Medicine, University of California at INS Angeles School of Medicine, Los Angeles, California, USA. Summary. Background: ... and in patients with cardiac disease,s-12 suggesting that auto- ... ually reviewed and edited by an experienced technician and a cardiologist ... from study entry to exit in body weight or proportion of.
Clin. Cardiol. 23, 615-620 (2000)
Exercise Conditioning and Heart Rate Variability: Evidence of a Threshold Effect YOSEF PARDO, M.D., c . NOELBAIREY MERZ,M.D., IVAN VELASQUEZ, M.D., MAURA PAUL-LABRADOR, M.P.H., AALOK AGARWALA, C. THOMAS PETER,M.D.
Division of Cardiology, Department of Medicine,Cedars-Sinai Research Institute,Cedars-Sinai Medical Center, and the Department of Medicine, University of California at INS Angeles School of Medicine,Los Angeles, California, USA
Summary
Background: A protective effect of exercise in preventing sudden cardiac death is supported by studies in healthy populations as well as in patients with cardiac disease. The mechanisms involved in this protective effect are unknown. Hypothesis: We hypothesized that exercise conditioning would beneficially alter autonomic nervous system tone, measured by heart rate variability. Methods: We prospectively studied 20 cardiac patients enrolled in a Phase 2 12-week cardiac rehabilitation program following a recent cardiac event. The patients underwent 24 h Holter monitoring at program entry and 12 weeks later. Heart rate variability analysis was assessed for both time domain and spectral analysis. Results: The group demonstrated a modest mean conditioning effect, indicated by an average reduction in resting heart rate from 8 I k 16 to 75 12 beatshin (p = 0.03),and an increase in training METS from 2.1 rt 0.4 to 3.3 f 1.1 (p< 0.OOOl). Overall, 15 of 20 (75%) patients demonstrated increased total and high-frequency power, and mean high-frequency power was significantly increased (3.9 rt 1.4 vs. 4.4 k 1.O In, p = 0.05). When stratified according to the magnitude of exercise conditioning, patients achieving an increase of > 1.5 training METS demonstrated significant increases in SDNN, SDANN index, SDNN index, pNN50, total power,
This work was supported in part by grants from the Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, and Marquette Electronics, Inc. Dr. Pardo was supported, in part, by the Save A Heart Foundation and the Kurian Family Foundation Fellowship, Los Angeles, California. Address for reprints: C. Noel Bairey Merz, M.D. Preventive and Rehabilitative Cardiac Center Division of Cardiology Cedars-Sinai Medical Center 444 S. San Vicente Boulevard, Suite 901 Los Angeles, CA 90048, USA Received: March 22, 1999 Accepted with revision: October 1. 1999
and high-frequency power (all p < 0.05) (see text for explanation of abbreviations). Conclusions: Exercise conditioning improves heart rate variability in cardiac patients, particularly in patients who achieve a threshold of > 1.5 training METS increase over a 12week period. These study results are supportive of the concept that exercise training lowers the risk of sudden cardiac death via increased vagal tone, which likely beneficially alters ventricular fibrillatory and ischemic thresholds.
Key words: exercise, heart rate variability
Introduction A protective effect of exercise in preventing sudden cardiac death in healthy persons is supported by many large studies.'.2 Meta-analyses of multiple randomized trials in patients post myocardial infarction undergoing cardiac rehabilitation have also shown a significant reduction in total and cardiovascular death rates with a predominant effect of sudden cardiac death red~ction.~,The mechanisms involved in this protective effect of exercise are unknown, although it has been hypothesized that modulation of the autonomic nervous system, indicated by resting bradycardia, associated with exercise conditioning may beneficially alter ventricular fibrillation and myocardial ischemia thresholds. Heart rate fluctuates on a beat-to-beat basis. These fluctuations in heart rate, termed heart rate variability (KRV), received marginal attention until digital processing facilitated their analysis and allowed for the practical use of these parameters in clinical research. Heart rate variability assessment is a noninvasive technique that can be used to evaluate the cardiac manifestation of autonomic nervous system function, specifically the cardiac sympathetic-parasympathetic intera~tion.~ Recently, a strong inverse relation between HRV and all-cause mortality has been demonstrated in both healthy subjects6and in patients with cardiac disease,s-12suggesting that autonomic nervous system function may be an important mechanistic link for investigation. Cross-sectional data in human^'^-'^ and a prospective study in dogs" have suggested that exercise training beneficially influences HRV via modulations in sympathetic and parasympathetic (vagal) nervous system balance. The impact of exer-
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cise on HRV in patients with cardiac disease has not yet been evaluated. Accordingly, we assessed whether exercise conditioning in cardiac patients would beneficially modify autonomic nervous system tone, measured by HRV.
