Associations between wellness and internal and external load variables in two intermittent small-sided soccer games

Accepted Manuscript Associations between wellness and internal and external load variables in two intermittent small-sided soccer games

Filipe Manuel Clemente PII: DOI: Reference:

S0031-9384(18)30619-X doi:10.1016/j.physbeh.2018.09.008 PHB 12319

To appear in:

Physiology & Behavior

Received date: Revised date: Accepted date:

7 August 2018 13 September 2018 16 September 2018

Please cite this article as: Filipe Manuel Clemente , Associations between wellness and internal and external load variables in two intermittent small-sided soccer games. Phb (2018), doi:10.1016/j.physbeh.2018.09.008

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ACCEPTED MANUSCRIPT Original Work

Associations between wellness and internal and external load variables in two intermittent small-sided soccer games Filipe Manuel Clemente1,2

Instituto Politécnico de Viana do Castelo, Escola Superior de Desporto e Lazer, Melgaço, Portugal

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Instituto de Telecomunicações, Delegação da Covilhã, Portugal

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Corresponding author: Filipe Manuel Clemente, [email protected], Adress: Complexo

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Desportivo e Lazer de Melgaço – Monte de Prado, 4960-320, Melgaço, Portugal

ACCEPTED MANUSCRIPT Associations between wellness and internal and external load variables in two intermittent small-sided soccer games Abstract The purpose of this study was to test the associations between wellness and internal and

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external load variables during two intermittent small-sided games (SSGs). Ten male

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amateur soccer players (age: 19.8 1.6 years; experience: 8.3 2.1 years; height: 177.4 3.8 cm; weight: 71.7 4.2 kg) voluntarily participated in this study. The 5x5 format was played in 3x6 min and 6x3 min regimens. Muscle soreness (DOMS), stress,

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fatigue, and sleep quality were rated before each session. Perceived exertion (RPE); mean heart rate (HRmean); total (TD), jogging (JD), running (RD), and sprinting (SD)

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distances; player’s training load (PTL); and total accelerations (TAc) were monitored during SSGs. In the case of the 3x6’ regimen, large negative correlations between

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DOMS and TD (-0.68, [-0.89;-0.20]), JD (-0.66, [-0.89;-0.17]) and SD (-0.63, [-0.88;0.12]) were found, and very large negative correlations between DOMS and PTL (-0.84,

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[-0.95;-0.53]) were found. Very large (-0.73, [-0.91;-0.30] and large (-0.61, [-0.87;-

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0.09]) negative correlations between DOMS and HRmean and PTL, respectively, were observed during the 6x3’ regimen. Regarding the associations between load variables,

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during the 6x3’ regimen, RPE was very largely correlated with TD (0.77, [0.37;0.93]), JD (0.70, [0.25;0.90]) and largely correlated with TAc (0.67, [0.19;0.89]). In the 3x6’ regimen, large correlations were found between RPE and SD (0.62, [0.10;0.87]) and TAc (0.61, [0.09;0.87]). Overall, PTL was nearly perfectly correlated with TD (0.96, [0.86;0.99]) and JD (0.94, [0.81;0.98]), very largely correlated with TAc (0.87, [0.61;0.96]), and largely correlated with RD (0.72, [0.29;0.91]). The results of this study suggest that wellness status may influence workload in SSGs; in particular, DOMS may be moderately-to-largely detrimental to both internal and external load variables.

ACCEPTED MANUSCRIPT Moreover, it was confirmed that RPE is moderately-to-largely correlated to objectively measured external load variables. Keywords: Association football; load monitoring; well-being; SSGs; RPE; GPS.

Introduction

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Small-sided games (SSGs) are very popular training drills used in soccer to

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maintain a high-exertion level while technical and tactical skills are worked on (1,2). The use of SSGs in training may help to specify training stimuli with the dynamics of a game (3). Despite the low reproducibility of SSGs to replicate some forms of workload

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stimulation (4,5), there is evidence revealing that the proper application of these games may ensure a proper stimulation that conducts to improvements in different fitness

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categories, similarly to running-based activities (6,7).

