University School of Physical Education in Wrocław University School of Physical Education in Poznań University School of Physical Education in

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University School of Physical Education in Wrocław

University School of Physical Education in Poznań

University School of Physical Education in Kraków

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University School of Physical Education in Poznań (Akademia Wychowania Fizycznego im. Eugeniusza Piaseckiego w Poznaniu) University School of Physical Education in Kraków (Akademia Wychowania Fizycznego im. Bronisława Czecha w Krakowie)

Human movement quarterly

vol. 13, number 2 (June), 2012, pp. 91 – 194

editor-in-Chief alicja Rutkowska-Kucharska

University School of Physical Education, Wrocław, Poland associate editor Wiesław osiński

University School of Physical Education, Poznań, Poland andrzej Klimek

University School of Physical Education, Kraków, Poland editorial Board

Tadeusz Bober University School of Physical Education, Wrocław, Poland Jan Celichowski University School of Physical Education, Poznań, Poland Lechosław B. Dworak University School of Physical Education, Poznań, Poland Ewa Kałamacka University School of Physical Education, Kraków, Poland Tadeusz Koszczyc University School of Physical Education, Wrocław, Poland Stanisław Kowalik University School of Physical Education, Poznań, Poland Juliusz Migasiewicz University School of Physical Education, Wrocław, Poland Edward Mleczko University School of Physical Education, Kraków, Poland Łucja Pilaczyńska-Szcześniak University School of Physical Education, Poznań, Poland Zbigniew Szyguła University School of Physical Education, Kraków, Poland Aleksander Tyka University School of Physical Education, Kraków, Poland Marek Zatoń University School of Physical Education, Wrocław, Poland advisory Board

Wojtek J. Chodzko-Zajko University of Illinois, Urbana, Illinois, USA Charles B. Corbin Arizona State University, East Mesa, Arizona, USA Gudrun Doll-Tepper Free University, Berlin, Germany

Józef Drabik University School of Physical Education and Sport, Gdańsk, Poland Kenneth Hardman University of Worcester, Worcester, United Kingdom

Andrew Hills Queensland University of Technology, Queensland, Australia Zofia Ignasiak University School of Physical Education, Wrocław, Poland Slobodan Jaric University of Delaware, Newark, Delaware, USA

Toivo Jurimae University of Tartu, Tartu, Estonia

Han C.G. Kemper Vrije University, Amsterdam, The Netherlands

Wojciech Lipoński University School of Physical Education, Poznań, Poland Gabriel Łasiński University School of Physical Education, Wrocław, Poland Robert M. Malina University of Texas, Austin, Texas, USA

Melinda M. Manore Oregon State University, Corvallis, Oregon, USA Philip E. Martin Iowa State University, Ames, Iowa, USA Joachim Mester German Sport University, Cologne, Germany Toshio Moritani Kyoto University, Kyoto, Japan

Andrzej Pawłucki University School of Physical Education, Wrocław, Poland John S. Raglin Indiana University, Bloomington, Indiana, USA

Roland Renson Catholic University, Leuven, Belgium

Tadeusz Rychlewski University School of Physical Education, Poznań, Poland James F. Sallis San Diego State University, San Diego, California, USA James S. Skinner Indiana University, Bloomington, Indiana, USA Jerry R. Thomas University of North Texas, Denton, Texas, USA Karl Weber German Sport University, Cologne, Germany Peter Weinberg Hamburg University, Hamburg, Germany

Marek Woźniewski University School of Physical Education, Wrocław, Poland Guang Yue Cleveland Clinic Foundation, Cleveland, Ohio, USA

Wladimir M. Zatsiorsky Pennsylvania State University, State College, Pennsylvania, USA Jerzy Żołądź University School of Physical Education, Kraków, Poland

Translation: Michael Antkowiak, Tomasz Skirecki Design: Agnieszka Nyklasz

Copy editor: Beata Irzykowska

Proofreading: Michael Antkowiak, Anna Miecznikowska

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http://156.17.111.99/hum_mov Editorial Office Secretary: Dominika Niedźwiedź

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IS THE PREVALENCE OF EXERCISE-ASSOCIATED HYPONATREMIA

HIGHER IN FEMALE THAN IN MALE 100-KM ULTRA-MARATHONERS?

CHRISTOPH ALEXANDER RÜST 1_{, BEAT KNECHTLE}1, 2_*_{, IRENA JOLESKA}2_{, PATRIZIA KNECHTLE}2_,

ANDREA WIRTH 2_{, REINHARD IMOBERDORF}3_{, OLIVER SENN}1_{, THOMAS ROSEMANN}1

1_{Institute for General Practice and for Health services Research, University of Zurich, Zurich, switzerland}

2_{Gesundheitszentrum st. Gallen, st. Gallen, switzerland}

3_{Klinik für Innere Medizin, Kantonsspital Winterthur, Winterthur, switzerland}

ABsTRAcT

Purpose. The prevalence of exercise-associated hyponatremia (EAH) has mainly been investigated in male endurance athletes. The aim of the present study was to investigate the prevalence of EAH in female 100-km ultra-marathoners and to compare them to male ultra-runners since females are considered more at risk of EAH. Methods. changes in body mass, hematocrit, [Na+] and [K+] levels in both plasma and urine, plasma volume, urine specific gravity, and the intake of energy, fluids and electrolytes was determined in 24 male and 19 female 100-km ultra-marathoners. Results. Three male (11%) and one female (5%) ultra-marathoners developed asymptomatic EAH. Body mass decreased, while plasma [Na+], plasma [K+] and hematocrit remained stable in either gender. Plasma volume, urine specific gravity and the potassium-to-sodium ratio in urine increased

in either gender. In males, fluid intake was related to running speed (r = 0.50, p = 0.0081), but not to the change in body mass,

in post-race plasma [Na+], in the change in hematocrit and in the change in plasma volume. Also in males, the change in

he-matocrit was related to both the change in plasma [Na+] (r = 0.45, p = 0.0187) and the change in the potassium-to-sodium

ratio in urine (r = 0.39, p = 0.044). sodium intake was neither related to post-race plasma [Na+] nor to the change in plasma

volume. Conclusions. The prevalence of EAH was not higher in female compared to male 100-km ultra-marathoners. Plasma volume and plasma [Na+] were maintained and not related to fluid intake, most probably due to an activation of the renin-angiotensin-aldosterone-system.

Key words: ultra-endurance, electrolyte disorder, fluid overload, sport nutrition doi: 10.2478/v10038-012-0009-2

2012, vol. 13 (2), 94– 101

* corresponding author.