Methods We prospectively assessed 24 consecutivepatients initiating a Phase 2 cardiac rehabilitation program following a recent cardiac event, who consented to participate in the study. Patients with sigmficant comorbid conditions that might influence exercise conditioning (renal failure, n = 1, intercurrent stroke, n = l), or arrhythmiasthat would affect HRV measurement (atrial fibrillation, n = l), or patients who did not return after the first exercise monitoring visit (n = 1) were excluded. The investigation was performed in accordance with the Declarationof Helsinki. Patients underwent supervised, electrocardiographically monitored, aerobic exercise training sessions three times per week for a maximum of 12 weeks (total of 36 sessions), as previously described. Resting heart rate was obtained at gym entry in the standing position. Each hour session included continuous telemetry monitoring with rest and exercise blood pressure measurement.The intensity of exercise training was calculated to achieve a target training heart rate by the Karvonen formula [(50-75% maximal heart rate - resting heart rate) + resting heart rate], and/or below the heart rate when ischemia was demonstrated during exercise stress testing. For patients who had not yet undergone stress testing at program entry, the exercise prescription was set at a range of 20 to 30 beats/& above the resting heart rate. Patients underwent 24-h Holter monitoring (Marquette model 8000, Marquette Electronics, Inc., Milwaukee, Wis., USA) using leads V2 and a modified inferior position with a calibration signal of 1 mV= 10mm on the day of cardiac rehabilitation orientation, prior to initiating their exercise training program. They also filled out a physical activity regarding baseline daily physical activities outside of the cardiac rehabilitation program. A second 24-h Holter monitor and physical activity log was performed 12 weeks later. The physical activity log provided a measure of kilocalorie energy expenditureover a 2-week period. During both Holter monitoring periods, patients continued taking their usual medication. Analysis of Heart Rate Variability
The 24-h Holter tape recordings were digitized on a Marquette 8000 scanner with a signal sampled at 128Hz,as previously described.20QRS complex recognition and arrhythmia detectionwere done automaticallyby template matching with the standardMarquette algorithmsfor QRS labeling and editing (version 5.8 software). This system classified each complex as normal sinus, atrial or ventricular premature complex, artifact,or unclassified.The computerclassifcationwas manually reviewed and edited by an experienced technician and a cardiologist. Editing included verification and correction of (1) beat morphology, (2) longest and shortest R'R intervals,
(3) arrhythmias, and (4) maximum and minimum heart rates. This editing typically took approximately40 min per tape. A tape was eligible for this study if it had > 12 h of analyzable data, had half of the nighttimeand daytime periods analyzable, and if > 50%of the recording revealed sinus rhythm. After completing the review and editing of the Holter recording, HRV was analyzed using the Marquette software version OO2A. This program permits detailed analysis of HRV in the time domain as follows: (1) Mean NN = the average of all normal RR intervals of a 24-h recording, (2) SDNN = the standarddeviation of all normal RR intervals of a 24-h recording, (3) S D A " index = the standard deviation of the average normal RR intervals for all 5-min segments of a 24-h recording, (4) SDNN index = the mean of the standard deviations of all normal RR intervals for all 5 min segments of a 24-h recording, ( 5 ) r-MSSD = the root mean square of successive differences