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Changes in markers of body composition of professional male soccer players during pre-season



To evaluate changes achieved in whole-body and regional (upper limbs, lower limbs, and trunk) estimates of body composition, twenty professional male soccer players (7 defenders, 7 midfielders, 6 forwards) underwent dual-energy x-ray absorptiometry (DXA) analysis at the beginning and end of pre-season. Measures included: mass, fat mass (FM), fat-free mass (FFM), and body fat per cent (BF%). Players’ activity during on-field training sessions was monitored using Global Positioning System (GPS) units, with GPS data used to obtain estimations of energy expenditure (EE). Whole-body mass remained unchanged across the pre-season. Moderate significant increases and decreases were achieved in whole-body FFM (Pre: 59.58±5.27 kg; Post: 60.61±5.18 kg; P=0.001; d=0.87) and FM (Pre: 10.60±1.88 kg; Post: 9.56±1.81 kg; P=0.001; d=0.85), respectively. Moderate significant decreases were achieved in whole-body BF% (Pre: 14.4±2.3 %; Post: 12.9±2.0 %; P<0.001; d=0.94). No significant inter-positional differences were observed for the changes achieved in any global or regional estimate of body composition. Total EE was significantly correlated with ΔFM (r=0.65, P=0.002), ΔFFM (r=0.46, P=0.03), and ΔBF% (r=0.67, P=0.002). The total EE of pre-season training accounted for 42%, 21%, and 45% of the variance in ΔFM, ΔFFM, and ΔBF%, respectively. These findings suggest that the pre-season period is a suitable time for initiating favourable alterations in body composition following the off-season in elite soccer players.
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Changes in markers of body composition of professional male soccer players during
Gary Paul McEwan, Franchek Drobnic, Antonia Lizarraga, Antonio Gómez Díaz,
Eduard Pons, Antonio Dello Iacono, Viswanath Unnithan
PII: S2666-3376(20)30044-5
Reference: SMHS 32
To appear in: Sports Medicine and Health Science
Received Date: 27 July 2020
Revised Date: 31 August 2020
Accepted Date: 31 August 2020
Please cite this article as: McEwan GP, Drobnic F, Lizarraga A, Díaz AG, Pons E, Iacono AD, Unnithan
V, Changes in markers of body composition of professional male soccer players during pre-season,
Sports Medicine and Health Science,
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Changes in markers of body composition of professional male soccer
players during pre-season
Authorship: 3
Gary Paul McEwan
, Franchek Drobnic
, Antonia Lizarraga
, Antonio Gómez Díaz
, 4
Eduard Pons
, Antonio Dello Iacono
, Viswanath Unnithan
Division of Sport and Exercise, University of the West of Scotland, Glasgow, UK 6
Medical Department, Shanghai Greenland Shenhua FC, Shanghai, China
Medical, Sport Science and Health Department, FC Barcelona, Barcelona, Spain 8
Sports Performance Department, FC Barcelona, Barcelona, Spain 9
Corresponding Authors: 10
Professor Viswanath Unnithan and Mr Gary P McEwan 11
Division of Sport and Exercise, University of the West of Scotland, Glasgow, UK, G72 0LH 12
Email addresses: and 13
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Changes in markers of body composition of professional male soccer
players during pre-season
Abstract 21
To evaluate changes achieved in whole-body and regional (upper limbs, lower limbs, and 22
trunk) estimates of body composition, twenty professional male soccer players (7 defenders, 23
7 midfielders, 6 forwards) underwent dual-energy x-ray absorptiometry (DXA) analysis at the 24
beginning and end of pre-season. Measures included: mass, fat mass (FM), fat-free mass 25
(FFM), and body fat per cent (BF%). Players’ activity during on-field training sessions was 26
monitored using Global Positioning System (GPS) units, with GPS data used to obtain 27
estimations of energy expenditure (EE). Whole-body mass remained unchanged across the 28
pre-season. Moderate significant increases and decreases were achieved in whole-body FFM 29
(Pre: 59.58±5.27 kg; Post: 60.61±5.18 kg; P=0.001; d=0.87) and FM (Pre: 10.60±1.88 kg; 30
Post: 9.56±1.81 kg; P=0.001; d=0.85), respectively. Moderate significant decreases were 31
achieved in whole-body BF% (Pre: 14.4±2.3 %; Post: 12.9±2.0 %; P<0.001; d=0.94). No 32
significant inter-positional differences were observed for the changes achieved in any global 33
or regional estimate of body composition. Total EE was significantly correlated with ΔFM 34
(r=0.65, P=0.002), ΔFFM (r=0.46, P=0.03), and ΔBF% (r=0.67, P=0.002). The total EE of 35
pre-season training accounted for 42%, 21%, and 45% of the variance in ΔFM, ΔFFM, and 36
ΔBF%, respectively. These findings suggest that the pre-season period is a suitable time for 37
initiating favourable alterations in body composition following the off-season in elite soccer 38
players. 39
Key Words: DXA; association football; team sport; GPS 40
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Introduction 43
The maintenance of an appropriate body composition is an important requisite in the physical 44
conditioning of elite soccer players.
Intuitively, excess body fat represents an inert load 45
likely to impair physical performance and predispose players to a heightened risk of all-cause 46
Conversely, the fat-free compartment of the body, constituting lean muscle and bone 47
mineral mass, plays a key role in strength and power performance and forms an integral 48
component of the physical make-up of the elite soccer player.
The routine measurement of 49
body composition is therefore a useful practice in professional soccer and is commonly 50
undertaken to assess players’ readiness for competition and monitor the effectiveness of 51
dietary and training interventions.
As is commonplace within many team sports, soccer adopts a cyclical pattern of competition, 53
where the cessation of the competitive season is followed by a period of planned rest and 54
recuperation. This period of inactivity and reduced training, the so-called off-season, 55
typically lasts between 4-6 weeks and may adversely affect measures of body composition.
In a recent study of 19 elite male soccer players, significant increases in body fat per cent 57
(BF%) were observed following a 6-week off-season period, with changes mediated through 58
increases in whole-body fat mass (FM) and reductions in the fat-free mass (FFM) of the 59
lower limbs.
Given the deleterious effects that such outcomes may have on parameters of 60
match-related fitness such as speed, power, and high-intensity running performance,
reversing such trends prior to the return to competition is desirable. 62
To facilitate the return to competition and provide players with the opportunity to establish 63
the physical base from which technical and tactical development can occur, an intensive pre-64
season training programme is commonly undertaken.
In comparison to the in-season where 65
professional players may complete an average of three training sessions per week,
training 66
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loads (TL) are generally increased up to 2-4 times during the pre-season.
Consequently, 67
energy expenditure (EE) will be elevated during this phase of the season.
Accordingly, 68
energy intake (EI) should be modified in relation to the body composition goals of the 69
During the in-season, players typically match EI with their daily energy 70
requirements to avoid hindering performance.
However, when alterations in FM and body 71
composition are sought, energy deficits may be necessary.
Pre-season therefore represents 72
a unique opportunity to reverse the negative changes observed in markers of body 73
composition following the off-season through the systematic increase of TL and the provision 74
of individualised dietary regimes. 75
In contrast to the volume of research concerning the impact of pre-season training on 76
parameters of physical fitness,
a dearth of information appears available on its impact on 77
markers of body composition. Literature examining seasonal changes in the body 78
composition of professional soccer players have typically observed decreases in FM and 79
increases in FFM from the beginning of pre-season to mid-season.
However, without a post 80
pre-season assessment of body composition, the role of the pre-season in these changes is 81
unclear. Additionally, studies utilising two-compartment techniques such as skinfolds and 82
bioelectrical impedance analysis (BIA) have returned conflicting results.
Although such 83
techniques represent practical methods within the field, the accuracy in which BF% may be 84
estimated from skinfold thicknesses can vary greatly depending upon the anthropometric 85
equation utilised.
Dual-energy x-ray absorptiometry (DXA), however, is widely regarded 86
as the criterion reference standard for assessing body composition within team sport athletes 87
given its potential to provide a three-compartmental overview of body composition.
Nevertheless, the use of DXA within most sports settings is often limited due to its associated 89
logistical and financial costs.
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To our knowledge, two studies have previously utilised DXA to document body composition 91
changes during the pre-season within elite soccer players.