Besides the technical and tactical demands, SSGs are often used to stimulate acute

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physiological and physical responses, and for that reason, it is extremely important to monitor the workload associated with them (8). Usually, internal (biological responses

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to physical stimulation) and external (physical demands associated to the stimuli) load

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variables are monitored during SSGs – in particular, heart rate (HR), rated of perceived exertion (RPE), and distances covered at different speeds (1). These load variables

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measure different categories of performance, and for that reason, they are independent of each other and provide different information despite being associated (9). In the case of load monitoring, RPE seems to be highly dependent on session distance and not so dependent on high-speed running (10,11). Partially in line with these results, a study conducted in elite soccer players also found that high-speed running, number of impacts, and accelerations were moderately correlated with RPE (12). Therefore, it seems evident that internal (perceptive or objectively measured) and

ACCEPTED MANUSCRIPT external load variables are closely related despite the correlation variations between specific measures (13). Despite the apparently moderate-to-large associations between RPE and external load, it seems that such a relationship is dependent on training mode (13). In a metaanalysis conducted on the interactions between internal and external measures, it was

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found that correlations between RPE and total distance were possibly very large (13).

skills-based

tasks and

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Nevertheless, in such meta-analysis reductions in the correlation levels were found in neuromuscular training.

Trying to observe specific load

relationships within a drill-based task, it was found that the correlations between

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internal and external load variables were trivial-to-small with the exception of moderate

measures) during SSGs (14).

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correlations between player load (external measure) and HR and RPE (internal

More than considering the relationships between internal and external load

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measures, workload variables seem to be wellbeing-dependent (15). Usually, research on this topic tries to explain the well-being variations considering over- or de-load (16).

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Generally, most of the studies suggest that muscle soreness, fatigue, stress, mood, and

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sleep quality are sensitive to the load imposed (17,18). However, there are no studies that have tried to identify the opposite relationships in soccer (i.e., to explain load based

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on the well-being status of the day). As mentioned above, relationships between load variables seem to be not so dependent during drill-based tasks. However, to the best of our knowledge, only one study has tested such a hypothesis in the case of SSGs (14). The confirmation (or lack thereof) of such evidence will help identify the most proper load-monitoring methods to track players’ performance and to re-interpret the consequences of load stimulation during SSGs. Moreover, the study of the relationship levels between load and well-

ACCEPTED MANUSCRIPT being measures would help to identify the impact of wellness during drill-based tasks. Such observations may help coaches to manage load stimulus and, most of all, the type of SSG regimens to apply based on players’ perceptions of muscle soreness, fatigue, and stress. Based on these reasons, the purpose of the present study was to analyze the

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relationships between wellness and internal and external load variables during two

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intermittent SSGs. Primarily, wellness variables (muscle soreness, sleep quality, stress, and fatigue) will be correlated with internal (HR and RPE) and external (distances at different speed thresholds, player’s training load, and total accelerations) load variables.

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Secondly, internal and external load variables will be tested for its associations during

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two intermittent SSGs regimens.

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Methods

Participants

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Ten male amateur soccer players (age: 19.8 1.6 years; experience: 8.3 2.1 years; height: 177.4 3.8 cm; weight: 71.7 4.2 kg) voluntarily participated in this study. They

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competed at the regional level, training four times a week and playing one match each weekend. Prior to the study, participants were informed of the experimental approach

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and study design, as well as of the potential risks, benefits, and implications of participating. After being informed, players signed an informed consent. The study was conducted following the ethical standards for the study in humans as suggested by the Declaration of Helsinki.

Experimental Approach A cross-sectional design was used to associate the player’s perception of muscle soreness, fatigue, stress and quality of sleep with the RPE, mean heart rate (HRmean),

ACCEPTED MANUSCRIPT total distance (TD), running distance (RD), sprinting distance (SD), total accelerations (TAc) and player’s training load (PTL) of the players in two 5x5 intermittent regimens: 6 sets of 3 minutes with 2 minutes of rest (6x3’ regimen) and 3 sets of 6 minutes with 2 minutes

of rest

(3x6’

regimen).

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regimens were chosen based

on the

recommendations of a previous review in the SSGs for the specific format of play (2).

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The study was conducted during 2 weeks before the end of the season.

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Each training regimen was applied twice, with a week between them to maintain the experimental conditions and to avoid contextual differences. Only one regimen was conducted in each session. In the first week, the 6x3’ regimen was implemented first

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and the 3x6’ regimen was implemented 48 hours afterward, without any other training sessions in between. In the second week, the inverse sequence was employed.

