Tuesday, February 4, 2025

DFA a1, Respiratory Rate as measures of Durability

Although there have been 2 published studies examining DFA a1 behavior after either long duration low (ultramarathon) and short duration high intensity exercise (post ramp to failure), there has been a lack of a dedicated look at a1 through more typical scenarios. With this in mind, the just released article looking at a1 trajectory over the course of a time to task failure (TTF) trial is of great interest. Further, to add even more value to these observations, we have measures of VO2, lactate, glucose and respiratory rate (fB) to report.

 

The article....

Abstract
Purpose. Field based measures of durability (exercise-related physiologic deterioration over time) for assessing athletic fitness often rely on changes in maximal power profiles or heart rate (HR) drift. This study aimed to determine whether an index of HR variability based on the short-term exponent of Detrended Fluctuation Analysis (DFA a1) along with respiratory frequency (fB) could demonstrate changes in durability during a Time to Task Failure (TTF) Trial.
Methods. Ten participants performed a cycling TTF at an intensity of 95% of the respiratory compensation point (RCP) on two occasions, Control and a “Reward” where a monetary incentive was offered when task failure was signaled. Metabolic responses including oxygen uptake (V̇O2), lactate and glucose along with HR, DFA a1 and fB were measured and compared over each quarter of the TTF up to the time of signaling (Q1,Q2,Q3,Q4).
Results. The elapsed time of TTF sessions was statistically similar (p = 0.54). After initial equilibration, metabolic responses remained largely stable over Q2-Q4. Both HR, DFA a1 and fB displayed drift over Q2-Q4 with significant ANOVA. Repeatability of quarterly HR, DFA a1, fB between Control and Reward sessions was high with ICC between 0.73- 0.94, Pearson’s r between 0.83-0.98 with no difference in mean values by paired t-testing.
Conclusion. HR, fB and DFA a1 are useful metrics representing alteration in physiologic characteristics demonstrating durability loss during an endurance exercise session. These measures were repeatable across sessions and have the potential to be monitored retrospectively or in real time in the field with low-cost consumer equipment.
 
