The ACSM yearly meeting and abstracts begin today. Here is our entry for those who don't have access. I would like to thank both Laurent and Thomas for their assistance, guidance and cleaning up all my mistakes.
What is the significance of this work? Mainly that the DFA a1 based HRVT aerobic threshold relationship holds even in a non athletic, cardiac population with coronary disease and heart failure (most on beta blockers). So for those out there getting cardiac rehab and/or on beta blockade, the info to date on this method appears valid.
Aerobic threshold identification in a cardiac disease population based on correlation properties of heart rate variability
Bruce Rogers, University of Central Florida, Laurent Mourot, University of Bourgogne Franche-Comté, Besançon, France, Thomas Gronwald, MSH Medical School Hamburg
PURPOSE: Recently, encouraging data has indicated that a nonlinear index of fractal correlation properties of heart rate (HR) time series based on the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1) can provide insights into physiologic regulation during exercise as well as indicate zone 1 transition in a population of healthy recreational athletes (1-5). Whether that observation extends to other, more diverse populations is unknown. Given the importance of proper intensity zone assessment in cardiac rehabilitation outcome and guidance, an assessment of agreement between a DFA a1 based HRV threshold (HRVT) with the VT1 obtained by gas exchange would be of great interest. If DFA a1 behavior is similar in both athletes and those with cardiac disease, this could lead to more widespread interest in using this HRV index as a modality for training guidance in the low to moderate intensity areas for the purpose of intensity distribution in therapy and rehabilitation.
METHODS: Sixteen volunteers with stable coronary disease or heart failure performed an incremental cycling ramp to exhaustion PRE and POST a 3-week training intervention. All participants were on beta blockade therapy except for one. The exercise training intervention consisted of cycle ergometer sessions (30 minutes per day, 5 times per week), at an intensity approximating the participants VT1 heart rate and gymnastic (calisthenic) sessions of 50 minutes per day. Oxygen uptake (VO2) and HR at VT1 were obtained from a metabolic cart. An ECG was processed for DFA a1 and HR. The HR variability threshold (HRVT) was defined as the VO2, HR or power where DFA a1 reached a value of 0.75. A DFA a1 value of 0.75 was chosen based on previous study in recreational athletes (4). This value is also the midpoint between a fractal, well correlated behavior of the HR time series of 1.0 (seen with very light exercise) and an uncorrelated value of 0.5 which represents white noise, random behavior (seen with high intensity exercise) (1,2). Plotting of DFA a1 vs time was then performed, generally showing a reverse sigmoidal curve with a stable area above 1.0 at low work rates, a rapid, near linear drop reaching below 0.5 at higher intensity, then flattening without major change (4).
RESULTS: Mean VT1 was reached at 16.82±5.72 ml/kg/min, HR of 91.3±11.9 bpm and power of 67.8±17.9 watts compared to HRVT at 18.02±7.74 ml/kg/min, HR of 94.7±14.2 bpm and power of 73.2±25.0 watts. Linear relationships were seen between modalities, with Pearson’s r of 0.95 (VO2), 0.86 (HR) and 0.87 (power) (Figure 1). Bland Altman assessment showed mean differences of 1.20 ml/kg/min (Figure 2), 3.4 bpm and 5.4 watts. Regression analysis for comparisons of VT1 vs HRVT (as VO2) calculated from all ramp tests performed (both PRE and POST) is shown in Figure 1. Strong correlations were seen between VT1 based determinations and those derived from HRVT. Pearson’s r was calculated at 0.95, 0.86, 0.87 (all p < 0.001) for gas based VO2 (ml/kg/min), HR and power comparisons, respectively. Mean peak VO2 and VT1 did not change after training intervention. However, the correlation between PRE to POST change in VO2 at VT1 with the change in VO2 at HRVT was significant (r = 0.84, p < 0.001). A similar correlation was seen between both gas exchange-based HR (r = 0.72) and power (r = 0.81) to that determined by HRVT.