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The players were grouped into two teams. The groups were constituted based on skill level, fitness status, and playing positions, aiming to homogenize as much as

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possible. Both teams kept the same players throughout the study. Each player wore vests equipped with GPS and HR sensors. Before each sessions begins, players rated the

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wellness status in an appropriate questionnaire. The study was conducted in a synthetic

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turf at 5:00 p.m. at a temperature of 21.2(2.1)ºC and a relative humidity of 63.4(3.6)% without rainy weather conditions.

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A standardized warm-up protocol consisting of 5 minutes of low-intensity running, 5 minutes of lower-limb dynamic stretching and mobility exercises, 5 minutes of agility and acceleration/deceleration drills, and 5 minutes of a ball-possession game was employed before 5x5 formats.

The 5x5 format

ACCEPTED MANUSCRIPT Two training regimens were conducted using the 5x5 format: 6x3’/2’ rest and 3x6’/2’ rest. The 5x5 format was conducted without goalkeepers and with small goals (2x1 m) centered on the end line. Players were distributed by the teams, and each team had 2 defenders, 2 midfielders, and 1 forward with similar skills levels and fitness status. However, no strategic or tactical advice was conceded to players during the

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study. Only verbal encouragement was provided to maintain high levels of commitment

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and effort. The 5x5 format followed the majority of soccer rules, with exception of offside and scoring method. Six balls were placed around the pitch to be easily and

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quickly replaced every time the in-play ball went out of bounds.

Rate of Perceived Exertion

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The CR-10 point scale (19) was used to classify the effort of each game. On this scale, 1 represents “very light activity,” and 10 represents “maximal exertion.” Players

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rated each set of SSGs. The ratings were made individually to minimize the influence of external factors and to prevent players from hearing other players’ ratings. The players

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were previously familiarized with the scale to maximize the accuracy of the answers

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during the experiments.

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Wellness measures

The 7-point scale of Hooper (20) was used to quantify the player’s perception of muscle soreness, fatigue, stress and quality of sleep. In the scale 1 means very, very low and 7 is very, very high (for stress, fatigue and muscle soreness) and 1 is very, very good and 7 is very, very bad for the sleep quality. Players was informed about the scale and the meaning and the questionnaire was applied 30 minutes before study begins in each session. The rates were made individually by the players.

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Heart rate (HR) and Global Positioning System (GPS) Individual chest belts with a heart rate sensor (Polar H7, Polar Electro, OY, Kempele, Finland) were worn by the players. Data was recorded each second during SSGs. After SSGs, the data was imported into the Polar Team application and the

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HRmean (bpm) was collected for each set.

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Vests with a tracker, placed in the dorsal region, (JOHAN Sports, Noordwijk, The Netherlands) consisting of a GPS sensor (10 Hz, including EGNOS correction), accelerometer, gyroscope, and magnetometer (100 Hz, 3 axes) were also worn by the

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players during the SSGs. The geolocation tracker was validated and tested for its reliability in a previous study (21). After collecting information, the raw data was

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processed in the JOHAN application. The following variables were collected from the tracker devices: a) total distance (meters); b) running distance at 14-19.9 km/h (meters);

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c) sprinting distance at > 20.0 km/h (meters); d) total number of accelerations > 2 m/s2 ; and f) player’s training load (PTL) (g), consisting of the accumulation of instantaneous

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acceleration data collected for the three axes estimated by the accelerometer. The

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players were previously familiarized with the use of both HR and geolocation trackers

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to minimize any initial discomfort.

Statistical Procedures Results were presented in form of text, tables, and figures as either means with standard deviation (SD), means with a 90% confidence interval (90% CI) or coefficient of variation (CV) where specified. Pearson’s product-moment correlation coefficients tested the associations between the wellness and training load measures. The p was set at 0.05 and the tests were executed in the SPSS software (version 21.0, IBM, USA). The

ACCEPTED MANUSCRIPT magnitude of correlation (r[90% CI]) between variables were assessed with the following thresholds: < 0.1, trivial; 0.1-0.3, small; 0.3-0.5, moderate; 0.5-0.7, large; 0.70.9, very large; and > 0.9, nearly perfect (22). In the case of 90% confidence limits overlapped positive and negative values, the magnitude was deemed unclear and if not the magnitude was deemed to be the observed magnitude as proposed in a previous

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study (23).

