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This review aimed to synthesize evidence regarding interventions based on heart rate variability (HRV)-guided training for VO2max improvements in endurance athletes and address the issues that impact this performance enhancement. The Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection, Global Health, Current Contents Connect, and the SciELO citation index were searched. Inclusion criteria were: randomized controlled trials; studies with trained athletes enrolled in any regular endurance training; studies that recruited men, women, and both sexes combined; studies on endurance training controlled by HRV; studies that measured performance with VO2max. A random-effects meta-analysis calculating the effect size (ES) was used. Moderator analyses (according to the athlete's level and gender) and metaregression (according to the number of participants in each group) were undertaken to examine differences in ES. HRV-guided training and control training enhanced the athletes' VO2max (p < 0.0001), but the ES for the HRV-guided training group was significantly higher (p < 0.0001; ESHRVG-CG = 0.187). The amateur level and female subgroup reported better and significant results (p < 0.0001) for VO2max. HRV-guided training had a small (ES = 0.402) but positive effect on endurance athlete performance (VO2max), conditioned by the athlete's level and sex.
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Int. J. Environ. Res. Public Health 2020, 17, 7999; doi:10.3390/ijerph17217999 www.mdpi.com/journal/ijerph
Review
HRV-Based Training for Improving VO
2
max in
Endurance Athletes. A Systematic Review with
Meta-Analysis
Antonio Granero-Gallegos
1,†
, Alberto González-Quílez
2,†
, Daniel Plews
3,4
and
María Carrasco-Poyatos
1,
*
1
Health and Public Administration Research Center, Department of Education, University of Almeria,
04120 Almeria, Spain; agranero@ual.es
2
Department of Education, University of Almeria, 04120 Almeria, Spain; albertillo_gq@hotmail.com
3
Sports Performance Research Institute, The Waikato University, Hamilton 3216, New Zealand;
daniel.plews@aut.ac.nz
4.
School of Sport & Recreation, Auckland University of Technology, Auckland 92006, New Zealand
* Correspondence: carrasco@ual.es
A.G.-G. and A.G.-Q. contributed equally to this work.
Received: 29 September 2020; Accepted: 28 October 2020; Published: 30 October 2020
Abstract: This review aimed to synthesize evidence regarding interventions based on heart rate
variability (HRV)-guided training for VO
2max
improvements in endurance athletes and address the
issues that impact this performance enhancement. The Cochrane Central Register of Controlled
Trials (CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection,
Global Health, Current Contents Connect, and the SciELO citation index were searched. Inclusion
criteria were: randomized controlled trials; studies with trained athletes enrolled in any regular
endurance training; studies that recruited men, women, and both sexes combined; studies on
endurance training controlled by HRV; studies that measured performance with VO
2max
. A random-
effects meta-analysis calculating the effect size (ES) was used. Moderator analyses (according to the
athlete’s level and gender) and metaregression (according to the number of participants in each
group) were undertaken to examine differences in ES. HRV-guided training and control training
enhanced the athletes’ VO
2max
(p < 0.0001), but the ES for the HRV-guided training group was
significantly higher (p < 0.0001; ES
HRVG-CG
= 0.187). The amateur level and female subgroup reported
better and significant results (p < 0.0001) for VO
2max
. HRV-guided training had a small (ES = 0.402)
but positive effect on endurance athlete performance (VO
2max
), conditioned by the athlete’s level and
sex.
Keywords: performance; heart rate variability; high-level athletes; maximal oxygen uptake
1. Introduction
1.1. Description of the Condition
The key components in any training program are the volume (i.e., how much), intensity (i.e.,
how hard), and frequency (i.e., how often) of the exercise sessions, and the combination of these
‘training impulses’ determines the magnitude of adaptive responses that improve the physical
condition of an athlete or increase fatigue [1]. Combining these key elements to optimize training in
athletes for better performance represents a relevant area of research within exercise physiology and
sports medicine [2]. It is recognized that a standard training program applied to a group of athletes
Int. J. Environ. Res. Public Health 2020, 17, 7999 2 of 22
can induce diverse responses in terms of performance and physiological adaptations [3,4]. Therefore,
individualization is recognized as a training principle [1] as well as the need to adjust training stimuli
to the psychophysical load capacity and individual tolerance of each athlete, if individual responses
to training and recovery loads are intended for optimal performance [5]. The maximal oxygen uptake
(VO
2max
) is considered one of the main indicators for measuring an athlete’s performance and
cardiovascular adaptation to training loads [6]. The VO
2max
is defined as the largest volume of oxygen
that the body can capture, use, and transport during intense exercise [7] and is a determining factor
of endurance performance [7,8]. As Vesterinen et al. [4,9] state, although some athletes show great
endurance performance improvements after standardized group training (even up to 40% in VO
2max
),
other athletes show no changes or benefits, and sometimes even show a decrease in endurance
performance. In recent years, research has looked at whether heart rate variability (HRV)-guided
training has positive effects on athletic performance, given that this type of training allows daily
adjustment of the training and recovery stimuli, individually based on HRV records [4,5,10].
1.2. Description of the Intervention
HRV is an indicator that enables the noninvasive analysis of autonomic nervous system activity
in both its sympathetic and parasympathetic branches [11]. This is relevant if we consider that an
important component of the interindividual variability in physiological responses to training is
related to the balance between the parasympathetic (PNS) and sympathetic (SNS) activity of the
autonomic nervous system (ANS) [12]. According to Huang et al. [13], HRV is considered the
variation in the time interval between two consecutive heartbeats and obtained by calculating the
time interval between two consecutive R waves (i.e., RR interval fluctuation) in the electrocardiogram
(ECG). Since the elapsed time between beats is not constant, high vagally related HRV values are
associated with efficient ANS, promoting behavioral adaptation and cognitive flexibility during
stress [14], while low HRV is indicative of an inefficient ANS, resulting in maladaptive responses to
stress and perceived threats [13]. HRV analysis is considered a useful method for measuring the
heart’s ability to adapt to endogenous and exogenous loads [15]; therefore, it can be used for the
individual assessment of responses to training loads and recovery adaptation [4,16]. High HRV
measurements indicate more parasympathetic than sympathetic activation, which is indicative of
better recovery and preparedness for facing high-intensity training sessions [17]. HRV-guided
training starts with a preparation period of about four weeks, which serves as a standardized data
collection phase to obtain the baseline HRV values (e.g., LnrMSSD; the natural logarithm of the
square root of the mean value of the sum of the squares of the differences between the adjacent RR
intervals) and their normal range (upper and lower limits) for each athlete [9,18]. Once the normal
range of HRV measurements has been established, the training prescribed (moderate- or high-
intensity session) is based on this calculation, which is normally updated weekly [19]. Traditionally,
the vagally related HRV index has been measured with ECG [20], and quantified by means of rMSSD
[17]. Currently, the development and validation of new applications (i.e., smartphone applications:
Kubios-HRV, Elite-HRV, Mobile Lab, or HRV4Training) facilitate daily HRV measurements and their
quantification and, thus, the individual adaptation of training loads and recovery.
1.3. How the Intervention Might Work
Bellenger et al. [21], in a recent systematic review with meta-analysis, highlighted the need to
use monitoring systems that accurately reflect the athletes’ adaptations to the training stimulus.
Although there have been numerous research studies using the HRV measure to check wellness and
training adaptation in athletes [22,23], these have not focused on performance improvement based
on HRV-guided training but have followed training interventions based on a traditional and
nonindividualized methodology.
In contrast, evidence exists supporting the use of HRV-guided training for improved
performance in endurance athletes. With this type of training monitoring, some studies have found
significant VO
2max
improvements in athletes who have developed individualized endurance training
programs based on daily HRV values. These studies alternated moderate-intensity sessions with
Int. J. Environ. Res. Public Health 2020, 17, 7999 3 of 22
high-intensity sessions [4,10] or even rest sessions, vigorous-intensity training, and moderate-
intensity exercise [5]. However, Javaloyes et al. [18], in a program with similar characteristics
developed with professional cyclists, found no significant improvements in VO
2max
. Likewise,
significant improvements have been found among athletes following HRV-guided training in other
variables; for example, for lactate in maximal test [10], speed in maximal test [4], time in maximal test
[4,10], or muscle strength [24]. At the level of perceived recovery, significant improvements have also
been found in variables such as general stress, emotional stress, lack of energy, and even overall mood
disturbance [25]. HRV-guided training may, therefore, function as an alternative method for
improving performance in resistance athletes.
1.4. Why Is This Review Important?
In the search to improve athletic performance, different training methods have been tried and
studied, such as intensified training [2] or submaximal tests [26]. However, it has also been
recognized that the same training program followed by a group of athletes can provoke a wide range
of reactions in terms of performance and physiological adaptations [3]. Overuse injuries occur due to
repetitive submaximal loading of the musculoskeletal system when there is inadequate rest to allow
for structural adaptation to take place [27]. In recent years, HRV-guided training has shown itself to
be a promising method for improving different performance variables (e.g., VO
2max
) compared to
predefined training (traditional training) through the monitoring and individualization of endurance
athletes’ training [4,28]. HRV-guided training has been investigated in randomized trials on samples
from different endurance sports, such as skiers [28], runners [4,25], and cyclists [18]), as well as
athletes of different ages and levels: elite [18,28] and recreational endurance athletes [5,24,25].
Therefore, it is important to carry out a systematic review and meta-analysis of the different
experimental studies conducted so far on endurance athletes in order to assess whether HRV-guided
training is an effective method for performance improvement.
2. Objectives
As mentioned above, this review aimed to analyze the effect of HRV-guided training on VO
2max
in endurance athletes.
We asked the following research questions regarding HRV-guided training in endurance
athletes:
Research Question 1: Does HRV-based training have an effect on VO
2max
?
Research Question 2: Is the effect of this type of training superior to that of traditional training?
Research Question 3: Is the level of the athletes decisive in obtaining an effect on the VO
2max
?
