Ultra-endurance triathlon (UET) combines three disciplines (3.8-km swimming, 180-km cycling, 42.2-km running) and involves from 8 to 17 hours of competition depending on the fitness level and efficiency rates of the triathlete (Laursen and Rhodes, 2001). During a UET, dehydration and glycogen depletion are the main causes of metabolic fatigue, whereas gastrointestinal problems, hyperthermia and hyponatremia are potential threats to the triathletes' health (Jeukendrup et al., 2005).
Dehydration decreases endurance performance (Cheuvront and Kenefick, 2014) and increases the injury risk (Oppliger and Bartok, 2002). Furthermore, it is an important factor in race completion in ultra-endurance events (Knechtle et al., 2015). However, the lack of a 'gold-standard' marker of hydration status must be emphasized. The assessment of body hydration status is a dynamic and complex process and no method is valid for all situations (Armstrong, 2007). Finding a method which is sensitive to the type (intra- or extra-cellular) and magnitude of dehydration is necessary (Cheuvront and Kenefick, 2017).
A common method to assess dehydration in endurance athletes has been pre- and post-exercise body mass (BM) control (McGarvey et al., 2010). Ultra-endurance athletes may suffer great BM losses (Hew-Butler et al., 2007; Laursen et al., 2006; Sharwood et al., 2004), principally due to the sweat rate (Cheuvront and Kenefick, 2017). Other possible sources are the respiratory and urinary/fecal water losses (Cheuvront and Kenefick, 2017).
Literature supports that reductions in total BM of [greater than or equal to] 2% generate negative effects on the endurance performance of the athletes (Cheuvront and Kenefick, 2014; McDermott et al., 2017). However, well-trained ultra-triathletes should expect to lose about 3% of their BM without any adverse consequences (Laursen et al., 2006). Therefore, despite the fact that measuring BM variation provides a simple estimate of post-race hydration status in athletes (Maughan et al., 2007; McGarvey et al., 2010), it is not always a reliable measure. Furthermore, it may give rise to misleading results since, for instance, a significant loss of BM may be observed without an effective hypohydration resulting (Cheuvront and Kenefick, 2017; Maughan et al., 2007).
In this regard, the bioelectrical impedance vector analysis (BIVA) emerges as a technique to assess hydration status with no inherent errors of bioimpedance equations or requirements for biological assumptions such as the constant tissue hydration (Lukaski and Piccoli, 2012; Norman et al., 2012). The method is used in the clinical context for the analysis of both homeostatic state and possible vector variations resulting from modifications in body fluid status (Norman et al., 2012; Piccoli, 2010). In the exercise context, as mentioned in Heavens et al. (2016), directional changes in vector values from serial measurements are consistent with fluid loss according to the theory (Piccoli et al., 1994; Piccoli et al., 2002). Therefore, since any vector change is a function of extracellular water -ECW- changes (Segal et al., 1991) because a 50 kHz current does not penetrate cells (De Lorenzo et al., 1997), a potential strength of BIVA would be to afford insight into ECW dehydration (Heavens et al., 2016). Moreover, it might help to provide additional information about hydration changes in ultra-endurance events than, for instance, BM loss alone. Thus, due to the already mentioned deleterious effects of dehydration and to the potential strengths of the method, BIVA is especially interesting for hydration assessment in both sport training and competitive event (Carrasco-Marginet et al., 2017; Koury et al., 2014).
In this way, this study aimed at providing the first description of the whole-body bioelectrical impedance vector in a group of ultra-endurance triathletes, and to assess the variation in the BM and the bioimpedance vector of the athletes evoked during a UET event. We hypothesized that a specific distribution of BIVA would be found in the triathletes when compared to the reference population, and that the BM and the directional changes of the vector in this type of events would be consistent with a decrease in body fluids, according to the literature.
An advertisement was placed on the triathlon race webpage to recruit non-professional male triathletes. The inclusion criteria were to train at least 10 h per week and the participation in a minimum of one UET during the past 3 years.
Sample size was calculated based on a potential increase of the impedance vector module (Z) of 4% based on our previous observations in synchronized swimmers after a high-intensity training session (Carrasco-Marginet et al., 2017) as the only available estimate for short-term (
Nine experienced, well-trained, non-professional ultra-endurance male triathletes volunteered for the study [mean [+ or -] SD: age 36.6 [+ or -] 5.5 years; body mass (BM) 76.0 [+ or -] 6.9 kg; height 1.75 [+ or -] 0.06 m; body mass index (BMI) 24.8 [+ or -] 2.0 kg/[m.sup.2]; [??][O.sub.2max] = 66.3 [+ or -] 4.3 ml/kg/min]. The participants had an average of 10 [+ or -] 3 years of experience in UET and ultra-endurance events, and they had been training regularly for approximately 14-20 hours per week for at least three years. All participants passed a medical examination before the race and gave their informed written consent prior to their participation. The study was performed following the Helsinki Declaration Statement and was approved by the Ethics Committee for Clinical Sport Research of Catalonia.