Results

Descriptive statistics of wellness categories and load variables can be observed in

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table 1. The average of sets revealed that RPE (5.97, [5.50;6.43]), HRmean (169.67, [163.84;175.49]), TD (591.86, [565.63;618.10], JD (258.47, [239.64;277.31]), RD

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(56.39, [47.84;64.93]), SD (4.33, [2.94;5.73]), PTL (38.26, [35.50;41.03]) and TAc

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(14.13, [12.50;15.76]) were higher in 3x6’ regimen compared to 6x3’ regimen.

Table 1. Descriptive statistics (mean[90% confidence interval]) of wellness and load variables per SSG regimen and overall.

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6x3’ Reg. 3x6’ Reg. General DOMS (A.U.) 2.35 [1.79;2.91] 3.20 [2.60;3.80] 2.78 [2.36;3.19] Sleep (A.U.) 2.35 [2.08;2.63] 2.80 [2.39;3.21] 2.58 [2.33;2.82] Fatigue (A.U.) 2.20 [1.57;2.83] 2.80 [2.49;3.11] 2.50 [2.16;2.84] Stress (A.U.) 2.70 [1.89;3.51] 2.20 [1.41;2.99] 2.45 [1.92;2.98] RPE (A.U.) 5.43 [4.90;5.95] 5.97 [5.50;6.43] 5.70 [5.36;6.04] HRmean (bpm) 169.52 [163.86;175.17] 169.67 [163.84;175.49] 169.59 [165.87;173.32] TD (m) 323.42 [313.05;333.79] 591.86 [565.63;618.10] 457.64 [402.85;512.44] JD (m) 150.02 [139.76;160.28] 258.47 [239.64;277.31] 204.25 [180.59;227.91] RD (m) 35.84 [32.56;39.12] 56.39 [47.84;64.93] 46.11 [40.26;51.97] SD (m) 2.70 [1.69;3.71] 4.33 [2.94;5.73] 3.52 [2.66;4.37] PTL (g) 20.79 [19.07;22.52] 38.26 [35.50;41.03] 29.53 [25.75;33.30] TAc > 2m/s 2 (n) 7.93 [7.22;8.64] 14.13 [12.50;15.76] 11.03 [9.56;12.51] 6x3’ reg.: 6x3 minutes SSG regimen; 3x6’ reg.: 3x6 minutes SSG regimen; DOMS: muscle soreness; RPE: rated of perceived exertion; HRmean: mean heart rate; TD: total distance; JD: jogging distance; RD: running distance; SD: sprinting distance; PTL: player’s training load; TAc: total accelerations (>2 m/s 2 ). Load variables were not split by the time.

Associations between wellness categories and load variables are presented in table 2. Very large (-0.73, [-0.91;-0.30] and large (-0.61, [-0.87;-0.09]) negative correlations

ACCEPTED MANUSCRIPT were observed between DOMS and HRmean and PTL during 6x3’ regimen, respectively. In the case of 3x6’ regimen it were found large negative correlations between DOMS and TD (-0.68, [-0.89;-0.20]), JD (-0.66, [-0.89;-0.17]) and SD (-0.63, [-0.88;-0.12]) and very large correlations between DOMS and PTL (-0.84, [-0.95;0.53]). Overall, magnitudes were deemed unclear.

[-0.88;-0.13]) during 6x3’ regimen. However,

TAc (-0.64,

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[-0.88;-0.14]) and

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Large and negative correlations were found between sleep quality and RPE (-0.64,

magnitudes were deemed unclear in 3x6’ regimen and overall. Regarding fatigue, the correlations were deemed unclear in both regimens and overall.

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Stress was largely and positively correlated with RPE (0.59, [0.06;0.86]) and RD (0.66, [0.17;0.89]) during 6x3’ regimen. Running distance was also largely and

magnitudes were deemed unclear.