Introduction
Over the past several years it has been suggested that assessment of endurance exercise performance should incorporate measures beyond commonly used outcomes such as maximum oxygen uptake (V̇O2max), mechanical efficiency, critical power (CP) or the intensity reached at the maximal metabolic steady state (MMSS) (Maunder et al. 2021; VAN Erp et al. 2021; Mateo-March et al. 2022; Jones 2023). One such measure is the capacity to withstand exercise induced performance loss (i.e., performance fatigability) over time (Maunder et al. 2021; Jones 2023). This concept has been described by terms such as “durability” (Maunder et al. 2021) and “physiologic resilience” (Jones 2023). Maunder et al. defined “durability” as “deterioration in physiological-profiling characteristics over time during prolonged exercise”. Alternatively, Jones defined physiologic resilience as “the ability to resist fatigue and maintain performance”. Therefore, these concepts recognize both the existence of performance degradation over time, and the importance of its objective quantification. From a practical standpoint, a group of individuals may have similar V̇O2max, CP or MMSS values, but variations in their “durability/resilience” may lead to markedly disparate race results, recovery needs, and training targets (Muriel et al. 2022; Leo et al. 2024; Hamilton et al. 2024).
Various measures have been used to identify aspects of endurance exercise performance degradation. These include hormonal/metabolic, neuromuscular, and central nervous system elements with few being practical during an ongoing endurance activity, especially under field conditions (Gandevia 2001; Lambert 2005; Ament and Verkerke 2009; Noakes 2011). Examples include salivary hormone markers (Deneen and Jones 2017), muscle enzyme elevation (Martínez-Navarro et al. 2019), blood lactate concentration (Jastrzębski et al. 2015), markers of substrate availability (Schader et al. 2020), cortical activity (Ludyga et al. 2016), functional testing such as the counter movement jump (Wu et al. 2019) and measures of running economy (Scheer et al. 2018), with few being practical for ongoing activity. One commonly used field based method is the upward “drift” in heart rate (HR) that occurs with prolonged exercise (Maunder et al. 2021; Smyth et al. 2022). Heart rate drift is a complex process dependent on multiple factors including fluid balance, skin or core temperature, cardiac preload dynamics and stroke volume change (Souissi et al. 2021; Billat et al. 2022). Additionally, upward HR drift has been noted to be absent or even downward under some circumstances where there is a reduction in work rate to keep the metabolic demand constant (Billat et al. 2012; Zuccarelli et al. 2018) or even during very prolonged endurance running (Mattsson et al. 2011). Therefore, dependence on the extent of HR drift as the sole piece of evidence demonstrating durability change could lead to erroneous conclusions, underscoring the need for additional types of confirmatory data. Other field based tests to assess performance fatigability rely upon the change in all-out efforts, time to task failure (TTF) bouts, maximal power profiles, or time trial efforts (Sanchez-Jimenez et al. 2023; Spragg et al. 2023; Almquist et al. 2023; Bitel et al. 2024). These types of evaluations might be problematic for individuals physically unable or unwilling to perform them due to training schedule, or simply in connection to health concerns or logistic restrictions. Potential examples are individuals who are not able to perform these high impact sessions including those with peripheral vascular or ischemic cardiac disease, recent orthopedic injury or post operative procedures as well as athletes interested in assessments during lower intensity training cycles or prior to race events. Other situations where more conventional TTF or maximal volitional efforts may not be feasible are in cases of depression or issues with motivation (Silvia et al. 2016). Therefore, it would be beneficial to find additional indicators of durability that are practical for widespread usage.
Potential methods to assess exercise durability from the autonomic nervous system (ANS) perspective include respiratory frequency (fB) (Syabbalo et al. 1994; Nicolò et al. 2017) and heart rate variability (HRV) (Greco et al. 2019; Rogers and Gronwald 2022). Previous data indicated that the fB is more dependent on muscular afferent signaling and central nervous system input than metabolic components such as acidosis or blood lactate concentration [La-]b (Nicolò et al. 2017). The fB is also highly associated with the change in the rating of perceived exertion (RPE), a recognized marker of endurance performance fatigability (Pires et al. 2011; Nicolò et al. 2016). Although studies demonstrate that resting HRV may provide information on ANS status (Boullosa et al. 2014; Düking et al. 2021), neither the resting nor post session modalities19 can answer the question of whether a specific exercise endeavor is leading to ANS perturbation as the activity occurs. Recently, the study of HRV during endurance exercise using a nonlinear index, alpha 1 of Detrended Fluctuation Analysis (DFA a1) has received attention as a means of assessing ANS status (Rogers and Gronwald 2022). DFA a1 is a measure of the fractal nature of the cardiac beat sequence (Goldberger 1996; Gronwald and Hoos 2020). This fractal behavior can also be mathematically quantified as “correlation properties” (not to be confused with statistical correlation coefficients) of the cardiac beat repetition patterns over variable time spans. To better understand the notion of correlation properties, comparisons to a random walk have been drawn (Hardstone et al. 2012). For example, during a random walk, at each next step, the walker can choose to go either left or right. If the choice the walker makes is not random but based on the previous sequence (series of left or right decisions), the pattern is described as being well “correlated” (DFA a1 near or above 1.0), since the future pattern is based on the past history. Reports indicate that DFA a1 decreases with increasing work rates, starting with well correlated values (i.e., DFA a1 above 1.0) at very low intensities, then moving through a “partially” correlated zone at moderate intensities (between 1.0 to 0.5), passing the “uncorrelated” value of 0.5 near the heavy/severe intensity boundary, and finally reaching values below 0.5, signifying an “anticorrelated” pattern in the severe intensity domain (Gronwald and Hoos 2020; Rogers and Gronwald 2022). Therefore, DFA a1 values possess excellent dynamic range, encompassing all intensity domains. Moreover, these observations have been leveraged into the concept of using certain benchmark degrees of correlation properties (represented by DFA a1) as surrogates of the gas exchange threshold (GET) or respiratory compensation point (RCP) intensity (Gronwald et al. 2020; Rogers and Gronwald 2022). However, since this index is dependent on ANS status, it may also be an appropriate measure of autonomic durability if it changes over the course of prolonged endurance exercise (Rogers et al. 2021c; Gronwald et al. 2021b; Schaffarczyk et al. 2022a; Van Hooren et al. 2023). In other words, combinations of exercise time and intensity may lead to a reduction of DFA a1 from what it normally would have been expected in a non-fatigued state. In a study looking at DFA a1 levels during a fixed low intensity treadmill session before and immediately after a 6 hour ultramarathon run, DFA a1 was markedly suppressed after the 6 hours (Rogers et al. 2021c). Additionally, a recent report showed suppression of DFA a1 relative to exercise intensity immediately after an incremental running ramp to exhaustion (Van Hooren et al. 2023), supporting the notion that this index can serve as a marker of autonomic durability during activity. Therefore, since exercise related HRV and fB can be evaluated with consumer grade equipment (Nicolò et al. 2020; Rogers et al. 2022a, b; Schaffarczyk et al. 2022b), they become prime candidates for examining exercise durability under field conditions, both retrospectively and potentially in real-time(Gronwald et al. 2021a).
Thus, this study explored the utility of two available field-based metrics of ANS status (i.e., fB and DFA a1) along with HR to assess exercise durability loss over a cycling TTF performed at an intensity in the upper boundary of the heavy intensity domain. Since the performed intensity is below the MMSS, we hypothesized that metabolic indicators such as oxygen consumption (V̇O2), [La-]b, and blood glucose concentration ([Gluc]) would remain stable after initial equilibration (Laughlin 1999; Keir et al. 2018) but there will be an upward drift of fB and progressive decline in DFA a1 over the TTF denoting alteration of ANS durability.
 

