Physical Activity is Associated with Improved Aerobic Exercise Capacity over Time in Adults with Congenital Heart Disease

Abstract

Background

Impaired exercise capacity is common in adults with congenital heart disease (ACHD). This impairment is progressive and is associated with increased morbidity and mortality. We studied the influence of the frequency of at least moderately strenuous physical activity (PhysAct) on changes in exercise capacity of ACHD patients over time.

Methods

We studied ACHD patients ≥21 years old who had repeated maximal (RER≥1.09) cardiopulmonary exercise tests within 6 to 24 months. On the basis of data extracted from each patient’s clinical records, PhysAct frequency was classified as (1) Low: minimal PhysAct, (2) Occasional: moderate PhysAct <2 times/week, or (3) Frequent: moderate PhysAct ≥2 times/week.

Results

PhysAct frequency could be classified for 146 patients. Those who participated in frequent exercise tended to have improved pVO₂ (ΔpVO₂=+1.63±2.67 ml/kg/min) compared to those who had low or occasional activity frequency (ΔpVO₂=+0.06±2.13 ml/kg/min, p=0.003) over a median follow-up of 13.2 months. This difference was independent of baseline clinical characteristics, time between tests, medication changes, or weight change. Those who engaged in frequent PhysAct were more likely to have an increase of pVO₂ of ≥1SD between tests as compared with sedentary patients (multivariable OR=7.4, 95%CI 1.5-35.7). Aerobic exercise capacity also increased for patients who increased activity frequency from baseline to follow-up; 27.3% of those who increased their frequency of moderately strenuous physical activity had a clinically significant (at least +1SD) increase in pVO₂ compared to only 11% of those who maintained or decreased activity frequency.

Conclusions

ACHD patients who engage in frequent physical activity tend to have improved exercise capacity over time.

INTRODUCTION

As a consequence of improvements in medical and surgical care, adults with congenital heart disease (ACHD) now outnumber children with congenital heart disease in developed nations.[1] Survival is not, however, synonymous with optimal functional capacity and quality of life.[2] The importance of these less easily quantifiable outcomes is becoming increasingly appreciated.[3, 4]

ACHD patients have lower peak oxygen consumption (pVO₂), a measure of exercise capacity, than seen in the general population. Low pVO₂ in ACHD has strong independent prognostic value; it is associated with increased risk of morbidity and mortality and with worse quality of life.[5, 6] An accelerated age-related decline has also been described. Several factors may account for these observations. Residual hemodynamic and electrophysiological defects are often present following surgical “repair.” Congenital heart disease (and its treatments) may also cause, or be associated with, impairment of other organs including the pulmonary and systemic vascular beds, lung and airways, central nervous system, and neuroendocrine system,[7, 8, 9] that can affect exercise capacity.

The adverse physiological consequences of residual hemodynamic inefficiency and other medical problems are compounded by deconditioning related to a sedentary lifestyle.[10] The majority of ACHD patients do not engage in physical activity (PhysAct) as frequently as recommended by published guidelines for the general population.[11, 12]

While PhysAct frequency does seem to be associated with higher aerobic capacity in ACHD at a single point in time,[13] it is unclear whether increasing frequency of PhysAct results in maintenance of aerobic capacity over time.

To better characterize the impact of frequent PhysAct upon exercise capacity in ACHD patients, we undertook a retrospective study of the relationship between the frequency of at least moderately strenuous PhysAct and changes in objective measures of exercise capacity.

METHODS

Subjects

We identified ACHD patients≥21 years old that underwent cycle ergometry exercise testing at Boston Children’s Hospital between January 2006 and July 2011. Patients with 2 maximal (RER≥1.09) tests during the period of study, separated by 6-24 months (median=13.2mos, IQR 10.8-16.8), were included. Exclusion criteria included pregnancy at or between tests, intervening cardiac percutaneous or surgical interventions with the potential to impact exercise capacity, or acute illness at the time of exercise testing. The first 2 exercise tests fulfilling these requirements were used for this analysis.

