Lactate, Lactic Acid, and Lactate-Threshold Assessment:
A Structured Narrative Review for Endurance and Hybrid-Sport Practice

Abstract

Background: Lactate is one of the most widely used but frequently misunderstood markers in exercise physiology. In applied sport settings, "lactic acid" is still often blamed for fatigue, soreness, and the burning sensation of hard exercise, even though lactate predominates at physiological pH and functions as an important metabolic intermediate rather than a simple waste product. Objective: This structured narrative review clarifies the distinction between lactate and lactic acid, synthesizes lactate-threshold terminology, and translates threshold-informed evidence into practical guidance for endurance and hybrid athletes. Methods: A targeted literature synthesis was conducted using peer-reviewed mechanistic, methodological, and applied papers on lactate metabolism, blood lactate testing, threshold concepts, maximal metabolic steady state, exercise-intensity prescription, interval training, and wearable lactate monitoring. Because no original participant dataset is reported, the manuscript is positioned as a structured narrative review rather than an empirical study. Synthesis: The evidence supports lactate as a central metabolite, transport substrate, and interpretive marker of exercise intensity. However, "lactate threshold" is not a single universal event: LT1, LT2, OBLA, MLSS/MMSS, ventilatory thresholds, and critical power or speed each represent different operational constructs. Fixed percentages of HRmax, VO2max, or maximal speed often fail to normalize internal physiological strain across individuals, whereas threshold-based anchors can improve individualization when the testing method and target training domain are explicit. Field tests and wearable technologies can support monitoring, but they should be treated as estimates rather than replacements for validated protocols. Conclusion: Lactate-informed coaching is strongest when the practitioner reports the threshold definition, understands the limits of the measurement method, and interprets results alongside pace, power, heart rate, rating of perceived exertion, performance durability, and recovery trends.

Keywords

Lactate; lactic acid; lactate threshold; LT1; LT2; OBLA; maximal lactate steady state; maximal metabolic steady state; exercise-intensity prescription; endurance training; hybrid athletes; wearable biosensors

Highlights

• Lactate should be framed as a metabolically useful molecule and interpretive marker, not as the direct toxic cause of fatigue.
• Threshold terminology must be explicit because LT1, LT2, OBLA, MLSS/MMSS, ventilatory thresholds, and critical power or speed are not interchangeable.
• Threshold-based prescription can better individualize internal load than fixed percentages of HRmax, VO2max, or maximal pace, but the best anchor depends on the session goal.
• Field tests, smartwatches, and wearable lactate sensors can assist coaching decisions, but current evidence supports cautious use rather than uncritical substitution for laboratory testing.

Abbreviations

BLa, blood lactate concentration; CP, critical power; CS, critical speed; HR, heart rate; HRmax, maximal heart rate; LT, lactate threshold; LT1, first lactate threshold; LT2, second lactate threshold; MLSS, maximal lactate steady state; MMSS, maximal metabolic steady state; OBLA, onset of blood lactate accumulation; RPE, rating of perceived exertion; VO2max, maximal oxygen uptake.

Introduction

Lactate sits at the intersection of metabolism, performance testing, and coaching language. It is measured in laboratories, discussed on training floors, shown on wearable-technology roadmaps, and used to organize training zones for endurance athletes. Yet it is also one of the most oversimplified concepts in sport science. Public-facing explanations still use "lactic acid" as a shorthand for pain, fatigue, or post-exercise soreness, while some modern discussions swing too far in the opposite direction and imply that lactate is always beneficial or simple to interpret. A publication-ready account needs to avoid both errors. Lactate is useful, but its usefulness depends on method, context, and interpretation.

At physiological pH, lactic acid is largely dissociated into lactate and hydrogen ions. This makes the routine statement that "lactic acid builds up in the muscles" chemically imprecise. More importantly, blood lactate concentration is not a direct one-to-one measure of acid, fatigue, anaerobic metabolism, or exercise quality. It reflects the dynamic balance among lactate appearance, clearance, transport, oxidation, gluconeogenic use, and distribution across tissues (Gladden, 2004; Goodwin et al., 2007). Lactate is therefore better understood as an active metabolite and a marker of metabolic flux than as an inert waste product.

The second challenge is terminology. "Lactate threshold" is often used as if it refers to one clear physiological breakpoint. The literature does not support that simplification. Faude et al. (2009) identified 25 lactate-threshold concepts and emphasized that different threshold definitions show different relationships with endurance performance and maximal lactate steady state. The practical consequence is straightforward: a test result labeled "threshold" is incomplete unless the protocol, sample type, analyte, mathematical model, and intended application are reported.

