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Journal
of
Science
and
Medicine
in
Sport
20
(2017)
606–610
Contents
lists
available
at
ScienceDirect
Journal
of
Science
and
Medicine
in
Sport
journal
h
om
epa
ge:
www.elsevier.com/locate/jsams
Original
research
Heart
rate
variability
and
psychometric
responses
to
overload
and
tapering
in
collegiate
sprint-swimmers
Andrew
A.
Flatt∗,
Bjoern
Hornikel,
Michael
R.
Esco
The
University
of
Alabama,
Department
of
Kinesiology,
United
States
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
13
June
2016
Received
in
revised
form
3
September
2016
Accepted
18
October
2016
Available
online
17
November
2016
Keywords:
Smartphone
Parasympathetic
Fatigue
Autonomic
Monitoring
a
b
s
t
r
a
c
t
Objectives:
The
purpose
of
this
study
was
to
evaluate
cardiac-parasympathetic
and
psychometric
responses
to
competition
preparation
in
collegiate
sprint-swimmers.
Additionally,
we
aimed
to
deter-
mine
the
relationship
between
average
vagal
activity
and
its
daily
fluctuation
during
each
training
phase.
Design:
Observational.
Methods:
Ten
Division-1
collegiate
sprint-swimmers
performed
heart
rate
variability
recordings
(i.e.,
log
transformed
root
mean
square
of
successive
RR
intervals,
lnRMSSD)
and
completed
a
brief
wellness
questionnaire
with
a
smartphone
application
daily
after
waking.
Mean
values
for
psychometrics
and
lnRMSSD
(lnRMSSDmean)
as
well
as
the
coefficient
of
variation
(lnRMSSDcv)
were
calculated
from
1
week
of
baseline
(BL)
followed
by
2
weeks
of
overload
(OL)
and
2
weeks
of
tapering
(TP)
leading
up
to
a
championship
competition.
Results:
Competition
preparation
resulted
in
improved
race
times
(p
<
0.01).
Moderate
decreases
in
lnRMSSDmean,
and
Large
to
Very
Large
increases
in
lnRMSSDcv,
perceived
fatigue
and
soreness
were
observed
during
the
OL
and
returned
to
BL
levels
or
peaked
during
TP
(p
<
0.05).
Inverse
correlations
between
lnRMSSDmean and
lnRMSSDcv were
Very
Large
at
BL
and
OL
(p
<
0.05)
but
only
Moderate
at
TP
(p
>
0.05).
Conclusions:
OL
training
is
associated
with
a
reduction
and
greater
daily
fluctuation
in
vagal
activity
com-
pared
with
BL,
concurrent
with
decrements
in
perceived
fatigue
and
muscle
soreness.
These
effects
are
reversed
during
TP
where
these
values
returned
to
baseline
or
peaked
leading
into
successful
competi-
tion.
The
strong
inverse
relationship
between
average
vagal
activity
and
its
daily
fluctuation
weakened
during
TP.
©
2016
Sports
Medicine
Australia.
Published
by
Elsevier
Ltd.
All
rights
reserved.
1.
Introduction
In
competitive
sporting
events
such
as
swimming,
overload
training
and
tapering
are
typical
periodization
strategies
used
to
achieve
peak
performance
at
competition.
Intensified
training
may
facilitate
performance
supercompensation
that
is
realized
as
fatigue
dissipates
in
response
to
a
reduction
in
training
load
(i.e.,
tapering).1However,
the
increased
training
load
associated
with
intensified
training
may
put
athletes
at
risk
of
non-functional
over-
reaching
or
experiencing
illness
or
injury.2An
alternative
strategy
of
maintaining
loads
within
the
recovery
capacities
of
athletes
during
intensification
phases
may
support
greater
performance
improvements
than
pursuing
purposeful
overreaching.2Therefore,
monitoring
the
training
response
of
athletes
throughout
this
time
∗Corresponding
author.
E-mail
address:
aflatt@crimson.ua.edu
(A.A.
Flatt).
period
may
be
useful
for
evaluating
individual
training
adaptation
and
thus
guiding
the
training
process.3
A
physiological
parameter
for
monitoring
training
effects
in
swimmers
and
growing
in
popularity
among
coaches
and
sports
medicine
practitioners
is
heart
rate
variability
(HRV).4HRV
reflects
central
regulation
of
the
heart
via
autonomic
innervation
and
can
be
acquired
non-invasively
in
field
settings.