Introduction

Exercise-associated hyponatremia (EAH) is defined as a serum sodium concentration of ([Na+_{]) < 135 mmol/L}

and was described first in scientific literature in 1985 by Noakes et al. [1] in male ultra-marathoners in south Africa as being due to “water intoxication”. EAH is a well-known and a well-described fluid and electrolyte disor-der in marathoners [2–8]. The prevalence of EAH varies between 3% and 22% in marathoners depending upon the number of studied athletes, their gender and fitness level [2–6]. There is abundant literature about the pre-valence of EAH in marathoners [2, 4–7]. studies inves-tigating EAH in ultra-marathoners are rare, in which exclusively male athletes have been investigated [9–11]. In marathoners presenting EAH, an association between excessive fluid intake and both an increase in body mass and a decrease in plasma sodium [Na+_{] has been}

demonstrated [2, 5, 6, 12, 13]. In ultra-marathoners, however, dehydration is a more common finding [14], resulting in a decrease of body mass and an increase in urine specific gravity [15, 16]. In cases of excessive fluid

intake with fluid overload during endurance perfor-mance [17], we would also expect in ultra-runners a stable or increased body mass [13, 17], a decrease in plasma [Na+_{] [12, 13, 18], an increase in plasma volume [18] and}

a decrease in hematocrit due to haemodilution [12]. Risk factors for fluid overload and subsequent EAH are the female gender, a slow running pace and a high frequency of fluid intake [2, 3, 19].Following Noakes, three independent mechanisms explain why some ath-letes develop EAH during and after prolonged exercise: (i) overdrinking due to biological or psychological fac-tors; (ii) an inappropriate secretion of antidiuretic hormone (ADH), in particular, the failure to suppress ADH-secretion in the face of an increase in total body water; and (iii) a failure to mobilize Na+_from

osmoti-cally inactive sodium stores or the alternatively inap-propriate osmotic inactivation of circulating Na+_[13].

Because the mechanisms causing factors (i) and (iii) are unknown, it follows that the prevention of EAH requires that athletes be encouraged to avoid overdrinking during exercise [13]. since ultra-marathoners run at a rather slow pace [20, 21],they may be at an especially high risk of fluid overload.

The aim of the present study was to investigate the prevalence of EAH in both female and male ultra-marathoners in the “100 km Lauf Biel” in Biel,

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switzer-c.A. Rüst et al., Hyponatremia in ultra-marathoners

land. This race is the most famous 100-km ultra-mara-thon in Europe. The organizers offer a total of 17 aid stations and the athletes may be accompanied by a cyclist providing continuous fluid and nutrition support while running. since the female gender, a slow running pace and excessive drinking behaviour [13, 19], combined with a high frequency of fluid consumption [2, 13], are considered as the main risk factors for fluid overload, and subsequently developing EAH, we hypothesized that (i) the prevalence of EAH would be higher in 100-km ultra-marathoners compared to existing reports on ma-rathoners and that (ii) the prevalence of EAH would be significantly higher in female than in male ultra-marathoners.

Material and methods

After receiving approval by the Institutional Review Board for use of Human subjects of st. Gallen, switzer-land, all the participants of the 50th_{annual “100 km}

Lauf Biel” in Biel, switzerland in 2008 were contacted via a separate newsletter, three months before the race, where they were asked to participate in the current study. Out of about 2,000 runners who were to start in the race, 31 male and 19 female, non-professional, experi-enced ultra-runners agreed to take part in this study, with all of them providing their informed written con-sent. The race began in the night of 13 to 14 June 2008, with the runners beginning on 13 June at 10:00 p.m. and had to finish the 100 km distance with a total climb in altitude of 645 metres within a time limit of 21 hours. Two-thirds of the course was on asphalt

with the remaining third on unpaved roads. Through-out the 100 km there were 17 aid stations at intervals of ~5 km that provided a variety of food and beverages. The organizers offered isotonic sports drinks, tea, soup, caffeinated drinks, water, bananas, oranges, energy bars and bread. The athletes were allowed to be supported by a cyclist in order to have access to food and clothing, if necessary. At the start of the race the temperature was 15° celsius. During the night, the temperature dropped to 8° celsius and then rose to 18° celsius the next morning by 10:00 a.m. A maximum temperature of 31°c was reached at 01:00 p.m. on 14 June 2008.

Out of the initial group of participants, twenty-seven male and all female participants finished the race within the 21 h time limit, with one male runner finishing in the top three. Table 1 shows the age, anthro-pometric characteristics, training and pre-race experi-ence of the subjects. Before the start of the race and after arrival at the finish line, every participant un-derwent analysis to determine body mass, take blood samples and be subject to urinary sampling. Body mass was measured to the nearest 0.1 kg using an electronic balance (Beurer, Germany) after voiding the urinary bladder. The athletes were weighed pre- and post-race in an identical manner in their running wear excluding shoes. samples of urine were collected for the determi-nation of urine creatinine, urine [Na+_{], urine [K}+_{] and}

urine specific gravity. Urine specific gravity was ana-lysed using a clinitek Atlas®_{Automated Urine}

chem-istry Analyser (siemens Healthcare Diagnostics, UsA). creatinine in urinary samples was measured using a cOBAs INTEGRA®_{800 (Roche Diagnostics,}

switzer-Table 1. comparison of age, anthropometric characteristics, training and pre-race experience between male and female subjects. Results are presented as mean (sD)

Male finishers (N = 27) Female finishers (N = 19)

Age (years) 46.7 (8.0) 44.0 (10.4)

Body height (m) 1.78 (0.06) 1.67 (0.09)**

Body mass (kg) 74.3 (10.2) 61.0 (10.1)**

Body mass index (kg/m2₎ _{23.3 (2.2)} _{21.5 (2.3)*}

Number of years participating in running (years) 11.2 (8.4) 10.3 (8.3)

Weekly distance ran (km) 73.7 (28.7) 66.3 (19.5)

Hours ran per week (h) 7.8 (3.2) 6.9 (2.3)

Number of weekly training units (n) 4.3 (1.5) 4.0 (0.7)

Minimal distance per week (km) 26.6 (21.4) 29.4 (19.0)

Maximal distance per week (km) 85.6 (56.7) 74.0 (35.4)

Distance per run training session (km) 18.8 (12.8) 14.9 (3.0)

Duration of run training sessions (min) 88.0 (24.9) 87.9 (24.5)

Mean speed of the training sessions (km/h) 10.7 (1.5) 9.5 (1.6)**

Yearly running distance (km) 3,158.9 (1,568.1) 2,185.8 (924.1)

Yearly hours ran (h) 307.1 (171.5) 222.4 (80.8)

Number of finished marathons (n) 30.9 (38.5) (n = 27) 20.0 (14.3) (n = 17)

Personal best time in a marathon (min) 207.8 (31.3) 231.2 (20.4)**

Number of finished 100 km ultra-marathons (n) 4.9 (6.9) (n = 18) 2.8 (3.5) (n = 5)

Personal best time in a 100 km ultra-marathon (min) 621.6 (250.2) 831.8 (173.3)

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c.A. Rüst et al., Hyponatremia in ultra-marathoners

land). Electrolytes in the urine samples were determined using an IsE IL 943 Flame Photometer (GMI, Inc., UsA). [Na+_{] and [K}+_{] in urine were normalised for creatinine}

in urine. At the same time, blood was sampled to deter-mine hematocrit, plasma [Na+_{] and plasma [K}+_{] using}

an i-sTAT®_{1 system (Abbott Laboratories, UsA). The}

changes in plasma volume were calculated according Beaumont’s equation [22].