Methods
Experimental Approach
All participants came to the laboratory on three occasions to complete: i) a step ramp step (SRS) test (Keir et al. 2022) to determine V̇O2max, peak power output (PPO), and to estimate the power output (PO) associated with the MMSS; ii) an initial TTF performed at a target intensity of 95% of the estimated MMSS (Control); and iii) a second TTF at the same intensity but with an offer of a small monetary reward made near task failure (Reward). During each TTF, participants provided a visual signal to the examiner about 1 minute before initial task failure. Comparisons between tested metrics (V̇O2, [La-]b, [Gluc], fB, DFA a1) were developed using a variation of the “isotime” method (Nicolò et al. 2019). This method was chosen to optimize individual response metrics, reduce between subject variability in TTF duration with no loss of data in comparison to traditional group comparisons (Nicolò et al. 2019; Souron et al. 2022). For this study, the isotime was defined as a fixed portion of total time duration. In other words, each participant’s TTF active duration (up to the signal time) was segmented into quarters (Q1, Q2, Q3, Q4) and these quarterly values were compared in two fashions. For example, if Participant 1 had a total TTF duration of 60 minutes, the quarterly isotimes would be 15 minutes each. If Participant 2 had a duration of 40 minutes, each quarterly isotime would be 10 minutes each. Quarterly mean values per metric were compared for all TTFs performed (e.g., DFA a1 for 10 participants times two TTF trials, resulting in 20 TTF measurements per quarter with mean quarterly DFA a1 compared). Additionally, evaluation of each metric’s repeatability was assessed between Control and Reward trials (e.g., 10 participants quarterly DFA a1 during Control vs the same 10 participants during Reward trials).

 
Participants
Data from 10 volunteers (5 males, 5 females) are included in this study. These participants are a subset of a larger cohort (n= 18) evaluating the effects from the unexpected offer of a small monetary reward close to task failure on exercise performance (i.e., duration). Eight participants from the full data set evaluating a different research question were excluded from the current study due to a malfunction in the Polar H10 unit making it unable to record HRV during the testing session. Participants were between 18-30 years of age, able to pass the CSEP Physical Activity Readiness Questionnaire-Plus (PAR-Q+), and physically active for 1-4 hours of regular exercise per week. Exclusion criteria included recent injury, BMI > 30 kg/m2, history of tobacco use and alcohol usage (males > 15 drinks/week; females > 7 drinks/week). None admitted having cardiovascular or metabolic disease. Approval was obtained from the Conjoint Health Research Ethics Board at the University of Calgary (REB21-1855). Prior to all exercise, participants signed a written informed consent form. All procedures were in accordance with the latest description of the Declaration of Helsinki.


Cycling Ergometer and TTF Testing Protocols

Testing sessions were performed on an electromagnetically braked cycle ergometer (Velotron: RacerMate, Seattle, WA), in an environmentally controlled room (temperature: 19-20°C; humidity: 50-60 %), with at least 48 hours between each session, and at a similar time of the day (±1 hr). Prior to each session, participants were instructed to avoid the consumption of food and caffeinated and/or alcoholic beverages for at least 2 and 12 hours, respectively, and to abstain from strenuous physical activity for at least 24 hours. Participants self-selected their cadence (70-90 revolutions/min (rpm)) and maintained it throughout the entirety of the study. Participants were blinded to PO and elapsed time. The definition of task failure included either volitional exhaustion that resulted in task termination, or the inability to continue cycling within 10 rpm of the selected cadence for greater than 5 s despite strong verbal encouragement.
The SRS cycling protocol included: i) a moderate-intensity step-transition to estimate the V̇O2 mean response time (MRT) (Iannetta et al. 2020) which involved cycling for 4 min at 20 W, followed by 6 min cycling at 60-100 W with the PO selected based on predicted fitness level of the participant to maximize increases in the V̇O2 value while ensuring moderate intensity domain response); ii) an incremental ramp cycling test that included a 4-min baseline at 20 W followed by the PO being increased in a ramp-like manner by 30 W·min−1 (1 W every 2 s) until task failure; iii) a 30-min rest period followed by participants transitioning from a 2-min 20 W baseline to a steady-state exercise for 12 min corresponding to a PO of 50-65% peak PO representing the heavy domain (HVY). The HVY bout allowed for an estimation of the dissociation between the incremental ramp cycling test and constant load V̇O2 to PO relationship so that the ramp-corrected PO at the respiratory compensation point could be retrieved as a proxy for the PO at the MMSS (Iannetta et al. 2020).
Both Control and Reward trials consisted of a baseline period of 4-min at 20 W, followed by the TTF trial. Instructions were given to signal the researcher conducting the test when the participant was nearing task failure (approximately 1 min of exercise ability remaining). To communicate this signal, participants raised their hand at eye-level and displayed their index finger. The reward trial was conducted in an identical fashion, however, once the 1 min task failure signal was given participants were verbally informed that they would win a reward if they could continue the exercise trial. The reward offered was two-fold: i) 1 raffle ticket won for every additional 1 min interval of exercise; ii) a $10 pre-paid credit card earned for every additional 5 min interval of exercise. Each raffle ticket was added to a draw to win a $250 pre-paid credit card while the $10 pre-paid credit cards were immediately distributed. As an example, in TTF 1, the participant gave a signal at 60 minutes, and “failed” at 61 minutes. They then came back to the lab for another TTF (not knowing about any potential reward), did the TTF 2 and gave a “signal” at 56 minutes that they would fail in 1 minute. It was at that point that a reward was offered. We only examined data up to the “failure signal” in both TTF 1 (60 minutes) and TTF 2 (56 minutes). Participants signed a consent form thinking they were part of a reliability study. For the purpose of this study, they were blinded until the second TTF failure signal and no signs of doubts about the reliability study were observed during the sessions. Participants were informed with the same prepared script and as the TTF was extended, and our research group believes the reward was effectively communicated. Furthermore, a post session chat confirmed the extension of performance due to the presented reward.