Vagal
indices
of
HRV
have
been
shown
to
be
sensitive
to
training
phase
(e.g.,
overload
and
taper)
and
performance
in
competitive
swimmers.
For
example,
Garet
et
al.
found
reduced
nocturnal-HRV
and
400
m
performance
in
regional
level,
teen-aged
swimmers
during
an
intensive
train-
ing
period.5Additionally,
increases
in
HRV
during
tapering
have
been
related
to
improved
performance
in
swimmers
of
a
vari-
ety
of
race
distances.5–7 However,
the
majority
of
investigations
pertaining
to
HRV
and
competition
preparation
in
swimming
and
among
other
sports,
almost
exclusively
involve
endurance
events.
For
example,
recent
reviews
concern
HRV
as
it
relates
primarily
to
aerobic
adaptations,
endurance
performance
and
the
monitor-
http://dx.doi.org/10.1016/j.jsams.2016.10.017
1440-2440/©
2016
Sports
Medicine
Australia.
Published
by
Elsevier
Ltd.
All
rights
reserved.
A.A.
Flatt
et
al.
/
Journal
of
Science
and
Medicine
in
Sport
20
(2017)
606–610
607
Table
1
External
training
load
details
for
each
phase
preceding
competition.
Training
load
Baseline
Overload
–
Taper
Week
1
Week
2
Week
3
Week
4
Week
5
Week
6
Total
distance
(m)
32,004
38,405
33,832
34,747
32,918
25,603
Distance
>160
bpm
(m)
3932
5761
5304
3658
3200
1829
Distance
Race
Int.
(m)
2560
4664
3704
2652
1920
1280
m
=
meters;
–
=
week
of
which
heart
rate
variability
and
wellness
data
were
not
included
in
the
analysis;
Distance
>160
bpm
=
weekly
distance
covered
with
a
pulse
rate
>160
beats
per
minute;
Distance
Race
Int.
=
weekly
distance
covered
at
race
intensity.
ing
of
endurance-sport
athletes.3,8,9 Therefore,
how
useful
HRV
monitoring
is
for
athletes
participating
in
anaerobic
events
such
as
sprint-swimming
remains
to
be
determined.
The
use
of
one
time-domain
HRV
parameter
reflective
of
cardiac-parasympathetic
activity,
the
logarithm
of
the
root
mean
square
of
successive
R–R
intervals
(lnRMSSD),
has
been
proposed
as
the
preferred
marker
for
use
among
athletes
in
ambulatory,
resting
conditions.3,8,9 Due
to
the
labile
nature
of
cardiac-parasympathetic
activity,
it
is
recommended
that
lnRMSSD
be
monitored
near
daily
and
averaged
(i.e.,
lnRMSSDmean)
to
derive
meaningful
information
pertaining
to
training
status.10,11 However,
no
previous
longitudi-
nal
investigation
involving
swimmers
has
acquired
HRV
data
with
such
frequency,
likely
due
to
time
constraints
when
using
tra-
ditional
or
nocturnal
HRV
recording
methodology.
An
additional
benefit
of
daily
acquisition
of
HRV
is
that
it
enables
the
quantifica-
tion
of
daily
fluctuation,
assessed
via
the
coefficient
of
variation
(lnRMSSDcv),
representing
perturbations
to
cardiac-autonomic
homeostasis.12 An
increased
lnRMSSDcv has
been
associated
with
lower
fitness,13,14 higher
perceived
fatigue
and
increased
training
load
in
team-sports.15 Moreover,
a
recent
study
found
that
higher
lnRMSSDmean was
related
to
a
smaller
lnRMSSDcv (r
=
−0.53),
sug-
gesting
that
greater
vagal
activity
may
reflect
greater
resilience
or
capacity
for
training
stress.16 Thus,
the
evolution
of
ones
HRV
trend
(i.e.,
increasing
or
decreasing)
and
the
degree
of
fluctuation
within,
appear
to
be
valuable
characteristics
that
coaching
and
sports
medicine
staff
can
use
for
evaluating
individual
responses
to
train-
ing.
However,
lnRMSSDcv has
not
been
reported
in
many
previous
studies
as
a
result
of
isolated
and
infrequent
HRV
recordings.
