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Purpose: This study examined the physiological, perceptual, and performance responses to a 2-week block of increased training load and compared whether responses differ between high-intensity interval (HIIT) and low-intensity (LIT) endurance training. Methods: Thirty recreationally trained males and females performed a two-week block of 10 HIIT-sessions (INT, n = 15) or 70 % increased volume of LIT (VOL, n = 15). Running time in the 3000 m and basal serum and urine hormone concentrations were measured before (T1) and after the block (T2), and after a recovery week (T3). In addition, weekly averages of nocturnal heart rate variability (HRV) and perceived recovery were compared to the baseline. Results: Both groups improved their running time in the 3000 m from T1 to T2 (INT -1.8 ± 1.6 %, p = 0.003; VOL -1.4 ± 1.7 %, p = 0.017) and T1 to T3 (INT -2.5 ± 1.6 %, p < 0.001; VOL -2.2 ± 1.9 %, p = 0.001). Resting norepinephrine concentration increased in INT from T1 to T2 (p = 0.01) and remained elevated at T3 (p = 0.018). The change in HRV from the baseline was different between the groups during the first week (INT -1.0 ± 2.0 % vs. VOL 1.8 ± 3.2 %, p = 0.008). Muscle soreness increased only in INT (p < 0.001) and the change was different compared to VOL across the block and recovery weeks (p < 0.05). Conclusions: HIIT and LIT blocks increased endurance performance in a short period of time. Although both protocols seemed to be tolerable for recreational athletes, a HIIT-block may induce some negative responses such as increased muscle soreness and decreased parasympathetic activity.
Physiological, Perceptual, and Performance
Responses to the 2-Week Block of High- versus
Low-Intensity Endurance Training
Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, FINLAND;
KIHU Research Institute for Olympic
Sports, Jyväskylä, FINLAND;
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio,
Department of Internal Medicine, Central Finland Health Care District, Jyväskylä, FINLAND
NUUTTILA, O.-P., A. NUMMELA, H. KYRÖLÄINEN, J. LAUKKANEN, and K. HÄKKINEN. Physiological, Perceptual, and Performance
Responses to the 2-Week Block of High- versus Low-Intensity Endurance Training. Med. Sci. Sports Exerc., Vol. 54, No. 5, pp. 851
860, 2022.
Purpose: This study examined the physiological, perceptual, and performance responses to a 2-wk block of increased training load and com-
pared whether responses differ between high-intensity interval (HIIT) and low-intensity training (LIT). Methods: Thirty recreationally trained
males and females performed a 2-wk block of 10 HIIT sessions (INT, n= 15) or 70% increased volume of LIT (VOL, n= 15). Running time in
the 3000 m and basal serum and urine hormone concentrations were measured before (T
) and after the block (T
), and after a recovery week
). In addition, weekly averages of nocturnal heart rate variability (HRV) and perceived recovery were compared with the baseline. Results:
Both groups improved their running time in the 3000 m from T
to T
(INT = 1.8% ± 1.6%, P= 0.003; VOL = 1.4% ± 1.7%, P= 0.017) and
from T
to T
(INT = 2.5% ± 1.6%, P< 0.001; VOL = 2.2% ± 1.9%, P= 0.001). Resting norepinephrine concentration increased in INT from
to T
(P= 0.01) and remained elevated at T
(P= 0.018). The change in HRV from the baseline was different between the groups during the
first week (INT = 1.0% ± 2.0% vs VOL = 1.8% ± 3.2%, P= 0.008). Muscle soreness increased only in INT ( P< 0.001), and the change was
different compared with VOL across the block and recovery weeks ( P<0.05).Conclusions: HIIT and LIT blocks increased endurance perfor-
mance in a short period. Although both protocols seemed to be tolerable for recreational athletes, a HIIT block may induce some negative re-
sponses such as increased muscle soreness and decreased parasympathetic activity. Key Words: BLOCK PERIODIZATION, RUNNING,
The aim of the athletic training process is to produce ad-
equate stimuli that would lead to positive training ad-
aptations. In endurance training, the variables that are
typically modified to induce desirable responses are the inten-
sity, duration, and frequency of training (1). In long-term peri-
odization, it seems necessary to perform high volumes of en-
durance training at low intensity (1). However, in short-term
periodization, block periodizationaltering focus between
volume and intensity (2)or polarized periodizationmixing
low- and high-intensity training (3)have both been suggested
to be the most favorable training organization methods.
Block periodization protocols have typically focused on
high-intensity interval training (HIIT) consisting of 1- to 3-wk
microcycles of multiple weekly or even daily high-intensity ses-
sions (4). On the other hand, studies examining the effects of
high-volume microcycles have most often included overload
periods increasing both low- and high-intensity training volume
(5). The length of the periods has varied predominantly between
2 and 6 wk, during which training volume has been increased
by 30%100% from the volume previously used by an individ-
ual (69). High-intensity and high-volume endurance training
periods have mainly been studied separately, but possible dif-
ferences in the physiological, perceptual and performance re-
sponses are not well established.
When there is a substantialincrease in training load from the
previous load, there is also an increased risk of injuries (10) and
maladaptation or overreaching (5). To avoid such consequences,
it would be critical to detect early signs that may predict compro-
mised training adaptations. Monitoring of training and recovery
typically consists of regular assessments of physiological,
Address for correspondence: Heikki Kyröläinen, Ph.D., F.A.C.S.M., Faculty
of Sport and Health Sciences, University of Jyväskylä, PO Box 35 (VIV),
FIN-40014 Jyväskylä, Finland; E-mail:
Submitted for publication October 2021.
Accepted for publication December 2021.
Supplemental digital content is available for this article. Direct URL citations
appear in the printed text and are provided in the HTML and PDF versions
of this article on the journals Web site (
Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
on behalf of the American College of Sports Medicine. This is an open access
article distributed under the Creative Commons Attribution License 4.0 (CCBY),
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provided the original work is properly cited.
DOI: 10.1249/MSS.0000000000002861
perceptual, or performance-related markers that are estimated to
provide valuable information about the recovery and training state
of an athlete (11). On one end of the monitoring tool spectrum are
extensive laboratory tests, such as hormonal or biochemical exam-
inations from blood or urine (6,12), whereas perceptual markers
such as subjective surveys (13,14) or session RPE (15) represent
the other end of the spectrum. In addition, noninvasive assess-
ments of physiological markers, like heart rate variability
(HRV) recordings at rest (16), heart rate (HR) during exercise
(17), and performance-related markers such as various jumping
tests (18), could be used in monitoring. The purpose of the mon-
itoring process is to follow whether an athlete is adapting to the
stimulus as expected and to influence decisions for the forth-
coming training load (18) or session intensity (16,17).
Although monitoring has clear advantages during the train-
ing process, previous studies have disclosed several contradic-
tions and limitations, especially regarding responses of physio-
logical markers. In the case of submaximal HR and resting
HRV, it is a well-known dilemma that a similar type of response
may be observed after both a positive training adaptation and in
the state of parasympathetic hyperactivity, which is associated
with a decrease in maximal performance (8,19). Furthermore,
plasma volume expansion may, at least acutely, affect HRV
(20), regardless of the recovery state. Resting levels of catechol-
amines, which correlate with sympathetic nervous system activ-
ity, have previously been reported as unchanged in female en-
durance athletes (7) and male triathletes (21) but decreased in
well-trained runners (6) after an intensified training period. In
the same studies, acute responses of catecholamines to maximal
exercise have also varied between unchanged (6,21) and de-
creased (7,21) after a period of intensified training. It has been
suggested that, in general, hormonal responses to maximal exer-
cise may be altered more than resting levels in the overtraining
state (12), making regular hormonal assessments in athletes
rather difficult. Acknowledging these challenges with physiolog-
ical markers, subjective estimations of recovery may provide
valuable triangulatinginformation that improves interpretation
of athlete status during training (13,14) and helps to contextu-
alize complicated physiological changes (19).
The aim of the present study was to examine the physiological,
perceptual, and performance responses to blocks of increased train-
ing load, and to compare whether these responses would differ be-
tween high-volume low-intensity training (LIT) and HIIT periods
in recreationally trained male and female participants. Another aim
was to explore whether training adaptation would be associated
with the responses of the monitoring variables. We hypothesized
that both types of training blocks would improve endurance per-
formance after the recovery week but induce acute fatigue imme-
diately after the training period, observed as decreased or un-
changed performance and impaired perceptual recovery (8,19).