Cardiopulmonary Testing

Cardiopulmonary exercise testing (CPX) involved symptom-limited cycle-ergometry using a standard ramp protocol with electrocardiographic monitoring and breath-by-breath expiratory gas analysis (CardiO₂ exercise testing system, Medical Graphics, Minneapolis, Minnesota). Calculations and predicted values were obtained as described by Wasserman et al.[14]

Physical Activity Frequency Assessment

PhysAct frequency was assessed from patients’ clinical notes based upon the following guidelines (with representative clinical descriptions):

1. Low: minimal PhysAct, e.g., “He has a recumbent bike at home but has not been using this.”

2. Occasional: light activity, or moderately strenuous activity (activity that makes the patient sweat or breathe hard) <2 times weekly and/or <40 minutes per session, e.g., “He has been exercising regularly, spending 10-20 minutes on the treadmill about 3 times a week.”

3. Frequent: at least moderately strenuous activity ≥ 2 times weekly for ≥40 minutes, i.e. “He is now going to the gym two hours a day Monday, Wednesday, and Friday.”

4. Undetermined: inadequate documentation to assign a level.

Changes in PhysAct frequency as defined above (low, occasional, frequent), between the clinic note at the time of the first CPX and at the time of the second CPX, were assessed. Changes were classified as: stable, increased or decreased.

Two investigators (AUT, JR) independently classified PhysAct frequency and change in PhysAct frequency for 50 subjects to assess inter-observer variability. The weighted kappa for PhysAct frequency was 0.79; the value for change in PhysAct between visits was lower (0.66), but still acceptable.

The baseline median percent predicted pVO₂ (pVO_2-%pred) for univentricular (56.5%) and biventricular (66.7%) physiology was determined, and patients with pVO_2-%pred above this value were classified as having “above average” exercise capacity for their underlying physiology; those with pVO2_-%pred below this value were classified as having “below average” exercise capacity. The proportion of patients with values above/below the median pVO_2-%pred baseline value at the second CPX was assessed.

Statistical Analysis

Change in pVO₂ over time, including the change in absolute pVO₂ (pVO2_-abs expressed in l/min), weight normalized pVO₂ (pVO_2-perkg, expressed in ml/min/kg) and percent predicted pVO₂ (pVO_2-%pred), was the primary outcome of interest. Secondary outcomes of interest were changes in the peak heart rate (pHR), peak O₂pulse, and VE/VCO₂ slope.

Continuous variables are presented as mean±SD and categorical variables as counts and percentages. Because characteristics of the low and occasional PhysAct frequency groups were similar, these were combined into a single comparison group for most analyses. The relationships between PhysAct frequency categories and individual CPX variables were analyzed using ANOVA or the Kruskal-Wallis test. Paired t tests were used to compare the initial and final values for each normally distributed CPX variable (Wilcoxon rank sum test for non-normal distributions). Given varying time between studies for each patient, we also analyzed relationships between predictors of interest and change in exercise test variables per year (e.g. Δ pVO_2-perkg/year). We performed linear regression analysis, with change in an exercise parameter of interest (e.g. Δ pVO_2-perkg or Δ pVO_2-perkg/year) as the dependent variable, to assess the association between the level of PA during the interval between the visits, or the change in level of PhysAct reported at the two visits, and the dependent variables under analysis. Multivariable models were used to assess whether the observed associations were independent of potential confounders including age, sex, CHD diagnosis, time between tests, baseline pVO_2-perkg(or other CPX variable), baseline height and weight (or BMI), weight change between studies, tobacco use, presence of a pacemaker, presence of systemic ventricular dysfunction by echocardiography (none, mild, moderate/severe), baseline heart rate, and use of specific cardiac medications (digoxin, beta-blocker, ACEI/ARB, diuretics). We also assessed the proportion of patients in each PhysAct group who changed from below to above median baseline pVO_2-%pred categories (or vice versa) during the interval between exercise tests, and those who had an increase or decrease of pVO_2-%pred by >1SD around the mean difference.