Ventilatory and gas exchange measurements

All ventilatory and gas exchange variables were measured continuously during the test using the breath-by-breath option with a metabolic cart (Quark, CPET; COSMED, Rome, Italy). The system consisted of a low dead space turbine as well as oxygen (O2) and carbon dioxide (CO2) gas analyzers; these were calibrated with a syringe of known volume (3 L) and a gas-mixture of known concentration (16% O2; 5% CO2; balance nitrogen), respectively. A face mask was connected to a turbine and a sampling line to measure ventilatory rates and gas exchange, respectively.

Threshold assessment
The GET and the RCP were assessed by three independent experienced evaluators. The GET corresponded the point at which V̇CO2 began to increase disproportionally in relation to V̇O2, which was accompanied by the first breakpoint in the minute ventilation (V̇E) against V̇O2 relationship, while the end-tidal pressure of CO2 (PetCO2) remained stable during a period of isocapnic buffering (Keir et al. 2022). This point was also confirmed by evaluating breakpoints observed in the end-tidal pressure of expired O2 (PetO2) plotted against the V̇O2. The RCP corresponded to the point at which there was a continued fall in the PetCO2 following a period of isocapnic buffering (Keir et al. 2022). Confirmation of the RCP was made by a second breakpoint in the V̇E against V̇O2 and examining the V̇E/V̇CO2, V̇E/V̇O2 against V̇O2 relationship. The average value from the three evaluators was used. If the evaluators had a disagreement of more than 100 mL∙min-1 in the V̇O2 results associated to the GET and RCP, a second round of evaluation was performed together until a consensus was reached. The ramp-corrected PO at the RCP (i.e., the PO estimated to represent that at the MMSS) was identified after aligning the V̇O2 at the RCP with its steady-state equivalent(Iannetta et al. 2020). The V̇O2 and PO coordinates corresponding to GET and to the HVY bout were used to establish the V̇O2-PO relationship in the heavy-intensity domain. Thereafter, projection of this relationship to the estimated V̇O2 at RCP allowed identification of the corresponding PO.


V̇O2 and fB analysis
During all testing sessions, breath-by-breath respiratory data was cleaned by removing data points lying ± 3 standard deviation (SD) from the local mean, followed by a linear interpolation to 1 s intervals (Origin, Origin Lab, Northampton, MA). Interpolated data from the incremental ramp cycling test was converted into a 20 s rolling average and the highest values were considered maximal values (i.e., V̇O2max, HRMAX, and fBMAX). Each participant’s TTF quarterly response for V̇O2 or fB was obtained by averaging the cleaned data values for each time interval segment. For example, if a participant had a TTF of 40 minutes total duration until signal time, both mean V̇O2 and fB for each 10-minute quarterly interval would be determined (e.g., mean Q1 data from start to 10 minutes elapsed, mean Q2 data from 10 to 20 minutes elapsed, etc.)
Blood lactate and glucose concentration
[La-]b and [Gluc] measurements were performed by wiping a finger with an alcohol swab, followed by a finger-prick, and collection of a 20 μL blood sample with a capillary tube, which was mixed in a EKF prefilled safe lock plastic tube containing a heparinized solution for analysis using a laboratory device (Biosen C-Line Clinic, EKF Industrie, Elektronik GmbH, Barleben, Germany). Appropriate manufacturer recommended calibration was performed on each test session and quality control assessment was performed monthly. [La-]b and [Gluc] were measured at rest and elapsed times of 5, 10, 15, 30, 40, 50, 60, 70, 80 minutes depending on TTF duration. Quarterly values were set as the last measurement done during that quarter.


RR Measurements, HR and DFA a1 analysis:
Each participant’s RR time series was recorded using a Polar H10 strap (Polar Electro, Kempele, Finland). Before TTF trials and SRS ramps, the Polar H10 ECG waveform was visually inspected with the Android app ECG Logger (https://ecglogger.en.aptoide.com/app). The strap was shifted slightly to the left if the R peak amplitude was lower than the S wave in order to optimize DFA a1 measurements (Rogers and Gronwald 2022). H10 data was recorded via Bluetooth using an Android smartphone running an open-source application (FatMaxxer, https://github.com/IanPeake/FatMaxxer) and offloaded to a PC for further analysis by Kubios Scientific Software Version 4.02 for measurement of HR and HRV. Preprocessing RR detrending method was set at “Smoothness priors” (Lambda = 500). RR-interval series were corrected by the Kubios “automatic threshold” method. DFA a1 window width was defined as 4 ≤ n ≤ 16 beats (Rogers et al. 2021a). Fatmaxxer ECG recordings of artifacts were inspected for all participants. To minimize DFA a1 bias from artifact correction, the acceptable limit of artifact was kept at or below 5% (Rogers et al. 2021b). For each quarterly segment of the TTF, HR and DFA a1 were determined using a measurement window encompassing that particular quarter. For instance, if the TTF length until signal time was 40 minutes total, the Q1 DFA a1 or HR measurement window of 10 minutes would be calculated from the start of the TTF to 10 minutes elapsed, Q2 would be calculated from a measurement window from 10 to 20 minutes elapsed and so on.