Research Question 4: Does the effect of HRV-guided training determine VO
2max
scores according to the
gender of the athlete?
3. Methods
The methods detailed below are reported in accordance with the Campbell Collaboration
policies and guidelines for systematic reviews [29].
3.1. Criteria for Considering Studies for This Review (Eligibility Criteria)
3.1.1. Types of Studies
We included randomized controlled trials (RCTs) and the first period of cross-over RCTs and
experimental studies using a random method for the treatment assignment in order to reduce the risk
of allocation bias. We restricted study eligibility by language. We did not restrict study eligibility by
publication status.
Int. J. Environ. Res. Public Health 2020, 17, 7999 4 of 22
3.1.2. Types of Participants
We included studies with trained athletes enrolled in any form of regular endurance training
(e.g., runners, triathletes, skiers, and cyclists). We included studies that recruited both men and
women, or men and women separately.
3.1.3. Types of Interventions
We included studies on endurance training controlled by heart rate variability to improve the
athletes’ performance. We considered designs comprising any dose, frequency, and duration. We
also considered studies with the following types of comparisons:
Endurance training controlled by HRV versus no specific training intervention (e.g., habitual
physical activity).
Endurance training controlled by HRV versus another training intervention (e.g., traditional
endurance training or another type of traditional training).
Endurance training controlled by HRV versus another training intervention (i) versus a further
training intervention (ii).
Endurance training controlled by HRV (i) versus endurance training controlled by HRV (ii)
versus another training intervention versus no specific training intervention.
3.1.4. Types of Outcome Measures
Primary
o Maximal oxygen consumption (VO
2max
)
3.2. Search Methods to Identify the Studies
3.2.1. Electronic Searches
The register contains studies identified from the Cochrane Central Register of Controlled Trials
(CENTRAL), MEDLINE, EMBASE, CINAHL Complete, the Web of Science Core Collection, Global
Health, Current Contents Connect, and the SciELO citation index.
The search is up to date as of 15 June 2020. The language was restricted, considering only English
or Spanish. The terms used to search the databases were: (amateur OR elite OR train*) AND (HRV-
guided OR “heart-rate variability guided”).
3.2.2. Searching Other Resources
We checked the reference lists of all the included studies and systematic reviews for additional
references. We contacted experts in the field and the authors of the included studies to identify
additional unpublished studies. We also checked the results of completed trials registered on the US
National Institutes of Health Ongoing Trials Register, ClinicalTrials.gov, the World Health
Organization International Clinical Trials Registry Platform (WHO ICTRP), and proceedings of
conferences for relevant research.
3.3. Data Collection and Analysis
We conducted the following data collection and analysis in accordance with the
recommendations in the Cochrane Handbook for Systematic Reviews of Interventions [30].
3.3.1. Selection of Studies
Two review authors independently screened the titles and abstracts of all the retrieved
references in Microsoft Excel 2018 (Microsoft, New York, United States) for Windows. The full-text
study reports were retrieved for all the citations that at least one review author considered potentially
relevant. Two review authors independently screened the full-text articles and identified studies for
inclusion; they also identified and recorded the reasons for excluding studies in the excluded studies
Int. J. Environ. Res. Public Health 2020, 17, 7999 5 of 22
characteristics. Any disagreements were resolved through discussion. The selection process is
detailed in a PRISMA flow diagram [31].
3.3.2. Data Extraction and Management
We used a standardized piloted data collection form in Microsoft Excel 2018 for Windows and
extracted the following study characteristics and outcome data: (i) Methods: study design; (ii)
Participants: randomized number, study participants’ mean age or age range, study location and
setting, recruitment methods, inclusion and exclusion criteria, and type of endurance sport; (iii)
Interventions: a description of the training intervention characteristics, the dose and duration of the
training intervention, a description of the comparison intervention characteristics, the length of
follow-up, the number of withdrawals, and the reasons for withdrawal; (iv) Outcomes: a description
of the primary and secondary outcomes in the review that were reported in the trial and a listing of
other outcomes collected in the trial; (v) Notes: the trial funding and notable conflicts of interest of
the trial authors; (vi) a ‘risk of bias’ assessment. Two review authors independently extracted the
outcome data from the included studies into Microsoft Excel 2018 spreadsheets and compared the
data to identify any discrepancies in the data entries. Any disagreements were resolved by consensus.
In the Characteristics of Included Studies section, we noted down if a trial did not report outcome
data in a usable way. We then transferred all the outcome data into the Comprehensive Meta-
Analysis software version 2.2.064 (Biostat, Englewood, United States) [32].
3.3.3. Risk-of-Bias Assessment in the Included Studies
Two review authors (M.C.P., A.G.G.) independently assessed the risk of bias for each included
trial using the Cochrane risk-of-bias tool [30]. Any disagreements were resolved by discussion. The
risk of biases were assessed for the following domains: random sequence generation (selection bias),
allocation concealment (selection bias), blinding of participants and personnel (performance bias),
blinding of outcome assessment for each outcome (detection bias), incomplete outcome data (attrition
bias), selective outcome reporting (reporting bias), and other biases (such as the validity of outcome
measure and baseline comparability). Each potential source of bias was assessed as either high, low,
or unclear, and a quotation from the study report was provided together with a justification for the
judgment in the ‘risk of bias’ tables. The judgments across the different studies were summarized for
each of the domains listed.
3.3.4. Treatment Effect Measures
The outcome data for each study were uploaded into the data tables of the Comprehensive Meta-
Analysis software to calculate the treatment effects. We used the mean difference (MD) for
continuous outcomes reported on the same scal, and the standardized mean difference (SMD) for
continuous outcomes measured on different scales in different trials (SMD = M
HRV guided training
−M
control
group
/Standard deviation) [33]. Uncertainty was expressed with 95% confidence intervals (CIs) for all
the effect estimates.
3.3.5. Assessment of Heterogeneity and Reporting Bias
Heterogeneity was assessed qualitatively between studies in three ways: a visual examination
of the forest plots, the Chi
2
test (p ≤ 0.10) for heterogeneity, and the I
2
statistic. The implications of the
observed I
2
statistic value were considered as follows: 0% to 40%—might not be important; 30% to
60%—may represent moderate heterogeneity; 50% to 90%—may represent substantial heterogeneity;
75% to 100%—considerable heterogeneity [30]. Publication bias was assessed by examining the
asymmetry of a funnel plot using Egger’s test. If studies were distributed symmetrically around the
mean effect size (ES), there was an absence of publication bias [33]. Subgroup analysis was carried
out using the outcome for athlete level (elite vs. amateur) and sex (men, women, and both sexes
combined). Metaregression was used to assess the relationship between the studies and the variable
sample size.
Int. J. Environ. Res. Public Health 2020, 17, 7999 6 of 22
3.3.6. Sensitivity Analysis
A sensitivity analysis was carried out to check whether the results varied according to the
endpoint data.
4. Results
4.1. Description of the Studies
4.1.1. Search Results
The search produced a total of 36 studies, with 222 additional records identified through other
sources. The removal of duplicates resulted in eleven studies, which were screened by the two
authors based on the title and abstract. Three studies were excluded. Eight full-text articles were
assessed for eligibility. Two more studies were excluded, and six studies were included either in the
qualitative analysis or in the quantitative metasynthesis. The PRISMA flow chart illustrates the search
and selection process (Figure 1).
Figure 1. Study flow diagram following the Preferred
Reporting
Items for Systematic Reviews and
Meta-Analyses Guidelines [31], where n is the number of papers and k is the number of individual
studies.
4.1.2. Included Studies
Six studies carrying out HRV-guided training with elite or amateur athletes were included in
this review [4,5,10,18,19,28], which were identified by the first author and publication date:
Javaloyes_2019, Kiviniemi_2007, Kiviniemi_2010, Nuuttila_2017, Schmitt_2018, and Vesterinen_2016.
Int. J. Environ. Res. Public Health 2020, 17, 7999 7 of 22
Study location
Schmitt_2018 conducted their study at the French National Ski-Nordic Center, while the
locations for the other five studies were not specified.
Study design
Every study included in this review was a randomized controlled trial.
Participants
A total of 195 participants (134 men and 61 women) were included in these studies.
Kiviniemi_2007, Javaloyes_2019, and Nuuttila_2017 considered only male samples of 30, 17, and 32
participants, respectively. In the rest of the studies, the samples were composed of men and women:
Kiviniemi_2010 included 24 men and 36 women, Schmitt_2018 incorporated 19 men and 5 women,
and Vesterinen_2016 assessed 20 men and 20 women. In the studies by Javaloyes_2019 and
Schmitt_2018, the samples were composed of professional athletes (cyclists and Nordic skiers,
respectively) while in the other four studies, the samples were of a nonprofessional level.
Interventions
According to the types of comparisons contemplated in the present systematic review ((a)
endurance training controlled by HRV versus no specific training intervention; (b) endurance
training controlled by HRV versus other training intervention; (c) endurance training controlled by
HRV (i) versus another training intervention (ii) versus another training intervention; (d) endurance
training controlled by HRV (i) versus endurance training controlled by HRV (ii) versus other training
intervention versus no specific training intervention. Kiviniemi_2007, Javaloyes_2019, Nuuttila_2017,
and Vesterinen_2016 were classified in Comparison B, Schmitt_2018 in Comparison C, and
Kiviniemi_2010 in Comparison D.
The interventions in the included studies focused on running (Kiviniemi_2007, Kiviniemi_2010,
Nuuttila_2017, and Vesterinen_2016), skiing (Schmitt_2018), and cycling (Javaloyes_2019). They were
from 6 to 15 weeks long. In most of the studies, three (Nuuttila_2017) or four (Javaloyes_2019,
Schmitt_2018, and Vesterinen_2016) low-intensity preparation weeks were followed either by the
experimental or control groups (standard training) before the intervention. An eight-week
intervention was carried out in Javaloyes_2019, Kiviniemi_2010, Nuuttila_2017, and Vesterinen_2016,
whereas Kiviniemi_2007 considered four weeks of training and Schmitt_2018 15 days. The
assessment weeks were treated separately from the intervention period in Javaloyes_2019,
Kiviniemi_2007, and Schmitt_2018.
