International  Journal  of
Environmental Research
and Public Health
Article
Blood Lactate Concentration Is Not Related to the
Increase in Cardiorespiratory Fitness Induced by
High Intensity Interval Training
Todd A. Astorino *, Jamie L. DeRevere, Theodore Anderson, Erin Kellogg, Patrick Holstrom,
Sebastian Ring and Nicholas Ghaseb
Department of Kinesiology, California State University—San Marcos, San Marcos, CA 92096, USA
* Correspondence: astorino@csusm.edu
Received: 9 July 2019; Accepted: 7 August 2019; Published: 9 August 2019


Abstract: Background: There is individual responsiveness to exercise training as not all individuals
experience increases in maximal oxygen uptake (VO2max), which does not beneﬁt health status
considering the association between VO2max and mortality. Approximately 50% of the training
response is genetic, with the other 50% accounted for by variations in dietary intake, sleep, recovery,
and the metabolic stress of training. This study examined if the blood lactate (BLa) response to high
intensity interval training (HIIT) as well as habitual dietary intake and sleep duration are associated
with the resultant change in VO2max (∆VO2max). Methods: Fourteen individuals (age and VO2max
= 27 ± 8 years and 38 ± 4 mL/kg/min, respectively) performed nine sessions of HIIT at 130% ventilatory
threshold. BLa was measured during the ﬁrst and last session of training. In addition, sleep duration
and energy intake were assessed. Results: Data showed that VO2max increased with HIIT (p = 0.007).
No associations occurred between ∆VO2max and BLa (r = 0.44, p = 0.10), energy intake (r = 0.38,
p = 0.18), or sleep duration (r = 0.14, p = 0.62). However, there was a signiﬁcant association between
training heart rate (HR) and ∆VO2max (r = 0.62, p = 0.02). Conclusions: When HIIT is prescribed
according to a metabolic threshold, energy intake, sleep status, and BLa do not predict ∆VO2max, yet
the HR response to training is associated with the ∆VO2max.
Keywords: high intensity exercise; blood lactate concentration; maximal oxygen uptake; individual
responsiveness to training
1. Introduction
One adaptation to moderate intensity continuous training (MICT) is a signiﬁcant increase in
maximal oxygen uptake (VO2max) [1], which reduces mortality risk [2]. It is apparent that some
individuals reveal marked increases in VO2max in response to training, whereas, others experience
little to no change [3]. Approximately 50% of the change in VO2max (∆VO2max) with training is
genetic [3], with the other 50% due to variations in habitual dietary intake, sleep status, physical activity,
and the speciﬁcs of the training regime [4]. Ross et al. [5] showed greater increases in VO2max in
response to high amount, higher intensity (75% VO2max) versus lower intensity MICT (50% VO2max).
This suggests that vigorous continuous exercise may optimize ∆VO2max, which seems important as
a 1 metabolic equivalent (MET) increase in VO2max is associated with a 19% reduction in all-cause
mortality [6].
One additional factor that may mediate training responsiveness is the absolute metabolic stress of
training [4]. Although there are various ways to monitor this, one approach is via the measurement of
blood lactate concentration (BLa). Previous studies show that this measure may serve as an index of
intramuscular stress [7] and may be related to the initiation of signaling pathways that regulate muscle
Int. J. Environ. Res. Public Health 2019, 16, 2845; doi:10.3390/ijerph16162845
www.mdpi.com/journal/ijerph
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plasticity in response to training [8]. It is apparent that the accumulation of BLa follows a threshold
response in that, at intensities below the work rate coincident with the lactate threshold, there is little
change in BLa, whereas, at work rates above the lactate threshold, BLa increases dramatically due to the
enhanced recruitment of higher threshold motor units and greater activation of glycolysis [9]. In three
groups of men (VO2max = 46–71 mL/kg/min) of similar aerobic ﬁtness, Scharhag-Rosenberger et al. [10]
showed dissimilar increases in blood lactate concentration (BLa) during prolonged cycling at 60% and
75% VO2max, which suggests discrepant metabolic strain at identical relative intensities. In addition,
these authors reported that some of their participants were unable to complete the prolonged exercise
bouts, which was partially explained by the onset of neuromuscular fatigue or enhanced fast-twitch
motor unit recruitment, which likely varied across subjects. Recently, the BLa response to moderate
intensity continuous exercise at 65 percent of peak power output (% PPO) was signiﬁcantly associated
with ∆VO2max seen with chronic training [11], which suggests that the metabolic response to acute
exercise may be predictive of the adaptive response. Whether this relationship also occurs in response
to high intensity interval training (HIIT) is unknown. Elucidating predictors of ∆VO2max with chronic
exercise such as HIIT is important considering that this modality elicits superior increases in VO2max
than MICT [12]. Moreover, VO2max is related to mortality [6], and better understanding predictors of
the VO2max response to training may help clinicians better utilize physical activity when employing
exercise-based rehabilitation.
