Turning Up the Heat: Passive Heating as a Stimulus for Systemic and Muscle-Level Adaptation

For a long time we treated heat mainly as a problem to be managed — something that degrades performance and has to be defended against. That framing is increasingly out of date. A growing body of work, including studies from my own group, points to the same conclusion: a controlled dose of heat is a genuine physiological stimulus in its own right, capable of driving adaptation from the whole-body oxygen-transport system all the way down to the regenerating muscle fibre. In this post I want to bring together two recent papers on the systemic side and connect them to what we have learned about heat at the muscle level.

The through-line is simple. If we stop thinking of heat as merely a stressor to survive and start treating it as an additional training input — layered on top of, or alongside, the usual mechanical and metabolic load — we open up practical ways to enhance adaptation without adding more hard training. That matters for athletes managing fatigue, for those returning from injury, and for anyone trying to hold on to hard-won gains.

The systemic story, part 1: passive heat and VO₂max

The first study I want to write about, from Jenkins and colleagues at Cardiff Metropolitan University and published in The Journal of Physiology, asked a clean question: can passive heat — hot-water immersion, which lets athletes keep training normally rather than compromising session quality — reproduce the haematological and aerobic benefits usually attributed to exercise-in-the-heat? Ten well-trained runners completed five weeks of hot-water immersion (5 × 45 min per week at ≥ 40 °C) in a within-subject, counterbalanced crossover against a time-matched control, alongside their habitual training.

INFOGRAPHIC · HEAT AS A PHYSIOLOGICAL STIMULUS

Passive heat boosts VO₂max

5 weeks of hot-water immersion · 10 well-trained runners · within-subject crossover · 5 × 45 min/week at ≥ 40 °C

+33 g

Haemoglobin mass — the strongest independent predictor of the VO₂max gain

+284 mL

Blood volume — expansion of total circulating volume

+10 mL

LV end-diastolic volume — greater cardiac filling, with strain unchanged

+0.8 km/h

Speed at VO₂max — the adaptation translating toward performance

+2.7 mL·kg⁻¹·min⁻¹ VO₂max

Coordinated gains across the oxygen-transport chain — without adding a single hard training session.

Jenkins et al., J Physiol 2025 · DOI 10.1113/JP289874

The results are striking for a passive intervention. Hot-water immersion increased haemoglobin mass by 33 g, expanded blood volume by 284 mL, and raised left-ventricular end-diastolic volume by 10 mL — without altering systolic or diastolic strain mechanics. Those coordinated changes drove a 2.7 mL·kg⁻¹·min⁻¹ improvement in VO₂max and a 0.8 km/h increase in the treadmill speed at VO₂max. Crucially, haemoglobin mass was the strongest independent predictor of the VO₂max gain, with cardiac adaptation adding further explanatory value. The take-home is that passive heat acts on multiple convective links of the oxygen-transport chain at once — more blood, more of it carrying oxygen, and a heart filling a little more with every beat.

The systemic story, part 2: locking in altitude gains

The second study, from the same group in Experimental Physiology, tackles a problem every altitude-camp practitioner knows too well: the haemoglobin mass you build at altitude tends to melt away within about a week of coming down. If heat can expand haemoglobin mass, could it also preserve an altitude-induced expansion after descent? Twenty-one adults spent 14 days at 3800 m and, on descending to 1250 m, were assigned either to hot-water immersion (45 min at 40 °C for four days) or to a control condition.

INFOGRAPHIC · HEAT AS A PHYSIOLOGICAL STIMULUS

Hot water locks in altitude gains

21 adults · 14 days at 3800 m, then descent to 1250 m · post-descent hot-water immersion (45 min at 40 °C × 4 days) vs control

+24 g haemoglobin mass gained

across all participants during the 14-day altitude sojourn

CONTROL: –18 g

gains lost within days of descent

HOT WATER: +9 g

expansion maintained

Preservation occurred independent of EPO — circulating erythropoietin fell equally in both groups, so the mechanism remains to be resolved.

Jenkins et al., Exp Physiol 2026 · DOI 10.1113/EP093944

The divergence after descent is the headline. Haemoglobin mass rose by 24 g at altitude across the whole cohort. Back at low elevation, the control group lost 18 g — the familiar wash-out — while the hot-water group actually held on, drifting up by 9 g. Interestingly, this preservation was not explained by sustained erythropoietin: EPO declined similarly in both conditions, and plasma-volume expansion was comparable. So heat protected the red-cell expansion through a route we have not yet pinned down. Mechanism aside, the applied message is immediate: a few days of hot-water immersion after an altitude block is a low-impact, practical way to defend the adaptation athletes travelled a long way to earn.

