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Heart Rate Variability and Training Load Among National Collegiate Athletic Association Division 1 College Football Players Throughout Spring Camp


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The purpose of this study was to determine if recovery of cardiac-autonomic activity to baseline occurs between consecutive-day training sessions among positional groups of a collegiate football team during Spring camp. A secondary aim was to evaluate relationships between chronic (i.e., 4-week) HRV and training load parameters. Baseline HRV (lnRMSSD_BL) was compared with HRV following ~20 h of recovery prior to next-day training (lnRMSSDpost20) among positional groups comprised of SKILL (n = 11), MID-SKILL (n = 9) and LINEMEN (n = 5) with a linear mixed model and effect sizes (ES). Pearson and partial correlations were used to quantify relationships between chronic mean and coefficient of variation (CV) of lnRMSSD (lnRMSSD_chronic and lnRMSSDcv, respectively) with the mean and CV of PlayerLoad (PL_chronic and PL_cv, respectively). A position × time interaction was observed for lnRMSSD (p = 0.01). lnRMSSD_BL was higher than lnRMSSDpost20 for LINEMEN (p <0.01; ES = Large) while differences for SKILL and MID-SKILL were not statistically different (p >0.05). Players with greater body mass experienced larger reductions in lnRMSSD (r = -0.62, p <0.01). Longitudinally, lnRMSSDcv was significantly related to body mass (r = 0.48) and PL_chronic (r = -0.60). After adjusting for body mass, lnRMSSDcv and PL_chronic remained significantly related (r = -0.43). The ~20 h recovery time between training sessions on consecutive days may not be adequate for restoration of cardiac-parasympathetic activity to baseline among LINEMEN. Players with a lower chronic training load throughout camp experienced greater fluctuation in lnRMSSD (i.e., lnRMSSDcv) and vice-versa. Thus, a capacity for greater chronic workloads may be protective against perturbations in cardiac-autonomic homeostasis among American college football players.
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Exercise Physiology Laboratory, Department of Kinesiology, University of Alabama, Tuscaloosa, Alabama;
Department of
Health Sciences, Armstrong State University, Savannah, Georgia;
Department of Athletics, Sports Medicine, University of
Alabama, Tuscaloosa, Alabama;
Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama; and
Department of Athletics, Sports Nutrition, University of Alabama, Tuscaloosa, Alabama
Flatt, AA, Esco, MR, Allen, JR, Robinson, JB, Earley, RL,
variability and training load among National Collegiate
Athletic Association Division 1 college football players
throughout spring camp. J Strength Cond Res 32(11):
3127–3134, 2018—The purpose of this study was to deter-
mine whether recovery of cardiac-autonomic activity to baseline
occurs between consecutive-day training sessions among posi-
tional groups of a collegiate football team during Spring camp. A
secondary aim was to evaluate relationships between chronic (i.e.,
4-week) heart rate variability (HRV) and training load parameters.
Baseline HRV (lnRMSSD_BL) was compared with HRV after
;20 hours of recovery before next-day training (lnRMSSDpost20)
among positional groups composed of SKILL (n= 11), MID-
SKILL (n= 9), and LINEMEN (n= 5) with a linear mixed model
and effect sizes (ES). Pearson and partial correlations were used
to quantify relationships between chronic mean and coefficient of
variation (CV) of lnRMSSD (lnRMSSD_chronic and lnRMSSDcv,
respectively) with the mean and CV of PlayerLoad (PL_chronic
and PL_cv, respectively). A position 3time interaction was
observed for lnRMSSD (p= 0.01). lnRMSSD_BL was higher than
lnRMSSDpost20 for LINEMEN (p,0.01; ES = large), whereas
differences for SKILL and MID-SKILL were not statistically differ-
ent (p.0.05). Players with greater body mass experienced larger
reductions in lnRMSSD (r=20.62, p,0.01). Longitudinally,
lnRMSSDcv was significantly related to body mass (r= 0.48) and
PL_chronic (r=20.60). After adjusting for body mass,
lnRMSSDcv and PL_chronic remained significantly related
(r=20.43). The ;20-hour recovery time between training ses-
sions on consecutive days may not be adequate for restoration of
cardiac-parasympathetic activity to baseline among LINEMEN.
Players with a lower chronic training load throughout camp expe-
rienced greater fluctuation in lnRMSSD (i.e., lnRMSSDcv) and
vice versa. Thus, a capacity for greater chronic workloads may
be protective against perturbations in cardiac-autonomic homeo-
stasis among American college football players.
KEY WORDS parasympathetic, autonomic, monitoring, sport
physiology, sport science, recovery
Despite a demanding training schedule and high
injury rate in American football (26), research
pertaining to training status monitoring among
American football players pales in comparison
with sports such as soccer and rugby. American football
players vary in physical and performance characteristics
because of unique positional requirements. For example, re-
ceivers and defensive backs (SKILL) experience the greatest
running demands and thus tend to have the fastest sprinting
speeds, lowest body and fat mass, and greatest aerobic fitness
level among positional groups (32,36). By contrast, offensive
and defensive linemen (LINEMEN) have the lowest run-
ning demands but regularly encounter physical bouts in
which they must displace their opponent to gain or defend
field position (26,36). LINEMEN, therefore, have the high-
est maximal strength, greatest body and fat mass, and lowest
aerobic fitness level of the various positional groups (32).
Linebackers, running backs, and tight-ends (MID-SKILL)
experience playing demands characteristic of both SKILL
and LINEMEN and thus tend to display physical and fitness
characteristics intermediate to these positions (26,32,36).
Given that body mass, playing demands and physiological
responses to training vary by position (26), recovery duration
Address correspondence to Andrew A. Flatt,
Journal of Strength and Conditioning Research
Ó2017 National Strength and Conditioning Association
VOLUME 32 | NUMBER 11 | NOVEMBER 2018 | 3127
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
requirements may also differ among position. However, this
topic has received little investigation.
Wearable devices using triaxial accelerometers (micro-
sensors) are capable of measuring movement profiles of
athletes during sports play. Wearable microsensors are
attractive to coaches because the physical demands of
competition or training can be quantified with minimal
burden to the athlete. Though training load monitoring is
becoming more popular in American football (35–37), its use
in conjunction with recovery status indicators among foot-
ball players has not been well studied. This research is
needed because external training load does not provide
information regarding internal physiological responses and
there exists substantial interindividual variation in responses
and adaptation to training (24).
A recovery status metric gaining popularity among
sports teams is resting heart rate variability (HRV). Heart
rate variability reflects autonomic modulation of the heart
and can be measured noninvasively with inexpensive field
tools such as smartphone applications (13,15,28). Vagally
mediated HRV is considered a global marker of homeo-
stasis and reflects cardiovascular recovery after a training
session (33). For example, vagal-HRV suppression is
observed for 24–48 hours after intense training (33) with
the return to baseline possibly reflecting the optimal
state for subsequent intensive training (22). Cardiac-
parasympathetic recovery from exercise is affected by fac-
tors such as fitness level, exercise intensity, and alterations
in fluid balance (33). Because fitness level, body mass, as
well as fluid balance and thermoregulatory responses to
training vary among positional groups in American foot-
ball (9,32), daily cardiac-autonomic responses inferred
from vagal HRV may also differ among positions and thus
provide useful recovery status information.