RESULTS

Patient characteristics

PhysAct was classifiable from clinical records at the time of the second test for 72.4%(n=147) of 203 patients who met study criteria. After examining distributions for percent change in pVO_2-abs we excluded 1 outlier whose pVO_2-abs increased 70.4% between the exercise tests (among the remaining subjects, ΔpVO_2-abs ranged from−24.2% to +35.5%). Of the 146 subjects included, 145 had PhysAct classifiable at the time of the first test. Demographic, clinical and CPX data were collected on all patients. There were no important differences between those excluded (n=57) and those included in the analysis (e.g. age 32.8 vs. 33.5y, p=0.65; time between studies 13.3 vs. 13.6mos, p=0.63; baseline pVO_2-abs 1.48 vs. 1.61 l/min, p=0.15; Δ pVO_2-%pred +2.4 vs. +3.0%, p=0.76).

Baseline data

Of the 145 patients classifiable at the time of the first test, 42%(n=61) had low PhysAct, 32.4%(n=47) participated in occasional PhysAct and 25.5%(n=37) engaged in frequent PhysAct. BMI and systolic BP tended to be higher in the lowest PhysAct group while pVO_2-perkg and O₂ pulse were significantly higher (p<0.01) in patients who exercised more frequently (Table 1). Data for the 146 patients with classifiable data at follow-up were similar.

Table 1.

Baseline demographic and clinical description based on physical activity frequency classification at the 1^st CPX

	Physical Activity Frequency
	Overall	Low	Occasional	Frequent	P
N	145	61	47	37
Age(y)	33.5±10.2	34.1±11.4	35.2±9.9	30.3±7.8	0.09
Male(%)	49.6	50.8	55.3	40.5	0.39
Height(cm)	167.8±9.9	166.9±.9.9	167.6±7.7	169.5±12.2	0.53
Weight(kg)	73.9±17.8	77.1±20.1	72.4±13.5	70.7±18.1	0.21
BMI, baseline(kg/m2)	26.1±4.9	27.5±5.8	26±4.0	24±4.0	0.01
Peak VO2-perkg, baseline(ml/min/kg)	21.9±6.7	19.0±5.0	21.4±6.1	27.4±6.7	<0.01
O2 pulse, baseline(mL/beat)	10.6±3.6	9.7±3.0	10.4±3.6	12.0±4.0	<0.01
Diabetes mellitus(%)	1.5	3.3	0	0	0.27
Tobacco(%)	9.9	15.8	6.7	3.3	0.15
Systolic BP(mmHg)	122±15	125±16	119±14	119±13	0.04
Diagnosis(%)					0.49
Tetralogy of Fallot	35.8	34.4	29.8	46.0
Fontan	10.3	14.8	6.4	8.1
Systemic right ventricle	22.8	16.4	29.8	24.3
Other	31.0	34.4	34.0	21.6
Ventricular dysfunction(%)					0.42
Moderate/severe	13.1	18.0	12.8	5.4
Mild	23.5	18.0	25.5	29.7
Normal	61.4	60.6	61.7	62.1
Unknown	2.1	3.2	0	2.7
Arrhythmia with exercise(%)					0.62
None	92.4	90.2	93.6	94.6
Atrial fibrillation/flutter/SVT	2.1	3.3	2.1	0
Ventricular tachycardia	0.7	0	2.1	0
Frequent APBs or VPBs	4.8	6.6	2.1	5.4
Pacemaker(%)	19.3	19.7	23.4	13.5	0.51
Medication(%)
ACEI	33.8	37.7	34.0	27.0	0.58
Beta blocker	33.8	36.0	46.8	13.5	<0.01
Digoxin	10.3	8.2	8.5	16.2	0.44
Diuretic	17.2	23.0	17.0	8.11	0.17

Descriptive statistics(mean±SD or %) based on physical activity frequency classification at the time of the first CPX, using ANOVA or Kruskal-Wallis and Fisher’s exact or Chi-squared tests for continuous and categorical variables respectively.

There were significant differences in baseline pVO_2-%pred between CHD diagnoses (Figure 1a). Median pVO_2-%pred was well below normal for all diagnoses, and more than 3/4^ths of patients in all diagnostic groups fell below the predicted value. On the other hand, there was no difference between diagnostic groups with regard to ΔpVO_2-%pred over the study (Figure 1b).

Figure 1. Baseline % predicted peak VO₂(A) and change in % predicted peak VO₂ between baseline and follow-up cardiopulmonary exercise tests(B) by congenital heart disease diagnostic category.