While running, the athletes consumed food and drinks ad libitum and recorded their intake of fluid and solid nutrition using paper and pencil at each aid station. At every station, beverages and food were pro-vided in same size portions. The ingestion of fluids, electrolytes and solid food between pre- and post-race measurements were determined by the reports of the athletes using a food table [23]. Energy expenditure was estimated using a stepwise calculation using body mass, mean velocity and the time spent during perfor-mance [24].

Pre-race, the participants were asked to maintain a comprehensive training diary consisting of their daily workouts, their distance and duration in preparation for the race. The training record consisted of the number of training units showing duration, kilometres, pace, the amount of kilometres ran per week, the amount of hours ran, the minimal and maximal amount of kilo-metres ran per week as well as the running speed during training in min/km were also recorded. Additionally, they reported on the number of years they had actively participated in running, the number of marathons and 100-km ultra-marathons they successfully completed and the best times that were achieved in these races. Following their arrival at the finish line, the subjects were asked if they felt the symptoms of EAH [19].

Data are presented as mean and standard deviation (sD). The measured parameters of both males and fe-males were compared using the Kruskal-Wallis test. The student’s t-test was used to compare the parameters

before and after the race. correlations in the changes in the parameters during the race were evaluated using the Pearson’s Product-Moment correlational Analysis. The significance level was set at p < 0.05.

Results

Three male (11%) and one female (5%) finishers were diagnosed with asymptomatic EAH, where one male and one female athlete showed post-race plasma [Na+_{] of 131 mmol/L, and two male athletes were found}

with plasma [Na+_{] of 134 mmol/L. Throughout the race,}

females ran slower, consumed less energy, expended less energy, ingested less fluid and less electrolytes than the males (see Tab. 2). Body mass decreased (p < 0.01) while plasma [Na+_{] and plasma [K}+_{] remained unchanged}

(p > 0.05) in either gender. For both genders, the de-crease in body mass was found not to be related to an energy deficit (p > 0.05). Also, the decrease in body mass was not related to running speed (p > 0.05). Hematocrit levels decreased non-significantly in the males (p > 0.05) and significantly in the females (p < 0.05), plasma volume increased by 5.5% in the males and by 6.4% in the females. For both the males and the females, race time was not correlated to post-race plasma [Na+_{] (}_p_{> 0.05).}

In the three male athletes with EAH, body mass de-creased by – 3.5 (1.2) kg.

Fluid intake was significantly and positively related to the running speed of males (see Fig. 1), but not for females (see Fig. 2). Running speed, however, was neither related to post-race plasma [Na+_{] (}_p_{> 0.05) nor to the}

change in plasma [Na+_{] (}_p_{> 0.05) in either gender. For}

both males and females, there was no association be-tween fluid intake and the following: the change in body mass, post-race plasma [Na+_{], the change in}

he-matocrit and the change in plasma volume (p > 0.05). sodium intake was not related to post-race plasma [Na+_]

and potassium intake was not related to post-race plas-Table 2. comparison of race time, energy turnover, fluid and electrolyte intake and body mass

between male and female subjects. Results are presented as mean (sD)

Race time (min) 689.9 (119.9) 770.5 (103.4)*

Running speed (km/h) 8.9 (1.6) 7.9 (1.1)*

Energy intake (kcal) 758.5 (302.3) 566.8 (229.3)*

Energy expenditure (kcal) 7,424.5 (1,666.4) 6,198.2 (1,366.8)*

Energy balance (kcal) –6,666.0 (1,648.5) –5,631.4 (1,187.6)*

Fluid intake (L/h) 0.52 (0.18) 0.32 (0.11)**

Fluid intake (L/kg body mass) 0.08 (0.02) 0.21 (0.03)**

sodium intake (mg/h) 445 (471) 364 (250)**

Potassium intake (mg/h) 146 (176) 62 (24)**

Body mass pre-race (kg) 74.3 (10.2) 61.0 (10.1)**

Body mass post-race (kg) 72.4 (10.1) 59.6 (10.0)**

Body mass change (kg) –1.9 (1.5)## _{–1.4 (0.9)}##

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(see Tab. 3). The potassium-to-sodium ratio in urine in-creased in both males and females (p < 0.01). The change in post-race potassium-to-sodium ratio in urine was sig-nificantly and positively related to the change in hemato-crit in males (see Fig. 4), but not in females (see Fig. 5). Figure 1. Hourly fluid intake during the race

was significantly and positively related to running speed

in males (N = 27) (r = 0.50; p = 0.0081)

Figure 2. In females, fluid intake and running speed

had no association (N = 19) (r = 0.00, p = 0.98)

ma [K+_{] in either gender (}_p_{> 0.05). In males, the change}

in plasma [Na+_{] was related to the change in hematocrit}

(see Fig. 3). Urine specific gravity increased in both male and female subjects (p < 0.01), urine [Na+_{] decreased}

(p < 0.01) and urine [K+_{] remained unchanged (}_p_{> 0.05)}

Table 3. comparison of race performance and results obtained during the race between male and female subjects. Results are presented as mean (sD)

Hematocrit pre-race (%) 44.1 (2.8) 41.5 (2.4)**

Hematocrit post-race (%) 43.0 (2.9) 40.3 (3.4)**

Hematocrit change (%) –1.1 (3.3) –1.2 (3.5)#

change in plasma volume (%) +5.5 (13.9) +6.4 (13.7)

Plasma sodium pre-race (mmol/L) 139.5 (1.4) 138.4 (1.7)*

Plasma sodium post-race (mmol/L) 139.6 (3.8) 137.7 (2.3)*

Plasma sodium change (mmol/L) 0.15 (4.13) –0.74 (2.23)

Plasma potassium pre-race (mmol/L) 4.9 (0.7) 4.7 (0.5)

Plasma potassium post-race (mmol/L) 5.3 (1.0) 4.7 (0.6)*

Plasma potassium change (mmol/L) 0.5 (1.2) 0.05 (1.0)

Urine sodium/creatinine pre-race (mmol/mmoL) 0.022 (0.009) 0.031 (0.016)*

Urine sodium/creatinine post-race (mmol/mmoL) 0.006 (0.004) 0.010 (0.002)

Urine sodium/creatinine change (mmol/mmoL) –0.016 (0.009)## _{–0.027 (0.015)*}##

Urine potassium/creatinine pre-race (mmol/mmoL) 0.008 (0.006) 0.011 (0.006)*

Urine potassium/creatinine post-race (mmol/mmoL) 0.010 (0.004) 0.009 (0.006)

