Scatter plot of 37 T100 race-days: median run split relative to each athlete's usual against peak race-day simplified WBGT. Most races sit near zero; the five Singapore races, the hottest, are 5 to 10 percent slower. Doha 2025 is near zero.
data analysis, glycogen, Sports Science, Triathlon

Heat Acclimation Won’t Fix Your Fuelling

Heat acclimation makes you a better thermoregulator, but it does not make you better at burning the carbohydrate you drink. A study published in The Journal of Physiology this week shows exogenous glucose oxidation drops by about 27% at 40°C, and stays down after nine days of heat acclimation.

The timing matters. Elite triathlon is in the Gulf right now: WTCS Abu Dhabi closed the Championship Series era on 13 November, the Dubai T100 follows today, and the Qatar T100 World Championship Final comes to Doha on 11 December.

The standard playbook for hot races is: acclimate, then fuel as you would anywhere else. This paper says the second half of that sentence needs rewriting.

What the study did and found

Tim Podlogar, Gareth Wallis and colleagues at Birmingham, Exeter and USARIEM tested 17 endurance-trained adults (10 men, 7 women). Each cycled for 90 minutes at a moderate 45% of maximal power while drinking glucose at 90 g/h.

They did this three times: at 20°C, at 40°C, and at 40°C again after nine days of isothermic heat acclimation spread over ten days. Relative humidity was 20% throughout. Dual stable-isotope tracers, indirect calorimetry and muscle biopsies tracked where the glucose went.

Dot plot of exogenous glucose oxidation: 0.51 g/min at 20°C, 0.37 g/min at 40°C, and 0.38 g/min at 40°C after nine days of heat acclimation, with 95% confidence intervals.
Data: Podlogar et al., J Physiol, 2026 (doi:10.1113/JP289304). 17 endurance-trained adults, 90 min cycling, 90 g/h glucose.

The acclimation worked on the usual markers: the authors report clear thermoregulatory adaptation. Yet the oxidation of ingested glucose did not move back towards the temperate value.

The mechanism is the interesting part. Glucose still entered the blood from the gut at the same rate in every condition. What fell in the heat was glucose clearance and the oxidation of plasma glucose. The bottleneck is not absorption; it is uptake and use by the working tissues.

It is not a dehydration story

The obvious objection is that heat studies often confound temperature with fluid loss. Two strands of evidence argue against that explanation.

First, a Loughborough study last year kept runners euhydrated. Ten trained runners ran 100 minutes at about 65% of VO₂max, at 34°C or 19°C, replacing about 90% of sweat losses and drinking 60 g/h of glucose. Average exogenous oxidation was still about 20% lower in the heat, and the body leaned harder on its own carbohydrate stores to make up the difference.

Second, the new study points to a peripheral limit. Gut-to-blood delivery was unchanged, so the effect sits downstream, in how muscle takes up and oxidises circulating glucose. Two labs, two exercise modes, two temperatures: the direction and rough size of the effect (20–27%) agree.

Why it matters: more grams in is not more grams used

The practical consequence is a larger draw on glycogen for the same intake. In the new study the deficit was about 0.14 g/min, roughly 8 g of ingested glucose per hour that is not oxidised.

That sounds small, but it compounds. As a rough extrapolation, not a measured result, a T100 lasting around 3.5 hours at that deficit means about 30 g less fuel from the bottle. In the Loughborough study data, endogenous carbohydrate use rose by about 0.24 g/min in the heat, which over a long race is a meaningful extra withdrawal from muscle and liver glycogen.

This collides with the current push towards 100–120 g/h intakes. If oxidation is capped lower in the heat, pushing intake harder may simply load the gut, exactly when heat is already making gastrointestinal problems more likely.

