The current study investigated the positioning of lactate thresholds in recreational female and male runners and evaluated how accurately different fixed intensity anchors could estimate lactate thresholds in terms of external (running speed) and internal (heart rate) load. The main findings were that LT1 occurred at a higher relative intensity (HR, speed and VO2) in females compared to males, suggesting that fixed intensity anchors should be considered separately for females and males. Furthermore, the running speed-derived lactate threshold estimations provided the most accurate results, while the RPE-derived estimations were the least accurate. As expected, the estimation of maximum values impaired the prediction accuracy significantly, showing that the optimal method also depends on the availability of the maximum values.

In the context of endurance training, exercise intensity is typically controlled using HR, speed, the power of exercise, or RPE level (Bellinger et al. 2020). Acute and chronic responses to endurance training are often studied using fixed values relative to the maximum (80%/HRmax). According to research evidence, however, the individually determined training intensity based on the metabolic thresholds could help standardize the metabolic stress of training between individuals (Meyler et al. 2021) and induce greater training adaptations (Meyler et al. 2024). The present study highlighted the potential challenges of using fixed intensity, if the aim of training is to induce comparable physiological strain between individuals. For instance, at an intensity of 80%/HRmax that is quite often applied in exercise interventions, 57% of the participants were exercising at the moderate intensity domain while 43% at the heavy intensity domain. Therefore, when using fixed anchors, it is important to choose methods and intensities that allow the most standardized stimulus between individuals. It is also critical to acknowledge the difference between intensities fixed according to estimated vs. measured values, since the use of estimated maximum values increases the magnitude of error significantly. Furthermore, when considering issues such as the heterogeneity of training responses or dose–response models, the potential challenges arising from the methods of defining the dose should be addressed.

In the current study, the approximate lactate thresholds as percentages of maximum HR and speed were at 80% and 65% at LT1, and at 90% and 80% at LT2, respectively. In previous studies, relative intensities at the lactate thresholds have varied. For example, Iannetta et al. (2020) reported lower percentages, LT1 at 75%/HRmax and 60%/VO2max in healthy adults, and Weltman et al. (1990) higher percentages, LT1 at 88%/HRmax and 83%/VO2max in trained runners. In turn, a recent study of Benítez-Muñoz et al. (2024a, b) reported very similar HR, VO2, and speed relative to maximum at VT1 and VT2 compared to the LT results of this study. An interesting finding in the present study was that LT1 appeared to be at a higher relative speed, HR, and VO2 along with LT2 at higher relative HR in females compared to males. The results were slightly in contrast to those reported by Gaskill et al. (2023) who found no sex differences in VT1 when the fitness level was standardized, which itself might affect lactate thresholds (Weltman et al. 1989, 1990). On the other hand, Iannetta et al. (2020), Støa et al. (2020), and Benítez-Muñoz et al. (2024a, b) reported similarly differing lactate threshold values between sexes, and that is why, the discrepancies might also relate to different analysis methods (i.e., ventilatory vs. lactate threshold). While the current data cannot explain the exact reasons behind the sex differences, the HR-based differences could hypothetically be associated with the lower cardiac output of females, largely due to their generally lower blood volume and oxygen carrying capacity (Diaz-Canestro et al. 2022). Furthermore, Benítez-Muñoz et al. (2024a, b) speculated that the sex difference of the thresholds might relate to greater reliance on fat oxidation at low-to-moderate intensities as well as greater proportion of the type 1 muscle fibers in females. Sex differences have also been reported in deoxygenation during fatiguing exercise, which in turn could contribute to variations in exercise tolerance and intensity-duration relationship (Ansdell et al. 2019). In any case, it seems that the use of fixed HR prescription (e.g., 80% or 90%/HRmax) is likely to lead males exercising at a higher intensity than females. Therefore, it should be more critically considered, whether similar exercise intensity recommendations apply to both sexes. If ignored, the observed sex differences may even have practical consequences for the training adaptations in the form of lesser or suboptimal stimuli in females undertaking exercise at the same fixed intensity or similar prescription as males (Ansdell et al. 2020).

The confidence intervals of the different lactate threshold estimation methods were quite wide (Fig. 2), demonstrating that the average-based values alone may not be very effective at the individual level. Interestingly, the lower limit of the confidence intervals for LT1 appeared to align closely with the moderate-to-vigorous transitions proposed by the ACSM in terms of HR, HRR, VO2 and VO2RES (American College of Sports Medicine 2021). In practice, these results suggest that if the aim is to ensure moderate intensity exercise prescription for a recreational runner, the intensity should be ≤ 55%/vPeak, ≤ 70%/HRmax, or ≤ 60%/VO2max. On the other hand, based on the upper limit of the confidence intervals for LT2, the relative intensity would need to be quite high (e.g., ≥ 90%/vPeak or ≥ 95%/HRmax) to ensure that the participants are exercising in the severe intensity domain. Overall, standardizing training at the heavy intensity domain seems challenging with fixed methods, as there was no fixed intensity that would have allowed all individuals to be exercising within the same domain. To maximize the proportion of individuals at the heavy intensity domain, the current data suggest the approximate values of 75%/vPeak, 80%/VO2max, and 85%/HRmax. It may be useful to note that critical speed can also be used to define the maximal metabolic steady state and the heavy-severe intensity boundary (Jones et al. 2019). Since critical speed can be estimated even with a single-visit field test (Galbraith et al. 2014) without the need for lactate or ventilatory measurements in a laboratory setting, it can be a feasible option for defining intensity domains more accurately for recreational runners. As a limitation in assessing the entire intensity spectrum, the moderate-heavy intensity boundary (i.e., LT1) cannot be accurately estimated from the critical speed only (Hunter et al. 2024).