between adjacent normal RR intervals of a 24-h recording, and (6) pNN50 = the percentage of differences> 50 ms between adjacent normal RR of a 24-h recording. The spectraldomain data contain the total power (0.01 to 1.OO Hz), the low-frequency band (0.04to < 0.15 Hz),and the high-frequency band (0.15 to 1 3,there were no differences in diagnosis, risk factors, functional class, or medication use among the three groups. Patients in the highest tercile (achieving a MET increase > 1 S ) had a lower resting systolic blood pressure and a greater exercise duration at study entry (both p < 0.01),and there was atrend toward younger patients in this higher MET increase (p = 0.06) (Table 111). Exercise training data, similarly stratified into terciles in Table III,demonstrated a graded physiologic conditioning effect among the three groups, evidenced by significant reductions and elevations in resting and training heart rate, respectively, with greater increases in training MET. Assessment of the physical activity log data, obtained to accommodate for exercise activity outside the cardiac rehabilitation program, revealed no significant baseline group differences in kilocalorie energy expenditure. Overall, 12 of 19 (68%) patients demonstrated net increases in kilocalorie expenditure over the 12-week period, with the highest MET increase group mean kilocalorie expenditure for a 14-day period increasing from 4,546 2,668 at baseline to 6,278 f 3,381 at exit. When stratified by the training MET increase tercile groups, only 2 of 7' (29%) in the lowest training MET increase group demonstrated an increase in kilocalorie expenditure, compared with 7 of 7 (100%) and 4 of 5 (83%) patients in the higher second and third training MET increase groups, respectively (p = 0.014). There was a positive correlation between in-
*
TABLEII Group heart rate variability response (n = 20)
Variable SDNN (ms) S D A " index (ms) SDNN index (ms) pNN.50 (%) r-MSSD (ms) Total power (In) High frequency (In) Low frequency (In)
Entry
Exit
A (range)
98 f 46 87241 37 f 20 7.8+ 12.2 27-c21 6.0 f 1.2 3.9+ 1.4 4.6 + 1.2
107541
-8.6+ 32.3 (-42-72) 8.7 2 28.9 (-45-67) 4.0f 1 5 3 - 3 4 3 1 ) 0.0+ 6.9 (-21-8.0) 2.4+ 10.1 (-28-18) 0.3 + 1.O (- 1.8-2.2) 0.4 f 0.9 (- 1.5-2.6) 0.4+ 1.1 (-2.1-2.7)
96f40 41 2 18 7.8f 9.0 30+ 19 6.3 f 0.9 4.4 f 1 .0 S.Of 1.0
p Value NS
NS NS NS NS NS 0.05
NS
The spectral domain data contain the total power (0.01 to 1 .OOHz), the low frequency band (0.04to < 0.15 Hz), and the high frequency band (0.I5 to < 0.40 Hz). Abbreviations: ms = milliseconds, In = natural logarithm, SDNN =the standard deviation of all normal RR intervals of a 24-h recording, SDANN index =the standard deviation of the average normal RR intervals for all 5-min segments of a 24-h recording, SDNN index = the mean of the standard deviations of all normal RR intervals for all 5-min segments of a 24-h recording, r-MSSD =the root mean square of successive differences between adjacent normal RR intervals of a 24-h recording, pNNS0 =the percentage of differences between adjacent normal RR > 50 ms of a 24- h recording.
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TABLEIU Exerciseconditiong data stratifiedby MET increase Variable
ISMET p (n=7) (n=7) (n=6) Value
~~
62+ 13 76f7 Age (years f SD) 76 f 13 30f8 22+7 Entry exercise dur 17f 5 33k3 30+8 Exercise visits (no.) 25 f 7 76+ 17 88+ 18 RestHR(beatdmin) 81 f 13 A Rest HR (beatdmin) 0.3 k7.7 -5.Of 11.8 - 14.7+ 9.8 A Training HR 2.6 k 8.3 8.3 + 3.5 (beatdmin) -3.7 + 3.9 139f I1 120f9 Rest SBP(mmHg) 122+ 11 76f11 78+3 Rest DBP (mmHg) 71 + 6
0.06 0.01 0.14 NS 0.045
0.006 0.007 NS
Abbreviations: DBP = diastolicblood pressure, dur = durationin min, H R = heart rate, no. = number, SD = standard deviation.