The third challenge is translation. Coaches and athletes need actionable training zones, but common prescriptions such as 70% of HRmax, 80% of VO2max, or a fixed percentage of best pace can impose very different physiological strain across athletes. Reviews and methodological critiques have argued that fixed maximal-percentage approaches often fail to normalize internal load, whereas threshold-based anchors may better match exercise-intensity domains when chosen carefully (Jamnick et al., 2020; Mann et al., 2013). Even threshold-based systems require caution, especially when moving from continuous endurance exercise to severe-domain interval training or mixed-modal hybrid sport (Bossi et al., 2023).

The purpose of this manuscript is to provide a structured narrative review of lactate physiology, lactate-threshold terminology, measurement considerations, and practical application for endurance and hybrid athletes. The goal is not to promote lactate testing as mandatory for every athlete. Rather, it is to make lactate-informed training more precise, transparent, and useful by distinguishing what is well supported from what remains estimated, method-dependent, or emerging.

Methods

This manuscript was developed as a structured narrative review and publication-ready redevelopment of an educational article on lactate and lactic acid. No original participant recruitment, experimental intervention, or unpublished athlete dataset was available. For that reason, the appropriate manuscript genre is a narrative review with translational applications rather than an original research article.

The evidence synthesis prioritized peer-reviewed sources that clarify five domains: lactate physiology and acid-base interpretation; blood lactate measurement and analysis; lactate-threshold concepts and their validity; exercise-intensity prescription and intensity-domain classification; and emerging wearable or field-based threshold-estimation methods. Foundational papers were retained when they remain central to the concept, and newer papers were added where they updated the prescription and wearable-technology discussion. Non-peer-reviewed media and commercial claims were excluded as evidence anchors, although practical coaching implications were retained when consistent with peer-reviewed physiology and methodology.

Because this was not a PRISMA-compliant systematic review, no claim is made that every relevant paper was captured. Instead, studies were selected for conceptual relevance, methodological importance, and translational value. Findings were synthesized thematically, with stronger weight given to review papers, methodological critiques, and original studies that directly addressed threshold validity, intensity normalization, or wearable-test agreement. Claims specific to hybrid sport are presented as evidence-informed extrapolations unless a cited study directly examined the relevant sport context.

Lactate physiology and the lactic-acid problem

The central correction is simple but important: lactate is not merely a waste product, and lactic acid is not the best term for the molecule measured during routine exercise testing. During intense exercise, glycolytic flux increases and pyruvate can be reduced to lactate through lactate dehydrogenase. This reaction helps sustain glycolysis and supports redox balance. Lactate can then be transported within and between cells, oxidized by working or less active tissues, used in gluconeogenesis, and involved in broader metabolic signaling (Brooks, 2018; Gladden, 2004).

The practical language problem is that "lactic acid" bundles several different phenomena into one phrase: high glycolytic flux, rising blood lactate concentration, declining pH, muscle discomfort, and fatigue. These phenomena may occur together during hard exercise, but they are not the same mechanism. Robergs et al. (2004) challenged the idea that lactate production itself is the primary source of exercise-induced acidosis, and contemporary explanations generally avoid presenting lactate as a direct toxin. A more accurate statement is that lactate accumulation is associated with high metabolic strain and limited homeostatic stability, while fatigue arises from multiple interacting processes.

This distinction matters for coaching. If lactate is treated as waste, the training goal becomes to "clear" or avoid it. If lactate is treated as a marker of metabolic state, the training goal becomes more nuanced: identify the intensity range being targeted, expose the athlete to appropriate stress, and evaluate whether appearance and clearance dynamics improve over time. In other words, lactate does not tell a coach whether a session was good or bad by itself; it helps interpret the physiological character of the session.

Figure 1. Conceptual lactate-intensity response and threshold terminology. The curve is illustrative rather than diagnostic; exact thresholds depend on protocol, sample type, mathematical model, exercise mode, and athlete characteristics.

Threshold concepts: useful, plural, and method-dependent

Threshold concepts are useful because they provide an individualized way to organize exercise intensity. However, their usefulness depends on recognizing that "threshold" is not one universally defined event. In practice, the term may refer to the first sustained rise in blood lactate above baseline, a second or more marked inflection point, a fixed concentration such as 4 mmol/L, the maximal lactate steady state, a ventilatory breakpoint, or a performance-derived construct such as critical power or critical speed.

Faude et al. (2009) showed that many lactate-threshold concepts correlate with endurance performance but are not interchangeable. This is a vital point for manuscript rigor and for coaching practice. A value obtained from a fixed 4 mmol/L criterion cannot automatically be equated with a Dmax-derived threshold, a ventilatory threshold, or MLSS. Each method may be useful, but it answers a slightly different question.