Fur-
thermore,
the
relationship
between
lnRMSSDmean and
lnRMSSDcv
has
not
been
evaluated
during
different
training
phases.
The
purpose
of
this
study
was:
(1)
to
determine
how
periods
of
overload
and
tapering
effect
lnRMSSD
trends
preceding
competi-
tion
and
(2)
to
assess
the
relationship
between
lnRMSSDmean and
lnRMSSDcv at
each
training
phase
in
collegiate
sprint-swimmers.
It
was
hypothesized
that
lnRMSSD
would
decrease
and
fluctuate
to
a
greater
extent
during
overload,
concurrent
with
a
reduction
in
perceived
wellness
and
that
these
effects
would
be
reversed
in
response
to
tapering.
In
addition,
we
hypothesized
that
greater
average
vagal
activity
would
relate
to
less
daily
fluctuation
at
each
phase
of
training.
2.
Methods
Ten
Division-1
sprint-swimmers
(n
=
7
male;
age
=
21
±
1.6
years;
height
=
187
±
7.3
cm;
weight
=
84.4
±
7.3
kg;
n
=
3
female;
age
=
21
±
1.5
years;
height
=
173.6
±
8.2
cm;
weight
=
68.8
±
5.2
kg)
from
the
National
Collegiate
Athletic
Association
(NCAA)
were
recruited
for
this
study.
These
athletes
compete
in
a
variety
of
short
distance
events
including:
50
m
free
(n
=
6),
100
m
free
(n
=
8),
200
m
free
(n
=
5),
100
m
back
stroke
(n
=
3),
100
m
fly
(n
=
4),
200
m
fly
(n
=
1),
100
m
breast
stroke
(n
=
1)
and
200
m
breast
stroke
(n
=
1).
Prior
to
participation,
all
subjects
provided
written
informed
con-
sent
and
obtained
medical
clearance
from
the
sports
medicine
staff.
Ethical
approval
for
this
study
was
granted
by
the
institutional
review
board
for
human
participants.
Data
collection
took
place
during
the
2015
NCAA
competi-
tive
season,
capturing
the
preparatory
period
of
a
championship
competition.
All
athletes
took
part
in
an
overload
and
tapering
phase
preceding
competition.
All
training
sessions
in
the
pool
were
planned
and
implemented
by
the
head
coach
while
strength
and
conditioning
(S&C)
training
was
implemented
by
the
S&C
coach.
Training
content
and
structure
were
not
influenced
by
the
researchers.
To
assess
the
effect
of
training
phase
on
cardiac-
parasympathetic
activity
and
psychometrics,
data
was
obtained
from
three
distinct
phases
including
baseline
(BL),
overload
(OL)
and
taper
(TP).
HRV
data
was
self-measured
daily
by
the
athletes
with
a
vali-
dated
smartphone
application
and
optical
pulse-wave
finger
sensor
(PWFS)
apparatus
(i.e.,
photoplethysmograph)
that
inserts
into
the
headphone
slot
of
a
mobile
device.
This
tool
has
been
shown
to
provide
accurate
time-domain
HRV
analysis
compared
with
electrocardiography.17 Each
morning
after
waking
and
elimination,
the
subjects
would
perform
a
seated
HRV
recording.
Once
seated
comfortably,
the
subjects
were
instructed
to
insert
their
left
index
finger
into
the
PWFS
and
open
the
ithleteTM HRV
application
on
their
mobile
device.
After
allowing
1-min
for
stabilization18 the
subjects
initiated
an
HRV
recording
while
remaining
motionless,
breathing
spontaneously
and
with
their
left
hand
held
still,
within
20
cm
of
their
chest.
The
application
utilizes
a
1-min
HRV
recording
to
determine
lnRMSSD19 and
expresses
this
value
on
a
100-point
scale
by
multiplying
it
by
20
(i.e.,
lnRMSSD
×
20).20
Psychometrics
were
evaluated
daily
via
the
smartphone
appli-
cation
immediately
following
HRV
recordings.
Subjects
rated
their
perceived
level
of
sleep
quality,
fatigue,
muscle
soreness,
stress
and
mood
on
a
9-point
scale
from
an
electronic
questionnaire
adapted
from
McLean
et
al.21 Ratings
closer
to
1
and
9
represented
poorer
and
greater
wellness
perceptions,
respectively.