Urine potassium/creatinine change (mmol/mmoL) 0.002 (0.006) –0.002 (0.008)

Potassium-to-sodium ratio pre-race 0.36 (0.19) 0.39 (0.21)

Potassium-to-sodium ratio post-race 2.10 (1.12) 2.25 (1.03)

Potassium-to-sodium ratio change 1.72 (1.15) ## _{1.86 (1.08)}##

Urine specific gravity pre-race (g/mL) 1.013 (0.008) 1.011 (0.007)

Urine specific gravity post-race (g/mL) 1.026 (0.005) 1.024 (0.004)

Urine specific gravity change (g/mL) 0.012 (0.007) ## _{0.013 (0.007)}##

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The weekly running distance (r = –0.48, p = 0.0122), the mean running speed during training (r = –0.52, p = 0.0053), the personal best time in a marathon (r = 0.62, p = 0.0005) and the personal best time in a 100-km ultra-marathon (r = 0.79, p = 0.0002) were related to the achieved race time in the group of males. In females, the training variables were not related to race time (p > 0.05), however, the personal best time in a mara-thon (r = 0.59, p = 0.0136) and the personal best time in a 100-km ultra-marathon (r = 0.82, p = 0.0091) were associated with their race time.

Discussion

The aim of the present study was to investigate the prevalence of EAH in both female and male ultra-marathoners in a 100-km ultra-marathon. since the female gender, a slow running pace and excessive drink-ing behaviour with a high frequency of fluid con-sumption were considered as the main risk factors for fluid overload, we hypothesized (i) that the prevalence of EAH would be higher in 100-km ultra-marathoners as based on the available reports on marathoners and (ii) be especially higher in females than in male ultra-marathoners. Three males (11%) and one female (5%) developed asymptomatic EAH. The 11% prevalence of EAH in the male ultra-marathoners was the same rate as had been recently found in marathoners in the Lon-don Marathon [5]. The prevalence rates for EAH for marathoners seem, however, to vary between 3% [6] to 22% [3] depending upon weather and temperature [6]and the fitness level of the subjects [3]. For the female

ultra-marathoners, the 5% prevalence of EAH was con-siderably lower compared to the males.

Excessive fluid intake leading to fluid overload is considered to be the most important risk factor for EAH [2, 13, 19]. Fluid intake was significantly and positively related to running speed for males (see Fig. 1), where faster male athletes were drinking more compared to slower ones. However, fluid intake was not associated with the decrease in body mass, post-race plasma [Na+_],

Figure 5. In females, the change in the post-race potassium-to-sodium ratio in urine showed no association with the change in hematocrit

(N = 19) (r = –0.01, p = 0.96)

Figure 3. The change in plasma [Na+_{] was significantly}

and positively related to the change in hematocrit in males

(N = 27) (r = 0.48, p = 0.015)

Figure 4. The change in the post-race potassium-to-sodium ratio in urine was significantly and positively related

to the change in hematocrit in males

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the change in hematocrit and the change in plasma volume. For fluid overload, fluid intake would have to have been far greater and the athletes would have had to gain weight as described by speedy et al., where one Ironman triathlete with EAH and who presented plasma [Na+_{] < 130 mmol/L drank 16 L over the course of the}

event and gained 2.5 kg in body mass [17]. The athletes in this race, compared to a classical marathon, had the opportunity to be supported by a cyclist. This cyclist could carry food and drinks as well as additional cloth-ing. We assume that the faster runners were followed by a cyclist who provided fluids between the aid stations, so they did not have to stop at each aid station to replen-ish their fluid level. However, the increased availability of fluids did not lead to fluid overload and EAH.

Apart from the female gender, event inexperience and a slow running pace are also considered as risk factor for EAH [19]. In the present subjects, training volume regarding the distance ran each year and the amount of hours ran was not different between genders. The female ultra-marathoners ran slower during training and had a slower personal best marathon time; how-ever, the personal best time in a 100-km ultra-mara-thon was not different between genders. Experienced ultra-runners with a fast race time were obviously able to consume rather large amounts of fluids so that nei-ther dehydration nor fluid overload occurred. We as-sume that pre-race experience is an important factor in preventing EAH in ultra-marathoners. In these sub-jects, weekly running distance, mean running speed during training, personal best time in a marathon and personal best time in a 100-km ultra-marathon were all related to race time. Recent reports on 100-km ultra-marathoners reported that pre-race experience such as a high training volume in the distance ran in a week, a fast running speed during training and a fast per-sonal best time in a marathon were associated with race time in a 100-km ultra-marathon [25–27]. A high train-ing volume [25, 26] and a fast runntrain-ing speed while training [25–27] were especially significant indicators for a fast 100-km race time. We presume that these subjects were both highly trained and highly experi-enced ultra-runners which might explain that the prevalence of EAH was lower in these athletes com-pared to existing reports on marathoners.

The mean hourly fluid intake was 0.52 (0.18) L for males and 0.32 (0.11) L for females, where males were consuming more fluids compared to females. Faster male runners drank more than slower runners (see Fig. 1), whereas no association between running speed and fluid intake existed in females (see Fig. 2). We specu-lated that a slower running pace during the race cou-pled with a frequent fluid intake would lead to fluid overload and EAH. In contrast, the faster runners drank more when compared to slower ones while run-ning speed showed no association with either post-race plasma [Na+_{] or the change in plasma [Na}+_{]. The}

fact that no fluid overload occurred in the faster run-ners although they drank more might be explained by a higher perspiration rate in these runners. We assume that the rather low amount of fluids despite ad libitum fluid consumption was responsible for the fact that no fluid overload occurred. Although aid stations were pro-vided every ~5 km and athletes could be followed by a support crew to provide fluids, both male and female athletes were found to not overdrink. In general, amounts greater than 0.8 L per hour to 1.6 L per hour are recom-mended to maintain hydrated in performances lasting 1–3 h [25]. However, hourly amounts of ~0.5 L could also lead to fluid overload and a decrease in serum [Na+_]

concentration [12, 18]. stuempfle et al. reported fluid consumptions of 0.3 (0.1) L per hour in an ultra-dis-tance race [12],and speedy et al. described a mean hourly fluid intake of 0.7 L in Ironman triathletes [18].In both studies, subjects developing EAH had evidence of fluid overload despite a moderate fluid intake. stuempfle et al. concluded that the current recommendations for ultra-distance athletes to consume 0.5 L to 1.0 L per hour may be too high [12],and speedy et al. summarised that subjects developing EAH had evidence of fluid over-load despite modest fluid intakes [18]. Therefore, recom-mendations for fluid intake, especially in ultra-endur-ance performultra-endur-ances, should be adapted to take into account these recent findings, where Gisolfi and Duch-man have already recommended reducing hourly fluid intake to 0.5 L to 1.0 L for endurance performances lasting longer than 3 h [28]. Their recommendations for fluid intake are as follows: a possible starting point suggested for marathon runners (who are hydrated from the outset) is they drink ad libitum from 0.4 L per hour to 0.8 L per hour, with the higher rate suggested for faster, heavier individuals competing in warm environ-ments while the lower rate for the slower, lighter per-sons competing in cooler environments [29–32].

