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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0007">Introduction</span><p id="para0005" class="elsevierStylePara elsevierViewall">Today&#44; technology allows monitoring and to describe sport demands with greater precision than ever before&#46;<a class="elsevierStyleCrossRefs" href="#bib0001"><span class="elsevierStyleSup">1&#8211;3</span></a> During the last decade&#44; the physical demands of basketball have been described through several studies<a class="elsevierStyleCrossRefs" href="#bib0004"><span class="elsevierStyleSup">4&#8211;6</span></a>&#59; however&#44; not many of them are explicitly related to female youth athletes&#46; The development of smaller and more reliable devices has opened a new field of research in sports sciences&#44; including new perspectives on the study of the physical demands and physiological responses of team sports&#44; and specifically&#44; within basketball contexts&#46;<a class="elsevierStyleCrossRef" href="#bib0004"><span class="elsevierStyleSup">4</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRefs" href="#bib0007"><span class="elsevierStyleSup">7&#8211;9</span></a> Recent studies using technology have described basketball as a high demanding sport considering its aerobic and anaerobic requirements&#44; its changes of direction&#44; accelerations&#44; decelerations&#44; jumps&#44; sprints&#44; contacts and&#44; specific skills&#46; Thus&#44; performance in elite basketball competition is closely related to the players&#8217; fitness level&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a> Additionally&#44; basketball imposes significant cognitive demands surrounding decision-making and anticipatory processes&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">10</span></a> The measurement of the specific physical sport demands and the athlete&#39;s physiological responses&#44; using different models&#44; allows coaches and practitioners to obtain objective information about individual and collective training loads&#46; The advances in the field of load monitoring are helping researchers and staff to optimize the sport-specific practices to raise player&#39;s fitness&#44; promote specific adaptations&#44; decrease the possibility of getting injured or experience non-functional overreaching&#44; and overtraining syndrome or undertraining&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0011"><span class="elsevierStyleSup">11</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a></p><p id="para0006" class="elsevierStylePara elsevierViewall">Various models to monitor training loads in team sports exist&#44; but the validity of these and their competitive development has important limitations&#46; Thus far&#44; to quantify sport demands and athletes&#8217; responses&#44; two different categories of load units have been utilized&#46; External load &#40;EL&#41; is defined as the dose of external stimulus applied to the athlete during practices or competitions&#46;<a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a> In recent years&#44; the use of global positioning systems &#40;GPS&#41; has become a reference method to measure EL in various field sports&#46; However&#44; its signal interference and imprecision in indoor sports has caused the development of local positioning systems &#40;LPS&#41;&#44; which are based in radiofrequency and the commercialization of devices incorporating accelerometers&#44; gyroscopes&#44; and magnetometers&#46; Not many previous publications have monitored these physical demands through absolute or relative &#40;min<span class="elsevierStyleSup">&#8722;1</span>&#41; EL variables e&#46;g&#46;&#44; using Player Load &#40;PL&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0013"><span class="elsevierStyleSup">13</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0014"><span class="elsevierStyleSup">14</span></a> However&#44; we can find several studies where the authors use secondary parameters derived from accelerometry&#44; like speeds in the distances traveled<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">15</span></a>&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">On the other hand&#44; internal load &#40;IL&#41; is an individual response to an external stimulus&#44; resulting in physiological and psychological stress responding to the requirements imposed on the athlete&#46; IL can be monitored through different metrics<a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a> such as 1&#41; heart rate &#40;HR&#41;&#44; 2&#41; oxygen uptake &#40;VO<span class="elsevierStyleInf">2</span>&#41;&#44; 3&#41; biochemical indicators&#44; 4&#41; rating of perceived exertion &#40;RPE&#41;&#44; and 5&#41; questionnaires&#46; We can also find within the literature&#44; different indices<a class="elsevierStyleCrossRef" href="#bib0016"><span class="elsevierStyleSup">16</span></a>&#58; 1&#41; Banister&#39;s training impulse &#40;TRIMP<span class="elsevierStyleInf">B</span>&#41;<a class="elsevierStyleCrossRef" href="#bib0017"><span class="elsevierStyleSup">17</span></a>&#59; 2&#41; session-RPE &#40;sRPE&#41;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a>&#59; 3&#41; Edward&#39;s summated heart rate zones model &#40;SHRZ&#41;<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a>&#46;</p><p id="para0008" class="elsevierStylePara elsevierViewall">The models mentioned above may allow a better understanding of the specific demands of basketball and individual acute and chronic changes&#46; Variations in acute and chronic loads are the result of the athlete&#39;s cumulative IL during the training process&#46; The IL of each player will be affected by physical demands so that higher EL values could involve increased energy costs during and after the activity&#46; Understanding this relationship could allow a precise periodization of team and athlete activity during the season&#44; lengthening their sports careers&#44; following the dose-response paradigm in sports training&#46; For this reason&#44; we find some studies examining this association in team sports&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0021"><span class="elsevierStyleSup">21</span></a> However&#44; to the best of our knowledge&#44; only one study has investigated the relationship between these models in adult basketball players<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a>&#46; However&#44; no pieces of research looking for this association in female youth basketball players can be found&#46;</p><p id="para0009" class="elsevierStylePara elsevierViewall">To understand the relationship between internal and external loads could be a possible solution to improve the training and rehabilitation processes and managing more effectively the exposure minutes&#46; Therefore&#44; the main aim of this study was to determine the relationship between internal and external loads during basketball practices in elite female youth basketball players&#46;</p></span><span id="sec0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0008">Material and methods</span><span id="sec0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0009">Design</span><p id="para0010" class="elsevierStylePara elsevierViewall">This study followed a longitudinal descriptive research design&#46; Training loads data were collected during a seven-week in-season period &#40;February-April&#41; of the 2016-17 Second Division competition of the Spanish professional basketball league &#40;LF2&#41;&#46; Each player contributed with a mean &#40;&#177;SD&#41; of 13&#46;67&#177;5&#46;96 sessions&#46; For the analysis&#44; the inclusion criteria were&#58; &#40;1&#41; to complete the basketball team-training session&#59; and &#40;2&#41; to collect all the variables of the session &#40;minutes&#44; heart rate&#44; sRPE&#44; and accelerometry&#41;&#46; Load data were collected across 35 sessions&#44; with a total of 164 registered events&#46;</p><p id="para0011" class="elsevierStylePara elsevierViewall">Player internal and external load values were collected across all the basketball training sessions&#46; Two indicators of internal load were used&#58; HR and sRPE&#46; Besides&#44; the external load was calculated using players&#39; accelerometer outputs that were measured during practices&#46; All training sessions were indoor in similar environmental conditions&#46;</p></span><span id="sec0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0010">Participants</span><p id="para0012" class="elsevierStylePara elsevierViewall">Thirteen elite female youth basketball players &#40;age 16 &#177; 1 years&#44; height 181 &#177; 6 cm and body mass 71 &#177; 9 kg&#44; and 7&#177;1 years of playing experience&#41;&#44; participating in a basketball national player development program&#44; volunteered for this study&#44; and were monitored across the first seven weeks of the season &#40;26&#46;9&#37; of the weeks belonging to the competitive period&#41;&#46; All of the participants competed in the Second Division of the Spanish professional