External
training
load
data
was
obtained
from
the
head
coach.
Training
load
parameters
include
weekly
total
distance
covered,
weekly
distance
covered
with
pulse
rate
>160
beats
per
minute
(bpm)
and
weekly
distance
covered
at
race
intensity
(Table
1).
Total
distance
covered
was
20%
and
5.7%
above
BL
during
week
1
and
2
of
OL,
respectively.
Subsequently,
total
distance
was
reduced
to
BL
and
20%
below
BL
during
the
final
two-weeks
of
TP,
respectively.
Distance
covered
with
pulse
rate
>160
bpm
was
44.6%
and
34.9%
above
BL
during
week
1
and
2
of
OL,
respectively.
Subsequently,
distance
covered
with
pulse
rate
>160
bpm
was
reduced
to
18.6%
and
53.5%
below
BL
during
the
final
two-weeks
of
TP,
respectively.
Distance
covered
at
race
intensity
was
82%
and
44.7%
above
BL
during
week
1
and
2
of
OL,
respectively.
Subsequently,
distance
cov-
ered
at
race
intensity
was
reduced
to
25%
and
50%
below
BL
during
the
final
two-weeks
of
TP,
respectively.
Therefore,
the
OL
period
was
characterized
with
a
substantial
increase
in
training
intensity
with
total
volume
only
varying
by
≥20%.
Though
training
was
pre-
planned,
variations
for
individuals
were
made
at
the
discretion
of
the
coach
based
on
factors
such
as
perceived
fatigue,
performance
in
the
pool
and
pulse
rate
recovery
between
sets.
BL
training
con-
sisted
of
19.5
h
of
total
training
time
including
three
1-h
resistance
training
sessions
and
nine
1.5–2-h
pool
sessions.
The
training
time
and
structure
remained
the
same
during
the
OL
weeks,
however
608
A.A.
Flatt
et
al.
/
Journal
of
Science
and
Medicine
in
Sport
20
(2017)
606–610
Table
2
Comparison
of
heart
rate
variability
and
wellness
parameters
between
training
phases.
Phase
(mean
±
SD)
Comparison
statistics
(p,
ES)
BL
OL
TP
BL
vs.
OL
OL
vs.
TP
BL
vs.
TP
lnRMSSDmean 82.5
±
6.7
77.9
±
7.1
84.8
±
4.4
<0.01,
−0.67
<0.01,
1.18
0.09,
0.41
lnRMSSDcv 6.7
±
1.8
10.1
±
4.5
6.4
±
2.0
<0.01,
−0.98
<0.01,
1.05
0.43,
−0.18
Sleep
6.4
±
1.0
5.9
±
1.0
7.0
±
0.9
0.17,
−0.50
<0.01,
1.16
0.04,
0.63
Fatigue
5.8
±
1.2 4.3
±
0.9 5.9
±
0.7
0.01,
−1.41
<0.01,
1.98
0.85,
0.10
Soreness
5.8
±
1.3
4.6
±
0.9
6.0
±
0.9
0.04,
−1.07
<0.01,
1.56
0.59,
0.18
Stress
6.6
±
1.4
6.3
±
1.5
6.7
±
1.6
0.49,
−0.21
0.39,
0.26
0.66,
0.07
Mood
7.1
±
1.2
6.8
±
1.4
7.2
±
1.2
0.24,
−0.23
0.13,
0.31
0.31,
0.08
ES
=
Effect
Size;
BL
=
Baseline;
OL
=
Overload;
TP
=
Taper;
lnRMSSDmean =
the
mean
logarithm
of
the
root
mean
square
of
successive
RR
intervals
multiplied
by
20;
lnRMSSDcv =
coefficient
of
variation
of
the
logarithm
of
the
root
mean
square
of
successive
RR
intervals
multiplied
by
20.
training
content
differed
(Table
1).
During
TP,
weekly
total
train-
ing
time
was
reduced
to
approximately
15.5
h
and
included
three
1-h
resistance
training
sessions
and
seven
1.5–2-h
pool
sessions.
Sundays
were
reserved
for
passive
rest
throughout
each
phase.
Data
are
expressed
as
mean
±
SD.
The
Kolmogorov–Smirnov
test
was
used
to
assess
data
normality.