In every study, the experimental groups trained at moderate or high intensities according to
their daily HRV scores. The control groups (standard training) followed a predefined training design
at high, moderate, and low intensities (Javaloyes_2019), high and moderate intensities
(Kiviniemi_2010 and Nuuttila_2017), high and low intensities (Kiviniemi, 2007) or moderate and low
intensities (Vesterinen_2016). The control group (standard training) design was not explained in
Schmitt_2018.
Outcomes
The primary outcome analyzed in the included studies was VO
2max
. The secondary outcomes
were: ventilatory thresholds (Javaloyes_2019, Kiviniemi_2007) and power in the cycling test
(Javaloyes_2019); rMSSD or RR interval (Javaloyes_2019, Kiviniemi_2007, and Schmitt_2018); basal
heart rate (Nuuttila_2017, Kiviniemi_2010, and Schmitt_2018); maximal heart rate in the ergometer
test (Nuuttila_2017); speed in the treadmill test (Kiviniemi_2007, Nuuttila_2017, and
Vesterinen_2016); maximal speed in the 10 m test (Nuuttila_2017); time and lactate in the 3000 m test
(Nuuttila_2017); maximal load in the ergometer test (Kiviniemi_2007 and Kiviniemi_2010); and
oxygen saturation and VO
2
at the second ventilatory threshold (Schmitt_2018).
Further details about participants, interventions, comparators, and outcomes are provided in
Table 1.
Int. J. Environ. Res. Public Health 2020, 17, 7999 8 of 22
Table 1. Overview of the studies included in the review.
Author, Year Method Participants Intervention Outcomes Results
Risk of Bias
Bias Author’s
Judgment Support for the Judgment
Javaloyes_2019
Randomized
controlled
trial
Trained male
cyclist, mean age
of 38.42 years.
N = 17: EG = 9 +
CG = 8
Location: not
specified.
Recruited from
local clubs
Inclusion criteria:
at least 2 years of
experience in
cycling.
Exclusion criteria:
not specified.
15 weeks (4 weeks of
baseline period to
capture baseline HRV +
8 weeks of training + 3
weeks of assessments);
4–7 sessions/week; time
depended on the
training intensity.
EG: HRV-G-based
training before each
session; training MICT
and HIIT according to
HRV.
CG: 4 high-intensity
training sessions + 4
high-intensity interval
training sessions + 6
moderate-intensity
training sessions + 2–5
low-intensity training
sessions/week.
No follow-up periods.
No withdrawals
Primary: VO
2max
(maximal bicycle
ergometer test, direct
measurement).
Secondary: ventilatory
thresholds in the
graded test, peak
power output in the
graded test, rMSSD
with a heart rate
monitor + kubios
(LnrMSSD) in a supine
position for 90 s, mean
power output during a
40 min all-out cycling
test.
VO
2max
: no significant
differences between
intragroups and
intergroups. Moderate
training load:
significant intergroup
differences (EG = 24%;
CG = 27%).
VT2: significant
improvements in EG
(36.11 ± 3.73W). Peak
power output:
significant
improvements in EG
(17.45 ± 3.91W).
LnrMSSD: significant
differences between
intergroups for the
percentage of change
(EG = 0.85 ± 3.21%, CG
= –2.02 ± 5.21%). Mean
power 40M: significant
improvements in EG
(17.67 ± 3.03W)
Selection Unclear
Insufficient information about
the sequence generation
process and allocation to
permit judgment of ‘low risk’
or ‘high risk’.
Performance High
Incomplete blinding, and the
outcome is likely to be
influenced by lack of blinding.
Detection Unclear The study did not address this
outcome.
Attrition Low No missing outcome data.
Reporting Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Other Low The study appears to be free of
other sources of bias.
Kiviniemi_2007
Randomized
controlled
trial
30 healthy
recreational male
runners
N = 30: TRA:
predefined
training group (n
= 10) + HRV:
HRV-guided
training (n = 10) +
CG: Control
group (n = 10).
Location: Not
specified
Recruitment: The
candidates were
interviewed with
a standardized
6 weeks: 1-week
baseline resting +
pretest Intervention: 4-
week training period (6
days per week)
consisting of running
sessions at either a low-
or high-intensity level
according to
recommendations by
the American College
of Sports Medicine:
low-intensity: 40 min of
jogging at 65% of
maximal HR; high-
intensity exercise
included 5 min warm-
up and cool-down
Primary: VO2peak
(maximal treadmill
ergometer test: direct
measurement).
Secondary: high-
frequency power of
RR interval with
software while
standing for 5 min,
maximal load in the
ergometer test,
maximal running
velocity in the
ergometer test;
ventilatory threshold
(VT) from the relation
of running velocity
VO2peak: significant
intragroup
improvements in the
HRV group (pretest =
56 ± 4; post-test = 60 ± 5
mL/kg/min).
High-frequency power
of RR interval:
significant intragroup
improvements in TRA
(pretests = 4.7 ± 0.4;
post-test = 5.5 ± 0.8 ln
ms2), and HRV
(pretests = 4.8 ± 0.6;
post-test = 5.2 ± 0.8 ln
ms2). Maximal load:
significant intragroup
improvements in TRA
Selection
Performance
Detection
Attrition
Reporting
Other
Unclear
Unclear
Unclear
Unclear
Unclear
Low
Insufficient information about
the sequence generation
process and allocation to
permit judgment of ‘low risk’
or ‘high risk’.
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
The study did not address this
outcome.
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’
The study appears to be free of
other sources of bias.
Int. J. Environ. Res. Public Health 2020, 17, 7999 9 of 22
scheme to
ascertain their
medical history
and levels of
physical activity.
Inclusion criteria:
healthy men.
Exclusion criteria:
subjects who had
done regular
physical exercise
training less than
twice a week for
the past 3 months,
competing
athletes, and
subjects with
diabetes mellitus,
asthma, or
cardiovascular
disorders were
excluded.
periods at 65% of the
maximal HR before and
after 30 min of running
at 85% of maximal HR.
The last week for the
post-test.
HRV: exercised at low-
or high-intensity or
rested based on their
daily HRV
measurements at home.
If HRV increased or did
not change, vigorous-
intensity training on
that day. If HRV
decreased, moderate-
intensity exercise or
rest.
TRA: weekly training
started with low-
intensity exercise
followed by two
sessions of high-
intensity exercise on
successive days. This 3-
day period was
repeated before a day
of rest.
CG: no intervention
No follow-up period.
4 withdrawals: TRA (2);
HRV (1); CG (1).
and selected
ventilatory
parameters.
(pretest = 15.1 ± 1.3
km/h; post-test = 15.7 ±
1.2 km/h); significant
intergroup differences
between CG (post-test
= 14.9 ± 1.5 km/h) and
TRA (post-test = 15.7 ±
1.2 km/h), TRA and
HRV (post-test = 16.4 ±
1.0 km/h), and between
CG and HRV. VT:
significant intragroup
improvements in HRV
(pretest = 12.2 ± 0.6
km/h; post-test = 16.4 ±
1.0 km/h)
Kiviniemi_2010
Randomized
controlled
trial
Healthy men and
women. Mean age
of 34.57 years.
N = 60. Men, n =
24; women, n =
36).
ST: standard
training (8 men +
8 women) + HRV-
I: HRV-guided
training for men
and women (EG: 8
men + 8 women) +
HRV-II: HRV-
guided training
tailored for
8 weeks of aerobic
exercise sessions (40
min), vigorous-
intensity level: HR
between 85% of the
HRpeak-5 bpm lower
limit; moderate-
intensity exercise was
70% of the HRpeak-5
bpm lower limit.
HRV-I: if HRV
increased or did not
change, vigorous-
intensity training on
that day. If HRV
decreased, moderate-
Primary: VO
2max
(maximal bicycle
ergometer test: direct
measurement).
Secondary: HR, RR
interval with a heart
rate monitor (SD1)
while standing for 3
min, maximal load in
the ergometer test.
VO
2max
: significant
intragroup
improvements in ST
(men subgroup)
(pretest = 50 ± 7; post-
test = 53 ± 7
mL/kg/min), ST
(women subgroup)
(pretest = 35 ± 5; post-
test = 37 ± 4
mL/kg/min), HRV-I
(men subgroup)
(pretest = 50 ± 6; post-
test = 54 ± 6
mL/kg/min), HRV-I
(women subgroup)
Selection High
Allocation based on the results
of a laboratory test or a series
of tests.
Performance Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Detection Unclear The study did not address this
outcome.
Attrition High High rates of loss to follow-up.
Reporting Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Other Low The study appears to be free of
other sources of bias.
Int. J. Environ. Res. Public Health 2020, 17, 7999 10 of 22
women (12) + CG
(8 men + 8
women).
Location: Not
specified
Recruitment:
advertisement
local newspaper
Inclusion criteria:
healthy men and
women
Exclusion criteria:
smoker, BMI ≥ 30
kg/m
2
; regular
physical exercise
training more
than twice a week
for the last 3
months,
competing
athletes, mellitus,
asthma, or
cardiovascular
disorders.
intensity exercise or
rest.
HRV-II: vigorous-
intensity exercise only
when HRV had
increased.
ST group: two
moderate-intensity and
three vigorous-intensity
exercises weekly.
CG: no intervention
No follow-up period.
7 withdrawals: ST (1
man + 1 woman) +
HRV-I (7 men + 7
women); CG (7 men + 8
women) + HRV-II (10);
4 because of illness or
injury and 3 because of
insufficient compliance.
(pretest = 36 ± 4; post-
test = 39 ± 3
mL/kg/min), and in
HRV-II (women
subgroup) (pretest = 37
± 5; post-test = 40 ± 5
mL/kg/min).
HR: RR interval:
significant intragroup
improvements in HRV-
I (men subgroup)
(pretests = 13.7 ± 6.7;
post-test = 16.9 ± 8.7
ms). Maximal load:
significant intragroup
improvements in ST
(men subgroup)
(pretest = 275 ± 28W;
post-test = 293 ±35W),
ST (women subgroup)
(pretest = 179 ± 32W;
post-test = 198 ± 35W),
HRV-I (men subgroup)
(pretest = 270 ± 29W;
post-test = 300 ± 25W),
HRV-I (women
subgroup) (pretest =
174 ± 28W; post-test =
189 ± 25W), and in
HRV-II (women
subgroup) (pretest =
177 ± 26W; post-test =
194 ± 23W)
Nuuttila_2017
Randomized
controlled
trial
Males, 19–37
years.
N = 24. EG = 13
and CG = 11.
Location: not
specified.
Recruitment: not
specified.
Inclusion criteria:
recreationally
endurance
training.
Exclusion criteria:
not specified
11 weeks (3 weeks of
control + 8 weeks of
training). EG: 2–5
sessions/week; CG: 6
sessions/week; time
depended on the
training intensity.
EG: 4 moderate-
intensity endurance
training sessions + 20
high-interval intensity
training sessions.
Training MICT and
HIIT according to HRV.
CG: 22 moderate-
intensity endurance
Primary: VO
2max
(maximal treadmill
test: direct
measurement).
Secondary: basal heart
rate, maximal heart
rate, lactate in the
treadmill test, Vmax in
the treadmill test,
Vmax in the 10m test,
time and lactate in the
3000 m test, rMSSD
with a heart rate
monitor + Firstbeat in
a supine position for 3
min.
VO
2max
: significant
intragroup changes
(EG = 3.1 ± 0.8
mL/kg/min; CG = 2.2 ±
0.6 mL/kg/min).
Basal HR: significant
intragroup decrease
(EG = 4.4 ± 0.6bpm; CG
= 3.6 ± 0.1bpm). Vmax
in the treadmill test:
significant intragroup
improvements (EG =
0.9 ± 0.1km/h; CG = 0.5
± 0.1 km/h). Vmax in 10
m: decreased
significantly in CG
Selection High
Allocation based on the results
of a laboratory test or a series
of tests.
Performance Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Detection Unclear The study did not address this
outcome.
Attrition High High rates of loss to follow-up.
Reporting Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Other Low The study appears to be free of
other sources of bias.
Int. J. Environ. Res. Public Health 2020, 17, 7999 11 of 22
training sessions + 20
high-interval intensity
training sessions + 4
high-intensity strength
training sessions
No follow-up periods.
9 withdrawals: illness
(n = 1), injuries (n = 2),
personal reasons (n = 3),
lack of adherence (n =
3).
Other: body weight,
height in
countermovement
jump, strength in the
concentric dynamic
leg press, nocturnal
heart rate variability,
testosterone, and
cortisol (blood
samples), % of fat
(InBody 720).
from pre to mead test
(0.08 ± 0.04 m/s). Time
in the 3000 m test:
significant intragroup
decrease (EG = 35 ± 2s;
CG = 35 ± 6s). Lactate
in the 3000 m test:
significant intragroup
improvements (EG = 12
± 18.4%) from mead-to
post-test; CG = 16–0 ±
23.5%) from pre-to
post-test. rMSSD:
significant
improvements in EG
(13 ± 3ms)
Schmitt_2018
Randomized
controlled
trial
24 elite Nordic
skiers (19 men,
age 23.3 ± 3.6; 5
women, age 22.8 ±
4.1).
N = 24; H-HRV,
HRV-guided
training
normobaric
hypoxic group (n
= 9) + H, sleeping
in normobaric
hypoxia group (n
= 9); N, normoxia
group (n = 6).
Location: French
National Ski-
Nordic Center.
Recruitment:
members of the
cross-country ski
and Nordic
combined French.
Inclusion criteria:
elite Nordic
skiers.
Exclusion criteria:
a history of
altitude-related
sickness and
health risks that
could
compromise the
Prior to pretest: 3 low-
intensity training weeks
(base training) with
progressive training
volume + 1-week
recovery; Intervention:
pretest + 15 days
training (training load
was organized into four
training zones
depending on the
intensity and quantified
as in Mujika et al.
(1996), adapted to
Nordic skiing (the
threshold for training
adjustment was chosen
as 30% of the mean of
the previous day) +
postest1 + 1 week +
postest2. Similar
training content for
each group.
H-HRV group: sleeping
normobaric in hypoxia
(simulated altitude of
2700 m) with HRV-
guided training; daily
hypoxic dose was
similar between H-HRV
and H; Night SpO2 was
similar between H-HRV
Primary: VO
2max
(maximal treadmill
test: direct
measurement).
Secondary: basal HR,
peripheral oxygen
saturation (SpO2), RR
interval with a heart
rate monitor (HF and
LF) 5 min in a supine
position and 5 min in
standing, VO2 at the
second ventilatory
threshold.
Others: duration of
hypoxic exposure, HR,
blood parameters
(erythrocyte
concentration,
hemoglobin,
hematocrit, ferritin),
questionnaire of
overtraining.
VO
2max
: significant
intragroup changes in
H-HRV (3.8 ± 3.1%).
Basal HR: significant
intergroup differences
(H-HRV = 55.38 ± 10.02
vs. H = 55.59 ± 4bpm;
H-HRV = 55.38 ± 10.02
vs. N = 47.11 ±
6.21bpm). SpO2:
significant intergroup
differences (H-HRV =
90.4 ± 1.3 vs. N = 94.2 ±
0.8%). RR interval: no
significant differences
between intergroups
(H-HRV = 9561.10 ±
9436.02 ms
2
; H =
12,199.41 ± 1293.14 ms
2
;
N = 7441.2 ± 4954.16
ms
2
). VO2 second VT:
significant intragroup
changes for H-HRV
(6.7 ± 6.1%).
Selection High
Allocation based on the results
of a laboratory test or a series
of tests.
Performance Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Detection Unclear The study did not address this
outcome.
Attrition
Low
No missing outcome data.
Reporting High
Not all of the study’s
prespecified primary
outcomes have been reported.
Other Low The study appears to be free of
other sources of bias.
Int. J. Environ. Res. Public Health 2020, 17, 7999 12 of 22
subject’s safety
during training
and/or hypoxic
exposure.
and H, but lower than
in N.
H: traditional training
sleeping in hypoxia
(simulated altitude of
2700 m).
N: traditional training
sleeping in normoxia.
Follow-up (post-test21
after 3 weeks of end
postest1)
Vesterinen_2016
Randomized
controlled
trial
Recreational
endurance
runners (men =
20; women = 20)
N = 40: EXP = 20 +
TRAD = 20
Location: not
specified.
Recruitment:
advertisement
and social media
Inclusion criteria:
2 years’ regular
endurance
running training.
Exclusion criteria:
disease or regular
medication for
chronic or long-
term diseases.
12 weeks (4 weeks of
preparation + 8 weeks
of training). The same
volume as before the
study for PREP and the
same volume as for
PREP for INT.
EXP: training MICT
and HIIT according to
HRV.
TRAD: 50% sessions at
low-intensity and 50%
sessions at
moderate/high-
intensity. Week
periodization, 3:1.
No follow-up periods.
9 withdrawals:
sicknesses (n = 2),
injuries (n = 2), lack of
adherence (n = 5)
Primary: VO
2max
(maximal treadmill
test: direct
measurement).
Secondary: Speed in
Lactate 1, speed in
Lactate 2, mean speed
in the 3000 m test, time
in the 3000 m test, RR
intervals (rMSSD) 4
min in a supine
position.
VO
2max
: significant
intragroup
improvements (EXP =
3.7 ± 4.6%, TRAD = 5.0
± 5.2%).
Speed in L1 significant
intragroups
improvement in EXP
(2.8 ± 3.7%). Speed in
L2 significant
intragroups
improvement in EXP
(2.6 ± 3.3%) and TRAD
(1.9 ± 2.2%). Time in the
3000 m test: significant
intragroup
improvements in EXP
(–14.3 ± 14.1 s)
Selection High
Allocation based on the results
of a laboratory test or a series
of tests.
Performance Unclear
Insufficient information to
permit judgment of ‘low risk’
or ‘high risk’.
Detection Unclear The study did not address this
outcome.
Attrition High High rates of loss to follow-up.
Reporting High
Not all of the study’s
prespecified primary
outcomes have been reported.
Other Low The study appears to be free of
other sources of bias.
Int. J. Environ. Res. Public Health 2020, 17, 7999 13 of 22
4.1.3. Excluded Studies
As indicated in Figure 1, five studies were excluded from the qualitative analysis. Three studies
were excluded because the VO
2max
was not considered as an outcome [24,25,34], and two studies were
excluded because they were not RCTs [35,36].
4.2. Risk of Bias in the Included Studies
The risk of bias in the included studies is summarized in Table 2. This assessment was made
following the Cochrane Collaboration guidelines [30]. In addition, publication bias was assessed
using a funnel plot (Figure 2). The Egger test provided statistical evidence of funnel plot symmetry,
suggesting the absence of a significant publication bias (p = 0.101).
Table 2. Risk of bias in the included studies.
Study
Risk-of-Bias Domains
Selection Performance Detection Attrition Reporting Other
Overall
Risk of Bias
Javaloyes_2019 Unclear High Unclear Low Unclear Low Unclear
Kiviniemi_2007
Unclear
Unclear
Unclear
Unclear
Unclear
Low
Unclear
Kiviniemi_2010 Unclear Unclear Unclear High Unclear Low Unclear
Nuuttila_2017
High
Unclear
Unclear
High
Unclear
Low
Unclear
Schmitt_2018 High Unclear Unclear Low High Low Unclear
Vesterinen_2016 High Unclear Unclear High High Low Unclear
Figure 2. Funnel plot of standard error by standard differences in means (17 comparison; black circle,
HRV-guided training; white circle, traditional training).
