Validation of a non-linear index of heart rate variability to determine aerobic and anaerobic thresholds during incremental cycling exercise in women

Training zone demarcation is often based on physiological thresholds of blood lactate concentration or gas exchange under monitored laboratory or field conditions (Skinner and Mclellan 1980; Mann et al. 2013). Apart from the complexity and costs of these procedures, it is recognized that depending on the lactate threshold approach used, testing protocol, type of exercise, or the expertise of the gas exchange test interpreter (Yeh et al. 1983; Shimizu et al. 1991; Meyer et al. 1996), dissimilar threshold calculations can result in differing training recommendations (Jamnick et al. 2018). The need for objective, non-invasive and low-cost markers for threshold demarcation is apparent. Heart rate (HR) variability (HRV) measures have been tested for years as a potential monitoring tool, although mostly during resting conditions and with focus on vagal-related HRV indices (Buchheit 2014; Michael et al. 2017). Recently, studies have endeavored to evaluate the accuracy of HRV correlation properties, in particular, the short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA-alpha1) for delineation of physiological thresholds during endurance exercise (Gronwald et al. 2020b, 2021; Rogers et al. 2021a). This index represents the fractal, self-similar nature of cardiac beat-to-beat intervals. For DFA-alpha1 calculation, the root mean square fluctuation of the integrated and detrended data is measured in observation windows of different sizes. This is done using a logarithmic plot of the data against the size of the window. The resulting slope of the line relating the (log) fluctuation to the (log) window size represents the scaling exponent (Mendonca et al. 2010). At low exercise intensity, DFA-alpha1 values usually are near 1.0 or slightly above, signifying a well correlated, fractal pattern (Gronwald et al. 2020b). As intensity rises, the index will drop past 0.75 near the aerobic threshold (AT) then approach uncorrelated, random patterns represented by values near 0.50 and below at higher work rates (Rogers et al. 2021a, b). These observations are attributable to changes in autonomic nervous system balance, namely the withdrawal of the parasympathetic and enhancement of the sympathetic branch as well as the influence of other non-neural factors during endurance exercise (Persson 1996; White and Raven 2014; Qu et al. 2014; Gronwald et al. 2020b). Moreover, DFA-alpha1 appears to be an index that reflects the overall systemic state of internal load (Gronwald and Hoos 2020; Rogers et al. 2021d). In contrast to other typical HRV indexes such as time domain SDNN (total variability as the standard deviation of all normal RR-intervals) which reaches a nadir at the first ventilatory and lactate thresholds (staying suppressed afterward) (Tulppo et al. 1996; Karapetian et al. 2008), DFA-alpha1 possesses a wide dynamic range spanning all exercise intensity domains (Gronwald et al. 2020b). Furthermore, its dimensionless nature allows application independent of an individual’s fitness status and without the need for prior normalization to blood lactate concentration or gas exchange kinetics (Gronwald et al. 2020b; Rogers et al. 2021d, e). Recent investigations showed agreement in the intensities reached at DFA-alpha1 derived thresholds marked as 0.75 (HRVT1) and 0.50 (HRVT2) with the first (VT1) and second ventilatory threshold (VT2) during an incremental treadmill protocol in recreational male runners (Rogers et al. 2021a, b). However, since an electrocardiogram (ECG) was used for RR interval data recording with artifact level lower than 3%, it remained unclear whether the same results can also be confirmed in the general application with commercial chest straps and divergent error presence. A follow-up study examining the effects of missed beat artifact on both DFA-alpha1 as well as the HTVT1 showed minimal effects at levels below 5% using the Kubios “automatic” correction method. In addition, there was a small but significant degree of bias of the HRVT1 (4 bpm) between ECG and the Polar H7 (Rogers et al. 2021c). In addition, the HRVT1 was shown to strongly agree with that of the VT1 in a male cardiac disease population (Rogers et al. 2021e). What remains unclear is the question of whether the HRVT1 and HRVT2 thresholds agree with that of the VT1 and VT2 in a group of female participants. If the use of the fixed 0.75 and 0.50 DFA-alpha1 values for threshold identification is confirmed in that demographic, widespread application for intensity distribution monitoring could result. In addition to benefits for routine sports laboratory work, this metric could provide real-time information about absolute exercise intensity based on systemic internal load without prior lactate or gas exchange testing with only HRV monitoring. However, a generalization of the applicability, especially due to the few trials for large parts of the population as well as exercise protocols requires further investigation. There is a particular need for data on women as it has been demonstrated that there are significant gender differences in HRV-related cardiac stress responses and these may also be reflected in non-linear metrics (Adjei et al. 2018). The reasons behind this disparity could be attributed to hormonal, neuroanatomical and cognitive differences in the sexes with age being an essential modulator (Ramaekers 1998; Balhara et al. 2012). Since the gender difference in cardiac autonomic function narrows between the age of 40 and 50 with women becoming menopausal during this age range, the female hormone estrogen is suspected of having a significant influence which can furthermore be observed during the menstrual cycle (Ramaekers 1998; Yildirir et al. 2001). Therefore, this study aims to extend the validity of DFA-alpha1-based intensity thresholds to female participants tested in a cycling ramp protocol.