From the bloodstream to the muscle fibre

If those two papers make the case for heat as a systemic stimulus, our own recent work makes the complementary case at the tissue level. In a study led by colleagues at Aspetar and published in The Journal of Physiology, we examined how different thermal treatments influence human muscle regeneration after a simulated injury. Thirty-four participants underwent an electrically stimulated eccentric-contraction protocol that triggers genuine myofibre necrosis and regeneration, then completed ten days of daily lower-body immersion in cold (12 °C), thermoneutral (32 °C), or hot (42 °C) water, with muscle biopsies before and at five and eleven days post-damage.

The findings ran against the reflex to reach for ice. Hot-water immersion produced lower perceived muscle pain and lower circulating creatine kinase and myoglobin than both thermoneutral and cold water. It up-regulated heat-shock proteins 27 and 70 and raised the anti-inflammatory cytokine interleukin-10, while blunting the rise in nuclear factor-κB seen in the other conditions. Cold-water immersion, by contrast, did not improve pain or reduce markers of damage, and appeared to dampen the heat-shock-protein response. In short: heat supported the muscle’s own regenerative machinery; cold did not. This is a muscle-level adaptation — a shift in the molecular environment toward repair — driven by the same physical stimulus that, systemically, expands haemoglobin mass and cardiac filling.

Why this matters: heat as an additional stimulus

Read together, these three studies tell a coherent story. At the systemic level, passive heat expands haemoglobin mass, blood volume and cardiac filling to lift VO₂max, and can preserve the haemoglobin expansion won at altitude. At the muscle level, heat tilts the local environment toward regeneration through heat-shock proteins and a more favourable inflammatory profile. The common thread is that heat is not merely a comfort measure or a stressor to be tolerated — it is a controllable physiological input that produces real, measurable adaptation on two fronts at once.

For practitioners, that reframing is the point. Heat can be programmed deliberately: to add an aerobic-adaptation stimulus in athletes who cannot absorb more mechanical load, to protect red-cell mass in the days after an altitude camp, and to support tissue repair during return-to-play rather than reflexively cooling everything down. The doses in these studies were modest and passive — 45 to 60 minutes of hot-water immersion — which makes them realistic to implement. As always, individual responses vary and heat carries its own cardiovascular and hydration considerations, so it should be dosed and monitored like any other training variable. But the direction of travel is clear, and I suspect we are only beginning to map what a well-designed heat stimulus can do.

I will keep writing about this as the evidence develops. If you are applying heat with your athletes and seeing effects — systemic or muscular — I would be glad to hear about it.

References

  • Jenkins EJ, Killick JA, Zerilli O, Douglas AJM, Corr L, Hughes MG, Tremblay JC, Stembridge M. Long-term passive heat acclimation enhances maximal oxygen consumption via haematological and cardiac adaptation in endurance runners. The Journal of Physiology, 2025. DOI: 10.1113/JP289874 (via PubMed).
  • Jenkins EJ, Koep JL, Douglas AJM, Maier LE, Howe CA, Sheitelman S, Corr LD, Siebenmann C, Hughes MG, Tremblay JC, Ainslie PN, Gibbons TD, Stembridge M. Daily hot-water immersion preserves altitude-induced haemoglobin mass expansion following descent independent of erythropoietin. Experimental Physiology, 2026. DOI: 10.1113/EP093944 (via PubMed).
  • Dablainville V, Mornas A, Normand-Gravier T, et al., Cardinale M, Candau R, Bernardi H, Racinais S. Muscle regeneration is improved by hot water immersion but unchanged by cold following a simulated musculoskeletal injury in humans. The Journal of Physiology, 2025;603(23):7603-7625. DOI: 10.1113/JP287777 (via PubMed).

What’s New in the Lactate Threshold App: Anaerobic Speed Reserve, Flexible Training Zones, Maximum Speed and More

The Lactate Threshold app started as a simple tool to turn a step test into a clean set of threshold values. Over the past few development cycles it has grown into something closer to a complete profiling and prescription workspace. This post walks through the most significant additions — anaerobic speed reserve, maximum speed determination, switchable 3- and 5-zone models — and the smaller refinements that came with them.

Anaerobic Speed Reserve (ASR)

The headline feature is the ability to determine an athlete’s anaerobic speed reserve — the velocity range that sits between the speed at maximal oxygen uptake (vVO2max, or maximal aerobic speed) and maximal sprinting speed (MSS). Everything an athlete does above their aerobic ceiling happens inside this band, which is exactly why it matters for events decided by surges, kicks and repeated high-intensity efforts.

The concept owes much to the work of Dr Gareth Sandford and colleagues, who showed that ASR and maximal sprint speed are “untapped tools” for differentiating the world’s best middle-distance runners and for understanding the complexity of athlete profiles that traditional aerobic categories miss. Two athletes with an identical vVO2max can have very different reserves above it — and therefore very different tolerances to supramaximal work — information that is invisible if you only look at threshold and VO2max.