A
paired-samples
t-test
was
used
to
compare
race
times
recorded
from
the
current
competition
with
preceding
best
race
times
from
the
2014–2015
season.
One-
way
analysis
of
variance
for
repeated
measures
with
Bonferonni
post-hoc
tests
were
used
to
evaluate
differences
in
HRV
and
well-
ness
parameters
across
B,
OL
and
TP
training
periods.
Effect
sizes
(ES)22 were
calculated
for
all
comparisons
to
assess
the
magnitude
of
changes
in
HRV
and
wellness
parameters
across
the
three
train-
ing
phases.
ES
were
interpreted
qualitatively
using
the
following
thresholds:
<0.2,
trivial;
0.2–0.6,
small;
0.6–1.2,
moderate;
1.2–2.0,
large;
2.0–4.0,
very
large.23 Pearson
correlation
coefficients
(r)
were
used
to
quantify
the
relationship
between
individual
lnRMSSDmean
and
lnRMSSDcv values
during
each
training
phase.
The
thresholds
used
for
qualitative
assessment
were:
<0.1,
trivial;
0.1–0.3,
small;
0.3–0.5,
moderate;
0.5–0.7,
large;
0.7–0.9,
very
large;
>0.9
nearly
perfect.23 In
order
to
capture
the
effects
of
the
various
training
phases
on
HRV
and
wellness
parameters,
BL
values
were
derived
from
week
1,
OL
values
were
derived
from
weeks
2–3
and
TP
was
derived
from
weeks
5–6.
Week
4
was
omitted
because
of
its
simi-
larity
to
BL
and
thus
may
obscure
the
effects
of
the
varying
training
load
on
HRV
and
wellness
parameters
(Table
1).
Statistical
signif-
icance
was
set
at
p
<
0.05.
Analyses
were
performed
using
SPSS
software
(Version
22.0,
IBM
Corp,
New
York,
NY,
USA)
and
Microsoft
Excel
2016
(Redmond,
WA,
USA).
3.
Results
The
Kolmogorov–Smirnov
test
was
not
significant
(p
>
0.05)
indicating
that
the
assumption
of
data
normality
was
met.
Com-
petition
performance
significantly
improved
(p
<
0.01)
with
a
−1.02
±
0.61
s
(or
−1.71
±
1.31%)
reduction
in
race
times.
Signifi-
cant
reductions
in
lnRMSSDmean,
were
observed
during
the
OL
and
returned
to
BL
levels
or
peaked
during
TP.
lnRMSSDcv,
and
per-
ceptions
of
fatigue
and
soreness
significantly
increased
during
OL
and
returned
to
BL
levels
during
TP.
Perceived
sleep
quality
signif-
icantly
improved
during
TP.
Comparison
statistics
are
displayed
in
Table
2.
lnRMSSDmean was
significantly
related
to
lnRMSSDcv at
BL
(r
=
−0.72,
p
=
0.018,
very
large),
during
OL
(r
=
−0.71,
p
=
0.021,
very
large)
but
not
during
TP
(r
=
−0.38,
p
=
0.277,
moderate).
Notable
training
interventions
for
one
athlete
(Subject
A)
were
made
by
the
coach
throughout
the
training
period
due
to
fatigue
and
per-
formance
decrements.
The
HRV
and
wellness
trend
for
this
athlete
(Subject
A)
along
with
another
male
(Subject
B)
and
female
(Subject
C)
from
the
group
are
graphically
displayed
in
Fig.
1
for
comparison.
4.
Discussion
This
study
evaluated
changes
in
HRV
and
wellness
parameters
in
response
to
OL
and
TP
in
Division-1
collegiate
sprint-swimmers
preceding
competition.
We
found
that
OL
training
was
associated
with
a
reduction
along
with
greater
daily
fluctuation
in
lnRMSSD,
concurrent
with
decrements
in
perceived
fatigue
and
muscle
sore-
ness.
These
effects
were
reversed
during
TP,
where
these
values
returned
to
BL
or
peaked
leading
into
successful
competition.
We
found
very
large
negative
relationships
between
lnRMSSDmean and
lnRMSSDcv during
BL
and
OL
while
the
relationship
was
only
mod-
erate
during
TP.