The percentage of artifacts was calculated based on the Kubios automatic correction method for each participant’s test data. Since only a portion of the entire exhaustive test was used for the linear interpolation of DFA-alpha1, the artifact percentage listed refers to that section only. Artifact percentage for the linear plotted data series was between 0 and 1.6% (Table 2).

The average VT1 VO₂ and VT2 VO₂ were 25.2 ml/kg/min (± 2.8) and 31.5 ml/kg/min (± 3.6) compared to 26.5 ml/kg/min (± 4.0) and 31.9 ml/kg/min (± 4.5) obtained by the DFA-alpha1-derived thresholds HRVT1 and HRVT2. The average HR at VT1 VO₂ was 147 bpm (± 15.6) and 167 bpm (± 12.7) for VT2 VO₂ compared to 152 bpm (± 15.5) and 166 bpm (± 13.2) obtained by HRVT1 and HRVT2. High correlations could be found between the variables represented in Pearson’s r values of 0.81 (VT1 vs. HRVT1, p < 0.001, ICC_3,1 = 0.77) and 0.86 (VT2 vs. HRVT2, p < 0.001, ICC_3,1 = 0.84) for the VO₂ comparisons and 0.87 (VT1 vs HRVT1, p < 0.001, ICC_3,1 = 0.87) and 0.90 (VT2 vs. HRVT2, p < 0.001, ICC_3,1 = 0.90) for the respective HRs (Fig. 1). Whereas the comparison of VT2 and HRVT2 showed no differences (VO₂: p = 0.385, d = − 0.173; HR: p = 0.661, d = 0.086), VT1 vs. HRVT1 revealed significant differences with regard to both metrics (VO₂: p = 0.010, d = − 0.549; HR: p = 0.005, d = − 0.601). Bland Altman analysis for VT1 vs. HRVT1 for VO₂ showed a mean difference of − 1.3 ml/kg/min (± 2.4) with upper and lower limits of 3.3 and − 6.0 ml/kg/min (Fig. 2a). Regarding HR there was a difference of − 4,7 bpm (± 7.8) with upper and lower limits of 10.6 and − 20.0 bpm (Fig. 2b). Bland Altman analysis for VT2 vs. HRVT2 for VO₂ (Fig. 2c) showed a mean difference of − 0.4 ml/kg/min (± 2.3) with upper and lower limits of 4.1 and − 4.9 ml/kg/min. In addition, a mean deviation of 0.5 bpm (± 5.7) with upper and lower limits of 11.8 and − 10.8 bpm for the respective HR was shown (Fig. 2d).

The present study followed recent published approaches (Rogers et al. 2021a, b, e) to compare workloads at fixed DFA-alpha1 values of 0.75 and 0.50 to the corresponding work rate at VT1 and VT2 during an incremental test. These prior investigations were done in healthy male participants and male participants with cardiac disease. The resulting findings showed strong similarities in terms of VT1 vs. HRVT1 for both demographic groups, whereas VT2 vs. HRVT2 was only studied in the former population, but also showed a good relation in terms of HR (Rogers et al. 2021a, b, e). Since no data were previously available for female participants, the findings of this study are intended to test the utility of DFA-alpha1 for these purposes.