In a cohort of 18 A-League 92
soccer players, improvements in body composition were achieved following a ~3-month pre-93
Similarly, favourable alterations (decreases/increases in FM/FFM, respectively) 94
were observed within English Premier League players following 6 weeks of pre-season 95
However, given the potential impact that differences in culture, competition 96
demands and playing position have upon playing style and training practices,
additional 97
research is required to document the changes incurred in body composition during the pre-98
season amongst elite cohorts from varying competitions. Furthermore, the extent to which 99
changes in body composition may be attributed to the TL and EE induced during pre-season 100
remains unclear. Understanding such relationships may be of benefit to support staff working 101
with elite soccer players during the pre-season, and provide valuable information from which 102
to base the prescription of TL and nutritional guidelines. This investigation therefore aimed 103
to expand upon the available literature and document body composition changes induced 104
during the pre-season within players belonging to one of Europe’s leading soccer clubs (FC 105
Barcelona). Accordingly, we sought to: 1) evaluate changes in global and regional markers 106
of body composition over the pre-season period; 2) examine the impact of playing position 107
upon these changes; and 3) explore what proportion of the variability in changes in body 108
composition may be explained by the total EE associated with pre-season training. 109
Methods 110
Participants 111
Twenty male soccer players (age: 25.1±4.1 y; stature: 177.0±6.9 cm; body mass: 73.8±6.0 112
kg) belonging to the first team of FC Barcelona provided informed consent to participate in 113
this study. Players were separated into their outfield playing positions for analyses 114
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(defenders: n=7; midfielders: n=7; forwards: n=6). Over the course of the two competitive 115
seasons evaluated (2014-2015 and 2015-2016), the club achieved considerable success, 116
winning two La Liga titles, two Copa del Rey titles, and one UEFA Champions League title. 117
Whilst assessments of body composition were completed as part of the routine medical 118
screening of players, data procurement and handling conformed to the recommendations 119
outlined within the Declaration of Helsinki and the study received approval from the Ethics 120
Committee for Clinical Research of the Catalan Sports Council. 121
Design 122
Measures of whole-body and regional (upper limb, lower limb, trunk) mass, FM, FFM, and 123
BF% were assessed by DXA (GE Healthcare Lunar, Madison, WI). Assessments were 124
undertaken 38±10 days apart at the start and end of the pre-season during either the 2014-15 125
(n=16) or 2015-16 (n=4) competitive seasons. These timeframes were dictated by the club 126
and reflect previous literature.
In accordance with standardised protocols that are 127
recognised as best practice, scans were performed and analysed by the same trained operator 128
and were undertaken in a rested and hydrated state.
Specifically, players presented to the 129
lab having not undertaken any exercise on the morning of the scans and had refrained from 130
the consumption of food in the 3-4 hours previous.
The players had also been instructed to 131
consume fluids ad-libitum prior to scans. The test-retest reliability of the DXA scanner for 132
measurement of whole-body and regional body composition in non-obese adults, with 133
repositioning between scans, has been previously documented.
Briefly, the coefficient of 134
variation (CV) for whole-body FM and FFM were 1.0% and 0.5%, respectively. Regional 135
reliability estimates for FM were as follows: upper limbs, 2.8%; lower limbs, 1.6%; and 136
trunk, 2.0%. Regional reliability estimates for FFM were: upper limbs, 1.6%; lower limbs, 137
1.3%; and trunk, 1.0%. 138
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Pre-season training and nutritional practices 139
Pre-season training, as programmed and directed by the coaching staff, was comparable prior 140
to the start of both the 2014-15 (36 sessions, 5 matches) and 2015-16 (33 sessions, 8 matches) 141
competitive seasons. The mean number of on-field training sessions performed was 21±10 142
sessions, with variation attributable to each player’s unique circumstances (date of return to 143
pre-season following international competition, injury, fatigue management etc.). In line 144
with the unique playing and training philosophy embedded at FC Barcelona, training sessions 145
incorporated integrated content whereby tactical, technical, and physical factors were 146
Players’ locomotive activities were recorded during each training session 147
using portable 10-H
Global Positioning System (GPS) units (Viper Pod, Statsports, Northern 148
Ireland). To avoid inter-unit error, each player wore the same device during the entirety of 149
the study period.
Following each training session, GPS data were extracted and analysed 150
using propriety software (Viper, Statsports) to derive: total distance (TD); high-speed running 151
distance (HSR; 19.8-25.09 km·h
); sprinting distance (SPR; 25.1 km·h
); and number of 152
accelerations (ACC; >3 s
) and decelerations (DEC; >3 s
). Intensity thresholds were 153
established based upon previous literature and are consistent with those commonly used by 154
professional soccer clubs.
GPS data were also used to estimate the average metabolic 155
power (AMP; W·kg
), total energy cost (EC; kJ·kg
), and EE (kcal) associated with on-field 156
training sessions according to the approach of di Prampero and Osgnach (2018).
In 157
particular, this approach relies on the theoretical construct that metabolic demands can be 158
estimated from the instantaneous speed and acceleration, and the known EC of 159
accelerated/decelerated locomotion which characterise soccer-related activities.
Acceptable 160
accuracy and validity have been advocated in support of these metabolic estimates as 161
practical tools in team-sport demands analysis, especially following a recent update of the 162
original model,
which now differentiates between the EC of running and walking, and also 163
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considers the cost of air resistance. The full GPS data set included 18 players with weekly 164
analyses inclusive only of those who completed 75% of on-field sessions for that week. 165
Nutritional practices were informed by the club’s nutritional adviser and remained consistent 166
across each pre-season. The general objective of the nutritional advice provided to the 167
players was to support an optimal adaptation to the enhanced TL induced during pre-season 168
as well as promote decreases/increases in FM/FFM, respectively. Additionally, players were 169
provided with individualised nutritional goals based upon the result of their baseline DXA 170
measurement. Subject to the specific nutritional goals of the player, recommended 171
macronutrient intakes were as follows: carbohydrate (4.0-6.0 g/kg/day); protein (1.8-2.0 172
g/kg/day); and fat (1.0 g/kg/day). Based upon these guidelines, the recommended daily EI 173
was ~2795 kcal (range: 2648-3080 kcal). During pre-season, all meals and snacks were 174
provided by the club and players were encouraged to work closely with the club’s nutritional 175
advisor to translate their recommended nutrient guidelines into food equivalents. 176
Statistical Analysis 177
Prior to the use of parametric statistical test procedures, normality of distribution and 178
homogeneity of variance were verified using Shapiro-Wilk’s and Levene’s tests, respectively. 179
Changes in estimates of body composition were assessed using one-way analysis of variance 180
(ANOVA) with two-way ANOVAs (factor: time; playing position) used to examine whether 181
the changes achieved in estimates of body composition were related to playing position. 182
Mean standardised differences are reported as Cohen’s d with the following criteria used to 183
interpret the practical significance of findings: trivial, <0.2; small, 0.21-0.6; moderate, 0.61-184
1.2; large, 1.21-1.99; and very large, 2.0.
In addition, the smallest worthwhile change 185
(SWC) was calculated for each variable as the between-player standard deviation multiplied 186
by 0.2.
To assess the relationship between independent (total EE of pre-season training) 187
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and dependent (ΔFM, ΔFFM, ΔBF%) variables, multiple linear regressions were performed. 188
Prior to regression analyses, Pearson’s correlation coefficients (r) were used to indicate 189
whether the magnitude of the relationship between variables was small (0.10-0.29), moderate 190
(0.30-0.49), large (0.50-0.69), very large (0.70-0.89), and nearly perfect (0.90).
Data are 191
presented as means and standard deviations (mean±SD). Weekly TL and EE data are 192
presented for descriptive purposes only. Statistical procedures were completed using 193
Statistical Package for Social Sciences (SPSS 22.0, IBM, USA) and statistical significance 194
was set at P<0.05. 195
Results 196
Changes in global and regional markers of body composition 197
Table 1 reports the changes in markers of body composition between the beginning and end 198
of pre-season. 199
Whole-body total mass remained unchanged following pre-season (d=0.00; 95% CI: -0.66 to 200
0.66 kg; P=0.99). When analysed regionally, no differences were observed in total mass of 201
the upper limbs (d=0.37; 95% CI: -0.15 to 0.02 kg; P=0.11), lower limbs (d=0.19; 95% CI: -202
0.36 to 0.15 kg; P=0.41), or trunk (d=0.34; 95% CI: -0.12 to 0.76 kg; P=0.14). 203
Moderate significant reductions in whole-body FM (d=0.85; 95% CI: -0.47 to -0.28 kg; 204
P=0.001) were observed following pre-season with reductions occurring in the upper limbs 205
(d=0.69; 95% CI: -0.18 to -0.04 kg; P=0.006), lower limbs (d=0.97; 95% CI: -0.53 to -0.19 206
kg; P<0.001) and trunk (d=0.70; 95% CI: -0.91 to -0.18 kg; P=0.006). 207
Moderate significant increases in whole-body FFM (d=0.87; 95% CI: 0.48 to 1.59 kg; 208
P=0.001) were mediated through increases in FFM of the lower limbs (d=0.51; 95% CI: 0.02 209
to 0.48 kg; P=0.03) and trunk (d=1.17; 95% CI: 0.52 to 1.21 kg; P<0.001). The FFM of the 210
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upper limbs remained unchanged (d=0.22; 95% CI: -0.05 to 0.13 kg; P=0.34) following pre-211
season. 212
Moderate significant decreases occurred in whole-body BF% following pre-season (d=0.94; 213
95% CI: -2.2 to -0.7%; P<0.001) with reductions occurring in the upper limbs (d=0.68; 95% 214
CI: -2.1 to -0.4%; P=0.006), lower limbs (d=1.03; 95% CI: -1.89 to -0.70%; P<0.001), and 215
trunk (d=0.87; 95% CI: -2.91 to -0.87%; P=0.001). 216
No significant inter-positional differences were observed for any global (P0.10; d0.50) or 217
regional (P0.08; d0.59) estimate of body composition across the pre-season period. 218
Weekly training load (TL) and energy cost (EC) of on-field training sessions 219
An overview of the TL accumulated across weeks 1-6 of the pre-season is presented in Table 220
2 whilst the mean EC of on-field training sessions during each of these weeks is presented 221
within Table 3. The data presented pertains to on-field training sessions only and does not 222
include data from pre-season match play. 223
Relationship between changes in body composition and the total energy expenditure (EE) 224
associated with pre-season training 225
The total EE of pre-season training was 9369±5424 kcal. Total EE was significantly 226
correlated with ΔFM (r=0.65, P=0.002), ΔFFM (r=0.46, P=0.032), and ΔBF% (r=0.67, 227
P=0.002). The total EE of pre-season training accounted for 42%, 21%, and 45% of the 228
variance in ΔFM, ΔFFM, and ΔBF%, respectively. 229
Discussion 230
We sought to investigate the changes incurred in global and regional markers of body 231
composition during the pre-season within a cohort of elite male soccer players. At the global 232
level, reductions in BF% were mediated through reductions in FM and increases in FFM. 233
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Regionally, reductions in the FM of the upper limbs, lower limbs and trunk were 234
accompanied by concomitant increases in the FFM of the lower limbs and trunk. Moreover, 235
the changes observed in all global and regional estimates of body composition were similar 236
between playing positions. An additional aim was to identify what proportion of the 237
variability in changes in body composition could be explained by the total EE associated with 238
pre-season training. The total EE of pre-season training accounted for 42%, 21%, and 45% 239
of the variance in ΔFM, ΔFFM, and ΔBF%, respectively. 240
As evidenced, pre-season presents a suitable time by which favourable alterations in body 241
composition may be achieved within elite soccer players. Our findings support those 242
observed within English Premier League players whereby reductions (~2%) in BF% were 243
achieved following a 6-week pre-season.
Conversely, the FFM and BF% of French League 244
1 players remained unchanged following pre-season.
Although we are unable to discount 245
whether such discrepancies are simply an artefact of the different assessment methods, one 246
explanation may reside in potential disparities in the pre-season duration. In the present 247
study, measurements were obtained ~6 weeks apart; however, the duration of the pre-season 248
employed by Carling and Orhant was not noted.
The duration of pre-season employed 249
within the aforementioned study may therefore have been insufficient to promote such 250
changes; however, without such information, this remains speculative. Another explanation 251
likely relates to the type and intensity of the pre-season training stimulus. As shown, 252
differences in the total EE associated with pre-season training accounted for 42% of the 253
variance in ΔFM. The increased energy cost of training reflects the up-regulation of 254
oxidative metabolism, with exercise intensity representing the greatest moderator of substrate 255
Additionally, the intermittent nature of soccer-specific exercise has been found 256
to promote a high rate of lipolysis and the release of free fatty acids into the blood.
In the 257
context of the studied club, a unique training philosophy is embedded whereby there is a 258
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marked disposition for on-field game-related training, through which tactical, technical and 259
physical factors are amalgamated.
Conversely, only 21% of the variance in ΔFFM was 260
attributable to the total EE associated with pre-season training. This is perhaps unsurprising 261
given that training-induced increases in muscle hypertrophy are not driven by EE per se.
As discussed, favourable alterations in DXA-derived estimates of body composition have 263
previously been reported within elite English Premier League players following ~6-weeks of 264
pre-season training.
Nevertheless, as differences in culture and competition demands may 265
result in distinct training practices, data derived from additional competitions is warranted.
Additionally, the present investigation is the first to report body composition changes induced 267
during pre-season within elite soccer players, in relation to metrics of TL. This represents an 268
important contribution to the literature as indications of TL and the associated EE are 269
necessary when interpreting alterations in body composition.
When considering the TL 270
accumulated across each week of pre-season, the mean weekly TD and HSR distance 271
observed in the present study was 23193 m and 1048 m, respectively. Such values exceed 272
those reported for elite players during the in-season,
thus supporting observations of 273
enhanced TL during the pre-season in comparison to the in-season.
Interestingly, the mean 274
weekly TD observed within the present study is substantially lower than that reported for elite 275
English Premier League players during pre-season.
As FC Barcelona adopt a unique playing 276
style, such discrepancies in loading patterns likely relate to differences in training practices.
Based upon measures of metabolic power, the mean EC of on-field training during weeks 1-3 278
was ~36 kJ·kg
Whilst such figures remain below those associated with match play,
they 279
exceed the ~25 kJ·kg
reported for elite players during the in-season.
Conversely, the 280
reduced training volume observed during weeks 4-6 resulted in a mean EC of training more 281
closely aligned with that of the in-season. A likely explanation for this pertains to the more 282
frequent scheduling of matches during the latter stages of pre-season. Whilst we report TL 283
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and EE data to aid the interpretation of our findings, this data relates to on-field sessions only, 284
and is not reflective of gym-based training sessions or the load associated with match play. 285
Consequently, a conservative account of the demands imposed during pre-season is 286
presented. Nevertheless, given that the estimated EE of match play ranges from 1200-1500 287
the greater match participation exhibited during weeks 4-6 is likely to have 288
compensated for the reduced training volume. This represents an important consideration for 289
applied practitioners preparing athletes during the pre-season, as the scheduling of matches 290
will impact upon the programming of training, and will ultimately contribute towards their 291
physical conditioning. 292
Several limitations warrant consideration when interpreting these results. Whilst we provide 293
surrogate markers of EE to contextualise our findings, the limitations of the adopted approach 294
should be acknowledged in comparison to the doubly labelled water method. Additionally, 295
although we provide details concerning the nutritional guidelines provided to the players, we 296
are unable to confirm their actual EI. Future research incorporating direct measurements of 297
EE and EI is therefore necessary in order to fully examine the interaction of energy balance 298
and body composition changes within elite soccer players during the pre-season. Another 299
limitation relates to our relatively small sample size, a challenge commonly encountered 300
when conducting applied research with elite cohorts. In the context of the present 301
investigation, the two studied pre-seasons coincided with international competitions (FIFA 302
World Cup 2014 and CONMEBOL Copa América 2015). Consequently, although we report 303
the impact of ~6 weeks of pre-season (38±10 days) on markers of body composition, the 304
return of players to the club for pre-season training was dictated by the player’s individual 305
commitments to their national teams. This represents an important consideration in the 306
applied setting as the timing of a player’s return will influence the programming of pre-307
season training. Future research may wish to examine the impact that such logistical 308
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challenges have upon the loading patterns programmed during the pre-season and their 309
subsequent impact upon markers of physical performance and body composition. 310
The present findings provide additional evidence supporting the pre-season to be a suitable 311
period for elite soccer players to achieve concomitant reductions/increases in FM/FFM, 312
respectively. These findings have practical implications for support staff working to prepare 313
elite soccer players for the upcoming competitive season and further demonstrate the 314
potential for favourable alterations in body composition to be achieved during a ~6-week pre-315
season period. 316
Acknowledgements 317
The authors wish to express their gratitude to all the players who participated in this 318
investigation and would like to thank FC Barcelona for their cooperation and input to this 319
article. 320
Submission statement 321
The data reported in this manuscript have not been published elsewhere and the manuscript is 322
not under consideration for publication in another journal. 323
Authors’ contributions 324
All authors have participated in the research and/or article preparation. All authors have 325
approved the final submitted article. Study concept and design: GPM, FD, VU; Data 326
collection: FD, AL, AGD, EP; Data analysis: GPM, ADI, VU; Manuscript development: 327
GPM, VU; Contribution to manuscript: FD, AL, AGD, EP, ADI. 328
Ethics statement 329
The study received approval from the Ethics Committee for Clinical Research of the Catalan 330
Sports Council and all players provided informed written consent to participate in the study. 331
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Disclosure of interest 332
The authors report no conflicts of interest. 333
References 334 335
1. Sutton L, Scott M, Wallace J, Reilly T. Body composition of English Premier League 336
soccer players: Influence of playing position, international status, and ethnicity. J 337
Sports Sci. 2009;27(10):1019-1026. 338
2. Nikolaidis PT. Association between body mass index, body fat per cent and muscle 339
power output in soccer players. Cent Eur J Med. 2012;7(8):783-789. 340 341
3. Milsom J, Naughton R, O’Boyle A, Iqbal Z, Morgans R, Drust B, Morton JP. Body 342
composition assessment of English Premier League players: a comparative DXA 343
analysis of first team, U21 and U18 squads. J Sports Sci. 2015;33(17):1799-1806. 344 345
4. Silva JR, Brito J, Akenhead R, Nassis GP. The transition period in soccer: a window 346
of opportunity. Sports Med. 2016;46(3):305-313.
0419-3 348
5. Requena B, García I, Suárez-Arrones L, Sáez de Villarreal E, Naranjo Orellana J, 349
Santalla A. Off-season effects on functional performance, body composition, and 350
blood parameters in top-level professional soccer players. J Strength Cond Res. 351
2017;31(4):939-946. 352
6. Esco MR, Fedewa MV, Cicone ZS, Sinelnikov OA, Sekulic D, Holmes CJ. Field-353
based performance tests are related to body fat percentage and fat-free mass, but not 354
body mass index, in youth soccer players. Sports (Basel). 2018;6(4):105. 355 356
Journal Pre-proof
7. Caldwell BP, Peters DM. Seasonal variation in physiological fitness of a 357
semiprofessional soccer team. J Strength Cond Res. 2009;26(5):1370-1377. 358 359
8. Gaudino P, Iaia FM, Strudwick AJ, Hawkins RD, Alberti G, Atkinson G, Gregson W. 360
Factors influencing perception of effort (session rating of perceived exertion) during 361
elite soccer training. Int J Sports Physiol Perform. 2015;10(7):860-864. 362 363
9. Malone JJ, Di Michele R, Morgans R, Burgess D, Motron JP, Drust B. Seasonal 364
training-load quantification in elite English premier league soccer players. Int J Sports 365
Physiol Perform. 2015;10(4):489-497. 366
10. Raizel R, da Mata Godois A, Coqueiro AY, Voltarelli FA, Fett CA, Tirapegui J, da 367
Paula Ravagnani FC, de Faria Coelho-Ravagnani C. Pre-season dietary intake of 368
professional soccer players. Nutr Health. 2017;23(4):215-222. 369 370
11. Loucks AB, Kiens BK, Wright HH. Energy availability in athletes. J Sports Sci. 371
2011;29(S1):S7-S15. 372
12. Anderson L, Orme P, Naughton RJ, Close GL, Milsom J, Rydings D, O’Boyle A, Di 373
Michele R, Louis J, Hambly C, Speakman JR, Morgans R, Drust B, Morton JP. 374
Energy intake and expenditure of professional soccer players of the English Premier 375
League: evidence of carbohydrate periodization. Int J Sports Nutr Exerc Metab. 376
2017;27(3):228-238. 377
13. Manore MM. Weight management for athletes and active individuals: a brief review. 378
Sports Med. 2015;45(Suppl 1):S83-S92. 379
14. Buchheit M, Racinais S, Bilsborough JC, Bourdon PC, Voss SC, Hocking J, Cordy J, 380
Mendez-Villanueva A, Coutts AJ. Monitoring fitness, fatigue and running 381
Journal Pre-proof
performance during a pre-season training camp in elite football players. J Sci Med 382
Sport. 2013;16(6):550-555. 383
15. Jaspers A, Brink MS, Probst SGM, Frencken WGP, Helsen WF. Relationships 384
between training load indicators and training outcomes in professional soccer. Sports 385
Med. 2017;47:533-544. 386
16. Milanese C, Cavedon V, Corradini G, De Vita F, Zancanaro C. Seasonal DXA-387
measured body composition changes in professional male soccer players. J Sports Sci. 388
2015;33(12):1219-1228. 389
17. Carling C, Orhant E. Variation in body composition in professional soccer players: 390
interseasonal and intraseasonal changes and the effects of exposure time and player 391
position. J Strength Cond Res. 2010;24(5):1332-1339. 392 393
18. Owen AL, Lago-Penas C, Dunlop G, Mehdi R, Chtara M, Dellal A. Seasonal body 394
composition variation amongst elite European professional soccer players: An 395
approach of talent identification. J Hum Kinet. 2018;62:177-184. 396 397
19. Reilly T, George K, Marfell-Jones M, Scott M, Sutton L, Wallace JA. How well do 398
skinfold equations predict percent body fat in elite soccer players? Int J Sports Med. 399
2009;30:607-613. 400
20. Suárez-Arrones L, Petri C, Maldonado RA, Torreno N, Munguia-Izquierdo D, Di 401
Salvo V, Mendez-Villanueva A. Body fat assessment in elite soccer players: cross-402
validation of different field methods. Sci Med Football. 2018;2(3):203-208. 403 404
Journal Pre-proof
21. Egan E, Wallace J, Reilly T, Chantler P, Lawler J. Body composition and bone 405
mineral density changes during a premier league season as measures by dual-energy 406
x-ray absorptiometry. Int J Body Compos Res. 2006;4(2):61-66. 407
22. Devlin BL, Kingsley M, Leveritt MD, Belski R. Seasonal changes in soccer players’ 408
body composition and dietary intake practices. J Strength Cond Res. 409
2017;31(12):3319-3326. 410
23. Martín-García A, Gómez Díaz A, Bradley PS, Morera F, Casamichana D. 411
Quantification of a professional football team’s external load using a microcycle 412
structure. J Strength Cond Res. 2018;32(12):3511-3518. 413 414
24. Nana A, Slater GJ, Hopkins WG, Halson SL, Martin DT, West NP, Burke LM. 415
Importance of standardised DXA protocol for assessing physique changes in athletes. 416
Int J Sport Nutr Exerc Metab. 2016;26(3):259-267. 417 418
25. Rothney MP, Martin FP, Xia Y, Beaumont M, Davis C, Ergun D, Fay L, Ginty F, 419
Kochhar S, Wacker W, Rezzi S. Precision of GE Lunar iDXA for the measurement of 420
total and regional body composition in nonobese adults. J Clin Densitom. 421
2012;15(4):399-404. 422
26. Buchheit M, Al Haddad H, Simpson BM, Palazzi D, Bourdon PC, Di Salvo, Mendez-423
Villanueva A. Monitoring accelerations with GPS in football: time to slow down? Int 424
J Sports Physiol Perform. 2014;9(3):442-445.
0187 426
27. Osgnach C, Poser S, Bernardini R, Rinaldo R, di Prampero PE. Energy cost and 427
metabolic power in elite soccer: a new match analysis approach. Med Sci Sports 428
Exerc. 2010;42(1):170-178. 429
Journal Pre-proof
28. Owen AL, Djaoui L, Newton M, Malone S, Mendes B. A contemporary multi-modal 430
mechanical approach to training monitoring in elite professional soccer. Sci Med 431
Football. 2017;216-221. 432
29. di Prampero PE, Osgnach C. Metabolic power in team sports – part 1: An update. Int 433
J Sports Med. 2018;39(8):581-587. 434
30. Minetti AE, Moia C, Roi GS, Susta D, Ferretti G. Energy cost of walking and running 435
at extreme uphill and downhill slopes. J Appl Physiol. 2002;93(3):1039-1046. 436 437
31. Polglaze T, Hoppe MW. Metabolic power: a step in the right direction for team 438
sports. Int J Sports Physiol Perform. 2019;14(3):407-411. 439 440
32. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies 441
in sports medicine and exercise science. Med Sci Sports Exerc, 2009,41(1):3-13. 442 443
33. Buchheit M. The numbers will love you back in return – I promise. Int J Sports 444
Physiol Perform. 2016;11(4):551-554. 445
34. van Loon LJ, Greenhaff PL, Constantin-Teodosiu D, Saris WH, Wagenmakers AJ. 446
The effects of increasing exercise intensity on muscle fuel utilisation in humans. J 447
Physiol. 2001;536(1):295-304. 448
35. Bangsbo J, Mohr M, Krustrup P. Physical and metabolic demands of training and 449
match-play in the elite soccer player. J Sports Sci. 2006;24(7):665-674. 450 451
36. Rennie MJ, Wackerhage H, Spangenburg EE, Booth FW. Control of the size of the 452
human muscle mass. Annu Rev Physiol. 2004;66:799-828. 453 454
Journal Pre-proof
37. Gaudino P, Iaia FM, Alberti G, Strudwick AJ, Atkinson G, Gregson W. Monitoring 455
training in elite soccer players: systematic bias between running speed and metabolic 456
power data. Int J Sports Med. 2013;34(11):963-969.
1337943 458
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Table 1. Changes in global (whole-body) and regional (upper limbs, lower limbs, trunk) markers of body composition between start and end of pre-season within outfield
players (n = 20).
Defenders (n = 7) Midfielders (n = 7) Forwards (n = 6) All players (n = 20) SWC
Start End Start End Start End Start End
Total mass (kg) 75.37 ± 5.64 74.79 ± 5.85 72.34 ± 4.35 73.76 ± 4.64 73.51 ± 8.15 75.25 ± 9.14 73.75 ± 5.93 73.75 ± 6.35 1.19
FM (kg) 9.84 ± 1.89 8.62 ± 1.77 11.55 ± 2.07 10.43 ± 1.65 10.40 ± 1.36 10.04 ± 1.75 10.60 ± 1.88 9.56 ± 1.81
FFM (kg) 61.83 ± 4.58 62.49 ± 4.47 57.36 ± 2.72 59.81 ± 3.10 59.54 ± 7.58 61.55 ± 7.99 59.58 ± 5.27 60.61 ± 5.18
BF% (%) 13.0 ± 1.9 11.5 ± 1.7 16.0 ± 1.8 14.1 ± 1.5 14.0 ± 2.3 13.4 ± 2.0 14.4 ± 2.3 12.9 ± 2.0
Upper limbs
Total mass (kg) 8.54 ± 0.56 8.41 ± 0.54 8.01 ± 0.59 8.18 ± 0.63 8.20 ± 0.98 8.31 ± 1.06 8.25 ± 0.72 8.19 ± 0.73 0.14
FM (kg) 0.98 ± 0.12 0.86 ± 0.11 1.13 ± 0.16 1.00 ± 0.19 0.94 ± 0.11 0.89 ± 0.15 1.02 ± 0.15 0.91 ± 0.14
FFM (kg) 7.10 ± 0.54 7.09 ± 0.47 6.47 ± 0.47 6.75 ± 0.46 6.83 ± 0.98 6.97 ± 1.04 6.80 ± 0.70 6.84 ± 0.66 0.14
BF% (%) 11.6 ± 1.6 10.2 ± 1.2 14.0 ± 1.5 12.1 ± 1.7 11.6 ± 2.4 10.9 ± 2.3 12.4 ± 2.1 11.1 ± 1.9
Lower limbs
Total mass (kg) 27.81 ± 2.33 27.46 ± 2.14 26.72 ± 1.95 27.21 ± 2.30 27.23 ± 2.52 27.92 ± 3.25 27.25 ± 2.19 27.15 ± 2.35 0.44
FM (kg) 3.36 ± 0.69 2.99 ± 0.55 4.05 ± 0.64 3.71 ± 0.69 3.51 ± 0.52 3.37 ± 0.42 3.65 ± 0.67 3.29 ± 0.58
FFM (kg) 22.91 ± 2.20 22.93 ± 1.92 21.25 ± 1.43 22.04 ± 1.63 22.24 ± 2.46 23.02 ± 3.03 22.13 ± 2.07 22.38 ± 2.08
BF% (%) 12.1 ± 2.6 10.9 ± 1.9 15.1 ± 1.8 13.6 ± 1.6 13.0 ± 2.5 12.2 ± 2.0 13.4 ± 2.5 12.1 ± 2.0
Total mass (kg) 33.41 ± 3.21 33.45 ± 3.41 31.81 ± 2.16 32.70 ± 2.01 32.48 ± 4.43 33.52 ± 4.56 32.57 ± 3.22 32.89 ± 3.36 0.64
FM (kg) 4.50 ± 1.12 3.81 ± 1.18 5.32 ± 1.43 4.71 ± 0.90 4.95 ± 1.12 4.79 ± 1.30 4.92 ± 1.23 4.38 ± 1.21
FFM (kg) 27.82 ± 2.29 28.56 ± 2.31 25.46 ± 1.37 26.93 ± 1.35 26.45 ± 3.90 27.62 ± 3.69 26.58 ± 2.70 27.44 ± 2.55
BF% (%) 13.4 ± 2.3 11.2 ± 2.3 16.6 ± 3.4 14.3 ± 2.0 15.3 ± 3.3 14.3 ± 2.8 15.1 ± 3.2 13.2 ± 2.7
Data presented as mean ± SD.
FM, fat mass; FFM, fat-free mass; BF%, body fat per cent; SWC, smallest worthwhile change.
significantly different from start of pre-season (P < 0.05)
significantly different from start of pre-season (P < 0.01)
significantly different from start of pre-season (P < 0.001)
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Table 2. Accumulated training loads (TL) reported across each 1-week microcycle of the pre-season
(min) TD
(m) HSR
(m) SPR
(m) ACC
(n) DEC
Week 1
DEF (n = 1) 565 ± 0 32752 ± 0 942 ± 0 61 ± 0 938 ± 0 860 ± 0
MID (n = 3) 535 ± 45 30219 ± 3568 1467 ± 1307 112 ± 157 896 ± 77 842 ± 81
ATT (n = 2) 655 ± 346 27740 ± 2161 830 ± 337 85 ± 74 250 ± 45 273 ± 18
ALL (n = 6) 580 ± 168 29815 ± 3094 1167 ± 903 95 ± 107 688 ± 344 655 ± 301
Week 2
DEF (n = 2) 529 ± 250 31354 ± 14137 907 ± 341 130 ± 93 1035 ± 540 953 ± 538
MID (n = 7) 544 ± 176 31013 ± 10533 1736 ± 1439 76 ± 79 962 ± 388 870 ± 431
ATT (n = 3) 262 ± 114 14507 ± 5617 523 ± 342 65 ± 58 262 ± 311 281 ± 270
ALL (n = 12) 471 ± 202 26943 ± 11861 1294 ± 1215 82 ± 73 799 ± 482 737 ± 466
Week 3
DEF (n = 2) 645 ± 175 36580 ± 6205 1061 ± 1 231 ± 123 1049 ± 268 961 ± 212
MID (n = 7) 739 ± 39 39933 ± 3531 2144 ± 1502 32 ± 27 1114 ± 158 1079 ± 109
ATT (n = 4) 363 ± 301 21370 ± 15834 832 ± 580 144 ± 174 467 ± 529 394 ± 534
ALL (n = 13) 609 ± 237 33705 ± 12118 1574 ± 1277 97 ± 124 905 ± 426 850 ± 428
Week 4
DEF (n = 5) 215 ± 108 12437 ± 6327 454 ± 229 84 ± 161 351 ± 164 323 ± 158
MID (n = 7) 332 ± 105 17644 ± 6358 976 ± 658 71 ± 75 483 ± 195 446 ± 204
ATT (n = 4) 258 ± 102 16104 ± 5225 653 ± 295 153 ± 134 318 ± 248 304 ± 225
ALL (n = 16) 277 ± 111 15632 ± 6137 732 ± 510 96 ± 118 400 ± 201 372 ± 195
Week 5
DEF (n = 5) 242 ± 51 14835 ± 2722 424 ± 124 88 ± 92 410 ± 79 377 ± 67
MID (n = 7) 248 ± 97 14762 ± 5684 795 ± 641 16 ± 14 383 ± 146 383 ± 144
ATT (n = 4) 204 ± 109 12231 ± 6799 520 ± 146 83 ± 105 274 ± 218 277 ± 203
ALL (n = 16) 235 ± 85 14152 ± 5046 611 ± 450 55 ± 76 364 ± 151 355 ± 141
Week 6
DEF (n = 5) 386 ± 44 22468 ± 2292 895 ± 169 228 ± 87 572 ± 43 535 ± 59
MID (n = 7) 379 ± 84 20365 ± 5572 956 ± 502 61 ± 66 494 ± 186 474 ± 184
ATT (n = 4) 256 ± 175 13323 ± 8547 854 ± 1146 121 ± 65 301 ± 284 279 ± 266
ALL (n = 16) 345 ± 119 18912 ± 6772 908 ± 657 128 ± 98 460 ± 214 435 ± 206
Data presented as mean ± SD and are stratified by playing position (DEF, defender; MID, midfielder; ATT, attacker; ALL, all positions).
TD, total distance; HSR, high-speed running distance (m >19.8 km·h
); SPR, sprinting distance (m >25.2 km·h
); ACC, number of accelerations (>3 s
); DEC, number of
decelerations (<-3 m·s
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Table 3. Mean estimated energy cost of on-field training sessions during each 1-week microcycle of the pre-
) EC
) EE
Week 1
DEF (n = 1) 5.5 ± 0.5 46.1 ± 12.4 824 ± 222
MID (n = 3) 5.5 ± 0.2 42.8 ± 12.3 727 ± 223
ATT (n = 2) 6.8 ± 0.7 31.9 ± 11.1 581 ± 201
ALL (n = 6) 5.9 ± 0.2 36.6 ± 10.8 641 ± 179
Week 2
DEF (n = 2) 5.6 ± 0.5 40.0 ± 14.4 715 ± 258
MID (n = 7) 5.5 ± 0.4 36.8 ± 9.2 636 ± 162
ATT (n = 3) 5.6 ± 1.2 26.7 ± 11.6 435 ± 189
ALL (n = 12) 5.5 ± 0.5 35.4 ± 8.4 611 ± 148
Week 3
DEF (n = 2) 5.0 ± 1.0 46.7 ± 22.9 836 ± 409
MID (n = 7) 5.0 ± 0.8 46.1 ± 21.1 796 ± 363
ATT (n = 4) 5.6 ± 0.8 40.7 ± 27.9 644 ± 400
ALL (n = 13) 5.2 ± 0.8 37.5 ± 24.0 650 ± 399
Week 4
DEF (n = 5) 5.0 ± 0.4 23.4 ± 9.8 421 ± 171
MID (n = 7) 4.9 ± 0.5 27.4 ± 13.7 477 ± 239
ATT (n = 4) 6.4 ± 1.8 22.7 ± 8.7 368 ± 134
ALL (n = 16) 6.1 ± 1.7 24.6 ± 10.2 418 ± 172
Week 5
DEF (n = 5) 5.6 ± 0.1 38.6 ± 16.3 708 ± 310
MID (n = 7) 5.4 ± 0.5 43.1 ± 17.2 780 ± 258
ATT (n = 4) 6.0 ± 1.3 25.7 ± 19.7 422 ± 314
ALL (n = 16) 6.0 ± 1.3 24.2 ± 16.9 418 ± 302
Week 6
DEF (n = 5) 5.3 ± 1.2 26.8 ± 7.1 490 ± 134
MID (n = 7) 5.1 ± 1.2 25.1 ± 7.8 433 ± 135
ATT (n = 4) 5.1 ± 0.9 23.3 ± 5.8 424 ± 114
ALL (n = 16) 5.2 ± 1.2 25.2 ± 7.0 447 ± 129
Data presented as mean ± SD and are stratified by playing position (DEF, defender; MID, midfielder; ATT,
attacker; ALL, all positions).
