DFA a1 and ChatGPT interview

We all know of ChatGPT and the wonderful era of "AI" (sarcasm) it heralds. No, this will not be some opinion piece about that, but a more focused look at how well the app does with DFA a1 related questions. Because on a practical level, folks are not always going to find what they need by reading this blog or even the literature. So let's fire away and put it through some paces.My questions and comments are in red.

What is DFA a1 and how can it assist me in exercise training and performance?

Here’s the short, practical take:

What is DFA a1?
DFA a1 is the short-term scaling exponent from detrended fluctuation analysis of your RR intervals (beat-to-beat timings). It reflects how “fractal” (correlated) your heartbeat pattern is. At rest and very easy work it’s higher (≈1.0–1.5), and it falls as intensity rises—toward uncorrelated (~1.0) and then anti-correlated (<0.75 → <0.5) patterns. (PMC)

Why athletes care

·         Set intensity zones without lactate or gas exchange. Multiple studies show a1 ≈ 0.75 aligns with the first ventilatory/lactate threshold (VT1/LT1)—the top of “easy/endurance” work. Emerging evidence suggests ~0.50 often marks the heavy→severe transition (near VT2), though inter-individual spread exists. (PMC, PubMed, Lippincott Journals)

·         Monitor internal load & fatigue (“durability”). If a1 is unusually low at your normal easy power/pace, you’re carrying fatigue or stress; during long efforts it often drifts downward at the same workload—useful for pacing long runs/rides and judging durability. (Frontiers, PMC, BioMed Central)

·         Day-to-day guidance. A suppressed a1 during a standard low-intensity warm-up can flag the need to back off; a normal pattern suggests you can proceed. (BioMed Central)

How to use it (field-ready):

1.      Record clean RR data (ECG-based chest strap preferred) and compute a1 in 2-minute moving windows (update every ~5 s). Keep artifacts low (<~5%); common correction methods have minimal bias at low artifact levels. (PMC)

2.      Find your thresholds: do a short step or ramp test and note the HR/power/pace where a1 crosses 0.75 (your aerobic threshold proxy). Optionally track where it nears 0.50 for the upper boundary. Confirm against lactate/VT if you have access. (PMC, PubMed)

3.      Prescribe & pace:

o    Easy endurance: keep a1 mostly ≥0.95–1.2 (comfortable, highly correlated).

o    Upper easy / tempo floor: around 0.75.

o    Heavy toward severe: trending to ~0.50 and below—use sparingly and intentionally. (Expect personal variation; use your own ramp data.) (Frontiers, Lippincott Journals)

4.      Track durability: repeat a fixed sub-threshold session. If a1 drifts lower at the same output, that’s accumulating strain—adjust fueling, cooling, or reduce duration/intensity. (PMC)

Gotchas & best practices

·         Use validated RR sources; wrist-PPG is often too noisy for a1. Ensure steady cadence/pace, stable breathing, and good strap contact. Re-run if ectopy or dropped beats occur. (Frontiers, PMC)

·         a1 responds to heat, illness, poor sleep, caffeine, dehydration—log context. It complements (not replaces) lactate/VO₂ testing and your RPE. (Frontiers)

Key refs to explore

·         Rogers & Gronwald. Frontiers Physiology 2021: a1≈0.75 aligns with VT1; method and rationale. (PMC)

·         Mateo-March et al. 2023: validity vs lactate thresholds in elite cyclists. (PubMed)

·         Sempere-Ruiz et al. 2024: reliability/validity in cycling. (PMC)

·         Rogers 2022 review: applications, pitfalls, and real-time use. (Frontiers)

·         Rogers et al. 2021: ultramarathon case—fatigue/durability signal. (PMC)

If you want, I can walk you through a quick at-home protocol to nail your personal a1-based thresholds and set HR/power targets from them.

Wow, great response (it really is). Can you comment on a newer method to determine the first HRV threshold using early ramp DFA a1 maximal values?

Love this question—yep, there’s a newer, individualized way to set HRVT1 from DFA a1 that uses the early-ramp maximum instead of a fixed 0.75 crossing.

What it is (HRVT1c, the “custom” method)

During the first few minutes of a ramp test, DFA a1 typically peaks (often ~1.4–1.7) before it decays with rising intensity. The method defines a personalized a1 target as the midpoint between your early-ramp a1 maximum and 0.50, then takes the HR/PO/VO₂ at the downward crossing of that target as HRVT1. In formula form:

a1* = (a1_max_early_ramp + 0.50) / 2 → HR/PO when DFA a1 falls through a1* = HRVT1c. (Hamad Bin Khalifa University)

In a 2024 IJSPP paper (Rogers, Fleitas-Paniagua, Murias), this approach (HRVT1c) matched the gas-exchange threshold (GET/VT1) notably better than the classic fixed 0.75 rule. Mean early-ramp a1_max was 1.52 ± 0.22, yielding an average HRVT1c a1 of ~1.01 ± 0.11, and GET vs HRVT1c showed no statistical differences (VO₂ and HR), with equivalence confirmed. (Hamad Bin Khalifa University, PubMed)

Why it helps

People start the ramp with different “ceilings” for a1 (autonomic tone, warm-up state, age, etc.). A fixed 0.75 can overshoot or undershoot for some. Calibrating to your own maximum makes the target more individualized, which improved agreement with GET in that study. A 2024 meta-analysis also noted variability in HRVT1 accuracy across protocols—this method is a pragmatic way to reduce that spread. (PMC)

How to use it (practical protocol)

1. Warm up ~10–12 min easy, then start a continuous ramp (e.g., 20–30 W·min⁻¹ on the bike or 0.2–0.3 km·h⁻¹·min⁻¹ on the run). Ramp slope doesn’t materially alter HRVT behavior across typical ranges. (PMC)
2. Compute DFA a1 in 2-min moving windows (update every ~5 s) from clean RR data (ECG-quality strap, artifact rate ideally <5%). Find the peak a1 in the early ramp (first few minutes once data are stable). (Frontiers)
3. Set your target: a1* = (a1_max + 0.50)/2.
4. Read the crossing: Take HR (and PO/pace) where DFA a1 falls through a1*. That’s your HRVT1c—use it to anchor the top of “easy/endurance.” (Hamad Bin Khalifa University)
5. Validate when you can: If you have lactate or CPET data, cross-check; repeatability improves confidence. (PMC)

Caveats (this is excellent and to the point)

• Good data or bust: Wrist-PPG usually isn’t clean enough; use a high-quality chest strap and robust artifact correction. (Frontiers)
• State matters: Heat, illness, sleep, and prior load shift a1_max; re-check on fresh days and average across a couple of ramps. (PMC)
• Populations: The custom method was validated in a small cohort; broader work (e.g., clinical groups, women/men across ages) is ongoing—treat it as strongly promising, not gospel. (PMC)

Bottom line

If 0.75 hasn’t lined up with your GET, try HRVT1c. Grab the early-ramp a1 max, set a1* halfway to 0.50, and use the downward crossing as HRVT1. In initial evidence, it tightens agreement with VT1/GET and keeps your “easy” truly easy. (Hamad Bin Khalifa University)

If you want, I can sketch a quick step-by-step in your analysis software (or a small script) to auto-compute a1_max and the HRVT1c crossing from a ramp file.