The present analysis showed high correlations for the comparison of VT1 vs. HRVT1. The mean difference for VO₂ (1.3 ml/kg/min) was nearly in line with the corresponding deviation for men with cardiac disease (1.2 ml/kg/min) (Rogers et al. 2021e) as well as for healthy male participants (0.3 ml/kg/min) (Rogers et al. 2021a). The upper and lower LoA for the comparison of VT1 vs. HRVT1 in the present study showed a wider range for both VO₂ and HR similar to the group of participants with cardiac disease. However, this difference was rated as minimal from a clinical and practical standpoint. With regard to the VT2 vs. HRVT2 comparison, all analyses confirmed strong agreement in terms of VO₂ and HR. With a Pearson’s correlation coefficient of 0.90 for the HR comparison, it was even higher than in the study by Rogers et al. (2021b), which yielded a coefficient of 0.78.

Potential factors for DFA-alpha1 bias include artifact correction, recording device type (Rogers et al. 2021c), unsuitable detrending (Voss et al. 2015), fatigue, and stress (Rogers et al. 2021d). Although artifact correction can induce a proportional bias resulting in DFA-alpha1 to decline relatively later in the exercise ramp (Rogers et al. 2021c), this does not apply to the present data since artifact percentage was very low (0.2%). With respect to device induced bias, it has been shown that the H7 Polar chest strap induced an early drop in DFA-alpha1 during incremental testing (Rogers et al. 2021c), not a late one as in our study. Proper detrending was ensured using Kubios HRV software (Tarvainen et al. 2014) mirroring the methods of Rogers et al. (2021a). Finally, fatigue can also cause a premature decline in DFA-alpha1 (Rogers et al. 2021d), but the participants in the present study were well rested.

It seems reasonable that the observed delayed decline and wider LoA of DFA-alpha1 at HRVT1 during incremental ramp testing in many participants could be due to sex specific hormonal differences. It is known that estrogen enhances parasympathetic control of the heart which is why premenopausal women experience higher vagal activity compared to males and postmenopausal women (Adjei et al. 2018). Furthermore, it has been shown in previous studies that the balance of ovarian hormones reflected in the menstrual cycle might play a significant role on the regulation of autonomic tone (Yildirir et al. 2001; Bai et al. 2009). There is a plethora of data on linear HRV indexes with respect to the female cycle, the vast majority of which demonstrate a decrease of the vagal dominance on the heart from the follicular to the luteal cycle phase (von Holzen et al. 2016). To date, there is very limited data on non-linear HRV properties and no data regarding intensity thresholds. Although the few existing studies also point to a loss of complexity in the late cycle phase, the results must be treated with caution due to methodological limitations like low data sampling rates, processing without Kubios HRV software or different measurement time points of assessing HRV (Bai et al. 2009; Rawal 2014).

The present study is the first to examine DFA-alpha1 behavior during exercise in females. Based on the present findings, it is possible that the HRVT1 slightly shifts with the fluctuations in ovarian hormones across the menstrual cycle or with female hormone status in general. Definitive evaluation of this situation is problematic since every woman possesses an individual endogenous hormonal profile which is further affected by exogenous factors such as physical activity and exercise habits, performance level, diet habits, hormonal contraceptives or hormone replacement therapy (Elliott-Sale et al. 2021). This can be best seen in the partially contradictory results on the influence of premenstrual syndrome (PMS) or premenstrual dysphoric disorder (PMDD) on HRV (von Holzen et al. 2016). The delay in DFA-alpha1 decline during the exercise ramp resulting in slight HRVT1 bias may also relate to the observation that women are more resistant to neuromuscular fatigue during endurance exercise (Temesi et al. 2015). It is plausible that reduction or delay in the onset of neuromuscular fatigue can affect DFA-alpha1 kinetics (Rogers et al. 2021d).

