Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete

Abstract

A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.

Aerobic Threshold Identification in a Cardiac Disease Population Based on Correlation Properties of Heart Rate Variability

“…Given its dimensionless nature, calibration with gas exchange should not be needed. Therefore, the DFA a1 threshold of 0.75 can potentially be observed over an exercise session, enabling subjects to track and limit their intensity accordingly in a real-time monitoring assessment [ 32 ].…”

“…Although more investigation needs to be done regarding usage over longer time frames, values above the 0.75 level should correspond to intensities below the aerobic threshold and suitable for zone 1 training. With chest belt recording devices, smartphone or smartwatch form factors and appropriate software already available, real-time tracking of DFA a1 could aid with both aerobic threshold estimation as well as enforcement of a low-intensity limit in cardiac rehabilitation and athletic training [ 32 ].…”

Variabilidad del ritmo cardiaco e índices antropométricos en hombres universitarios de Santiago de Chile

“…En cuanto al sobrepeso y obesidad se sabe modulan el comportamiento de la VRC, siendo también influenciado este por otros estímulos externos predominantes sobre el estatus de adiposidad (5,(28)(29)(30) . En este sentido, el valor reportado para el IMC (25,48 ± 2,19 kg/m 2 ) hace presumir una activación simpática y retirada vagal, mientras que la evidencia indica que un estado nutricional normopeso (IMC < 24,9 kg/m 2 ) se puede relacionar con una activación vagal, no obstante, los hallazgos reportados en este estudio son controversiales, puesto que los parámetros de VRC muestran un comportamiento normal comparados con los reportes establecidos por la literatura (LF/HF entre 1,5 a 2; SD1>20 ms; α1=1) (9,(31)(32)(33)(34) .…”

“…Esta controversia puede entenderse parcialmente al observar los valores medios del IC-C (0,81 ± 0,02), puesto que este es un mejor predictor del control nervioso cardíaco y balance simpático vagal ya que se correlaciona directamente con la capacidad de almacenamiento de tejido adiposo intrabdominal y riesgo cardiometabólico, independientemente de la masa corporal total evaluada por medio del IMC (16,35) . Por otro lado, este planteamiento también puede explicarse en parte al observar la asociación inversa entre los índices antropométricos (IMC e IC-C) y el análisis fractal α-1, puesto que un valor ~1 (creciente o decreciente) se relaciona con pérdida hemodinámica, considerándose los valores en torno a 0,5 como el límite entre propiedades fractales aleatorias y no aleatorias expresadas en una menor adaptabilidad frente a diversos estímulos (33,34) . Estos hallazgos cobran gran importancia sobre estudiantes universitarios ligados al deporte en profesiones como preparadores físicos, profesores de educación física o científicos del deporte debido a que estos comúnmente están expuestos a múltiples parámetros moduladores de la VRC.…”

Analysis of Fractal Correlation Properties of Heart Rate Variability during an Initial Session of Eccentric Cycling

“…If the variable DFA a1 decline seen with unaccustomed ECC is due to a corresponding variation in autonomic stress, real-time monitoring of this metric could be of great value in assuring patient safety as well as providing individual training intensity prescription guidelines. A smartphone application developed for many available devices (Android and iOS) using consumer grade heart rate monitors has also been explored for both DFA a1 threshold testing and in adhering to a polarized training model [ 32 ]. Further assessment of the usage of DFA a1 for real-time monitoring during ECC seems a worthwhile endeavor especially in at-risk patient populations.…”