basketball league &#40;LF2&#41;&#46;</p><p id="para0013" class="elsevierStylePara elsevierViewall">Before starting the study&#44; the health of all the participants was checked by the team&#39;s physicians&#46; Participants and their legal guardians received detailed written and verbal information about the possible risks and discomforts associated with the testing procedures&#46; Written informed consent was obtained from all the participants and their legal guardians&#44; respectively&#46; Procedures performed in the study followed the Declaration of Helsinki &#40;World Medical Association&#44; 1964&#41; and its later amendments&#46;<a class="elsevierStyleCrossRef" href="#bib0022"><span class="elsevierStyleSup">22</span></a></p></span><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0011">Methodology</span><p id="para0014" class="elsevierStylePara elsevierViewall">Internal and external loads were assessed via Polar Pro technology &#40;Polar Team Pro&#44; Polar Electro Oy&#44; Finland&#41;&#46; These devices incorporated HR and acceleration sensors with a sampling frequency of 200 Hz&#46; Only accelerations maintained at least 0&#46;5 seconds were included in the analysis&#46; Sensors were placed in the middle of the player&#39;s chest&#46; The devices were assigned to the same athlete throughout the duration of the whole study&#46; Following similar protocols&#44; RPE was collected 30 minutes after each practice&#46;</p><p id="para0015" class="elsevierStylePara elsevierViewall">Before initiating the study&#44; all players were familiarized with the devices and the procedures to report RPE&#46; Additionally&#44; before starting the data collection process&#44; individual HR<span class="elsevierStyleInf">max</span> and basal heart rate &#40;HR<span class="elsevierStyleInf">basal</span>&#41; were calculated using a single test using the 30-15 Intermittent Fitness Test<a class="elsevierStyleCrossRef" href="#bib0023"><span class="elsevierStyleSup">23</span></a> and measuring their resting heart rate for 5 minutes after waking up during 10 minutes&#46; Before starting every basketball-specific session&#44; players carried out a standardized warm-up with a duration of 10 min &#40;<a class="elsevierStyleCrossRefs" href="#fig0001">Figs&#46; 1 and 2</a>&#41;&#46;</p><elsevierMultimedia ident="fig0001"></elsevierMultimedia><elsevierMultimedia ident="fig0002"></elsevierMultimedia><p id="para0016" class="elsevierStylePara elsevierViewall">The internal load was determined through player HR applied to TRIMP<span class="elsevierStyleInf">B</span><a class="elsevierStyleCrossRef" href="#bib0017"><span class="elsevierStyleSup">17</span></a> and SHRZ<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a> and individual rate of perceived exertion using sRPE&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a> Outcomes of all models are expressed in arbitrary units &#40;AU&#41;&#46;</p><p id="para0017" class="elsevierStylePara elsevierViewall">The TRIMP<span class="elsevierStyleInf">B</span> model calculates the load through individual player HR<span class="elsevierStyleInf">max</span>&#44; HR<span class="elsevierStyleInf">basal&#44;</span> and HR<span class="elsevierStyleInf">mean</span> during practice&#46; The applied formula was the following&#58;<elsevierMultimedia ident="ueqn0001"></elsevierMultimedia></p><p id="para0018" class="elsevierStylePara elsevierViewall">Where&#44; e &#61; 2&#44;712&#59; x&#8239;&#61;&#8239;&#40;HR<span class="elsevierStyleInf">mean</span> - HR<span class="elsevierStyleInf">basal</span>&#41;&#93; &#47; &#91;&#40;HR<span class="elsevierStyleInf">max</span> - HR<span class="elsevierStyleInf">basal</span>&#41;&#46;</p><p id="para0019" class="elsevierStylePara elsevierViewall">SHRZ was calculated using HR based on Edwards&#39;s model&#46;<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a> This model assumes as individual IL the sum of the time spent in arbitrary HR-zones weighted multiplying the accumulated time in each HR zone &#40;in minutes&#41; by a relative factor for each zone&#58;<elsevierMultimedia ident="ueqn0002"></elsevierMultimedia></p><p id="para0020" class="elsevierStylePara elsevierViewall">Where&#44; zone 1&#8239;&#61;&#8239;50-59&#37; HR<span class="elsevierStyleInf">max</span>&#59; zone 2&#8239;&#61;&#8239;60-69&#37; HR<span class="elsevierStyleInf">max</span>&#59; Zone 3&#8239;&#61;&#8239;70-79&#37; HR<span class="elsevierStyleInf">max</span>&#59; Zone 4&#8239;&#61;&#8239;80-89&#37; HR<span class="elsevierStyleInf">max</span>&#59; and Zone 5&#8239;&#61;&#8239;&#62;90&#37; HR<span class="elsevierStyleInf">max</span>&#46;</p><p id="para0021" class="elsevierStylePara elsevierViewall">Each player reported their RPE individually to the researchers using the category ratio scale &#40;CR-10&#41; by answering the question&#44; &#8220;How hard was the practice&#63;&#8221; 30 min after the end of each training session&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a> This value was collected using a mobile app Quanter &#40;Kvantia&#44; Helsinki&#44; Finland&#41;&#46; This method shows a good fit in HR models when compared with paper-pencil methods sRPE was obtained by multiplying the duration of each session &#40;in minutes&#41; by the RPE of each player&#46;<a class="elsevierStyleCrossRef" href="#bib0024"><span class="elsevierStyleSup">24</span></a><elsevierMultimedia ident="ueqn0003"></elsevierMultimedia></p><p id="para0022" class="elsevierStylePara elsevierViewall">Where RPE&#8239;&#61;&#8239;Borg&#39;s 1-10 category ratio scale&#46;</p><p id="para0023" class="elsevierStylePara elsevierViewall">Before starting the study&#44; all players familiarized themselves with the RPE scale for four weeks&#46; This model has been used consistently to monitor the psychophysiological loads in basketball&#46;<a class="elsevierStyleCrossRef" href="#bib0006"><span class="elsevierStyleSup">6</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">25</span></a></p><p id="para0024" class="elsevierStylePara elsevierViewall">Outcomes selected to quantify EL were categorized into absolute and relative to time measurements&#46; Absolute metrics were&#58; 1&#41; total accelerations and decelerations &#40;TA&#41; and 2&#41; the sum of all maximal accelerations and decelerations &#40;TAmax&#59; &#62;2&#46;0 m&#47;s<span class="elsevierStyleSup">2</span>&#8239;&#43;&#8239;&#60;-2&#46;0 m&#47;s<span class="elsevierStyleSup">2</span>&#41;&#46; These acceleration thresholds are similar to those used in previous male basketball research&#46;<a class="elsevierStyleCrossRef" href="#bib0026"><span class="elsevierStyleSup">26</span></a> The relative to time variables used were&#58; 1&#41; total accelerations per minute &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 2&#41; accelerations per minute &#40;Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 3&#41; decelerations per minute &#40;Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#46;</p></span><span id="sec0006" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0012">Statistical analyses</span><p id="para0025" class="elsevierStylePara elsevierViewall">Due to the data sample size &#40;n&#61;164&#41;&#44; the Kolmogorov-Smirnov test was used to examine if variables were normally distributed&#46; As normality of the dataset was assumed&#44; relationships between the internal and external load models were determined using Pearson&#39;s Rho product-moment correlation with 95&#37; confidence intervals&#46; Correlation magnitudes were defined according to the Hopkins&#39;s criteria<a class="elsevierStyleCrossRef" href="#bib0027"><span class="elsevierStyleSup">27</span></a> trivial&#58; 0-0&#46;09&#59; low&#58; 0&#46;10-0&#46;29&#59; moderate&#58; 0&#46;30-0&#46;49&#59; large&#58; 0&#46;50-0&#46;69&#59; very large&#58; 0&#46;70-0&#46;89&#59; nearly perfect 0&#46;90-0&#46;99&#59; 1 perfect&#46; The significance level was set at p &#8804; 0&#46;001&#46; The coefficient of determination &#40;R<span class="elsevierStyleSup">2</span>&#41; was determined to understand the adjustment between the control models of the external and internal load&#46; Means &#40;&#177;SD&#41; were calculated for all descriptive and outcome measures&#46; All statistical analyses were performed using IBM Statistical Package for the Social Sciences &#40;SPSS&#44; version 21 for macOS&#44; SPSS Inc&#44; Chicago&#44; IL&#41;&#46;</p></span></span><span id="sec0007" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0013">Results</span><p id="para0026" class="elsevierStylePara elsevierViewall">The mean &#177; SD external &#40;TA&#44; TA<span class="elsevierStyleInf">max</span>&#44; TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span> and&#44; Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41; and internal &#40;TRIMP<span class="elsevierStyleInf">B</span>&#44; SHRZ&#44; and sRPE models&#41; loads of the basketball training events are shown in <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#46; The level of correlations and coefficients of determination between external and