 
Statistics
Statistical analysis of means and standard deviations (SD) were calculated for the listed variables (V̇O2, [La-]b, [Gluc], HR, DFA a1, fB). Normal distribution of data was checked by Shapiro-Wilk testing, visual inspection of data histograms and all were normally distributed. Single factor, repeated-measures ANOVA was performed across Rest and all quarters of TTF trials (both Control and Reward) for listed metrics with Bonferroni post hoc analysis performed with a p ≤ 0.05 as statistically significant. ANOVA effect sizes were reported as eta2 (ɳ2). The repeatability between quarterly Reward TTF and Control TTF individual isotime metrics were assessed using Pearson’s r correlation coefficient and Intraclass correlation coefficient (ICC3,1) with 95% confidence intervals (CI) and paired t testing. The Coefficient of Repeatability (CR), also known as the Smallest Real Difference (SRD) was determined by multiplying 2.77 by the Standard Error of Measurement (SEM) (Vaz et al. 2013) for each quarter of TTF1 vs TTF 2 comparative responses. Hedge’s g effect sizes were reported for paired t testing (Lakens 2013). The size of Pearson’s r correlations was evaluated as follows: 0.3 ≤ r < 0.5 low; 0.6 ≤ r < 0.8 moderate and r ≥ 0.8 high. ICC3,1 correlation strength was classified according to the following ranges: <0.40 as poor, 0.40 to 0.59 as fair, 0.60 to 0.74 as good, and 0.75 to 1.00 as excellent. Analysis was performed using Microsoft Excel 365 with Real Statistics Resource Pack software (Release 6.8) and GraphPad Prism (version 10.4 for Windows, GraphPad Software, Boston, Massachusetts USA). SEM testing was done using Jamovi (the Jamovi project 2024, Version 2.55, retrieved from https://www.jamovi.org) and the SimplyAgree module by Aaron Caldwell (https://aaroncaldwell.us/SimplyAgree/index.html).
 
Results
Participant demographics, ramp incremental and TTF testing characteristics are presented in Table 1. 


Table 1 – Participant characteristics, Age; Stature; Body mass; PPO, greatest ramp incremental PO; V̇O2MAX, maximal oxygen consumption; [La-]b MAX, maximal ramp incremental blood lactate concentration; HRMAX, maximal heart rate; fBMAX, maximal ramp incremental respiratory frequency; V̇O2 @ GET, oxygen consumption at gas exchange threshold; V̇O2 @ RCP, oxygen consumption at respiratory compensation point.
Age (yrs)    23 ± 3
Stature (cm)    175 ± 7
Body mass (kg)    74.1 ± 9.4
PPO (Watts)    275± 21
V̇O2max (L·min-1)    2.97 ± 0.36
V̇O2max (mL·kg-1·min-1)    40.5 ± 6.1
[La-]b MAX (mmol·L-1)    10.4 ± 1.4
HRMAX (bpm)    179 ± 13
fBMAX (b·min-1)    55 ± 13
V̇O2 @ GET (L·min-1)    1.79 ± 0.17
V̇O2 @ RCP (L·min-1)    2.43 ± 0.24


Responses from Control and Reward TTF
Mean time durations (±SD) until the one minute signal before the anticipated task failure were 46.6 ± 18.9 minutes for the Control TTF, and 45.3 ± 21.3 minutes for the Reward TTF (p = 0.54, g = 0.20). The TTF was performed at a PO corresponding to 95% of the PO estimated to represent the MMSS from the SRS test. Group mean responses with standard deviations during Rest and for each quarter (Q1, Q2, Q3, Q4) of both Control and Reward TTF trials combined are presented in Figure 1 with detailed values reported in Supplementary Table 1. Single factor repeated-measures ANOVA did show statistical differences across Rest to Q4 for the metabolic measures of [La-]b (F = 43.0, p < 0.001, ɳ2 = 0.70), [Gluc] (F = 8.1, p < 0.001, ɳ2 = 0.30), and V̇O2 (F = 460.2, p < 0.001, ɳ2 = 0.96). Single factor repeated-measures ANOVA did show statistical differences across Rest to Q4 for the measures of HR, DFA a1 and fB (F = 369.2, p < 0.001, ɳ2 = 0.95, F = 29.06, p < 0.001, ɳ2 = 0.63, F = 58.09, p < 0.001, ɳ2 = 0.76 respectively). Post hoc testing between progressive quarters is presented in Figure 1.