Selection bias
In Javaloyes_2019, Kiviniemi_2007, and Kiviniemi_2010, neither the random component in the
sequence generation nor the allocation concealment were described; therefore, the risk-of-bias
selection was considered unclear. In Nuuttila_2017, Schmitt_2018, and Vesterinen_2016, the risk of
bias was considered high because the randomization sequence was, in the first stage, based on the
results of certain physical condition tests, sport discipline, age, or gender. Furthermore, in the second
stage, the random component or the allocation concealment was not described.
Performance and detection bias
Int. J. Environ. Res. Public Health 2020, 17, 7999 14 of 22
The detection bias was considered unclear in all of the included studies because they did not
address this outcome. The performance bias was also unclear in every study but Javaloyes_2019,
which was considered high because only the participants were blinded, thus the blinding was
incomplete.
Attrition bias
In Javaloyes_2019 and Schmitt_2018, the attrition bias was considered low because there were
no missing outcome data. On the other hand, Kiviniemi_2010, Nuuttila_2017, and Vesterinen_2016
presented high rates of follow-up loss for different reasons. These might be relevant in the ES
observed. Moreover, no statistical procedure, such as intention-to-treat, was used to minimize this
risk of bias. Therefore, they were considered as having a high risk of attrition bias. Finally, in
Kiviniemi_2007, the attrition bias was unclear because this outcome was not addressed in the study.
Reporting bias
The study protocols for the included studies were not available. Accordingly, Javaloyes_2019,
Kiviniemi_2007, Kiviniemi_2010, and Nuuttila_2017 were considered as having an unclear reporting
bias. For their part, Schmitt_2018 and Vesterinen_2016 did not report every outcome and were thus
considered as having a high risk of reporting bias.
Other biases
The included studies appear to be free from other sources of bias.
4.3. Synthesis of Results
The Kiviniemi_2010 and Schmitt_2017 studies were segmented for quantitative analysis
according to their intervention groups. The comparisons were: Kiviniemi_2007 a, HRV (male
subgroup, HRV-guided training) vs. standard training (ST); Kiviniemi_2010 a, HRV-1 (male
subgroup, HRV-guided training) vs. standard training (ST); Kiviniemi_2010 c, HRV-I (female
subgroup, HRV-guided training) vs. standard training (ST); Kiviniemi_2010 g, HRV-II (female
subgroup, HRV-guided training tailored for women) vs. HRV-I (female subgroup, HRV-guided
training); Kiviniemi_2010 f, HRV-II (female subgroup, HRV-guided training tailored for women) vs.
standard training (ST); Schmitt_2017 a HRV (HRV-guided training) vs. N (traditional training and
normoxia sleeping); Schmitt_2017 b HRV (HRV-guided training) vs. H (traditional training and
hypoxia sleeping). Therefore, the total number of individual studies analyzed were 17 (k = 7 for the
experimental group; k = 10 for the control group).
Primary outcome measures
There were five studies (Kiviniemi_2007, Kiviniemi_2010, Nuuttila_2017, Schmitt_2017 and
Vesterinen_2016) with significant intragroup VO
2max
improvements in the HRV-guided training
group (n = 95), while no significant changes were found in Javaloyes_2019 (n = 9). On the other hand,
in three studies (Kiviniemi_2010, Nuuttila_2017, and Vesterinen_2016), there were also significant
intragroup VO
2max
improvements in the control group (n = 47). The overall risk of bias was considered
high in every study but for Javaloyes_2019, which was considered unclear. A random-effects meta-
analysis of the six studies revealed a statistically significant (p < 0.0001) treatment effect for VO
2max
in
the HRV-guided training intervention (ES = 0.402; 95%CI = 0.273, 0.531). Moreover, the other training
intervention was also statistically beneficial (p < 0.0001) for VO
2max
improvements in the control group
(ES = 0.215; 95% CI = 0.101, 0.329). However, the ES for the VO
2max
was significantly higher (p < 0.0001)
in the HRV-guided training group. The heterogeneity observed in the meta-analysis was significant
and high in the overall analysis (p < 0.0001; I
2
= 94.24%) and for the experimental (p < 0.0001; I
2
= 9.36%)
and the control group (p < 0.0001; I
2
= 92.26%) (Figure 3).
Int. J. Environ. Res. Public Health 2020, 17, 7999 15 of 22
Figure 3. Standard differences in means (SDM) between post- and premeasures for VO
2max
in included
studies, segmented by the control group (CG) and heart-rate-variability-guided training group (HRV-
G). Squares represent the SDM for each trial; the diamond represents the pooled SDM across trials;
weight determines how much each individual study contributes to the pooled estimate; 95%CI,
confidence interval.
Moderator analyses
Owing to the high heterogeneity observed in the meta-analysis, the potential moderating effect
of the following was considered to be of interest: (a) the athletes’ level (elite vs. amateur) and (b) the
sex of the participants (‘men vs. women’ vs. ‘men and women’). We had originally planned to take
into account the intervention duration; however, it was not finally included as a subgroup owing to
there being only one study that considered an intervention period of 15 days (Schmitt_2017) while
the others conducted an eight-week intervention. The sample size was used for the metaregression.
Following the moderating variables (Table 3), the athletes’ level (elite vs. amateur) brought about
statistically significant improvements (p < 0.0001) in both subgroups, while there were statistically
significant differences between the subgroups (p < 0.0001) in favor of the nonprofessional subgroup
(elite, ES = 0.17; amateur, ES = 0.36). According to the sex subgroups (‘men vs. women’ vs. ‘men and
women’), there were statistically significant improvements (p < 0.0001) in the three subgroups and
statistically significant differences (p < 0.0001) between the three subgroups in favor of the women
(men, ES = 0.33; women, ES = 0.40; men and women, ES = 0.19). The metaregression findings (Figure
4) revealed that the sample size of the studies was directly related to the ES magnitude (regression
coefficient = −0.016; standard error = 0.003; lower limit = −0.023; upper Limit = −0.011; Z-value = −5.42;
p ≤ 0.0001).
Int. J. Environ. Res. Public Health 2020, 17, 7999 16 of 22
Table 3. Subgroup analyses for measuring their impact on VO
2max
.
Research Studies Variable: VO
2max
Group No
Studies References SMD (95% CI) I
2
p p-Difference
Athlete level
Elite 3 Javaloyes_2019; Schmitt_2018 a; Schmitt_2018
b
0.17
(0.03; 0.30) 89.63 <0.001
<0.001
Amateur 5
Kiviniemi_2010 a; Kiviniemi_2007 a;
Kiviniemi_2010 c; Kiviniemi_2010 g;
Nuuttila_2017; Vesterinen_2016
0.36
(0.24; 0.48) 94.66 <0.001
Sex
Women 3
Kiviniemi_2010 c; Kiviniemi_2010 f;
Kiviniemi_2010 g
0.40
(0.25; 0.56) 88.36 <0.001
<0.001 Men 4
Javaloyes_2019; Kiviniemi_2007 a;
Kiviniemi_2010 a; Nuuttila_2017
0.33
(0.17; 0.48) 94.98 <0.001
Men and
women 3
Schmitt_2017 a; Schmitt_2017 b;
Vesterinen_2016
0.19
(0.06; 0.33) 92.10 0.006
Note: SMD, standard mean difference; CI, confidence interval; VO
2max
, maximal oxygen uptake; I
2
= I-squared.
Figure 4. Metaregression of the number of participants (sample size) on standard differences in means
(Std diff in means).
5. Discussion
5.1. Summary of Main Results
Six RCT studies evaluating the effects of an HRV-guided training intervention on endurance
athletes were included in this review. The results of the meta-analyses provide some evidence that
either HRV-guided training or traditional training may improve their performance in terms of VO
2max
(HRV-G: ES = 0.402, p < 0.0001; CG: ES = 0.215, p < 0.0001). However, more favorable outcomes (p <
0.0001) for the experimental groups compared to the control groups were recorded across the studies.
Moderators indicated larger effect sizes for interventions involving amateur endurance athletes (ES
= 0.36, p < 0.0001) and women (ES = 0.40, p < 0.0001). On the other hand, the sample size of the studies
was directly related to the ES magnitude (p < 0.0001).
5.2. Overall Completeness and Applicability of the Evidence
The total sample size of the studies meeting our original inclusion criteria was sufficiently large
to warrant restricting the results to a meta-analysis of the RCTs. Data on the primary outcome
(VO
2max
) were measured directly using a gas exchange analysis system and a maximal test in each
study. This is the most accurate way to obtain cardiorespiratory data. However, some studies
Int. J. Environ. Res. Public Health 2020, 17, 7999 17 of 22
implemented this test using a treadmill (Kiviniemi_2007, Nuuttila_2017, Schmitt_2017, and
Vesterinen_2016) and others using a cycle ergometer (Javaloyes_2019 and Kiviniemi_2010). In the
first case, training was based on running (Kiviniemi_2007, Nuuttila_2017, and Vesterinen_2016) and
skiing (Schmitt_2017), which implies similar technical execution in the test. In the second case, the
Javaloyes_2019 study was carried out on cyclists, whereas the Kiviniemi_2010 study sample was
composed of runners. Statistical improvements regarding VO
2max
were found in the Kiviniemi_2007
and Kiviniemi_2010 studies. However, the specificity of the test may be a source of variability and
potential imprecision in the second study results. Following the training specificity principle [37], the
body’s physiological and metabolic responses and training adaptations are specific to the type of
exercise and the muscle groups involved. Thus, the evaluation method should be as similar as
possible to the training in order to obtain the most reliable results. This needs to be taken into account
when interpreting the results.