internal load models during basketball practices from this research study is shown in <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#46; We observed different correlation magnitudes between the internal and external models described in this research&#46; A very large relationship was found between TA and TRIMP<span class="elsevierStyleInf">B</span> model &#40;0&#46;78&#41; and a large correlation between TA and SHRZ model &#40;0&#46;63&#41; and sRPE model &#40;0&#46;62&#41;&#46; In the case of TA<span class="elsevierStyleInf">max</span> and the observed internal methods&#44; a moderate association with SHRZ and sRPE &#40;0&#46;41&#41; and a large with TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;54&#41; were observed&#46; A moderate or lesser correlation was found between EL relative to time outcome and IL models&#46; All relationships were statistically significant &#40;<span class="elsevierStyleItalic">p</span> &#8804; 0&#46;01&#41;&#46;</p><elsevierMultimedia ident="tbl0001"></elsevierMultimedia><elsevierMultimedia ident="tbl0002"></elsevierMultimedia></span><span id="sec0008" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0014">Discussion</span><p id="para0027" class="elsevierStylePara elsevierViewall">The main finding of this study is a low to a large correlation between models to monitor EL and IL in youth female basketball players&#44; especially within IL models and TA&#46; On the contrary&#44; this research did not find significant correlations between the models related to the number of accelerations per unit of time and the IL&#46;</p><p id="para0028" class="elsevierStylePara elsevierViewall">This relationship&#44; a priori&#44; seems logical since a more significant physical demand requires the contribution of energy substrates and oxygen to the muscle tissue&#44; increasing cardiac effort&#44; and oxygen consumption&#46; Despite this&#44; there is a need to establish a proper relationship between physical demands and the physiological response of the athlete&#44; in order to understand the dose-response relationship when training youth female basketball players&#46;</p><p id="para0029" class="elsevierStylePara elsevierViewall">Our findings observed a significant relationship among the studied models and various magnitudes of the relationship between external and internal load in female basketball &#40;<span class="elsevierStyleItalic">r</span>&#8239;&#61;&#8239;0&#46;21-0&#46;78&#59; <span class="elsevierStyleItalic">p</span> &#8804; 0&#46;01&#41;&#46; Our results also display a large correlation between TA and models using heart rate activity&#44; TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;78&#41; and SHRZ models &#40;0&#46;63&#41;&#44; and a large correlation between TA and sRPE &#40;0&#46;62&#41;&#46; This relationship has already been shown in basketball&#44; emphasizing that external and internal loads are two separated constructs&#46; It seems like this correlation between load variables was not sufficient&#44; requiring a coefficient of determination higher than 50&#37; &#40;<span class="elsevierStyleItalic">r</span>&#8239;&#61;&#8239;0&#46;38-0&#46;61&#59; R<span class="elsevierStyleSup">2</span>&#8239;&#61;&#8239;0&#46;14&#8211;0&#46;38&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> Compared to our results&#44; we obtained larger correlation values&#44; but also with lower R<span class="elsevierStyleSup">2</span> values &#40;0&#46;045-0&#46;613&#41;&#46; Conversely&#44; a meta-analysis including 13 studies on the relationship between physical demand and physiological responses in team sports&#44; concluded showing consistent relationships between both constructs of load&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a></p><p id="para0030" class="elsevierStylePara elsevierViewall">To the authors&#8217; knowledge&#44; no publications are showing a correlation between TA and internal load models&#46; However&#44; we have found within the literature related to Player Load &#40;PL&#41; models&#44; a similar parameter based on the sum of accelerations&#44; showing in different contributions a large and very large correlation with SHRZ models &#91;0&#46;61 &#40;0&#46;38-0&#46;77&#41;&#59; 0&#46;80 &#40;0&#46;71-0&#46;86&#41;&#93;&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> In the case of TRIMP<span class="elsevierStyleInf">B</span>&#44; several reports have shown a &#8220;possibly large&#8221; correlation with PL values &#91;0&#46;54 &#40;0&#46;40-0&#46;66&#41;&#93;&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a> Regarding the perceptual model &#40;sRPE&#41; we observed a large correlation with TA &#40;0&#46;62&#41;&#46; Considering the perceptual model &#40;sRPE&#41;&#44; we also observed a large correlation with the TA&#46; In adult male basketball players&#44; some authors have investigated this relationship between a perceptual model and PL<span class="elsevierStyleSup">TM</span> &#40;Player Load<span class="elsevierStyleSup">TM</span>&#41; obtaining different results&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0028"><span class="elsevierStyleSup">28</span></a> Svilar<a class="elsevierStyleCrossRef" href="#bib0028"><span class="elsevierStyleSup">28</span></a> showed a very large correlation &#40;r&#61;0&#46;80&#59; <span class="elsevierStyleItalic">p</span>&#8804;0&#46;05&#41; while Scanlan has found a lower correlation &#40;r&#61;0&#46;49&#59; <span class="elsevierStyleItalic">p</span>&#8804;0&#46;05&#41; between variables&#46; Values showed by Svilar belong to Euroleague players gathered during the competitive period&#46; We infer that these top-level players compete in most cases more than one game per week&#44; exposing players to high-intensity competitive stress&#46; So&#44; it might be necessary to get a higher control of the training process&#44; receiving smaller training doses&#44; probably with less volume and more intensity&#44; thus reporting greater values of external load and perceived effort&#44; and finally obtaining higher values in correlation models&#46; In contrast&#44; Scanlan&#39;s study participants were semi-professional players with a lesser competitive load during weeks and maybe exposing players to different number and kind of tasks during practice&#44; with variable volume and intensity&#46; Despite using the same devices and gathering the same variables in both studies&#44; the differences in correlation could be explained by the competitive level and&#44; especially by the intensity and volume in the practices of the teams included in the sample&#46; In the case of our results&#44; the difference could also be explained by the sample characteristics&#44; players with just one game per week&#44; season period registered&#44; type of training proposed&#44; and the devices employed&#46; These facts could be the reason why our results are more similar to those obtained in Scanlan&#39;s publication&#46;</p><p id="para0031" class="elsevierStylePara elsevierViewall">The correlations between models&#44; previously described &#40;EL&#58; TA&#59; IL&#58; SHRZ&#44; TRIMP&#44; and sRPE&#41;&#44; could be explained due to the time dependency of all the variables&#46; Longer exposure time indicates a higher load value&#44; with more accelerations or more heart activity that will be multiplied by time&#46; Thus&#44; we could suggest that all of these variables are quantifying the training or competition volume&#46; It might be for this reason that our results are in agreement with some other authors&#8217; results&#44; showing a large and very large correlation between internal and external load models in basketball and other sports&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a> On the contrary&#44; we only found a large correlation between TAmax and TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;54&#41;&#44; within internal models&#46; This association could be explained by the fact that TRIMP<span class="elsevierStyleInf">B</span> model considers the average and maximum heart rate value in every effort&#44; so continuous and maximal accelerations could raise these parameters&#46; The cardiovascular system works with a slight lag regarding the movements performed by the player&#44; depending on the heart rate activity and the intensity of physical effort&#46;<a class="elsevierStyleCrossRef" href="#bib0029"><span class="elsevierStyleSup">29</span></a> The same occurs between TAmax and sRPE &#40;0&#46;41&#41;&#44; as the main factor that modifies the perception of the athlete is the intensity and duration of the physical efforts performed&#44; more intensity higher fatigue perceived&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a></p><p id="para0032" class="elsevierStylePara elsevierViewall">On the contrary&#44; our results seem to have a moderate relationship between TRIMP<span class="elsevierStyleInf">B</span> and SHRZ models with TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; and Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span> and show a low correlation between sRPE and