 
Figure 1. Summary of all participant responses at rest and quarters one through four (Q1, Q2, Q3, Q4). V̇O2, Oxygen consumption; [La-]b, Blood lactate concentration; [Gluc], Blood glucose concentration; HR, heart rate; DFA a1; fB, respiratory frequency. Plots are presented as means ± SD. Bonferroni post hoc calculations with a p < 0.05 between quarters, ⋇ signifies difference from Rest, † signifies difference from Q1, ‡ signifies difference from Q2, § signifies difference from Q3.

Repeatability of participant responses, Control vs Reward TTF

Values for ICC with 95% confidence intervals and Pearson’s r, between Control and Reward sessions for each participant at rest and per each quarter are presented in Table 2 along with CR values. No significant difference in mean values were noted (p >0.05, see Supplementary Table 2). ICCs were above 0.7 and r values above 0.8 for the three metrics of exercise durability tested (HR, DFA a1 and fB) (see Table 2 for exact values).

Table 2. Repeatability of each participant response over the two TTF trials. Comparison of paired values at Rest, Quarters one through four (Q1, Q2, Q3, Q4) including Intraclass correlation coefficient (ICC) with 95% confidence intervals, Pearsons’s (r), Coefficient of Repeatability (CR) for V̇O2, Oxygen consumption; [La-]b, Blood lactate concentration; [Gluc], Blood glucose concentration; HR, heart rate; DFA a1; fB, respiratory frequency.
 




 
Discussion
The aim of this study was to determine whether ANS based markers consisting of fB and DFA a1 along with HR have the potential to reflect changes in exercise durability during a cycling TTF in the heavy intensity domain. The results indicate that there was a persistent, significant drift of both HR and fB upwards, accompanied by a steady decline in DFA a1, signifying loss of correlation properties and ANS perturbation. In contrast, metabolic markers such as V̇O2, [La-]b and [Gluc] remained largely stable once past an initial equilibration (Figure 1), consistent with physiological responses below the MMSS (Joyner and Coyle 2008; Billat et al. 2022; Flockhart and Larsen 2024).

 
Heart Rate drift
The results presented show that mean HR rose consistently during the TTF (Figure 1). ANOVA testing showed a significant difference between Rest and all quarterly values with Post Hoc analysis confirming significant HR rise from Rest through each progressive quarter. Individual responses were repeatable with excellent correlation (Table 2) with no changes between Control vs Reward trials per quarter by paired t testing. The presence of HR drift has been well recognized during prolonged endurance sport for many years (Coyle and González-Alonso 2001; Maunder et al. 2021; Souissi et al. 2021). This concept has been explained by various concepts such as skin blood flow induced stroke volume decrease with secondary HR rise, primary HR stimulation from increased sympathetic outflow (and/or receptor sensitivity) with secondary stroke volume  decrease and alterations in the cardiac force-frequency relationship over prolonged exercise duration (Coyle and González-Alonso 2001; Souissi et al. 2021). Additionally, upward HR drift may even diminish or turn downward after very prolonged exercise, weakening the value of this finding as a sole metric of endurance related durability (Mattsson et al. 2011; Billat et al. 2012). The HR drift pattern over the course of the current TTF is similar to that of a recent report concerning the disparity between the HR seen at the RCP obtained from ramp incremental testing and that of the MMSS (Iannetta et al. 2023) testing at various time points. Hence, HR upward drift appears to be a potential metric for exercise durability, at least in the case of the current exercise protocol. Interestingly, part of the rationale behind the HR drift seen here could be consistent with increased sympathetic outflow (White and Raven 2014; Souissi et al. 2021) which is also a mechanistic cornerstone for DFA a1 behavior (Tulppo et al. 2001; Gronwald et al. 2020).


Respiratory frequency
Findings indicate that fB steadily increased during each quarter of the TTF (Figure 1), mimicking the pattern of the upward HR drift. ANOVA testing showed a significant difference between Rest and all quarterly values. As with HR, Post Hoc analysis confirmed significant fB rise from Rest through each progressive quarter. Individual responses were repeatable with excellent correlation (Table 1) with no changes between Control vs Reward sessions per quarter by paired t testing. The upward drift seen is not surprising in view of the factors thought to control fB. These include muscle afferent inputs, central regulatory centers and peripheral vagal ganglia rather than only from metabolic factors such as acidosis or CO2 alteration (Nicolò and Sacchetti 2023). fB is also believed to be a marker of perceived effort, as its control includes factors such as stress, emotional and anticipatory states which may also play a role during a TTF (Kreibig 2010; Tipton et al. 2017). A recent publication presented data supporting the notion of monitoring the fB upward drift in assessing “acute performance decrement” (APD), which appears to be another manner of portraying exercise durability (Passfield et al. 2022). APD was described as “a decrease in time to task failure (TTF) or time-trial (TT) performance” after a given exercise session. The results seen in the present study appear consistent with these ideas. Furthermore, the current data resembles that of both Syabbalo et al (Syabbalo et al. 1994) and Baron et al (Baron et al. 2008) who also observed a continual rise in fB over the course of a TTF in the heavy intensity domain and at the MMSS respectively. Furthermore, upward drift of the fB was seen at the GET after a prolonged, 2 hour cycling session, underscoring the potential alterations of ANS responses (Stevenson et al. 2024). The method of fB measurement employed in the present study was through direct calculation from metabolic cart ventilatory response. However, relatively accurate approaches such as strain gauge vests/garments, HRV or ECG derived respiratory rate (EDR) are available (Smith et al. 2019; Liu et al. 2019; Nicolò et al. 2020; Rogers et al. 2022b). Therefore, currently available consumer hardware and software applications make fB monitoring feasible for field-based use.


