 Slope

OUES

0.64* 0.50*
0.48


0.79*
0.66* 0.58*

0.7* 0.63*
0.51*

in moderate-

0.12


0.46*

0.33

in vigorous-

0.55*


0.59*

0.34

Variable 1. Total steps 2. Percentage of time 3. Percentage of time intensity activities 4. Percentage of time intensity activities 5. Percentage of time intensity activities

active in light-

Abbreviations as in Table 1. *Significant Pearson correlation (P ! .05).

638 Journal of Cardiac Failure Vol. 19 No. 9 September 2013

Fig. 1. Scatterplots of the peak oxygen consumption (VO2), oxygen uptake efficiency slope (OUES) and minute ventilation/carbon dioxide production (VE/VCO2) slope in subjects with heart failure (n 5 16).

Ventilatory efficiency based on CPX variables, commonly assessed by the VE/VCO2 slope, has become a central CPX variable in the HF population, with important clinical implications.25 More recently, the OUES has gained attention as another measure of ventilatory efficiency with clinical significance in the HF population.20,32 Both the VE/VCO2 slope (r 5 0.59e0.79) and the OUES (r 5 0.51e0.70) were significantly correlated with PA measures. In fact, the VE/VCO2 slope demonstrated higher correlations with PA measurements than any other CPX variable included in the current analysis. This variable represents, among other things, matching of ventilation and perfusion within the pulmonary system25 and is related to peripheral muscle weakness in HF patients.33 Thus, the relationship between the VE/VCO2 slope and PA patterns demonstrated in the present study may be explained by the fact that skeletal muscle wasting and ventilatory inefficiency commonly found in HF patients is associated with both the VE/VCO2 slope and PA patterns. Moreover, the VE/VCO2 slope has emerged as an independent prognostic marker in HF patients.19 A significant correlation between OUES and lowintensity activities (r 5
0.51; P ! .05) in HF patients was found in this study, demonstrating that lower values of OUES may predict lower PA levels in this population. An increase of OUES is achieved with lower ventilatory cost at submaximal workloads.34 Thus, the demonstrated relationship between both the VE/VCO2 slope and OUES with PA patterns may indicate that objective measurements of PA could be an important prognostic marker in HF

patients. Future studies should be directed toward determining the prognostic significance of objective PA measurements in this population. Although we found statistically significant correlations between both total daily steps and high-intensity activities and peak VO2, these correlations were less than that found for ventilatory efficiency. Even so, it does seem clear that a higher peak VO2 is associated with more favorable PA patterns. This relationship may also have important implications for clinical practice in that PA patterns reflect aerobic capacity. Such information can be used to obtain a baseline status and track changes in PA levels longitudinally, the latter of which would be important for tracking disease progression and titrating advice/guidance on PA habits. It seems plausible to suspect that, as HF disease severity progresses, PA patterns would decrease. Such information may be an important trigger for more extensive testing and aggressive clinical management strategies. Therefore, future research investigating the value of tracking PA patterns in a serial fashion and determining its clinical value would be advantageous. There are obvious limitations to the present investigation that warrant discussion. Our dataset is relatively small, limiting our ability to perform multivariate linear regression in an attempt to determine if a combination of PA measurements improves the ability to predict the CPX response. Moreover, our cohort included only male subjects with ischemic HF, potentially limiting the applicability of our findings to patients with other HF etiologies as well as female patients. Future research

Physical Activity and CPX in HF

should be directed toward addressing these issues. Even so, the current analysis is novel in the context of assessing the relationship between CPX responses and accelerometry data. Our hope is that these initial findings will spur a new line of function-related research in patients with HF. In summary, a relationship between daily PA and CPX variables was found in HF patients. These findings suggest that PA monitoring may provide insight into functional patterns and identify patients with a higher likelihood for a poor CPX response. Moreover, monitoring PA in the clinical setting may allow for refined guidance on exercise/activity prescription as well as providing valuable information of disease severity progression. Disclosures None. References 1. Braunschweig F, Mortensen PT, Gras D, Reiser W, Lawo T, Mansour H, et al. Monitoring of physical activity and heart rate variability in patients with chronic heart failure using cardiac resynchronization devices. Am J Cardiol 2005;95:1104e7. 2. Defilippi CR, de Lemos JA, Tkaczuk AT, Christenson RH, Carnethon MR, Siscovick DS, et al. Physical activity, change in biomarkers of myocardial stress and injury, and subsequent heart failure risk in older adults. J Am Coll Cardiol 2012;60:2539e47. 3. Cowie A, Thow MK, Granat MH, Mitchell SL. A comparison of home and hospital-based exercise training in heart failure: immediate and long-term effects upon physical activity level. Eur J Cardiovasc Prev Rehabil 2011;18:158e66. 4. Garcia-Aymerich J, Lange P, Benet M, Schnohr P, Anto JM. Regular physical activity reduces hospital admission and mortality in chronic obstructive pulmonary disease: a population based cohort study. Thorax 2006;61:772e8. 5. Wouters EF, Franssen FM, Spruit MA. Survival and physical activity in COPD: a giant leap forward! Chest 2011;140:279e81. 6. Leeper NJ, Myers J, Zhou M, Nead KT, Syed A, Kojima Y, et al. Exercise capacity is the strongest predictor of mortality in patients with peripheral arterial disease. J Vasc Surg 2013;57:728e33. 7. Pruitt LA, Glynn NW, King AC, Guralnik JM, Aiken EK, Miller G, et al. Use of accelerometry to measure physical activity in older adults at risk for mobility disability. J Aging Phys Act 2008;16: 416e34. 8. Cao ZB, Miyatake N, Higuchi M, Miyachi M, Tabata I. Predicting VO2max with an objectively measured physical activity in Japanese men. Eur J Appl Physiol 2010;109:465e72. 9. Spruit MA, Wouters EF, Eterman RM, Meijer K, Wagers SS, Stakenborg KH, et al. Task-related oxygen uptake and symptoms during activities of daily life in CHF patients and healthy subjects. Eur J Appl Physiol 2011;111:1679e86. 10. Nguyen HQ, Steele BG, Dougherty CM, Burr RL. Physical activity patterns of patients with cardiopulmonary illnesses. Arch Phys Med Rehabil 2012;93:2360e6. 11. Myers J, Arena R, Dewey F, Bensimhon D, Abella J, Hsu L, et al. A cardiopulmonary exercise testing score for predicting outcomes in patients with heart failure. Am Heart J 2008;156:1177e83. 12. Guazzi M, Adams V, Conraads V, Halle M, Mezzani A, Vanhees L, et al. Clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations. Circulation 2012;126: 2261e74.



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