The participants completed a UET race, specifically, the "Extreme Man Salou-Costa Daurada Triathlon", composed of three segments consisting of a 3.8 km swim, 180 km cycle with a positive elevation over 2600 m and a 42.2 km marathon run. The mean (range) ambient temperature was 26[degrees]C (13-30[degrees]C), the water temperature was 21[degrees]C (20.8-21.2[degrees]C) and the relative humidity was 77% (64%-94%). The mean wind speed was 1.3 m/s (range 0.3-5.0 m/s). All the triathletes undertook the tests measurements designed for the study at three time points: before the race (PRE), after finishing the race (POST) and 48h after POST measurements (POST48h). Detailed information about the study design, race characteristics and procedures related to the performance variables analyzed in the present study (racing time, internal workload and energy deficit) can be consulted in a previously published article (Barrero et al., 2014).
Anthropometric and bioelectrical variables were obtained by the same trained investigator in a thermally neutral room (25.0 [+ or -] 1.0[degrees]C).
Anthropometric assessment: Anthropometric measurements were performed according to the standard criteria of The International Society for the Advancement of Kinanthropometry (ISAK) (Stewart et al., 2011). Body height (h) was assessed to the nearest 1 mm using a telescopic stadiometer (Seca 220[R], Hamburg, Germany). BM was measured to the nearest 0.05 kg using a calibrated weighing scale (Seca 710[R], Hamburg, Germany). BMI (kg/[m.sup.2]) was calculated as body mass / [height.sup.2]. The circumferences of the left and right thigh -[C.sub.LT] and [C.sub.RT], respectively- (taken at mid-thigh) and the left and right calf -[C.sub.LC] and [C.sub.RC], respectively- (taken at the greater perimeter of the calf) were measured to the nearest 1 mm using an anthropometric tape (Lufkin Executive[R], Lufkin, USA), in order to evaluate possible variations between the different time points. This is important since the whole-body impedance can be significantly reduced if a lower limb affected by swelling is in the same side as the electrodes (Codognotto et al., 2008).
Whole-Body Bioimpedance assessment: BIVA uses raw bioelectrical impedance parameters, i.e., resistance ("R", the opposition to flow through intra- and extracellular ionic solutions) and reactance ("Xc", additional opposition from the capacitance effect of cell membranes and tissue interfaces), standardized by height in order to remove the effect of conductor length, yielding a vector, which is plotted in an RXc graph (Piccoli et al., 1994). The vector direction (PA) is the geometric relationship between R and Xc. PA is a validated indicator of cellular health (Norman et al., 2012; Yanovski et al., 1996) and has been interpreted as an indicator of fluid distribution between intra- and extracellular compartments (Goovaerts et al., 1998), reporting an inverse correlation with the ECW--total body water (TBW) ratio (Chertow et al., 1995). The length of the vector states hydration status from fluid overload (short vector) to exsiccosis (longer vector), and lateral migration of the vector projects a decrease or increase in the dielectric mass (membranes and tissue interfaces) of soft tissues (Piccoli, 2005). Individual vectors can be normalized to Z scores and classified on the RXc score graph, according to the tolerance ellipses (50%, 75% and 95%) of a reference population, independently of the bioimpedance analyzer used (Piccoli et al., 2002). Individuals positioned within the 50% tolerance ellipses, according to the literature (Lukaski, 2013; Lukaski and Piccoli, 2012) are considered "normally hydrated".
In the present study, R and Xc were measured by a previously calibrated multifrequency bioimpedance analyser (Z-Metrix[R], BioparHom[R], Bourget du Lac, France) that emitted 77 [micro]A alternating sinusoidal current at different frequencies (1 to 325 kHz). The device provides impedance values with an accuracy characterized by an average error of 0.95% [+ or -] 1.58% and an average repeatability errors of 0.55% [+ or -] 0.38% for all the frequency range (Moreno, 2015). The 50-kHz frequency was selected for BIVA (Piccoli, 2010). The bioimpedance module [Z = [square root of ([R.sup.2] + [Xc.sup.2]) ] and phase angle...