While baseline pVO_2-%pred differed by diagnosis (Kruskal-Wallis, p=0.006), there was no such difference in ΔpVO_2-%pred between diagnoses (Kruskal-Wallis, p=0.96). RV=right ventricular lesion, LV=left ventricular lesion, AVCD=atrioventricular canal defect, ASD/VSD=atrial/ventricular septal defect, TOF=tetralogy of Fallot

Predictors of change in peak VO₂

The strongest predictor of % change in ΔpVO_2-abs was BMI (Table 2). Patients with higher baseline BMI tended to have lower pVO_2-abs at the second test (for +5 kg/m² BMI, pVO_2-abs on repeat testing was 3.0% lower, p=0.001, r²=0.07). Height was not associated with ΔpVO_2-abs(p=0.26) while baseline weight was (for +10kg, pVO_2-abs decreased by 1.6% between tests, p=0.002, r²=0.06). Those with higher baseline pVO_2-abs tended to have slightly lower follow-up VO_2-abs. For every 10% lower baseline pVO_2-abs, pVO_2-abs increased by 1.4%(p=0.01, r²=0.04). No other baseline clinical or demographic variable was associated with ΔpVO_2-abs(Table 2). There was no relationship between chronic cardiac medication use of any class or initiation of a new cardiac medication during the study interval and ΔpVO_2abs (beta-blockers n=10 p=0.27, ACEi/ARB n=5 p=0.21, digoxin n=2 p=0.82, diuretics n=7 p=0.22) were associated with ΔpVO_2-abs. Patients who started a beta-blocker tended to have lower peak heart rate (Δ=−13.5bpm, p<0.001) on follow-up study but higher O₂ pulse (Δ=+14ml/beat, p<0.001). None of the other medications were associated with changes in either peak heart rate or O₂ pulse.

Table 2.

Predictors of % change in absolute peak VO₂

	r2	β	P
Age(/y)	0.012	−0.12	0.19
Sex(male)	<0.001	0.51	0.79
BMI(/kg/m2)	0.07	−0.62	0.001
Height(/cm)	0.009	−0.11	0.26
Weight(/kg)	0.063	−0.16	0.002
Tobacco	<0.001	−0.35	0.92
Diabetes mellitus	<0.001	−0.76	0.93
Pacemaker	<0.001	−0.21	0.93
Severe systemic ventricular dysfunction	0.017	4.46	0.12
Baseline pVO2- perkg(mL/kg/min)	0.045	−4.41	0.01
MAP(mmHg)	0.002	0.05	0.6

Univariate linear regression of various predictors of % change in absolute peak VO₂.

Predictors of other measures of ΔpVO₂ (i.e. Δ pVO_2-%pred, Δ pVO_2-perkg) were equivalent with similar magnitudes of association to that seen with ΔpVO_2-abs.

Effect of season on exercise testing results

There was no significant difference in pVO_2-%pred achieved on baseline exercise test by month (p=0.34) or season (October-March versus April-September, p=0.74) of testing. Season of baseline versus follow-up testing did not affect change in pVO_2-%pred. Those who were re-tested in the same season as the baseline test (n=82, Δ pVO_2-%pred =+1.8±1.1), those who had baseline testing in the winter and repeat in the summer (n=35, Δ pVO_2-%pred =+2.0±1.8) and those who had the converse (n=29, Δ pVO_2-%pred =+2.2±1.6) had the same mean change in pVO_2-%pred (p=0.98).

Impact of physical activity on exercise parameters

No significant demographic and clinical differences were found between groups patients categorized according to their PhysAct levels reported at the time of the second CPX test (i.e., the activity level that they sustained during the interval between the exercise tests) except for BMI (p=0.02), peak VO₂ (p<0.01) that were better in those who were more active and tobacco users that were more frequent in the moderate physical activity group (p<0.01). Fewer patients were classified as having low PhysAct at the follow-up visit, as compared with the baseline visit (n=42 vs. 61).