In a state of fluid overload, we would expect a stable or rather increased body mass [19].We found, however, a significant decrease in body mass and a significant increase in urine specific gravity in the studied ultra-runners. since the energy deficit during the race was not related to the change in body mass, the decrease in body mass must be therefore associated with dehy-dration. In cases of dehydration resulting from ultra-marathon running [14],body mass should decrease and urine specific gravity should increase [15, 16]. Re-garding our results, a loss of ~2.5% in body mass and an increase in urine specific gravity to ~1.025 g/mL indi-cated severe dehydration, according to Kavouras [15].

Hematocrit decreased non-significantly in males and significantly in females, plasma volume increased by 5.5% in males and by 6.4% in females. A transient ex-pansion in plasma volume is reported after endurance events [33].The increase in plasma volume, however, was not related to fluid intake. A possible explanation for the increase in plasma volume could be a retention

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of [Na+_{] as a consequence of increased aldosterone}

activ-ity since both fluid and sodium intake were not related to post-race plasma [Na+_{] [12]. After intense exercise,}

aldosterone increases and rises with growing exercise intensity [34]. The activation of the renin-angiotensin-aldosterone system (RAAs) leads to an enhanced reten-tion of plasma [Na+_{] and water, consequently resulting}

in an increase in plasma volume. An increased activity in aldosterone should lead to an increase in plasma [Na+_]

according to the findings of Wade et al. from a 20-day 500-km race [35].We found, however, no change in plasma [Na+_{] while urine [Na}+_{] declined. The}

potassium-to-sodium ratio in urine was, however, increased. The potassium-to-sodium ratio in urine is a physiological reflection of the [K+_{] excretion in the distal tubulus}

and when compared to [Na+_{] re-absorption as an}

esti-mate of the aldosterone activity in serum. We see the increase in the potassium-to-sodium ratio in urine as a stimulation of the RAAs. During the race, more urine [K+_{] than urine [Na}+_{] was excreted and a positive ratio}

for urine [K+_{] to urine [Na}+_{] suggests an increased}

ac-tivity of aldosterone. A recent study on male 100-km ultra-marathoners showed a significant and positive association between the change in aldosterone and both the change in the potassium-to-sodium ratio in urine and the post-race transtubular potassium gradient [36]. A potassium-to-sodium ratio in urine > 1.0 reflects a contraction of the effective extra-cellular volume leading to a hyperreninemic hyperaldosteronemia. since the change in hematocrit was positively related with both the change in plasma [Na+_{] (see Fig. 3) and the}

post-race potassium-to-sodium ratio in urine (see Fig. 4) for males, we assume that both the change in hemato-crit and the increase in plasma volume was due to an increased activity of aldosterone and not due to fluid intake. However, the decrease in hematocrit could also be a result of intravascular hemolysis while running.

One limitation of this study is that we did not re-cord the urine output of the athletes during the race. Fluid balance might be estimated better with fluid in-take and urine output. Future studies should include fluid balance with an estimation of urines loss.

Conclusion

To summarize, the prevalence of EAH in these 100-km ultra-marathoners was not higher compared to existing reports on marathoners and EAH was not more frequent in female than in male ultra-maratho-ners. Although body mass decreased, plasma volume and plasma [Na+_{] were maintained. Fluid intake showed}

neither an association with the decrease in body mass, nor with post-race plasma [Na+_{] and the increase in}

plasma volume. We assume that the rather low fluid intake was responsible for the low prevalence of EAH. The potassium-to-sodium ratio in urine increased post-race to >1.0 and showed a significant and positive

as-sociation with the change in hematocrit. Maintained fluid homeostasis in these ultra-runners was most pro-bably due to a stimulation of the RAAs. Future studies investigating EAH in ultra-marathoners should deter-mine the activity of aldosterone and include larger sam-ples of female ultra-marathoners.

Acknowledgments

We wish to thank the organisers of ‘100 km Lauf Biel’ and the athletes for their help in collecting data. In addition we wish to thank Mary Miller from England who helped us with translating this manuscript.

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Paper received by the Editors: March 12, 2011 Paper accepted for publication: December 5, 2011

Correspondence address Beat Knechtle Facharzt FMH für Allgemeinmedizin Gesundheitszentrum st. Gallen Vadianstrasse 26 9001 st. Gallen, switzerland e-mail: beat.knechtle@hispeed.ch

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MAXIMAL ISOKINETIC PEAK TORQUE AND EMG ACTIVITY

DETERMINED BY SHORTER RANGES OF MOTION

ANDERSON S.C. OLIVEIRA 1, 2_{, ROGÉRIO B. CORVINO} 3, 4_{, MAURO GONÇALVES} 2_,

FABRIZIO CAPUTO 3, 4_{, BENEDITO S. DENADAI}3_*

1_{Biomechanics Laboratory, são Paulo state University, Rio claro, Brazil}

2_{Department of Health science and Technology, Aalborg University, Aalborg, Denmark}

3_{Human Performance Laboratory, são Paulo state University, Rio claro, Brazil}

4_{center for Health and sport science, santa catarina state University, Florianópolis, Brazil}

ABsTRAcT

Purpose. Isokinetic tests are often applied to assess muscular strength and EMG activity, however the specific ranges of motion used in testing (fully flexed or extended positions) might be constrictive and/or be painful for patients with injuries or under-going rehabilitation. The aim of this study was to examine the effects of different ranges of motion (RoM) when determining maximal EMG during isokinetic knee flexion and extension with different types of contractions and velocities. Methods. Eighteen males had EMG activity recorded on the vastus lateralis, vastus medialis, semitendinosus and biceps femoris muscles during

five maximal isokinetic concentric and eccentric contractions for the knee flexors and extensors at 60° · s–1_{and 180° · s}–1_{. The}

root mean square of EMG was calculated at three different ranges of motion: (1) a full range of motion (90°–20° [0° = full knee extension]); (2) a range of motion of 20° (between 60°–80° and 40°–60° for knee extension and flexion, respectively) and (3) at a 10° interval around the angle where peak torque is produced. EMG measurements were statistically analyzed (ANOVA) to test for the range of motion, contraction velocity and contraction speed effects. coefficients of variation and Pearson’s correlation

coefficients were also calculated among the ranges of motion. Results. Predominantly similar (p > 0.05) and well-correlated

EMG results (r > 0.7, p 0.001) were found among the ranges of motion. However, a lower coefficient of variation was found for

the full range of motion, while the 10° interval around peak torque at 180° · s–1_{had the highest coefficient, regardless of the}

type of contraction. Conclusions. shorter ranges of motion at around the peak torque angle provides a reliable indicator when recording EMG activity during maximal isokinetic parameters. It may provide a safer alternative when testing patients with injuries or undergoing rehabilitation.