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positively correlated with stress (0.60, [0.07;0.86]) during 3x6’ regimen. Overall,

Stress 3x6’ Reg. DOMS Sleep Fatigue Stress General DOMS Sleep Fatigue Stress

0.18 [-0.41;0.67] -0.64 [-0.88;-0.14] 0.24 [-0.36;0.70] 0.59 [0.06;0.86]

-0.73 [-0.91;-0.30] -0.07 [-0.60;0.50] -0.39 [-0.78;0.21] -0.25 [-0.70;0.35]

-0.16 [-0.65;0.43] 0.20 [-0.40;0.68] 0.35 [-0.25;0.75] -0.27 [-0.72;0.33] 0.14 [-0.45;0.64] -0.02 [-0.57;0.54] 0.34 [-0.26;0.75] 0.12 [-0.46;0.63]

TD

JD

RD

SD

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T Ac

-0.08 [-0.60;0.50] -0.46 [-0.81;0.13] -0.08 [-0.60;0.50] 0.48 [-0.10;0.82]

-0.34 [-0.75;0.26] -0.39 [-0.77;0.21] -0.22 [-0.69;0.38] 0.28 [-0.32;0.72]

0.37 [-0.23;0.77] -0.38 [-0.77;0.21] 0.21 [-0.39;0.68] 0.66 [0.17;0.89]

0.38 [-0.21;0.77] -0.17 [-0.66;0.42] 0.55 [-0.01;0.84] 0.50 [-0.07;0.82]

-0.61 [-0.87;-0.09] -0.29 [-0.73;0.31] -0.45 [-0.80;0.13] 0.32 [-0.28;0.74]

0.34 [-0.26;0.75] -0.64 [-0.88;-0.13] 0.42 [-0.17;0.79] 0.71 [0.26;0.91]

0.20 [-0.39;0.68] -0.12 [-0.63;0.46] 0.12 [-0.46;0.63] -0.36 [-0.76;0.25]

-0.68 [-0.89;-0.20] 0.3 [-0.30;0.73] -0.04 [-0.58;0.53] 0.29 [-0.31;0.73]

-0.66 [-0.89;-0.17] 0.41 [-0.19;0.78] 0.05 [-0.52;0.59] 0.41 [-0.19;0.78]

-0.22 [-0.69;0.38] 0.12 [-0.47;0.63] 0.31 [-0.30;0.73] 0.60 [0.07;0.86]

-0.63 [-0.88;-0.12] 0.39 [-0.20;0.78] 0.21 [-0.39;0.68] -0.06 [-0.59;0.51]

-0.84 [-0.95;-0.53] 0.38 [-0.22;0.77] -0.16 [-0.66;0.43] -0.13 [-0.63;0.46]

-0.47 [-0.81;0.11] 0.59 [0.06;0.86] 0.46 [-0.13;0.81] 0.16 [-0.43;0.66]

-0.22 [-0.69;0.38] -0.09 [-0.61;0.49] -0.19 [-0.67;0.40] -0.30 [-0.73;0.30]

0.29 [-0.31;0.73] 0.38 [-0.21;0.77] 0.33 [-0.27;0.75] -0.11 [-0.62;0.47]

0.17 [-0.42;0.66] 0.41 [-0.19;0.78] 0.29 [-0.32;0.72] -0.03 [-0.57;0.53]

0.25 [-0.35;0.70] 0.26 [-0.34;0.71] 0.37 [-0.23;0.77] 0.26 [-0.34;0.71]

-0.03 [-0.57;0.53] 0.32 [-0.28;0.74] 0.45 [-0.14;0.80] 0.09 [-0.49;0.61]

0.11 [-0.47;0.62] 0.40 [-0.19;0.78] 0.22 [-0.38;0.69] -0.15 [-0.65;0.44]

0.23 [-0.37;0.69] 0.46 [-0.12;0.81] 0.46 [-0.12;0.81] 0.01 [-0.55;0.56]

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Fatigue

HRmean

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Sleep

RPE

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6x3’ Reg. DOMS

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Table 2. Pearson’s product-moment coefficients [90% confidence intervals] for wellness and load variables during intermittent SSGs.

6x3’ reg.: 6x3 minutes SSG regimen; 3x6’ reg.: 3x6 minutes SSG regimen; DOMS: muscle soreness; RPE: rated of perceived exertion; HRmean: mean heart rate; TD: total distance; JD: jogging distance;

ACCEPTED MANUSCRIPT RD: running distance; SD: sprinting distance; PTL: player’s training load; TAc: total accelerations (>2 m/s 2 )

Correlations between RPE and the remaining load variables can be observed in figure 1. During 6x3’ regimen the RPE was very largely correlated with TD (0.77, [0.37;0.93]), JD (0.70, [0.25;0.90]) and largely correlated with TAc (0.67, [0.19;0.89]).