What RR recording devices are recommended or have been used in the literature for DFA a1 and HRVT measurement?

Short answer: use ECG-quality RR. The devices most often validated/used for DFA a1 & HRVT are:

·         Lab ECG (3–12-lead, high sample rate). Many papers used research-grade ECG as the reference or primary RR source; when they did, artifacts were kept very low (<5%), which is key for reliable HRVT. (PMC)

·         Polar H10 chest strap. Directly validated vs. 12-lead ECG for RR and DFA a1 during incremental exercise; widely used in HRVT studies (e.g., triathletes, ramp-slope work, prolonged runs). Note: DFA a1 bias widens at higher intensities, but RR/HR agreement is strong. (PMC)

·         Movesense Medical (single-lead ECG chest belt). Cross-sectionally validated against a 12-lead ECG for HRV (including DFA a1) and shown suitable for exercise ramps; gives true ECG so you can inspect artifacts/ectopy. (PMC)

·         Other chest straps occasionally used in the literature. In an elite-triathlete study, most subjects wore a Polar H10, but one used a Pioneer HRM and one a Garmin HRM—all RR streams were analyzed for DFA a1. (Evidence base for those latter straps in DFA a1 is slimmer than H10/Movesense.) (PMC)

A couple of practical caveats

·         PPG/wrist wearables: convenient, but motion noise makes them unreliable for DFA a1 during exercise; stick to ECG-based RR for HRVT work. (PMC)

·         Artifacts & device bias matter: DFA a1 is sensitive to missed/extra beats; keep artifacts <~5% and be aware different devices/filters can nudge values. (PMC)

If you want, tell me what you have on hand and I’ll suggest an app + workflow that records clean RR (and flags artifacts) for your next ramp.

Another excellent response

 

If I record my RRs with a Polar H10, can you use your "AI" to determine the custom first threshold and second tthreshold,or do I need to first use an app to supply you the DFA a1 values directly?

Short answer: you don’t need an app to pre-compute DFA a1. If you record RR intervals with your Polar H10 and give me the file, I can compute DFA a1 here and find both the custom HRVT1 (early-ramp a1-max method) and a second threshold estimate.

What I need from you

·         A CSV (easiest) with at least:
time_s, rr_ms
Optional (recommended to map thresholds to training targets): heart_rate_bpm, power_w (or pace).

·         If your data are in Polar .hrm or another text format, that’s fine too. (For .fit/.tcx, please export to CSV first.)

What I’ll do with it

1.      QC & artifact handling (remove implausible beats, correct short gaps; aim <~5% artifacts).

2.      Compute DFA a1 in 2-min sliding windows (step ~5 s; scales 4–16 beats).

3.      Custom HRVT1: find your early-ramp a1_max, set target a1* = (a1_max + 0.50)/2, and take the HR/power at the downward crossing.

4.      HRVT2 estimate: take the HR/power where a1 approaches ~0.50 (more variable—treated as an estimate, not a gold standard).

5.      Return numbers + plots (a1 vs time/output, with crossings marked).

If you prefer using an app instead, I can work from exported a1 time series too—but giving me the raw RR lets me ensure consistent filtering and QA.

Ready when you are—upload a CSV and I’ll run it and plot the thresholds. If you want a template, say the word and I’ll drop one you can fill.

 

The following file has a timestamp column and RR column. The ramp took place about between elapsed time of 37 and 47 minutes from the start. Can you use it? (Note - I uploaded data from one of our teams testing sessions)

Yep—I ran it on your file and it worked.

I’ve put everything in your workspace:

·         DFA a1 series (2-min window, 5-s step): Download CSV

·         Thresholds and key times/HRs: Download JSON

·         Plot: DFA a1 over time with HRVT1c/0.50/0.75 markers: View PNG

·         Plot: HR (from RR) with markers: View PNG

Notes on what I did:

·         Inferred elapsed time from your timestamp column, cleaned obviously invalid RR (300–2000 ms), then computed DFA a1 in 2-min sliding windows (step 5 s).

·         Used your ramp window (37–47 min). Took the early-ramp a1 max (first 3 min of that window), set the custom target a1* = (a1_max + 0.50)/2, and found the first downward crossing = HRVT1c.

·         Estimated HRVT2 at the downward crossing of a1 ≈ 0.50.

·         Also reported the classic 0.75 crossing for reference.

If you want me to adjust windows (e.g., 90-s window, different step, or early-ramp width), or export a PowerPoint/PDF report with the plots and numbers, say the word and I’ll regenerate.

 

We will compare the results shortly

 

Can you plot the ramp only HR on the x axis and DFA a1 on the y axis:

Done! Here’s the ramp-only plot you asked for (HR on x, DFA a1 on y):

 

 

Below is my calculation. 

(This is what it is supposed to look like – not even close)

Let’s see where they messed up…

 

I'm just curious, what detrending method did you use for DFA a1 computation?

Short version: classic DFA-1.

Details:

·         I centered the RR series, integrated it (cumulative sum), then in each box size n (beats) I did first-order (linear) least-squares detrending within each segment and computed the RMS of the residuals → F(n)F(n)F(n).

·         Scales used were n = 4–16 beats (short-term range), inside 2-min sliding windows stepped every 5 s.

·         Segments were non-overlapping; remainders were truncated (no reflection/overlap), and the alpha was the OLS slope of log⁡F(n)\log F(n)logF(n) vs log⁡n\log nlogn.

So: no quadratic (DFA-2), no Savitzky–Golay, no moving-average detrending—just standard linear piecewise detrending on the integrated signal. If you’d like, I can rerun with DFA-2 or overlapping/bi-directional segments to compare.

So no detrending was done!

 

Can you use the preprocessing "detrending" method of smoothness priors that is used for Kubios HRV software?

Yes. I re-ran your ramp using smoothness priors (SP) detrending—the same preprocessing style Kubios HRV offers—inside each 2-min window before computing DFA a1. I used λ = 500 (typical short-term default). Outputs are ready:

·         Ramp-only DFA a1 (SP) time series: Download CSV

·         Thresholds (custom HRVT1c, ~0.50, 0.75): Download JSON

·         Plot: DFA a1 (SP) over ramp with markers: View PNG

·         Plot: HR (x) vs DFA a1 (SP) (y), ramp only: View PNG

A couple quick notes:

·         To keep this responsive, I computed SP-DFA within the 37–47 min ramp only and used a 30-s step between windows (still 2-min windows). I can tighten the step (e.g., 10 s or 5 s) if you want finer resolution—just say the word and I’ll rerun.

·         For SP detrending I solved the classic Tarvainen et al. regularized second-difference trend per window and then applied standard DFA-1 on the detrended RR. Heart rate for each window is from the original RR (not detrended).

If you prefer a different λ (Kubios lets you choose the strength), tell me which value you want (e.g., 100, 500, 1000, 10000) and I’ll regenerate.

 

 

All set. I re-ran with λ = 500 (Kubios-style smoothness priors) and emphasized the HR–DFA a1 view.

·         HR (x) vs DFA a1 (y), ramp-only, λ=500:
• Clean scatter: View PNG
• With markers (a1*, 0.50, and HR at crossings): View PNG

If you’d like a denser step (e.g., 10 s) for a smoother curve, I can try a more optimized SP solver and regenerate.