These
results
are
in
agreement
with
a
previous
investiga-
tion
in
female
soccer
players
where
increases
in
training
load
resulted
in
Small
reductions
in
supine
and
standing
lnRMSSDmean,
Moderate
increases
in
lnRMSSDcv and
Large
reductions
in
per-
ceived
wellness.15 An
inverse
bell-shaped
trend
for
RMSSD
in
response
to
OL
and
TP
derived
from
isolated
HRV
recordings
has
been
observed
previously
among
athletes
from
a
variety
of
endurance
sports
including
swimming6and
rowing.24 Schmitt
et
al.25 reported
reduced
and
“scattered”
vagal
activity
during
peri-
ods
of
fatigue
versus
non-fatigued
states
in
57
elite
Nordic
skiers,
agreeing
with
our
finding
of
reduced
lnRMSSDmean and
increased
lnRMSSDcv (i.e.,
scattering
of
the
values)
with
increased
fatigue
dur-
ing
OL.
Our
results
are
in
contrast
to
previous
investigations
that
observed
parasympathetic
hyperactivity
(i.e.,
bell-shaped
trend)
in
overreached
triathletes
in
response
to
OL
with
a
progressive
reduction
in
lnRMSSD
toward
BL
following
TP.11 These
conflicting
HRV
responses
fall
in
line
with
the
two
clinical
forms
of
over-
training,
characterized
by
either
sympathetic
or
parasympathetic
predominance.26 However,
none
of
the
athletes
in
the
aforemen-
tioned
studies
were
diagnosed
with
the
overtraining
syndrome,
but
rather
were
more
appropriately
described
as
overreached.
The
reduction
in
average
vagal
activity
during
OL
observed
in
the
cur-
rent
study
is
likely
a
result
of
the
increased
anaerobic
work
load
as
training
intensity,
more
so
than
volume
appears
to
have
a
greater
impact
on
autonomic
recovery
after
training.9,27 For
example,
Plews
et
al.27 found
that
waking
lnRMSSD
showed
Small
increases
when
training
below
the
first
lactate
threshold,
whilst
showing
Small
decreases
when
training
above
the
second
lactate
threshold
in
elite
rowers.27 Furthermore,
a
recent
review
of
the
literature
showed
that
parasympathetic
activity
is
not
fully
restored
to
base-
line
until
at
least
48
h
after
high
intensity
exercise.9
The
increase
in
lnRMSSDcv during
the
overload
may
be
explained
by
the
substantial
reductions
in
vagal
activity
fol-
lowing
high
intensity
training
and
subsequent
parasympathetic
rebound
roughly
48
h
later,
previously
observed
after
very
intense
exercise.28 The
parasympathetic
rebound
phenomenon
has
been
attributed
to
dehydration-induced
hypervolemia
which
stimulates
baroreflex
mediated
increases
in
vagal
activity.28 Thus,
in
prac-
tice,
lnRMSSDcv may
provide
valuable
insight
regarding
training
adaptation.12,29 For
example,
in
interpreting
the
individual
HRV
A.A.
Flatt
et
al.
/
Journal
of
Science
and
Medicine
in
Sport
20
(2017)
606–610
609
Fig.
1.
Individual
heart
rate
variability
and
perceived
fatigue
trends
for
selected
subjects
A,
B
and
C
throughout
baseline,
overload
and
tapering.
The
boxed
data
points
on
the
trend
for
Subject
A
represent
the
time
at
which
training
load
was
reduced
due
to
fatigue
and
sluggish
performance
in
the
pool.
Black
dots
represent
daily
logarithm
of
the
root
mean
square
of
successive
RR
intervals
multiplied
by
twenty
(lnRMSSD).
The
solid
black
line
represents
that
7-day
rolling
lnRMSSD
average.
The
horizontal
dashed
lines
represent
the
smallest
worthwhile
change
(0.5
of
the
coefficient
of
variation).12 The
vertical
gray
bars
represent
perceived
fatigue
ratings
(1
=
very
tired,
9
=
very
fresh).
responses
(Fig.
1),
it
is
conceivable
that
an
increased
lnRMSSDcv
(e.g.
Subject
C
during
the
first
week
of
OL)
may
reflect
the
ini-
tial
stage
of
physiological
stress.
In
contrast,
minimal
change
in
lnRMSSDcv may
indicate
that
the
training
is
well
tolerated
given
that
HRV
returns
to
near
baseline
values
within
24
h
of
a
ses-
sion
(e.g.,
Subject
B).