Despite the intervention durations being quite homogeneous in the included studies (eight
weeks for each study apart from Kiviniemi_2007 and Schmitt_2017), the total duration of the training
process, preparation weeks included, endurance sport modality, and training intensities used for the
control group (standard training) were different. There was also a marked heterogeneity in the
sample of the included studies: elite (Javaloyes_2019 and Schmitt_2017) and amateur
(Kiviniemi_2007, Kiviniemi_2010, Nuuttila_2017, and Vesterinen_2016) participants, or samples
comprising only men (Javaloyes_2019, Kiviniemi_2007, Kiviniemi_2010, and Nuuttila_2017), women
(Kiviniemi_2010), or men and women (Schmitt_2017 and Vesterinen_2016). A standardized training
protocol should be recommended to ensure the optimal benefits regarding VO
2max
.
5.3. Quality of the Evidence
The quality of the evidence from the included studies can be considered unclear. Despite each
study being a randomized controlled trial, the sequence generation or the allocation concealment was
considered skewed in half of them. The performance bias was high only in Javaloyes_20019, while
the detection bias was unclear in all the studies because incomplete blinding was considered.
Attrition was high in Kiviniemi_2010, Nuuttila_2017, and Vesterinen_2016 because of the high
follow-up rates. In addition, the reporting bias was generally unclear due to the lack of a registered
protocol.
5.4. Potential Biases in the Review Process
Although the systematic nature of the review process followed here decreases the potential for
bias, the risk of bias in the review process remains. The greatest risk of bias present in this review
was the study selection; specifically, the decision to limit the inclusion criteria to individual
endurance sports, thus reducing the number of studies included and causing a potential limitation in
the results.
Agreements and disagreements with other studies or reviews
Based on the results from this systematic review with meta-analysis, and in response to Research
Question 1, it is not surprising that the meta-analyzed results regarding improvements in athletes’
VO
2max
were associated with both training methodologies. According to Bartlett, O’Connor, Pitchford,
Torres-Ronda, and Robertson [2] and Heyward [37], adequate prescribed training should maximize
athletic performance when the specificity, overload, progression, initial level, individualization,
diminishing return, and reversibility principles are followed. However, it was also found that the
individual training adaptation according to the endurance athletes’ daily HRV scores produced better
VO
2max
results than the standardized prescribed training, which answers Research Question 2. As
pointed out by Vesterinen et al. [4,9], not every athlete improves their VO
2max
after standardized
group training. Similarly, Gallo, Cormack, Gabbett, Williams, and Lorenzen [38] reported that, in
footballers, the internal load (perceived effort) of each athlete was different for a given external load;
this definitely affects their individual performance during training and will be reflected in their
individual performance improvements. Thus, daily individual HRV monitoring and training
Int. J. Environ. Res. Public Health 2020, 17, 7999 18 of 22
guidance balancing the sympathetic and parasympathetic autonomic nervous system leads to greater
athletic performance in endurance athletes compared to standardized prescribed training. This is
relevant if training optimization is the objective, supporting the idea that training should be
prescribed appropriately to avoid overtraining and/or injury [38]. In the same vein, it is also
interesting to point out that, according to studies such as Hulin, Gabbett, Lawson, Caputi, and
Sampson [39] and Williams et al. [16], training individualization is also related to minimizing overuse
and reducing the injury risk, which may be a correlative benefit in the pursuit of endurance athlete
training optimization.
On the other hand, the meta-analyzed results show that VO
2max
improvements were greater
when the sample comprised amateur endurance athletes. This answers Research Question 3.
According to the initial training level principle [37], individuals with a low initial level of physical
fitness should achieve more significant relative increases than those of average or high levels. This is
in accordance with the results of Sanchez-Sanchez et al. [40], where greater performance
improvements were obtained in lower-level football players compared to the higher-level players,
concluding that the lower the athlete’s initial fitness level, the higher the available window of
adaptability. Conversely, in the systematic review with meta-analysis by Hammami, Gabbett,
Slimani, and Bouhlel [41], the athlete’s level was not a determinant variable in terms of VO
2max
enhancement since it improved if they were elite or amateur players. It should be noted that this
review was conducted on football players and that randomized and nonrandomized controlled trials
were included.
According to our meta-analyzed results, and in response to Research Question 4, there were
higher effect sizes regarding VO
2max
improvements when the sample was not mixed, especially in the
case of women. There is controversy concerning the influence of sex on sport performance. Recent
studies conducted on endurance athletes concluded that either sex was not a predictor variable of
performance [42] or that performance between men and women was different in swimming, cycling,
and running [43]. In the case of the present systematic review with meta-analyses, we consider that
the initial level of the sample influenced the result, given that, in the Kiviniemi_2010 study, when
female samples were analyzed, the participants were amateur level athletes. Thus, a higher relative
performance increment is predictable based on the athletes’ level.
6. Conclusions
6.1. Practical Implications
Training optimization to enhance performance in endurance athletes is a goal that is undergoing
a constant process of improvement. Finding a procedure to objectively individualize the training
would be ideal for achieving this goal. The meta-analyses results considered in this review suggest
that HRV is a good indicator of physiological responses to training in endurance athletes.
Consequently, using daily HRV scores for training individualization and prescription is an effective
method for optimizing performance in endurance athletes. This is reflected in the improved VO
2max
results when the training is guided by HRV, considering VO
2max
as one of the main performance
indicators. In addition, it should be taken into account that a lower initial athlete fitness level will be
relevant in achieving greater VO
2max
improvement. Although gender may be a variable that
influences the performance gains, in our opinion, this result is primarily conditioned by the level of
the athletes included in the analyzed studies. Therefore, we do not consider it to be a variable that
clearly affects VO
2max
improvements.
6.2. Research Implications
The results from this review suggest that, while there is evidence that HRV-guided training is
effective at improving VO
2max
in endurance athletes, there is still work to be done in terms of
identifying the characteristics of the interventions that contribute to this effect and the characteristics
of participants who are more likely to respond to such interventions. The most important point is
that more research is required since only five studies were included in this review. Moreover, only
Int. J. Environ. Res. Public Health 2020, 17, 7999 19 of 22
two of the studies used samples composed of elite endurance athletes, which gave different results
regarding VO
2max
improvement. Consequently, the research should be extended to the professional
field in order to clarify the effect of guiding training on VO
2max
. This would also help to clarify
whether the endurance sport modality is determinative of the VO
2max
enhancement when following
this training methodology.
Using daily HRV scores to control the training load and intensity over eight weeks is enough to
improve VO
2max
in endurance athletes. Nonetheless, the training protocol should be further
standardized in terms of adjusting the number of preparation weeks or considering the measurement
weeks within or around the training period, factors that determine the training duration. Moreover,
the standardized training protocol used in the control groups varied between the studies, which
considered low, moderate, or high training intensities, as well as different numbers of sessions per
week and session durations. This might very well have influenced the VO
2max
results. Therefore, it is
necessary to reach a consensus regarding a standardized training protocol to use in future studies. In
this line, it has been recently published a protocol [44] that could clarify the studies design. Similarly,
although each study in this review used the most accurate method available to obtain the
cardiorespiratory data, in the future, we should consider using a measuring instrument that allows
us to implement the most specific sport technique in order to minimize result variability and
imprecision.
Regarding the quality of the studies, authors should consider: improving the sequence
generation or allocation concealment, the blinding of the participants, personnel, and outcome
assessors, the rates of follow-up loss, using statistical procedures such as intention-to-treat to
minimize attrition bias, and registering their protocols before starting the randomized controlled
trial.
Lastly, to reinforce knowledge regarding performance optimization in endurance athletes, a
good way to supplement the effect of HRV-guided training might be to register the risk of injuries
associated with overuse using tools such as the Oslo Sports Trauma Research Center Overuse Injury
Questionnaire, since this considers additional aspects affecting the execution of athletes’ training.
Author Contributions: Research concept and study design, A.G.-G. and M.C.-P.; literature review, A.G.-G.,
M.C.-P., and A.G.-Q.; data collection, A.G.-G., M.C.-P., and A.G.-Q.; data analysis and interpretation, A.G.-G.,
M.C.-P., and A.G.-Q.; statistical analyses, A.G.-G. and M.C.-P.; writing of the manuscript, A.G.-G., M.C.-P., and
D.P.; reviewing/editing the draft of the manuscript, M.C.-P. and D.P. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
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... Recent reviews have aimed to consolidate available findings. Granero-Gallegos, González-Quílez, Plews, Carrasco-Poyatos [25] reported that HRV-guided training had a significantly greater effect on VȮ2 max versus predefined training. However, this meta-analysis included the training group (i.e., HRV-guided training and predefined training) as the analysis unit. ...
... Various methodological approaches have been applied in HRV-guided training interventions that may influence outcomes and may possibly explain the lack of consensus in recent reviews [25,27,28]. Differences in HRV assessment (e.g., body position, pre-recording stabilization period, measurement duration, selection of the vagal-related HRV index, and respiration rate) and the criterion to modify training (e.g., use of single or average HRV values, and static or rolling baseline reference ranges) may influence HRV values and, consequently, training prescription. ...
... Aerobic fitness and performance have been the primary outcomes of interest in recent reviews [25,27,28]. Whether post-intervention changes in markers of cardiac-parasympathetic modulation vary as a function of prescription methodology is unclear. ...