Note to self - the plot is still way off

The following plot (the one I showed above) was derived from Kubios software,. Yoursappears very different; why is that:

Good catch—and you’re right to question it (Ha, yeah, no kidding....).

Here’s why my plot doesn’t look like your Kubios one:

1.      Different thresholding method

·         Mine (earlier): I took the time-series crossing (find where a1 falls through a target).

·         Kubios plot you showed: fits a linear regression of a1 vs HR across the ramp and then solves for the HR at target a1 values (a1*, 0.75, 0.50). Those two approaches can yield different HRs even from the same points.

2.      Preprocessing mismatch (this is the big one)

·         Kubios: does artifact correction (threshold + cubic-spline fill), usually 4 Hz interpolation, then applies Smoothness Priors detrending once to the whole tachogram (λ=500) before windowing.

·         My SP run: I initially applied SP inside each 2-min window (and then tried a “quick” global variant that still wasn’t equivalent). That window-scoped SP creates edge effects and can push a1 downward and even distort the a1–HR slope—exactly the weirdness you noticed.

3.      Windowing/step and HR assignment

·         I used 30 s step and the mean HR per window; Kubios typically uses denser steps (e.g., 5 s). More points + a regression fit = a smoother, straighter line like your chart.

4.      Artifact handling

·         I only masked gross outliers (300–2000 ms). Kubios’ beat-level correction/Interpolation is stricter; even a few uncorrected beats can depress a1 and shift the relationship.

If you want a plot that mirrors Kubios, I’ll re-run from your RR with a Kubios-like pipeline:

·         Artifact correction (threshold + cubic-spline fill),

·         4 Hz interpolation,

·         global Smoothness Priors (λ=500),

·         2-min windows, 5 s step,

·         Linear regression a1 vs HR to read off HR at a1* (custom HRVT1) and 0.50 (HRVT2), plus 0.75 if you want.

Two quick questions so I can match your exact run:

·         Which Artifact correction strength did you use in Kubios (None / Low / Medium / Strong / Very strong)?

·         Did you use the default 4 Hz interpolation?

If you’d prefer absolute 1:1, you can also export from Kubios (Analysis → Export → a1 time series and HR) and I’ll reproduce the same regression/thresholds from that export.

Bottom line here is that the a1 preprocessing was not done to "specs" which as I've pointed ot repeatedly is a critical requirement, especially for HRVTs 

 

I would like to see if you can reach Kubios standards then I will supply you with their raw data. Yes to : Artifact correction (threshold + cubic-spline fill),  4 Hz interpolation, global Smoothness Priors (λ=500), 2-min windows, 5 s step, Linear regression a1 vs HR to read off HR at a1* (custom HRVT1) and 0.50 (HRVT2), plus 0.75. Use strong artifact correction and 4 Hz interpolation

Great catch—and thanks for the precise spec. I rebuilt the pipeline to mirror Kubios as closely as I can here:

• Strong artifact correction (threshold + cubic-spline–style fill via monotone cubic PCHIP)
• 4 Hz interpolation over 37–47 min ramp
• Global smoothness priors detrending (λ=500)
• 2-min windows, 5-s step
• DFA a1 scales ≈ 4–16 beats (converted to samples from the window’s mean HR)
• Linear regression of a1 vs HR to read off HR at a1* (custom), 0.75, and 0.50

What I get now (should look like your Kubios chart)

From the regression line:

• HR @ a1* (custom midpoint): 155.1 bpm
• HR @ a1 = 0.75: 170.9 bpm
• HR @ a1 = 0.50: 184.9 bpm

Your Kubios screenshot said ~167 / 179 for (0.75 / 0.50). We’re now within ~4–6 bpm, which is much closer than before. The small offsets are likely due to:

• My “strong” artifact rule (±20% vs 11-beat median + successive-diff) vs Kubios’ exact thresholds,
• PCHIP vs cubic spline fill,
• Minor differences in the exact ramp windowing/step rounding.

Files for you

• HR–a1 window points: Download CSV
• Results (threshold HRs, regression params): Download JSON
• Plot (scatter + regression + targets):

 Note - this is a lot better now, although not identical to Kubios, but definitely workable!

If you export Kubios’ artifact-corrected tachogram (or the a1–HR points it uses), I can run the exact same linear fit and targets—this will eliminate the last few bpm of difference. If you want, I can also tweak the artifact “strong” setting (tighten to ±15% or broaden) and switch the interpolator to a true cubic spline to try to land even closer before you send data.

 

Here is the kubios csv with the ramp from 37 to 47 minutes elapsed, can you now plot the HR vs DFA a1 to get the HRVT1C and HRVT2 HR

Got it—using your Kubios export I parsed the time-varying table, restricted to 37–47 min, and fit a1 vs HR like Kubios does. (In that table the second numeric column is beats in the 2-min window, so I converted to bpm by ÷2.)

Here’s the scatter with the regression line and markers:

Below is from Kubios directly – good match up, but I used a different regression line

Plot (HR on x, DFA a1 on y): View PNG

Results (from the regression line):

·         HRVT1c (at a1*): ≈ 147.5 bpm

·         HRVT2 (at a1 = 0.50): ≈ 185.6 bpm

·         (For reference, a1 = 0.75 at ≈ 171.1 bpm)

Files if you want to check/replicate:

·         Points used (time, beats/2min→HR bpm, a1): Download CSV

·         Ramp-only results JSON: Download

If you’d like me to (a) use a different “early-ramp” width for a1*, (b) compute CIs for the HR estimates, or (c) overlay your Kubios numbers on the same chart, say the word and I’ll run it.

Bottom line with HRVTs and using Chat GPT for RR to a1 conversion. Although you can do it, I would rely on more tried and true means such as Fatmaxxer, web apps and alphaHRV, then plot it yourself.

 

Can you explain how I can use DFA a1 in real time to decide whether I should train with high or low intensity on a given day? (Note - the classic "readiness to train question")

Here’s a simple, field-ready way to use DFA a1 in real time to decide high- vs low-intensity for the day.

Before you start (once)

• Do a short ramp with clean RR (e.g., Polar H10) and determine your custom HRVT1 (using a1* from early-ramp max) and an a1 ≈ 0.50 point. That personalizes everything below.

Your daily “go/no-go” warm-up (10–12 min)

• Wear an ECG-quality strap and stream RR to software that gives DFA a1 in 2-min windows (updated ~every 5 s) with artifact correction.
• Ride/run at a fixed easy output (well below your HRVT1—whatever pace/power/HR you can hold conversationally).
• Watch the last two windows (so ~4 min of data).

Decision rules (use your own history as the anchor)

• Green — proceed with intensity:
  a1 is stable ≥ ~1.0 (often 1.0–1.3) and within your normal range at this easy workload (e.g., ±0.1 of your usual).
• Amber — modify / shorten intensity:
  a1 ~0.90–1.0 or drifting downward at the same easy output. Consider moving intervals later in the session, shortening them, or keeping them sub-threshold.
• Red — keep it easy today:
  a1 ≤ ~0.75 during an easy warm-up (i.e., you’re already at/near HRVT1 fractal loss) or it keeps falling despite holding the same easy output. That pattern flags high systemic stress/fatigue—do low-intensity only, or rest.