these external variables &#40;<a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#41;&#46; As we have mentioned previously&#44; SHRZ&#44; TRIMP<span class="elsevierStyleInf">B</span>&#44; and sRPE models quantify load volume&#44; and this might be the reason to explain the reduction of correlation value&#44; with the results shown previously&#46; In this case&#44; external variables &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; and Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41; are relative to time&#44; and therefore&#44; are more related to the density of load &#40;units per minute&#41;&#46;</p><p id="para0033" class="elsevierStylePara elsevierViewall">The main limitation of the study is the amount of data collected&#46; The initial data volume expected was higher&#44; but several events were discarded during the process due to data signal inaccuracies&#46; However&#44; correlations were found in the study according to previous research&#44; and new correlations and conclusions have been generated&#44; providing these results interesting information to the scientific community and sports professionals&#46; On the other hand&#44; the use of heart rate and perceptual-based models could present some limitations described previously to determine team sports&#39; physiological response&#46;</p><p id="para0034" class="elsevierStylePara elsevierViewall">In future investigations&#44; it would be advisable 1&#41; to use a bigger sample&#44; allowing the use of other statistical tests such as R-Squared&#44; aiming at obtaining higher predictive power between models&#59; 2&#41; combine other methods for the control of internal load that do not depend on heart rate or subjective methods&#59; 3&#41; establishing individual analyses could provide an understanding of the individual interactions within external and internal load values&#46; Provided that the analyses are performed individually&#44; we could find different conclusions since the physiological response to the same stimulus can differ significantly among players&#59; 4&#41; finally&#44; it could be interesting to understand these relationships&#44; specifically in competitive events&#46;</p><p id="para0035" class="elsevierStylePara elsevierViewall">Internal and external load models should be considered different constructs&#44; and therefore&#44; both must be quantified during the event &#40;practice or competition&#41; in youth female basketball&#46; These results respond to the combination of many different factors within similar drills &#40;space&#44; number of players&#44; number of opponents&#44; or competition format&#41; that contribute to determining the intensity in the context of intermittent team sports&#44; especially in indoor team sports&#46; For this reason&#44; the relationship values found in the literature show significant differences&#46;</p></span><span id="sec0009" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0015">Conclusion</span><p id="para0036" class="elsevierStylePara elsevierViewall">To the best of our knowledge&#44; this study is the first study aiming at establishing a relationship between external and internal loads in elite female youth basketball players&#46; We can conclude that the assumption of strong relationships between external and internal loads in team sports can be misleading&#44; as both models should be considered as different constructs&#46; The findings of this study show a different level of correlation between external and internal load models&#44; but current data highlights the importance of a continuous study of the complex relationship between physical demands and physiological responses in sports&#46;</p></span></span>"
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        "resumen" => "<span id="abss0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0002">Introduction</span><p id="spara007" class="elsevierStyleSimplePara elsevierViewall">Monitoring load has been a key point in team sports during last years&#46; This study aimed to determine the relationship between the external and internal training load during full basketball practices in elite female youth basketball players&#46;</p></span> <span id="abss0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0003">Material and methods</span><p id="spara008" class="elsevierStyleSimplePara elsevierViewall">Thirteen elite female youth basketball players &#40;age 16&#46;3&#177;1 years&#44; height 181&#46;7&#177;5&#46;8cm and body mass 71&#46;2&#177;9&#46;6kg&#41; had physical and physiological monitored over seven weeks&#46; Players&#8217; internal load was assessed using the session-rating of perceived exertion &#40;sRPE&#41;&#44; the Edward&#39;s summated heart rate zones model &#40;SHRZ&#41; and&#44; the Banister&#39;s training impulse &#40;TRIMP<span class="elsevierStyleInf">B</span>&#41;&#46; The external load was determined through&#58; 1&#41; total accelerations &#40;TA&#41;&#59; the sum of all accelerations and decelerations&#59; 2&#41; maximal accelerations and decelerations &#40;TAMax&#41;&#59; 3&#41; total accelerations per minutes &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 4&#41; accelerations per minute &#40;Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; and 5&#41; decelerations per minute &#40;Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#46; Heart rate-based and accelerometry models were assessed via Polar Pro technology&#46;</p></span> <span id="abss0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0004">Results</span><p id="spara009" class="elsevierStyleSimplePara elsevierViewall">Correlations between variables revealed different magnitudes&#46; SHRZ model showed a positive correlation with TA &#40;0&#46;63&#41;&#59; the TRIMP<span class="elsevierStyleInf">B</span> model showed a high degree of correlation with TA &#40;0&#46;78&#41;&#59; the sRPE model also presented a high correlation with TA &#40;0&#46;62&#41;&#46;</p></span> <span id="abss0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0005">Conclusion</span><p id="spara010" class="elsevierStyleSimplePara elsevierViewall">Our study establishes different levels of association between external and internal load models in elite female youth basketball players&#46; However&#44; we cannot assume that a high relationship between internal and external loads exists&#44; as both models should be considered as different constructs&#46;</p></span>"
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          "en" => "<p id="spara001" class="elsevierStyleSimplePara elsevierViewall">The mean &#177; SD &#40;A&#41; individual absolute physiological responses values during practice&#59; &#40;B&#41; absolute individual physical demand values during practice&#59; and &#40;C&#41; relative to time external load during basketball practice &#40;n&#8239;&#61;&#8239;164&#41;&#46; SHRZ Score&#8239;&#61;&#8239;Edward&#39;s summated heart rate zones model&#59; TRIMP<span class="elsevierStyleInf">B</span>&#8239;&#61;&#8239;Banister&#39;s training impulse&#59; sRPE&#8239;&#61;&#8239;session-rating of perceived exertion&#59; Total Accelerations&#8239;&#61;&#8239;total accelerations and decelerations during practice&#59; TAMax&#8239;&#61;&#8239;sum of all maximal accelerations and decelerations&#59; TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#8239;&#61;&#8239;total accelerations per minute&#59; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#8239;&#61;&#8239;accelerations per minute&#59; Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#8239;&#61;&#8239;decelerations per minute&#46;</p>"
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          "en" => "<p id="spara002" class="elsevierStyleSimplePara elsevierViewall">Correlations between external and internal load during practice&#58; &#40;A&#41; between TA and SHRZ&#59; &#40;B&#41; between TA and TRIMP<span class="elsevierStyleInf">B</span>&#59; &#40;C&#41; between TA and sRPE&#59; &#40;D&#41; between TAMax&#183;min<span class="elsevierStyleSup">&#8722;1</span> and TRIMP<span class="elsevierStyleInf">B</span>&#59; &#40;E&#41; between TAMax&#183;min<span class="elsevierStyleSup">&#8722;1</span> and TRIMP<span class="elsevierStyleInf">B</span>&#46; &#40;n&#8239;&#61;&#8239;164&#41;&#46; 95&#37; CI&#8239;&#61;&#8239;95&#37; confidence intervals&#46; R<span class="elsevierStyleSup">2</span>&#8239;&#61;&#8239;coefficient of determination&#46;</p>"
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                  \t\t\t\t</td><a name="en0060"></a><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;54&#42;&#42;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">&#60;0&#44;000&nbsp;\t\t\t\t\t\t\n
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Original Article
Relationship between internal and external load in elite female youth basketball players
Javier Espasa Labradora,b, Javier Peñac,d,
Corresponding author
javier.pena@uvic.cat

Corresponding author at: Sport Performance Analysis Research Group, University of Vic-Central University of Catalonia (UVic-UCC), Barcelona, Spain.