Health and ﬁtness practitioners implement exercise training in their clientele to promote gains
in ﬁtness and body composition which likely lead to improved health status. In addition, it is
likely that they try and identify speciﬁc variables that impact training responsiveness to optimize
exercise programming for their clientele. In this preliminary study, we measured changes in BLa
during a brief HIIT regime to determine if the adaptive response is predicted by BLa changes during
training. Participants’ sleep status and dietary intake were also measured, as Hawley et al. [13]
and Samuels et al. [14] suggest that they are related to training responsiveness. In the case of sleep
deprivation, it is associated with fatigue and may make individuals more susceptible to overtraining [13].
In addition, Mann et al. [4] postulated that the eﬀect of habitual dietary intake on resultant variations in
the training response is unknown. Overall, it is plausible that adequate energy intake as well as sleep
serve to promote recovery from individual sessions, which in turn may beneﬁt the resultant adaptation
to training. It is hypothesized that the BLa response to training is signiﬁcantly related to ∆VO2max.
2. Materials and Methods
Participants: Three men and eleven women (age and VO2max = 27 ± 8 years and 38 ± 4 mL/kg/min,
respectively) who perform >150 min/week of exercise in the last 12 months, including resistance
training, aerobic exercise, noncompetitive sport, and group exercise, participated in this study. They
were recruited via convenience sampling. They completed a standard health-history questionnaire,
which contained information pertaining to their current physical activity regimen. Participants also
provided written informed consent to take part in the study, whose procedures were approved by the
University Institutional Review Board. The study was carried out in accordance with the rules of the
Declaration of Helsinki.
Design: VO2max was assessed before and after nine sessions of HIIT. On days 1 and 9 of training,
BLa was measured. Changes in sleep status and dietary intake were monitored during the study.
Data concerning ∆VO2max and ventilatory threshold in response to this regimen were previously
reported [15]. The present study summarized changes in these outcomes (BLa, sleep status, and
dietary intake) that were only obtained from the experimental group who underwent interval training.
Sessions occurred at the same time of day within participants, were preceded by a 24 h abstention
from physical activity, and were separated by ≥24 h. Participants were instructed to record habitual
physical activity during the study in a written log, and were instructed to maintain this behavior
during the study.
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Testing of maximal oxygen uptake: Participants performed incremental exercise on an electrically
braked cycle ergometer (Velotron Dynaﬁt Pro, RacerMate, Seattle, WA, USA). After a 2 min warm-up at
40–70 Watt (W), the work rate was increased by 20–35 W/min until volitional exhaustion, represented
by cadence <50 rev/min. After 10 min of pedaling at 10% PPO, participants pedaled at 105% PPO
to volitional exhaustion [16]. VO2max was identiﬁed as the average of these two values. VO2max
testing was repeated at least 48 h after the last training session. During all bouts, HR was measured
continuously using telemetry (Polar Electro, Beth Page, NY, USA), and gas exchange data were acquired
every 15 s using indirect calorimetry (ParvoMedics True One, Sandy, UT, USA).
Assessment of blood lactate concentration, sleep status, and dietary intake: During sessions 1 and
9 of HIIT, BLa was determined using a portable monitor (Lactate Plus, Nova Biomedical, Waltham,
MA, USA) and lancet (Owen Mumford, Inc., Marietta, GA, USA). After a 5 min rest, the ﬁngertip was
washed with a damp paper towel and dried, and BLa was measured by using the second drop of blood,
as the ﬁrst one was wiped away. This procedure was repeated immediately after intervals 4 and 8. The
change in BLa during the session of HIIT was calculated as the diﬀerence between the pre-exercise
and interval 8 values. In addition, the change in BLa in response to training was calculated as the
diﬀerence in the sum of BLa recorded after intervals 4 and 8 on day 9 versus day 1 of training. On day
1, participants recorded their dietary intake in the 24 h prior and replicated this prior to session 9 to
standardize the fed state before these sessions.