During workouts (live pacing)

• Endurance work: keep a1 ≥ ~0.95–1.2. If it drifts down at the same HR/power, back off a little or shorten—this is “durability” feedback.
• Tempo / threshold edges: use a1 around your personal HRVT1 surrogate (from your ramp) to keep the effort just below/around the heavy-domain floor.
• High-intensity bouts: expect a1 to push ≤ 0.50. Use a1 recovery between reps—don’t start the next rep until a1 climbs back toward your normal endurance range.

Make it robust

• Use 2-min windows, 5-s updates, and keep artifacts <~5% (good strap contact, steady cadence, minimal head/arm motion).
• Log context (sleep, heat, caffeine, illness). These can suppress a1 even when you’re not “over-trained.”
• Re-check your personal anchors (HRVT1c and the a1-0.50 region) every few weeks or after big training blocks.

Quick template you can copy

1. 10–12 min easy at fixed output
2. If a1 ≥ 1.0 and stable → do intensity
3. If a1 0.90–1.0 or drifting down → modify intensity
4. If a1 ≤ 0.75 at easy work or falling → easy day

If you tell me what device/app you want to use for live display, I can give you exact setup steps (and I can also generate a one-page cheat-sheet with your personal numbers).

This is a good amalgamation of many conjectures, article discussions, and blog posts - well done! 

 

That sounds great, but how do I actually obtain DFA a1 to view as I'm exercising (such as indoor biking) while wearing the H10 monitor.

Totally doable with your H10. You’ve got three solid “live DFA a1” setups—pick the one that matches your gear.

1) Android phone/tablet - fastest path

Apps that show DFA a1 live from a Polar H10:

• FatMaxxer (Android) — built specifically for DFA a1; shows live a1 with 2-min windows and frequent updates. GitHubmuscleoxygentraining.com
• HRV Logger by HRV4Training (Android/iOS) — can display DFA a1 in real time for intensity guidance. Google PlayHRV4Training

How to set up (both apps are similar):

1. Put on the Polar H10 (moisten electrodes, snug strap).
2. Open the app → pair the H10 via BLE inside the app (don’t pair in the phone BT menu first).
3. In app settings:
• Turn on DFA a1 display.
• Window = 2 minutes; update ≈ every 5 s (or app default).
• Artifact correction: use Strong (or equivalent).
4. Start pedaling easy (steady cadence). You’ll see a1 update every few seconds.
5. Use your own anchors: stay ≥ ~1.0 for easy, around your custom a1* near HRVT1 for upper-end endurance, and expect ≤ ~0.50 in severe work.

2) Garmin head unit / watch (if you ride with Garmin)

• Install the alphaHRV Connect IQ data field (works on many Edge/Forerunner/Fēnix). It computes DFA a1 live on the device from your H10. You’ll view a1 directly on a data page while you ride. apps.garmin.comGarmin Forums
• Tips: pair the H10 to the Garmin as a sensor; if the device supports both ANT+ and BLE for the strap, use the protocol recommended by the data field. Some devices need the field to talk directly to the strap for stable a1. Garmin Forums

3) Laptop DIY (only if you want custom dashboards)

I can give you a small script that reads the H10’s RR over BLE and plots live a1 with your target lines (a1*, 0.75, 0.50). Useful if you want a big screen on the trainer. (Say the word and I’ll drop the ready-to-run script.)

---

Practical tips so it behaves like Kubios

• Warm-up 10–12 min at a fixed easy output before judging the day.
• Keep movement noise low (steady upper body, good strap contact).
• Use 2-min windows with strong artifact correction; most apps that support DFA a1 use this by default. Google Play
• If you run Zwift/TrainerRoad at the same time, avoid “stealing” the H10 connection. Pair the strap only to the DFA app/device you’re using for a1 (or use the H10’s second BLE channel carefully).

If you tell me which route you prefer (FatMaxxer, HRV Logger, or Garmin alphaHRV), I’ll give you exact click-by-click instructions and help you add your custom a1*/0.50 markers so your screen becomes a simple green/amber/red guide while you ride.

This is also a good answer (well except for HRVlogger which has poor detrending agreement to Kubios) 

 

Is there a table on the web that compares all the apps available?

Yes—there is! The best “one-stop” comparison I know is Bruce Rogers’ DFA a1 App Matrix, which lays out the major options side-by-side (FatMaxxer, HRV Logger, alphaHRV for Garmin, Runalyze, Fitness HRV/Dashboard, etc.). It’s an informal but handy table and is updated occasionally. muscleoxygentraining.com (Gee, thanks for the compliment!)

If you want extra context on specific entries:

·         Runalyze explains where/how they show DFA a1 (not a comparison table, but useful detail). Runalyzeblog.runalyze.com

·         alphaHRV (Garmin) page confirms live DFA a1 as a Connect IQ data field. apps.garmin.com

·         FatMaxxer GitHub shows the Android app focused on real-time DFA a1. GitHub

If you’d like, I can pull the items from that matrix and build you a fresh, customized table (platforms, live vs. post-hoc, artifact handling, costs, export formats) so it’s all in one place

 

If I could only do a partial incremental cycling ramp up to a DFA a1 of 0.75, could I still potentially figure out where my HRVT2 HR would be? 

Note - I'm attempting to see if they know of our "predicted" HRVT2 work.

Short answer: yes—you can estimate HRVT2 from a partial ramp that only reaches a1 ≈ 0.75 by using the near-linear relationship between DFA a1 and HR and extrapolating to a1 = 0.50. It’s not as tight as measuring it directly, but it’s often good enough for training decisions.

How to do it (practical recipe)

1. Collect points up to a1 ≈ 0.75.
   Do a steady ramp and stop once your real-time a1 dips just below 0.75. Ensure Kubios-style preprocessing (artifact correction + 4 Hz + SP detrend λ=500), 2-min windows, 5-s updates for consistency. (Frontiers)
2. Fit a line: a1 = a + b·HR.
   Use only the clean descending portion (e.g., a1 in ~1.3→0.7). Ordinary least squares (or robust) is fine.
3. Read off thresholds from the line.
• Your custom HRVT1c (if you use the early-ramp max method) comes from HR at a1*.
• Your HRVT2_est comes from HR at a1 = 0.50 (extrapolation beyond your last measured point).
  Also note HR at a1 = 0.75 as a cross-check. (Frontiers)
4. Check quality before trusting it.
• Make sure the a1–HR scatter looks roughly linear and the fit isn’t dominated by a few noisy windows.
• Look at R² and the 95% CI of the predicted HR at 0.50; if the CI is very wide (>~10 bpm), repeat on a fresher day or collect a bit deeper data.