When the lactate threshold estimations were based on the sex-specific mean values, the MAPE for the running speed ranged from 5.3% (speed) to 8.3% (RPE) at LT1, and from 3.4% (speed) to 6.1% (RPE) at LT2. Based on these results, it seems that the location of LT2 can be more accurately estimated than the location of LT1. Especially, the estimates of LT2 appear surprisingly accurate, considering the daily variability and reliability of metabolic threshold assessments (Pallarés et al. 2016). The results also suggest that fixing the exercise intensity according to the external speed or power output relative to the maximum is the most suitable method if a direct measurement of thresholds is not possible. However, it should be underlined that the error substantially increased when the maximum speed was estimated using the ACSM’s submaximal treadmill test formula. Fixing the thresholds as percentages of the estimated vPeak increased the MAPE to 9.2% at LT1 and 8.2% at LT2. If the assessment of the maximum performance and metabolic thresholds is not possible, the RPE-based prescription may perform better than HR- or speed-derived methods, as highlighted by the smaller MAPE in RPE-based threshold determination when compared to estimated maximal speed or HRmax-derived thresholds. Although the RPE did not perform particularly well as an independent method, it could potentially aid in determining the intensity if the HR range is also described with the corresponding target RPE (Lehtonen et al. 2022). The precision of RPE in exercise prescription may also depend on the individual’s training experience (Johnson et al. 2017), and therefore, less experienced individuals may require more thorough induction into the RPE. In addition to the actual RPE, at least athletes may have the ability to assess the maximal metabolic steady state in self-paced trials by estimating the highest sustainable intensity they can maintain for the intended duration (e.g., 30 min) (Mattioni Maturana et al. 2017).

Given the importance of determining the exercise intensity domains and the challenges with fixed thresholds demonstrated in the present study, it would be beneficial to find feasible ways to estimate intensities with sufficient accuracy. Regarding the performance estimations, studies have focused more on VO2max, while indirect threshold estimation has received somewhat less attention. Indirect and submaximal methods are mainly related to subjective methods (Bok et al. 2022) such as the “talk test” (Reed and Pipe 2014) or “Rabbit-test” (Giovanelli et al. 2020). Another option is to use non-invasive physiological markers such as HR- (Jones and Doust 1997) or HR variability-based thresholds (Kaufmann et al. 2023). Indirect methods usually provide reasonably accurate estimates and at least moderate correlations with the actual thresholds, but they do not seem to perform significantly better than the fixed intensity anchors reported in the present study. Thus, the LoA, MAE, and MAPE results of this study can also be considered in the future as reference level for assessing the accuracy of the estimates provided by such indirect methods. If a new method is not more accurate than the average-based vPeak or HR estimates, it is probably not worth using. As wearable technologies advance, new options for indirect threshold estimation (even live-monitoring of thresholds) are likely to emerge (Andriolo et al. 2024). Moreover, the training data recorded with wearables could help determine critical speed (Smyth et al. 2022) without laboratory testing.

The current study involved a large, pooled dataset from five separate studies performed in the same laboratory. It is important to emphasize that the participants were recreationally trained, and the threshold values from this study should not be applied directly to different populations. Interestingly, Gaskill et al. (2023) reported that VT1 occurred at a higher relative intensity in well-trained and sedentary individuals, while they were lower in moderately trained individuals, indicating a U-shaped pattern between the fitness level and threshold positioning. On the other hand, Benítez-Muñoz et al. (2024a, b) found that the VO2max did not correlate with the %/VO2max, %/HRmax or %/vPeak at ventilatory thresholds. However, the higher the training status (classified as low, medium, high), the higher the relative fraction of the maximum was. Similar to Benítez-Muñoz et al. (2024a, b), no strong correlations were found between fitness levels (vPeak) and the relative location of the thresholds in the current study. Nevertheless, it is likely there would be more differences if the participants’ exercise backgrounds were more heterogeneous than in this study. Since these studies have been cross-sectional by nature, it is not possible to argue that the thresholds would shift due to training adaptations, and consequently, longitudinal studies are needed to examine the adaptability of such characteristics. When interpreting the results of this study, it is also relevant to consider the potential impact of the testing mode (e.g., bicycle ergometer vs. running (Vainshelboim et al. 2020)) and the testing protocol (Bentley et al. 2007) on the thresholds. Therefore, the current absolute or relative results may not necessarily apply universally to different testing modes or protocols. Finally, the thresholds were analyzed as lactate thresholds, and it is acknowledged that different threshold determination methods have their strengths and weaknesses (Faude et al. 2009; Jamnick et al. 2020). It is important to be aware of the daily variability associated with gold-standard methods, which can easily be overlooked when evaluating alternative methods. In the future, large database studies could help to understand in a more detailed manner the effects of testing protocols and various background factors on the location of intensity domains. This would allow the creation of more tailored exercise intensity guidelines that can also take the target group into account.