Dietary intake was measured for three days prior to and for three days during the last week of
training. Participants recorded all food and drink ingested over this period in a log. This information
was analyzed using software (http://ndb.nal.usda.gov/ndb/foods/list) to determine energy intake (in
kilocalories). Sleep status was quantiﬁed every day of training, as participants reported the number of
hours they slept on the night prior, and an average value was calculated.
High intensity interval training: At least 48 h after baseline testing, participants initiated HIIT at a
work rate equal to 130% ventilatory threshold (VT), which was equal to 70% PPO in our participants.
Ventilatory threshold was estimated by two experienced reviewers independently using the methods of
Caiozzo et al. [17]. If their evaluation diﬀered, consensus was reached by consulting a third investigator.
Sessions were held three days/week for three weeks and were performed on the same cycle ergometer.
Subjects performed eight 1 min intervals on days 1–3 of training, nine on days 4–6, and ten on days
7–9 [18]. Sessions were preceded by a 5 min warm-up at 10% PPO, and intervals were separated by a
75 s active recovery at 10% PPO. Heart rate (HR) was measured continuously using telemetry (Polar
Electro USA, Beth Page, NY, USA). The HR response to training was represented by the average HR
attained at the end of each interval across all sessions of training.
Data analyses: Data were expressed as mean ± standard deviation (SD) and were analyzed using
SPSS Version 24.0 (IBM, Armonk, NY, USA). The Shapiro–Wilk test was used to assess normality. A
two-way analysis of variance (training = pre versus post, time = three levels) with repeated measures
was performed to identify diﬀerences in BLa. The Greenhouse–Geisser correction was used to account
for the sphericity assumption of unequal variances across groups. If a signiﬁcant F ratio occurred,
Tukey’s post hoc test was used. Pearson’s pairwise correlation was used to determine relationships
between variables. Statistical signiﬁcance was set as p < 0.05.
3. Results
Training ﬁdelity: Training elicited 90% ± 5% PPO (79%–96% across participants), which veriﬁes
the intensity of this regime. VO2max was increased by 6% with HIIT, and this change was equal to
0.15 ± 0.13 L/min (range = −0.06–0.47 L/min) and 2.4 ± 1.8 mL/kg/min (range = −0.9–4.7 mL/kg/min)
across participants.
Changes in blood lactate concentration in response to training: BLa increased during HIIT
(p < 0.001) yet there was no diﬀerence from day 1 to 9 of HIIT (p = 0.91) or trainingXtime interaction
(p = 0.87). The change in BLa during session 1 (10.5 ± 2.2 mM) did not diﬀer compared to session 9
(10.3 ± 2.2 mM). The overall change in BLa from pre- to post-training was equal to −0.36 ± 2.4 mM.
Int. J. Environ. Res. Public Health 2019, 16, 2845
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Four participants showed a greater than 1 mM reduction in BLa from pre- to post-training, whereas,
ﬁve participants showed increases in BLa above 1 mM.
Changes in sleep status and energy intake: Sleep duration was equal to 7.5 ± 0.7 h and ranged
from 6.0–8.8 h per night across participants. Dietary intake did not change from pre- (2050 ± 686 kcal)
to post-training (2074 ± 639 kcal) (p = 0.39), although it varied from 950–3400 kcal/d across participants.
Relationship between ∆VO2max and BLa, sleep status, and dietary intake: No correlation was
shown between absolute ∆VO2max and the change in BLa (r = 0.44, p = 0.10) (Figure 1a). In addition,
no correlation was shown between ∆VO2max and the mean BLa on day 1 (r = 0.17, p = 0.57) or 9 of
training (r = 0.48, p = 0.08) (Figure 1b,c). In addition, results showed no correlation between ∆VO2max
and sleep status (r = 0.14, p = 0.62) or energy intake (r = 0.38, p = 0.18) (Figure 2a,b).
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Changes in sleep status and energy intake: Sleep duration was equal to 7.5 ± 0.7 h and ranged 
from 6.0–8.8 h per night across participants. Dietary intake did not change from pre- (2050 ± 686 kcal) 
to post-training (2074 ± 639 kcal) (p = 0.39), although it varied from 950–3400 kcal/d across 
participants. 
Relationship between ΔVO2max and BLa, sleep status, and dietary intake: No correlation was 
shown between absolute ΔVO2max and the change in BLa (r = 0.44, p = 0.10) (Figure 1a). In addition, 
no correlation was shown between ΔVO2max and the mean BLa on day 1 (r = 0.17, p = 0.57) or 9 of 
training (r = 0.48, p = 0.08) (Figure 1b and 1c). In addition, results showed no correlation between 
ΔVO2max and sleep status (r = 0.14, p = 0.62) or energy intake (r = 0.38, p = 0.18) (Figure 2a and 2b). 
