Kubios Analytics for Sports and Exercise updated

The sport and exercise analytics offers detailed performance analytics and heart rate recovery for different types of training and exercise sessions. The analytics have been recently updated to include estimates for ventilatory thresholds (VT1 and VT2) and oxygen uptake.

About Kubios sport and exercise analytics

Kubios sport and exercise analytics is designed for professionals who require detailed performance analytics for different types of training and exercise sessions. The analytics is based on beat-to-beat heart rate variability (HRV) data, which does not require expensive lab equipment and can be easily measured also outside on the field, says Mika Tarvainen, CEO of Kubios. We have over 20 years of experience in HRV and we are proud to present our latest algorithms for ventilatory threshold and oxygen uptake estimation to improve sports performance monitoring, he adds.

For a coach, the analytics that Kubios is able to provide are very useful because it provides the key physiological performance benchmarks without the need of lab equipment, says Riccardo Proietti, sport physiologist and strength/conditioning coach, working in professional football and with elite endurance athletes. I use the analytics also personally to monitor my own training, as ex-triathlete I’m still training for pleasure and see the Kubios training report after any swim/bike/run session because it keeps me more interested and motivated to reach my limit, Riccardo adds.

Riccardo Proietti training

Kubios sport and exercise analytics is available in the following Kubios HRV software products:

1. Kubios HRV Scientific – HRV analysis software designed for research and professional use (runs in Win & macOS).
2. Kubios sports and exercise analysis service – Cloud service for companies who want to integrate Kubios analytics in their training, sports or exercise application (documented API available upon request)

Features of sport and exercise analysis

Kubios sport and exercise analytics provides information about the cardiorespiratory function, training effect, and metabolic profile of the athlete. In addition, heart rate recovery, which is known to correlate with cardiorespiratory fitness, is provided.

Cardiorespiratory function is assessed via heart rate (HR) and respiratory rate (RESP), where RESP is estimated from the beat-to-beat HRV data by using a validated algorithm [1, 2]. RESP is an important parameter in exercise physiology since it is a strong marker of “physical effort”. Compared to oxygen consumption (VO2) and HR, RESP responds rapidly to fast changes in workload (characteristic especially for high intensity interval training, HIIT), and thus, RESP is strongly associated with the on/off metabolic demand [3]. Instantaneous values of HR and RESP as well as time spent at different HR and RESP zones are reported as illustrated in the figure below.

Cardiorespiratory function sports analytics

Training effect is assessed by using the training impulse (TRIMP), which increases exponentially as a function of exercise intensity, modeling lactate accumulation during exercise [4]. In Kubios analytics, the TRIMP is computed using beat-to-beat HR values, and therefore, the instantaneous value of TRIMP (TRIMP/min) can be reliably derived to represent training intensity. The accumulation of TRIMP, on the other hand, reflects training load accumulation. An illustration of training effect assessment is shown below.

training effect sports analytics

Metabolic profile of the athlete can be assessed via ventilatory thresholds and oxygen uptake (VO2). In exercise prescription, training efforts are typically separated into three intensity zones based on aerobic and anaerobic thresholds, which are generally defined by the first and second ventilatory or lactate thresholds. Kubios analytics provides validated estimates for the ventilatory thresholds VT1 and VT2. Ventilatory thresholds are estimated based on instantaneous values of HR, RESP, and DFA alpha1, where DFA alpha1 reflects fractal behavior of HRV [6]. The accuracy of the Kubios VT algorithm is -1.2 ± 10.7 bpm (mean ± SD) for the VT1 and -1.0 ± 6.7 bpm for the VT2. In addition to ventilatory thresholds, oxygen uptake during the exercise is estimated by using a well known relationship between the HR and VO2 [6, 7]. Instantaneous VT and VO2 values as well as time spent at specified zones are reported as illustrated below.

Metabolic profile sports analytics

Heart rate recovery (HRR) tells how much HR drops at specific time frames after exercise cessation. Similar to HRV, HRR has been linked with training status due to its association with the autonomic nervous system. Well trained athletes show faster HRR compared to untrained individuals. In addition, longitudinal studies have shown faster HRR with an improvement in training status after a training intervention [8, 9]. HRR is reported at 60s, 120s, and 300s increments. In addition, a fast 30s recovery (T30) within 0-40s after exercise cessation is reported.

heart rate recovery sports analytics
About Kubios

Kubios is a software company specializing in heart rate variability (HRV) analysis. HRV is a physiological phenomenon where the time intervals between consecutive heartbeats vary from beat-to-beat. The variation is due to continuous regulation of the heart rate by the autonomic nervous system. HRV is the most commonly used tool for objective assessment of physiological stress and recovery, but requires accurate analysis tools. Kubios HRV software products provide the most detailed HRV analysis in the market and they are widely used by researchers and professionals around the world.



1. Lipponen JA and Tarvainen MP. Accuracy of Kubios HRV software respiratory rate estimation algorithms. Kubios white paper, 2021.
2. Rogers B, Schaffarczyk M, and Gronwald T. Estimation of respiratory frequency in women and men by Kubios HRV software using the Polar H10 and Movesense Medical ECG sensor during an exercise ramp, Sensors, 22(Article 7156):1-12, 2022.
3. Nicolò A, Massaroni C, and Passfield L. Respiratory frequency during exercise: the neglected physiological measure. Front Physiol, 8(Article 922):1-8, 2017.
4. Morton RH, Fitz-Clarke JR, and Banister EW. Modeling human performance in running. Environ Exerc Physiol, 69(3):1171-7, 1990
5. Rogers B and Gronwald T. Fractal correlation properties of heart rate variability as a biomarker for intensity distribution and training prescription in endurance exercise: an update, Front Physiol, 13(Article 879071):1-11, 2022.
6. Christensen CC, Frey HM, Foenstelien E, Aadland E, and Refsum HE. A critical evaluation of energy expenditure estimates based on individual O2 consumption/heart rate curves and average daily heart rate. Am J Clin Nutr, 37(3):468-472, 1983.
7. Ceesay SM Prentice AM, Murgatroyd PR, Goldberg GR, Scott W, and Spurr GB. The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry. Br J Nutr 61(2):175-186, 1989.
8. Daanen HAM, Lamberts P, Kallen VL, Jin A, and Van Meeteren NLU. A systematic review on heart rate recovery to monitor changes in training status in athletes. Int J Sports Physiol Performance, 7:251-260, 2012.
9. Fecchio RY, Brito L, Leicht AS, Forjaz CLM, and Peçanha T. Reproducibility of post-exercise heart rate recovery indices: A systematic review. Autonom Neurosci Basic Clin, 221(Article 102582):1-9, 2019.

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