Three caveats keep this honest:

  • Glucose only. The new study used glucose alone. An older Birmingham study found a glucose–fructose mix oxidised about 36% faster than glucose in the last hour of cycling at about 32°C. Whether multiple transportable carbohydrates escape the heat penalty is the obvious next experiment.
  • Moderate intensity. 45% of maximal power is well below race pace. At race intensity, carbohydrate demand and heat production are both higher.
  • Performance is not oxidation. A 2026 systematic review found carbohydrate’s benefit in the heat equivocal: five of nine trials showed gains, four did not, and seven of nine never measured gut symptoms.

What the T100 race data shows

If heat erodes fuel availability late in a race, run splits should suffer more in hot races than in cool ones. I tested that on every T100 result from 2024 to 2026 on PTO Stats: 37 race-days and 555 finishes by 91 athletes who raced at least three times.

Each split is expressed against that athlete’s own median, so +5% means five per cent slower than their usual T100 run. Heat is the simplified wet-bulb globe temperature (sWBGT) at the day’s peak, calculated from hourly temperature and dew point in the Open-Meteo historical weather archive.

Scatter plot of 37 T100 race-days: median run split relative to each athlete's usual against peak race-day simplified WBGT. Most races sit near zero; the five Singapore races, the hottest, are 5 to 10 percent slower. Doha 2025 is near zero.
Data: PTO Stats, T100 2024–2026 (37 race-days, 555 finishes, 91 athletes with 3+ races). Weather: Open-Meteo archive; sWBGT = 0.567T + 0.393e + 3.94, daily peak 07:00–18:00 local. Dubai 2025 men excluded (shortened run).

Most race-days sit within about 5% of zero whatever the weather. The exception is Singapore: all five editions, men and women, ran 5–10% slower than usual, roughly three to six minutes on an hour-long run.

Across all races, each extra degree of sWBGT was associated with a 0.48% slower run (95% CI 0.19 to 0.76; athlete fixed effects, errors clustered by race). The bike showed no such link (−0.29% per degree, CI −0.70 to 0.12).

That slope is really a Singapore effect. Remove the five Singapore races and the run slope falls to zero (−0.06% per degree, CI −0.30 to 0.17). Between roughly 18 and 29°C sWBGT, race-day heat explains very little of how elite athletes run.

The bike works as an internal control. If Singapore were simply a slow venue or a bad day, the bike should slow too.

Dot plot with 95% confidence intervals of run and bike splits relative to each athlete's usual, by heat band. Cool: run 0.0%, bike +1.0%. Warm: run -0.6%, bike -1.7%. Hot: run +4.2%, bike 0.0%.
Same dataset. Race-day bands by peak sWBGT; medians of athlete-relative splits, 95% CIs from 2,000 bootstrap resamples of races.

It didn’t. In the hot band the median bike split matched athletes’ usual, while the run was about 4% slower. The interval for the run crosses zero because Miami, also hot, showed no penalty. A run-specific slowdown confined to the hottest, most humid races fits a heat-and-fuel story, but it cannot prove one.

What this means for Doha. Last year’s final sat in the warm band (sWBGT 26.8°C), and its run splits were normal: +0.7% for women and −0.2% for men against each athlete’s usual. On that evidence, December in Doha does not trigger the run penalty seen in Singapore.

One limitation to state up front: race data cannot separate fuel from thermoregulation, pacing or tactics. A steeper fade in the heat is consistent with this mechanism; it does not prove it. December in Doha is also typically warm rather than extreme, well short of the 40°C used in the lab (hopefully as I will be in the race too, injuries permitting!), so the effect at the final may be smaller.

Three further caveats on the data:

  • Singapore is one venue. Its course, its start time and its five editions all travel together, so a slow run course could mimic a heat effect. The normal bike splits there argue against a general slow-venue explanation, but not against a slow run course.
  • Weather is approximate. sWBGT uses the day’s peak temperature and humidity, not conditions at race time, and ignores sun and wind.
  • One race excluded. Dubai 2025 men’s run splits were around 50–55 minutes, far quicker than any other edition, which suggests a shortened course; it is left out.