CPX data from the baseline test and change between tests, classified by PhysAct level sustained between tests, are presented in Table 3. Frequent PhysAct was associated with an improvement in both pVO_2-perkg and pVO_2%-pred(i.e., ΔpVO_2-perkg and ΔpVO_2%-pred were positive, p=0.003 and 0.04 respectively). There was a trend towards improved pVO_2-abs(p=0.07), and a significant improvement in pVO_2-abs when expressed on a per year basis(p=0.04). These relationships are shown graphically in Figure 2, comparing the lower 2 PhysAct frequency levels to those who engaged in frequent PhysAct.

Table 3.

Exercise data by physical activity frequency at the 2^nd CPX

		Physical Activity Frequency
	Overall	Low	Occasional	Frequent	P
N	146	42	49	55
Time between tests(mos.)	13.6±4.7	14.3±4.5	13.7±5.2	13.0±4.5	0.58
BMI, baseline(kg/m2)	26.1±4.9	27.7±5.6	26.1±4.8	24.9±4.0	0.03
Weight(kg)
baseline	74.0±17.7	78.0±19.9	74.1±16.3	70.8±16.9	0.14
Δ	−0.2±3.7	0.5±3.0	0.2±3.9	−1.2±3.7	0.008
Rest MAP(mmHg)
baseline	91.0±9.4	92.9±11.3	90.3±9.0	90.2±7.9	0.43
Δ	0.0±9.0	−0.6±9.7	2.1±8.9	−1.3±8.5	0.19
Rest HR(bpm)
baseline	77.3±13.9	81.6±12.9	76.8±13.5	74.6±14.4	0.05
Δ	−0.6±12.0	0.3±13.2	−2.0±9.3	0.1±13.1	0.81
Peak HR(bpm)
baseline	153.2±24.7	153.6±29.8	152.2±24.0	153.8±21.3	0.95
Δ	0.7±12.4	−0.8±13.5	−0.5±11.9	3.0±11.8	0.34
Peak Work(W)
baseline	148.6±56.6	134.9±46.9	148.6±60.3	159.0±58.6	0.14
Δ	3.3±16.5	−0.7±18.0	3.0±16.9	6.7±14.3	0.21
pVO2-perkg(ml/kg/min)
baseline	22.0±6.7	19.3±5.7	22.0±6.1	24.0±7.3	0.005
Δ	0.6±2.7	0.07±2.1	0.0±3.0	1.6±2.7	0.004
Δ(ml/min/kg/year)	0.7±2.9	0.06±2.0	−0.07±2.77	1.8±3.2	0.002
pVO2-%pred (%)
baseline	67.3±16.7	61.2±13.1	67.3±12.8	71.9±20.6	0.02
Δ	1.9±9.9	0.5±8.6	0.2±9.7	4.6±10.5	0.04
Δ(%pred/year)	1.9±9.9	0.3±8.0	−0.5±9.0	5.2±11.2	0.01
pVO2-abs(L/min)
baseline	1.6±0.6	1.49±0.53	1.64±0.62	1.68±0.60	0.29
Δ	0.03±0.18	0.01±0.17	0.00±0.19	0.08±0.19	0.07
Δ(%/year)	2.3±14.4	1.6±10.1	0.2±12.2	6.6±13.3	0.04
O2pulse(ml/beat)
baseline	10.6±3.6	9.7±2.9	10.9±4.0	10.9±3.6	0.19
Δ	0.1±1.3	0.1±1.4	0.0±1.4	0.2±1.2	0.62
Δ(%/year)	2.3±14.4	2.0±13.6	0.8±12.8	3.8±16.3	0.56
VE/VCO2 Slope
baseline	28.2±4.6	29.5±4.4	27.9±4.2	27.5±5.0	0.02
Δ	−0.01±4.16	−0.1±5.4	−0.4±3.0	0.3±4.0	0.37

CPX data at baseline and change in CPX data between both tests, based on PA classification at follow-up

Figure 2. Change in peak VO₂ by frequency of moderate or strenuous physical activity, <2 vs. ≥2 times per week.

Panel A shows change in pVO_2-abs while panel B shows change in pVO_2-%pred between exercise tests. P values represent linear regression adjusting for baseline pVO_2-%pred.