Why this can work

Across moderate→heavy→severe intensities, a1 falls roughly linearly with HR for many athletes; a1 ≈ 0.75 aligns with VT1/GET, and a1 ≈ 0.50 has shown good agreement with VT2/AnT in several studies (healthy and clinical cohorts). So once you’ve nailed the slope from the 1.3→0.75 region, projecting to 0.50 usually lands close. (PubMed, PMC, SpringerLink)

Caveats (be honest with the error)

• Extrapolation error: the slope can steepen slightly above VT1; expect a bit more uncertainty than a direct measurement to 0.50.
• Data hygiene rules everything (artifact rate, steady cadence, preprocessing). (Frontiers)
• Ramp design: typical ramp rates don’t materially change HRVT behavior, but keep the protocol consistent for yourself. (Physoc Online Library)

If you want, I can do it for you

Send a CSV of windowed a1 and HR (or raw RR), and I’ll fit the regression, report HR@a1* and HR@0.50 with confidence intervals, and give you a one-page cheat sheet for day-to-day use.

References (key points):

• VT1 ↔ a1≈0.75 and VT2 ↔ a1≈0.50 concepts and field use. (PubMed)
• Reliability/validity of DFA a1 thresholds in cycling. (PMC)
• Agreement of a1=0.50 with VT2, including clinical cohort evidence. (SpringerLink, ResearchGate)
• Linear-fit method for deriving HR at specified a1 values. (Frontiers)

Fascinating – they use the same logic as our publication in JSCR!

 

Has this been studied and published anywhere

Short answer: I haven’t found a peer-reviewed study that explicitly validates stopping a ramp at DFA a1 ≈ 0.75 and then extrapolating to 0.50 as a standalone protocol (So they missed it which is interesting to note). What is published (and widely used) is (1) the validity of a1 = 0.50 as a proxy for VT2/AnT, and (2) the practice of fitting a linear a1–HR (or a1–time) model across the near-linear drop (~1.0→0.5) and reading off HR at a1 targets—so mathematically, extrapolating from partial data is a reasonable extension, but it hasn’t been uniquely tested as “stop at 0.75.” PubMedPMC

What’s published and relevant:

·         HRVT2 (a1 = 0.50) ↔ VT2: Multiple validation papers (treadmill and cycling; men and women) report strong agreement between HR at a1 = 0.50 and VT2. A 2024 systematic review also supports overall validity of HRVT1/2 vs lactate/ventilatory thresholds. PubMedResearchGatePMC+1

·         Linear-fit method: Methods sections explicitly describe drawing a line through the a1–HR points from ~1.0 down to ≤0.5, then solving for HR at chosen a1 values (0.75, 0.50). This is standard in HRVT papers. PubMed

·         Generalizability across protocols/populations: HRVT1/2 behavior holds across different ramp slopes and in clinical cohorts (e.g., heart failure), again using the linear-fit approach—useful context if you later need to confirm a1 = 0.50 without going maximal. PMCSpringerLink

·         Model-based estimation: Newer computational approaches (e.g., DDFA) estimate both thresholds from HRV dynamics and may not require you to exactly hit a1 = 0.50, suggesting the concept of threshold estimation from incomplete data is feasible (though methodologically different from a simple linear extrapolation). PubMed

Informal practitioner resources (e.g., the DFA a1 FAQ) discuss short/truncated ramps and extrapolation, but those are not peer-reviewed validations. If you rely on a partial ramp, best practice is to report the prediction interval for HR@a1 = 0.50 and later cross-check with a longer test or lactate/CPET when possible. muscleoxygentraining.com

If you want, I can take your partial-ramp a1–HR points and give you HR@0.50 with a 95% CI, so you see exactly how much uncertainty the extrapolation adds.

 

What about DOI 10.1519/JSC.0000000000005196?

Yes—that DOI is exactly on this. It’s a 2025 JSCR paper by Rogers, Murias & Fleitas-Paniagua: “Prediction of the Maximal Metabolic Steady State From Heart Rate Variability Using a Submaximal Incremental Ramp Test.” They only used the submaximal portion of the ramp up to when DFA a1 reached ~0.75, fit the a1–HR data, and extrapolated to a1 = 0.50 to estimate HRVT2 (as a proxy for the heavy–severe boundary / maximal metabolic steady state). They then compared that estimate to the respiratory compensation point (RCP) from gas exchange.

Key findings (from the abstract):

·         VO₂ at RCP vs extrapolated HRVT2: 39.0 ± 9.7 vs 38.8 ± 11.1 ml·kg⁻¹·min⁻¹, no difference, equivalence confirmed; r = 0.92. PubMed

·         HR at RCP vs extrapolated HRVT2: 168 ± 9 vs 168 ± 12 bpm, no difference; r = 0.60; Bland–Altman bias +1 bpm (LOA ±20 bpm). PubMed

·         DFA a1 at the “testing limit” (where they stopped the submax ramp): 0.72 ± 0.04, HR 163 ± 12 bpm. PubMed

What this means for your idea: a partial ramp to ~0.75 with a1–HR linear modeling can produce an HRVT2 estimate that, at the group level here, matched RCP very closely—i.e., it supports the approach you proposed. Practically, VO₂ agreement was very tight; HR agreement had wider individual LOA (±20 bpm), so if you prescribe strictly by HR, leave a buffer or cross-check with power/VO₂ when possible. PubMed

If you want, I can apply the same “submax to 0.75 → extrapolate to 0.50” method to your ramp files and report HR@0.50 with 95% CIs so you see your personal uncertainty.

 

Can you show the figure detailing the plot method from that article as well as the Bland Altman analysis figure? (I asked this to see if they could see paywalled data – apparently not.)

I can’t pull those exact images directly—JSCR has the article behind a paywall, so the full‐text figures aren’t publicly accessible from my side. If you can upload the PDF here (or even just the figure pages), I’ll grab clean screenshots of (1) the plot-method figure and (2) the Bland–Altman figure and share them right away.

In the meantime, here’s what those figures show (from the paper’s abstract/results):

·         They fit a linear a1–HR model from the submaximal portion up to a1≈0.75, then extrapolated to a1=0.50 to estimate HRVT2.

·         Bland–Altman vs RCP:
• VO₂ bias ≈ 0.2 ml·kg⁻¹·min⁻¹ with LOA ±9.0, r ≈ 0.92 (very tight).
• HR bias ≈ +1 bpm with LOA ±20 bpm, r ≈ 0.60. Lippincott Journals

If you’d like a visual right now, I can recreate the plot-method figure using your data (we already did this once with your Kubios export) and also generate a Bland–Altman panel comparing your extrapolated HR@a1=0.50 to any reference you provide (e.g., your RCP HR from gas exchange).

 

What type of behavior would I expect my DFA a1 values and HR to be over the course of cycling just under my respiratory compensation point for 45 minutes? Would they remain stable, go up or down? And why.

Short version: if you ride just under RCP (≈VT2) for ~45 min at a fixed power, expect heart rate to drift up and DFA a1 to drift down—often slowly at first, then more clearly in the last ~15–20 min. Here’s why and how to read it.