What practitioners should do before Doha

Keep heat acclimating; just stop expecting it to solve fuelling. A practical checklist:

  1. Acclimate anyway. Lower core temperature, better sweating and a lower heart rate are real gains for performance and safety. The new study confirms they happen; it shows they are not the whole story.
  2. Start with full glycogen stores. If more of the race is fuelled from the inside, the tank you start with matters more. Carbohydrate loading deserves attention before hot races, not only long ones.
  3. Prefer multiple transportable carbohydrates. Glucose–fructose blends have the best evidence for higher oxidation in the heat, though they have not yet been tested against heat acclimation.
  4. Gut-train in the heat, not just at home. Rehearse race intake in hot sessions and record gut symptoms. Tolerance in a cool training block does not transfer automatically.
  5. Don’t chase the highest number on the label. If oxidation is capped lower, extra grams mostly add gut risk. Find the highest intake an athlete tolerates in the heat, then hold it.
  6. Use cooling to protect pace. Pre-cooling, ice and cold fluids reduce heat strain directly; they may also buy back some of the metabolic penalty, though that is untested.
  7. Pace the first half conservatively. A heat-driven increase in glycogen use argues for restraint early, so there is something left for the run.

The bottom line

Heat acclimation fixes how hot you get, not how much of your drink you burn. In the best mechanistic data we have, ingested glucose oxidation runs 20–27% lower in the heat, regardless of hydration and regardless of nine days of acclimation. For athletes racing in the Gulf this winter, that shifts the emphasis towards starting glycogen, carbohydrate type, gut training and pacing, rather than simply drinking more.

The open question I most want answered: does a glucose–fructose blend, in heat-acclimated athletes at race intensity, close the gap?

References

  • Podlogar T, Gifford RE, Thomas J, et al. Heat acclimation does not alleviate the reduction in exogenous glucose oxidation that occurs when exercising in hot conditions. J Physiol. 2026. doi:10.1113/JP289304
  • Mougin L, Horner M, Edwards D, et al. Heat stress impairs exogenous carbohydrate oxidation during prolonged running when maintaining euhydration. J Appl Physiol. 2025;139(6):1436–1446. doi:10.1152/japplphysiol.00873.2025
  • Jentjens RLPG, Underwood K, Achten J, et al. Exogenous carbohydrate oxidation rates are elevated after combined ingestion of glucose and fructose during exercise in the heat. J Appl Physiol. 2006;100(3):807–816. doi:10.1152/japplphysiol.00322.2005
  • Salame A, Brown D, Oueijan K, McCullough D. Carbohydrate supplementation for endurance exercise in the heat: a systematic review with practical recommendations. J Int Soc Sports Nutr. 2026;23(1):2669307. doi:10.1080/15502783.2026.2669307

Race context: TRI247, WTCS Abu Dhabi 2026 results · PTO Stats, Qatar T100 World Championship Final

Waterfall chart: Ditlev's expected time on a typical 2025 to 2026 race day of 7:30:51, minus 4:37 for Florida 2026 conditions, minus 6:57 for his outperformance, equals 7:19:17.
Sports Science

Seven nineteen: how much of Ditlev’s Ironman world best is the athlete, and how much is Florida?

On 7 November 2026, Magnus Ditlev won IRONMAN Florida in 7:19:17: a 47:52 swim, a 3:51:16 bike and a 2:33:31 run, more than 14 minutes clear of Rudy Von Berg. It is the fastest full-distance triathlon ever recorded. It beats Sam Laidlow’s 7:21:04 at Challenge Roth in July and Kristian Blummenfelt’s 7:21:11 in Cozumel (2021) and 7:21:24 in Texas (April 2026). The bike split is also the fastest ever ridden at the distance, ahead of Cameron Wurf’s 3:53:32 in Texas in 2025.

Triathlon has no official world records. Courses, currents, wind, temperature and course measurement all differ too much, which is why careful outlets call it a “world best”. That leaves an obvious question for anyone who works with performance data: how much of 7:19 is Ditlev, and how much is Panama City Beach on the day? Results data can answer that more precisely than the headlines do.