Key words: torque, concentric, eccentric, knee extension, joint angle doi: 10.2478/v10038-012-0010-9

2012, vol. 13 (2), 102– 108

* corresponding author.

Introduction

Maximal strength is currently one of the most im-portant parameters tested in sports performance and rehabilitation programs. Even modest sports associa-tions have procedures for measuring maximal capacity (using standard resistance training equipment), while more sophisticated centers may make use of iso kinetic equipment (which provides constant velocity throughout the entire range of motion). In addition, an evaluation of muscular activity by use of electromyography [EMG] during maximal effort provides more accurate results and optimizes readings during testing and training [1, 2]. This isokinetic procedure usually requires a full range of motion (RoM) from which the moment of maximal torque is selected for analysis.

The optimized joint positions to produce torque are ~40–80° for knee extension [3–5] and 40–60° for knee flexion [4, 5] (0° = full extension). However, existing

literature presents some divergent results on maximal torque and joint positions: (1) the use of short RoMs (partitions of 15° to 30° throughout the full RoM) pro-vides similar results compared to full RoM at low and moderate speeds, but also presents noteworthy incon-sistencies [5, 6], while (2) other studies have verified that the further from the optimized length-tension joint position, the lower the maximal torque [3, 4, 7].

Despite these conflicting results, the use of isoki-netic measurements are also used for rehabilitation purposes, which itself presents a number of idiosyncra-sies of joint RoM. Patients after trauma or with chronic knee disease should avoid the use of full RoM during such strength measurements [8], which can lead to com-plications in not allowing maximal torque to be deter-mined as well as related EMG activity. concerned about how patients can adapt their neuromuscular system during restricted training programs, Barak et al. [8] found a transferability between the strength gains from using a RoM of 30–60° knee extension to other different RoMs (5°–30° and 60°–85°), which is useful for injured/rehabilitation patients. Thus, at least for re-habilitation purposes, the most likely angles providing maximal torque may be avoided.

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A.s.c. Oliveira et al., EMG at different ranges of knee motion

During isokinetic measurements both the contraction type and speed are very important issues; it is well known that concentric contractions show lower peak torque than eccentric contractions [7, 9], with the EMG activity for eccentric actions also being lower. contrac-tion speed, which affects generating peak torque, has also been widely investigated, but EMG activity was found not to follow the same pattern and shows no changes among different contraction speeds [2, 7, 9].

Recent studies have assessed EMG activity during maximal contractions at short RoMs [9, 10], which in-clude the range corresponding to the optimal length-tension relationship (i.e., the range that includes the angle of peak torque). sports performance and reha-bilitation research could be benefited by using shorter RoMs to evaluate maximal parameters, since knee joint disorders may affect afferent information, especially in the range of the injury [8]. It seems that by avoiding larger RoM, EMG may be more accurately represented. However there is no clear evidence in the reliability of using shorter RoMs to determine EMG respective to peak torque (PT) compared to the standard testing procedure using Full RoM [6].

With this in mind, our main hypothesis was that EMG during maximal isokinetic contractions, measured in different RoMs, may differ between each other, since different joint positions might reveal different muscle activations. A second hypothesis was that the changes verified between the different RoMs may be main-tained when different contraction speeds are executed, or even between different contractions types (eccentric × concentric). The objective of the present study was therefore to verify the differences in EMG activity during maximal isokinetic contractions when measured by different RoMs during concentric and eccentric actions at both slow and moderate speeds.

Material and methods

Eighteen physically active, though not specifically trained, males (mean ± sD: 22 ± 2 years old, height 179.1 ± 6.25 cm and weight 80.12 ± 9.56 kg) provided their informed consent to participate in the study. All subjects were healthy and free of cardiovascular, respira-tory and neuromuscular disease. The study was approved by the Institutional Research Ethics committee.

The subjects were tested on two occasions. During their first visit, all subjects were familiarized with the maximal concentric and eccentric isokinetic contrac-tions they were to perform (knee extension and flexion) at speeds of 60° · s–1_{and 180° · s}–1_{on an isokinetic}

dy-namometer (Biodex system 3, Biodex Medical systems, UsA). During the second test session, which took place at least five days later, the (already familiarized) sub-jects returned to the laboratory to perform five maximal concentric knee flexion and extension cycles at 60° · s–1

and 180° · s–1_{, and five maximal eccentric isokinetic}

knee flexion and extension cycles at 60° · s–1_{and 180° · s}–1_.

The order of concentric and eccentric contractions and the contraction velocity was randomized.

During their familiarization session, the subjects were fully instructed about the tasks they were to form on the dynamometer. Prior to the test, they per-formed a standardized warm-up consisting of cycling for 5 min at 70 Watts. After this, the subjects were po-sitioned and allowed to perform the submaximal eccen-tric and isomeeccen-tric contractions at the tested velocity. The subjects were instructed to work at maximal force when performing knee extensions and flexions. The order of the type of contraction during the familiari-zation and testing process was random, with 5–10 max-imal contractions for knee flexion and extension at each velocity (60° · s–1_{and 180° · s}–1_{). In order to standardize}

the nomenclature, concentric contractions at 60° · s–1

and 180° · s–1_{were named “cON-60” and “cON-180”,}

respectively, for the knee flexors and extensors, while the eccentric contractions at 60° · s–1_{and 180° · s}–1_were

named “Ecc-60” and “Ecc-180”, respectively, for the knee flexors and extensors.

For both the familiarization and maximal test ses-sions, the subjects were placed in a sitting position and securely strapped into the test chair. Extraneous movement of the upper body was limited by two cross-over shoulder harnesses and an abdominal belt. The trunk/thigh angle was 85°. The axis of the dynamome-ter was lined up with the right knee flexion-extension axis, and the lever arm was attached to the shank by a strap. The subject was asked to relax his leg so that passive determination of the effects of gravity on the limb and lever arm could be carried out. The RoM for the knee test was 70° for both concentric and eccentric contractions (from 90° to 20° [0° = full extension]). To ensure full extension, an anatomical 90° position was determined by manual measurement using a goniometer. All subjects were encouraged to give maximal effort by both visual feedback and strong verbal encourage-ment when pushing the lever up, and then down, as hard and as fast as possible during extension in the eccentric contractions.

The isokinetic (torque, position and velocity) con-tractions were analyzed using specific algorithms cre-ated in MatLab software (The MathWorks Inc., UsA). Torque and EMG measurements were collected through-out the whole ROM. From the isokinetic contractions, peak torque and angle of peak torque (PTANG) were

de-termined by using specific Matlab algorithms. Torque curves were smoothed by use of a 10 Hz Butterworth fourth-order zero-lag filter. After this, the contraction with the highest peak torque from five individual efforts was considered for further analysis. Peak torque was taken in an averaged window of 10° around peak torque [9]. The right leg was utilized for all test procedures.