, Toni Caparrós Ponsa,d, Michael Cooke, Azahara Fort Vanmeerhaeghef,g,h
a National Institute of Physical Education of Catalonia, University of Barcelona, Barcelona, Spain
b School of Health Sciences, TecnoCampus-Mataró, University of Pompeu Fabra, Barcelona, Spain
c Sport and Physical Activity Studies Centre (CEEAF), University of Vic-Central University of Catalonia (UVic-UCC), Barcelona, Spain
d Sport Performance Analysis Research Group, University of Vic-Central University of Catalonia (UVic-UCC), Barcelona, Spain
e Sport Performance Centre, Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Alberta, Canada
f Faculty of Psychology, Education and Sports Blanquerna, University of Ramon Llull, Barcelona, Spain
g Department of Sports Sciences, Ramon Llull University, FPCEE and FCS Blanquerna, Barcelona, Spain
h Segle XXI Female Basketball Team, Catalan Federation of Basketball, Barcelona, Spain
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0007">Introduction</span><p id="para0005" class="elsevierStylePara elsevierViewall">Today&#44; technology allows monitoring and to describe sport demands with greater precision than ever before&#46;<a class="elsevierStyleCrossRefs" href="#bib0001"><span class="elsevierStyleSup">1&#8211;3</span></a> During the last decade&#44; the physical demands of basketball have been described through several studies<a class="elsevierStyleCrossRefs" href="#bib0004"><span class="elsevierStyleSup">4&#8211;6</span></a>&#59; however&#44; not many of them are explicitly related to female youth athletes&#46; The development of smaller and more reliable devices has opened a new field of research in sports sciences&#44; including new perspectives on the study of the physical demands and physiological responses of team sports&#44; and specifically&#44; within basketball contexts&#46;<a class="elsevierStyleCrossRef" href="#bib0004"><span class="elsevierStyleSup">4</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRefs" href="#bib0007"><span class="elsevierStyleSup">7&#8211;9</span></a> Recent studies using technology have described basketball as a high demanding sport considering its aerobic and anaerobic requirements&#44; its changes of direction&#44; accelerations&#44; decelerations&#44; jumps&#44; sprints&#44; contacts and&#44; specific skills&#46; Thus&#44; performance in elite basketball competition is closely related to the players&#8217; fitness level&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a> Additionally&#44; basketball imposes significant cognitive demands surrounding decision-making and anticipatory processes&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">10</span></a> The measurement of the specific physical sport demands and the athlete&#39;s physiological responses&#44; using different models&#44; allows coaches and practitioners to obtain objective information about individual and collective training loads&#46; The advances in the field of load monitoring are helping researchers and staff to optimize the sport-specific practices to raise player&#39;s fitness&#44; promote specific adaptations&#44; decrease the possibility of getting injured or experience non-functional overreaching&#44; and overtraining syndrome or undertraining&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0011"><span class="elsevierStyleSup">11</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a></p><p id="para0006" class="elsevierStylePara elsevierViewall">Various models to monitor training loads in team sports exist&#44; but the validity of these and their competitive development has important limitations&#46; Thus far&#44; to quantify sport demands and athletes&#8217; responses&#44; two different categories of load units have been utilized&#46; External load &#40;EL&#41; is defined as the dose of external stimulus applied to the athlete during practices or competitions&#46;<a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a> In recent years&#44; the use of global positioning systems &#40;GPS&#41; has become a reference method to measure EL in various field sports&#46; However&#44; its signal interference and imprecision in indoor sports has caused the development of local positioning systems &#40;LPS&#41;&#44; which are based in radiofrequency and the commercialization of devices incorporating accelerometers&#44; gyroscopes&#44; and magnetometers&#46; Not many previous publications have monitored these physical demands through absolute or relative &#40;min<span class="elsevierStyleSup">&#8722;1</span>&#41; EL variables e&#46;g&#46;&#44; using Player Load &#40;PL&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0007"><span class="elsevierStyleSup">7</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0013"><span class="elsevierStyleSup">13</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0014"><span class="elsevierStyleSup">14</span></a> However&#44; we can find several studies where the authors use secondary parameters derived from accelerometry&#44; like speeds in the distances traveled<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">15</span></a>&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">On the other hand&#44; internal load &#40;IL&#41; is an individual response to an external stimulus&#44; resulting in physiological and psychological stress responding to the requirements imposed on the athlete&#46; IL can be monitored through different metrics<a class="elsevierStyleCrossRef" href="#bib0012"><span class="elsevierStyleSup">12</span></a> such as 1&#41; heart rate &#40;HR&#41;&#44; 2&#41; oxygen uptake &#40;VO<span class="elsevierStyleInf">2</span>&#41;&#44; 3&#41; biochemical indicators&#44; 4&#41; rating of perceived exertion &#40;RPE&#41;&#44; and 5&#41; questionnaires&#46; We can also find within the literature&#44; different indices<a class="elsevierStyleCrossRef" href="#bib0016"><span class="elsevierStyleSup">16</span></a>&#58; 1&#41; Banister&#39;s training impulse &#40;TRIMP<span class="elsevierStyleInf">B</span>&#41;<a class="elsevierStyleCrossRef" href="#bib0017"><span class="elsevierStyleSup">17</span></a>&#59; 2&#41; session-RPE &#40;sRPE&#41;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a>&#59; 3&#41; Edward&#39;s summated heart rate zones model &#40;SHRZ&#41;<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a>&#46;</p><p id="para0008" class="elsevierStylePara elsevierViewall">The models mentioned above may allow a better understanding of the specific demands of basketball and individual acute and chronic changes&#46; Variations in acute and chronic loads are the result of the athlete&#39;s cumulative IL during the training process&#46; The IL of each player will be affected by physical demands so that higher EL values could involve increased energy costs during and after the activity&#46; Understanding this relationship could allow a precise periodization of team and athlete activity during the season&#44; lengthening their sports careers&#44; following the dose-response paradigm in sports training&#46; For this reason&#44; we find some studies examining this association in team sports&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0021"><span class="elsevierStyleSup">21</span></a> However&#44; to the best of our knowledge&#44; only one study has investigated the relationship between these models in adult basketball players<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a>&#46; However&#44; no pieces of research looking for this association in female youth basketball players can be found&#46;</p><p id="para0009" class="elsevierStylePara elsevierViewall">To understand the relationship between internal and external loads could be a possible solution to improve the training and rehabilitation processes and managing more effectively the exposure minutes&#46; Therefore&#44; the main aim of this study was to determine the relationship between internal and external loads during basketball practices in elite female youth basketball players&#46;</p></span><span id="sec0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0008">Material and methods</span><span id="sec0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0009">Design</span><p id="para0010" class="elsevierStylePara elsevierViewall">This study followed a longitudinal descriptive research design&#46; Training loads data were collected during a seven-week in-season period &#40;February-April&#41; of the 2016-17 Second Division competition of the Spanish professional basketball league &#40;LF2&#41;&#46; Each player contributed with a mean &#40;&#177;SD&#41; of 13&#46;67&#177;5&#46;96 sessions&#46; For the analysis&#44; the inclusion criteria were&#58; &#40;1&#41; to complete the basketball team-training session&#59; and &#40;2&#41; to collect all the variables of the session &#40;minutes&#44; heart rate&#44; sRPE&#44; and accelerometry&#41;&#46; Load data were collected across 35 sessions&#44; with a total of 164 registered events&#46;</p><p