Article
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Purpose: This systematic review with meta-analysis was conducted to establish whether heart rate variability (HRV)-guided training enhances cardiac-vagal modulation, aerobic fitness, or endurance performance to a greater extent than predefined training while accounting for methodological factors. Methods: We searched Web of Science Core Collection, Pubmed, and Embase databases up to October 2020. A random-effects model of standardized mean difference (SMD) was estimated for each outcome measure. Chi-square and the I2 index were used to evaluate the degree of homogeneity. Results: Accounting for methodological factors, HRV-guided training was superior for enhancing vagal-related HRV indices (SMD+ = 0.50 (95% confidence interval (CI) = 0.09, 0.91)), but not resting HR (SMD+ = 0.04 (95% CI = -0.34, 0.43)). Consistently small but non-significant (p > 0.05) SMDs in favor of HRV-guided training were observed for enhancing maximal aerobic capacity (SMD+ = 0.20 (95% CI = -0.07, 0.47)), aerobic capacity at second ventilatory threshold (SMD+ = 0.26 (95% CI = -0.05, 0.57)), and endurance performance (SMD+ = 0.20 (95% CI = -0.09, 0.48)), versus predefined training. No heterogeneity was found for any of the analyzed aerobic fitness and endurance performance outcomes. Conclusion: Best methodological practices pertaining to HRV index selection, recording position, and approaches for establishing baseline reference values and daily changes (i.e., fixed or rolling HRV averages) require further study. HRV-guided training may be more effective than predefined training for maintaining and improving vagal-mediated HRV, with less likelihood of negative responses. However, if HRV-guided training is superior to predefined training for producing group-level improvements in fitness and performance, current data suggest it is only by a small margin.
... If the HRV is recorded daily, higher intensity sessions might be included in the training periodization, with the athletes benefiting from greater peripheral and central physiological adaptations associated to more polarized training. According to this assumption, a recent meta-analysis [8] showed that the individual training adaptation, based on the endurance athletes' daily HRV scores, produced better VO 2max results than the standardized training prescribed, with the training level being a determinant factor. However, when the sample is composed of runners, there is still a lack of consensus regarding the training design, when it is HRV-guided or traditionally prescribed [9,10], in terms of the total volume achieved and the proportion of training performed at high, moderate or low intensities. ...
... Therefore, the higher proportion of moderate-intensity training in HRV-G resulted in better physiological performance after the intervention than the higher proportion of low-intensity training followed by TRAD-G. These results are in the line with other similar studies carried out on professional or amateur athletes [2,3,10,14,8,21,23,18,24,25]. However, when a block periodization was followed in these studies, significant changes were found in variables such as the VO 2max or peak power in their HRV-guided training groups, or the Vmax in their traditional training groups. ...
Article
Purpose: to analyze the training structure following a heart rate variability (HRV) -guided training or traditional training protocol, determining their effects on the cardiovascular performance of professional endurance runners, and describing the vagal modulation interaction. Methods: This was an 8-week cluster-randomized controlled trial. Twelve professional endurance runners were randomly assigned to an HRV-guided training group (HRV-G; n=6) or a traditional training group (TRAD-G; n=6). The training methodology followed by the HRV-G was determined by their daily HRV scores. Training intensities were recorded daily. HRV4Training was used to register the rMSSD every morning and during a 60-second period. Cardiovascular outcomes were obtained through an incremental treadmill test. The primary outcome was the maximal oxygen uptake (VO2max). Results: total training volume was significantly higher in TRAD-G, but moderate intensity training was significantly higher in HRV-G (X±SDDif=1.98±0.1 %; P=0.006; d=1.22) and low intensity training in TRAD-G (X±SDDif=2.03±0.74 %; P=0.004; d=1.36). The maximal velocity increased significantly in HRV-G (P=0.027, d=0.66), while the respiratory exchange ratio increased in TRAD-G (P=0.017, d=1). There was a small effect on the LnRMSSD increment (P=0.365, d=0.4) in HRV-G. There were statistical inter-group differences in the ∆maximal heart rate when ∆LnrMSSD was considered as a covariable (F=7.58; P=0.025; d=0.487). There were significant and indirect correlations of LnRMSSDTEST with VO2max (r =-0.656, P=0.02), ∆LnrMSSD with ∆VO2max (r=-0.606, P=0.037), and ∆LnrMSSDCV with ∆VENT (r=-0.790, P=0.002). Conclusions: higher HRV scores suggest better cardiovascular adaptations due to higher training intensities, favoring HRV as a measure to optimize individualized training in professional runners.
... In a monitoring purpose the usage of DFA a1 may help inform an athlete about their recovery from previous training sessions. It has become commonplace for individuals to monitor and track resting HRV as a method to direct daily exercise intensity and volume (Granero-Gallegos et al., 2020;Düking et al., 2021). Unfortunately, resting HRV requires a regular day-to-day monitoring routine including standardization (e.g., time of day, nutrition; Bellenger et al., 2016) and logistically may not fit into an irregular schedule. ...
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While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.
... Moreover, HRV might also be useful to personalize training programs. Training prescription guided by resting HRV was already shown to enhance training effects of endurance training in younger adults (Düking et al., 2020;Granero Gallegos et al., 2020;Ruiz et al., 2020). ...
Article
Full-text available
Background: Monitoring phasic responses of heart rate variability (HRV) in terms of HRV reactivity [i. e., the absolute change from resting state to on-task (i.e., absolute values of HRV measured during exercise)] might provide useful insights into the individual psychophysiological responses of healthy middle-aged to older adults (HOA) to cognitive and physical exercises. Objectives: To summarize the evidence of phasic HRV responses to cognitive and physical exercises, and to evaluate key moderating factors influencing these responses. Methods: A systematic review with meta-analyses was performed. Publications up to May 2020 of the databases Medline (EBSCO), Embase, Cochrane Library, CINAHL, Psycinfo, Web of Science, Scopus, and Pedro were considered. Controlled clinical trials and observational studies measuring phasic HRV responses to cognitive and/or physical exercises in HOA (≥50 years) were included. Results: The initial search identified 6,828 articles, of which 43 were included into the systematic review. Compared to resting state, vagally-mediated HRV indices were significantly reduced during all types of exercises [Hedge's g = −0.608, 95 % CI (−0.999 to −0.218), p = 0.002] indicating a significant parasympathetic withdrawal compared to rest. The key moderating variables of these responses identified included exercise intensity for physical exercises, and participant characteristics (i.e., level of cognitive functioning, physical fitness), task demands (i.e., task complexity and modality) and the individual responses to these cognitive challenges for cognitive exercises. In particular, higher task demands (task complexity and physical exercise intensity) were related to larger HRV reactivities. Better physical fitness and cognition were associated with lower HRV reactivities. Additionally, HRV reactivity appeared to be sensitive to training-induced cognitive and neural changes. Conclusion: HRV reactivity seems to be a promising biomarker for monitoring internal training load and evaluating neurobiological effects of training interventions. Further research is warranted to evaluate the potential of HRV reactivity as a monitoring parameter to guide cognitive-motor training interventions and/or as a biomarker for cognitive impairment. This may facilitate the early detection of cognitive impairment as well as allow individualized training adaptations that, in turn, support the healthy aging process by optimizing individual exercise dose and progression of cognitive-motor training.
... As part of an athlete monitoring system, monitoring of heart rate (HR) and heart rate variability (HRV) can provide feasible assessment of activity and regulatory mechanisms of the cardiac autonomic nervous system and cardiorespiratory fitness (Achten & Jeukendrup, 2003;Buchheit, 2014; Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996). For many decades, HR(V) monitoring has been a topic of sport science research (Israel, 1982), and its applications in athletes continue to generate growing interest among practitioners and researchers (Akenhead & Nassis, 2016;Alexandre et al., 2012;Bellenger, Fuller, et al., 2016;Berkelmans et al., 2018;Bhati et al., 2019;Buchheit, 2014;Capostagno et al., 2021;Daanen et al., 2012;Düking et al., 2020;Granero-Gallegos et al., 2020;Halson, 2014;Kingsley & Figueroa, 2016;Mujika, 2017). This is in no small part due to technological advances in recent years, such as the use of wearables, smartphone apps, and teambased tracking systems to enable time-efficient and cost-effective recording of beat-by-beat HR data in almost any situation and context. ...
Thesis
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The monitoring of heart rate (HR) and heart rate variability (HRV) can contribute significantly to the individualization and optimization of training and recovery. However, the practical interpretation of longitudinal data is still challenging in some cases. The results of this dissertation can be summarized as follows: Practical interpretation of HR(V) data requires consideration of contextual factors such as training context and more detailed analysis of HR(V) time courses (Study 1). Orthostatic tests appear to be useful in identifying complex and training-specific HR(V) responses following short-term overload training and recovery (Study 2). Submaximal HR during standardized warm-up is sensitive to short-term periods of training and recovery, contrary to previous assumptions (Study 3). A future challenge is to effectively separate potential short-term from long-term effects. --- Das Monitoring von Herzfrequenz (HR) und Herzfrequenzvariabilität (HRV) wird zur Individualisierung und Optimierung von Training und Regeneration empfohlen. Die trainingspraktische Interpretation der Daten stellt jedoch nach wie vor eine Herausforderung dar. Die im Rahmen der Dissertation veröffentlichten Ergebnisse können wie folgt zusammengefasst werden: Die praxisnahe Interpretation von HR(V) Daten erfordert die Berücksichtigung kontextualer Faktoren wie die Trainingsstruktur und eine differenziertere Analyse von HR(V) Zeitverläufen (Studie 1). Orthostase Tests scheinen hilfreich zu sein, um die komple-xen, belastungsspezifischen HR(V) Reaktionen nach Kurzzeit-Überlastungstraining und Erholung identifizieren zu können (Studie 2). Die standardisiert im Training erfasste submaximale Belastungs-HR reagiert entgegen früherer Annahmen sensitiv auf kurze Trainings- und Erholungsphasen (Studie 3). Eine zukünftige Herausforderung besteht darin, Kurzzeit- von Langzeiteffekten zu isolieren.
... Heart rate variability (HRV) is now a widely used tool to provide valuable information about cardiorespiratory status and health in a large range of subjects, from elite athletes [1] to heart failure patients [2]. Briefly, a reduced HRV illustrates a cardiovascular system under stress or submitted to a pronounced fatigue [3,4]. ...
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... Recovery-based training has been studied recently among multiple populations. Individually adjusted training-based on resting heart rate variability (HRV) has induced superior improvements in maximal endurance performance [7] and VO 2max [8] compared to pre-planned training in recreationally trained participants. ...
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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.