creased exit kilocalorie expenditure measured by the physical activity log and training MET increase over the 12weeks (r = 0.56, p = 0.01).Although changes in kilocalorie expenditure did not significantly correlate with changes in HRV, they did correlate with baseline pNN50 (r = 0.48, p = 0.04). We next assessed HRV according to the magnitude of MET increaseduring the exercisetraining period. Unadjusted entry, exit, and change HRV measurements stratified by the MET increase terciles are demonstrated in Table IV.Overall, the patients who experienced a training increase of > 1.5 METS demonstrated significantly greater changes in SDNN, S D A " index, SDNN index, pNN50, total power, and highfrequency power. Following adjustment for baseline differences in resting systolicblood pressure and exercise duration, changes in SDNN (p < 0.02) and high-frequency power (p < 0.02) remained significantly greater in the highest MET increase tercile and paralleled the decrease in resting heart rate and increase in training heart rate seen, respectively (all p < 0.05) (Fig. 1).
Discussion These results demonstratethat exerciseconditioningsigntficantly improves HRV in cardiac patients, particularly among those who achieve a threshold of > 1.5 training METS increase over a 12-week period. This effect was demonstrable in a heterogeneous group of cardiac patients following a variety of cardiac events and using multiple cardiac medications. We were unable to demonstrate any association between baseline clinicalvariables and HRV change,and the HRV exerciseconditioningeffect appearedto be independent from beta-blocker use. Our finding of a threshold effect, for example, significant increases in multiple HRV measures in the group achieving a significant improvement in cardiac functional capacity measured by MET improvement,suggests that the observed HRV effect was related to exercise training rather than to spontaneous variability in HRV. Indeed, we have documented previously low intra- and interobservervariability and high subject
TABLElV Heart rate variability measurements stratified by MET increase 1.5 MET p (n=7) (n=7) (n=6) Value
Variable
h4"(ms) 807f 138 Entry Exit 823 + 107 Change 17f83 SDNN (ms) Entry 94+46 Exit 94 f 38 Change O + 14 S D A " index (ms) Entry 84f41 Exit 85 f 38 Change 1f 12 SDNN index (ms) Entry 35 f 23 Exit 38+ 14 Change 2f12 r-MSSD (ms) Entry 24+ 17 Exit 29f 10 Change 5f8 pNN50 (%) Entry 6 + 10 Exit 8f6 Change 2+4 Total power (In) Entry 5.9+ 1.3 Exit 6.3 + 0.8 Change 0.4 f 0.8 High frequency (In) Entry 3.82 1.4 Exit 4.5 f0.8 Change 0.7 k0.7 Low frequency (In) Entry 4.4 f 1.4 Exit 4.9 f 0.9 Change 0.6 f 1.O
889f119 879+ 120 -1Ok 128
816k181 791+382 -27+322
0.54 0.78 0.92
112f43 100f44 -11+25
87+52 128+39 41k33
0.61 0.32 0.004
100k42 96 f 49 -5 f 29
75244 109+35 33+31
0.56 0.60 0.03
43 f 20 38 f 22 -5f18
34+19 50k16 16f11
0.68 0.40 0.04
33 f 26 28 f 25 - 4 f 12
25k23 32f22 7+6
0.72 0.95 0.07
12* 17 6 f 10 -5 f 9
5 2 10 9 + 12 4f3
0.63 0.87 0.03
6.4 f 1.O 6.1 k 1.2 -0.4f0.9
5.7k 1.2 6.7f0.8 0.9f0.8
0.54 0.55 0.04
4.3+ 1.4 4.1 + 1.3 -0.2k0.8
3.7+ 1.5 4.6kl.l 0.920.9
0.65 0.71 0.04
5.1 f 1.0 4.7k1.3 -0.3f 1.1
4.421.2
0.52 0.47 0.06
5.4k0.8
1.020.8
Abbreviations as in Table Il.
200
*
80 60 40
20
FIG.1 Exercise program entry (openbars) and exit (closedbars) for resting heart rate (I-R)in beatshin, high frequency (IW) power (in natural log), and SDNN (the standarddeviation of all normal RR intervals of a 24-h recording) (ms) for the training group achieving > 1.5 MET increase (n = 6), adjusted for baseline exercise duration and resting systolic blood pressure. *p