AMP, average metabolic power; EC, energy cost; EE, energy expenditure.
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... However, our results revealed an increase in both cortisol and testosterone hormone. is result is in line with recent studies that emphasized that establishing this balance (anabolic and catabolic) during the PS is important for the quality of soccer-related physical performance throughout the season [47,58]. e PS is an appropriate phase for improving player's body composition [63]. Recent studies showed that low body fat percentage in soccer (depending on each field position) is important for maximizing sprint [64][65][66], high-speed running distance [65,67], vertical jump [68], and aerobic fitness performance in soccer players [65,66,69]. ...
... is is affirmed by previous studies [63,64,[70][71][72][73]. Body fat loss in soccer players in the PS was mostly related to the type and training intensity. ...
... It was recently observed that the total energy expenditure of PS explained 45% of the variance in body fat percentage. Accordingly, increased energy expenditure reflected the upregulation of aerobic metabolism through the exercise workload, which was a major mediator of substrate utilization [63]. Caldwell and Peters [64] explained that the reason for the loss in body fat after the PS can be associated with the levels aerobic and anaerobic attained in the PS phase. ...
Full-text available
Background: Physical conditions are recognized to be optimal after the pre-season (PS) phase in professional sports. Given that blood measures may also reveal variations, which in turn, may present associations with fitness changes. Objective: The aim of this study is to test the changes of blood markers and physical fitness outcomes at the beginning and following the PS phase. Additionally, we aimed also to analyze the associations of training adaptations between blood markers and the physical fitness measures. Methodology. 25 professional male soccer players (28.1 ± 4.6 years old, 2.0 ± 7.8 kg, and 176.7 ± 4.9 cm) were assessed for hematological and biochemical parameters, and physical fitness measures in the baseline and after the phase of PS. Results: Increases in platelets were observed after the PS phase (p = 0.001, η2 = 0.39). Regarding the biochemical parameters, significant increases between PS were found for creatinine (Cre) (p = 0.001, η2 = 0.66), alkaline phosphatase (ALP) (p = 0.001, η2 = 0.79), C-Reactive Protein (CRP) (p = 0.001, η2 = 0.74), cortisol (C) (p = 0.001, η2 = 0.63), and testosterone (T) (p = 0.001, η2 = 0.76), whereas significant decreases were found for albumin (Alb) (p = 0.004, η2 = 0.29), and calcium corrected (Ca Corr.) (p = 0.002, η2 = 0.32). Moderate correlations were found between albumin and the 5-meter linear sprint split (r = -0.44 (95%CI: -0.71; -0.05)) and CRP (r = -0.48 (95%CI: -0.74; -0.10)). Moderate correlations were found between VAMEVAL and hemoglobin (r = 0.44 (95%CI: 0.05; 0.71)). Conclusions: The overall physical fitness measures improved after the PS phase. Also, significant variations (decreases/increases) were observed for the case of biomchemical and hematological outcomes. Coaches should carefully consider the adaptative changes observed in blood parameters as the changes in whole organism and metabolism after specific critical phases as the PS in professional players. Thus, optimal management of stimulus/recovery can be warranted to minimize illness and injury rate and to follow the direction and dynamics of adaptative changes.
... This study's results confirm a significant reduction in the content of adipose tissue in the analyzed period, by other studies that describe fat reduction with a concomitant increase in lean body mass during the training period [29,30]. Indeed, body fat percentage has been shown to correlate with aerobic and anaerobic fitness capacities [31,32], while fat-free-mass strongly relates to movements that involve rapid skeletal-muscle activation [33], pointing to the importance of decreasing BF% and increasing FFM throughout a period of conditioning [34]. ...
... This study's results confirm a significant reduction in the content of adipose tissue in the analyzed period, by other studies that describe fat reduction with a concomitant increase in lean body mass during the training period [29,30]. Indeed, body fat percentage has been shown to correlate with aerobic and anaerobic fitness capacities [31,32], while fatfree-mass strongly relates to movements that involve rapid skeletal-muscle activation [33], pointing to the importance of decreasing BF% and increasing FFM throughout a period of conditioning [34]. ...
Full-text available
The COVID-19 pandemic has caused significant changes in global sustainability, but specifically, this study analyses the impact of lockdown on health and behavior in the game of football. The 2020/2021 Italian football competitive season (indicated as “post-COVID”), taking place following an obliged lockdown and longer than the normal summery season break, was characterized by very short recovery times and was compared to the 2018–2019 “pre-COVID” season, which had a regular course. The comparisons were about anthropometric and hormonal responses, muscle damage, and the physical performance of players in the major league (Serie A), and were made considering two extreme points of the competitive seasons: before the preparatory period (T0) and at the end of the season (T1). Turning to the results, it is significant to note the following: (1) body fat percentage was lower at the start (T0) of the post-COVID season than at the start of the pre-COVID season. During both seasons, serum CK and LDH increased in T1 and were significantly higher in both T0 and T1 of the post-COVID season. (2) Cortisol and testosterone concentrations increased in both seasons from T0 to T1; however, in the post-COVID season, concentrations of both were higher than in the previous season. The testosterone to cortisol ratio increased at the end of the pre-COVID season, whilst strongly decreasing at T1 of the post-COVID season. (3) Blood lactate concentrations significantly decreased during the pre-COVID season but remained unchanged during the post-COVID season. We may conclude that the enforced suspension period and the consequent rapid resumption of all activities influenced the physical and physiological state of professional footballers.
... A total of 74 studies were included in the systematic review, of which 32 were based on the use of anthropometry , 21 in BIA , 13 in DXA [71][72][73][74][75][76][77][78][79][80][81][82][83], 3 combined anthropometry and BIA [84][85][86], 3 combined anthropometry and DXA [87][88][89], and 2 combined BIA and DXA [90,91], while 73 articles were included in the meta-analysis ( Figure 1). Tables 2-4 show the qualitative characteristics of the included articles differentiated by measurement methods (anthropometry, BIA, and DXA, respectively). ...
... In the UEFA expert group statement, the average values of FM of elite male soccer players measured by DXA varied between 8-13%, although lower and higher values have also been reported [2]. Several of the included studies agreed with this range [71][72][73]75,76,78,90], with most of them exceeding it [74,77,[79][80][81][82][83][87][88][89]91], but in no case was it below this value. In fact, the mean of the meta-analysis performed provided a value of 13.46%. ...
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The performance of male soccer players (MSP) depends on multiple factors such as body composition. The physical demands of modern soccer have changed, so the ideal body composition (BC) requirements must be adapted to the present. The aim of this systematic review and meta-analysis was to describe the anthropometric, BC, and somatotype characteristics of professional MSP and to compare the values reported according to the methods and equations used. We systematically searched Embase, PubMed, SPORTDiscus, and Web of Science following the PRISMA statement. Random-effects meta-analysis, a pooled summary of means, and 95% CI (method or equation) were calculated. Random models were used with the restricted maximum likelihood (REML) method. Seventy-four articles were included in the systematic review and seventy-three in the meta-analysis. After comparing the groups according to the assessment method (kinanthropometry, bi-oimpedance, and densitometry), significant differences were found in height, fat mass in kilograms, fat mass percentage, and fat-free mass in kilograms (p = 0.001; p < 0.0001). Taking into account the equation used to calculate the fat mass percentage and ∑skinfolds, significant differences were observed in the data reported according to groups (p < 0.001). Despite the limitations, this study provides useful information that could help medical technical staff to properly assess the BC of professional MSP, providing a range of guidance values for the different BC.
... A study by McEwan et al. analyzed two extremities combined and showed a slight increase in fat-free mass in the upper extremities from 6.8 ± 0.7 kg to 6.84 ± 0.66 kg and an increase in FFM from 22.13 ± 2.07 kg to 22.38 ± 2.08 kg in the lower extremities during the preparatory period. McEwan et al. further found differences in fat mass: fat mass in the lower extremities decreased from 1.02 ± 0.15 kg to 0.91 ± 0.14 kg, and in the lower extremities, from 3.65 ± 0.67 kg to 3.29 ± 0.58 kg; the current study did not show the above changes [31]. ...