... Granero-Gallegos, González-Quílez, Plews, and Carrasco-Poyatos [27] performed a systematic review with meta-analysis to analyze the effect of HRV-guided training on VO 2max in endurance athletes. The methods were reported in accordance with the Campbell Collaboration policies and guidelines for systematic reviews. ...
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Continuous updates of knowledge among professionals in physical education (PE) and sport are essential for the goal of developing quality professional work [...]
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DOCTORADO EN EDUCACIÓN (RD09/11) ESCUELA INTERNACIONAL DE DOCTORADO Efectos fisiológicos del entrenamiento basado en la variabilidad de la frecuencia cardíaca en corredores de fondo-Physiological effect of training based on heart rate variability in endurance runners
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Physiological training responses depend on sympathetic (SNS) and parasympathetic nervous system (PNS) balance. This activity can be measured using heart rate variability (HRV). Such a measurement method can favor individualized training planning to improve athletes’ performance. Recently, HRV-guided training has been implemented both on professional and amateur sportsmen and sportswomen with varied results. There is a dearth of studies involving professional endurance athletes following a defined HRV-guided training protocol. The objectives of the proposed protocol are: (i) to determine changes in the performance of high-level athletes after following an HRV-guided or a traditional training period and (ii) to determine differences in the athletes’ performance after following both training protocols. This will be a 12-week cluster-randomized controlled protocol in which professional athletes will be assigned to an HRV-based training group (HRV-G) or a traditional-based training group (TRAD-G). TRAD-G will train according to a predefined training program. HRV-G training will depend on the athletes’ daily HRV. The maximal oxygen uptake (VO2max) attained in an incremental treadmill test will be considered as the primary outcome. It is expected that this HRV-guided training protocol will improve functional performance in the high-level athletes, achieving better results than a traditional training method, and thus providing a good strategy for coaches of high-level athletes.
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This study compares and describes relationships among stress-recovery indices, the heart rate variability index, and the Cooper and Yo-Yo IR1 tests among female soccer players during the last six weeks of the competitive season. Sixteen female soccer players engaged in a pre-test of all of the variables. After having their training monitored for six weeks, a post-test was administered. The results revealed significant (p < 0.05) differences in the specific stress-recovery scales of the RESTQ-sport and in the frequency-domain variables of the HRV, although there were no significant differences in the general stress or general recovery scales. The Yo-Yo IR1 test, the Cooper test scores, and the means of the time-domain HRV variables did not exhibit any significant differences between the pre- and the post-test. The RMSSD variations exhibited very large and large correlations with the performance test and the RESTQ-sport variables, respectively. The variations in the HRV frequency-domain variables exhibited significant moderate and large correlations among the variations of the RESTQ-sport scales. Monitoring athletes at the end of the season may reveal contradictions between some variables. To help with the interpretation of these scales, some external aspects, such as athlete strain and monotony of training, should be considered.
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Purpose: To analyze if live high-train low (LHTL) effectiveness is improved when daily training is guided by heart rate variability (HRV). Methods: Twenty-four elite Nordic skiers took part in a 15-day LHTL study and were randomized into a HRV-guided training hypoxic group (H-HRV, n = 9, sleeping in normobaric hypoxia, FiO2 = 15.0%) and two predefined training groups sleeping either in hypoxia (H, n = 9, FiO2 = 15.0%) or normoxia (N, n = 6). HRV and training loads (TL) were recorded daily. Prior (Pre), one (Post-1), and 21 days (Post-21) following LHTL, athletes performed a 10-km roller-ski test, and a treadmill test for determination of [Formula: see text] was performed at Pre and Post-1. Results: Some HRV parameters measured in supine position were different between H-HRV and H: low and high (HF) frequency power in absolute (ms2) (16.0 ± 35.1 vs. 137.0 ± 54.9%, p = 0.05) and normalized units (- 3.8 ± 10.1 vs. 53.0 ± 19.5%, p = 0.02), HF(nu) (6.3 ± 6.8 vs. - 13.7 ± 8.0%, p = 0.03) as well as heart rate (3.7 ± 6.3 vs. 12.3 ± 4.1%, p = 0.008). At Post-1, [Formula: see text] was improved in H-HRV and H (3.8 ± 3.1%; p = 0.02 vs. 3.0 ± 4.4%; p = 0.08) but not in N (0.9 ± 5.1%; p = 0.7). Only H-HRV improved the roller-ski performance at Post-21 (- 2.7 ± 3.6%, p = 0.05). Conclusion: The daily individualization of TL reduced the decrease in autonomic nervous system parasympathetic activity commonly associated with LHTL. The improved performance and oxygen consumption in the two LHTL groups confirm the effectiveness of LHTL even in elite endurance athletes.
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Introduction: This paper aimed to systematically review and meta-analyse the training effects of small-sided games (SSG) on physical fitness and specific skills related to team sport according to the level of play and the period of the season. Evidence acquisition: The search covered the following electronic databases (PubMed, Google Scholar, and ScienceDirect). The publications' search period ranged from 2000 to 2016. The terms (small- sided game, training, skill-based game, aerobic fitness, sprint, agility, jump and team sports) were used either singularly or combined in a systematic sequence. Appraisal of 16 articles (15 were meta-analysed) was performed after the application of exclusion criteria and quality assurance processes and the standardized mean effects were measured using random effects. Evidence synthesis: The results revealed that SSG training had a large beneficial effect on maximal oxygen uptake VO2max (effect size 1.94; 95 % CL 0.15, 3.74; I2 = 94 %), agility (-1.49; 95% CL -2.27, -0.71; I2 = 80%), and repeated sprint ability (-1.19; 95% CL - 2.17, -0.21; I2= 53%).There was a moderate beneficial effect on 10- and 20-m sprint performance (-0.89; 95 % CL -1.7, -0.07; I2 =88%), jump height (0.68; 95% CL 0.03, 1.33; I2= 79%), and intermittent endurance (0.61; 95% CL 0.17, 1.05; I2= 0%). The results also showed greater positive effects on specific skills (specific endurance and agility tests and techniques) after SSG when compared with generic or agility training. Conclusion: Small-sided games may represent an effective strategy of multicomponent training that can induce greater positive effects on specific skills tasks when compared with interval or agility training and moderate to large improvements in team sport-related physical fitness.
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Predefined training programs are common place when prescribing training. Within predefined training, block periodization (BP) has emerged as a popular methodology due to its benefits. Heart rate variability (HRV) has been proposed as an effective tool for prescribing training. The aim of this study is to examine the effect of HRV guided-training against BP in road cycling. Twenty well-trained cyclists participated in this study. After a preliminary baseline period to establish their resting HRV, cyclists were divided into two groups: an HRV-guided group and a BP group and they completed 8 training weeks. Cyclists completed three evaluations weeks, before and after each period. During the evaluation weeks, cyclists performed: (1) a graded exercise test to assess VO2max, peak power output (PPO) and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and (2) a 40-min simulated time-trial (40TT). The HRV-guided group improved VO2max (p = 0.03), PPO (p = 0.01), WVT2 (p = 0.02), WVT1 (p = 0.01) and 40TT (p = 0.04). BP group improved WVT2 (p = 0.02). Between-group fitness and performance were similar after the study. The HRV-guided training could lead to a better timing in training prescription than BP in road cycling. Keywords: cardiac autonomic regulation; cycling; endurance training; day-to-day; aerobic performance; HRV.
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Objective: To examine heart rate variability (HRV) at rest and with a 2-Back cognitive task involving executive function and sustained attention in athletes during the acute phase following concussion and compare them with the controls. Participants: Twenty-three male and female collegiate athletes (20 ± 1 years) following (4 ± 1 days) a sports-related concussion and 23 sports- and sex-matched noninjured controls. Procedure: Continuous R-R interval was acquired using 3-lead electrocardiogram for 3 minutes each at rest and during the 2-Back task. HRV was quantified as percent high-frequency (HF) power. Results: At rest, lower percent HF power was observed in the concussed athletes (23 ± 11) compared with the controls (38 ± 14; P = .0027). However, with the 2-Back task, an increase in HF power was observed in the concussed group (39 ± 12; P = .0008) from rest and was comparable with the controls (36 ± 15). No difference in HF power between rest and 2-Back task was observed in the controls. Conclusion: Lower HRV was observed at rest following concussion. An increase in HRV, suggestive of enhanced prefrontal cortex (PFC) functioning, was observed during a cognitive task in the concussed athletes. Therefore, cognitive tasks as early as 4 days after injury may increase PFC functioning from rest and expedite return to learn in collegiate athletes.
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Purpose: Road cycling is a sport with extreme physiological demands. Therefore, there is a need to find new strategies to improve performance. Heart rate variability (HRV) has been suggested as an effective alternative for prescribing training load against predefined training programs. The purpose of this study is to examine the effect of training prescription based on HRV in road cycling performance. Methods: Seventeen well-trained cyclists participated in this study. After an initial evaluation week (EW), cyclists performed 4 baseline weeks (BW) of standardized training to establish their resting HRV. Then, cyclists were divided into two groups, a HRV-guided group (HRV-G) and a traditional periodization group (TRAD) and they carried out 8 training weeks (TW). Cyclists performed two EW, after and before TW. During the EW, cyclists performed: (1) a graded exercise test to assess VO2max, peak power output (PPO) and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and (2) a 40-min simulated time-trial. Results: HRV-G improved PPO (5.1 ± 4.5 %; p = 0.024), WVT2 (13.9 ± 8.8 %; p = 0.004) and 40TT (7.3 ± 4.5 %; p = 0.005). VO2max and WVT1 remained similar. TRAD did not improve significantly after TW. There were no differences between groups. However, magnitude-based inference analysis showed likely beneficial and possibly beneficial effects for HRV-G instead of TRAD in 40TT and PPO, respectively. Conclusions: Daily training prescription based on HRV could result in a better performance enhancement than a traditional periodization in well-trained cyclists.