EMG signals from the vastus lateralis (VL), vastus medialis (VL), biceps femoris (BF) and

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semitendino-A.s.c. Oliveira et al., EMG at different ranges of knee motion

sus (sT) muscles were selected for analysis. The subjects were prepared for the placement of the EMG electrodes by having their skin shaven at each electrode site, which was then cleaned carefully with an alcohol wipe and lightly abraded. Two Medi-Trace Ag–Ag/cl electrodes (covidean, UsA), with a diameter of 2 cm and an inter-electrode distance of 2 cm, were used per muscle and placed according to procedure suggested by Hermens et al. [11]. The ground (reference) electrodes were posi-tioned on the tibia. To ensure that movement artefacts were kept to a minimum, the electrodes and cables were taped to the skin with surgical tape. The EMG activity was recorded by an EMG system800 (EMG system, Bra-zil) at 2000 Hz with a signal amplification of 2000x. surface EMG signals were high pass filtered (20 Hz) and low pass filtered (500 Hz), with the common mode rejection ratio set to 80 dB. All EMG data was stored together with the isokinetic measurements (torque, joint position, velocity) on a computer disk. The EMG data were low-pass filtered (15 Hz using a Butterworth fourth-order zero-lag filter), and the root mean square (RMs) was calculated for three different RoMs from the original signal: for the entire range of motion (FULLEMG), a RoM

of 20° (20EMG – 60–80° for knee extension, 40–60° for

knee flexion), and a RoM of 10° around peak torque (10EMG). These RoMs were selected to verify the

differ-ences in EMG determined by different joint positions (see Fig. 1 as an example).

Data are presented as mean ± standard deviation (sD) for torque measurements and mean ± standard error of mean (sEM) for EMG activity. A shapiro-Wilks test assessed the normality of distribution for torque (PT and PTANG) and EMG (FULLEMG, 20EMG and 10EMG)

mea-surements. The effects of the type of contraction and the velocity on PT and PTANG were accessed by two-way

analysis of variance (two contraction types [concen-tric × eccen[concen-tric], two velocities [60° · s–1_{× 180° · s}–1_{]) with}

Tukey’s HsD post-hoc test, when applicable. For the EMG measurements, the effects of the RoMs (FULLEMG,

20EMG and 10EMG) were assessed by a non-parametric

test, the Kruskal-Wallis Analysis of Variance. Differe-nces in EMG between concentric and eccentric con-tractions, and between 60° · s–1_{and 180° · s}–1 _were

ac-cessed by the non-parametric Wilcoxon test. To assess the relationships between the EMG measurements (FULLEMG ×20EMG; FULLEMG ×10EMG and 20EMG × 10EMG),

Pearson’s correlation coefficient (r) was used. For all sta-tistical tests, the significance level was set at p 0.05.

Results

For both knee extension and flexion the effects of the type of contraction were analyzed, where concen-tric contractions were found to present lower peak torque (PT) than eccentric contractions at 60° · s–1_and

180° · s–1 ₍_p_{0.01, Tab. 1). For PT}

ANG, there were no

dif-ferences in the contraction velocity, with the only

con-Figure 1. Representative diagram of the different ranges of motion used to calculate maximal EMG activity.

The example was extracted from a subject during

isokinetic knee extension at 60° · s–1_{, showing the torque}

curve, EMG for the vastus lateralis (EMG VL) and the vastus medialis (EMG VM). The peak torque (PT) was achieved at 61° (marked by a thick vertical black line and arrow). The more commonly-used method covered

the entire range of motion (FULLEMG), the second method

was calculated in a 20° fixed window (between 60° and 80° for knee extension – marked between the dotted

vertical lines), and the third method considered peak torque as a reference point, using 10° around this peak torque (in this example from 56° to 66° – marked between

the thin solid vertical lines)

Table 1. Mean ± sD peak torque (PT) and the angle of

peak torque (PTANG) during maximal isokinetic concentric

contractions at 60° · s–1_{(cON-60) and 180° · s}–1

(cON-180), and eccentric contractions at 60° · s–1

(Ecc-60) and 180° · s–1_(Ecc-180)

PT (Nm) PTANG (°) PT_(min)ANG PT_(max)ANG

EX T EN sOR s cON-60 234 ± 46*† _{63.7 ± 4.6} _54.4 _73.6 cON-180 166.8 ± 38 63.1 ± 5.8 47.3 71.8 Ecc-60 316.2 ± 72 70.7 ± 6.6 56.4 78.2 Ecc-180 317.7 ± 61* 65.7 ± 10.2 39.6 81.2 FL EXO R s cON-60 123.6 ± 22 † _{46.4 ± 9} _29.7 _61.9 cON-180 114 ± 24 55.3 ± 15 39.1 86.4 Ecc-60 183 ± 31 41.2 ± 7 23.8 54.8 Ecc-180 193 ± 34* 42.4 ± 12* 31.5 82.8

* denotes significant difference in relation to cON-180

(p 0.05)

†_{denotes significant difference in relation to EXc-60}

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traction that showed differences was during knee flex-ion, with higher PTANG during cON-180 compared to

Ecc-180 (p 0.05). The PTANG for knee extension and

flexion was predominantly reached within the defined RoM of 20EMG. However, there were cases in which

PTANG was not achieved at this RoM.

In general, the different ranges of RoM had no ef-fect in determining the EMG respective to PT for both knee extension (Fig. 2) and flexion (Fig. 3), regardless of the type of contraction and velocity. Except for the VL muscle during cON-180,20EMG was higher than

FULLEMG and 10EMG (p 0.05). However, the coefficient

of variation was frequently lower for FULLEMG in

com-parisonto the other measurements (Tab. 2). Qualitative comparisons between these coefficients of variation showed higher variations for VM and sT muscles for all EMG measurements. correlations between the dif-ferent EMG measurements presented generally good to strong coefficients of correlation (p 0.05) for the knee extensor muscles (Tab. 3). All muscles presented good and strong correlations between FULLEMG ×20EMG

† denotes significant difference in relation to the full range of motion and 10° (p 0.05)

* denotes significant difference in relation to eccentric contractions at the same velocity (p 0.05)

‡ denotes significant difference in relation to 180° · s–1_{at the same type}

of contraction (p 0.05)

Figure 2. Mean (sEM) root mean square (RMs) for the vastus lateralis (VL) and vastus medialis (VM) during

maximal isokinetic concentric contractions at 60° · s–1

(cON-60) and 180° · s–1_{(cON-180), and eccentric}

contractions at 60° · s–1_{(Ecc-60) and 180° · s}–1_(Ecc-180).