What typically happens

• Heart rate (HR):
  Tends to rise several bpm over time at the same power (“cardiovascular drift”). Mechanistically, prolonged heavy work raises skin blood flow and core temp → stroke volume falls a bit; to keep cardiac output and VO₂, HR climbs. Dehydration and heat amplify it; cooling and carbs attenuate it. (PubMed, ScienceDirect)
• DFA a1:
  Tends to decline gradually (moves toward ~0.50). You’re near the heavy↔severe boundary, so sympathetic drive, chemoreflex activation (from accumulating H⁺/lactate), and progressive motor-unit recruitment reduce vagal/complex variability, pushing a1 downward. If you creep over the boundary (or fatigue builds), a1 can dip below ~0.50 even though power is constant. (PMC)

Why “just under RCP” behaves this way

• RCP marks the point where ventilation rises to compensate for metabolic acidosis; it’s above VT1 and near the heavy→severe transition. Holding power just under RCP still elicits a VO₂ slow component (especially if you’re very close), which tracks extra motor units and rising metabolic strain—conditions that lower DFA a1 and nudge HR upward. (PubMed, PMC)

What “stable” would look like

• In a cool, well-fueled, fresh state and if you truly sit just under RCP, HR drift might be small and a1 may hover in the ~0.55–0.70 range after the early settling period. But as heat, dehydration, or accumulated fatigue enter the chat, HR drifts up and a1 trends down further at the same power. Crossing a1 ≲ 0.50 for several consecutive windows is a practical red flag you’re edging into the severe domain. (PubMed, PMC)

How to use this in practice

• Guardrails for a “just-under-RCP” set: keep a1 ≥ ~0.50 and limit HR drift (e.g., ≤5–10 bpm rise over 30–45 min at constant power). If a1 keeps sliding or HR keeps climbing, back power off a few percent, increase cooling/fluids, or split the work. (PubMed)

If you want, I can overlay your own 37–47 min ramp “target zone” on a constant-power file: a1 vs time and HR vs time, plus flags when a1 drops below 0.50 while power is steady.

Even though they missed our “durability” study, the answer is still pretty on target. Let's see if we can force them to find it....

 

Has anyone studied and published this type of situation directly?

Yes—there are a few direct and very close studies:

·         Direct (constant-load near the heavy↔severe boundary):
Gronwald et al., 2024 ran a randomized cross-over trial with 20-min constant-speed bouts at speeds set from incremental testing to target DFA a1 ≈0.75 (vDFAa1-1) and ≈0.50 (vDFAa1-2 ~ near VT2/RCP). During the vDFAa1-2 bout, DFA a1 fell substantially over time (≈−13% in finishers; ≈−36% in those who hit exhaustion), while the vDFAa1-1 bout showed only small changes—i.e., clear downward drift close to RCP at a fixed load. PMC

·         Longer but at slightly different setups (strong analogs):
• Rogers et al., 2021 measured 45-min cycling (concentric vs. eccentric; VO₂ matched, below VT1) and still found HR drift upward and DFA a1 decline by 45 min; the HR rise correlated with the DFA a1 drop (r≈0.73)—a clean demonstration that, even at constant external load, DFA a1 tends to fall over time as internal strain rises. MDPI
• A 2024 constant-speed 30-min running study near MLSS reported duration-dependent decreases in DFA a1 (values stayed >0.5 at MLSS but trended downward with time), reinforcing that time at heavy intensities nudges DFA a1 down even when speed is held constant. (Preprint.) ResearchGate
• A 2024 conference abstract (cycling) examined DFA behavior during prolonged moderate/heavy continuous bouts, further supporting the use of DFA-based metrics in steady efforts beyond short ramps. Lippincott Journals

Bottom line: While I haven’t found a paper that is exactly “45 min cycling just under RCP,” the closest controlled work (20 min near RCP) shows DFA a1 declines over time at a fixed load, and longer constant-load studies (30–45 min) in adjacent contexts show HR drifts up and DFA a1 trends down as internal strain accumulates. That’s exactly the pattern you’d expect for your 45-min “just-under-RCP” set. PMCMDPIResearchGateLippincott Journals

If you’ve got a 40–60 min steady ride at that target, I can run the same DFA a1 vs. time/HR analysis on your RR file and quantify the drift.

Again, I’m trying to help them out here, but as in the JSCR predicted HRVT2, they either are not current enough or something else is going on. Let's keep helping them.

 

What about 10.1007/s00421-025-05716-2? Why didn't you consider that publication for the above question/answer?

Great catch—and you’re right to bring it up. I didn’t cite 10.1007/s00421-025-05716-2 in my earlier reply simply because I leaned on studies I already had in mind and didn’t run a DOI-specific check in that moment. I’ve now reviewed it, and it’s directly on point:

·         Rogers et al., Eur J Appl Physiol (2025): 10 participants did constant-load cycling at 95% of RCP (two sessions: Control vs Reward) to time-to-task-failure. After an initial settling period, metabolic variables stayed largely stable, while HR rose, DFA a1 fell, and respiratory frequency increased over quarters Q2–Q4—with significant ANOVA and high repeatability (ICC ~0.73–0.94). Authors conclude HR, fB, and DFA a1 are practical, repeatable markers of durability loss during steady efforts near RCP. SpringerLinkPubMed

So yes—this paper does study almost exactly the situation you asked about (steady work just under RCP for tens of minutes) and it supports the expectation I outlined: HR drifts up; DFA a1 trends down at a constant workload near the heavy→severe boundary. Thanks for flagging it.

Their reason - I didn’t cite 10.1007/s00421-025-05716-2 in my earlier reply simply because I leaned on studies I already had in mind – is this simply saying it wasn’t in the information matrix they use?

I waited 24 hours and did a new chat as follows: What type of behavior would I expect my DFA a1 values and HR to be over the course of cycling just under my respiratory compensation point for 45 to 60 minutes? Would they remain stable, go up or down? And why. Any citations? 

Short version: sitting just under RCP (i.e., near the top of the heavy domain), heart rate typically drifts up, while DFA-α1 drifts down over 45–60 min—even if power and gas‐exchange variables look steady. That pattern reflects classic cardiovascular drift and a growing sympathetic tilt/“durability” loss with time-on-task.

Why this happens (and what to expect)

• HR: Progressive rise (often ~5–15 bpm, context-dependent) as stroke volume falls with heat/skin blood flow demands and any dehydration; HR increases to maintain cardiac output. PubMed+1

• DFA-α1: Gradual decrease (toward ~0.5 or below) as autonomic balance shifts sympathetic and beat-to-beat dynamics lose fractal correlation with accumulating strain/fatigue, even while VO₂ and lactate remain near steady. In a cycling trial at 95% of RCP, VO₂/lactate stabilized after the opening minutes, but HR and DFA-α1 showed significant drift across quarters of the session. dx.doi.org (Touchdown!! - correct)

• Duration effect near MLSS/RCP: In 30-min constant-speed running around MLSS, DFA-α1 was influenced by both intensity and duration (values tended to fall over time and remained above 0.5 at MLSS early on); using a fixed 0.5 (RCP-like) value from ramps can over-estimate steady-state intensity. SpringerLink

• Longer constant-work bouts: Extending constant work from 20 to 40 min produced a larger within-bout decrease in DFA-α1, highlighting sensitivity to time-on-task (durability) rather than only absolute workload. SpringerLink

• Background link to intensity: Across studies, DFA-α1 falls with rising intensity (≈0.75 near VT1; ≈0.5 near RCP/VT2 on ramps), so any upward HR drift at fixed power tends to push DFA-α1 lower over time. PMCFrontiers

Practical takeaways

• If the goal is to stay below severe: monitor DFA-α1 and keep it ≥ ~0.5; if it slips well below 0.5 during a steady effort, your internal load is drifting upward despite constant power—back off slightly, cool, and hydrate. SpringerLinkPubMed

If you’d like, share a recent steady-state file and I’ll plot HR and DFA-α1 over time to quantify your personal drift.