Stacked bars of swim, bike, transition and run splits for the four fastest full-distance triathlons: Ditlev Florida 2026 7:19:17, Laidlow Roth 2026 7:21:04, Blummenfelt Cozumel 2021 7:21:11, Blummenfelt Texas 2026 7:21:24.
Four ways to go about 7:20. Blummenfelt’s Cozumel time rests on a 39:40 swim with the current; Ditlev’s rests on the bike.

The problem: a time is the athlete multiplied by the day

A finish time is the product of two things we can’t observe directly: how good the athlete was that day, and how fast the course and conditions were. Comparing raw times across venues is like comparing sprint times without knowing the wind reading. The figure above shows the point. Four performances finish within about two minutes of each other, yet their make-up is completely different. Cozumel’s sea swim is current-assisted and comes in about eight minutes faster than the others. Ditlev and Laidlow built their times on the bike. Blummenfelt’s Texas run (2:30:47) carries his 2026 time.

The way through is to use the athletes themselves as the measuring stick. Most elite triathletes race several of these courses across several years. If the same people are consistently quicker on one day than on another, the difference belongs to the day, not to them.

How I did it, step by step

  1. Data. I took the men’s professional top-10 results (swim, bike, run and total) from the PTO stats database for the four courses that hold the fastest times: Florida (2019, 2021, 2023, 2026), Challenge Roth (2019, 2022–2026), Texas (2019, 2022–2026) and Cozumel (2019, 2021, 2022, 2024, 2025). That gives 21 races and 210 results. Cozumel 2023 is excluded because the swim was cancelled. I checked that every row’s splits plus transitions add up to the total.
  2. Model. I fitted a two-way fixed-effects regression on log finish time, with one term for each athlete and one for each race edition. The race terms estimate how fast each race day was for the same people. The model explains 94% of the variance, and the residual is about 0.9% of finish time, roughly four minutes. The network of athletes links all 21 races into a single connected set, which is what allows any course to be compared with any other.
  3. Out-of-sample test. I refitted the model without Ditlev’s Florida 2026 result, then asked what it expected him to do there. I did this for total time and for each discipline.
  4. Course adjustment. I re-expressed the fastest performances as if they had been raced on the Florida 2026 day, with 95% confidence intervals.
  5. Draft check. I compared the spread of top-10 bike splits before and after the 20 m draft zone came in (1 March 2026), alongside RaceRanger’s published drafting data from Roth.

Finding 1: Florida 2026 was a very fast day, but not an outlier

Dot plot of race-day speed for 21 full-distance races from 2019 to 2026. Texas 2026 and Florida 2026 are the fastest, about 14 to 15 minutes quicker than average; Florida 2021 is the slowest.
How fast each race day was for the same athletes, relative to the average race day in the sample, at a 7:30 finishing level.

For the same athletes, Florida 2026 was about 14 minutes faster than the average race day in the sample. Only Texas 2026 (about 15 minutes) was quicker. Roth 2026, where Laidlow set the previous best, comes out about 6 minutes slower than Florida 2026. The 95% interval on that gap runs from 3 minutes faster to 16 minutes slower, so treat it as likely, not certain. An independent rating from TriRating, using a different method, also puts Florida 2026 roughly five minutes quicker than this year’s Roth.

Two things stand out. First, conditions on one course swing a lot from year to year. Florida spans almost 40 minutes, from 2021, when a brutal, current-driven swim left the leaders around 58 minutes, to 2026. Second, the general drift is downwards: seven of the eight fastest race days are from 2023–2026. Part of that is the sport getting faster (equipment, fuelling, depth of field), and a fixed-effects model can’t fully separate that from conditions. I come back to this under limitations.

Finding 2: same course, same men, three years apart

Slope chart of IRONMAN Florida finish times in 2023 and 2026: Ditlev 7:48:52 to 7:19:17, Von Berg 7:34:41 to 7:33:25, Hanson 7:45:26 to 7:35:48.
The three men who were in the top 10 at Florida in both 2023 and 2026.