MLSS and the related broader term MMSS are often treated as reference concepts for the upper boundary of sustainable metabolic steady state. Jones et al. (2019) argued that MMSS is an important conceptual boundary because above it, fatigue accelerates and systemic or muscular homeostasis cannot be maintained. Yet MLSS testing is time-intensive because it usually requires multiple constant-load trials. This is why coaches often use incremental thresholds, ventilatory markers, critical speed, or field tests as practical estimates. The key is not to pretend that all markers are identical; it is to choose the anchor that best matches the training or research purpose.

Table 1. Key lactate and intensity terms for manuscript use.

Measurement and interpretation

Blood lactate data are only as useful as the measurement context is transparent. Goodwin et al. (2007) emphasized that practitioners should attend to sample type, analyzer validation, protocol design, and interpretation. Whole blood and plasma values are not the same. Capillary and venous samples may differ. Stage duration, step size, warm-up, prior nutrition, environment, and recent training can alter the curve. For publication, these details should not be treated as minor procedural notes; they are part of the result.

Incremental tests are popular because they are efficient and provide a curve from which threshold estimates can be derived. They are not the same as steady-state confirmation. A ramp or step protocol may identify a breakpoint or concentration value, but whether that value corresponds to an athlete-specific sustainable boundary depends on the method used and the exercise mode tested. This is why a publishable manuscript should specify the protocol before interpreting the number.

Field tests can be valuable when laboratories are unavailable. Time trials, critical speed tests, heart-rate trends, pace durability, RPE, and repeated standardized sessions can all provide actionable information. However, they should be described as estimates. A 30-minute time trial, a talk-test heuristic, or a smartwatch-derived threshold may help guide training, but it should not be presented as equivalent to direct blood-lactate testing or repeated MLSS trials unless validation evidence supports that equivalence in the target population.

Table 2. Common lactate-threshold assessment approaches and publication-ready interpretation.

Exercise-intensity prescription

One of the most important applied uses of lactate testing is intensity prescription. The traditional convenience of prescribing exercise as a percentage of HRmax, VO2max, maximal speed, or peak power is obvious: percentages are easy to calculate and communicate. The limitation is that equal percentages do not necessarily create equal physiological strain. Two athletes working at the same percentage of VO2max may be in different intensity domains; one may be below threshold while another is already accumulating lactate rapidly.

Mann et al. (2013) outlined why percentage-based methods can be physiologically imprecise, and Jamnick et al. (2020) extended this critique by emphasizing that intensity should be matched to exercise domains and homeostatic disturbance rather than to maximal anchors alone. Hansen et al. (2025) similarly argued for advancing aerobic exercise prescription beyond standard maximal-percentage recommendations. This does not make percentage-based prescriptions useless; they remain accessible and may be adequate for some general-fitness purposes. It does mean that performance-oriented programming should use individual responses whenever the goal is to target a specific physiological domain.

Threshold-based prescription is not automatically superior in every setting. Bossi et al. (2023) reported that conventional methods for normalizing intensity performed poorly for exhaustive interval training, reinforcing the need to match the anchor to the session. Continuous tempo running, long threshold intervals, repeated severe-domain intervals, and mixed-modal hybrid race simulations are not the same prescription problem. For endurance development, LT1, LT2, ventilatory thresholds, MLSS/MMSS, or critical speed may be appropriate depending on the goal. For exhaustive HIIT, time-to-exhaustion, critical power or speed, and individual tolerance may matter more than a single lactate-derived number.

Applications for endurance and hybrid athletes

Endurance athletes can use lactate-informed data to organize training intensity with greater precision. The most defensible model is not to chase lactate values during every session, but to use standardized testing to define approximate domains, then triangulate daily training with pace or power, HR, RPE, and recovery response. This is especially useful when an athlete repeatedly performs too much work in the middle-heavy range: effortful enough to generate fatigue but not specific enough to maximize either aerobic volume or high-intensity quality.

Hybrid athletes present a harder translation problem because running or cycling physiology is only part of the performance demand. Functional stations, loaded carries, sled work, rowing, lunges, wall balls, and repeated transitions can alter local muscular fatigue, breathing pattern, grip, posture, and pacing. A threshold value from continuous running may inform the running component, but it does not fully characterize how the athlete will respond after station fatigue. For hybrid sport, lactate-informed training should therefore be integrated with station efficiency, transition practice, strength-endurance capacity, and event-specific split analysis.