DFA a1

With respect to DFA a1, there was a steady decline of mean values through each quarter of the TTF (Figure 1), with single factor repeated measures ANOVA testing showing differences between Q1, Q2, Q3 and Q4. As with HR and fB, individual responses were repeatable with excellent correlation along with no changes between Control vs Reward sessions per quarter by paired t testing (Table 2). Few published studies have explored the behavior of DFA a1 during fatiguing exercise. Evaluation of DFA a1 throughout a marathon run showed decreasing index values even in the presence of a reduction in running speed (Gronwald et al. 2021b). Another report evaluated the change elicited in DFA a1 after a 6-hour ultramarathon trail run (Rogers et al. 2021c). Participants were tested on a treadmill at an intensity in the moderate domain (below the GET), before and after the ultramarathon trail session. After the trail run, they were noted to have significant decline of DFA a1 compared to the initial levels, displaying values typically seen when in the severe intensity domain. Interestingly, there were no changes in mean HR despite the DFA a1 suppression, possibly due to the length of the exercise session (Mattsson et al. 2011). Another study evaluated the DFA a1 related first threshold during two consecutive treadmill ramps to exhaustion (Van Hooren et al. 2023). During the second ramp there was a clear reduction in DFA a1 at comparable running speeds, with no significant alterations in V̇O2 or HR at the GET in the second ramp. Additionally, the first DFA a1 threshold was markedly shifted after the fatiguing ramp, highlighting the effects of an acute high intensity load in the severe domain on this index. All of these observations should be considered in the context of DFA a1 being an element of “network physiology” (Gronwald et al. 2020; Balagué et al. 2020), which incorporates multiple neuromuscular, biochemical, peripheral and central nervous system inputs reflecting “organismic demand”. These inputs combine to modify the balance between the branches of the ANS and the degree of HRV correlation patterns (Goldberger 1991) resulting from sinoatrial pacemaker function. These correlation patterns may reflect a physiologic optimization and/or stabilization strategy to best suit internal load requirements (Goldberger 1996; Ivanov PCh et al. 1998; Fossion et al. 2018; Billman 2020). Therefore, at low to moderate exercise intensity where future physiologic requirements may be highly variable, cardiac ANS measures consistent with flexibility are preferred (DFA a1 is correlated, 0.75 ≤ n ≤ 1.5) but these measures become more rigid (DFA a1 is uncorrelated/anticorrelated, ≤ 0.5), in the heavy to severe domain as an ultimate protective response. Consequently, the changes seen in DFA a1 behavior with prolonged exercise could represent an integrative physiologic defensive strategy.
This study also highlights the presence of heterogeneity in the magnitude of DFA a1 suppression over the course of the TTF. Prior to this report, it was suspected that an individual’s DFA a1 would drop on a continuous basis, but it was unknown if all participants would reach values representing anticorrelated patterns (usually seen at intensities present in the severe domain). Anticorrelated behavior refers to self-correcting patterns associated with the potential failure of homeostatic regulation  and as an ultimate protective response that can only be maintained for short time spans (Seely and Macklem 2004; Muñoz 2018; Fossion et al. 2018). Therefore, it would not have been unreasonable to expect all participants to have these DFA a1 values as they approached task failure. The current results may imply that some individuals have better ANS resiliency/durability manifesting as less absolute DFA a1 suppression. As an analogy, females appear to have a more robust HRV status during mental stress(Adjei et al. 2018), possibly related to differing autonomic optimization strategies. 