The association between exercise frequency and ΔpVO_2-%pred was maintained despite multivariable adjustment for age, sex, and baseline pVO_2-%pred(p=0.02) and additional factors including BMI, time between tests, weight change, diabetes, tobacco use, and presence of a pacemaker(p=0.03). The relationship between PhysAct frequency and ΔpVO_2-perkg likewise persisted after equivalent adjustment for demographic and clinical data (p=0.002 and 0.02 respectively).

In order to better understand whether inter-group differences in ΔpVO_2-abs were clinically significant, we assessed the proportion of patients who had either an increase or decrease in pVO_2-abs of at least 1SD (mean change +3±11%, with +1SD defined as increase of 14% and −1SD as −8%; Figure 3). Patients with little or no PhysAct during the time interval between tests were more likely to sustain a decrease of at least 1SD in pVO₂ (19.8%) than an increase (11.0%). Conversely, those who participated in frequent PhysAct during that time interval were more likely to improve pVO_2-abs by at least 1 SD(27.3%), while in only 12.7% of those engaging in frequent PhysAct did pVO_2-abs drop by >1SD(p=0.03). This finding was independent of age, BMI and baseline VO_2-abs(multivariable logistic regression OR=7.4, 95%CI 1.5-35.7, for +1SD in pVO_2-abs for frequent PhysAct).

Figure 3. Proportion of patients having >1SD(±11%) change in peak VO_2-abs between exercise tests.

Most patients had stable pVO₂, independent of PA frequency. However, approximately twice as many patients in the lower exercise frequency group had a >1SD decrease in pVO₂ compared with a >1SD increase. Conversely, among those engaging in frequent PA, more than twice as many improved their pVO₂ >1SD compared to the number that had equivalently decreased pVO₂.

Change in physical activity frequency and pVO₂

Most patients (61.4%) maintained the same level of PhysAct over the study period; 29.7% increased and 9.0% decreased PhysAct frequency. A statistically significant relationship existed between the Δ PhysAct between tests, the concomitant ΔpVO_2-abs (l/min), and ΔpVO_2-%pred(Figure 4). Decreasing PhysAct frequency was associated with a decrease in pVO_2-perkg(−1.9±3.3ml/min/kg), while stable PhysAct frequency was associated with stable pVO_2-perkg(+0.5±2.7 ml/min/kg) and increased PhysAct frequency was associated with improved pVO_2-perkg(+1.6±2.1 ml/min/kg).

Figure 4. Change in peak VO₂ by change in PA frequency.

(decreased=”↓”, stable=”≈”, increase=”↑”) between the baseline and follow-up CPX. Panel A shows %change pVO_2-abs. Panel B shows ΔpVO_2-%pred. P values represent linear regression adjusting for baseline pVO_2-%pred.

DISCUSSION

We found frequent PhysAct of at least moderate intensity to be associated with maintenance of superior exercise capacity in ACHD patients. Patients who sustained a high level of PhysAct were more likely to maintain or improve their pVO₂ and to lose weight during the interval between their exercise tests. In contrast, patients who participated in less frequent PhysAct were more likely to gain weight and their pVO₂ was more likely to decline. Furthermore, not only did frequent baseline PhysAct correlate with a more favorable change in pVO₂ over time, the subset of patients who increased physical activity frequency between tests appeared to derive benefit while those who became more sedentary saw a decline in pVO₂. The change in peak VO₂ was not attributable to weight loss per se, as adjustment for weight change in multiple regression models did not affect the relationship between PA and change in peak VO₂.

Our data support the concept that the aerobic capacity of ACHD patients is not limited solely by fixed cardiac factors. They also imply that the time-related decline in aerobic capacity, frequently reported in past longitudinal studies of ACHD patients, is modifiable. For many ACHD patients, regular PhysAct appears to have the potential to attenuate or reverse this debilitating decline.

Regular aerobic activity has pleiotropic effects, a number of which could account for these results. These include peripheral factors(e.g., increased numbers of mitochondria and up-regulation of aerobic enzyme pathways within skeletal muscle cells, increased muscle size and capillary density[15], enhanced muscle pump function[16] and beneficial changes in peripheral vascular beds) which may enhance oxygen extraction and/or cardiac output during exercise[17], and central factors(e.g. increase in myocardial mass and improvements in myocardial systolic and diastolic function)[18] which also may enhance myocardial reserve and improve enhanced cardiac output during exercise.