The RMs was calculated considering the full range of motion (white bars), a fixed range of motion of 20° (grey bars) and a fixed range of motion of 10° (black bars)

(p 0.05), regardless of the type of contraction and velocity. Non-significant correlations (p 0.05) were found only for the knee flexor muscles, between FULLEMG

×10EMG and between20EMG × 10EMG.

concentric contractions at 60° · s–1_{presented higher}

EMG activity than eccentric contractions (p 0.05) for all tested muscles, except for VL at 10EMG and for sT at

20EMG. similarly, at 180° · s–1, concentric contractions

presented higher EMG readings than eccentric con-tractions (p 0.05) for all tested muscles except for sT at 20EMG (Fig. 2 and 3). The contraction velocity

af-fected the knee extensor muscles mainly at 10EMG. The

VL and VM muscles presented higher EMG activity during 60° · s–1_{in relation to 180° · s}–1₍_p_{0.05) for}

eccentric contractions at 10EMG (p 0.05) and for

con-centric contractions for VL (p 0.05). In addition, the VL muscle also presented higher EMG activity at 60° · s–1

in relation to 180° · s–1₍_p_{0.05) for eccentric}

contrac-tions at 20EMG (p 0.05). The contraction velocity had

minor effects on the EMG of the knee flexor muscles, regardless of the RoM (Fig. 3). The only significant

dif-* denotes significant difference in relation to the eccentric contractions at the same velocity (p 0.05)

‡ denotes significant difference in relation to 180° · s–1_{at the same type of}

contraction (p 0.05)

Figure 3. Mean (sEM) root mean square (RMs) for the semitendinosus (sT) and the biceps femoris (BF) during

maximal isokinetic concentric contractions at 60° · s–1

(cON-60) and 180° · s–1_{(cON-180), and eccentric}

contractions at 60° · s–1_{(Ecc-60) and 180° · s}–1_(Ecc-180).

The RMs was calculated considering the full range of motion (white bars), a fixed range of motion of 20° (grey bars) and a fixed range of motion of 10° (black bars)

(16)

ference was for the sT during concentric contractions at FULLEMG.

Discussion

The purpose of this study was to compare EMG ac-tivity at three different ranges of motion: (1) the more commonly used full range of motion, (2) at a fixed RoM of 20° at the point where PT is present (20EMG),

and (3) at a fixed RoM of 10° around the point where PT was found (10EMG). We expected some differences in

EMG due to the changes in the RoM. For instance, if the PTANG is found at 55° of knee extension, the EMG

related to PTANG is not included for 20EMG (between 60°

to 80°). This fact could cause differences between mea-surements, especially in relation to 10EMG, which always

contains the EMG for PTANG (within a range of 5° below

and 5° above peak torque). contrary to our first hy-pothesis, no substantial differences were found among FULLEMG, 20EMG and 10EMG except for only one

meas-urement (see Results). In this way, the EMG respective to the peak torque produced during isokinetic contrac-tions may be successfully obtained regardless of the RoM used, although caution needs to be exercised with res-pect to data variability.

In general, the subjects presented their PTANG within

the range that had been verified in previous studies [7]

and concurrent to the results expected for such torque measurements. These expected results include higher PT during eccentric contractions, higher PT under lower velocity during concentric knee extension [7, 10], and minimal effects of both contraction type and velocity on PTANG [12]. However, Ecc-180 presented higher

variability, caused in part by the complexity of per-forming faster isokinetic actions even after extensive familiarization procedures [5]. Therefore, avoiding the use of full RoM for eccentric contractions might provide less reliable results since the variability is not only higher but the probability that a given patient produces PT outside this range is also high. However, further inves-tigation is needed to confirm this theory.

Higher EMG activity during concentric contractions is related to reduced input to the motor cortex and/or increases in peripheral facilitation during eccentric con-tractions [12, 13]. In the same way, as was previously verified [2, 14], there were minimal changes in EMG related to movement velocity. This issue as of yet has no consensus in the literature on the subject, primarily because of the wide range of studied velocities [7].

With respect to the effects of RoM on EMG, motor unit recruitment is increasingly impaired as it reaches more extreme RoM positions (excessive flexion or ex-tension), where EMG activity is decreased in order to protect the knee joint against high toque [4, 7]. This Table 2. coefficient of variation (cV) of the root mean square (RMs) for the vastus lateralis (VL), vastus medialis (VM),

semitendinosus (sT) and biceps femoris (BF) during maximal isokinetic concentric contractions at 60° · s–1_(cON-60)

and 180° · s–1_{(cON-180), and eccentric contractions at 60° · s}–1_{(Ecc-60) and 180° · s}–1_{(Ecc-180). The RMs was calculated}

considering the full range of motion (F), a fixed range of motion of 20° (20°) and a fixed range of motion of 10° (10°)

VL VM sT BF F 20° 10° F 20° 10° F 20° 10° F 20° 10° cON-60 23% 28% 21% 39% 43% 43% 45% 45% 52% 25% 25% 24% EXc-60 25% 38% 30% 48% 51% 46% 31% 36% 43% 22% 23% 31% cON-180 25% 29% 45% 41% 44% 58% 45% 55% 59% 30% 38% 57% EXc-180 28% 39% 38% 46% 52% 60% 39% 51% 40% 29% 53% 46%

Table 3. Pearson’s correlation coefficient (r) for EMG RMs of the vastus lateralis, vastus medialis, semitendinosus and biceps

femoris muscles during maximal isokinetic concentric contractions at 60° · s–1_{(cON-60) and 180° · s}–1_{(cON-180), and}

eccentric contractions at 60° · s–1_{(Ecc-60) and 180° · s}–1_{(Ecc-180). The correlation coefficient was calculated between the}

full range of motion and a fixed range of motion of 20° (F × 20°), between the full range of motion and a fixed range

of motion of 10° (F × 10°) and between a fixed range of motion of 20° and a fixed range of motion of 10° (10° × 20°)

Vastus lateralis Vastus medialis semitendinosus Biceps femoris

F × 2 0° F × 10 ° 20 ° × 1 0° F × 2 0° F × 10 ° 20 ° × 1 0° F × 2 0° F × 10 ° 20 ° × 1 0° F × 2 0° F × 10 ° 20 ° × 1 0° cON-60 0.87* 0.72* 0.59* 0.76* 0.84* 0.69* 0.77* 0.38 0.34 0.74* 0.39 0.55* Ecc-60 0.98* 0.98* 0.96* 0.87* 0.92* 0.80* 0.94* 0.61* 0.73* 0.76* 0.74* 0.69* cON-180 0.97* 0.92* 0.92* 0.88* 0.69* 0.85* 0.96* 0.61* 0.71* 0.87* 0.45 0.52† Ecc-180 0.73* 0.77* 0.89* 0.75* 0.66* 0.56† _0.83* _0.45 _0.42 _0.82* _0.25 _0.05

University School of Physical Education in Wrocław University School of Physical Education in Poznań University School of Physical Education in

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