Greater daily fluctuation in cardiac-parasympathetic activity
(assessed by the coefficient of variation [CV]) has been
associated with lower maximal oxygen uptake (V
max) (14),
lower intermittent running performance (2,7,14,16), and greater
fatigue during intensified training (17,18). An athlete’s chronic
training load (i.e., 4-week average) is sometimes used as an
indicator of training capacity or fitness level (21). Hypotheti-
cally, players who perform greater chronic training loads (i.e.,
SKILL) should have a lower CV of vagal HRV, whereas those
who perform lower chronic workloads (i.e., LINEMEN) would
be expected to display a greater CV. This would build on pre-
vious findings that less fit individuals experience greater pertur-
bations in cardiac-autonomic homeostasis than more fit
individuals (7,14,16). However, no previous studies have eval-
uated relationships between chronic training load and chronic
vagal-HRV trends in American football players.
The purpose of this study was to determine whether
recovery of cardiac-autonomic activity to baseline occurs
between consecutive-day training sessions among positional
groups of a collegiate football team. A secondary aim was to
assess relationships between football players’ chronic work-
loads and chronic vagal-HRV trends (i.e., mean and CV)
from an annual Spring training camp.
Experimental Approach to the Problem
This was a prospective observational cohort study that
evaluated cardiac-autonomic responses to training among
positional groups of an elite college football team during
their 2016 Spring training camp. The research design and
methodology were devised according to the predetermined
program and structure of the training camp, which the
researchers did not influence. Vagal-HRV and external
training load were acquired each football training day
throughout the 4-week Spring camp. For the first objective,
we assessed whether vagal HRV returned to baseline levels
between consecutive-day training sessions (i.e., after ;20
hours of recovery) among positional groups. For the second
objective, relationships between chronic external training
load and chronic vagal HRV (i.e., mean and CV) from the
4-week camp were evaluated.
Twenty-five Division 1 football players from a National
Collegiate Athletics Association team volunteered for this
study. This was the national championship winning team
from the preceding competitive season. Only players aged 18
years and older and on athletic scholarship were included.
Volunteers were grouped based on position as previously
described (26) (SKILL: n= 11; age = 20 61 years; height =
187.7 64.0 cm; body mass = 90.2 64.3 kg; MID-SKILL: n
= 9; age = 20 61 years; height = 188.8 65.5 cm; body mass
= 103.8 64.4 kg; and LINEMEN: n= 5; age = 22 61 years;
height = 192.5 62.8 cm; body mass = 131.1 610.6 kg). All
athletes obtained medical clearance from the sports medi-
cine staff and provided written informed consent for their
involvement as research participants. Study approval was
granted by the University of Alabama institutional review
Spring Camp. Spring camp was held between mid-March and
mid-April and involved 4 weeks of football training. Two-
hour football practices were held on Monday, Wednesday,
and Friday of week 1; Monday, Wednesday, Friday, and
Saturday of weeks 2 and 3; and Tuesday, Thursday, and
Saturday of week 4. Forty-five minutes full-body resistance
training sessions of fewer sets and lower intensity (#85% of 1
repetition maximum) relative to workouts preceding Spring
camp were held on Tuesdays and Thursdays throughout
weeks 1–3. Sundays were reserved for passive rest. Before
Spring camp, all players participated in an 8-week off-season
strength and conditioning program. Outdoor temperatures
during training were 21.7 62.88C.
Training Load. Training load parameters were obtained from
football sessions using triaxial accelerometers (Catapult
HRV in College Football Players
Journal of Strength and Conditioning Research
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Innovations, Melbourne, Australia) at a sampling rate of 100
Hz. These devices measure full-body acceleration in 3
planes: anteroposterior, mediolateral, and vertical. Subjects
wore the same device each session, positioned between the
scapulae, and fixed in place on their shoulder pad in
a custom-built cartridge. After each practice session, training
load data were downloaded to a laptop for analysis. The
training load parameter used for this study was total Play-
erLoad. This parameter has demonstrated acceptable reli-
ability and reflects total external workload including running,
jumping, changes of direction, and body contacts (3). In
addition, variation in PlayerLoad has been related to injury
occurrence in college football players (37). PlayerLoad is
expressed as the square root of the sum of the squared
instantaneous rate of change in acceleration in each vector
and divided by 100 (3).
Physiological Assessments. Fasted body mass was measured on
a calibrated digital scale (Tanita Corporation, Arlington
Heights, IL, USA) at the training facility before Spring
camp. Heart rate variability data were obtained in the
athletic training facility at least 90 minute after team
breakfast and before any physical activity (25). Subjects ver-
bally confirmed that caffeinated beverages were not con-
sumed before data acquisition. Heart rate variability
recordings were obtained while subjects were seated com-
fortably on an athletic training table 60–90 minute before
training. Once seated, participants were handed a tablet
device (iPad2; Apple, Inc., Cupertino, CA, USA) with a val-
idated optical pulse-wave finger sensor (HRV Fit Ltd.,
Southampton, United Kingdom) inserted into the head-
phone slot (13). Subjects were instructed to insert their left
index finger into the finger sensor cuff and to select their
name from the team roster previously uploaded onto the
application (ithlete Team; HRV Fit Ltd). Subjects would
then initiate a 1-minute HRV recording while remaining
quiet and breathing naturally (30). All HRV recordings were
preceded by at least 1 minute for stabilization (24). The
application provides the time domain vagal-HRV index of
the natural logarithm of the root mean square of successive
RR intervals (lnRMSSD). The lnRMSSD is multiplied by 20
to fit an approximate 100-point scale for simplified interpre-
tation (15). The application is equipped with an irregular
pulse-rate detection algorithm which excludes interpulse in-
tervals ,500 ms and .2000 ms. In addition, adjacent normal
pulse–pulse (PPn) interval differences are automatically
examined using the following formula: (PPn 2[PPn 21])
,(40 3Exp [120/PRave])
, where PRave is the average
pulse rate calculated since commencement of the recording.
Statistical Analyses
Data normality was confirmed with the Shapiro-Wilk test (p.
0.05). To determine whether lnRMSSD returned to baseline
between consecutive-day football training sessions, we com-
pared baseline lnRMSSD (lnRMSSD_BL) to lnRMSSD after
the ;20-hour recovery period between training sessions
(lnRMSSDpost20). The intraindividual mean lnRMSSD from
Monday, Wednesday, and Friday of week 2 represented
lnRMSSD_BL as each of these training sessions were sepa-
rated by $44 hours. Saturday of the same week represented
lnRMSSDpost20 as it was preceded by only ;20-hour rest
before the next session. Compliance for HRV measures for this
component was 100%. A linear mixed model was used to
examine variation in lnRMSSD among positions and between
lnRMSSD_BL and lnRMSSDpost20. Position was included as
a fixed effect, time (lnRMSSD_BL vs. lnRMSSDpost20) as
a fixed within-subjects repeated measure, the position 3time
interaction as a fixed effect, and athlete identification as a ran-
dom effect. Tukey honest significant difference (HSD) was used
for post hoc analyses. We evaluated the magnitude of the
change in lnRMSSD between conditions with Cohen’s deffect
sizes (ES) 690% confidence limits (CLs) (8). Effect sizes were
interpreted qualitatively as follows: ,0.2, trivial; 0.2–0.59, small;
0.6–1.19, moderate; 1.2–1.9, large; and .2.0, very large (20).
The effect was deemed unclear if the CL crossed thresholds
for both substantially positive (0.20) and negative (20.20)
values (1). Individual change variables for lnRMSSD were
calculated (lnRMSSDpost20 2lnRMSSD_BL, DlnRMSSD).
TABLE 1. Model effects and the natural logarithm of the root mean square of successive RR interval difference
(lnRMSSD) values among and between positional groups and across time.*
effect Fdf p Mean 6SD lnRMSSD
Position 4.24 2, 22 0.028 SKILL: 82.4 67.6 MID-SKILL: 76.4 68.0 LINEMAN: 71.0 610.3
Time 18.47 1, 22 ,0.001 BL: 80.0 67.3 Post: 76.0 610.7z
5.46 2, 22 0.012 SKILL BL: 82.8 67.2 MID-SKILL BL: 78.4 66.2 LINEMAN BL: 76.6 68.6
SKILL post: 82.0 68.3 MID-SKILL post: 74.4 69.5 LINEMAN post: 65.4 69.5§
*BL = baseline; post = 20-hour post-training.
p#0.05 vs. SKILL across all time-points.
zp#0.05 vs. BL across all groups.
§p#0.05 vs. BL for LINEMEN.
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The relationship between DlnRMSSD and body mass was
quantified via Pearson correlation. PlayerLoad values derived
from the same days as lnRMSSD_BL were compared among
positions with a 1-way analysis of variance (ANOVA), Tukey
HSD post hoc analysis, and ES.
Pearson correlations were used to quantify relationships
between the mean and CV of PlayerLoad, (PL_chronic and
PLcv, respectively) with the mean and CV of lnRMSSD
(lnRMSSD_chronic and lnRMSSDcv, respectively) derived
from the entire 4-week training camp (i.e., all training days).
The thresholds used for qualitative assessment of the
correlations were as follows: ,0.1, trivial; 0.1–0.29, small;
0.3–0.49, moderate; 0.5–0.7, large; 0.7–0.89, very large;
and .0.9 nearly perfect (20). Positional differences in
lnRMSSDcv were evaluated
with a 1-way ANOVA, Tukey
HSD post hoc analysis, and ES.
Compliance for HRV measures
for this component was 93.4 6
8.7%. Statistical procedures were
performed using JMP Pro 13
(SAS Institute, Inc., Cary, NC,
USA) and Excel 2016 (Microsoft
Corp., Redmond, WA, USA).
pvalues #0.05 were considered
statistically significant. Data are
reported as mean 6SD unless
noted otherwise.
A significant effect was found for
PlayerLoad values derived from
the same days as lnRMSSD_BL
(p,0.0001). Baseline Player-
Load values for SKILL, MID-
623.7 660.6 au, 556.8 653.6
au, and 450.0 629.2 au, respec-
tively. Post hoc comparisons re-
vealed that PlayerLoad values
for SKILL were significantly
higher than LINEMAN (p,
0.0001, ES = 3.27 61.27, very
large) and MID-SKILL (p=
0.029, ES = 1.16 60.79, moder-
ate). In addition, MID-SKILL
baseline PlayerLoad was signifi-
cantly greater than LINEMEN
(p= 0.005, ES = 2.25 61.10,
very large).
A significant position 3
time interaction was found
for lnRMSSD. Model effects
and lnRMSSD values are pre-
sented in Table 1. Post hoc
comparisons revealed that lnRMSSD_BL was signifi-
cantly higher than lnRMSSDpost20 for LINEMEN (p,
0.01; ES = large), whereas differences for SKILL (p=
0.998; ES = unclear) and MID-SKILL (p= 0.343; ES =
unclear) were not statistically significant. Interaction ES
and individual lnRMSSD responses are graphically
displayed in Figure 1A, B, respectively.
SKILL had greater lnRMSSD than LINEMEN (Table 1;
ES = 1.35 60.96; large). In addition, evaluated as a group (n=
25), lnRMSSD_BL was higher than lnRMSS Dpost20 (Table 1;
ES = 0.44 60.47; small). We observed a large and significant
negative relationship between DlnRMSSD and body mass
(Figure 2A), indicating that greater reductions in lnRMSSD
occurred among players with greater body mass.
Figure 1. A) Effect size 690% confidence limits (ES 690% CLs) comparison between baseline natural
logarithm of the root mean square of successive RR interval differences (lnRMSSD_BL) with lnRMSSD after 20
hours of rest (lnRMSSDpost20) among positional groups. The horizontal dashed lines represent thresholds for
a small effect (0.20 to 20.20). B) Individual lnRMSSD_BL and lnRMSSDpost20 values among positional groups.
HRV in College Football Players
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A significant effect for lnRMSDcv was observed
values were 6.5 62.9%, 8.5 61.7%, and 10.5 62.7%, respec-
tively. Post hoc analysis showed that lnRMSSDcv for SKILL
was significantly lower than LINEMEN (p#0.05, ES = 21.41
60.97, large) but not MID-SKILL (p.0.05, ES = 20.82 6
0.76, moderate). In addition, MID-SKILL lnRMSSDcv was
not significantly different from LINEMEN (p.0.05, ES =
20.96 61.28, unclear).
The lnRMSSD_chronic did not show significant relation-
ships with PL_chronic (r=0.33,p= 0.109), PLcv (r= 0.16, p=
0.436), or body mass (r=20.36, p= 0.073). There was a large,
significant negative relationship between lnRMSSDcv and
PL_chronic (Figure 2B) and a moderate, significant positive
relationship between lnRMSSDcv and body mass (Figure
2C). The lnRMSSDcv did not relate with PLcv (r=0.025
p= 0.907). Body mass showed a large, significant negative
relationship with PL_chronic (Figure 2D). After adjusting for
body mass via partial correlation analysis, the negative relation-
ship between lnRMSSDcv and PL_chronic remained signifi-
cant (r=20.42, p= 0.034, moderate).
This study evaluated daily and chronic lnRMSSD responses
to training among an elite college football team throughout
Spring camp. The novel finding was that daily lnRMSSD
responses to a football training session differ by position.
;Twenty hours after a practice session, LINEMEN show
substantial reductions from baseline, whereas SKILL and
MID-SKILL were recovered to within or near baseline val-
ues. Longitudinally, lnRMSSDcv was inversely related to
PL_chronic, independent of body mass.
Previous studies among youth and elite adult soccer and
rugby players have found that daily cardiac-parasympathetic
activity is not significantly affected by training sessions at the
group level (4,12,34), but is substantially reduced 1-day post-
competition among youth players (4,12). During a ,1 week
training camp, elite adult rugby players showed small reduc-
tions in lnRMSSD after the first day of training and
remained suppressed for the duration of camp (25). Exclud-
ing LINEMEN, our findings tend to agree with the previous
literature considering SKILL players are reasonably compa-
rable to soccer players and MID-SKILL players are reason-
ably comparable to rugby players with regards to physical
characteristics (23).
Despite substantially smaller PlayerLoad values than
SKILL and MID-SKILL, LINEMEN demonstrated large
reductions in lnRMSSDpost20, indicating inadequate car-
diovascular recovery between sessions. A recent systematic
review determined that cardiac-parasympathetic recovery
Figure 2. A) Scatterplot representing the relationship of the difference in natural logarithm of the root mean square of successive RR interval differences from
baseline (lnRMSSD_BL) and 20-hour post-training (lnRMSSDpost20) (i.e., lnRMSSDpost20 2lnRMSSD_BL = DlnRMSSD) with body mass. B) Scatterplot
representing the relationship between the coefficient of variation of lnRMSSD (lnRMSSDcv) and average PlayerLoad (PL_chronic) from the entire 4-week camp.
C) Scatterplot representing the relationship between lnRMSSDcv and body mass. D) Scatterplot representing the relationship between PL_chronic and body
Journal of Strength and Conditioning Research
VOLUME 32 | NUMBER 11 | NOVEMBER 2018 | 3131
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from exercise is slower in individuals with lower aerobic
fitness and is suppressed for longer durations (;48 hours)
after high intensity, anaerobic exercise (33). We speculate
that numerous factors related to body mass, fitness, and
exercise intensity may have contributed to the suppressed
lnRMSSDpost20 in LINEMEN. For example, during 60 mi-
nutes of simulated football training, LINEMEN displayed an
average exercise intensity corresponding to 79% of maxi-
mum HR, a respiratory exchange ratio .0.90, and blood
lactate levels .5.0 mmol$L
(19). As such, LINEMEN
may experience a substantial anaerobic workload during
training due to both lower aerobic fitness and movement
demands that depend more heavily on the expression of
strength and power (e.g., repeated blocking, tackling, and short
sprints). Moreover, LINEMEN are more sedentary than other
positions between efforts because of smaller distances covered,
necessitating less jogging (29). This provides less active recov-
ery for LINEMEN in addition to reduced convective heat loss
compared with SKILL (11). Indeed, LINEMEN experience the
greatest internal core temperatures and fluid loss during training
which may increase cardiac strain (10). Deren et al. (10) dem-
onstrated that LINEMEN experience a progressive increase in
exercise HR beyond 30 minutes of cycling in the heat at a work-
load that elicits a heat production of 350 W$m
, whereas
SKILL demonstrated no such change in HR. Although con-
tinuous cycling is dissimilar from football training, the cardio-
vascular response may be relevant. Dehydration-induced
hypovolemia is thought to contribute to suppressed lnRMSSD
in the 24- to 48-hour postexercise period (33). Accordingly,
unloading of cardiopulmonary baroreceptors facilitates sympa-
thoexcitation to maintain peripheral resistance and combat the
reduction in cardiac output consequent to reduced stroke vol-
ume (31). It has been hypothesized that lnRMSSD may not
return to baseline after intense exercise until plasma volume has
been restored (6). Collectively, lower aerobic fitness, larger body
mass, greater reliance on anaerobic-glycolytic metabolism
during training, and disturbed fluid balance may all have con-
tributed to the delayed cardiac-parasympathetic recovery in
Whether unrecovered lnRMSSD affects performance or
injury risk in football players remains unclear, particularly
during periods when sessions areheldmorefrequently(e.g.,
preseason). Hypothetically, consistent inadequate recovery may
lead to suppressed lnRMSSD over prolonged periods which
has been associated with high perceived fatigue, illness, and
decrements in running performance (16,18,27). We speculate
that LINEMEN may be at greater risk of inadequate recovery
when training over several consecutive days based on their
daily lnRMSSD responses to training observed in this study.
Thus, future research evaluating cardiac-autonomic responses
to more frequent training among LINEMEN is warranted.
Relationships between chronic training load and
lnRMSSD parameters have not previously been investi-
gated in American football. Although indirect, chronic
training load is sometimes used as a surrogate for fitness
level (21). We did not find a significant relationship between
lnRMSSD_chronic and PL_chronic, possibly because weekly
changes in averaged lnRMSSD relate more with fitness (5,16)
than lnRMSSD averaged over chronic periods. Previous studies
found that lnRMSSDcv is inversely related to V
max and
intermittent-running performance in soccer players (2,7,14,16).
We observed a significant inverse relationship between
lnRMSSDcv and PL_chronic after adjusting for body mass.
This suggests that training capacity (inferred from PL_chronic)
may be a determinant of lnRMSSDcv in football players
during Spring camp, a period where overreaching is
unlikely to occur due to mostly nonconsecutive-day train-
ing. This distinction is important because more intensified
(e.g., preseason) or stressful (e.g., competitive season)
periods may increase lnRMSSDcv and alter its relation-
ship with PL_chronic. Another explanation may be that
lnRMSSDcv is reflecting position-specific training effects,
where LINEMEN experience more physiologically dis-
ruptive demands from line-play, exacerbated by their size
and fitness level relative to other positions. Additional
research is needed to determine what information
lnRMSSDcv provides in the context of fitness and training
adaptation in football players.
Although HRV data collection was standardized ac-
cording to recent recommendations (25), there is greater
potential for “noise” when acquired at the facility versus
postwaking and thus is a limitation of this study. In addi-
tion, HRV data were only collected on football training
days, and therefore the lnRMSSD values do not reflect
all days of the week. The inclusion of only 5 LINEMEN
is also a limitation, and thus further research is needed to
support the current findings. It must also be considered
that the lnRMSSD responses from this observation period
may not reflect what occurs during training in hotter con-
ditions with more frequent training sessions. Finally, rela-
tionships between chronic workloads and fitness have not
been investigated in American football players; thus, we
caution readers that our interpretation of lnRMSSDcv re-
flecting training capacity is, therefore, speculative.
Approximately 20 hours after a football training session,
lnRMSSD values were suppressed for LINEMEN, whereas
SKILL and MID-SKILL values returned to near or within
baseline. Players with greater body mass showed the greatest
reductions in lnRMSSD in response to training. Over the 4-
week camp, players who performed the lowest chronic
workloads experienced greater fluctuation in lnRMSSD (i.e.,
lnRMSSDcv) and vice versa. Therefore, training capacity
may be a determinant of lnRMSSD trends in football players
during Spring camp.
During Spring camp, LINEMEN did not attain cardiac-
parasympathetic recovery to baseline between consecutive-
day training sessions, potentially making them susceptible
to autonomic nervous system imbalance during more
HRV in College Football Players
Journal of Strength and Conditioning Research
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intensive training periods. Longitudinally, a capacity for
greater chronic workloads may be protective against daily
perturbations in cardiac-autonomic homeostasis based
on the inverse relationship between PL_chronic and
Obtaining compliance from athletes to perform daily HRV
measures at home after waking is challenging and in some
cases prohibitive of the implementation of HRV monitoring.
This study showed that standardized, 60-second lnRMSSD
recordings acquired via mobile devices at the training facility
may provide meaningful information regarding training
responses among football players. This may make HRV
monitoring a more convenient process for coaching and
support staff.
The authors extend their thanks to the Alabama football
players and staff for their participation in this study. The
authors also thank Aaron Brosz for his assistance throughout
the study. The authors have no conflicts of interest to
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HRV in College Football Players
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... Most of the papers dealt with the topic of training loads in American football athletes and all of them measured external loads. Eight papers [147,153,154,159,[164][165][166]168] measured internal load. Investigations of physical demands during practices were reported by five papers [144,151,157,163,167], whereas another five papers focused on game demands [145,148,150,158]. ...
... Quantification of average and maximum distances traveled in games external load:total distance, low (0 to 12.9 km/h), moderate (12.9 to 22.5 km/h), moderate-high (>19.3 km/h), high (>22.5 km/h) intensity distance; max range = computed as the range from the mean distance +1SD to max distance 0.82 [166] Sports science 17 Relationship between training load and next-day recovery internal load: physiological load is a heart rate-based metrics from 0 to 10 where 0 corresponds to 50% of age-predicted heart rate and 10 to 100% of max age-predicted HR; s-RPE x time practice; external load: mechanical load given by the peak acceleration along any direction scaled from 0 (0.5 g) to 10 In four of the analyzed papers [144,164,166,168], the internal load measures were extracted from wearable sensors. Another four papers published by Flatt et al., from 2018 to 2021 [153,154,159,165], opened up a new research line regarding the monitoring of HRV and cardiovascular recovery between training days in American football athletes. The HRV was sampled thanks to mobile devices and finger sensors just before each training session; the subjects wore GPS sensors to collect player load data. ...
... The metric used to understand the effect of the autonomic nervous system on cardiovascular function was a time-domain index of vagal tone. The researchers documented the behavior of HRV during spring camp [153], in-season practices [159], and across the entire season from preseason to postseason [165], stratifying the results by player position. Flatt et al. [154] reported on the daily fluctuations of HRV experienced by a concussed player. ...
Full-text available
American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.
... 1,6 For meaningful interpretation of adaptation to training using RMSSD data, it is important to consider the normal, day-to-day perturbations in RMSSD (assessed by the coefficient of variation, CV; %) from a variety of potential stimuli. 3,8,9 As such, significant daily fluctuations of vagal activity (CV of RMSSD) have been associated with greater fatigue during training sessions. 9 Thus, RMSSD and CV of RMSSD (RMSSD CV ) are the preferred metric for daily measurement by athletes in the field to indicate specific perturbations to training stimuli. ...
... RMSSD CV values represent the SE of the estimate (absolute reliability) within interday RMSSD M assessments. 3,8,9 By observing RMSSD CV from at least 3 morning HRV recordings, users can assess cardiac-autonomic modulations that inevitably occur throughout a week of training instead of a single, isolated value compressed over the full week. 2,8,9 Shapiro-Wilk test was performed to identify nonnormal data. ...
... 3,8,9 By observing RMSSD CV from at least 3 morning HRV recordings, users can assess cardiac-autonomic modulations that inevitably occur throughout a week of training instead of a single, isolated value compressed over the full week. 2,8,9 Shapiro-Wilk test was performed to identify nonnormal data. Due to the skewed nature of vagally derived indices of HRV, all daily RMSSD values were log-transformed (LnRMSSD M and LnRMSSD CV ), and these values were used for subsequent analyses. ...
Full-text available
Introduction: The parasympathetically derived marker of heart rate variability, root mean square of successive R-R differences (RMSSD), and the daily fluctuations as measured by the coefficient of variation (RMSSDCV) may be useful for tracking training adaptations in athletic populations. These vagally derived markers of heart rate variability may be especially pertinent when simultaneously considering a female athlete's menstrual cycle. Purpose: The purpose of this study was to observe the perturbations in RMSSDCV, while considering RMSSD, across a season in the presence and absence of menses with training load in female collegiate rowers. Methods: Thirty-six (20 [1] y, 25.6 [3.4] kg·m-2) National Collegiate Athletic Association Division I female rowers were monitored for 18 consecutive weeks across a full season. Seated, ultrashortened RMSSD measurements were obtained by the rowers on at least 3 mornings per week using a smartphone photoplethysmography device. Following the RMSSD measurement, athletes indicated the presence or absence of menstruation within the application. Individual meters rowed that week and sessions rate of perceived exertion were obtained to quantify training load. Results: Longitudinal mixed-effects modeling demonstrated a significant effect of menses and time, while also considering RMSSD, such that those who were on their period had a significantly greater RMSSDCV than those who were not (11.2% vs 7.5%, respectively; P < .001). These changes were independent of meters rowed, sessions rate of perceived exertion, body mass index, birth-control use, and years of rowing experience, which were all nonsignificant predictors of RMSSDCgV (P > .05). Conclusion: The presence of menses appears to significantly impact RMSSDCV when also considering RMSSD, which may allow coaches to consider individualized training plans accordingly.
... Despite changes in hormonal and wellness stressors, HRV remained unaltered, indicating HRV may not be as sensitive to high volumes and intensities of the preseason. One possible explanation is that the athletes in the current study had a high capacity for increased chronic workloads and had protective adaptations against daily alterations in cardiac-autonomic homeostasis (13). Irrespective, athletes in the current study demonstrated increased anabolism and decreased catabolism, worsened mood states, and unaltered HRV before entering in-season play. ...
... Furthermore, despite bidirectional associations between hormones and external loads, morning HRV was not associated with external load measures during afternoon practices or scrimmages. Yet, large correlations have been previously reported between HRV and weekly player load in NCAA DI football players (13). In addition, trivial relationships between HRV and total distance, acceleration, and high-speed distance have been observed in men elite soccer players (10). ...
... However, the necessary assumption for correlation analyses of independent samples may be rejected when scores are summed over a course of a week and thus may reveal falsely inflated relationships in the aforementioned studies. In addition, using HRV to monitor training responses is challenging as it is influenced by factors independent of the work performed, including body mass, fitness, exercise intensity, weather, and hydration (13). Thus, the lack of associations between HRV and external load observed in the current study may be partially attributed to the hot and humid preseason climate the athletes were exposed to (temperature average: 32.2°C). ...
Fields, JB, Merigan, JM, Gallo, S, White, JB, and Jones, MT. External and internal load measures during preseason training in men collegiate soccer athletes. J Strength Cond Res 35(9): 2572-2578, 2021-Collegiate athletes are exposed to high volume loads during preseason training. Monitoring training load can inform training and recovery periods. Therefore, the purpose was to examine changes in and bidirectional relationship between external and internal load metrics in men collegiate soccer athletes (n = 20; age, 20 ± 1 year). Internal load measures of heart rate variability (HRV), salivary testosterone (T) and cortisol (C), and self-assessment wellness and ratings of perceived exertion scales were collected daily. External load measures of total distance, player load, high-speed distance, high inertial movement analysis, and repeated high-intensity efforts were collected in each training session using global positioning system/global navigation satellite system technology. A 1-way analysis of variance determined weekly changes in external load, physiological, hormonal, and subjective self-assessment measures of internal load. Bidirectional prediction of external load markers and self-assessment measures on physiological and hormonal markers of internal load were assessed by hierarchical linear regression models (p < 0.05). External load measures, C, energy, sleep, and rate of perceived exertion (RPE) decreased (p < 0.01), whereas T, T:C ratio, anger, depression, and vigor increased (p < 0.01) from week 1 to week 2. Morning C positively predicted afternoon external load and post-training RPE (p < 0.05); T:C ratio negatively predicted afternoon external load and post-training RPE (p < 0.05); and morning HRV negatively predicted post-training RPE (p = 0.031). Despite reduced hormonal stress and external load across weeks, negative perceptions of fatigue increased, suggesting fatigue patterns may have a delayed response. Load may have a more belated, chronic effect on perceptions of fatigue, whereas hormonal changes may be more immediate and sensitive to change. Practitioners may wish to use a variety of external and internal load measures to understand athletes' stress responses to training.
... It has recently been demonstrated that linemen exhibit significantly slower interday HRV recovery than nonlinemen. 15,16 However, the implications of these findings are unclear as longitudinal changes in HRV throughout a competitive football season have yet to be characterized. ...
... Recordings were 55 seconds in duration and were preceded by at least 60 seconds for stabilization. 15,16,18 The application automatically detects and corrects for irregular interpulse intervals using the following algorithm: ...
... Previous investigation into HRV responses to football training are limited to short-term training periods (≤4 wk) with aims of delineating position-based differences in day-to-day cardiacautonomic recovery. 15,16 Linemen demonstrated progressive adaptation to football training throughout the annual cycle, reflected in progressively smaller physiological responses to recurrent training stress. 22 ES decrements in LnRMSSD observed ∼20 hours posttraining were qualitatively large during off-season spring camp, 16 moderate following day 1 of preseason camp, 18 and small following Tuesday sessions during the early competitive phase (all P < .05). 15 Nonlinemen showed consistently trivial to small reductions at the same time points. ...
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Purpose: To track cardiac-autonomic functioning, indexed by heart-rate variability, in American college football players throughout a competitive period. Methods: Resting heart rate (RHR) and the natural logarithm root mean square of successive differences (LnRMSSD) were obtained throughout preseason and ∼3 times weekly leading up to the national championship among 8 linemen and 12 nonlinemen. Seated 1-minute recordings were performed via mobile device and standardized for time of day and proximity to training. Results: Relative to preseason, linemen exhibited suppressed LnRMSSD during camp-style preparation for the playoffs (P = .041, effect size [ES] = -1.01), the week of the national semifinal (P < .001, ES = -1.27), and the week of the national championship (P = .005, ES = -1.16). As a combined group, increases in RHR (P < .001) were observed at the same time points (nonlinemen ES = 0.48-0.59, linemen ES = 1.03-1.10). For all linemen, RHR trended upward (positive slopes, R2 = .02-.77) while LnRMSSD trended downward (negative slopes, R2 = .02-.62) throughout the season. Preseason to postseason changes in RHR (r = .50, P = .025) and LnRMSSD (r = -.68, P < .001) were associated with body mass. Conclusions: Heart-rate variability tracking revealed progressive autonomic imbalance in the lineman position group, with individual players showing suppressed values by midseason. Attenuated parasympathetic activation is a hallmark of impaired recovery and may contribute to cardiovascular maladaptations reported to occur in linemen following a competitive season. Thus, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.
... Moreover, these data can help direct users in improving their HRV. A higher and more consistent HRV from day to day is typically associated with better health and improved fitness [83,84]. Thus, wearable devices can serve not only as a tracking tool but also as a personal training tool to help users get positive feedback, such as in HRVB, to improve their condition. ...
Full-text available
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
... By effectively processing this signal, it can be judged what kind of action the human body has made [6]. With the continuous improvement of microelectronic system technology, acceleration sensors are becoming smaller and cheaper, and they have been widely embedded in mobile phone devices, notebooks, electronic game consoles, etc., and are based on acceleration sensors [7]. Various studies provide a broader platform. ...
Full-text available
According to the algorithm of time difference and threshold value, this paper selects the more valuable data for motion state recognition and selects the characteristics, respectively selects the data of the combined acceleration and the combined angular velocity, and uses the data of the pitch angle and the roll angle more novelly. In the aspect of data preprocessing, the sliding segmentation window method is used for feature processing, and the time domain and frequency domain features of the data are extracted. A total of 108 dimensional features are extracted. In order to improve the calculation performance, PCA technology is used for data dimensionality reduction. In this paper, we collected data on changes in physiological parameters of 24 experimenters before and after exercise, collected 14 self-evaluated severely fatigued volunteers and self-evaluated severely stressed volunteers’ resting heart rate and blood pressure data as unhealthy data samples, and collected physiological data of 14 healthy experimenters as unhealthy data samples. For healthy samples, three sets of experiments were set up to analyze the changes of exercise heart rate, exercise blood pressure and exercise body temperature, and the effectiveness of fusion of physiological data to improve the performance of exercise recognition and the analysis of the health status of physiological parameters that introduce exercise interference. The experimental results show that during exercise, monitoring changes in systolic blood pressure is more meaningful than monitoring changes in diastolic blood pressure; it verifies the effectiveness of improving the performance of exercise recognition by fusion of physiological parameters. The addition of physiological data can effectively improve the recognition rate of exercise. The recognition rate has been increased from 93.7% to 96.3%; the effectiveness and applicability of the algorithm in this paper are analyzed through design experiments, and the results show that the recognition accuracy of the algorithm in this paper is above 87%. This result has a good classification recognition rate for a small sample.
... Finally, scientists have only recently shifted attention toward monitoring training load among collegiate student-athletes in the United States (Conte et al., 2018;Flatt et al., 2018;Govus et al., 2018;Hamlin et al., 2019;Huggins et al., 2019;Sampson et al., 2019). Previous research has demonstrated that the collegiate student-athlete population may experience elevated levels of stress and burnout due to perfectionistic tendencies, competing demands as student and athlete, insufficient self-regulation skills, as well as responses to training load and competition (Gould and Whitley, 2009;Dubuc-Charbonneau and Durand-Bush, 2015;Garinger et al., 2018;Huml et al., 2019). ...
Full-text available
The purpose of the current study was to examine the reliability and validity of the RESTQ-Sport-36 for use in the collegiate student-athlete population. A total of 494 collegiate student-athletes competing in National Collegiate Athletic Association Division I, II, or III sanctioned sport completed the RESTQ-Sport-36 and Brief Profile of Mood States (POMS). Structural equation modeling (SEM) procedures were used to compare first order to hierarchical model structures. Results of a confirmatory factor analysis (χ ² [528] = 1129.941, p < 0.001; SRMR = 0.050; CFI = 0.929) and exploratory structural equation modeling analysis (χ ² [264] = 575.424, p < 0.001; SRMR = 0.013; CFI = 0.963) indicated that the first order 12-factor structure demonstrated the best fit of all models tested. Support was not observed for the fit of any hierarchical model. Moderate to strong correlations were observed between stress and recovery subscales and mood states, thus supporting the construct validity of the abbreviated RESTQ measurement model. The current findings provide support for the measure’s use in this population and give pause as it relates to the scoring and interpretation of hierarchical factors such as Total Stress and Total Recovery . Overall, the current results indicate that the RESTQ-Sport-36 may be a useful tool for collegiate student-athlete training load and competition monitoring.
... While HRV has been examined across a range of collegiate sports [33,34], with emphasis on collegiate soccer [35][36][37][38], very little research has focused on student-athlete eligibility classification as a factor when documenting HRV in collegiate athletes [38]. Recently, Edmonds and colleagues found that female freshman volleyball players reported lower HRV compared to all other eligibility classifications over a collegiate season, despite similar HRV responses to games and similar perceived fatigue [38]. ...
Full-text available
The current study examined differences in heart rate (HR) variability (HRV) across student-athlete eligibility classifications within a men’s soccer team. The study also aimed to identify any differences in HRV while competing at home or away. Data collection covered an entire collegiate season, commencing in the preseason and concluding upon elimination from the NCAA Soccer tournament. Comparisons of HR and HRV, paired with self-reported subjective measures, were documented between student-athlete eligibility classifications, home versus away games, and based on soccer position (forward, midfielder, defender, goalkeeper). HR and HRV were similar based on student-athlete eligibility. Heart rate exhibited a small, but statistically significant decrease (β = −1.7 bpm (95% CI: −2.9, 0.57), p = 0.003) for the away games relative to home. HRV showed a statistically significant increase in the away game setting (β = 2.1 (95% CI: 0.78, 3.38), p = 0.002). No difference in HRV was observed across eligibility classification. This lack of difference may be attributed to a different perception of stress amongst male athletes. Athletes also exhibited a reduced HRV at home, likely as an indication of their readiness to compete paired with an increased self-confidence, given there was no difference in any subjective measures of mood or stress or between games played away or at home.
... Discomfort in the heart is a non-specific symptom that can occur in various conditions related to heart function, such as myocardial ischemia with excessive stretching of the heart chambers, which can occur with increasing end-diastolic size, a number of inflammatory diseases of different layers of the heart, aortic lesions, etc. [47][48][49][50]. These sensations can also have reflexive nature and be associated with the condition of the spine or excessive activation of the branches of the sympathetic ANS [51][52][53]. ...
The aim of the study is to identify the characteristic subjective features of highly qualified athletes with different types of cardiac rhythm regulation. Materials and methods. 202 highly qualified male athletes aged 22.6 ± 2.8 years and engaged in acyclic sports were examined. According to the designed survey protocol, all athletes were interviewed using a specifically designed questionnaire, which included 4 questions pools, each of them characterized certain components of athletes’ subjective assessment of their condition and attitude to it during the previous week, as well as studies using spiroarteriocardiorhythmography (SACR). Results. The SACR study allowed to divide athletes, taking into account heart rate variability (HRV) parameters, into 4 groups according to the types of their cardiac rhythm regulation. Subjective signs that might have clinical significance in the development of cardiovascular overexertion were uncomfortable sensations in the heart, feeling of interruption in the heart work, perspiration at rest, headache after sleep, perspiration at low loads, feeling of fatigue after sleep and night perspiration. Uncomfortable sensations in the heart occurred frequently in 1 % of cases and periodically in 15.3 % of cases, and feeling of interruption in the heart work occurred frequently in 0.5 % of cases and periodically in 14.9 % of cases. These indications were typical of people with cardiac rhythm regulations type I and II. In type III the least number of clinically significant features was noted. In type IV the number of significant features was less than in types I and II; however, this is nonsignificant. Probable differences in the features of perspiration at rest were noticed in athletes with type IV in comparison with type III. Conclusions. Subjective indications can be employed to verify the regulatory features of the cardiovascular system, which are associated with the centralization of effects. Questionnaires can be useful in differentiating states of overexertion according to parasympathetic type and a state of high training level in type IV cardiac rhythm regulation.
This chapter is concerned with the use of wearable devices for disabled and extreme sports. These sporting disciplines offer unique challenges for sports scientists and engineers. Disabled athletes often rely on and utilize more specialist equipment than able-bodied athletes. Wearable devices could be particularly useful for monitoring athlete-equipment interactions in disability sport, with a view to improving comfort and performance, while increasing accessibility and reducing injury risks. Equipment also tends to be key for so called “extreme” sports, such as skiing, snowboarding, mountain biking, bicycle motocross, rock climbing, surfing, and white-water kayaking. These sports are often practiced outdoors in remote and challenging environments, with athletes placing heavy demands on themselves and their equipment. Extreme sports also encompass disability sports, like sit skiing and adaptive mountain biking, and the popularity and diversity of such activities is likely to increase with improvements in technology and training, as well as with the support of organizations like the High Fives Foundation ( and Disability Snowsport, United Kingdom ( Within this chapter in these two sporting contexts, wearable devices are broadly associated with those that can be used to monitor the kinetics and kinematics of an athlete and their equipment. This chapter will first consider image-based alternatives and then focus on wearable sensors, in three main sections covering, (1) sports wearables, (2) disability sport and the use of wearables, and (3) extreme sport and the use of wearables, as well as making recommendations for the future.
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Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart rate sensor in the measurement of heart rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The root mean sum of the squared differences between R-R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% (90% CI) = 6.35 (5.13; 8.5)). When assessed as a standardised difference, all differences were deemed "Trivial" (average std. diff (90% CI) = 0.10 (0.08; 0.13). Both PPG and HR sensor derived measures had almost perfect correlations with ECG (R = 1.00 (0.99; 1:00). Conclusion: Both PPG and heart rate sensor provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.
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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 determine 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 compared 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 competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.
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The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability (HRV) in three different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated and standing positions. lnRMSSD was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 via ECG and 81.6 ± 11.3 via SPFS (p = 0.63, Cohen's d = 0.01) in the supine position, 76.5 ± 8.2 via ECG and 77.5 ± 8.2 via SPFS (p = 0.007, Cohen's d = 0.11) in the seated position, and 66.5 ± 9.2 via ECG and 67.8 ± 9.1 via SPFS (p < 0.001, Cohen's d = 0.15) in the standing positions. The SPFS showed a possibly strong correlation to the ECG in all three positions (r values from 0.98 to 0.99). In addition, the limits of agreement (CE ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Though significant differences were noted between the two methods in the seated and standing positions, the effect sizes were trivial.
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The aim of this study was to examine the intra-day and inter-day reliability of ultrashort- term vagal-related heart rate variability (HRV) in elite rugby union players. Forty players from the Brazilian National Rugby Team volunteered to participate in this study. The natural log of the root mean square of successive RR interval differences (lnRMSSD) assessments were performed on four different days. HRV was assessed twice (intra-day reliability) on the first day and once per day on the following three days (inter-day reliability). The RR interval recordings were obtained from 2-min recordings using a portable heart rate monitor. The relative reliability of intra- and inter-day lnRMSSD measures were analyzed using the intraclass correlation coefficient (ICC). The typical error of measurement (absolute reliability) of intra- and inter-day lnRMSSD assessments were analyzed using the coefficient of variation (CV). Both intra-day (ICC = 0.96; CV = 3.99%) and inter-day (ICC = 0.90; CV = 7.65%) measures were highly reliable. The ultra-short-term lnRMSSD is a consistent measure for evaluating elite rugby union players, in both intra- and inter-day settings. This study provides further validity to using this shortened method in practical field conditions with highly trained team sports athletes.
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Numerous studies have reported on the thermoregulation and hydration challenges athletes face in team and individual sports during exercise in the heat. Comparatively less research, however, has been conducted on the American Football player. Therefore, the purpose of this article is to review data collected in laboratory and field studies and discuss the thermoregulation, fluid balance, and sweat losses of American Football players. American Football presents a unique challenge to thermoregulation compared with other sports because of the encapsulating nature of the required protective equipment, large body size of players, and preseason practice occurring during the hottest time of year. Epidemiological studies report disproportionately higher rates of exertional heat illness and heat stroke in American Football compared with other sports. Specifically, larger players (e.g., linemen) are at increased risk for heat ailments compared with smaller players (e.g., backs) because of greater body mass index, increased body fat, lower surface area to body mass ratio, lower aerobic capacity, and the stationary nature of the position, which can reduce heat dissipation. A consistent finding across studies is that larger players exhibit higher sweating rates than smaller players. Mean sweating rates from 1.0 to 2.9 L/h have been reported for college and professional American Football players, with several studies reporting 3.0 L/h or more in some larger players. Sweat sodium concentration of American Football players does not seem to differ from that of athletes in other sports; however, given the high volume of sweat loss, the potential for sodium loss is higher in American Football than in other sports. Despite high sweating rates with American Football players, the observed disturbances in fluid balance have generally been mild (mean body mass loss ≤2 %). The majority of field-based studies have been conducted in the northeastern part of the United States, with limited studies in different geographical regions (i.e., southeast) of the United States. Further, there have been a limited number of studies examining body core temperature of American Football players during preseason practice, especially at the high school level. Future field-based research in American Football with various levels of competition in hotter geographical regions of the United States is warranted.
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BACKGROUND: Heart rate variability (HRV) is an objective physiological marker that may be useful for monitoring training status in athletes. However, research aiming to interpret daily HRV changes in female athletes is limited. The objectives of this study were (1) to assess daily HRV (i.e., log-transformed root mean square of successive R-R interval differences, lnRMSSD) trends both as a team and intra-individually in response to varying training load (TL) and (2) to determine relationships between lnRMSSD fluctuation (coefficient of variation, lnRMSSDcv) and psychometric and fitness parameters in collegiate female soccer players (n=10). METHODS: Ultra-short, Smartphone-derived lnRMSSD and psychometrics were evaluated daily throughout 2 consecutive weeks of high and low TL. After the training period, fitness parameters were assessed. RESULTS: When compared to baseline, reductions in lnRMSSD ranged from unclear to very likely moderate during the high TL week (effect size ± 90% confidence limits [ES ± 90% CL] = -0.21 ± 0.74 to -0.64 ± 0.78, respectively) while lnRMSSD reductions were unclear during the low TL week (ES ± 90% CL = -0.03 ± 0.73 to -0.35 ± 0.75, respectively). A large difference in TL between weeks was observed (ES ± 90% CL = 1.37 ± 0.80). Higher lnRMSSDcv was associated with greater perceived fatigue and lower fitness (r [upper and lower 90% CL] = -0.55 [-0.84, -0.003] large, -0.65 [-0.89, -0.15] large). CONCLUSIONS: Athletes with lower fitness or higher perceived fatigue demonstrated greater reductions in lnRMSSD throughout training. This information can be useful when interpreting individual lnRMSSD responses throughout training for managing player fatigue.
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Purpose: To quantify the mean daily changes in training and match load and any parallel changes in indicators of morning-measured fatigue across in-season training weeks in elite soccer players. Methods: Following each training session and match, ratings of perceived exertion (s-RPE) were recorded to calculate overall session load (RPE-TL) in 29 English Premier League players from the same team. Morning ratings of fatigue, sleep quality, delayed-onset muscle soreness (DOMS), as well as sub-maximal exercise heart rate (HRex), post-exercise heart rate recovery (HRR%) and variability (HRV) were also recorded pre-match day and one, two and four days post-match. Data were collected for a median duration of 3 weeks (range:1-13) and reduced to a typical weekly cycle including no mid-week match and a weekend match day. Data were analysed using within-subjects linear mixed models. Results: RPE-TL was approximately 600 AU (95%CI: 546-644) higher on match-day vs the following day (P<0.001). RPE-TL progressively decreased by ≈ 60 AU per day over the 3 days prior to a match (P<0.05). Morning-measured fatigue, sleep quality and DOMS tracked the changes in RPE-TL, being 35-40% worse on post-match day vs pre-match day (P<0.001). Perceived fatigue, sleep quality and DOMS improved by 17-26% from post-match day to three days post-match with further smaller (7-14%) improvements occurring between four days post-match and pre-match day (P<0.01). There were no substantial or statistically significant changes in HRex, HRR% and HRV over the weekly cycle (P>0.05). Conclusions: Morning-measured ratings of fatigue, sleep quality and DOMS are clearly more sensitive than HR-derived indices to the daily fluctuations in session load experienced by elite soccer players within a standard in-season week.
The aims of the present study were to 1) examine positional impact profiles of NCAA division I college football players using global positioning system (GPS) and integrated accelerometry (IA) technology, and 2) determine if positional differences in impact profiles during competition exist within offensive and defensive teams. Thirty-three NCAA Division I Football Bowl Subdivision players were monitored using GPS and IA (GPSports, Canberra, Australia) during 12 regular season games throughout the 2014 season. Individual player datasets (n = 294) were divided into offensive and defensive teams, and positional sub-groups. The intensity, number, and distribution of impact forces experienced by players during competition were recorded. Positional differences were found for the distribution of impacts within offensive and defensive teams. Wide receivers (WR) sustained more very light and light to moderate (5-6.5 G force) impacts than other position groups, while the running backs (RB) were involved in more severe (>10 G force) impacts than all offensive position groups, with the exception of the quarterbacks (QB) (p<0.05). The defensive back (DB) and linebacker (LB) groups were subject to more very light (5.0-6.0 G force) impacts, and the defensive tackle (DT) group sustained more heavy and very heavy (7.1-10 G force) impacts than other defensive positions (p<0.05). Data from the present study provide novel quantification of positional impact profiles related to the physical demands of college football games and highlight the need for position-specific monitoring and training in the preparation for the impact loads experienced during NCAA Division I football competition.
Relatively few studies have investigated the potential injury prevention value of data derived from recently developed wearable technology for measurement of body mass accelerations during the performance of sport-related activities. The available evidence has been derived from studies focused on avoidance of overtraining syndrome, which is believed to induce a chronically fatigued state that can be identified through monitoring of inertial load accumulation. Reduced variability in movement patterns is also believed to be an important injury risk factor, but no evidence currently exists to guide interpretation of data derived from inertial measurement units (IMUs) in this regard. We retrospectively analyzed archived data for a cohort of 45 NCAA Division 1-FBS football players who wore IMUs on the upper back during practice sessions to quantify any associations between average inertial load measured during practice sessions and occurrence of musculoskeletal sprains and strains. Both the coefficient of variation for average inertial load and frequent exposure to game conditions were found to be strongly associated with injury occurrence. Having either or both of the 2 risk factors provided strong discrimination between injured and non-injured players (χ = 9. 048; P = .004; OR = 8.04; 90% CI: 2.39, 27.03). Our findings may facilitate identification of individual football players who are likely to derive the greatest benefit from training activities designed to reduce injury risk through improved adaptability to rapidly changing environmental demands.