Full-text available
Body composition is an important indicator of the overall health and fitness of team sports athletes, including in football, and therefore, anthropometric profiling of elite football players is useful as part of determining their skills, strengths, and weaknesses to develop effective strength and conditioning programs. One of the tools available to coaches to track correlates of performance and health is routine body composition assessment. The purpose of this study is to describe and compare the body composition and anthropometric profiles of players using the Direct Segmental Multi-Frequency Bio-Electrical Impedance Analysis method, and to manage body composition throughout the round in the 2020–2021 season. The investigation was carried out during the Polish football league, PKO BP Ekstraklasa, spring round of the football season 2020–2021, in which male football players participated. Athletes between the ages of 18 and 25 (n = 16) made up the younger age group, while those between the ages of 26 and 31 (n = 22) made up the older age group. This manuscript is a continuation of the presentation of the results of the study, which was conducted between 7 January and 23 July 2021. At different stages of the macrocycle, participants underwent six different body composition analyses. The younger and older groups of athletes were compared, as well as measurements of time points 1–6. The dominant extremities, assisting extremities, and trunk had larger fat-free mass contents in the older age group. In the study groups, there was a difference in the fat-free mass content between measures 1–6 that was statistically significant. In the younger group, there was a statistically significant difference in the amount of fat mass content between measurements 1–6. In the older age group, no statistically significant changes were found. The study showed changes in fat-free mass and fat mass in body segments; differences were observed between age groups and between different moments of measurement. Age is an important factor in determining body composition and is also related to an athlete’s experience and seniority. Anthropometric profiling and comprehensive body composition analysis are important tools used in preparing athletes for competition.
... Analizar la estructura corporal de los futbolistas infiere hoy en día como uno de los métodos más oportunos para obtener estimaciones variadas del atleta en diferentes (López et al., 2017). Por ejemplo, McEwan et al. (2020). "En su estudio detallaron los cambios en marcadores de composición corporal de futbolistas profesionales durante la pretemporada" (p.3). ...
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Introduction: Soccer is a complex acyclic discipline, which is particularly demanding of very high physical aptitudes. Aim: To determine the anthropometric characteristics, body composition, and somatotype to unveil the morphological profile of professional Ecuadoran soccer players, and compare them according to the game positions. Materials and methods: An observational-descriptive study was conducted. A number of 73 Ecuadoran professional soccer players were evaluated, including seven goalkeepers, 25 defenders, 29 wingers, and 12 attackers. The international ISAK protocol was performed for measurements, with optimally calibrated equipment. An ANOVA was performed to describe the information, and a Student-T test was conducted to verify the significant differences (p<0.05) by game position. Results: Significant differences (p<0.05) were observed among the different game positions, especially between the goalkeepers and the other players. Compared to the international players, the Ecuadorans differed in terms of basic measurements and muscle mass. The other variables were similar. Conclusions: The anthropometric characteristics, body composition, and somatotype of professional Ecuadoran soccer players by position and overall average were determined. There were significant differences among the variables studied by game position. The Ecuadoran soccer players have different basic metrics from the group of international players, with lower muscle mass. Concerning fat, sum of 6 skinfold thickness, muscle-bone index, and somatotype, the similarities were remarkable.
... In relation to the method of measurement, on 12 occasions, they were based on the use of anthropometry [23][24][25][26][27][28][29][30][31][32][33][34], six in BIA [35][36][37][38][39][40], seven in DXA [41][42][43][44][45][46][47], and one combined anthropometry and BIA [48]. ...
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The performance of male soccer players (MSPs) depends on multiple factors, such as body composition. It is understandable to think that, due to the physical demands and specific functions during play, body composition may vary depending on the playing position. The aim of this systematic review and meta-analysis was to describe the anthropometric, BC, and somatotype characteristics of professional MSPs and to compare the reported values according to playing position. We systematically searched Embase, PubMed, SPORTDiscus, and Web of Science following the PRISMA statement. Random-effects meta-analysis, a pooled summary of means, and 95% CI (method or equation) were calculated. Random models were used with the Restricted Maximum Likelihood (REML) method. Twenty-six articles were included in the systematic review and the meta-analysis. After comparing the groups according to the playing position (goalkeeper, defender, midfielder, and forward), significant differences were found in age, height, weight, the sum of skinfolds, kilograms of muscle mass, and kilograms of fat-free mass (p = 0.001; p < 0.0001). No significant differences were observed in fat mass, percentage of fat-free mass, percentage of muscle mass, bone mass, and somatotype. Despite the limitations, this study provides useful information to help medical–technical staff to properly assess the BC of professional MSPs, providing reference values for the different positions.
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Professional football players are obligated to meet the physical demands and maintain the best possible performance throughout the whole macrocycle. It is important to assess the players' nutrition knowledge, identify areas that require increased nutrition awareness and identify the impact of knowledge on changes in body composition as this can affect the players' health and performance. This study aimed to assess changes in the body composition of professional football players during the macrocycle of the spring round of the football championship and to identify the correlation between nutrition knowledge and maintaining body composition. The study included 38 football players. The players' body compositions were analyzed 6 times during the macrocycle consisting of preparatory, competitive, and transition periods using the Direct Segmental Multi-Frequency Bioelectrical Impedance Analysis method. Athletes completed the Nutrition for Sport Knowledge Questionnaire to assess their nutrition knowledge. During the preparatory period, a statistically significant negative correlation was demonstrated between the players' knowledge about the subsections of micronutrients in the diet and the dispersion of the adipose percentage tissue content ( r = −0.36, p = 0.03). In the competitive period, there was a statistically significant negative correlation between the players' knowledge of sports nutrition and the dispersion of lean body mass ( r = −0.46, p = 0.004), and skeletal muscle mass ( r = −0.36, p = 0.03). During the transition period, a statistically significant negative correlation between the players' knowledge of weight control and the dispersion of body mass ( r = −0.47, p = 0.00) and BMI values ( r = −0.48, p = 0.00) was identified. The player's knowledge about the subsection of macronutrients significantly negatively correlated with the dispersion of skeletal muscle mass content ( r = −0.33, p = 0.05). Nutrition knowledge has an impact on the stability of body composition during all analyzed periods: preparatory, competitive, and transition periods.
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The ability of the “metabolic power” model to assess the demands of team-sport activity has been the subject of some interest— and much controversy—in team-sport research. Because the cost of acceleration depends on the initial speed and the costs of acceleration and deceleration are not “equal and opposite,” changes in speed must be accounted for when evaluating variablespeed locomotion. The purpose of this commentary is to address some of the misconceptions regarding “metabolic power,” acknowledge its limitations, and highlight some of the benefits that energetic analysis offers over alternative approaches to quantifying the demands of team sports.
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The primary aim of this study was to determine the association between body composition and performance outcomes in youth soccer players. Twenty-five competitive male youth soccer players (age = 13.7 ± 0.8 years, height = 167.4 ± 9.7 cm, weight = 57.6 ± 12.1 kg) volunteered to participate in this study. Height and weight were used to calculate body mass index (BMI). Body fat percentage (BF%) and fat-free mass (FFM) were determined with dual-energy X-ray absorptiometry. Each athlete performed the Pacer test, vertical jump, and t-test drill. Participants were predominantly normal weight (20.4 ± 2.7 kg·m−2). The body composition results were 20.3 ± 4.9% for BF% and 46.5 ± 8.7 kg for FFM. The results of the performance tests indicated a mean ± standard deviation (SD) of 1418 ± 332 m for Pacer, 57.2 ± 7.4 cm for vertical jump, 11.6 ± 0.7 s for t-test. Body mass index was not associated with any performance measure (r = 0.02 to −0.21, all p > 0.05). Body fat percentage was associated with the Pacer, vertical jump, and t-test (r = −0.62, −0.57, 0.61, respectively; all p < 0.01) and remained after accounting for BMI. Fat-free mass was only significantly related to t-test (r = −0.43, p < 0.01). However, after controlling for BMI, FFM was related to all three performance tests. Body fat percentage and FFM are associated with performance in youth soccer players, with stronger relationships reported in the former metric. The findings highlight the need for accurate body composition measurements as part of an assessment battery in young athletes.
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Martín-García, A, Gómez Díaz, A, Bradley, PS, Morera, F, and Casamichana, D. Quantification of a professional football team's external load using a microcycle structure. J Strength Cond Res 32(12): 3520-3527, 2018-The aims of this study were to (a) determine the external load of a football team across playing position and relative to competition for a structured microcycle and (b) examine the loading and variation the day after competition for players with or without game time. Training and match data were obtained from 24 professional football players who belonging to the reserve squad of a Spanish La Liga club during the 2015/16 season using global positioning technology (n = 37 matches and n = 42 training weeks). Training load data were analyzed with respect to the number of days before or after a match (match day [MD] minus or plus). Training load metrics declined as competition approached (MD-4 > MD-3 > MD-2 > MD-1; p < 0.05; effect sizes [ES]: 0.4-3.1). On the day after competition, players without game time demonstrated greater load in a compensatory session (MD + 1C) that replicated competition compared with a recovery session (MD + 1R) completed by players with game time (MD + 1C > MD + 1R; p < 0.05; ES: 1.4-1.6). Acceleration and deceleration metrics during training exceeded 50% of that performed in competition for MD + 1C (80-86%), MD-4 (71-72%), MD-3 (62-69%), and MD-2 (56-61%). Full backs performed more high-speed running and sprint distance than other positions at MD-3 and MD-4 (p < 0.05; ES: 0.8-1.7). The coefficient of variation for weekly training sessions ranged from ∼40% for MD-3 and MD-4 to ∼80% for MD + 1R. The data demonstrate that the external load of a structured microcycle varied substantially based on the players training day and position. This information could be useful for applied sports scientists when trying to systematically manage load, particularly compensatory conditioning for players without game time.