They got it this time - is it learning or luck?

 

 

On to a more difficult set of questions - identification of cardiac rhythm questions. Many may skip this but for those of you interested, the examples are instructive. 

What is the nature of this ECG rrhythm?This is a lead II from an ECG displayed in Kubios software

Short answer: sinus rhythm with intermittent premature ventricular beats (PVCs).

Why I say that:

·         The baseline rhythm is regular with upright P waves before the tall, narrow QRS in lead II → sinus rhythm.

·         Scattered beats have an early coupling interval, different/wider morphology (one appears predominantly negative), and are followed by a pause—features typical of PVCs rather than normal sinus beats or atrial premature beats.

·         Kubios’ R-markers with small circles typically indicate beats it has flagged as abnormal/ectopic, which line up with those morphologically different complexes.

It’s not atrial fibrillation (the RR isn’t irregularly irregular) and there’s no sustained tachyarrhythmia or AV block on this short strip. If you need to be certain about ectopic origin, confirm on a longer strip/12-lead (look for absence of a preceding P and a full compensatory pause).

This was a good answer and gave me motivation to test some more:

 

This is taken with a chest belt pPolarH10, what do you think of the circled beats?

Note to self – no Q waves, T wave subtly different, second QRS clearly wider

Likely artifact, not ectopic beats.

Why:

·         Timing: the R–R intervals on either side stay essentially constant—there’s no early beat or compensatory pause as you’d expect with a PVC/PAC.

·         Morphology/location: the odd deflection is a narrow, sharp negative notch in the ST/T area between two normal, tall, narrow QRS complexes. A true PVC would replace a QRS with a wide/bizarre complex; a PAC would usually shift the next R earlier.

·         Context: chest-strap single-lead (H10) recordings commonly show motion/electrode “pops” or myopotential spikes, especially as the strap shifts or dries. The second circled area also shows mild baseline wander consistent with movement.

What to do:

·         Treat those spots as artifact in Kubios (exclude or correct); don’t include them in HRV/DFA α1 windows.

·         Tighten/rewet the strap (or use a dab of gel), clean the electrodes, and minimize torso motion; if needed, reposition/rotate the strap for a steadier Lead II vector.

If you want, share the RR file segment; I can mark/remedy these artifacts so they don’t skew your analysis.

I could have missed this myself, but I was also wearing a 3 lead ECG Faros 180) with a lead II config. See next question below…

 

If this was the simultaneous lead 2 tracing with the same circled beats, what is your opinion? (Note, I only supplied the top pane)

Still looks like artifact, not ectopy.

Why, even on this “simultaneous lead II” view:

·         The R–R cadence stays nearly perfectly regular across the circled events—no premature R, no compensatory pause → argues strongly against a PVC or PAC.

·         The deflections occur between QRS complexes (in the ST/T segment) and have a very sharp, non-physiologic spike shape (the right one even looks like a brief amplifier saturation) → classic for electrode motion/“pop” or strap bounce, which is common with chest belts.

·         The QRS morphology of the surrounding beats is unchanged; a true PVC would replace a normal QRS with a wide, bizarre complex, and a PAC would advance the next QRS (often with a different P). Neither happens here.

What to do: mark these as artifact in Kubios and exclude the windows around them from HRV/DFA α1. To reduce recurrences, snug the strap, moisten/gel the electrodes, clean the contact surfaces, and minimize cable/strap tugging (especially with deep breaths or torso twist).

(You may still have other true PVCs elsewhere on your longer strip; but the two circled deflections here are best explained as motion/contact artifact.)

This was an eye opener for me and a caution for those of you looking at chest belt recordings. Although on very close inspection the H10 tracing did reveal some change, only on the lead II did the QRS really resemble a VPC. The "timing" was also still in spec due to the likleyhood of fusion of the VPC and normal beat.

 

Couldn't they be fusion beats with preserved R peak timing?

Good question—and in theory, fusion beats can keep (nearly) the same cycle length. But what’s on your strip doesn’t fit fusion physiology:

·         Where the distortion is. The odd deflections occur between normal QRS complexes, in the ST/T segment. A fusion beat would replace a QRS with a hybrid-looking QRS at the expected time, not create an extra sharp spike mid-diastole.

·         QRS morphology/timing. The surrounding R peaks are tall, narrow, and unchanged in shape and timing. With fusion you’d expect the QRS at that beat to look intermediate (notch/wider/different axis) at the R-peak moment.

·         Waveform physics. Those negative “knife-edge” notches are too brief and spiky (tens of ms) to be ventricular depolarization (normally 60–110 ms). That profile is classic for motion/electrode-contact artifact (“electrode pop,” strap bounce, or myopotentials), which is common with chest belts.

·         RR behavior. The RR intervals bracket the events almost perfectly—no subtle advance or delay of the next R. Fusion usually gives you at least a small change in coupling/PR or a subtly altered QRS.

These are weak arguments.  

Given all that, these circled deflections are far more consistent with artifact than fusion beats.

If you want to double-check on your end: zoom in and compare QRS width/axis of the “suspect” beats to neighbors; if they’re identical and the spike sits away from the QRS, call it artifact. A simultaneous second lead or a 12-lead would settle it—fusion must alter the QRS at the beat itself.

Practical tip: snug/rewet the H10 pads (a dab of gel helps), clean the contacts, and avoid strap tugging; then exclude those moments in Kubios before doing HRV/DFA α1.

 

Notice the shape of the T wave which is classic for a VPC.

You’re right that PVCs usually have a “discordant” T-wave (T opposite the main QRS), and an interpolated PVC can occur between two normal sinus beats without a big pause. But on your strip the circled events still don’t line up with PVC physiology:

·         They don’t replace a QRS. A PVC—interpolated or not—appears as an extra, wide/bizarre QRS. Here, every R peak is the same narrow, tall morphology and the cycle length stays essentially unchanged.

·         The deflection sits in the ST/T segment. The odd, sharp negative notch occurs ~200–300 ms after the preceding R, i.e., during repolarization. A PVC must begin at the QRS, not mid-T.

·         Too narrow to be ventricular depolarization. The spike is only a few tens of ms—far shorter than a ventricular QRS (≈60–120 ms). That knife-edge look is classic for motion/electrode “pop” or myopotential artifact—common with chest straps (H10).

·         No compensatory behavior or QRS change. Even with fusion/interpolation you’d expect some QRS alteration (axis/width/notching) at that beat. Your QRS complexes before and after the notches are indistinguishable.

So the “T-wave look” here is best explained by T-wave distortion from artifact, not a true VPC.

If you want to self-check quickly: measure the duration of the abnormal deflection and compare the “suspect” QRS width to neighbors; if the QRS width/shape is identical and the extra deflection is mid-T and very brief, call it artifact. For cleaner data, snug/rewet the strap (a dab of gel helps), clean contacts, and avoid torsional chest movements—then exclude those spots in Kubios before HRV/DFAα1 analysis.They still don't get it.

 

Here is another example from another day but same lead placement showing VPC activity. Don't they resemble the ones above that you are calling artifact?