 
Figure 1. Association between change in VO2max and (a) change in blood lactate concentration, (b) 
blood lactate concentration on day 1 of training, and (c) blood lactate concentration on day 9 of 
training. 
Relationship between ΔVO2max and training heart rate: Heart rate in response to training 
ranged from 88%–100% percent of maximal heart rate (HRmax), with a mean value equal to 95% ± 
3% HRmax. Results revealed a significant correlation between ΔVO2max and training HR (r = 0.62, p 
= 0.02) (Figure 2c). 
Figure 1. Association between change in VO2max and (a) change in blood lactate concentration,
(b) blood lactate concentration on day 1 of training, and (c) blood lactate concentration on day 9
of training.
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Figure 2. Association between change in VO2max and (a) sleep duration, (b) dietary intake, and (c) 
training intensity expressed as % HRmax. 
4. Discussion 
Previous studies document a heterogeneous BLa response to continuous exercise [10] and that 
the change in BLa in response to acute continuous exercise at 65% PPO predicts the resultant training 
response [11]. Our study examined if the BLa response to short-term HIIT is associated with 
ΔVO2max. The results oppose our hypothesis and they suggest that HR response to training is 
significantly associated with change in VO2max, yet BLa, sleep duration, and energy intake are not. 
These preliminary data suggest that the cardiovascular strain of HIIT prescribed according to a 
metabolic threshold may predict resultant responses to training. 
Our data showing no correlation between ΔVO2max and the BLa response to HIIT refute 
previous results [11]. However, there are methodological differences between studies which may 
explain these discrepancies. First, our sample contained men and women, while the former study 
included only men.  It is apparent that muscle fiber type may differ between men and women [19], 
leading to greater reliance on glycolysis and resultant blood lactate accumulation in men who have a 
greater proportion of higher threshold motor units. Moreover, men exhibit higher BLa in response to 
HIIT and sprint interval training (SIT) versus women, which may be due to the greater work 
completed during training due to a propensity to maintain a higher cadence, especially during SIT 
[20]. Second, HIIT was performed at an intensity equal to 90% PPO, whereas, in the former study, 
training was performed at 65% PPO. This different composition of training led to markedly different 
BLa responses, as our subjects exhibited BLa ranging from 7.6–14.1 mM, which was higher than 
values exhibited in their study (6–8 mM). Lastly, our regimen was prescribed according to VT, which 
Figure 2. Association between change in VO2max and (a) sleep duration, (b) dietary intake, and
(c) training intensity expressed as % HRmax.
Relationship between ∆VO2max and training heart rate: Heart rate in response to training ranged
from 88%–100% percent of maximal heart rate (HRmax), with a mean value equal to 95% ± 3% HRmax.
Results revealed a signiﬁcant correlation between ∆VO2max and training HR (r = 0.62, p = 0.02)
(Figure 2c).
4. Discussion
Previous studies document a heterogeneous BLa response to continuous exercise [10] and that
the change in BLa in response to acute continuous exercise at 65% PPO predicts the resultant training
response [11]. Our study examined if the BLa response to short-term HIIT is associated with ∆VO2max.
The results oppose our hypothesis and they suggest that HR response to training is signiﬁcantly
associated with change in VO2max, yet BLa, sleep duration, and energy intake are not.
These
preliminary data suggest that the cardiovascular strain of HIIT prescribed according to a metabolic
threshold may predict resultant responses to training.
Our data showing no correlation between ∆VO2max and the BLa response to HIIT refute previous
results [11]. However, there are methodological diﬀerences between studies which may explain these
discrepancies. First, our sample contained men and women, while the former study included only
men. It is apparent that muscle ﬁber type may diﬀer between men and women [19], leading to greater
reliance on glycolysis and resultant blood lactate accumulation in men who have a greater proportion
of higher threshold motor units. Moreover, men exhibit higher BLa in response to HIIT and sprint
interval training (SIT) versus women, which may be due to the greater work completed during training
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due to a propensity to maintain a higher cadence, especially during SIT [20]. Second, HIIT was
performed at an intensity equal to 90% PPO, whereas, in the former study, training was performed
at 65% PPO. This diﬀerent composition of training led to markedly diﬀerent BLa responses, as our
subjects exhibited BLa ranging from 7.6–14.1 mM, which was higher than values exhibited in their
study (6–8 mM). Lastly, our regimen was prescribed according to VT, which may potentially reduce
variability in metabolic stress versus their regimen, which may have had select participants training
above or below the workload associated with VT. Further work is merited to examine if the BLa
response to HIIT prescribed according to an absolute intensity is associated with ∆VO2max.