A total of 40 recreationally endurance-trained male and fe-
male runners were recruited to participate voluntarily in the
study. Participants were 2045 yr old, healthy, and experienced
in regular running training (>4 times per week). A cardiologist
checked electrocardiography of all potential participants before
the final acceptance to participate. One participant dropped out
before any measurement because of difficulties with the
timetable. In addition, six participants dropped out because
of sicknesses (n=2)orinjuries(n= 4) that occurred during
the preparatory period or at the beginning of the training pe-
riod. From the participants that finished the whole study pe-
riod, one participant was excluded from the final analysis
because of insufficient training adherence (<90%/main ses-
sions), and two participants for not following the training in-
structions during the preparatory or recovery periods. Baseline
characteristics of the participants that were included in the fi-
nal analysis (n= 30) are presented in Table 1. All participants
gave their written consent to participate, and the study proto-
col was approved by the ethics committee of the University
of Jyväskylä.
Study Protocol
The study consisted of four separate phases similar to the
protocol used by Le Meur et al. (8): a 2-wk preparatory period
(phase 1), the first recovery week (phase 2), a 2-wk training
period (phase 3), and the second recovery week (phase 4). Par-
ticipants were advised to continue their regular training in
terms of volume during the preparatory period and to decrease
training volume by 50% in the following recovery week. To
ensure a similar training intensity distribution before the train-
ing intervention, participants were asked to train below the first
lactate threshold, excluding one HIIT session (6 3 min), which
was performed to familiarize participantswith the interval pro-
tocol. At the end of the preparatory period, participants were
matched into pairs based on sex, 3000-m performance, v
and baseline HRV, and divided into the interval group (INT)
or volume group (VOL). During the 2-wk training period,
the INT group performed a total of 10, 6 3-min HIIT ses-
sions (5 sessions per week), whereas the VOL group increased
their low-intensity running volume (h) by 70%. Proper train-
ing load for the HIIT and VOL protocols was estimated based
on previous studies examining HIIT shock microcycles (4) or
volume-based overload periods (68). After the 2-wk training
period, a similar recovery week as the first was prescribed.
TABLE 1. Mean ± SD baseline characteristics of the participants.
INT (n=15) VOL(n=15)
Sex (male/female) 9/6 9/6
Age(yr) 33±7 37±7
Height(cm) 172±10 174±11
Body mass (kg) 72 ± 14 71 ± 13
) 10.8 ± 1.2 10.7 ± 1.4
) 13.3 ± 1.7 13.0 ± 1.6
) 16.6 ± 1.8 16.4 ± 1.8
) 50.4 ± 6.9 49.7 ± 6.4
3000 m (min:s) 12:29 ± 1:36 12:34 ± 1:35
INT, interval group; VOL, volume group; v
, running speed at the first lactate threshold;
, running speed at the second lactate threshold; v
maximal speed of the incremental
treadmill test;
, maximal oxygen uptake. Baseline characteristics were measured be-
fore the preparatory period (T
http://www.acsm-msse.org852 Official Journal of the American College of Sports Medicine
Performance in the 3000 m and countermovement jump
(CMJ) were measured, and fasting blood and urine samples
were taken and analyzed before the preparatory period (T
in the middle of the first recovery week (T
), 1 d after the in-
tensified training period (T
), and after the second recovery
week (T
). An incremental treadmill test was performed once
in the same week as the other T
tests to analyze lactate thresh-
olds (LT1 and LT2) and individual training intensity zones
among the participants. A day of rest was always prescribed
before testing days. Training and recovery were monitored
with multiple markers throughout the study.
Training Protocol
Both groups had five main sessions per week, which were
supervised and performed individually at the same time of
the day (±2 h) during the morning or afternoon and at the same
outdoor road/track, which was tight gravel (INT) or about 50/
50 combination of gravel and asphalt (VOL). The INT group
performed all the sessions as 6 3-min intervals, whereas
the VOL group performed only low-intensity sessions below
the first lactate threshold. If participants performed more than
five sessions during the preparatory period, these sessions
were also incorporated into the training period as low-intensity
training with the same duration (INT) or with increased dura-
tion (VOL) to match the requirement of the volume increment.
In case participants were accustomed to alternative endurance
training modes such as cycling, these modes were incorpo-
rated as part of the additional sessions with similar proportion
to the preparatory period.
Interval session. HIIT session was a 6 3-min interval with
2-min active recovery (walking). Intervals were performed at the
maximal sustainable effort (22). Before the session, a 15-min
warm-up, including three 30-s accelerations to the target speed,
was performed. After the session, a 10-min cooldown was pre-
scribed. Average running speed and HR were calculated sepa-
rately for each interval and for the entire session, and a session
RPE score was reported after each session (15).
Low-intensity sessions. The VOL group performed
four similar basic sessions (85%95% HR of the LT1) and
one long-distance session (75%90% HR of the LT1) in a
week. The aim was to increase the duration of running ses-
sions compared with preparatory period. The duration of these
sessions was individually scaled based on the training during
the preparatory period. The basic session was planned to be
approximately 1.50the average session duration during the
preparatory period (1:22 ± 0:10 h:min), whereas the long-
distance session was 1.66the duration of the basic session
(2:16 ± 0:16 h:min). Average running speed, average HR,
and HR running speed index (HR-RS index) (23) were calcu-
lated from all supervised sessions. In addition, session RPE
was estimated after all sessions (15).
Performance Tests
An incremental treadmill test was performed on a treadmill
(Telineyhtymä Oy, Kotka, Finland). The starting speed was
set to 7 or 8 km·h
for women and 8 or 9 km·h
for men.
Three-minute stages and speed increments of 1 km·h
used. After each stage, the treadmill was stopped, and partici-
pants stood still for the fingertip blood lactate samples, which
took approximately 1520 s. Incline was kept constant at 0.5°
throughout the test. Oxygen consumption was measured breath
by breath (Jaeger VyntusTM CPX, CareFusion Germany 234
GmbH, Hoechberg, Germany), and HR was monitored with
Garmin Forerunner 245 M (Garmin Ltd., Schaffhausen,
Switzerland). Maximal oxygen uptake ( ˙
was defined as the highest 60 s average of oxygen consump-
tion. Maximal running speed (v
) of the test was defined
as the highest completed speed, or if the stage was not fin-
ished, as a speed of the last completed stage (km·h
) + (run-
nning time (s) of the unfinished stage 30 s)/(180
30 s) 1km·h
. The first lactate threshold (LT1) and the sec-
ond lactate threshold (LT2) were determined based on lactate
values during the test. The LT1 was set at 0.3 mmol·L
the lowest lactate value and LT2 at the intersection point be-
tween 1) a linear model betweenLT1 and the next lactate point
and 2) a linear model for the lactate points measured after the
point when La increased at least 0.8 mmol·L
for the first
time. The same treadmill and lactate threshold estimation pro-
tocols have been used in previous studies (16,24,25).
The 3000-m running test was performed on a 200-m indoor
track. Before the test, 15-min low-intensity warm-up was per-
formed, including 3 2030 s accelerations to target pace at
the latter part of the warm-up. Verbal encouragement and split
times (1000 m, 2000 m) were given for all participants during
the test. The test was run in small groups (maximum seven
persons). All test attempts were performed individually at the
same time of the day (±2 h) during the afternoon or evening.
The CMJ test was performed before supervised sessions
and before the 3000-m running tests. In the test, participants
performed three maximal attempts on a contact mat with a
1-min recovery. The test was performed after a standardized
warm-up, including a short jog (~3 min) and two sets of differ-
ent kinds of squats (half squat, lunge, and squat jump). Jump
height (h) was calculated based on the measured flight time
with the following formula: h=gt
,wheretis the re-
corded flight time in seconds and gis the acceleration due to
gravity (9.81 m·s
) (26). The highest jump height (cm) was
used in the data analysis.