The practical role of lactate in hybrid programming is to sharpen intent. Low-intensity aerobic work develops the base that supports recovery between harder efforts. Threshold intervals develop the ability to sustain high aerobic work without early destabilization. Severe-domain intervals and race-specific pieces develop tolerance to accumulating disturbance. Station-specific work develops efficiency under local fatigue. Lactate concepts help prevent these goals from being blurred into one chronically hard training style.

Table 3. Applied prescription framework for endurance and hybrid athletes.

Wearable lactate monitoring and digital threshold estimates

Wearable lactate monitoring is an exciting but still developing field. Sweat lactate sensors and integrated wearable platforms are attractive because they promise continuous, non-invasive feedback during real-world exercise. Recent reviews describe rapid technological progress in sweat-based biochemical monitoring and specifically in sweat lactate devices for sport (Assalve et al., 2024; Yang et al., 2024). The translation issue is that sweat and blood are different matrices. Sweat rate, skin temperature, sensor placement, lag time, calibration, contamination, and biofluid dynamics can all influence the signal.

Digital threshold estimates from smartwatches should be treated with similar caution. A 2025 study comparing smartwatch-derived lactate-threshold heart rate and pace with graded exercise testing found that some heart-rate estimates were not significantly different at group level, but correlations and error patterns varied and lactate-threshold pace tended to be overestimated across devices (Lu et al., 2025). For practitioners, this means a smartwatch estimate may be useful as a trend or hypothesis, but it should not be used as an unquestioned diagnostic threshold for high-performance programming.

The strongest publishable position is neither dismissal nor hype. Wearable and algorithmic methods can contribute to athlete monitoring, particularly when repeated under standardized conditions and interpreted alongside performance and perceptual data. They should not yet replace transparent threshold definitions, validated laboratory protocols, or carefully repeated field tests when precision matters.

Figure 2. Decision framework for applying lactate-informed intensity prescription. The figure emphasizes method transparency, domain-specific anchoring, and repeated verification of athlete response rather than reliance on a single number.

Practical recommendations

• Use the term lactate when discussing exercise testing unless specifically describing the chemical acid form. This improves scientific accuracy and reduces confusion.
• Report the threshold method every time. A publication, test report, or athlete profile should specify whether the value represents LT1, LT2, OBLA, Dmax, MLSS/MMSS, a ventilatory threshold, critical speed, or a field estimate.
• Avoid treating fixed percentages of HRmax, VO2max, or maximal pace as physiologically equivalent across athletes. Use them as broad starting points only when individualized anchors are unavailable.
• When using field methods, standardize the protocol and environment as much as possible. Repeated comparable data are usually more useful than a single impressive test.
• Triangulate lactate or threshold estimates with HR, RPE, pace or power durability, session quality, split drift, symptoms, and next-day recovery.
• For hybrid athletes, validate running thresholds inside the reality of the sport. Station fatigue, loading, breathing constraints, and transitions may change the sustainable run output compared with a continuous run test.
• Treat wearable lactate and smartwatch-derived threshold values as supportive monitoring tools. They can inform hypotheses and trends, but they should not be framed as stand-alone replacements for validated testing in publication-ready claims.

Limitations

This manuscript is a structured narrative review rather than a systematic review or meta-analysis. It does not include a formal PRISMA screening record, risk-of-bias scoring, or quantitative pooling. The conclusions should therefore be read as a rigorous conceptual and translational synthesis rather than a complete evidence census.

A second limitation is that lactate-threshold terminology varies across studies. Even when authors use the same term, testing protocols, mathematical models, sample matrices, and exercise modes can differ. This heterogeneity limits direct comparison and reinforces the manuscript's central recommendation: threshold definitions must be reported explicitly.

A third limitation is that hybrid-sport applications remain partly extrapolated from endurance, cycling, running, interval-training, and general exercise-physiology research. More sport-specific studies are needed to evaluate how laboratory thresholds, field estimates, station splits, and wearable metrics relate to performance in mixed-modal events.

Future research

Future work should compare lactate-derived, ventilatory, critical-speed, and field-estimated anchors within the same athletes and across relevant exercise modes. Such studies should report protocol details, sample matrix, analyzer information, threshold algorithm, and test-retest reliability.

For hybrid athletes, prospective studies should combine laboratory profiling with race or simulation splits, station-specific fatigue metrics, training-load data, and recovery markers. This would clarify whether continuous-run thresholds predict hybrid performance directly or whether station-specific modifiers are needed.

Wearable lactate and smartwatch threshold estimates need independent validation against clearly described criterion methods. Researchers should examine not only group-level agreement but also individual error, bias across intensity ranges, device failure rates, environmental effects, and whether the resulting prescriptions improve training decisions.