Our data showed no association between ∆VO2max and sleep duration or calorie intake. Sleep
deprivation may mitigate the adaptive response to training by eliciting fatigue, which may reduce
performance [21]. Previous results exhibit that one (25–30 h of sleeplessness) [22] and three nights of
sleep deprivation (3 h of sleep per night) [23] decreased time to exhaustion and muscular strength
in athletes, whereas, in basketball players, two additional hours of sleep per night for up to seven
weeks were consequent with increased speed and shooting performance [24]. Our data show that the
participant with the lowest amount of sleep (6.0 h) did not exhibit increases in VO2max with HIIT.
However, two additional participants who received 8 h per night of sleep also showed minimal increases
in VO2max, which suggests that sleep duration by itself may not predict training responsiveness. Two
of these participants also completed 7 h/week of physical activity outside the HIIT regimen, so an
eﬀect of overreaching on their lack of response may exist. Our participants’ habitual dietary intake
ranged from 1.8–3.8 kcal/kg body mass. Two men and two women exhibiting substantial increases
in VO2max (0.13–0.47 L/min) revealed dietary intakes approaching 3.8 kcal/kg body mass, whereas,
two participants ingesting less than 2 kcal/kg revealed no change in VO2max in response to training.
These individual results reveal that nutritional state may impact the training response. Nevertheless,
in adults with diabetes, the addition of post-exercise protein did not aﬀect adaptation to training [25],
which may indicate that diet has little impact on training responsiveness.
A signiﬁcant association between HR response to training and ∆VO2max was revealed (Figure 2).
Our results reveal that participants with the greatest increases in VO2max exhibited training HR
above 95% HRmax. Whether this greater adaptation is due to some unique sympathetic response or
alternatively, maintaining a higher cadence during HIIT, which would elicit greater work, is unknown.
In sedentary men, six sessions of HIIT increased VO2max and PPO [26], with these outcomes associated
with the ratio of low to high frequency power of R-R oscillation, which represents sympathovagal
balance. Higher vagal activity has been shown to be directly related to ∆VO2max in response to
MICT [27], and further study is merited to conﬁrm this result in response to other HIIT regimens.
One limitation of our study is that dietary intake was quantiﬁed through self-reported logs, which
are prone to underreporting [28]. Studies show that the pre-exercise nutritional state may alter the
molecular response to training [13], so monitoring participants’ dietary patterns before and after each
session may be useful to better understand the eﬀects of nutrition on training responsiveness. Sleep
status was assessed by identifying the duration of sleep the night before each session, rather than using
various questionnaires, which may be more valid to assess sleep quality [29]. Thus, the reliability of
our relatively simple measure is unknown. In addition, our sample included both men and women
who may show unique responses to interval training [30], yet our study was underpowered to examine
this potential eﬀect of sex. No measure of critical power was performed in our study, so it is likely that
participants were training at diﬀerent workloads within the heavy and/or severe intensity domain.
Lastly, our training protocol was relatively brief, so data cannot be used to explain responsiveness to
prolonged regimens of interval training.
5. Conclusions
Overall, training HR was associated with the VO2max response to HIIT, yet dietary intake, sleep
duration, and BLa accumulation were not predictive of this response. Overall, these preliminary
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data suggest that the absolute cardiovascular strain may be a mediator of the adaptive response to
interval training.
Author Contributions: Conceptualization, T.A.A., J.L.D., and T.A.; methodology, T.A.A., J.L.D., and T.A.; formal
analysis, T.A.A.; resources, T.A.A.; data curation, T.A.A., J.L.D., T.A., E.K., P.H., S.R., and N.G.; writing—original
draft preparation, T.A.A.; writing—review and editing, T.A.A., J.L.D., T.A., E.K., P.H., S.R., and N.G.; supervision,
T.A.A.; project administration, T.A.A.
Funding: This research received no external funding.
Acknowledgments: The authors thank the participants for their eﬀort and dedication in completing the
study requirements.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Blood Lactate Concentration Is Not Related to the Increase in Cardiorespiratory Fitness Induced by High Intensity Interval Training.
Astorino, Todd A,DeRevere, Jamie L,Anderson, Theodore,Kellogg, Erin,Holstrom, Patrick,Ring, Sebastian,Ghaseb, Nicholas