Blood and Urine Samples
Fasting blood samples were taken after 12 h of fasting and
individually at the same time of the day (7:009:15 AM). Blood
samples were taken in a seated position from the antecubital
vein into 6 mL serum tubes using standard laboratory proce-
dures. Whole blood was centrifuged at 2250g(Megafuge 1.0 R,
Heraeus, Hanau, Germany) for 10 min, and the separated serum
was removed and frozen at 20 C until analysis. Serum cortisol
concentration was analyzed with a chemical luminescence tech-
nique (Immulite 2000 XPi, Siemens, New York City, NY).
The sensitivity of the cortisol assay was 5.5 nmol·L
RESPONSES TO 2-WK BLOCKS OF HIIT OR LIT Medicine & Science in Sports & Exercise
the intra-assay coefficient of variation was 5.3%. Free testos-
terone concentration was analyzed with ELISA (DYNEX
DS 2 ELISA processing system, DYNEX Technologies,
Chantilly, VA). The sensitivity of the free testosterone assay
was 0.6 pmol·L
, and the intra-assay coefficient of variation
was 6.0%. Serum creatine kinase activity was analyzed with
Indiko Plus Clinical Chemistry Analyzer (Thermo Fisher Sci-
entific, Vantaa, Finland). The sensitivity of the creatine kinase
assay was 2.2 L
, and the intra-assay coefficient of varia-
tion was 0.9%. Hemoglobin and hematocrit were analyzed
with an automated hematology analyzer (Sysmex XP-
300TM, Sysmex Inc., Kobe, Japan). Plasma volume was esti-
mated from the obtained hematocrit and hemoglobin values
based on the equation of Dill and Costill (27).
Urine sample collection was performed between 1900 and
0700 h during the night before fasting samples were taken.
Participants were asked to document the accurate starting
and ending times of the collection. After bringing the sample
to the laboratory, the urine volume was determined. For the
analysis of norepinephrine, a 10-mL sample was frozen at
20°C. The concentrations of hormones in the sample were
assessed by the liquid chromatography (HPLC) method (Labor
Dr. Kramer & Kollegen, Geesthacht, Germany). The intra-
assay coefficient of variation for the norepinephrine was
2.0%. Because of slight differences in collection times, the
concentration of hormones in the urine sample was multiplied
by the volume of the whole urine, then divided by the collec-
tion time in hours, and multiplied by 12 to represent a similar
12-h collection time for all participants similar to Hynynen
et al. (28).
Training and Recovery Monitoring
Participants wore an HR monitor (Garmin Forerunner
245 M) during all endurance training sessions. HR and GPS
data (distance covered, running speed) were analyzed from
all sessions. Training intensity distribution was analyzed with
a time in zone model (HR
, HR < LT1; HR
the training log basic information of each session performed
and estimated session RPE on a 010 scale (15).
Nocturnal HR and HRV were recorded with the Firstbeat
Bodyguard 2 device (Firstbeat Technologies LTD, Jyväskylä,
Finland). Participants were advised to start recording when go-
ing to sleep and stop the recording right after awakening. Re-
cordings were performed every night starting from the first re-
covery week. Recorded RR intervals were edited by an artifact
detection filter within the Firstbeat Sports software, which ex-
cluded all falsely detected, missed, and premature heartbeats.
If the error percentage representing the number of corrected
interbeat intervals shown by the software was higher than
33%, recordings were excluded from the analysis, as sug-
gested by Vesterinen et al. (24). One participant in the VOL
group had a high amount of erroneous data (error percentage
>33% more than 50% of the recorded nights) and was ex-
cluded from the nocturnal analysis. Average HR, natural
logarithm of high-frequency power (lnHF), and natural log-
arithm of the root-mean-square of the successive differences
(lnRMSSD) were analyzed from the sleep period of 0030
0430 h after going to bed. High intraclass correlation coef-
HF, respectively, when 4-h averages have been compared
between two consecutive nights after a similar training
day (29). Weekly average values were used as suggested
by Le Meur et al. (8): Pre, recovery week preceding the
training period; Week1, first week of the training period;
Week2, second week of the training period; Week3, recov-
ery week after the training period.
Participants filled out daily questionnaires on a 010 visual
analog scale (VAS) regarding estimated readiness to train,
sleep quality of the previous night, general fatigue, muscle
soreness of lower extremities, and perceived stressfulness dur-
ing the day. Questionnaires were modified from the previous
studies (13,14). Results were averaged similarly to nocturnal
HR and HRV results.
Statistical Analysis
Results are presented as mean ± SD. Before performing the
final analysis, we determined if the magnitude of changes in
the main variables differed between sexes (KruskalWallis
test). No significant differences were found; thus, female and
male participants were analyzed in combined groups. The nor-
mality of the data was assessed with the ShapiroWilk test. To
examine the main effects (time, group) and their interaction
(timegroup) in the monitoring variables (Pre, Week1,
Week2, and Week3), performance or laboratory tests (T
), and training characteristics of the main sessions (1st
vs 2nd10th sessions), repeated-measures ANOVA was ap-
plied. In the case of a significant main effect or interaction, a
Bonferroni post hoc test was used for within-group compari-
sons and simple contrasts for between-group comparisons.
Training characteristics (frequency, volume, running kilome-
ters, and training intensity distribution), creatine kinase, and
free testosterone results were not normally distributed; thus,
the Wilcoxon signed rank test was used for comparisons be-
tween time points and the MannWhitney U-test for
between-group comparisons, with Bonferroni correction ( P
values multiplied by the number of comparisons). To examine
the magnitude of observed changes, the effect size (ES) of
within-group absolute differences was calculated as Cohens
dfor the main variables, and after nonparametric tests by the
following formula: ES = Z(n)
,whereZis the z-score, and
nis are the number of observations on which Zis based. The
magnitude of changes was categorized as <0.2 trivial, 0.2
0.5 small, 0.50.8 moderate, and >0.8 large. In addition, the
Pearson correlation coefficient was used to analyze relation-
ships between the monitoring variables (absolute values at
Week2, changes from Pre to Week2 or changes from 1st ses-
sion to 10th session) and changes in the 3000-m running speed
Δ%, T
Δ%). The statistical significance
level was set to P< 0.05. Analyses were performed with
http://www.acsm-msse.org854 Official Journal of the American College of Sports Medicine
Microsoft Excel 2010 (Microsoft Corporation, WA) and IBM
SPSS Statistics version 26 programs (SPSS Inc, Chicago, IL).
Training. The INT group increased the weekly training
volume at HR
by 32 ± 22 min and at HR
by 55 ±
17 min from the preparatory to the training period, whereas
the VOL group increased training volume by 68% ± 5% and
running distances by 76% ± 25% (Table 2). Both groups per-
formed lower training volume ( P< 0.01) compared with the
preparatory period during the first (INT = 2.9 ± 1.1, VOL =
2.7 ± 1.2 h) and the second recovery weeks (INT = 2.9 ± 1.1 h,
VOL = 2.9 ± 1.5 h), and only LIT, except for the 3000-m run-
ning test, was reported during the recovery weeks.
Performance and session RPE values of all main sessions
are presented in Figure 1. In the VOL group, average running
speed and distance covered were 9.8 ± 1.5·h
and 13.5 ±
3.0 km in the basic sessions and 9.1 ± 1.5 km·h
and 21.0 ±
4.7 km in the long-distance sessions, respectively. In the INT
group, the average HR during the intervals decreased (P<0.05)
from the first session (90.7% ± 1.8% HR
) to the 6th, 7th,
9th, and 10th sessions (88.1%88.6% HR
). In the VOL group,
the average HR remained similar within-session type and was
on average 72.6% ± 4.9% HR
during the basic sessions and
69.0% ± 4.5% HR
during the long-distance sessions.
Physical performance. A significant main effect of time
(P< 0.001) was observed in the 3000-m running time as well
as HR
the test (Table3). In addition, a significant timegroup interac-
tion ( P< 0.001) was found in HR
and HR
improved the 3000-m running time from T
to T
(INT, P=
0.003; VOL, P= 0.017) and from T
to T
(INT, P<0.001;
VOL, P= 0.001) (Fig. 2). No significant main effects nor in-
teraction was observed in the CMJ performance, which was
tested before the 3000-m tests (Table 3) or in the tests that
were performed before the supervised sessions during the
training period (INT, lowest mean = 31.9 ± 5.5 vs highest
mean = 32.4 ± 5.1 cm; VOL, lowest mean = 31.0 ± 5.8 vs
highest mean = 31.6 ± 6.0 cm).