id="para0011" class="elsevierStylePara elsevierViewall">Player internal and external load values were collected across all the basketball training sessions&#46; Two indicators of internal load were used&#58; HR and sRPE&#46; Besides&#44; the external load was calculated using players&#39; accelerometer outputs that were measured during practices&#46; All training sessions were indoor in similar environmental conditions&#46;</p></span><span id="sec0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0010">Participants</span><p id="para0012" class="elsevierStylePara elsevierViewall">Thirteen elite female youth basketball players &#40;age 16 &#177; 1 years&#44; height 181 &#177; 6 cm and body mass 71 &#177; 9 kg&#44; and 7&#177;1 years of playing experience&#41;&#44; participating in a basketball national player development program&#44; volunteered for this study&#44; and were monitored across the first seven weeks of the season &#40;26&#46;9&#37; of the weeks belonging to the competitive period&#41;&#46; All of the participants competed in the Second Division of the Spanish professional basketball league &#40;LF2&#41;&#46;</p><p id="para0013" class="elsevierStylePara elsevierViewall">Before starting the study&#44; the health of all the participants was checked by the team&#39;s physicians&#46; Participants and their legal guardians received detailed written and verbal information about the possible risks and discomforts associated with the testing procedures&#46; Written informed consent was obtained from all the participants and their legal guardians&#44; respectively&#46; Procedures performed in the study followed the Declaration of Helsinki &#40;World Medical Association&#44; 1964&#41; and its later amendments&#46;<a class="elsevierStyleCrossRef" href="#bib0022"><span class="elsevierStyleSup">22</span></a></p></span><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0011">Methodology</span><p id="para0014" class="elsevierStylePara elsevierViewall">Internal and external loads were assessed via Polar Pro technology &#40;Polar Team Pro&#44; Polar Electro Oy&#44; Finland&#41;&#46; These devices incorporated HR and acceleration sensors with a sampling frequency of 200 Hz&#46; Only accelerations maintained at least 0&#46;5 seconds were included in the analysis&#46; Sensors were placed in the middle of the player&#39;s chest&#46; The devices were assigned to the same athlete throughout the duration of the whole study&#46; Following similar protocols&#44; RPE was collected 30 minutes after each practice&#46;</p><p id="para0015" class="elsevierStylePara elsevierViewall">Before initiating the study&#44; all players were familiarized with the devices and the procedures to report RPE&#46; Additionally&#44; before starting the data collection process&#44; individual HR<span class="elsevierStyleInf">max</span> and basal heart rate &#40;HR<span class="elsevierStyleInf">basal</span>&#41; were calculated using a single test using the 30-15 Intermittent Fitness Test<a class="elsevierStyleCrossRef" href="#bib0023"><span class="elsevierStyleSup">23</span></a> and measuring their resting heart rate for 5 minutes after waking up during 10 minutes&#46; Before starting every basketball-specific session&#44; players carried out a standardized warm-up with a duration of 10 min &#40;<a class="elsevierStyleCrossRefs" href="#fig0001">Figs&#46; 1 and 2</a>&#41;&#46;</p><elsevierMultimedia ident="fig0001"></elsevierMultimedia><elsevierMultimedia ident="fig0002"></elsevierMultimedia><p id="para0016" class="elsevierStylePara elsevierViewall">The internal load was determined through player HR applied to TRIMP<span class="elsevierStyleInf">B</span><a class="elsevierStyleCrossRef" href="#bib0017"><span class="elsevierStyleSup">17</span></a> and SHRZ<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a> and individual rate of perceived exertion using sRPE&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a> Outcomes of all models are expressed in arbitrary units &#40;AU&#41;&#46;</p><p id="para0017" class="elsevierStylePara elsevierViewall">The TRIMP<span class="elsevierStyleInf">B</span> model calculates the load through individual player HR<span class="elsevierStyleInf">max</span>&#44; HR<span class="elsevierStyleInf">basal&#44;</span> and HR<span class="elsevierStyleInf">mean</span> during practice&#46; The applied formula was the following&#58;<elsevierMultimedia ident="ueqn0001"></elsevierMultimedia></p><p id="para0018" class="elsevierStylePara elsevierViewall">Where&#44; e &#61; 2&#44;712&#59; x&#8239;&#61;&#8239;&#40;HR<span class="elsevierStyleInf">mean</span> - HR<span class="elsevierStyleInf">basal</span>&#41;&#93; &#47; &#91;&#40;HR<span class="elsevierStyleInf">max</span> - HR<span class="elsevierStyleInf">basal</span>&#41;&#46;</p><p id="para0019" class="elsevierStylePara elsevierViewall">SHRZ was calculated using HR based on Edwards&#39;s model&#46;<a class="elsevierStyleCrossRef" href="#bib0019"><span class="elsevierStyleSup">19</span></a> This model assumes as individual IL the sum of the time spent in arbitrary HR-zones weighted multiplying the accumulated time in each HR zone &#40;in minutes&#41; by a relative factor for each zone&#58;<elsevierMultimedia ident="ueqn0002"></elsevierMultimedia></p><p id="para0020" class="elsevierStylePara elsevierViewall">Where&#44; zone 1&#8239;&#61;&#8239;50-59&#37; HR<span class="elsevierStyleInf">max</span>&#59; zone 2&#8239;&#61;&#8239;60-69&#37; HR<span class="elsevierStyleInf">max</span>&#59; Zone 3&#8239;&#61;&#8239;70-79&#37; HR<span class="elsevierStyleInf">max</span>&#59; Zone 4&#8239;&#61;&#8239;80-89&#37; HR<span class="elsevierStyleInf">max</span>&#59; and Zone 5&#8239;&#61;&#8239;&#62;90&#37; HR<span class="elsevierStyleInf">max</span>&#46;</p><p id="para0021" class="elsevierStylePara elsevierViewall">Each player reported their RPE individually to the researchers using the category ratio scale &#40;CR-10&#41; by answering the question&#44; &#8220;How hard was the practice&#63;&#8221; 30 min after the end of each training session&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a> This value was collected using a mobile app Quanter &#40;Kvantia&#44; Helsinki&#44; Finland&#41;&#46; This method shows a good fit in HR models when compared with paper-pencil methods sRPE was obtained by multiplying the duration of each session &#40;in minutes&#41; by the RPE of each player&#46;<a class="elsevierStyleCrossRef" href="#bib0024"><span class="elsevierStyleSup">24</span></a><elsevierMultimedia ident="ueqn0003"></elsevierMultimedia></p><p id="para0022" class="elsevierStylePara elsevierViewall">Where RPE&#8239;&#61;&#8239;Borg&#39;s 1-10 category ratio scale&#46;</p><p id="para0023" class="elsevierStylePara elsevierViewall">Before starting the study&#44; all players familiarized themselves with the RPE scale for four weeks&#46; This model has been used consistently to monitor the psychophysiological loads in basketball&#46;<a class="elsevierStyleCrossRef" href="#bib0006"><span class="elsevierStyleSup">6</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">25</span></a></p><p id="para0024" class="elsevierStylePara elsevierViewall">Outcomes selected to quantify EL were categorized into absolute and relative to time measurements&#46; Absolute metrics were&#58; 1&#41; total accelerations and decelerations &#40;TA&#41; and 2&#41; the sum of all maximal accelerations and decelerations &#40;TAmax&#59; &#62;2&#46;0 m&#47;s<span class="elsevierStyleSup">2</span>&#8239;&#43;&#8239;&#60;-2&#46;0 m&#47;s<span class="elsevierStyleSup">2</span>&#41;&#46; These acceleration thresholds are similar to those used in previous male basketball research&#46;<a class="elsevierStyleCrossRef" href="#bib0026"><span class="elsevierStyleSup">26</span></a> The relative to time variables used were&#58; 1&#41; total accelerations per minute &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 2&#41; accelerations per minute &#40;Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 3&#41; decelerations per minute &#40;Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#46;</p></span><span id="sec0006" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0012">Statistical analyses</span><p id="para0025" class="elsevierStylePara elsevierViewall">Due to the data sample size &#40;n&#61;164&#41;&#44; the Kolmogorov-Smirnov test was used to examine if variables were normally distributed&#46; As normality of the dataset was assumed&#44; relationships between the internal and external load models were determined using Pearson&#39;s Rho product-moment correlation with 95&#37; confidence intervals&#46; Correlation magnitudes were defined according to the Hopkins&#39;s criteria<a class="elsevierStyleCrossRef" href="#bib0027"><span class="elsevierStyleSup">27</span></a> trivial&#58; 0-0&#46;09&#59; low&#58; 0&#46;10-0&#46;29&#59; moderate&#58; 0&#46;30-0&#46;49&#59; large&#58; 0&#46;50-0&#46;69&#59; very