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The primary aim of the investigation was to study the seasonal changes in body composition in elite European soccer players and identify key playing positional differences. Twenty-two players (age = 24 ± 3.7 years, body height = 180.45 ± 5.12 cm, body mass = 76.66 ± 5.34 kg) were tested. Players’ mass (kg), lean body mass (LBM), fat free mass (FFM), fat mass (FM), muscle girths (MG) and sum of 8 skinfolds (mm) were measured across 5 time points (T0 = Start of pre-season training; T1 = End of pre-season training; T2 = Mid-season; T3 = End of mid-season break; T4 = End of season). Players were divided into their tactical positional roles for analysis. The specific positions they were divided into included defenders (n = 8), midfielders (n = 8) and forwards (n = 6). Assessment of training and matchplay exposure were also recorded. Sites-4, Sites-7, Sites-8 and Fat Mass decreased dramatically from T0 to T1 and T2 in all playing positions (p < 0.01), while no remarkable differences were found between T2, T3 and T4. Except for defenders, calf girth and lean mass were higher in T2, T3 and T4 compared to T0 and T1 (p < 0.01). Midfielders were found to be leaner than forwards and defenders in all data collection time point sessions. Defenders showed higher values in calf girth and lean body mass than midfielders and forwards. It can be concluded from this investigation that there are large variances n positional body composition profiles amongst professional European soccer players. Furthermore, significant changes are prevalent and occur across the season from LBM, FFM, MG and skinfold assessment amongst European elite level soccer players.
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Background: Despite the well-documented importance of nutrition in optimizing performance and health, dietary intake of soccer players has attracted little attention. Aim: To assess the pre-season dietary intake of professional soccer players and the adequacy in macro and micronutrients. Methods: Pre-season dietary intake of 19 male athletes was assessed using a semi-structured 3-day food record. To determine dietary adequacy and excess, energy and macronutrient intake were compared with the Brazilian dietary reference values for athletes, and micronutrients were compared to the Estimated Average Requirement - EAR (minimum recommendation) and Tolerable Upper Intake Level – UL (maximum recommendation). Results: Mean daily energy intake (40.74±12.81 kcal/kg) was adequate. However, there was a low carbohydrate intake (5.44±1.86g/kg/day) and high amount of protein and fat (1.91±0.75 and 1.27±0.50 g/kg/day, respectively). Sodium intake (3141.77±939.76 mg/day) was higher than UL (2300 mg/day), while the majority of players showed daily intake of vitamin A (74%), vitamin D (100%), folate (58%), calcium and magnesium (68%) below the EAR (625, 10 and 320 µg/day, 800 and 330 mg/day, respectively). Conclusion: Dietary intake of professional soccer players was adequate in energy; however, inadequate in macro and micronutrients, which suggest the need of improving nutritional practices to sustain physical demands of soccer during pre-season. Key Words: Dietary intake; Athletes; Soccer; Nutrition; Nutrients.
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Objectives: Understanding movements and mechanical demands of elite soccer players during training and competitive stressors is important for the support provision of player performance. Continued appreciation to quantify and monitor training load (TL) is apparent, however reporting multi-modal approaches in-line with competitive match-play demands remain limited. The investigation aimed to highlight a multimodal training monitoring method and its relationship to match-play. Subjects: 29 elite European soccer players participated were assessed (26.7 ± 4.07 years, height 183.4 ± 5.87 cm, body mass 78.4 ± 8.03 kg, 57.55 ± 5.32⁻¹.min⁻¹ and body composition 54.12 ± 13.65 mm) with daily TL and competitive match-load data in order to quantify the relationship between both. Methods: Key match-day (MD) data and TL was analysed across a 20-week in-season period. Results: Results reported significant TL differences between training days (TDs) and TDs and competitive MD data, in addition to significant differences between TDs for both volume- and intensity- session scores (p < 0.05). No differences were found between MD-1 and MD-2 session scores. Conclusion: To specific specific multi-modal approach used allows practitioners to combine key mechanical volume and intensity metrics as part of an athlete or player monitoring strategy and ensure a greater focus on targeted physical stressors.
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In an attempt to better identify and inform the energy requirements of elite soccer players, we quantified the energy expenditure (EE) of players from the English Premier League (n=6) via the doubly labeled water method (DLW) over a 7-day in-season period. Energy intake (EI) was also assessed using food diaries, supported by the remote food photographic method and 24 h recalls. The 7-day period consisted of 5 training days (TD) and 2 match days (MD). Although mean daily EI (3186 ± 367 kcals) was not different from (P>0.05) daily EE (3566 ± 585 kcals), EI was greater (P<0.05) on MD (3789 ± 532 kcal; 61.1 ± 11.4 LBM) compared with TD (2956 ± 374 kcal; 45.2 ± 9.3 LBM, respectively). Differences in EI were reflective of greater (P<0.05) daily CHO intake on MD (6.4 ± 2.2 compared with TD (4.2 ± 1.4 Exogenous CHO intake was also different (P<0.01) during training sessions (3.1 ± 4.4 g.h(-1)) versus matches (32.3 ± 21.9 g.h(-1)). In contrast, daily protein (205 ± 30, P=0.29) and fat intake (101 ± 20, P=0.16) did not display any evidence of daily periodization. Although players readily achieve current guidelines for daily protein and fat intake, data suggest that CHO intake on the day prior to and in recovery from match play was not in accordance with guidelines to promote muscle glycogen storage.
Team sports are characterised by frequent episodes of accelerated/decelerated running. The corresponding energy cost can be estimated on the basis of the biomechanical equivalence between accelerated/decelerated running on flat terrain and constant speed running uphill/downhill. This approach allows one to: (i) estimate the time course of the instantaneous metabolic power requirement of any given player and (ii) infer therefrom the overall energy expenditure of any given time window of a soccer drill or match. In the original approach, walking and running were aggregated and energetically considered as running, even if in team sports several walking periods are interspersed among running bouts. However, since the transition speed between walking and running is known for any given incline of the terrain, we describe here an approach to identify walking episodes, thus utilising the corresponding energy cost which is smaller than in running. In addition, the new algorithm also takes into account the energy expenditure against the air resistance, for both walking and running. The new approach yields overall energy expenditure values, for a whole match,≈14% smaller than the original algorithm; moreover, it shows that the energy expenditure against the air resistance is≈2% of the total.
Objective: In soccer players, body fat mass (FM) is commonly estimated by assessment methods such as skinfolds, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). The main aim was to cross-validate the 3 field methods for quantifying body fat against a recent model of DXA. Methods: This study involved a group of 18 international-level, elite male soccer players belonging to the squad of a Serie A club in Italy. Results: All skinfold equations (with the exception of Deuremberg) showed large-to-very large positive correlations (r from 0.61 to 0.82, p ≤ 0.01) with DXA, however, BIA %FM data showed unclear relationship. All the sum of skinfolds showed moderate-to-very large positive correlations with DXA (r from 0.46 to 0.79, p values from 0.061 to 0.000). The combination of triceps and iliac crest skinfolds selected by the stepwise procedure explained 78.6% variance in DXA total %FM. Conclusion: All different methods employed in the present study are likely to differ, which prevent their use interchangeably. Almost all the equations showed positive correlations, but with different values in comparison with a recent model of DXA. Finally, the sum of skinfolds appears to be a good alternative when limited time and budged is available.
The aims of this study were two-fold: to determine seasonal changes in dietary intake and body composition in elite soccer players and to evaluate the influence of self-determined individual body composition goals on dietary intake and body composition. This longitudinal, observational study assessed body composition (total mass, fat-free soft tissue mass and fat mass) using dual-energy x-ray absorptiometry and dietary intake (energy and macronutrients) via multiple pass 24-hour recalls, at four time points over a competitive season in elite soccer players from one professional club in the Australian A-League competition. Self-reported body composition goals were also recorded. Eighteen elite male soccer players took part (25 ± 5 years, 180.5 ± 7.4 cm, 75.6 ± 6.5 kg). Majority (≥67%) reported the goal to maintain weight. Fat-free soft tissue mass increased from the start of preseason (55278 ± 5475 g) to the start of competitive season (56784 ± 5168 g; p<0.001) and these gains were maintained until the end of the season. Fat mass decreased over the preseason period (10072 ± 2493 g to 8712 ± 1432 g; p<0.001), but increased during the latter part of the competitive season. Dietary intake practices on training days were consistent over time and low compared to sport nutrition recommendations. The self-reported body composition goals did not strongly influence dietary intake practices or changes in body composition. This study has demonstrated that body composition changes over the course of a soccer season are subtle in elite soccer players despite relatively low self-reported intakes of energy and carbohydrate.