Practical applications
The current report is the first in-depth evaluation of DFA a1 with accompanying measures of various metabolic parameters, during prolonged, constant intensity exercise in the heavy intensity domain. It had been previously proposed that DFA a1 may be a useful measure of exercise durability based on limited findings (Rogers and Gronwald 2022). The results presented here confirm that notion as well as show the index to have numerical repeatability over relatively lengthy time spans. Therefore, comparison of DFA a1 values (over similar time/workloads) through training cycles could be followed for assessment of autonomic durability as a performance metric. This type of monitoring has been previously employed using HR (Smyth et al. 2022) but can now be extended to both DFA a1 and fB. This close association should not be surprising as there may be some commonalities in both HRV and fB regulation. fB control is partly under CNS “central command” (periaqueductal gray area), along with other factors, most notably muscle afferent input (Nicolò and Sacchetti 2023). Additionally, the precise CNS centers responsible for RR interval timing (HRV) and fB may overlap (Gourine et al. 2016; Ernst 2017; Devarajan et al. 2022). Cardiac vagal fibers originating in the nucleus ambiguous are predominantly responsible for vagal HRV effects, however, significant crosstalk with respiratory control mechanisms may also be present in the peripheral vagal ganglia (Devarajan et al. 2022). Taken as components of the “network” concept of exercise physiology (Balagué et al. 2020), fB and DFA a1 are both reflections of autonomic disturbance that are achievable in a field setting. It is also important to point out that metabolic indicators such as V̇O2, [La-]b and [Gluc] remained largely stable once past an initial equilibration, thereby not predictive of exercise termination. In other words, exercise failure near the MMSS is largely independent of commonly measured physiologic responses (Baron et al. 2008), highlighting the value of ANS marker assessment.
Beyond the applications of both fB upward and DFA a1 downward drift over time as indicators of ANS durability, perhaps of equal importance is the potential inclusion of these markers in the related concepts of training load, daily directed training and recovery (Passfield et al. 2022; Rogers and Gronwald 2022; Schaffarczyk et al. 2022a). Since both DFA a1 and fB values change over both time and intensity, incorporating this information into the aggregate of one’s training load seems advantageous. For example, if the goal of a given training intervention is recovery, real time monitoring of DFA a1 and/or fB to observe for inappropriate drift denoting autonomic stress is feasible (Gronwald et al. 2021a; Rogers and Gronwald 2022). As another example, a pilot study (Schaffarczyk et al. 2022c) examining the suitability of DFA a1 to assess ANS status during a low intensity warmup as a surrogate metric for “readiness to train” indicated suppressed values within 36 hours post running sessions in the severe domain. Conversely, if the goal of a training session is to improve autonomic durability, purposeful maneuvers to continually suppress DFA a1 may be of benefit.
It is important to note that DFA a1 related exercise intensity thresholds may lose their agreement with the GET or RCP over the course of a given session due to drift. Hence, it is recommended to not equate data recorded in a “fresh” to that of a fatigued state for threshold purposes. Alternatively, comparing DFA a1 thresholds or numerical values per submaximal power profile, represents an opportunity to assess durability without formal maximal power profiles, TTF or time trial efforts. Lastly, we have included the Coefficient of Repeatability (CR), also known as the Smallest Real Difference (SRD) for use in future comparisons and setting the boundaries of the minimal detectable true change.


Experimental considerations
Concerning measurement of DFA a1, proper software preprocessing (detrending), low artifact containing data, optimal chest belt placement are key considerations that have been reviewed elsewhere (Rogers and Gronwald 2022). In addition, defective HRV recording equipment is also a potential issue. Though Control and Reward TTF trials were not technically identical due to the small monetary inducement offered near task failure, the section used for quarterly analysis included only data until the signal for task failure was given, making both TTF conditions matching. Another consideration is that the presence of ANS durability/fatigue markers may or may not coincide with other established neuromuscular measures. Whether they occur before, during or after DFA a1 suppression would be of great interest.
As this report presents both DFA a1 and fB as potential markers for durability, this does represent an addition to the original description by Maunder et al. which highlighted HR drift as a preferred metric for this assessment. Since it was also noted that “It is also possible that quantifying and monitoring durability may require development of new technologies focused on this goal”, we believe these statements are in accord with the usage of ANS metrics including those we have presented here.


Conclusions
Although more research is needed to better elucidate the roles of HR, fB and DFA a1 as markers of durability, the data presented suggest that they can be useful, complimentary metrics in examining deterioration in physiologic characteristics during an endurance exercise session, likely representing durability/resilience loss. In circumstances where HR drift is ambiguous, both fB and DFA a1 behavior may still be able to reveal changes in physiologic parameters during ongoing endurance exercise. These metrics were highly repeatable making longitudinal observations feasible. Further, these measures have the potential to be monitored retrospectively or in real-time in the field with readily available, low-cost consumer equipment.

I would like to thank all my coauthors. This was a team project that could not have been completed without each and every one. I especially want to thank Mackenzie for allowing the use of part of her thesis data.

Summary and Observations:

  • Changes in DFA a1 or fB over the course of an exercise session can be considered part of the choice of metrics available for assessments of durability. To demonstrate this to yourself - simply choose a power/running speed below the MMSS (zone2), position your H10 appropriately, record the RRs/or use alphaHRV/Fatmaxxer and see what happens to the a1 or fB. I've shown some of my data in prior posts.
  • This is a repeatable phenomenon, therefore potentially useful as an indicator of improving or declining fitness.
  • As mentioned above, long durations of "suppressed" a1 for a given power should be considered a warning sign of ANS stress. If one were attempting rest/recovery, try to avoid low a1, even if you are feeling well.
  • The findings presented also confirms the recommendation not to assess thresholds unless you are "fresh", and without recent exertion.
  • As per Figure 1 above, there is a range of "personal" fB and a1 values that can occur, even at task failure. Using all 3 parameters (HR, a1, fB drifts) in concert is advised. For example, having a minimal fB response to a TTF may mislead you to assume that there is little performance degradation (red ellipse). But in this case, the participant still had a "normal" a1 drop (red dots).

 



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