large&#58; 0&#46;70-0&#46;89&#59; nearly perfect 0&#46;90-0&#46;99&#59; 1 perfect&#46; The significance level was set at p &#8804; 0&#46;001&#46; The coefficient of determination &#40;R<span class="elsevierStyleSup">2</span>&#41; was determined to understand the adjustment between the control models of the external and internal load&#46; Means &#40;&#177;SD&#41; were calculated for all descriptive and outcome measures&#46; All statistical analyses were performed using IBM Statistical Package for the Social Sciences &#40;SPSS&#44; version 21 for macOS&#44; SPSS Inc&#44; Chicago&#44; IL&#41;&#46;</p></span></span><span id="sec0007" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0013">Results</span><p id="para0026" class="elsevierStylePara elsevierViewall">The mean &#177; SD external &#40;TA&#44; TA<span class="elsevierStyleInf">max</span>&#44; TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span> and&#44; Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41; and internal &#40;TRIMP<span class="elsevierStyleInf">B</span>&#44; SHRZ&#44; and sRPE models&#41; loads of the basketball training events are shown in <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#46; The level of correlations and coefficients of determination between external and internal load models during basketball practices from this research study is shown in <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#46; We observed different correlation magnitudes between the internal and external models described in this research&#46; A very large relationship was found between TA and TRIMP<span class="elsevierStyleInf">B</span> model &#40;0&#46;78&#41; and a large correlation between TA and SHRZ model &#40;0&#46;63&#41; and sRPE model &#40;0&#46;62&#41;&#46; In the case of TA<span class="elsevierStyleInf">max</span> and the observed internal methods&#44; a moderate association with SHRZ and sRPE &#40;0&#46;41&#41; and a large with TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;54&#41; were observed&#46; A moderate or lesser correlation was found between EL relative to time outcome and IL models&#46; All relationships were statistically significant &#40;<span class="elsevierStyleItalic">p</span> &#8804; 0&#46;01&#41;&#46;</p><elsevierMultimedia ident="tbl0001"></elsevierMultimedia><elsevierMultimedia ident="tbl0002"></elsevierMultimedia></span><span id="sec0008" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0014">Discussion</span><p id="para0027" class="elsevierStylePara elsevierViewall">The main finding of this study is a low to a large correlation between models to monitor EL and IL in youth female basketball players&#44; especially within IL models and TA&#46; On the contrary&#44; this research did not find significant correlations between the models related to the number of accelerations per unit of time and the IL&#46;</p><p id="para0028" class="elsevierStylePara elsevierViewall">This relationship&#44; a priori&#44; seems logical since a more significant physical demand requires the contribution of energy substrates and oxygen to the muscle tissue&#44; increasing cardiac effort&#44; and oxygen consumption&#46; Despite this&#44; there is a need to establish a proper relationship between physical demands and the physiological response of the athlete&#44; in order to understand the dose-response relationship when training youth female basketball players&#46;</p><p id="para0029" class="elsevierStylePara elsevierViewall">Our findings observed a significant relationship among the studied models and various magnitudes of the relationship between external and internal load in female basketball &#40;<span class="elsevierStyleItalic">r</span>&#8239;&#61;&#8239;0&#46;21-0&#46;78&#59; <span class="elsevierStyleItalic">p</span> &#8804; 0&#46;01&#41;&#46; Our results also display a large correlation between TA and models using heart rate activity&#44; TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;78&#41; and SHRZ models &#40;0&#46;63&#41;&#44; and a large correlation between TA and sRPE &#40;0&#46;62&#41;&#46; This relationship has already been shown in basketball&#44; emphasizing that external and internal loads are two separated constructs&#46; It seems like this correlation between load variables was not sufficient&#44; requiring a coefficient of determination higher than 50&#37; &#40;<span class="elsevierStyleItalic">r</span>&#8239;&#61;&#8239;0&#46;38-0&#46;61&#59; R<span class="elsevierStyleSup">2</span>&#8239;&#61;&#8239;0&#46;14&#8211;0&#46;38&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> Compared to our results&#44; we obtained larger correlation values&#44; but also with lower R<span class="elsevierStyleSup">2</span> values &#40;0&#46;045-0&#46;613&#41;&#46; Conversely&#44; a meta-analysis including 13 studies on the relationship between physical demand and physiological responses in team sports&#44; concluded showing consistent relationships between both constructs of load&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a></p><p id="para0030" class="elsevierStylePara elsevierViewall">To the authors&#8217; knowledge&#44; no publications are showing a correlation between TA and internal load models&#46; However&#44; we have found within the literature related to Player Load &#40;PL&#41; models&#44; a similar parameter based on the sum of accelerations&#44; showing in different contributions a large and very large correlation with SHRZ models &#91;0&#46;61 &#40;0&#46;38-0&#46;77&#41;&#59; 0&#46;80 &#40;0&#46;71-0&#46;86&#41;&#93;&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a> In the case of TRIMP<span class="elsevierStyleInf">B</span>&#44; several reports have shown a &#8220;possibly large&#8221; correlation with PL values &#91;0&#46;54 &#40;0&#46;40-0&#46;66&#41;&#93;&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a> Regarding the perceptual model &#40;sRPE&#41; we observed a large correlation with TA &#40;0&#46;62&#41;&#46; Considering the perceptual model &#40;sRPE&#41;&#44; we also observed a large correlation with the TA&#46; In adult male basketball players&#44; some authors have investigated this relationship between a perceptual model and PL<span class="elsevierStyleSup">TM</span> &#40;Player Load<span class="elsevierStyleSup">TM</span>&#41; obtaining different results&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0028"><span class="elsevierStyleSup">28</span></a> Svilar<a class="elsevierStyleCrossRef" href="#bib0028"><span class="elsevierStyleSup">28</span></a> showed a very large correlation &#40;r&#61;0&#46;80&#59; <span class="elsevierStyleItalic">p</span>&#8804;0&#46;05&#41; while Scanlan has found a lower correlation &#40;r&#61;0&#46;49&#59; <span class="elsevierStyleItalic">p</span>&#8804;0&#46;05&#41; between variables&#46; Values showed by Svilar belong to Euroleague players gathered during the competitive period&#46; We infer that these top-level players compete in most cases more than one game per week&#44; exposing players to high-intensity competitive stress&#46; So&#44; it might be necessary to get a higher control of the training process&#44; receiving smaller training doses&#44; probably with less volume and more intensity&#44; thus reporting greater values of external load and perceived effort&#44; and finally obtaining higher values in correlation models&#46; In contrast&#44; Scanlan&#39;s study participants were semi-professional players with a lesser competitive load during weeks and maybe exposing players to different number and kind of tasks during practice&#44; with variable volume and intensity&#46; Despite using the same devices and gathering the same variables in both studies&#44; the differences in correlation could be explained by the competitive level and&#44; especially by the intensity and volume in the practices of the teams included in the sample&#46; In the case of our results&#44; the difference could also be explained by the sample characteristics&#44; players with just one game per week&#44; season period registered&#44; type of training proposed&#44; and the devices employed&#46; These facts could be the reason why our results are more similar to those obtained in Scanlan&#39;s publication&#46;</p><p id="para0031" class="elsevierStylePara elsevierViewall">The correlations between models&#44; previously described &#40;EL&#58; TA&#59; IL&#58; SHRZ&#44; TRIMP&#44; and sRPE&#41;&#44; could be explained due to the time dependency of all the variables&#46; Longer exposure time indicates a higher load value&#44; with more accelerations or more heart activity that will be multiplied by time&#46; Thus&#44; we could suggest that all of these variables are quantifying the training or competition volume&#46; It might be for this reason that our results are in agreement with some other authors&#8217; results&#44; showing a large and very large correlation between internal and external load models in basketball and other