Physiological responses. A significant main effect of
time was observed in hemoglobin (P< 0.001), hematocrit
(P= 0.001), and norepinephr ine ( P< 0.001) (Table 4). In ad-
dition, a significant increase was observed in CK activity of
VOL from T
to T
(P= 0.036). Norepinephrine increased
in INT from T
to T
(P= 0.018). Hemoglobin concentration ( P=0.011)and
hematocrit ( P= 0.037) decreased from T
to T
in VOL,
whereas hemoglobin tended to decrease from T
to T
0.065) and increased from T
to T
(P= 0.029) in the INT
group. When plasma volume changes were estimated based
on hemoglobin and hematocrit values, T
changes trans-
lated to 4.3% ± 5.0% and 5.1% ± 6.7% expansion in the
plasma volume of INT and VOL, respectively.
A significant main effect of time ( P= 0.001) was found in
nocturnal HR, and a significant timegroup interaction was
found in nocturnal HR ( P= 0.001), nocturnal lnHF ( P=
0.036) (Fig. 3), and nocturnal lnRMSSD ( P= 0.027). Noctur-
nal HR decreased in INT from Pre to Week3 (P=0.002,
ES = 0.36) and from Week2 to Week3 (P<0.001,ES=
0.30). Changes in HR from Pre to Week1 (INT = 1.9% ± 4.0%
vs VOL = 1.6% ± 5.1%, P= 0.045) and from Week2 to
Week3 (INT = 3.8% ± 3.2% vs VOL = 0.1 ± 2.9, P=
0.003) were different between the groups. In lnHF, no signif-
icant within-group changes were found, but change from Pre
TABLE 2. Mean ± SD average weekly training characteristics during the 2-wk preparatory
and the 2-wk training periods of high-intensity (HIIT block) or low-intensity training (LIT
INT (n=15) VOL(n=15)
Preparatory HIIT Block Preparatory LIT Block
Training volume (h) 5.8 ± 1.7 5.2 ± 1.1 5.4 ± 2.1 9.0 ± 3.4**
Training frequency per week 5.6 ± 1.4 5.8 ± 1.3 5.3 ± 1.9 5.9 ± 1.8*
Running volume (km) 45.8 ± 12.6 49.8 ± 9.3 44.6 ± 14.7 77.0 ± 22.7**
(%) 91.5 ± 5.7 61.9 ± 7.0** 92.2 ± 3.9 99.6 ± 0.7**
(%) 6.3±4.7 18.4±6.0** 5.4±2.9 0.4±0.7**
(%) 2.2±2.6 19.7±5.2** 2.4±1.7 0.0±0.0**
*P<0.05,**P< 0.01 compared with the preparatory period.
INT, intensity group; VOL, volume group; HR
, HR below the first lactate threshold;
, HR between the first and the second lactate threshold; HR
ond lactate threshold.
FIGURE 1A, Mean ± SD average running speed during the 6 3-min intervals performed at maximal sustainable effort, and session RPE of each inter-
val session prescribed. B, Mean ± SD HR-RS index and session RPE of basic (14,69) and long-distance (5,10) LIT sessions prescribed. *P<0.05,
**P<0.01,***P< 0.001 compared with the first session.
RESPONSES TO 2-WK BLOCKS OF HIIT OR LIT Medicine & Science in Sports & Exercise
to Week1 was different between the groups (INT = 1.0% ±
2.0% vs VOL = 1.8% ± 3.2%, P= 0.008). The same pattern
was observed in lnRMSSD, which remained unaffected
through the training and recovery weeks in INT (4.18 ±
0.52 ms vs 4.14 ± 0.50, 4.21 ± 0.48 and 4.24 ± 0.42 ms) and
VOL (4.03 ± 0.43 ms vs 4.10 ± 0.40, 4.06 ± 0.42 and
4.05 ± 0.41 ms), but change from Pre to Week1 differed be-
tween the groups ( P=0.014).
Perceptual responses. A significant main effect of time
was found in muscle soreness ( P< 0.001), and a significant
timegroup interaction was found in the readiness to train
(P= 0.008) and muscle soreness ( P= 0.001) (Fig. 4). Readi-
ness to train decreased in INT from Pre to Week3 (P=0.045,
ES = 0.57) and tended to decrease from Pre to Week2
(P=0.057,ES=0.72). In addition, the change in readiness
to train from Pre to Week3 was different between the groups
(P= 0.002). Muscle soreness increased in INT ( P<0.001)
from Pre to Week1 (ES = 0.86) and Week2 (ES = 0.94), and
the change was different between the groups from Pre to Week1
(P< 0.001), Week2 ( P= 0.012), and Week3 ( P= 0.001).
Relationships between monitoring variables and
changes in endurance performance. A significant pos-
itive correlation was found between the relative change in av-
erage running speed from 1st to 10th interval session and rel-
ative change in the 3000-m running speed from T
to T
INT (r=0.656,P= 0.008). In VOL, a tendency for negative
correlation was found between the change in HR-RS index
from 1st to 10th low-intensity session and relative change in
the 3000-m running speed from T
to T
(r=0.510, P=
0.052). In addition, the relative change in the nocturnal HR
from Pre to Week2 correlated positively with the relative
change in the 3000-m running speed from T
to T
in VOL
(r=0.538,P= 0.047). Among the perceptual markers and
INT group, muscle soreness at Week2 correlated negatively
(r=0.564, P= 0.028), and change in the readiness to train
from Pre to Week2 correlated positively (r=0.529,P=
0.043) with the relative change in the 3000-m running speed
from T
to T
. The change in the stress from Pre to Week2
was the only marker that correlated significantly with the rela-
tive change in the 3000-m running speed from T
to T
in INT
(r=0.637,P= 0.011). When groups were pooled, fatigue
(r=0.449, P= 0.013) and muscle soreness (r=0.375,
P= 0.041) at Week2 both correlated negatively with the rela-
tive change in the 3000-m running speed from T
to T
results of all correlation analyses are presented in Supplementary
Table 1 (see Table, Supplemental Digital Content 1, Pearson corre-
lation coefficient between monitoring variables and relative change
in the 3000-m running speed,
The main findings of the study were that 2-wk blocks of
HIIT or LIT both improved the 3000-m running performance,
and no differences were found between the groups in the train-
ing adaptations. Based on physiological and perceptual re-
sponses during the blocks, both periods could be tolerable
for recreational athletes, although the HIIT block induced
some negative responses compared with the LIT block, such
as increased muscle soreness and decreased HRV. Running
speed during the interval sessions and resting-state perceptual
TABLE 3. Mean ± SD average performance test results before the 2-wk training period (T
), immediately after the training period (T
), and after a recovery week (T
INT (n=15) VOL(n=15)
3000 m (min:s) 12:19 ± 1:32 12:06 ± 1:32**, ES = 0.14 12:00 ± 1:27***, ES = 0.21 12:33 ± 1:33 12:22 ± 1:30*, ES = 0.12 12:16 ± 1:29**, ES = 0.18
(%/max) 94.3 ± 2.4 92.2 ± 2.6***,
,ES=0.85 93.8 ± 2.2
,ES=0.24 94.7 ± 2.1 94.9 ± 2.1, ES = 0.08 95.3 ± 2.2, ES = 0.33
(%/max) 99.4 ± 1.9 96.6 ± 2.4***,
,ES=1.29 98.1 ± 1.7**,
,ES=0.71 98.9 ± 2.3 99.9 ± 2.6, ES = 0.40 99.9 ± 2.3, ES = 0.45
CMJ(cm) 33.0±6.2 32.6±5.6,ES=0.07 33.5 ± 5.5, ES = 0.09 32.6 ± 6.4 33.1 ± 5.9, ES = 0.07 33.1 ± 6.1, ES = 0.08
*P<0.05,**P< 0.01, ***P< 0.001 within -group changes compared with T
P< 0.001 between-group changes compared with T
Difference observed from T
to T
and T
to T
INT, intensity group; VOL,volume group; HR
, average HR ofthe 3000-m running testin relation to the maximum HR of the incremental treadmill test; HR
, peak HR of the 3000-m running
test in relation to the maximum HR of the incremental treadmill test; CMJ, countermovement jump test; ES, effect size of the changes from T
FIGURE 2Relative individual (white plots) and mean changes (black rectangle) in the 3000-m running time immediately after the 2-wk training period
) and after a recovery week (T
). The gray area represents the smallest worthwhile change area (±1.41%), which was the coefficient of variation
between T
and T
tests. *P<0.05,**P< 0.01, ***P< 0.001 compared with T
http://www.acsm-msse.org856 Official Journal of the American College of Sports Medicine
recovery seemed to be useful monitoring tools for acute re-
sponses to intensified training blocks.