The simplest check needs no model. Three athletes finished in the top 10 at Florida in both 2023 and 2026. Von Berg, who set the 2023 course record, was only 1:16 faster. Hanson was 9:38 faster. Ditlev was 29:35 faster. In 2023 he rode 3:59:02 and then lost the race on the run (2:52:40). This year he took 7:46 off his own bike course record and ran 19 minutes faster. If the 2026 day had been 30 minutes quicker than 2023, Von Berg would have seen it too. The Florida day did help, but most of Ditlev’s improvement on this course is Ditlev.

Finding 3: he beat his expected time by about seven minutes

Actual minus model-expected times for Ditlev at Florida 2026: swim 26 seconds faster, bike 3:25 faster, run 4:22 faster, total 6:57 faster, all inside the 95% prediction intervals.
Model trained without his Florida 2026 result. Grey bands are 95% prediction intervals.

Given his seven other results in the data and how fast the rest of the field went in Florida, the model expected Ditlev to finish in 7:26:14 (95% prediction interval 7:10 to 7:43). He went 7:19:17, which is 6:57 better than expected. The gain came almost entirely from the bike (−3:25) and the run (−4:22), not the swim (−0:26). That is a very good day, but not an implausible one: it sits inside the prediction interval. The run gain is the more interesting one for coaches. His 2023 Florida run fell apart after a sub-four-hour ride. This time a much faster ride was followed by his best marathon in the data.

Finding 4: where the 7:19 came from

Waterfall chart: Ditlev's expected time on a typical 2025 to 2026 race day of 7:30:51, minus 4:37 for Florida 2026 conditions, minus 6:57 for his outperformance, equals 7:19:17.
Decomposition relative to Ditlev’s expected time on an average modern (2025–26) race day.

Put the pieces together. On an average 2025–26 race day in this sample, the model puts Ditlev at about 7:30:51. The Florida 2026 course and conditions take off about 4:37. His outperformance on the day takes off another 6:57. Roughly, 40% of the gap to his “typical” modern time is the venue, and 60% is the athlete. Measured against the full 2019–26 average day instead, the venue-and-era share grows to about 13 minutes, a reminder that the reference point drives the answer.

Finding 5: adjust for the day and the “best” is a statistical tie

Fastest full-distance performances adjusted to Florida 2026 conditions, with 95% confidence intervals: Laidlow Roth 2026 7:15:13, Blummenfelt Cozumel 2021 7:19:06, Ditlev Florida 2026 7:19:17, Ditlev Roth 2024 7:19:27, Blummenfelt Texas 2025 7:20:22, Blummenfelt Texas 2026 7:22:38. The intervals overlap heavily.
Hollow circles are actual times; filled circles re-express each performance on the Florida 2026 day.

Now move the other fast performances onto the Florida 2026 day. Laidlow’s Roth 7:21:04 becomes about 7:15. Blummenfelt’s Cozumel 7:21:11 becomes 7:19:06. Ditlev’s own Roth 2024 win (7:23:24) becomes 7:19:27, within ten seconds of what he did on Saturday. Blummenfelt’s Texas 2026 time actually gets slower (7:22:38), because Texas 2026 was an even faster day. The confidence intervals run to about ±9 minutes and overlap almost completely. On this evidence, Ditlev’s Florida race is one of five or six performances that are statistically indistinguishable. If anything, Laidlow’s Roth ride on a slower day may be the strongest of them.

Finding 6: did the 20 m draft rule matter?

Line chart of the interquartile range of top-10 men's bike splits at Texas, Roth and Florida from 2019 to 2026. Texas fell to 53 seconds in 2026 even after the 20 metre rule; Florida is 4:44 and Roth 7:02.
A small spread of bike splits among the top 10 is the signature of a group riding together.

IRONMAN and Challenge Roth increased the professional draft zone from 12 m to 20 m from March 2026, citing aerodynamic testing that showed a much smaller benefit at 20 m (Triathlon Magazine Canada). At Roth, RaceRanger reported that the top-10 men’s average illegal drafting time fell from 1:47 to 0:23 (Endurance Sportswire).