In
support
of
this
assessment,
athletes
of
higher
training
status
and
fitness
show
faster
parasympathetic
reactivation
following
exercise
and
typically
present
a
smaller
lnRMSSDcv.9,13,14 Finally,
suppression
of
vagal
activity
beyond
the
typical
48-h
period
(>3
days
below
baseline)
may
reflect
a
more
severe
level
of
physiological
perturbation
and
possible
need
for
training
intervention
(e.g.,
Subject
A).
Of
note,
Subject
A
presented
with
the
highest
lnRMSSDcv at
baseline
(11.1%
vs.
group
average
of
6.75%)
and
subsequently
responded
the
least
favorably
to
the
commencement
of
OL,
requiring
more
rest
and
reduced
training
than
other
subjects.
However,
the
interventions
made
for
subject
A
were
effective
as
competition
performance
improved
in
his
3
events
by
an
average
of
1.04
s.
Nakamura
et
al.16 found
a
large
relationship
(r
=
−0.53)
between
lnRMSSDmean and
lnRMSSDcv in
professional
futsal
play-
ers
throughout
a
5-week
preseason.
The
current
study
differed
by
evaluating
this
relationships
during
each
training
phase.
Interest-
ingly,
the
relationship
observed
in
the
current
study
changed
from
Very
Large
during
BL
and
OL
to
Moderate
during
TP.
We
speculate
that
this
may
be
explained
by
the
heterogeneity
among
swim-
mers
in
response
to
the
taper
and
time
required
to
fully
recover
610
A.A.
Flatt
et
al.
/
Journal
of
Science
and
Medicine
in
Sport
20
(2017)
606–610
and
achieve
peak
performance.30 This
may
reflect
appropriateness
of
the
taper
duration
and
training
content
on
an
individual
basis.
Future
research
is
needed
to
explore
this
hypothesis.
Limitations
of
the
current
study
include
recording
only
external
training
load
and
lack
of
performance
assessment
during
OL.
There-
fore,
it
is
unclear
whether
athletes
were
functionally-overreached
and
if
the
performance
improvement
was
a
result
of
supercompen-
sation.
The
use
of
a
pulse-wave
finger
sensor
for
daily
HRV
measures
can
also
be
considered
a
limitation,
however
the
convenience
and
affordability
of
this
method
facilitates
subject
compliance
and
frequent
data
collection.
Future
experimental
research
on
HRV-
guided
training
during
competition
preparation
as
well
case
studies
highlighting
individual
responses
to
OL
and
TP
are
encouraged
to
improve
upon
the
practical
application
of
cardiac-parasympathetic
monitoring
in
field
settings.
5.
Conclusion
Overload
training
in
collegiate
sprint-swimmers
resulted
in
a
reduction
and
greater
daily
fluctuation
in
cardiac-parasympathetic
activity,
as
well
as
greater
perceived
fatigue
and
muscle
soreness
compared
with
baseline.
These
responses
were
reversed
during
a
taper
where
these
values
returned
to
baseline
or
peaked
lead-
ing
into
successful
competition.
The
strong
relationships
between
average
vagal
activity
and
its
daily
fluctuation
observed
at
baseline
and
overload
weakened
during
the
taper.
Practical
implications
•Monitoring
competition
preparation
in
sprint-swimmers
with
ultra-short
lnRMSSD
and
a
brief
wellness
questionnaire
derived
from
a
smartphone
application
demonstrated
sensitivity
to
vari-
ations
in
training
phase.
•Reduced
lnRMSSDmean with
greater
day-to-day
fluctuation
(i.e.,
increased
lnRMSSDcv)
may
serve
as
an
indication
of
inadequate
recovery.
•Tracking
of
these
variables
in
conjunction
with
other
markers
of
recovery
status
(e.g.,
perceived
wellness)
may
therefore
be
use-
ful
for
monitoring
the
effects
of
overload
periods
and
guiding
training
load
manipulation
leading
into
competition.
Acknowledgments
No
external
funding
was
provided
for
this
study.
The
web-based
athlete
management
software
was
provided
by
HRV
Fit
Ltd.
The
authors
have
no
financial
relationship
with
any
products
used
in
this
study.
We
would
like
to
thank
Coach
Jonty
Skinner
and
the
sprint-swim
team
for
their
participation
in
this
study.
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