Training and performance. HIIT microcycles have pre-
sessions (4), whereas typical volume periods have increased
training volume by 30%100% for 26wk(69). The current
protocols were chosen to produce a significant but tolerable in-
crease in the training load via either training intensity or training
volume, but not at the same time. The 3000-m running perfor-
mance improved in both groups already at T
, but no significant
differences were found after the recovery week between T
. Therefore, the training load seemed to be suitable on aver-
age, and neither of the blocks induced significant acute fatigue
at group level. It has been suggested that LIT training would
more likely lead to positive (30) or very positive (31) training
adaptations compared with HIT training. The present results
did not support these findings, at least among the block period-
ization, as peak performance improved more than the coeffi-
cient of variation of the 3000-m test out of 14/15 participants
in the INT and 9/15 in the VOL groups. Although both of the
current 2-wk block protocols induced significant improvements
in endurance performance, previous studies suggest that a com-
bination of HIT and LIT may be needed for the optimal long-
term development of endurance capacity (1,3).
After overload protocols, positive training adaptation is typ-
ically delayed because of acute fatigue or overreaching effect
(5). Previously, after the high-volume 3-wk overload period,
peak performance has been obtained after a 2-wk taper (9). Af-
ter a high-frequency 3-wk HIIT period, the peak performance
was achieved after 12 d (32). From this perspective, it was in-
teresting that 4/15 participants of the INT group impaired their
running performance after the recovery week, whereas there
was only one clear impairment in the VOL group. This could
partially relate to tapering, which included no HIIT sessions.
Although the intensity is suggested to be maintained in opti-
mal tapering (33), no HIIT sessions were prescribed to allow
a similar recovery week for both groups in the present study.
Therefore, it may be possible that some individuals have expe-
rienced some type of detraining effect after the low-volume
and low-intensity recovery week.
Although the performance improved similarly in both groups
immediately after the training period, peak and average HR dur-
ing the running test decreased only in the INT group, and peak
HR remained decreased at T
. This may relate to decreased ac-
tivity of the sympathetic nervous system via a reduced adren-
ergic response during exercise (21) or the down-regulation of
β-adrenoreceptors (34) due to repetitive training at high inten-
sity. A similar trend was observed during the intervals, where
average HR decreased, especially during the second week of the
training period, despite maintained or even increased running
speed. It would be interesting to know whether the decrement
was compensated with improved cardiac stroke volume,
which has occurred after various HIIT protocols (32,35).
Based on previous studies of volume overloads (6,8), it was
expected that the LIT block would also decrease HR in the
3000-m tests, possibly by increased blood volume (36) and
parasympathetic hyperactivity (8). Lack of changes in the
VOL group could partially be related to the lower absolute
training volumes of recreational athletes compared with previ-
ous studies of well-trained athletes (6,8).
Physiological and perceptual responses to train-
ing. Although studies targeting overload may provide
TABLE 4. Mean ± SD average blood and urine sample results before the 2-wk training period (T
), immediately after the training period (T
), and after a recovery week (T
Cor (nmol·L
) 422±88 419±80,ES=0.03 442 ± 115, ES = 0.20 410 ± 106 459 ± 88, ES = 0.51 465 ± 111, ES = 0.51
Ftesto (pmol·L
) 40.4 ± 27.2 40.6 ± 26.0, ES = 0.00 42.9 ± 28.1, ES = 0.02 35.7 ± 23.3 36.0 ± 26.0, ES = 0.00 39.5 ± 26.2, ES = 0.04
CK (μmol·L
) 103±64 124±53,ES=0.09 122±130,ES=0.08 107±35 178±102*,ES=0.52 126±78,ES=0.13
Hb (g·L
) 140±9 136±10,ES=0.37 140 ± 9*
, ES = 0.03 145 ± 12 141 ± 11*, ES = 0.36 143 ± 13, ES = 0.16
Hct (%) 42.3 ± 2.7 41.4 ± 3.0, ES = 0.33 42.7 ± 2.8, ES = 0.13 43.8 ± 3.1 42.8 ± 2.7*, ES = 0.35 43.3 ± 2.9, ES = 0.18
NE (μmol) 0.11 ± 0.04 0.15 ± 0.04*, ES = 0.91 0.15 ± 0.04*, ES = 1.03 0.12 ± 0.05 0.13 ± 0.05, ES = 0.19 0.15 ± 0.06, ES = 0.53
*P<0.05,**P< 0.01 compared with T
Difference observed from T
to T
INT, intensity group; VOL, volume group; Cor, serum cortisol concentration; Ftesto, serum-free testosterone concentration; CK, serum creatine kinase activity; Hb, hemoglobin concentration;
Hct, hematocrit fraction; NE, urine norepinephrine concentration; ES, the effect size of the changes from T
FIGURE 3Mean ± SD average weekly nocturnal HR (A) and lnHF (B) at baseline (Pre), during the training period (Week1 and Week2), and recovery
week (Week3). INT, interval group; VOL, volume group. **P< 0.01, ***P< 0.001 compared with respective time points in INT.
between-group changes compared with respective time points.
RESPONSES TO 2-WK BLOCKS OF HIIT OR LIT Medicine & Science in Sports & Exercise
information regarding the state of overreaching, the effects of
the increased volume or intensity per se seem to remain un-
solved. It is possible that physiological responses to intensified
training periods have varied depending on the method of in-
creasing training load. In the current study, the only significant
change in hormonal markers was found in the resting norepi-
nephrine concentration, which increased in INT and remained
elevated also after the recovery period. The finding somewhat
contradicted previous studies, which have shown that resting
values either remained similar (7,21) or decreased after inten-
sified training (6). Based on the increase in resting norepineph-
rine and the decrease in peak HR during the 3000-m test after a
demanding block of frequent HIIT training, a longer recovery
period may be advisable to restore normal autonomic nervous
system function at rest and during exercise. From the other
biomarkers, creatine kinase increased in VOL at T
it was anticipated thatHIIT block wouldalso increase CKcon-
centration (37,38). It is possible that the higher training vol-
ume of VOL, as well as a long-distance session 2 d before
the CK assessment, may have induced more structural damage
in the musculoskeletal tissue compared with HIIT. Previously,
Quinn and Manley (39) observed elevated CK values even
72 h after a 26-km run at 60%75% HR
to the long-distance session of the VOL group performed in
the present study.
Resting HRV is a marker used to analyze the restoration of
cardiovascular homeostasis and the stress/recovery state in
general (40). Although increments in HRV are typically a pos-
itive sign of the increased parasympathetic activity and a good
state of recovery (40), the so-called parasympathetic hyperac-
tivity is an abnormal response to a sudden increase in training
load (8,19). Previously, overload microcycles have induced
significant increases in HRV with the concurrent increase of
fatigue (8,19). In the current study, no significant changes in
HRV were found, although the response to the first week dif-
fered between the groups (a slight decrease in INT vs an in-
crease in VOL). It seems that the parasympathetic hyperactiv-
ity may be related to the overreaching/fatigue state itself rather
than to the increased training load, as fatigue was not increased
at the group level in the current study. The HRV response to
the increased training load, an increase or decrease, seems to
be individual, despite the type of training (25). Interestingly,
nocturnal HR decreased significantly during the recovery pe-
riod in the INT group, with no changes at any week in VOL.
Similar findings have been observed previously (25), suggest-
ing that high-intensity training may induce different cardiac
adaptations compared with high-volume training. Although
resting HR or HRV and catecholamine concentration are thought
to reflect the autonomic nervous system function from another
perspective, itseems that responses to intensified training may
differ between these markers.