I see why they look suspicious, but these beats still don’t behave like PVCs. Here’s what your new strip shows:

·         All R peaks are tall, narrow, and essentially identical. A PVC should replace one of those with a wide/bizarre QRS (often ≥120 ms) and an oppositely directed T. I don’t see a single replaced/wide complex here—just normal QRS spikes at a steady cadence.

·         The “extra” deflections are mid-cycle, in the ST/T segment, ~200–300 ms after the prior R. That’s too late to be the onset of ventricular depolarization. It’s the classic location for motion/electrode-contact artifact on chest belts (brief, knife-edge spikes or notches that distort the T wave).

·         RR timing doesn’t show a premature beat. With a PVC (even an interpolated one), you’d expect a shortened coupling interval to the ectopic QRS and/or a compensatory pause. Here the R–R intervals bracketing those deflections stay nearly unchanged.

·         No fusion pattern. A fusion beat would alter the QRS at that beat (intermediate morphology), not insert a narrow spike in the T wave while leaving the surrounding QRS unchanged.

So while you may well have PVCs elsewhere in longer recordings, the circled “VPC activity” in this panel (like the prior ones) is most consistent with artifact—likely strap bounce/electrode pop or myopotentials—rather than true ventricular ectopy.

If you want to verify quickly in Kubios:

1.      Measure QRS width and compare suspect beats to neighbors—if identical and <120 ms, not a PVC.

2.      Check RR before/after the event. PVC: notable shortening then lengthening (or an extra premature QRS if interpolated). Your strip: essentially unchanged.

3.      Build a median template of normal beats and cross-correlate the suspect QRS; a PVC will have a low correlation and different axis; artifact in the T wave won’t.

Practical tweaks to reduce this: snug/rewet the H10 pads (a bit of gel helps), clean contacts, rotate the strap slightly to improve the Lead-II vector, and minimize torsional chest motion. For definitive arrhythmia confirmation, a 3-electrode adhesive patch or 12-lead snapshot will settle it immediately. I give up!

Bottom line here is that the VPCs were totally missed despite multiple attempts to nudge the evidence in the right direction. Obviously, this is not extending a reassuring degree of trust. Although some of these responses are spot on, many are way off and/or misleading.

 

Some observations on ChatGPT:

Can we trust it?

Some say yes, but many sources are skeptical. I’m somewhere in between. I’ve used it for some of my endocrinology lectures, tough cases and random searches that would have been time consuming using the "big G engine" that yields 10 million hits. Most of the time it’s uncannily accurate, but as in the arrhythmia example above, not quite always ready for prime time.

Funny that this was a headline in today’s news:

On the other hand, according to my ChatGPT query, this is a summary of accuracy stats:

My advice to you is use it but verify the source or citation. It’s also a good idea to do a Google Scholar or PubMed search for the most up to date material.

As a final observation, the following is a video clip of one of the questions being answered. The point being, the steps and amount of time it spent on "thinking" about it. The speed is 10x of original:

 

 

Predicting the max metabolic steady state with submax HRV

 

One of my "wish list" items has been identifying my "maximal lactate steady state" without actually doing the maximal effort testing. After all, these are tough tests that impact recovery and are generally (very) unpleasant, especially as we age.

Well over 1 year ago, I had an idea of a way to determine the second HRVT using only data up to the "heavy" intensity domain/zone 2. HRVT2 intensity corresponds to the "maximal metabolic steady state", (AKA - the RCP, second lactate threshold, MLSS, FTP, critical power, heavy severe domain boundary, and zone 2:3 transition). The HRVT2 is usually measured by ramp efforts to failure or at least passing the severe intensity domain (which is not trivial). 

Here are some graphic plots regarding zones/domains:

Or something like this:

The idea of using submaximal ramp data was based on several years of interpreting exercise DFA a1 trajectories that usually are linear from 1.5 to 0.5, as noted below:

As we can see in the figure above, the linear relationship from 1.5 to about 0.75 should suffice to get us a "predicted" HRVT2 without actually reaching that intensity. This would be a major boon to those who can't do a full ramp to exhaustion for various reasons. Indeed, one of those reasons can even be the desire to "take it easy," yet still identifying the HRVT2 with decent precision.

I ran this idea by Juan and Pablo over a year ago, who (as usual) were supportive of the investigation. Many may not realize that the time lag between the "hypothesis" and publication can be substantial. This paper was submitted many months ago, and although I wanted to share the results, one can't do that until acceptance and the "embargo" ends.

Below is the full text with references and a short take home summary. Many thanks to Juan and Pablo for the raw data, support, and encouragement. This is our seventh publication together, and it's been a blast working with both of them. I've learned a tremendous amount academically but also some skills in the "how-to" publication sphere. 

Here is the link to the official publisher website.

 

Take home summary:

It is possible to predict the HRVT2 heart rate (AKA - second lactate/ventilatory threshold, RCP, MLSS, CP, FTP) with only submaximal HRV data. This prediction is usually quite close to the standard HRVT2 derived from a full ramp to failure.

But, several factors must be considered:

• Make sure the chest belt placement is optimal - check R wave morphology/voltage before doing the test. This is one of the most common mistakes in a1 research from my point of view. As we've seen in other posts, it makes a difference.
• Don't perform the test unless you are fresh and healthy. For example - Doing this the day after an exhaustive effort may lead to erroneous results from autonomic suppression. The plot may be linear, but not associated with the MMSS.
• Trustworthy results should have an unambiguous line of regression - if the a1 plot is equivocal, do the test again. Ideally, you will see a plot as in Figure 1 above. However, if there is a1 undulation (drops, rises, drops, rises) I would not trust the test.
• Lastly, many thanks to Ian Peake for the brilliant work creating Fatmaxxer. The app was critical to the success of this project!

Since this is submaximal data, the test can be repeated often, without affecting training regimes or rest days.

To Blog index........

 

 

 

 

HRVTs in Hypoxia - are they still valid?

Perhaps someone has wondered whether HRVTs based on DFA a1 are still aligned with established standards when tested at altitude. It's not an unreasonable question, and one where adverse consequences could occur. I remember a trip to 6500 ft altitude in my younger days and even trivial exertion was surprisingly difficult. Well, we now have some data about DFA a1 behavior at altitude that I would like to share:

 

Before getting into the details, I would like to thank my co-authors and especially Juan and Youmna. It was a great fun working with the data, exploring HRV physiology under hypoxia and of course, the teamwork interactions.

One last point before you read through the file - although not part of the headline, a major highlight of this study is the tight correspondence of both the HRVT1 and HRVT2 with the GET/RCP. This reinforces our belief in the "custom" method of HRVT1 derivation.

You may notice some emphasis on the importance of preprocessing, notably detrending. I must thank one of our reviewers for that, they asked and I provided the explanation below:

 

Summary points:

• This study showed excellent alignment of HRVTs with gas exchange standards with normoxia.
• The new "custom" HRVT1 was confirmed to be valid.
  
• Hypoxia related HRVTs were still aligned, but there was much more "scatter". Variable hypoxic related ANS response was probably a factor.
• Detrending does matter, as does signal quality. 

Blog index

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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See HRVTs in hypoxia for more DFA a1 and autonomic tidbits