Bike-split spread tells a more mixed story. In Texas the interquartile range of top-10 bike splits shrank from 1:51 in 2025 to 53 seconds in 2026. All ten rode between 3:55:35 and 3:58:16. Legal or not, that is a group, and it helps explain why Texas 2026 comes out as the fastest race day in the model. Florida 2026 looks completely different. Ditlev rode alone at the front and was 8:43 faster than the next-best bike split (Sam Long, 3:59:59). Whatever helped him on Saturday, it wasn’t a wheel. For me that makes his 3:51 a more convincing benchmark than any bike split ridden in a pack.

What practitioners should take from this

  • Benchmark athletes against themselves and the field, not against the clock. A simple athlete-plus-race model turns noisy finish times into expected-versus-actual performance. That is far more useful for evaluating a training block than a PB.
  • Read the splits, not just the total. Cozumel’s swim and the Texas group ride both “buy” time that has nothing to do with the athlete’s physiology. Normalise each discipline separately before drawing conclusions about fitness.
  • The bike–run trade-off isn’t fixed. Ditlev’s two Florida races are a natural experiment: a faster ride followed by a much faster run. Pacing, fuelling and durability (the ability to hold power and economy late in the race), not just threshold power, decide whether a big ride turns into a big finish.
  • Report uncertainty. A ±9-minute interval on a course adjustment is the honest answer. “World best” headlines that rank times seconds apart across different venues are reporting noise.

Limitations

  • Top-10 men only, from four courses: 210 results in total. The women’s races and full fields would sharpen the estimates.
  • The athlete terms are fixed over 2019–26, so athletes improving over time partly shows up as faster recent race days. A model with athlete trajectories (random slopes) is the natural next step.
  • No weather covariates. Adding wind, air and water temperature, and whether wetsuits were allowed, would let the model say why a day was fast, not just that it was.
  • Courses change: swim layouts, bike routes, and run measurement. The race-day term absorbs all of this, which is useful for comparing days but not for diagnosing causes.

Bottom line

Florida gave Ditlev a fast day, worth around four to five minutes against a typical modern race day. The rest was the athlete: about seven minutes better than even a model that knew how fast the field went, delivered by riding alone and then running his best marathon. Is it the “best ever”? Adjust for the day and it is part of a cluster of five or six performances within a few minutes of each other, and the statistics can’t separate them. Ditlev’s 7:19 tells you who was in the best shape of his career on Saturday. It does not, on its own, settle who is the fastest long-distance triathlete ever.

Data and sources

Method note: OLS on log(finish time) with athlete and race-edition fixed effects (statsmodels), n = 210, R² = 0.94. Expected times come from refitting with the target result held out; intervals are 95% prediction or confidence intervals as labelled. The dataset and code are available on request.

Lactate Threshold Analyzer showing velocity and heart rate at 4 mmol/L, curve fit quality, estimated maximal aerobic speed, and the lactate performance curve with threshold markers
Sports Science

What’s new in the Lactate Threshold app: evidence-based interval session formats

Over the summer I wrote about the Lactate Threshold Analyzer and then about the anaerobic speed reserve, maximum speed and 3- and 5-zone models. Since then I have kept working on the second half of the tool, the Interval Session Designer, because turning a threshold into a good session is where most coaches actually need help. This post is a quick tour of what has changed.

Lactate Threshold Analyzer showing velocity and heart rate at 4 mmol/L, curve fit quality, estimated maximal aerobic speed, and the lactate performance curve with threshold markers
The app as it looks today, with the sample runner’s step test loaded.

Session formats from a network meta-analysis

The headline change is that the designer now offers eight session formats, taken from the network meta-analysis by Held and colleagues (2026) comparing HIIT protocol designs on accumulated time at or above 90% of V̇O2max. Alongside the classic custom designer you can now pick even long intervals (the reference format in the review), even short intervals, short-interval blocks, 1:1 blocks, decreasing intensity, varied intensity and decreasing duration.