Although physiological markers provide objective informa-
tion about the biological processes, perceptual markers may
also provide valuable information to predict maladaptation to
training (13,14) and help contextualize changes in the physio-
logical markers (8,19). In the present study, the most signifi-
cant changes were found in muscle soreness, which increased
in INT and differed from changes in VOL along all the training
and recovery weeks. Interestingly, the result somewhat
contradicted the result of CK, which increased only in VOL.
Concerning possible explanations, HIIT running differs from
LIT from a biomechanical point of view (cadence, ground re-
action forces) (41), and HIIT would most likely induce more
strain in the type II motor units compared with LIT (1). Alto-
gether, relative unfamiliarity combined with the abnormally
high HIT frequency may have increased muscle soreness lo-
cally in the running muscles. Furthermore, CK may be ele-
vated without an increase in muscle soreness after low-
intensity running (39).
Relationships between monitoring variables and
changes in running performance. Because responses to
endurance training periods are quite individual, it may be chal-
lenging to find unambiguous connections between monitoring
variables and changes in performance (25). In the current
study, there were associations among several markers of sub-
jective recovery (fatigue, muscle soreness, readiness to train,
and stress) and changes in running performance. Previous
studies have also shown that subjective markers, such as
FIGURE 4Mean ± SD average weekly values of perceptual recovery at baseline (Pre), during the trainingperiod (W1 and W2), and recovery week (W3).
INT, interval group; VOL, volume group. *P<0.05,**P<0.01,***P< 0.001 compared with Pre in INT.
P< 0.05
P< 0.01,
P< 0.001 between-group
changes compared with Pre.
Compared with the previous week in VOL.
http://www.acsm-msse.org858 Official Journal of the American College of Sports Medicine
fatigue and readiness to train (14), or the sum of multiple well-
being ratings (13) may be useful indicators in the prediction of
overreaching or overtraining. Therefore, maintaining these pa-
rameters within the normal range seems desirable during in-
tensified training periods. Interestingly, change in stress from
Pre to Week2 was positively associated with the final training
adaptation in INT. Ruuska et al. (42) have previously found
that mental stress may impair training adaptation to endurance
training, and it is generally suggested that intensive training
may not be recommended during periods of increased stress.
Because absolute stress values remained rather low through
the training block, the current association was most likely co-
incidence, and it highlights the importance of reliable refer-
ence values when assessing individual responses in the subjec-
tive monitoring variables.
Another interesting finding was that the change in running
speed from the 1st to the 10th interval session correlated with
the change in the 3000-m running speed from T
to T
ever, the same change did not correlate with the change from
to T
. Therefore, maximal sustainable running speed during
the intervals seemed to represent the current performance
level, rather than predicting the final training adaptation after
a sufficient recovery period. In the VOL group, a similar but
negative tendency was found between the change in the HR-
RS index and the change in running speed from T
to T
though the HR-RS index may be a useful tool in the long-term
monitoring of training adaptation (23,25), in this type of short-
term blocks where submaximal HR tend to drop, it or other
HR-based markers should be used in accordance with percep-
tual markers (8,19).
Limitations. In the current study, responses to HIIT and
LIT blocks were examined in males and females, but the
number of participants did not, unfortunately, allow true
comparisons between the sexes. Participants of the present study
were recreationally trained, meaning these results should not be
extrapolated uncritically to either untrained or well-trained
athletes. Changes in endurance performance were assessed
only with the 3000-m running test, so we cannot identify spe-
cific physiological adaptations underpinning the measured
performance improvements.
In conclusion, both the 2-wk block of HIIT and LIT elicited
statistically significant and practically meaningful short-term
performance improvements. Based on the responses observed
in the monitoring variables, both blocks seemed tolerable for
recreational athletes. However, the HIIT block induced some
negative responses not observed in response to a comparable
VOL overload. This may indicate higher demands of training
compared with LIT and less margin for errorwhen design-
ing this block training intervention in practice. Ensuring suffi-
cient recovery especially after such a period would therefore
be of importance. Monitoring subjective recovery alongside
performance and objective markers may provide the most
valid and actionable assessment of current readiness to train.
The authors thank Elisa Korhonen, Kaisa Liikanen, Salla Pekkala,
and Taru Teikari for their assistance during the data collection as well
as Susanna Luoma and Tanja Toivanen for conducting laboratory
analysis. The authors also thank Macey Higdon for the revision of
the language.
HR monitors were received from the Firstbeat Analytics, and this re-
search was funded by a grant from the Foundation of Sports Institute
and The Finnish Sports Research Foundation.
The authors declare no conflicts of interest. The results of the study
are presented clearly, honestly, and without fabrication, falsification, or
inappropriate data manipulation. The results of the present study do
not constitute endorsement by the American College of Sports Medicine.
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... Perceived exertion or effort could also be applied in the interval training prescription. More traditionally intervals are prescribed based on a certain speed or HR relative to maximum [13], whereas the "maximal sustainable effort" -method targets the power or speed that the individual estimates to be sustainable through the session [14,15,16]. In this approach, the intensity is regulated based on perceptual responses without relying on any predetermined intensity level. ...
... The study consisted of two separate data sets that were collected during previous intervention studies [16,17]. In both datasets, participants were recreationally endurance-trained male and female runners ( Table 1). ...
... Data for the studies were extracted from intervention studies [16,17] that used same kind of 6x3-min interval sessions and executed either a 3-km track running test or a 10-km road running test as a performance outcome test before and after the interval training period. The timing (T1 and T2) of the 6x3-min interval sessions and test runs with respect to the training interventions are presented in Figure 1. ...
This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running performance were investigated. Either 6-week (10-km group, n = 29) or 2-week (3-km group, n = 16) interval training periods were performed by recreational runners. A linear model was created for both groups based on the running speed of the first 6x3-min interval session and the test run of the preceding week (T1). The accuracy of the model was tested with the running speed of the last interval session and the test run after the training period (T2). Pearson correlation was used to analyze relationships between changes in running speeds during the tests and interval sessions. At T2, the mean absolute percentage error of estimate for 3-km and 10-km test times were 2.3 % and 3.4 %, respectively. The change in running speed of intervals and test runs from T1 to T2 correlated (r = 0.75, p < 0.001) in both data sets. Thus, the maximal sustainable effort intervals were able to predict 3-km and 10-km running performance and training adaptations with good accuracy, and current results demonstrate the potential usefulness of intervals as part of the monitoring process.
... Recreational male and female runners were recruited for a larger study project, 24 during which the current data collection was executed. Subjects whose full nocturnal HR and HRV data were available after the 2 first consecutive LIT sessions of the training period (n = 15) and/ or the night before and after the second 3000-m test of the study (n = 23) were involved in the analysis. ...
... Despite the fact that this was confirmed "only" by estimated readiness to train, the marker has been useful in the prediction of functional overreaching, 29 and has also been associated with the acute changes in performance followed by highintensity interval training block. 24 When evaluating current responses, changes of HR and HRV were greatest in magnitude in 4H and FULL segments, and the averages exceeded the typical error. Previously, the responses to endurance exercises in nocturnal recordings have been somewhat contradictory, as some have found greater increases in HR and decrements in HRV 16,19 compared with the present study, while others have found changes only in HR 15,20,21 despite late-time exercise. ...
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Purpose: To assess the reliability of nocturnal heart rate (HR) and HR variability (HRV) and to analyze the sensitivity of these markers to maximal endurance exercise. Methods: Recreational runners recorded nocturnal HR and HRV on nights after 2 identical low-intensity training sessions (n = 15) and on nights before and after a 3000-m running test (n = 23). Average HR, the natural logarithm of the root mean square of successive differences (LnRMSSD), and the natural logarithm of the high-frequency power (LnHF) were analyzed from a full night (FULL), a 4-hour (4H) segment starting 30 minutes after going to sleep, and morning value (MOR) based on the endpoint of the linear fit through all 5-minute averages during the night. Differences between the nights were analyzed with a general linear model, and intraclass correlation coefficient (ICC) was used for internight reliability assessments. Results: All indices were similar between the nights followed by low-intensity training sessions. A very high ICC (P < .001) was observed in all analysis segments with a range of .97 to .98 for HR, .92 to .97 for LnRMSSD, and .91 to .96 for LnHF. HR increased (P < .001), whereas LnRMSSD (P < .01) and LnHF (P < .05) decreased after the 3000-m test compared with previous night only in 4H and FULL. Increments in HR (P < .01) and decrements in LnRMSSD (P < .05) were greater in 4H compared with FULL and MOR. Conclusions: Nocturnal HR and HRV indices are highly reliable. Demanding maximal exercise increases HR and decreases HRV most systematically in 4H and FULL segments.