Dr. Martin Buchheit’s research extends this directly into programming. Prescribing high-intensity work as a percentage of maximal aerobic speed alone ignores the differing mechanical ceilings between individuals, so the same session can impose very different relative stress on two athletes. Anchoring supramaximal efforts to a percentage of the ASR instead normalises that stress, and the evidence shows it reduces the inter-individual variability of physiological adaptation. The app now makes that calculation a single step rather than a spreadsheet exercise.

Maximum Speed Determination

Because ASR depends on having a reliable upper anchor, the app now supports maximum sprint speed (MSS) determination as a first-class input. Enter the result of a short maximal sprint and the app uses it as the top of the reserve, pairing it with the aerobic anchor derived from the step test. This closes the loop: from a single profiling session you get the threshold values, the aerobic speed, the sprint ceiling, and the reserve that connects them.

Flexible Training Zones: 3 or 5

Training-zone prescription is now configurable. You can choose between a 3-zone model — the classic below-LT1, between-thresholds, above-LT2 structure favoured in polarised approaches — and a more granular 5-zone model for coaches who want finer resolution across the intensity spectrum. Zones are generated directly from the athlete’s own threshold and speed anchors rather than from generic percentages, so the prescription reflects the individual profile the test produced.

Switching between the two models takes a tap, which makes it easy to align the output with whichever periodisation philosophy a given athlete or training block calls for.

Other Improvements

Alongside the marquee features, this round of work brought a number of refinements: cleaner presentation of the threshold detection results, a more consistent workflow from data entry through to zone output, and better handling of the speed-based inputs that the ASR and MSS features rely on. The aim throughout has been to keep the app fast to use rink-side or track-side while quietly adding depth for those who want it.

Development is ongoing, and I’ll keep posting updates here as new capabilities land. If you’re using the app and have feedback or feature requests, I’d be glad to hear them. If you use it for any purposes make sure you reference it:

A note for team-sport coaches: if you are specifically after a tool to plan HIIT sessions with change-of-direction (COD) prescriptions, I’d recommend Dr Martin Buchheit’s dedicated COD shuttle prescription app, available here. It is purpose-built for that use case and complements the profiling work the Lactate Threshold app is designed for. A screenshot is below.

Key References

  • Sandford GN, Allen SV, Kilding AE, Ross A, Laursen PB. Maximal Sprint Speed and the Anaerobic Speed Reserve Domain: The Untapped Tools that Differentiate the World’s Best Male 800 m Runners. Sports Medicine, 2019.
  • Sandford GN, Laursen PB, Buchheit M. Anaerobic Speed/Power Reserve and Sport Performance: Scientific Basis, Current Applications and Future Directions. Sports Medicine, 2021.
  • Buchheit M, Laursen PB. High-Intensity Interval Training, Solutions to the Programming Puzzle. Part II: Anaerobic Energy, Neuromuscular Load and Practical Applications. Sports Medicine, 2013.

Lactate Threshold Testing: Why It Matters and How to Analyze Your Data

Why Lactate Threshold Testing Matters in Sport

Blood lactate testing has been a cornerstone of endurance sports science for decades. When you exercise at increasing intensities, your muscles produce lactate as a byproduct of anaerobic metabolism. The rate at which lactate accumulates in the blood — and specifically the intensities at which it rises sharply — reveals critical information about your aerobic fitness, training readiness, and optimal training zones.

Two thresholds are particularly important:

  • Lactate Threshold 1 (LT1) — Aerobic Threshold: The intensity above which lactate begins to rise measurably above baseline. Training below LT1 is predominantly aerobic and supports fat oxidation, cardiovascular development, and recovery. Most successful endurance athletes spend the majority of their training volume in this zone.
  • Lactate Threshold 2 (LT2) — Anaerobic Threshold / MLSS: The highest intensity at which lactate production and clearance are in equilibrium — also called the maximal lactate steady state (MLSS). This is often the best predictor of endurance race performance and correlates strongly with an athlete’s sustained power or pace over distances from ~20 minutes to several hours.

Introducing the Lactate Threshold Analyzer

To make lactate analysis more accessible, I’ve developed an interactive Lactate Threshold Analyzer — a free, browser-based tool that takes your incremental test data (load, heart rate, and blood lactate) and applies scientifically validated algorithms to detect LT1 and LT2 automatically. The tool was developed with Claude Code (Opus 4.8).

