Ventilatory threshold estimation based on HRV

When we exercise, our body shifts from using mainly oxygen to burning more energy in anaerobic ways, producing lactate. This shift happens at key points called ventilatory thresholds (VT1 and VT2), which are crucial for understanding your exercise intensity and performance. Traditionally measured in labs, these thresholds can now be estimated non-invasively through heart rate variability (HRV), making it easier to monitor your training intensity and progress.

About ventilatory thresholds

Ventilatory thresholds (VT1 and VT2) are crucial markers in exercise physiology, indicating the transition from aerobic to anaerobic metabolism. Specifically, VT1 marks the onset of lactate accumulation, while VT2 signifies the beginning of exercise-induced metabolic acidosis. Accurately determining these thresholds is essential for optimizing training intensity.

Traditionally, these thresholds are determined in a laboratory environment through cardiopulmonary exercise testing (CPET) or lactate measurements. In CPET, ventilatory thresholds are determined from respiratory gasses by addressing increases in minute ventilation and its relationship to oxygen uptake (VO2) and carbon dioxide production (VCO2). Alternatively, lactate measurements pinpoint turning points in lactate production to define lactate thresholds.

Cycling

Heart rate variability and ventilatory thresholds

Over the past 25 years, numerous studies have highlighted heart rate variability (HRV) as a promising non-invasive method for estimating ventilatory thresholds [1]. As exercise intensity increases, the autonomic nervous system shifts from parasympathetic to sympathetic dominance [2]. This shift affects HRV in a detectable way, particularly when using detrended fluctuation analysis (DFA). During low-intensity exercise, the short-term scaling exponent α1 of DFA typically ranges between 1.0 and 0.75, with VT1 often identified at DFA-α1 reaching 0.75 [3]. As intensity rises, DFA-α1 decreases further, with 0.5 indicating VT2 [4]. During maximal effort, DFA-α1 generally falls below 0.5.

Kubios VT estimation algorithm

The ventilatory threshold estimation algorithm developed by Kubios leverages heart rate (relative to individual HR reserve), respiratory rate, and DFA-α1 to estimate VT1 and VT2. HR reserve has been linked to oxygen uptake reserve at ventilatory thresholds [5], and respiratory rate is also associated with ventilatory thresholds [6]. By incorporating these additional physiological measures, the accuracy of VT1 and VT2 estimates is significantly enhanced.

The respiratory rate is derived from HRV time series data and, when available, raw electrocardiogram (ECG) waveform data for greater accuracy [7]. Thus, the Kubios VT algorithm only requires HRV data, with optional raw ECG data to refine the respiratory rate estimate. Thereby, this algorithm can be easily applied in both laboratory and field settings, making it a valuable tool for exercise testing, intensity monitoring, and training load assessment.

Heart rate (HR), oxygen uptake (VO2), respiratory frequency (RF), DFA-α1, and VT algorithm

Figure: Heart rate (HR), oxygen uptake (VO2), respiratory frequency (RF), DFA-α1, and VT algorithm output for a representative study participant undergoing an incremental CPET on an ergometer.

Accuracy of Kubios VT algorithm

The accuracy of the Kubios VT algorithm was validated using CPET data from 64 recreationally active participants.The reference ventilatory threshold values (true VT1 and VT2) were determined from the CPET data by an experienced exercise physiologist. Compared to these reference thresholds, the Kubios algorithm provided accurate estimations for both VT1 and VT2.

First ventilatory threshold (VT1)

For VT1, the Kubios algorithm demonstrated a strong correlation with heart rate (r=0.62) and an even stronger correlation with oxygen uptake (VO2) (r=0.81). The average heart rate at the estimated VT1 was similar to the true VT1 (142 vs. 141 bpm), with a standard error of estimate (SEE) of 11 bpm. VO2 values at the estimated VT1 were slightly higher than at the true VT1 (1.89 vs. 1.74 l/min, SEE 0.28 l/min). The Kubios algorithm outperformed DFA-α1 in accuracy for VT1 estimation.