sports&#46;<a class="elsevierStyleCrossRef" href="#bib0008"><span class="elsevierStyleSup">8</span></a><span class="elsevierStyleSup">&#44;</span><a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">20</span></a> On the contrary&#44; we only found a large correlation between TAmax and TRIMP<span class="elsevierStyleInf">B</span> &#40;0&#46;54&#41;&#44; within internal models&#46; This association could be explained by the fact that TRIMP<span class="elsevierStyleInf">B</span> model considers the average and maximum heart rate value in every effort&#44; so continuous and maximal accelerations could raise these parameters&#46; The cardiovascular system works with a slight lag regarding the movements performed by the player&#44; depending on the heart rate activity and the intensity of physical effort&#46;<a class="elsevierStyleCrossRef" href="#bib0029"><span class="elsevierStyleSup">29</span></a> The same occurs between TAmax and sRPE &#40;0&#46;41&#41;&#44; as the main factor that modifies the perception of the athlete is the intensity and duration of the physical efforts performed&#44; more intensity higher fatigue perceived&#46;<a class="elsevierStyleCrossRef" href="#bib0018"><span class="elsevierStyleSup">18</span></a></p><p id="para0032" class="elsevierStylePara elsevierViewall">On the contrary&#44; our results seem to have a moderate relationship between TRIMP<span class="elsevierStyleInf">B</span> and SHRZ models with TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; and Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span> and show a low correlation between sRPE and these external variables &#40;<a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a>&#41;&#46; As we have mentioned previously&#44; SHRZ&#44; TRIMP<span class="elsevierStyleInf">B</span>&#44; and sRPE models quantify load volume&#44; and this might be the reason to explain the reduction of correlation value&#44; with the results shown previously&#46; In this case&#44; external variables &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#44; and Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41; are relative to time&#44; and therefore&#44; are more related to the density of load &#40;units per minute&#41;&#46;</p><p id="para0033" class="elsevierStylePara elsevierViewall">The main limitation of the study is the amount of data collected&#46; The initial data volume expected was higher&#44; but several events were discarded during the process due to data signal inaccuracies&#46; However&#44; correlations were found in the study according to previous research&#44; and new correlations and conclusions have been generated&#44; providing these results interesting information to the scientific community and sports professionals&#46; On the other hand&#44; the use of heart rate and perceptual-based models could present some limitations described previously to determine team sports&#39; physiological response&#46;</p><p id="para0034" class="elsevierStylePara elsevierViewall">In future investigations&#44; it would be advisable 1&#41; to use a bigger sample&#44; allowing the use of other statistical tests such as R-Squared&#44; aiming at obtaining higher predictive power between models&#59; 2&#41; combine other methods for the control of internal load that do not depend on heart rate or subjective methods&#59; 3&#41; establishing individual analyses could provide an understanding of the individual interactions within external and internal load values&#46; Provided that the analyses are performed individually&#44; we could find different conclusions since the physiological response to the same stimulus can differ significantly among players&#59; 4&#41; finally&#44; it could be interesting to understand these relationships&#44; specifically in competitive events&#46;</p><p id="para0035" class="elsevierStylePara elsevierViewall">Internal and external load models should be considered different constructs&#44; and therefore&#44; both must be quantified during the event &#40;practice or competition&#41; in youth female basketball&#46; These results respond to the combination of many different factors within similar drills &#40;space&#44; number of players&#44; number of opponents&#44; or competition format&#41; that contribute to determining the intensity in the context of intermittent team sports&#44; especially in indoor team sports&#46; For this reason&#44; the relationship values found in the literature show significant differences&#46;</p></span><span id="sec0009" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0015">Conclusion</span><p id="para0036" class="elsevierStylePara elsevierViewall">To the best of our knowledge&#44; this study is the first study aiming at establishing a relationship between external and internal loads in elite female youth basketball players&#46; We can conclude that the assumption of strong relationships between external and internal loads in team sports can be misleading&#44; as both models should be considered as different constructs&#46; The findings of this study show a different level of correlation between external and internal load models&#44; but current data highlights the importance of a continuous study of the complex relationship between physical demands and physiological responses in sports&#46;</p></span></span>"
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            1 => "Monitoring"
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        "resumen" => "<span id="abss0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0002">Introduction</span><p id="spara007" class="elsevierStyleSimplePara elsevierViewall">Monitoring load has been a key point in team sports during last years&#46; This study aimed to determine the relationship between the external and internal training load during full basketball practices in elite female youth basketball players&#46;</p></span> <span id="abss0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0003">Material and methods</span><p id="spara008" class="elsevierStyleSimplePara elsevierViewall">Thirteen elite female youth basketball players &#40;age 16&#46;3&#177;1 years&#44; height 181&#46;7&#177;5&#46;8cm and body mass 71&#46;2&#177;9&#46;6kg&#41; had physical and physiological monitored over seven weeks&#46; Players&#8217; internal load was assessed using the session-rating of perceived exertion &#40;sRPE&#41;&#44; the Edward&#39;s summated heart rate zones model &#40;SHRZ&#41; and&#44; the Banister&#39;s training impulse &#40;TRIMP<span class="elsevierStyleInf">B</span>&#41;&#46; The external load was determined through&#58; 1&#41; total accelerations &#40;TA&#41;&#59; the sum of all accelerations and decelerations&#59; 2&#41; maximal accelerations and decelerations &#40;TAMax&#41;&#59; 3&#41; total accelerations per minutes &#40;TA&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; 4&#41; accelerations per minute &#40;Acc&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#59; and 5&#41; decelerations per minute &#40;Dec&#183;min<span class="elsevierStyleSup">&#8722;1</span>&#41;&#46; Heart rate-based and accelerometry models were assessed via Polar Pro technology&#46;</p></span> <span id="abss0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0004">Results</span><p id="spara009" class="elsevierStyleSimplePara elsevierViewall">Correlations between variables revealed different magnitudes&#46; SHRZ model showed a positive correlation with TA &#40;0&#46;63&#41;&#59; the TRIMP<span class="elsevierStyleInf">B</span> model showed a high degree of correlation with TA &#40;0&#46;78&#41;&#59; the sRPE model also presented a high correlation with TA &#40;0&#46;62&#41;&#46;</p></span> <span id="abss0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0005">Conclusion</span><p id="spara010" class="elsevierStyleSimplePara elsevierViewall">Our study establishes different levels of association between external and internal load models in elite female youth basketball players&#46; However&#44; we cannot assume that a high relationship between internal and external loads exists&#44; as both models should be considered as different constructs&#46;</p></span>"
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                  \t\t\t\t  " align="" valign="top">174&#44;87&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><a name="en0055"></a><td class="td" title="\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="top">0&#46;54&#42;&#42;&nbsp;\t\t\t\t\t\t\n
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        "texto" => "<p id="para0038" class="elsevierStylePara elsevierViewall">The current project has not received any external funding&#46; The authors would like to give explicit thanks to the o the coaching staff and players of the Segle XXI program that kindly collaborated in the study&#46;</p>"
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ISSN: 26665069
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