Interestingly, despite the issues surrounding agreement between HRVT1 and VT1 in the female participants, the conformity between HRVT2 and VT2 was similar to that reported in men (Rogers et al. 2021b). A possible reason revolves around the underlying nature of the HRVT2 which corresponds to a loss of physiologic sustainability and organismic destabilization (Rogers et al. 2021b), therefore, more resistant to hormonal changes.

Overall, the agreements obtained in this study seem comparable to those of conventional surrogate markers. Denis et al. (2021), for example, showed similar differences in the thresholds between blood lactate and gas exchange during a stage protocol in 14 males. It is also important to mention that the agreement of physiological markers for threshold determination may also vary due to changes in the work rate increase during incremental tests (Hughson and Green 1982), pedaling frequency (Hughes et al. 1982), inter- and intra-rater agreement (Gladden et al. 1985), glycogen depletion (Hughes et al. 1982) and determination methods used (Gaskill et al. 2001). Additionally, the mean differences presented in this study are of similar magnitudes as Functional Threshold Power (FTP) vs. Maximal Lactate Steady State (MLSS) (Klitzke Borszcz et al. 2019) or deoxygenated hemoglobin breakpoints vs. MLSS (Bellotti et al. 2013) and thus show similar practical utility as a non-invasive marker for an anaerobic threshold.

From a practical standpoint, the cutoff values of 0.75 and 0.50 seem to be well-suited markers for zone 1 to 2 and 2 to 3 transition (in a three zone model) in most of the women observed. This method of estimation of physiological breakpoints demonstrates the usability of DFA-alpha1 as a marker of organismic demands and internal load. Unlike other physiological and subsystemic indicators (like gas exchange or blood lactate concentration indicators), the correlation properties of HRV offer a more integrated perspective of autonomic nervous system regulation (Gronwald et al. 2020b). These regulation patterns could be seen as an outcome of the concepts underlying network physiology (Balagué et al. 2020) which embody the complex dynamic interplay of electro-physiological, hemodynamic, and humoral variables, along with the effects of the autonomic and central nervous system regulation (Gronwald et al. 2020b). When intensity reaches a moderate to high level, a disintegrating process is initiated, while culminating into progressive segregation of subsystems and mechanization (e.g. performance attractor) of the entire system (Gronwald et al. 2020a, b).

Future work should further investigate possible factors influencing the course of DFA-alpha1 in women. Due to the varied endogenous hormone profiles influenced by external factors like hormonal contraceptives, diet or exercise habits, specific considerations should be made regarding participant selection criteria and adaptations for experimental design according to the recommendations of Elliott-Sale et al. (2021). This would demand a recruitment based on pre-defined, standardized criteria as well as methodological decisions in terms of the timing of testing throughout the menstrual cycle (Elliott-Sale et al. 2021). In addition, exploration of endurance exercise induced neuromuscular fatigue between male and females in association with DFA-alpha1 behavior may yield interesting insights into alteration of gender specific autonomic balance during these kinds of sporting activities.

Since the the recording methodologies prescribed involve a post-session data interpretation, it would be recommendable to follow-up on the work of Gronwald et al. (2021) who illustrated the potential of real-time DFA-alpha1 and intensity distribution monitoring during endurance exercise. This could serve as a milestone for a further development of apps or even smart devices (e.g. smart watches) able to display this metric and thus give immediate feedback of systemic internal load during exercise (Rogers and Gronwald 2022).

Non-linear heart rate variability analysis during a cycle ergometer ramp demonstrated good agreement between the heart rate and oxygen uptake at first and second ventilatory threshold and the metrics related with DFA-alpha1 cutoff values of 0.75 and 0.50 in a population of randomly selected women. Although promising, additional study is recommended in terms of validating commercially available recording methods, the verification of other exercise protocols and sporting activities. Furthermore, it would be interesting to investigate DFA-alpha1 behavior in women in the context of their menstrual cycle, and pre vs post menopause status. If investigations into these matters confirm the present results, it may be feasible to extend traditional approaches for intensity distribution assessment (e.g. based on lactate or gas exchange measurement) with real-time calculation of DFA-alpha1 via a wearable device during endurance exercise.