Inadequate PhysAct is common among adults with CHD and >70% report moderate or extreme concern about participating in PhysAct.[19] This apprehension often stems from inappropriate advice or overprotection during childhood and adulthood. Historically, health-care providers restricted PhysAct in patients with CHD,[20] or did not provide specific exercise guidelines. A recent study of patients with aortic stenosis suggested, however, that exercise restriction does not prevent adverse outcomes, and the detrimental effects of exercise restriction on cardiovascular risk factors, exercise capacity and psychological well-being are often overlooked.[21]

Our observations are consistent with experience in other populations. In the general population, regular PhysAct is associated with lower mortality, improved quality of life and reduction in the incidence of primary [22, 23] and secondary [24] cardiovascular events. Interventions to increase PhysAct appears to provide benefit in a number of medical conditions including chronic heart failure,[25] obesity,[26] and diabetes [27].

The observed impact of frequent PhysAct on exercise capacity exceeds that reported for some ACHD surgical and interventional catheterization procedures.[28, 29] The few studies of cardiac rehabilitation in ACHD have reported promising results vis a vis exercise capacity and quality of life.[30, 31]^,[32] Cardiac rehabilitation programs have also been extensively studied in patients with acquired heart disease, and have been shown to increase exercise capacity, reduce morbidity, mortality, and medical costs.[33] The number of patients with ACHD is increasing, and the healthcare costs of these patients are increasing even more quickly.[34, 35] The impact of inexpensive lifestyle interventions such as providing an exercise prescription or referral to cardiac rehabilitation could improve the “natural history” and quality of life for many adults with CHD while at the same time lowering associated healthcare costs.

The finding that BMI and weight are significant clinical predictors of change in pVO₂ suggests that, in conjunction with specific exercise training, educational, nutritional and behavioral interventions may have independent benefit.

Our data support the need for prospective clinical trials to assess the impact of various strategies to increase PhysAct and improve aerobic capacity in this population. Most critically, it remains unclear whether improving pVO_2-%pred bestows a benefit in terms of quality of life, cardiovascular events, and mortality.

Limitations

Causality (i.e., frequent PhysAct causes ↑pVO₂ over time) cannot be inferred from these data. The fact that a change in lifestyle in terms of PhysAct frequency was associated with a corresponding change in pVO_2, provides some support for a causal relationship. It is also important to note that these data do not provide insights into the mechanisms by which frequent PhysAct results in ↑pVO₂. PhysAct frequency was extracted from clinical notes and assessment is therefore somewhat imprecise. It is possible that some patients might provide inaccurate descriptions of PhysAct frequency. Any misclassification, however, would generally tend to bias our results to the null (e.g., if sedentary patients reported frequent PhysAct, this would tend to minimize the apparent effect of frequent PhysAct). The observed baseline variation in pVO₂ and correlations between weight change and both PhysAct level and ΔpVO₂ supports the accuracy of PhysAct classification.

Patient characteristics differed between PhysAct groups. There were differences in baseline resting heart rate, pVO₂, and VE/VCO₂ slope. These characteristics tended to be “better” in the frequent PhysAct group. One might expect a more prominent decline in patients with higher baseline pVO₂ as a result of regression to the mean. Our data demonstrated this phenomenon; for each increase of 1mL/kg/min in baseline pVO_2-perkg there was a 0.056 mL/kg/min decline in pVO_2-rel between tests(p=0.07). The fact that we observed a strong relationship between PhysAct and ΔpVO₂, despite this supports the validity of our findings.

CONCLUSION

Adults with CHD who engage in frequent PhysAct maintain higher pVO₂ over time compared with sedentary patients. Given the strong prognostic significance of pVO₂ in this population, strategies to increase the frequency of PhysAct in the ACHD population may have the potential to positively impact morbidity and mortality. Further studies are needed to determine the effect of specific interventions to modify PhysAct on pVO₂ and clinical outcomes in adults with congenital heart disease.