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In the case of 3x6’ regimen, large correlations were found between RPE and SD (0.62,

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[0.10;0.87]) and TAc (0.61, [0.09;0.87]). Overall, the RPE was largely correlated with

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TAc (0.56, [0.02;0.85]).

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6x3’ reg.: 6x3 minutes SSG regimen; 3x6’ reg.: 3x6 minutes SSG regimen; RPE: rated of perceived exertion; HRmean: mean heart rate; TD: total distance; JD: jogging distance; RD: running distance; SD: sprinting distance; PTL: player’s training load; TAc: total accelerations (>2 m/s 2 )

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Figure 1. Correlation coefficients [90% CI] between RPE and the remaining load variables.

The relationships between HRmean and the remaining load variables can be found in figure 2. Magnitudes were deemed unclear in both SSG regimens and overall.

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6x3’ reg.: 6x3 minutes SSG regimen; 3x6’ reg.: 3x6 minutes SSG regimen; RPE: rated of perceived exertion; HRmean: mean heart rate; TD: total distance; JD: jogging distance; RD: running distance; SD: sprinting distance; PTL: player’s training load; TAc: total accelerations (>2 m/s 2 )

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Figure 2. Correlation coefficients [90% CI] between HRmean and the remaining load variables. The associations between PTL and the remaining load variables can be found in

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figure 3. PTL was very largely and positively correlated with TD (0.71, [0.26;0.91]) and JD (0.80, [0.44;0.94]) during 6x3’ regimen. In the case of the 3x6’ regimen, the PTL

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was very largely correlated with SD (0.79, [0.41;0.93]) and largely correlated with TD (0.68, [0.21;0.90]), JD (0.60, [0.07;0.86]) and TAc (0.57, [0.02;0.85]). Overall, the PTL

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was nearly perfect correlated with TD (0.96, [0.86;0.99]) and JD (0.94, [0.81;0.98]),

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very largely correlated with TAc (0.87, [0.61;0.96]) and largely correlated with RD

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(0.72, [0.29;0.91]).

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6x3’ reg.: 6x3 minutes SSG regimen; 3x6’ reg.: 3x6 minutes SSG regimen; RPE: rated of perceived exertion; HRmean: mean heart rate; TD: total distance; JD: jogging distance; RD: ru nning distance; SD: sprinting distance; PL: playerload; TAc: total accelerations (>2 m/s 2 )

Figure 3. Correlation coefficients [90% CI] between PTL and the remaining load

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variables.

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Discussion

This study shows that wellness status influences the physical demands monitored

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throughout GPS and accelerometers. DOMS was the main variable that was inversely and meaningfully correlated with more external load measures, thus suggesting its

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detrimental impact on players during SSGs. A previous study that conducted

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comparisons between DOMS and training load also revealed inverse relationships, confirming the detrimental potential of DOMS on the physical capacity of players

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during training sessions(16). Interestingly, DOMS was largely correlated with HRmean and PTL during shorter bouts (6x3’) and largely correlated with total, jogging, and sprinting distances and player’s training load during longer bouts (3x6’), suggesting that the type of SSG regimen may influence the level of associations, which is in line with the findings of a previous study that revealed that some load variables and their associations change according to the type of exercise being performed (13). The results obtained about the relationships between DOMS and load variables suggest that players with greater muscle soreness will experience decreases in player’s training load and

ACCEPTED MANUSCRIPT HRmean during longer bouts but will not be so severally affected in distances covered at different speed thresholds in shorter bouts. This fact may also be explained by the higher level of intensity (m/min) that may occur during shorter bouts (24). For that reason, players may be more sensitive to muscle soreness in shorter bouts. Stress was also a well-being variable that showed meaningful relationships with

with running distance during both regimens. These results are somewhat

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and

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load variables. Specifically, stress was largely correlated with RPE during shorter bouts

unexpected based on the fact that stress contributed to a higher load in terms of running distance. In fact, despite being deemed unclear, stress was also moderately correlated

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with sprinting distance during shorter bouts, suggesting that the increase in running did not compromise the decrease in high-speed running. Therefore, individual zones of

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optimal function (25) may play a role in addition to coping strategies (26) to manage the stress to optimize the performance during training and games.