Conclusion

Lactate is best understood as a useful metabolite and interpretive marker of exercise intensity, not as a waste product or the direct cause of fatigue. The scientific challenge is not whether lactate matters, but how precisely the term is used. "Lactate threshold" represents a family of operational constructs, and the practical value of any threshold depends on the protocol, threshold definition, exercise mode, and intended training domain. For endurance and hybrid athletes, lactate-informed training can improve intensity prescription when it is method-aware and integrated with performance, perceptual, and recovery data. The most publishable message is therefore one of disciplined translation: specify the method, respect the limits of the marker, and use lactate information to guide smarter training rather than to chase a single number.

References

Assalve, G., Lunetti, P., Di Cagno, A., De Luca, E. W., Aldegheri, S., Zara, V., & Ferramosca, A. (2024). Advanced wearable devices for monitoring sweat biochemical markers in athletic performance: A comprehensive review. Biosensors, 14(12), 574. https://doi.org/10.3390/bios14120574

Bossi, A. H., Cole, D., Passfield, L., & Hopker, J. (2023). Conventional methods to prescribe exercise intensity are ineffective for exhaustive interval training. European Journal of Applied Physiology, 123(8), 1655-1670. https://doi.org/10.1007/s00421-023-05176-6

Brooks, G. A. (2018). The science and translation of lactate shuttle theory. Cell Metabolism, 27(4), 757-785. https://doi.org/10.1016/j.cmet.2018.03.008

Faude, O., Kindermann, W., & Meyer, T. (2009). Lactate threshold concepts: How valid are they? Sports Medicine, 39(6), 469-490. https://doi.org/10.2165/00007256-200939060-00003

Gladden, L. B. (2004). Lactate metabolism: A new paradigm for the third millennium. The Journal of Physiology, 558(1), 5-30. https://doi.org/10.1113/jphysiol.2003.058701

Goodwin, M. L., Harris, J. E., Hernandez, A., & Gladden, L. B. (2007). Blood lactate measurements and analysis during exercise: A guide for clinicians. Journal of Diabetes Science and Technology, 1(4), 558-569. https://doi.org/10.1177/193229680700100414

Hansen, D., Cipriano Junior, G., Milani, J. G. P. O., Milani, M., Gojevic, T., Machado, F. V. C., Kambic, T., Eijsvogels, T. M. H., Bonifazi, M., Dendale, P., & D'Ascenzi, F. (2025). Advancing aerobic exercise training intensity prescription in health and disease beyond standard recommendations: A call to action. Sports Medicine, 55(9), 2111-2135. https://doi.org/10.1007/s40279-025-02272-9

Iannetta, D., Inglis, E. C., Mattu, A. T., Fontana, F. Y., Pogliaghi, S., Keir, D. A., & Murias, J. M. (2020). A critical evaluation of current methods for exercise prescription in women and men. Medicine & Science in Sports & Exercise, 52(2), 466-473. https://doi.org/10.1249/MSS.0000000000002147

Jamnick, N. A., Pettitt, R. W., Granata, C., Pyne, D. B., & Bishop, D. J. (2020). An examination and critique of current methods to determine exercise intensity. Sports Medicine, 50(10), 1729-1756. https://doi.org/10.1007/s40279-020-01322-8

Jones, A. M., Burnley, M., Black, M. I., Poole, D. C., & Vanhatalo, A. (2019). The maximal metabolic steady state: Redefining the gold standard. Physiological Reports, 7(10), e14098. https://doi.org/10.14814/phy2.14098

Lu, C., Cui, W., Zhu, Z., Wu, Y., Xing, Q., Pan, B., & Shen, Y. (2025). Validity of smartwatch-derived estimates of lactate threshold heart rate and pace compared to graded exercise testing. Frontiers in Physiology, 16, 1621996. https://doi.org/10.3389/fphys.2025.1621996

Mann, T., Lamberts, R. P., & Lambert, M. I. (2013). Methods of prescribing relative exercise intensity: Physiological and practical considerations. Sports Medicine, 43(7), 613-625. https://doi.org/10.1007/s40279-013-0045-x

Robergs, R. A., Ghiasvand, F., & Parker, D. (2004). Biochemistry of exercise-induced metabolic acidosis. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 287(3), R502-R516. https://doi.org/10.1152/ajpregu.00114.2004

Yang, G., Hong, J., & Park, S.-B. (2024). Wearable device for continuous sweat lactate monitoring in sports: A narrative review. Frontiers in Physiology, 15, 1376801. https://doi.org/10.3389/fphys.2024.1376801