First ventilatory thrreshold (VT1)
First ventilatory threshold (VT1) and oxygen uptake (VO2)

Second ventilatory threshold (VT2)

At VT2, the Kubios algorithm showed very high correlations for both heart rate (r=0.82) and VO2 (r=0.93). The estimated and true HR at VT2 were nearly identical (170 vs. 169 bpm), with a SEE of less than 7 bpm. The VO2 values at the estimated VT2 matched the true values exactly (2.40 vs. 2.40 l/min, SEE 0.20 l/min).

Second ventilatory threshold (VT) and heart rate (HR)
Second ventilatory threshold (VT2) and oxygen uptake (VO2)

Conclusions and practical applications

The Kubios VT algorithm represents a significant advancement in HRV-based ventilatory threshold estimation. Its high accuracy offers a practical, non-invasive method for athletes, coaches, and clinicians to monitor training intensity and optimize performance without needing laboratory equipment. Additionally, it provides valuable insights into internal loading and fatigue management during exercise.

 

For more detailed information, you can read the full paper:

Eronen T, Lipponen JA, Hyrylä VV, Kupari S, Mursu J, Venojärvi M, Tikkanen HO, and Tarvainen MP. Heart Rate Variability Based Ventilatory Threshold Estimation – Validation of a Commercially Available Algorithm. medRxiv 2024.08.14.24311967.

Frequently Asked Question (FAQ)

What are ventilatory thresholds?

Ventilatory thresholds (VT1 and VT2) mark the points during exercise where the body transitions from aerobic to anaerobic metabolism. VT1 indicates the onset of lactate accumulation, while VT2 marks the point where the body can no longer buffer metabolic acidosis.

 

How does HRV estimate ventilatory thresholds?

HRV, specifically the DFA-α1 metric, changes as exercise intensity increases, reflecting the shift in autonomic nervous system towards sympathetic dominance. The Kubios VT-algorithm uses HRV data, along with HR reserve and respiratory frequency for enhanced accuracy, to estimate ventilatory thresholds.

 

How accurate is the Kubios VT-algorithm compared to traditional methods?

The Kubios VT-algorithm shows strong correlations with traditional CPET-derived thresholds, offering a highly accurate and non-invasive alternative for estimating VT1 and VT2.

 

Who can benefit from HRV-based ventilatory threshold estimation?

Athletes, coaches, and clinicians can use this method to monitor training intensity, optimize performance, and manage fatigue without the need for expensive laboratory equipment.

References 

  1. Michael S, Graham KS, Davis GM. Cardiac autonomic responses during exercise and post-exercise recovery using heart rate variability and systolic time intervals – A review. Front Physiol, 8:301, 2017.
  2. Sandercock GRH, Brodie DA. The use of heart rate variability measures to assess autonomic control during exercise. Scand J Med Sci Sports, 16(5):302–313, 2006.
  3. Rogers B, Giles D, Draper N, Hoos O, Gronwald T. 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, 2021.
  4. Rogers B, Giles D, Draper N, Mourot L, Gronwald T. Detection of the anaerobic threshold in endurance sports: Validation of a new method using correlation properties of heart rate variability. JFMK. 6(2):38, 2021.
  5. Gaskill SE, Skinner JS, Quindry J. Ventilatory threshold related to V̇O2 reserve, heart rate reserve, and rating of perceived exertion in a large varied sample. Med Sci Sports Exercise, 55(10):1876–85, 2023.
  6. Cross TJ, Morris NR, Schneider DA, Sabapathy S. Evidence of break-points in breathing pattern at the gas-exchange thresholds during incremental cycling in young, healthy subjects. Eur J Appl Physiol, 112(3):1067–76, 2012.
  7. Lipponen JA and Tarvainen MP. Accuracy of Kubios HRV software respiratory rate estimation algorithms. Kubios white paper, June 2021.