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Sleep was also negatively and largely correlated with RPE during shorter bouts. The relationships between wellness variables and RPE found in our study is not in line

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with a previous study that revealed that RPE is consistent and not influenced by well-

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being variables during submaximal exercise training sessions (20). It seems that the higher intensity that occurred in shorter SSG bouts may promote changes in the

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individual perception of load, and for that reason, coaches should adopt supplementary monitoring methods to avoid erroneous classifications of load rated by players with low sleep quality. Relationships between load variables were also tested in the present study. Main evidence revealed that RPE was largely correlated with sprinting distance and total accelerations during longer bouts and with total and jogging distances and total accelerations during shorter bouts. Such evidence suggests that, as previously discussed,

ACCEPTED MANUSCRIPT RPE and its capacity to be associated with external load may be influenced by the type of exercise being performed. In this case, RPE was more strongly correlated with highdemanding load variables during longer bouts, suggesting that the perception of players may be affected by the intensity of longer periods of effort. In the case of shorter bouts, the perception may be more strongly associated with the overall activity. Despite this

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sensitiveness of RPE to the training regimen, the evidence confirms that RPE is a low-

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cost, easy-to-use and effective method to track the intensity of practice (13), particularly during SSGs. On the other hand, the correlations between HRmean and the remaining load variables were deemed unclear, probably owing to the fact that this measure should

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be more drill-based specific using TRIMP approach or maximal HR (27). Player’s training load was also tested for its relationship with load measures.

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Correlations between player’s training load and internal load measures were deemed unclear, suggesting that they provide different information, and for that reason, coaches

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should interpret the values carefully when using only one monitoring method. Despite this, player’s training load was largely-to-nearly perfectly correlated with other external

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load variables – in particular, with total and jogging distances and with total

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accelerations. Therefore, player’s training load is more closely related to overall activity and accelerations than to high-speed running that occurs sparsely in SSGs (28).

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This study had some limitations. The competitive level of the players (amateur) may have resulted in specific inferences that may not generalize to professional players, especially considering the influence of wellness status on SSG performance. Future studies should extend this approach to a larger number of players and to players of a higher competitive level. Moreover, playing positions were not compared and for that reason some variations in the results can be caused by the activity profile. Another study limitation was that wellness status was monitored only with subjective scales, but this

ACCEPTED MANUSCRIPT method seems to be more sensitive to high load periods than objective measures based on heart rate variability (29). Despite that, future studies should include objective measures of well-being to identify possibly different associations with load variables. A final study limitation is that the study was conducted at the end of the season and probably the results can be different for different moments as pre-season or in-season

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and depending on the players’ fitness status.

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This is the first study that has tested the associations between wellness status and load variables during SSGs and is one of few that has tested the relationships between internal and external load variables during different SSGs regimens. In terms of

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practical applications, coaches should consider adjusting intermittent training regimens according to the wellness status of players, especially when players are at dangerous

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levels of DOMS, fatigue, or stress. Moreover, coaches should also be aware of the importance of subjective intensity measures, such as RPE, to monitor effort during

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SSGs. However, coaches should also consider that RPE may be sensitive to some

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wellness status factors and training regimens.

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Conclusions

It was found that, during shorter SSG bouts (6x3’), the greater DOMS was largely

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detrimental to HRmean and PTL. Moreover, lower quality of sleep was largely detrimental to RPE and total accelerations; and greater stress largely contributed to an increase in RPE and running distances. In the case of longer SSG bouts (3x6’), the greater DOMS was largely detrimental to total, jogging, and sprinting distances and for player’s training load. It was also observed that more stress was largely correlated to increases in running distance. Regarding the relationships between load variables, it was found that RPE was largely correlated with sprinting distances and player’s training

ACCEPTED MANUSCRIPT load during longer bouts and with total and jogging distances and total accelerations during shorter bouts. However, the magnitudes of correlations of HRmean with other load variables were deemed unclear. Player’s training load was clearly and largely-tonearly perfectly correlated with external load variables. These findings suggest that time-motion demands are wellness-dependent, and for that reason, coaches should be

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aware of the importance of tracking players’ well-being to adjust the intensity and load

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stimuli of SSGs. Moreover, our findings suggest that the type of exercise and training regimen may promote variations in the association levels of internal and external load variables, and thus, coaches should consider adjusting the monitoring process in

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accordance to the typology of exercises.

Halouani J, Chtourou H, Gabbett T, Chaouachi A, Chamari K. Small-Sided

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1.

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