Choosing a format loads the protocol structure used in the studies behind it, and then translates it into speeds, paces and heart rates for your athlete. You can anchor the work on maximal aerobic speed, on the threshold you selected from the test, or on critical velocity if you have it from a separate test. If you choose critical velocity and do not enter one, the app tells you it is falling back on the 4 mmol·L−1 velocity and that the targets may be a little conservative. Every value is editable, and there is a button to reset back to the protocol used in the review.

Interval Session Designer with the eight session formats from Held et al. 2026 and their P-scores, showing the decreasing-duration format with intensity anchor options, work and recovery ladders, and session targets
The eight session formats, here with decreasing-duration intervals selected: 3, 2, 1 min, 45 s and 30 s of work with shrinking recoveries.

Below the inputs, the designer builds a step-by-step prescription table (time, target speed, pace and heart rate for every work and recovery bout) and a plot of the intensity profile of the whole session. The heart rate targets come from the athlete’s own heart rate–speed relationship in the test, capped at HRmax, with a reminder that during short supramaximal bouts heart rate lags and will not reach those values.

The evidence, shown honestly

This is the part I care about most. Each format comes with its own evidence panel: the effect size against even long intervals with its confidence interval, the P-score ranking, how many protocol arms it rests on, the certainty of the evidence, and the actual protocols used in the studies. A small forest plot shows where every format sits.

Intensity profile of the decreasing-duration session and evidence panel comparing each format with even long intervals for accumulated time at or above 90% VO2max, with the Held et al. 2026 reference
The intensity profile of the session and the evidence panel underneath it.

The P-scores look tempting. Decreasing duration ranks top at 89.6%, varied intensity at 76.7%, decreasing intensity at 68.4%. But I did not want the app to turn a ranking into a recommendation. In the review, no format was significantly better than even long intervals, the certainty of evidence is very low, and the top-ranked format rests on just two protocol arms from the same research group, with sessions that were not time-matched to the comparison. The app says all of this next to the numbers, not in a footnote. My advice is the same as the review’s: pick the format on feasibility, the athlete’s profile and matching the workload, and treat the ranking as exploratory.

A ranking is not a recommendation. The evidence panel is there so coaches can see how much, or how little, sits behind each choice.

Choosing the anchor that drives everything

The threshold comparison table now lets you tap any of the six detection methods (V4/OBLA from the curve or by linear interpolation, LT at baseline +0.4 mmol·L−1, Dmax, modified Dmax and the log-log turnpoint) to make it the anchor. Training zones and interval targets update straight away, so you can see how much a choice of method changes the prescription before you commit to one.

Threshold comparison table showing velocity, heart rate, lactate and pace for six detection methods, above the training zones panel with 3-zone and 5-zone options anchored on the selected threshold
Six threshold methods side by side, with the selected one driving the 3- or 5-zone model.

What’s next

I am now working on turning the tool into an iOS and Android app, with athlete and test history, pitch-side data entry, PDF and CSV export, and club or clinic branding. I will post here when it is ready. In the meantime the web version is free to use and nothing needs installing. As always, if you use it and something looks wrong or you would like a feature, let me know.

Try the app

If you use it in your work, please cite it:

Cardinale, M. (2026). Lactate Threshold and HIT designer [Web application]. Retrieved from https://lactatethresholdhitdesigner.netlify.app.

Reference

  • Held, S., Rappelt, L., Micke, F., Wiedenmann, T., & Donath, L. (2026). Comparison of high-intensity interval training protocol designs on accumulated time ≥ 90% V̇O2max: a network meta-analysis. BMC Sports Science, Medicine and Rehabilitation, 18, 336. https://doi.org/10.1186/s13102-026-01891-7

The tool is intended for education and performance monitoring. Thresholds, zones and session targets should be interpreted alongside the athlete’s full physiological and training profile.