Figure 1. Regression plots for all participant ramp tests performed. A: VT1 vs HRVT for VO2; B: VT1 vs HRVT for HR; Bisection lines in light gray.
A: Bland Altman Plot of VT1 vs HRVT for all participant ramp tests performed for VO2;
B: Analysis of individual improvement PRE vs POST exercise intervention for all participants, measured as VT1 vs HRVT for VO2
DISCUSSION: The results presented indicate that the HRVT and gas exchange based VT1 parameters agreed strongly. The average numerical differences between the VT1 and HRVT based on VO2, HR or power were small. However, it should be noted that the limit of agreements for the listed measures were 5.8 ml/kg/min, 14 bpm and 25 watts which are relatively wide. The second major finding of this report is that the post exercise intervention change in HRVT did relate to the change in VT1. Although there was little net improvement in post exercise VO2PEAK or VT1 for the entire group, there were individual changes in the various fitness parameters with the modest exercise training intervention employed. The change in HRVT measurement as VO2, HR or power occurred in parallel to that of the change in gas exchange based data, demonstrating the usefulness of fractal HR dynamics to longitudinally follow the aerobic threshold. This would be valuable from multiple standpoints including adjustment of training zones, estimating intervention success and pointing out potential non responders to a given exercise protocol. Lastly, there is little information on DFA a1 based HRVT retest reliability. The strong correlation of HRVT with VT1 both pre and post exercise intervention does support the retest consistency of an individual’s DFA a1 response to exercise intensity
LIMITATIONS: Participants in this study comprised a mixed group with diagnoses of both ischemic cardiac disease and heart failure. Subgroup investigations of heart failure vs ischemic disease related HRVT agreement were not done due to the low sample size. An in depth look at the HRVT in homogenous groups with heart failure or ischemic vascular disease or even in those with cardiac valvular disease would be ideal. Additionally, no female patients were evaluated, which is an area that needs exploration since few studies examining DFA a1 behavior in women exist. While it appears that beta adrenergic blockade therapy does not affect HRVT determination, we did not have a comparison group of patients without this medication. The basis for assuming the lack of effect of beta blockade on the HRVT centers around the similarity of the results presented here and that seen in healthy subjects (4).
CONCLUSIONS: A heart rate variability threshold based on fractal correlation properties, DFA a1, was closely related to the first ventilatory threshold in a group of individuals with either congestive heart failure or ischemic vascular disease. Although there were small differences in VO2, heart rate, and cycling power between modalities, they were minimal from a clinical and practical standpoint. In addition, exercise training associated change in the first ventilatory threshold strongly agreed with the change seen in the heart rate variability threshold. Therefore, even though the improvement of the first ventilatory threshold was not universal, a non-invasive measure is able to predict that change. It also appears that the usage of beta-adrenergic blocker therapy did not alter the relationship of the heart rate variability threshold to the first ventilatory threshold and its application in exercise therapy.
(1) Gronwald, T. & Hoos, O. (2020). Correlation properties of heart rate variability during endurance exercise: a systematic review. Ann. Noninv. Electrocardiol., 25 (1), e12697.
(2) Gronwald, T., Rogers, B. & Hoos, O. (2020). Fractal correlation properties of heart rate variability: A new biomarker for intensity distribution in endurance exercise and training prescription? Front. Physiol., 11, 550572.
(3) Rogers, B., Giles, D., Draper, N., Mourot, L. & Gronwald, T. (2021). Influence of artefact correction and recording device type on the practical application of a non-linear heart rate variability biomarker for aerobic threshold determination. Sensors, 21 (3), 821. doi: 10.3390/s21030821
(4) Rogers, B., Giles, D., Draper, N., Hoos, O. & Gronwald, T. (2021). A new detection method defining the aerobic threshold for endurance exercise and training prescription based on fractal correlation properties of heart rate variability. Front. Physiol., 11, 596567
(5) Rogers, B. & Gronwald, T. (2021). From laboratory to roadside: Real-time assessment and monitoring of the aerobic threshold in endurance-typed sports. Br. J. Sports. Med. Blog 13. Februar 2021: