Submitted to Applied Physiology, Nutrition, and Metabolism
Title: Physiological responses to partial-body cryotherapy performed during a
concurrent strength and endurance session
Authors: Alessandro Piras; Francesco Campa; Stefania Toselli; Di Michele Rocco;
Affiliation: Department of Biomedical and Neuromotor Sciences University of
Dr. Alessandro Piras,
Department of Biomedical and Neuromotor Sciences
University of Bologna
Piazza di Porta S. Donato, 2
40126 Bologna – Italy
Ph: (0039) 051 - 2091740
Fax: (0039) 051 - 2091737
Running head: Cryotherapy and concurrent training
Number of pages: 24
Number of words in abstract: 241
Number of figures: 3
Number of tables: 4
Number of references: 38
Word count: 4329
Page 1 of 33
This investigation examined the effect of partial-body cryostimulation (PBC)
performed in the recovery time between a strength training and an interval running
(IR). Nine rugby players [age 23.7±3.6, BMI 28.0±2.6 kg/m
] were randomly
exposed to two different conditions: i) PBC: 3-min at -160°C; ii) passive recovery at
21°C. We recorded the bioelectrical impedance analysis (BIA), temperature, and
cardiac autonomic variables in three moments: at baseline, after strength training
(R0) and after 90-min of recovery (R90). Additionally, the blood lactate
concentration was measured 1-min before and 2.5-min after the IR. The heart rate,
energy cost, minute ventilation, oxygen uptake and metabolic power were assessed
during the IR. The homeostatic hydration status was affected by the execution of
intense strength training sub-session. Then, after PBC the BIA vector was restored
back, close to normohydration status. Autonomic variables changed over time in both
conditions, although the mean differences and effect sizes were higher in the PBC
condition. During IR, the heart rate was 3.5% lower after PBC, and the same result
was observed for the oxygen uptake
(~4.9%) and ventilation (~6.5%). The energy
cost measured after cryotherapy was ~9.0% lower than after passive recovery.
Cryotherapy enhances recovery after a single strength training, while during the
subsequent interval running it shows a reduction in cardiorespiratory and metabolic
parameters. PBC may be used in those athletes who compete or train more than once
in the same day to improve recovery between successive training sessions or
Keywords: baroreflex sensitivity; energy cost; heart rate variability; interval
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Page 3 of 33
A recent strategy used for post-exercise recovery in sports is cryotherapy,
which involves local, partial-body (PBC), and whole-body exposures (WBC). The
major differences in the two systems are the temperatures (-110°C for WBC and -
160°C during PBC), the head exposure (only during WBC), and the source of cold
stimulation (compressor for WBC and nitrogen gas during PBC) (Hausswirth et al.,
2013). The cryotherapy has a vasoconstrictive effect during recovery after exercise,
reducing the inflammation responses through a decrease of the cell metabolism,
leading to improvements in blood and oxygen supply if performed between two
consecutive training sessions. Moreover, stimulates the autonomic nervous
parasympathetic activity favouring acute recovery (Hausswirth et al. 2013). Instead,
performed before submaximal exercise reduces heart rate leading to higher stroke
In sports, athletes combine strength and endurance training within the same
training cycle, a procedure defined ‘concurrent training’ (Leveritt et al. 1999). As
pointed out by Leveritt et al. (1999), some studies have addressed the issue of
assessing the inhibition in strength development after concurrent strength and
endurance training with respect to strength training alone. This matter is still
controversial (Fyfe and Loenneke 2018). A recent systematic review and meta-
analysis (Sabag et al. 2018) has showed that high intensity interval training (HIIT)
can be performed with resistance exercise without negatively impacting hypertrophy,
and that any attenuation of lower body muscular strength might be improved by
prescribing running instead of cycling, with adequate rest between HIIT and
resistance sessions. Possible causes of the above-mentioned interferences could be
the residual fatigue in the neuromuscular system and the overtraining produced by
imbalances in the athlete recovery processes. Rugby players, one time a week,
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perform two-a-day workouts, where the interval between training sessions represents
their actual recovery time. Deficiency of proper recovery may result in the athlete
being unable to train at the required intensity or complete the required load in extra
time (Barnett 2006). Therefore, during these training sessions, a key issue is the
recovery modalities that should be followed by the athletes. Until now, only two
studies have investigated the effects of cryotherapy on recovery between two training
sessions. Schaal et al. (2013) demonstrated that WBC yields a significant increase in
vagal-related heart rate variability (HRV) indices and a decrease in heart rate (HR)
during 70 min of recovery between two synchronized swimming performances.
Ferreira-Junior et al. (2014) found a significant increase, after 40 min of strength
recovery, on eccentric peak torque and total work when subjects were exposed to
PBC with respect to passive recovery.
Partial-body cryotherapy has become a popular mode of cryotherapy
(Hausswirth et al. 2013), but only few studies have investigated this method on
recovery ( Ferreira-Junior et al. 2014; Hausswirth et al. 2013; Holmes and
Willoughby 2016). Moreover, no study has examined the influence of cryotherapy
during concurrent training in team sports. Therefore, the present study aimed to
evaluate the effect of cryotherapy performed during the recovery time between a
strength training and an interval running in a group of rugby players. We
hypothesized that the physiological responses to this treatment will accelerate
strength recovery, improving the subsequent interval running in athletes who train
more than once on the same day. The rationale of our hypothesis is that, cryotherapy
would induce lower submaximal heart rate due to lower thermal stress, as well as the
increase of cardiac parasympathetic activity (Stanley et al. 2014), favouring acute
recovery after strength training. In addition, cryotherapy causes a peripheral
vasoconstriction that leads to improvements in blood and oxygen supply in the
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working musculature (Krüger et al. 2015) improving the cardiorespiratory efficiency
or less cardiovascular stress during the subsequent intermittent exercise. From a
practical perspective, PBC might speed up recovery in those athletes who compete or
train more than once in the same day.
An a priori power analysis was conducted to determine the sample size for the
study (G*Power 126.96.36.199, Germany). The following design specifications were
considered: α = 0.05; (1-β) = 0.8; effect size f = 0.5; test family = F test and
statistical test = ANOVA repeated measures, within-between interaction. The sample
size estimated according to these specifications was 8 subjects. Thus, we selected 9
rugby league players who volunteered to participate in this study (Table 1). All
subjects were instructed to continue normal daily activities and to refrain from
training the day before testing. Additionally, all the exclusion criteria suggested by
Podbielska et al. (2006) were respected. This study was approved by the Bioethic
Committee of the University of Bologna, and all participants were informed of the
benefits and risks of the investigation prior to signing an institutionally approved
informed consent document to participate in the study.
The athletes visited the laboratory 4 times. All tests were performed at the
same time of the day (9:00 – 12:00 AM), in a quiet room with stable temperature
(21°C; 52% of humidity). During the first visit, the athletes completed an
incremental running test to determine their maximal oxygen uptake (V’O
to familiarize with the interval running on treadmill. The second visit was used to
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determine their maximal whole-body muscular strength (1-RM). The recording
sessions were performed during the third and fourth visits when the athletes were
tested in a randomized, counterbalanced, crossover design (Figure 1).
Maximal oxygen consumption assessment (VO
max). The expired gas analysis
(Quark CPET, Cosmed, Italy) was performed on motor-driven treadmill (Cosmed,
Italy) at 8 km/h for 3-min as a warm-up, followed by 1 min at 10 km/h with 1% of
incline, and an instantaneous increase of 0.5 km/h every min until exhaustion. The
maximal exercise test lasted until attainment of oxygen uptake (V’O
) plateau or the
attainment of at least one of the two additional criteria: (i) a plateau of heart rate
despite an increased velocity, or (ii) exercise cessation due to substantial fatigue.
plateau was defined as an increase in V’O
≤ 50 ml min-1 during the last 30 s
despite increased velocity (Yoon et al. 2007). The highest V’O
during the exercise phase of the incremental test were considered as the V’O
Strength assessment (1-RM). Maximal whole-body muscular strength was assessed
using the following five multiarticular exercises: bench press, squat, barbell row,
deadlift, and overhead press. The participants first completed ten warm-up
repetitions, and after a suitable rest period, one repetition maximum (1-RM) values
were assessed according to the following equation: 1-RM=weight lifted/[1.0278-
(0.0278 x N° repetitions)] (Brzycki 1993). It has been easy to establish with accuracy
their 1-RM as they were athletes accustomed to strength exercises. Proper exercise
execution was followed and enforced by one of the investigators who supervised and
documented each testing session.
Recording sessions. During the third and fourth visits, separated by one-week, the
athletes were tested in a randomized, counterbalanced, crossover design (Figure 1):
1) PBC; partial body cryotherapy: 3-min at -160°C during 90-min of recovery; 2)
CON; control: 90 min of passive recovery at 21°C. We measured bioelectrical-
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impedance standards, temperature, and cardiac autonomic regulation parameters in
three moments: at baseline, after strength training (start of recovery - R0), and after
90-min of recovery (R90). Additionally, the blood lactate concentration (La) was
measured 1-min before and 2.5-min after the IR sub-session, while heart rate (HR),
energy cost (EC), minute ventilation (VE), oxygen uptake (V’O
), and metabolic
power (Pmet) were assessed during the IR sub-session (Figure 1). PBC was
performed 15 minutes after strength training in a head-out cryochamber using
gaseous nitrogen (Space Cabin Cryomed, Slovakia) at -160°C for 3-min. Participants
wore bathing suits, gloves, socks and shoes with thermic protection for their
extremities. For the remaining recovery period, athletes seated at the laboratory with
a temperature of 21°C. The CON condition consisted of passive recovery, during
which athletes remained seated on a chair in the laboratory at 21°C. During the entire
procedure (exercise and recovery), drinking or eating was forbidden.
Body temperature (head, right hand, right foot) was measured by an infrared
thermometer (Tecnimed, Italy) in three moments: at baseline, after strength training
(R0), and after 90-min of recovery (R90). The measurement areas were marked with
a pen before each condition.
*****Figure 1 near here*****
Strength protocol. Each sub-session started with a 5-min warm-up. The conditioning
phase involved the same five resistance exercise stations used for the maximal
whole-body muscular strength assessment. The program involved performing five
sets of progressively heavier weights and lower reps, separated by 3 min of rest. It
was an “ascending pyramid” program (increase weight, decrease reps), and consisted
of performing a light set (50% of 1RM) for 10 reps, followed by 8 reps at 60%, 6
Page 8 of 33
reps at 70%, 4 reps at 80%, and 2 reps at 90% of 1RM (Brown and National Strength
& Conditioning Association (U.S.), 2007).
IR protocol. Each sub-session was performed on the same motor-driven treadmill
used during the V’O2max test and monitored through the expired gas analysis and
HR monitoring. It consisted of 1-min sprints at 95% of V’O
max followed by1-min
of active rest at 45% of VO
max repeated for a total of 10-min of running (Denadai
et al. 2006). Blood lactate samples were performed 1-min before and 2.5-min after
interval running. Both the strength and IR sessions were supervised by three
Body composition assessment
All body composition measurements were performed in resting conditions.
Body height was measured by a wall stadiometer to the nearest 0.1 cm and body
mass was measured using a balance to the nearest 0.1 kg. Fat-free mass and fat mass
were determined with the equation of Kotler et al.(1996).
Bioelectrical impedance vector analysis (BIVA), studied and validated for
healthy population, as well as for specific patient subgroups and athletes (Campa and
Toselli 2018; Lukaski 2013; Micheli et al. 2014) was performed with a phase-
sensitive impedance plethysmograph (BIA-101, Akern-RJL Systems, Italy) at
baseline; after strength training; and after 90 min of recovery. The electrodes were
placed on the right hand and foot dorsum while the subjects lied in a supine position.
The bioelectrical-impedance vector analysis calculates the body composition
considering the impedance components like resistance (R) that depends on lean
tissue mass and tissue hydration, and reactance (Xc), associated with cell size and
integrity of the cell membranes (Lukaski, 2013). We standardized R and Xc by the
subjects’ height (H) to remove the effect of conductor length, thus expressing values
Page 9 of 33
in Ohm/m. The combination of R and Xc return an impedance vector (arc tangent of
Xc/R expressed in degrees), and its direction is defined as the phase angle (PA°).
PA° is an indicator of cellular health and an index of fluid distribution between the
intracellular and extracellular compartments. A main advantage of the use of PA° is
that it can be applied even under unstable tissue hydration conditions (Selberg and
Selberg 2002), because it indicates progressive changes in tissue hydration; from
dehydration to hyperhydration with apparent edema (Lukaski 2013).
Cardiac autonomic regulation assessment (HRV and BRS)
Athletes were asked to stay in a supine position for 15 min, in a comfortable
bed, with a respiratory frequency at 12-15 breaths/min. Only 5 min, from 10’ to 15’
were used for analysis (Camm et al. 1996). Participants underwent non-invasive
continuous blood pressure monitoring using the servo-controlled infrared finger
plethysmography (Portapres device; TNO/BMI) for analysis of HRV and baroreflex
sensitivity (BRS) (Piras et al. 2017; Piras and Gatta 2017).
HRV analysis. The Portapres recordings were used to extract time series of R-R
intervals and systolic as well as diastolic pressures, to analyse HRV and BRS. Data
were analysed with Kubios HRV software (v. 3.0, 2017, Finland), in which all time
series were filtered to exclude artefacts. Time domain indices for HRV analysis were
the standard deviation of the R-R interval (SDNN) and the square root of the mean
squared differences of successive R-R intervals (RMSSD). The power spectrum
density of HRV was estimated by an autoregressive method with model validation.
Powers in the bands of low frequency (LF, 0.04–0.15 Hz) and high frequency (HF,
0.15–0.4 Hz) were calculated. It has been shown that the HF spectral component of
HR variability (HFRR) is an index of the vagal tone, whereas both sympathetic and
vagal activities contributed to the LF (LFRR) spectral component of HRV (Camm et
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al. 1996). Both indices were expressed in absolute and normalized units. Such
normalized units are obtained by dividing the power of each component by total
variance from which the very-low-frequency component (below 0.04 Hz) had been
subtracted and multiplying this value by 100 (Pagani et al. 1986).
BRS analysis. Baroreflex sensitivity was computed from RR intervals and systolic
blood pressure sequence subtracted from the finger arterial pressure waveform.
These data were then used to define the oscillations in both heart rate and systolic
arterial pressure measures. Beatscope version 1.1a (TNO/BMI, The Netherlands) was
used to evaluate spontaneous BRS, with a BRS add-on module that computes the
time-domain cross correlation BRS.
Energy cost assessment during IR
In both running conditions, HR, V′O
, V′E and respiratory exchange ratio
were collected breath by breath (Quark CPET, Cosmed, Italy). A blood sample was
obtained from the index finger to determine La (Lapre; Lapost) (Lactate Scout,
Germany) 1-min before and 2.5-min after running.
Figure 2 shows a typical tracing of a subject’s oxygen uptake as a function of
time during IR in both CON and PBC conditions. V′O
increases during each bout of
exercise at about 95%VO
max and decreases during active recovery at about
max. Metabolic data collected during the working phase were averaged and
used in the following analysis.
The energy cost (EC) of interval running was calculated based on values of
net and of blood lactate concentration (Zamparo et al. 2015):
1. The breath-by-breath net oxygen uptake (V’O
net, ml/kg/min), was
calculated by subtracting the resting V’O
values. The energy
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derived from aerobic energy sources (EO
, ml/kg) was calculated by
net for the total exercise duration (min).
2. The energy derived from anaerobic lactic energy sources (ELa, ml/kg)
was calculated by multiplying La
(mM), e.g. the difference between the
largest value of La recorded at the end of the test and La recorded at rest,
by an energy equivalent of 3.3 ml/kg/mM (Di Prampero and Ferretti
3. The net energy cost of interval running was then calculated as follows:
EC = [ EO2 + ELa ] / d, (1)
where d is the total distance covered. EC was expressed in J m/kg using an
energy equivalent (J ml/O
) which considers the respiratory exchange ratio.
Moreover, the instantaneous P
expressed in W/kg, was obtained
multiplying EC by the running speed (expressed in m/s).
*****Figure 2 near here*****
Shapiro-Wilk tests were used to check the normal distribution of data.
Measures with skewed distribution were log transformed (Ln) before analysis.
Time (baseline; R0; R90) x condition (PBC; CON) repeated measures
ANOVAs were performed to analyse temperature, HRV, and BRS.
For all metabolic variables recorded during IR sub-sessions, the mean values
of the PBC vs. CON condition were compared with Student’s t test for paired data.
Effect sizes were calculated as the mean difference standardised by the between-
subject standard deviation and interpreted according to the following thresholds:
trivial, <0.20; small, >0.20-0.60; moderate, >0.60-1.20; large, >1.20-2.00; very large,
>2.00-4.00; extremely large, >4.00 (Hopkins et al. 2009). Statistical significance was
set at p < 0.05. Post hoc tests were corrected with the Bonferroni procedure. Data
Page 12 of 33
were analysed with SPSS v22.0 (SPSS, Chicago, IL, USA). Intra-class correlation
coefficient was used to test the reliability of mean speed of both IR tests.
The mean speed maintained by the athletes during both IR sub-sessions was
2.76±0.17 m/s, with high degree of reliability between measurements, in which the
average measure ICC was 0.959 with a 95% confidence interval from 0.819
*****Table 1 near here*****
Body composition and temperature
Repeated measure analysis indicated no significant differences between
conditions at baseline and after strength training (Table 2, all P>0.05). After 90 min
of recovery, a significant time x condition interaction effects was observed. Post-hoc
tests showed that, after PBC, R/H (P<0.001, d=0.78, moderate), Xc/H (P<0.001,
d=0.90, moderate), and PA (P=0.013, d=0.52, small) increased, meanwhile, hand
(P=0.000, d=-11.95, extremely large) and foot temperature (P<0.001, d=-14.19,
extremely large) decreased. From baseline to R0 the vector decreased in both PBC
and CON condition (P<0.001), meanwhile, a significant increase was observed
between R0 and R90 only after PBC (Table 2).
*****Table 2 near here*****
Cardiac autonomic regulation
ANOVAs showed a significant time main effect for all autonomic variables
investigated (P<0.05), meaning that variables changed over time in both CON and
PBC condition. In addition, mean differences and effect sizes were higher on PBC in
all cardiac autonomic regulation parameters (Table 3).
Page 13 of 33
*****Table 3 near here*****
Energy cost analysis
The metabolic data obtained from the IR are reported in Table 4. Respiratory
exchange ratio, La, and E’O
were not significantly affected by cryotherapy (0.469 <
P > 0.116; 0.63 < d > 0.15), while all other metabolic parameters were lower during
IR performed after PBC (0.006 < P > 0.000; 1.40 < d > 0.44). Heart rate was 3.5%
lower during IR performed after PBC than in the CON condition, and the same result
was observed for V’O
(~4.9%) and V’E (~6.5%). The EC measured after
cryotherapy was ~9.0% lower than after passive recovery (P<0.001, d=-1.40, large).
Figure 3 displays the EC of the athletes during IR in the two testing conditions, in
which individual differences ranged from -3.98 to -17.27%. Although the variable of
main interest in this study was EC, a comparison of P
mean values was also
performed, and analysis showed that P
was higher in CON than in PBC (~6.7%).
*****Figure 3 and Table 4 near here*****
In the present study we investigated, in rugby players, the influence of a
single session of cryotherapy performed during the 90-min of recovery between a
strength training and a high-intensity interval running sub-sessions on body
composition, temperature, and cardiac autonomic responses, and its effects on
energetic cost during the subsequent interval running. The main findings of this study
were as follows: (i) Bioelectric impedance analysis showed that strength training
affected hydration parameters and PBC enhanced recovery compared to passive rest.
(ii) PBC after strength training influenced the pattern of autonomic function
recovery, in which indexes reflecting tonic cardiac vagal outflow such as SDNN,
RMSSD, and HF power reflected moderate to large effect sizes. Furthermore,
consistent changes were observed in BRS, which is a measure of reflex cardiac vagal
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responsiveness (Piras et al. 2017; Piras et al. 2015; Piras and Gatta, 2017). (iii)
Cardiorespiratory and metabolic parameters were reduced during a single bout of IR
after PBC, indicated by lowered HR, V’O
, V’E, EC, and PMET with respect to
Bioelectrical impedance analysis
The bioelectrical impedance analysis is commonly used to monitor changes in
hydration and body composition induced by training (Piccoli et al. 1995). In our
study, the mean of the impedance vector between baseline and R0 differed in the
same way in both conditions, returning to normal when repeated 90-min later only
after PBC. An intense physical training may affect cellular membrane stability, and
passive recovery is unable to restore homeostasis (Korthuis 2011). Immediately after
strength training, we found a decrease of the phase angle in both conditions, a
situation characterised by an increase in body fluid volume, maybe due to muscle
swelling. Then, only after PBC the PA° was restored close to normohydration status.
Because the rate of fluid movement across the microcirculation initially exceeds the
drainage capacity of the lymphatic system, interstitial fluid volume can almost
double within 15 min of the onset of intense rhythmic exercise (Korthuis 2011).
Intense training could induce muscle damage and subsequent inflammation indicated
by muscle soreness, swelling, and prolonged loss of muscle function. It has been
already demonstrated that PBC stimulated physiological reactions of an organism
which result in analgesic, anti-swelling, antalgic immune and circulatory system
reactions and then could improve recovery after muscle injury from muscular trauma
(Ferreira-Junior et al. 2015). A possible reason for these results may be related to a
decrease in core, muscle, and skin temperatures after cryotherapy exposure (Bleakley
et al. 2014). This physiological response may lead to increased vasoconstriction,
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reducing blood vessel permeability and thus decreasing the cellular inflammatory
process (Ferreira-Junior et al. 2015; Hausswirth et al. 2011).
In our study, temperature decreased after cryotherapy exposure. We are aware
that the lack of muscle and core temperatures is a limitation because such
measurements may have provided useful information on the body heat variations
during each recovery procedure. However, skin temperature of hand and foot in the
present study dropped respectively 3.4±0.8 and 4.2±0.6 °C immediately after PBC
compared to the basal level.
Regardless of the great sympathetic response necessary to support maximal
strength production rates during exercise, we demonstrated that autonomic function
recovered fully at the cardiac level within 90-min in both CON and PBC conditions.
Furthermore, we found higher effect sizes after cold treatment in all cardiac
autonomic regulation parameters, mainly on vagal-mediated HRV and BRS indices
(Table 3). Schaal et al. (2013), examining the influence of WBC during recovery
between two training sessions, showed that swimmers were able to repeat the same
maximal workload with similar autonomic, metabolic, and subjective responses.
Hausswirt et al. (2013) determined whether PBC was as effective as a WBC in
stimulating a parasympathetic activation. Whatever the cryotherapy technique used,
results showed that a single 3-min cryostimulation induced a strong autonomic
response. Same results were found by Westerlund et al. (2006) on how an extreme
cold air exposure influences cardiac autonomic regulation and its adaptation effects
on healthy women.
Perhaps, the high level of training and endurance at high loads of our athletes
resulted in an immediate recovery of the cardiac autonomic parameters. Therefore,
Page 16 of 33
parasympathetic reactivation after maximal exercise was not a limiting factor to
recovery but could have affected the subsequent high intensity exercise capacity,
seen that a link was found between the magnitude of parasympathetic reactivation
and the cardiorespiratory and metabolic parameters recorded during subsequent
interval running after PBC compared to passive recovery.
Cryotherapy had induced a reduction of the cardiorespiratory and metabolic
parameters recorded during IR. Heart rate was about 3.5% lower after PBC than
CON condition, and the same result was observed for V’O
(~4.9%), V’E (~6.5%),
EC (~9.0%) and PMET (~6.7%). These results are in agreement with those of Krüger
et al. (2015) who found, after 3-min of WBC at -110°C, lowered values of heart rate,
, rate of perceived exertion, and a higher muscle oxygenation during running at
submaximal intensity. The beneficial effects of cold treatment are commonly
believed to be associated with rapid reduction in core temperature, which in turn may
reduce fatigue associated with hyperthermia and suppressed muscle blood flow and
metabolic activity (Holmes and Willoughby 2016). Schmidt and Bruck (1981)
claimed that decreasing the body temperature at the onset of exercise, the critical
environmental heat stress limits for exercising would decrease. This may result in
performance enhancement of both endurance time and increased work rate (Ferretti
et al. 1995). Metabolic load was reduced during running after PBC, indicated by
, energy cost, and metabolic power. We observed an average additional
cost of 0.34 J/kg/m during running after passive recovery. Reduced submaximal
suggests that running after cold treatment seems somewhat less energy
demanding than after passive recovery, with less effort needed to complete the
intermittent exercise or a lower V’O
of the passive muscles due to peripheral
Page 17 of 33
vasoconstriction and therefore lower blood and oxygen directed to non-working
musculature (Krüger et al. 2015). Ferretti et al. (1995) found that the minimum
mechanical power necessary to elicit V’O
max during cycling is about 21 W lower at
31°C than at 36°C. Stanley et al. (2014) found that V’O
kinetics and muscle oxygen
utilization were reduced after cold water immersion compared with passive recovery.
Schaal et al. (2013) found and equal or greater VO
on WBC in
comparison to passive recovery, where swimmers were able to repeat the same
maximal workload with similar autonomic, metabolic, and subjective responses.
Conversely, Drust et al. (2000) found no significant differences for the oxygen
consumption or heart rate under normal (20°C), heated (26°C) or pre-cooled (cold
shower at 24°C) conditions during intermittent exercise in soccer players, although
there was a tendency for heart rate to be lower during the pre-cooled condition.
These data therefore suggest that cold treatment might improve oxygen uptake
efficiency at muscle level with respect to passive recovery, possibly through different
mechanisms. First, cooling results in reduced muscle perfusion and edema formation,
all factors that reduce the transit distance between capillaries and muscle fibers
facilitating oxygen delivery to the muscle cells (Ihsan et al. 2013). Second, decreased
peripheral blood flow may increase central blood volume and enhance blood delivery
to the working muscles (Lee and Haymes 1995), resulting in a greater contribution of
the aerobic system to energy supply, combined with the low level of blood lactate
that we have found at the end of the IR effort. Third, the increase in muscle blood
flow redistribution and reduction in maximal heart rate, together with the left shift of
the oxygen dissociation curve known to occur when temperature is decreased, can
explain the observed lower V’O
during running (Ferretti et al. 1995).
Page 18 of 33
In conclusion, cryotherapy enhances recovery after a single strength training
sub-session, while during the subsequent interval running it shows a reduction in
cardiorespiratory and metabolic parameters. These findings agree with the suggested
mechanisms by which cooling between successive workouts has been recommended
to enhance muscle recovery and as such it can be used in sports. Therefore, from a
practical perspective, cryotherapy may be used in those athletes who compete or train
more than once in the same day (i.e. soccer, rugby) to improve recovery between
successive trainings or competitions.
Conflict of Interests: The author(s) declare(s) that there is no conflict of interests
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Table 1. Anthropometric, training (1-
RM) and physiological parameters
Training week (h)
Bench Press (kg)
Barbell Row (kg)
Overhead Press (kg)
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Table 2. Mean
± SD of body
Abbreviations: FM, fat mass; FFM, fat free mass; R/H, resistance divided by body height; Xc/H, reactance divided by body height;
PA, phase angle.
*Significant time x condition interaction effect (P<0.05) after 90 min of recovery.
Baseline R0 R90
CON PBC CON PBC CON PBC
FM (kg) 15.35±5.39 16.42±5.14 13.84±5.25 13.42±4.68 15.84±4.53 15.43±4.58
FFM (Kg) 75.50±7.84 75.32±7.15
R/H (Ohm/m) 218.99±22.18 220.78±19.46 211.68±22.61
Xc/H (Ohm/m) 31.52±3.76 31.54±3.40 29.38±3.48 28.78±3.55 30.89±4.24 32.19±4.64*
PA (degrees) 8.20±0.60 8.13±0.52
Head temperature (°C) 36.38±0.40 36.38±0.28 36.20±0.28 36.06±0.35 36.46±0.33 35.76±0.83
Hand temperature (°C) 33.94±2.51 33.40±2.64 35.21±0.59 35.24±0.62 34.94±0.52 30.03±0.44*
Foot temperature (°C) 34.45±1.87 34.17±1.67
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Table 3. Mean differences (with Cohen’s d) of autonomic nervous system parameters after recovery (R0 vs. R90) in both conditions
LF/HF LF (nu) HF (nu) HR (bpm) SDNN (ms) RMSSD (ms) BRS (ms/mmHg)
CON -1.08 (-0.75) -11.77 (-0.72) 11.81 (0.72) -8.75 (-1.15) 23.36 (0.95) 17.41 (1.01) 5.86 (0.78)
PBC -1.19 (-0.95) -12.11 (-0.73) 12.14 (0.73) -9.20 (-1.21) 24.50 (1.00) 21.13 (1.34) 9.41 (0.96)
Abbreviations: LF, low frequency; HF, high frequency; HR, heart rate; RMSSD, root mean square of standard deviations;
BRS, baroreflex sensitivity.
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Table 4. Physiological data (mean ± SD) collected during the IR sub-sessions
CON PBC diff % P Cohen's d
35.94±3.81 34.23±3.12 -4.9 0.006* -0.49
V′E (l/min) 88.75±12.44 83.41±11.91 -6.5 0.005* -0.44
HR (bpm) 142.62±10.98
137.71±9.76 -3.5 0.001* -0.47
RER 0.95±0.05 0.93±0.05 -2.3 0.469 -0.38
(mmol/l) 2.02±0.43 2.30±0.45 9.9 0.116 0.63
(mmol/l) 2.67±0.65 2.89±0.69 4.4 0.335 0.33
2.0 0.348 0.15
EC (J/Kg/m) 4.11±0.29 3.77±0.18 -9.0 0.000* -1.40
Pmet (W/Kg) 11.28±1.25 10.57±1.02 -6.7 0.006* -0.62
, oxygen uptake; V′E, minute ventilation; HR, heart rate; RER,
respiratory exchange ratio; La, blood lactate concentration (pre- and post-IR); E′O
net energy expenditure derived from aerobic energy sources; EC net energy cost;
, metabolic power.
* Significant differences between conditions (P<0.05).
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Figure. 1 Graphical overview of the testing protocol with the timeline of events.
Exercise bout 1 (strength training), exercise bout 2 (IR), start of recovery (R0), end
of recovery (R90), heart rate variability (HRV), baroreflex sensitivity (BRS),
temperature (T°), bioelectric impedance (BIA), partial-body cryotherapy (PBC),
control condition (CON), energy cost (EC), minute ventilation (V’E), heart rate
(HR), oxygen consumption (V’O2), metabolic power (P
Figure. 2 Representative profile of an athlete’s oxygen uptake as a function of time
(sec) during IR in both CON (black) and PBC (grey) conditions. V′O2 increases
during each bout of exercise at about 95%V’O2max and decreases during active
recovery at about 45%V’O2max.
Figure. 3 Individual differences (9 subjects) in energetic cost (EC) between control
(CON) and partial-body cryotherapy (PBC) conditions.
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Figure. 1 Graphical overview of the testing protocol with the timeline of events. Exercise bout 1 (strength
training), exercise bout 2 (IR), start of recovery (R0), end of recovery (R90), heart rate variability (HRV),
baroreflex sensitivity (BRS), temperature (T°), bioelectric impedance (BIA), partial-
body cryotherapy (PBC),
control condition (CON), energy cost (EC), minute ventilation (V’E), heart rate (HR), oxygen consumption
(V’O2), metabolic power (PMET).
101x31mm (300 x 300 DPI)
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Figure. 2 Representative profile of an athlete’s oxygen uptake as a function of time (sec) during IR in both
CON (black) and PBC (grey) conditions. V′O2 increases during each bout of exercise at about 95%V’O2max
and decreases during active recovery at about 45%V’O2max.
80x49mm (300 x 300 DPI)
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Figure. 3 Individual differences (9 subjects) in energetic cost (EC) between control (CON) and partial-body
cryotherapy (PBC) conditions.
65x49mm (300 x 300 DPI)
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