... When implementing HRV-guided training, practitioners should be aware of the divergent changes in HRV seen with modifications in training volume or intensity [112]. Further, it is worth noting that all of these studies compared a pre-determined training plan with HRV-guided training, whereas athletes often work with an exercise professional such as a coach who may adapt training according to athlete response. ...
Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.
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Purpose: Long-term development of endurance performance requires a proper balance between strain and recovery. While responses and adaptations to training are highly individual, this study examined whether individually-adjusted endurance training based on recovery and training status would lead to greater adaptations compared to a predefined program. Methods: Recreational runners were divided into predefined (PD, n = 14) or individualized (IND, n = 16) training groups. In IND, the training load was decreased, maintained or increased twice a week based on nocturnal heart rate variability, perceived recovery, and heart rate-running speed index. Both groups performed three-week preparatory, six-week volume and six-week interval periods. Incremental treadmill tests and 10 km running tests were performed before the preparatory period (T0) and after the preparatory (T1), volume (T2), and interval (T3) periods. The magnitude of training adaptations was defined based on the coefficient of variation between T0 and T1 tests (high >2 x, low <0.5 x). Results: Both groups improved (p < 0.01) their maximal treadmill speed (vMax) and 10 km time from T1 to T3. The change in the 10 km time was greater in IND compared to PD (-6.2 ± 2.8 % vs. -2.9 ± 2.4 %, p = 0.002). In addition, IND had more high responders (50 vs. 29 %) and fewer low responders (0 vs. 21 %) compared to PD in the change of vMax and 10 km performance (81 vs. 23% and 13 vs. 23 %) respectively. Conclusions: PD and IND induced positive training adaptations, but the individualized training seemed more beneficial in endurance performance. Moreover, IND increased the likelihood of high response and decreased the occurrence of low-response to endurance training.
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The purpose of the study was to examine the effects of progressively increased training intensity or volume on the nocturnal heart rate (HR) and heart rate variability (HRV), countermovement jump, perceived recovery, and heart rate-running speed index (HR-RS index). Another aim was to analyze how observed patterns during the training period in these monitoring variables were associated with the changes in endurance performance. Thirty recreationally trained participants performed a 10-week control period of regular training and a 10-week training period of either increased training intensity (INT, n = 13) or volume (VOL, n = 17). Changes in endurance performance were assessed by an incremental treadmill test. Both groups improved their maximal speed on the treadmill (INT 3.4 ± 3.2%, p < 0.001; VOL 2.1 ± 1.8%, p = 0.006). In the monitoring variables, only between-group difference (p = 0.013) was found in nocturnal HR, which decreased in INT (p = 0.016). In addition, perceived recovery decreased in VOL (p = 0.021) and tended to decrease in INT (p = 0.056). When all participants were divided into low-responders and responders in maximal running performance, the increase in the HR-RS index at the end of the training period was greater in responders (p = 0.005). In conclusion, current training periods of increased intensity or volume improved endurance performance to a similar extent. Countermovement jump and HRV remained unaffected, despite a slight decrease in perceived recovery. Long-term monitoring of the HR-RS index may help to predict positive adaptations, while interpretation of other recovery-related markers may need a more individualized approach.
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Purpose: Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency. Methods: Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak ([Formula: see text]) and submaximal ([Formula: see text]) oxygen uptake, velocity at lactate thresholds (V2, V4)] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed. Results: [Formula: see text], V2, V4 and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). [Formula: see text] improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4%); and HFT (15.4%) than HVLIT (7.6%). The number of very positive responders was higher following HVLIT (38.5%) than HFT (23%) or HIIT (7.7%). 46% of the runners responded positively to two mesocycles, while 23% did not respond to any. Conclusion: On a group level, none of the interventions altered [Formula: see text], V2, V4 or TTE, while HVLIT and HFT improved [Formula: see text]. The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46% responded positively to two mesocycles, while 23% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.
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Purpose: Low injury rates have previously been correlated with sporting team success, highlighting the importance of injury prevention programs. Recent methods, such as acute:chronic workload ratios (ACWR) have been developed in an attempt to predict and manage injury risk; however, the relation between these methods and injury risk is unclear. The aim of this systematic review was to identify and synthesize the key findings of studies that have investigated the relationship between ACWR and injury risk. Methods: Included studies were critically appraised using the Downs and Black checklist, and a level of evidence was determined. Relevant data were extracted, tabulated, and synthesized. Results: Twenty-seven studies were included for review and ranged in percentage quality scores from 48.2% to 64.3%. Almost perfect interrater agreement (κ = 0.885) existed between raters. This review found a high variability between studies with different variables studied (total distance versus high speed running), as well as differences between ratios analyzed (1.50-1.80 versus ≥1.50), and reference groups (a reference group of 0.80-1.20 versus ≤0.85). Conclusion: Considering the high variability, it appears that utilizing ACWR for external (eg, total distance) and internal (eg, heart rate) loads may be related to injury risk. Calculating ACWR using exponentially weighted moving averages may potentially result in a more sensitive measure. There also appears to be a trend towards the ratios of 0.80-1.30 demonstrating the lowest risk of injury. However, there may be issues with the ACWR method that must be addressed before it is confidently used to mitigate injury risk. Utilizing standardized approaches will allow for more objective conclusions to be drawn across multiple populations.
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There are variable responses to short-term periods of increased training load in endurance athletes, whereby some athletes improve without deleterious effects on performance, while others show diminished exercise performance for a period of days to months. The time course of the decrement in performance and subsequent restoration, or super compensation, has been used to distinguish between the different stages of the fitness–fatigue adaptive continuum termed functional overreaching (FOR), non-functional overreaching (NFOR) or overtraining syndrome. The short-term transient training-induced decrements in performance elicited by increases in training load (i.e. FOR) are thought be a sufficient and necessary component of a training program and are often deliberately induced in training to promote meaningful physiological adaptations and performance super-compensation. Despite the supposition that deliberately inducing FOR in athletes may be necessary to achieve performance super-compensation, FOR has been associated with various negative cardiovascular, hormonal and metabolic consequences. Furthermore, recent studies have demonstrated dampened training and performance adaptations in FOR athletes compared to non-overreached athletes who completed the same training program or the same relative increase in training load. However, this is not always the case and a number of studies have also demonstrated substantial performance super-compensation in athletes who were classified as being FOR. It is possible that there are a number of contextual factors that may influence the metabolic consequences associated with FOR and classifying this training-induced state of fatigue based purely on a decrement in performance may be an oversimplification. Here, the most recent research on FOR in endurance athletes will be critically evaluated to determine (1) if there is sufficient evidence to indicate that inducing a state of FOR is necessary and required to induce a performance super-compensation; (2) the metabolic consequences that are associated with FOR; (3) strategies that may prevent the negative consequences of overreaching.
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Dolci, F, Kilding, AE, Chivers, P, Piggott, B, and Hart, NH. High-intensity interval training shock microcycle for enhancing sport performance: A brief review. J Strength Cond Res XX(X): 000-000, 2019-High-intensity interval training (HIIT) is a powerful strategy to develop athletes' fitness and enhance endurance performance. Traditional HIIT interventions involve multiple microcycles (7-10 days long) of 2-3 HIIT sessions each, which have been commonly supported to improve athletic performance after a minimum period of 6 weeks training. Regardless of the efficacy of such an approach, in recent years, a higher frequency of HIIT sessions within a unique microcycle, commonly referred to as an HIIT shock microcycle, has been proposed as an alternative HIIT periodization strategy to induce greater and more efficient endurance adaptation in athletes. This review article provides an insight into this new HIIT periodization strategy by discussing (1) HIIT shock microcycle format and design; (2) the sustainability of this training strategy; (3) effects on performance and physiological parameters of endurance; and (4) potential mechanisms for improvements. Evidence advocates the sustainability and effectiveness of HIIT shock microcycle in different athletes to improve intermittent and continuous running/cycling performance and suggests mitochondria biogenesis as the main acute physiological adaptation following this intervention.