What the Tool Does

  • Interactive data entry: Enter your stage data (load in watts, speed, or pace; heart rate; blood lactate concentration) in a clean table. Load one of four built-in presets (cyclist, runner, rower, elite athlete) to explore immediately.
  • Multiple detection methods: Choose from Dmax, Modified Dmax, fixed 2 mmol/L, fixed 4 mmol/L, 3.5 mmol/L, or the Log-Log method — each with a brief scientific description.
  • Lactate curve visualisation: A real-time chart overlays the fitted spline curve on your raw data points, with LT1 and LT2 marked as vertical dashed lines. Heart rate is shown as a secondary overlay when available and you can compare a previous test too entering the data.
  • 5-zone training model: Based on detected thresholds, the tool generates a personalised 5-zone training model with load and heart-rate ranges for each zone.
  • Scientific interpretation: Each detection method is described with plain-language guidance on training implications for each zone.
  • CSV and PNG export: Download your results as a spreadsheet or save the chart as an image for reports.

The Science Behind the Methods

Dmax Method (Cheng et al., 1992)

A geometric method that identifies the point on the lactate curve that is maximally distant from the straight line connecting the first and last data points. It reliably corresponds to MLSS and is well-validated across cycling, running, and rowing.

Modified Dmax (Bishop et al., 1998)

An adaptation of Dmax that starts the reference line from the point at which lactate rises by 0.4 mmol/L above resting baseline — making it more robust when warm-up or baseline lactate is already elevated.

Fixed Concentration Methods (Mader et al., 1976)

The classic 4 mmol/L criterion (Mader criterion) and the more conservative 2 mmol/L threshold (aerobic threshold) are the simplest approach. While easy to apply, they do not account for inter-individual variation — two athletes may reach MLSS at 3 mmol/L and 6 mmol/L respectively.

Log-Log Method (Beaver et al., 1985)

By plotting log(lactate) against log(workload), the curve approximates a bilinear function, and the breakpoint in this log-log space corresponds closely to the ventilatory threshold — making it useful when you want to cross-reference with respiratory data.

How to Use the Analyzer

  1. Enter athlete information — name, sport, load unit (watts, km/h, pace), and test date.
  2. Enter stage data — for each stage of your incremental test, enter the load, heart rate (optional), and blood lactate value. Minimum 4 stages required. You can use a preset to explore immediately.
  3. Select a detection method — Dmax is recommended for most athletes. Use Fixed 4 mmol/L only when comparing to historical data that used that criterion.
  4. Click Analyze — the tool fits a cubic spline to your lactate curve, detects LT1 and LT2, calculates HR at each threshold, and generates training zones.
  5. Review the results — check the chart, threshold values, and 5-zone model. Use the interpretation panel for training recommendations.
  6. Export — save as CSV for your records or export the chart as PNG for reports.

Practical Implications for Training

  • Polarised training: Research by Seiler and colleagues consistently shows that elite endurance athletes perform ~80% of training below LT1 and ~20% at or above LT2. Correctly identifying LT1 is therefore essential to ensure that “easy” training is truly easy — the so-called “grey zone” between thresholds is associated with excessive fatigue without specific aerobic or anaerobic adaptation.
  • Race pace prediction: LT2 load (watts or speed) often predicts performance in events from ~20 minutes to several hours. Track changes in LT2 over a training block to assess adaptation.
  • Overtraining monitoring: A downward shift of LT1 and LT2 over repeated tests — without change in maximal load — can be an early sign of non-functional overreaching.
  • Return from illness/injury: Lactate testing provides an objective readiness metric that heart rate alone cannot supply.
  • Planning High Intensity Intermittent Exercise Sessions: you have a simple tool to plan a session using the data from testing.

Try the Tool

The Lactate Threshold Analyzer is free to use directly in your browser — no installation or account required. I plant to keep working on it to improve and offer some training sessions design options.

Access it here:


The tool is intended for educational and performance monitoring purposes. Lactate testing should be conducted by qualified sports scientists under standardised protocols. Threshold values and training zones should be interpreted in the context of an athlete’s full physiological and performance profile.

References

  • Beaver WL, Wasserman K, Whipp BJ. (1985). Improved detection of lactate threshold during exercise using a log-log transformation. J Appl Physiol, 59(6):1936-40.
  • Bishop D, Jenkins DG, Mackinnon LT. (1998). The effect of stage duration on the calculation of peak VO2 during cycle ergometry. J Sci Med Sport, 1(3):171-8.
  • Cheng B, Kuipers H, Snyder AC, Keizer HA, Jeukendrup A, Hesselink M. (1992). A new approach for the determination of ventilatory and lactate thresholds. Int J Sports Med, 13(7):518-22.
  • Mader A, Liesen H, Heck H, et al. (1976). Zur Beurteilung der sportartspezifischen Ausdauerleistungsfähigkeit im Labor. Sportarzt und Sportmedizin, 27(4):80-88.
  • Seiler KS, Kjerland GØ. (2006). Quantifying training intensity distribution in elite endurance athletes. Scand J Med Sci Sports, 16(1):49-56.