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Background: Block periodization (BP) has been proposed as an alternative to traditional (TRAD) organization of the annual training plan for endurance athletes. Objective: To our knowledge, this is the first meta-analysis to evaluate the effect BP of endurance training on endurance performance and factors determinative for endurance performance in trained- to well-trained athletes. Methods: The PubMed, SPORTdiscus and Web of Science databases were searched from inception to August 2019. Studies were included if the following criteria were met: 1) the study examined a block-periodized endurance training intervention; 2) the study had a one-, two or multiple group-, crossover- or case-study design; 3) the study assessed at least one key endurance variable before and after the intervention period. A total of 2905 studies were screened, where 20 records met the eligibility criteria. Methodological quality for each study was assessed using the PEDro scale. Six studies were pooled to perform meta-analysis for maximal oxygen uptake (VO2max) and maximal power output (Wmax) during an incremental exercise test to exhaustion. Due to a lower number of studies and heterogenous measurements, other performance measures were systematically reviewed. Results: The meta-analyses revealed small favorable effects for BP compared to TRAD regarding changes in VO2max (standardized mean difference, 0.40; 95% CI=0.02, 0.79) and Wmax (standardized mean difference, 0.28; 95% CI=0.01, 0.54). For changes in endurance performance and workload at different exercise thresholds BP generally revealed moderate- to large-effect sizes compared to TRAD. Conclusion: BP is an adequate, alternative training strategy to TRAD as evidenced by superior training effects on VO2max and Wmax in athletes. The reviewed studies show promising effects for BP of endurance training; however, these results must be considered with some caution due to small studies with generally low methodological quality (mean PEDro score =3.7/10).
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Specific physiological responses and their relationship were analyzed in 12 recreational endurance athletes (43.8 ± 7.9 years) during a period of intensified cycling training. Heart rate (HR), HR variability (HRV), serum creatine kinase (S-CK) and haematocrit (Hct) were measured in the mornings before (PRE) and following three consecutive days of intensified training (POST 1-3). Morning HR increased during this period (PRE: 52.2 ± 6.7 bpm, POST 1: 58.8 ± 7.0 bpm POST 2: 58.5 ± 8.1 bpm, POST 3: 57.9 ± 7.2 bpm; F(3,33) = 11.182, p < 0.001, ηp2 = 0.554). Parasympathetic HRV indices decreased from PRE to POST (F(3,33) = 11.182, p < 0.001, ηp2 ≥ 0.563), no effect was found for sympathetically modulated HRV (F(3,33) = 2.287, p = 0.101, ηp2 = 0.203). Hct decreased (PRE: 49.9 ± 4.0%, POST 1: 46.5 ± 5.1%, POST 2: 45.5 ± 3.8%, POST 3: 43.2 ± 3.4%; F(3,33) = 11.909, p < 0.001, ηp2 = 0.520) and S-CK increased during the training period (PRE: 90.0 ± 32.1 U/L, POST 1: 334.7 ± 487.6 U/L, POST 2: 260.1 ± 303.4 U/L, POST 3: 225.1 ± 258.8 U/L; F(3,33) = 3.996, p = 0.017, ηp2 = 0.285). S-CK release was associated with HR (r = 0.453, p = 0.002, n = 44), RMSSD (r = -0.494, p= 0.001, n = 44) and HF-Power (r = -0.490, p = 0.001, n = 44). A period of intensified training was associated with haemodilution, parasympathetic withdrawal and S-CK-increase. Cardiac autonomic control at morning rest correlated with the S-CK-release; and thus, may serve as a practical mean to complementary monitor and prescribe training load in this population.
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Background Overtraining syndrome (OTS), functional (FOR) and non-functional overreaching (NFOR) are conditions diagnosed in athletes with decreased performance and fatigue, triggered by metabolic, immune, hormonal and other dysfunctions and resulted from an imbalance between training stress and proper recovery. Despite previous descriptions, there is a lack of a review that discloses all hormonal findings in OTS/FOR/NFOR. The aim of this systematic review is to evaluate whether and which roles hormones play in OTS/FOR/NFOR. Methods A systematic search up to June 15th, 2017 was performed in the PUBMED, MEDLINE and Cochrane databases following PRISMA protocol, with the expressions: (1)overtraining, (2)overreaching, (3)overtrained, (4)overreached, or (5)underperformance, and (plus) (a)hormone, (b)hormonal, (c)endocrine, (d)adrenal, (e)cortisol, (f)GH, (g)ACTH, (h)testosterone, (i)IGF-1, (j)TSH, (k)T4, (l)T3, (m)LH, (n)FSH, (o)prolactin, (p) IGFBP-3 and related articles. Results A total of 38 studies were selected. Basal levels of hormones were mostly normal in athletes with OTS/FOR/NFOR compared with healthy athletes. Distinctly, stimulation tests, mainly performed in maximal exercise conditions, showed blunted GH and ACTH responses in OTS/FOR/NFOR athletes, whereas cortisol and plasma catecholamines showed conflicting findings and the other hormones responded normally. Conclusion Basal hormone levels are not good predictor but blunted ACTH and GH responses to stimulation tests may be good predictors of OTS/FOR/NFOR.
Background: Quantifying total running distance is valuable, as it comprises some aspects of the mechanical/neuromuscular, cardiovascular, and perceptual/psychological loads that contribute to training stress and is partially predictive of distance running success. However, running distance is only one aspect contributing to training stress. Clinical questions: The purpose of this commentary is to highlight: 1) problems with only using running distance to quantify running training and training stress; 2) the importance of alternative approaches to quantify and monitor training stress; 3) moderating factors (effect-measure modifiers) of training loads; and 4) the challenges of monitoring training stress to assess injury risks. Key results: Training stress is influenced by external (i.e., application of mechanical load) and internal (i.e., physiological/psychological effort) training load factors. In running, some commonly used external load factors include volume and pace, while physiological internal load factors include session ratings of perceived exertion, heart rate, or blood lactate level. Running distance alone might vastly obscure the cumulative training stress on different training days and, ultimately, misrepresent overall training stress. With emerging and novel wearable technology that quantify external load metrics beyond volume or pace, the future of training monitoring should have an ever-increasing emphasis on biomechanical external load metrics coupled with internal (i.e., physiological/psychological) load metrics. Clinical application: It may be difficult to change the running culture's obsession with weekly distance, but advanced and emerging methods to quantify running training discussed in this commentary will, with research confirmation, improve training monitoring and injury risk stratification. J Orthop Sports Phys Ther, Epub 1 Aug 2020. doi:10.2519/jospt.2020.9533.
Purpose: The aim was to compare mesocycles with progressively increasing workloads and varied training intensity distribution (TID), i.e. high-intensity (HIGH, > 4 mmol·L blood lactate), low-intensity (LOW, < 2 mmol·L blood lactate) or a combination of HIGH and LOW (referred to as "polarized", POL) on 5000-m running time and key components of endurance performance in recreational runners. Methods: Forty-two runners (peak oxygen uptake (VO2peak): 45.2 ± 5.8 mL·min·kg) were systematically parallelized to one of three groups performing a 4-wk mesocycle with equal TID (2-4 training sessions) followed by a 3-wk mesocycle with increased weekly TRIMP (i.e. 50% increase compared to the first 4-wk mesocycle) of either HIGH, LOW or POL and one week tapering. VO2peak, velocity at lactate threshold and running economy were assessed at baseline (T0), after four (T1), seven (T2) and eight weeks (T3). Results: The 5000-m time decreased in all groups from T0 to T2 and T3. VO2peak increased from T0 to T2 and T3 (p < 0.03) with HIGH and from T0 to T2 (p = 0.02) in LOW and from T0 to T3 (p = 0.006) with POL. Running economy improved only from T1 to T3 and from T2 to T3 (p < 0.04) with LOW. An individual mean response analysis indicated a high number of responders (n=13 of 16) in LOW, with less in HIGH (n=6/13) and POL (n=8/16). Conclusion: On a group level, HIGH, LOW and POL improve 5000-m time and VO2peak. Changes in running economy occurred only with LOW. Based on the individual response of recreational runners the relative risk of non-responding is greater with HIGH and POL compared to LOW.