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  • Author: João Brito x
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Pedro Figueiredo, George P. Nassis and João Brito

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

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Ciro José Brito, Aendria Fernanda Castro Martins Roas, Igor Surian Souza Brito, João Carlos Bouzas Marins, Claudio Córdova and Emerson Franchini

The aim of this study was to investigate the methods adopted to reduce body mass (BM) in competitive athletes from the grappling (judo, jujitsu) and striking (karate and tae kwon do) combat sports in the state of Minas Gerais, Brazil. An exploratory methodology was employed through descriptive research, using a standardized questionnaire with objective questions self-administered to 580 athletes (25.0 ± 3.7 yr, 74.5 ± 9.7 kg, and 16.4% ± 5.1% body fat). Regardless of the sport, 60% of the athletes reported using a method of rapid weight loss (RWL) through increased energy expenditure. Strikers tend to begin reducing BM during adolescence. Furthermore, 50% of the sample used saunas and plastic clothing, and only 26.1% received advice from a nutritionist. The authors conclude that a high percentage of athletes uses RWL methods. In addition, a high percentage of athletes uses unapproved or prohibited methods such as diuretics, saunas, and plastic clothing. The age at which combat sport athletes reduce BM for the first time is also worrying, especially among strikers.

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Júlio A. Costa, João Brito, Fábio Y. Nakamura, Eduardo M. Oliveira and António N. Rebelo

Purpose: To assess the sensitivity of nocturnal heart-rate-variability-monitoring methods to the effects of late-night soccer training sessions in female athletes. Methods: Eleven female soccer players competing in the first division of the Portuguese soccer league wore heart-rate monitors during sleep at night throughout a 1-wk competitive in-season microcycle, after late-night training sessions (n = 3) and rest days (n = 3). Heart rate variability was analyzed through “slow-wave sleep episode” (10-min duration) and “hour by hour” (all the RR intervals recorded throughout the hours of sleep). Training load was quantified by session rating of perceived exertion (281.8 [117.9] to 369.0 [111.7] arbitrary units [a.u.]) and training impulse (77.5 [36.5] to 110.8 [31.6] a.u.), added to subjective well-being ratings (Hopper index = 11.6 [4.4] to 12.8 [3.2] a.u.). These variables were compared between training and rest days using repeated-measures analysis of variance. Results: The log-transformed slow-wave sleep-episode cardiac autonomic activity (lnRMSSD [natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal RR intervals] varying between 3.92 [0.57] and 4.20 [0.60] ms; ηp2=.16; 95% confidence interval, .01–.26), lnHF (natural logarithm of high frequency), lnLF (natural logarithm of low frequency), lnSD1 (natural logarithm of short-term beat-to-beat variability), and lnSD2 (natural logarithm of long-term beat-to-beat variability), and the nontransformed LF/HF were not different among night-training session days and rest days (P > .05). Considering the hour-by-hour method (lnRMSSD varying between 4.05 [0.35] and 4.33 [0.32] ms; ηp2=.46; 95% confidence interval, .26–.52), lnHF, lnLF, lnSD1, and lnSD2 and the nontransformed LF/HF were not different among night-training session days and rest days (P > .05). Conclusion: Late-night soccer training does not seem to affect nocturnal slow-wave sleep-episode and hour-by-hour heart-rate-variability indices in highly trained athletes.

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Júlio A. Costa, João Brito, Fábio Y. Nakamura, Eduardo M. Oliveira, Ovidio P. Costa and António N. Rebelo

Purpose: To analyze whether exercise training conducted at night disturbs sleep and affects nocturnal cardiac autonomic control in high-level female athletes. Methods: A total of 18 high-level female soccer players (mean [SD] age 20.4 [2.1] y) wore actigraphs and heart-rate (HR) monitors during night sleep throughout night training days (n = 8) and resting days (n = 8), for 3 consecutive weeks. This was a longitudinal study that measured internal training load, sleep, nocturnal cardiac autonomic activity, and well-being ratings prior to training sessions. Results: Training load varied across training days (eg, training impulse range, mean [SD]; effect size, ES [95% confidence interval]: 72.9 [18.8] to 138.4 [29.6] a.u.; F 4,62 = 32.331; ηp2=.673 [.001–.16], large effect; P < .001). However, no differences in subjective well-being ratings were observed, although ES was large. Total sleep time (training days vs resting days: 07:17 [00:47] h vs 07:51 [00:42] h; ES = 0.742 [0.59–0.92], P = .005; moderate effect) and sleep-onset time (00:58 [00:19] h vs 00:44 [00:16] h; ES = 0.802 [0.68–0.94], P = .001; moderate effect) were negatively affected after night training. In addition, small effects were detected for wake-up time, time in bed, and sleep latency (P > .05). No differences were detected in HR variability during sleep (range of lnRMSSD: 4.3 [0.4] to 4.5 [0.4] ln[ms] vs 4.6 [0.3] to 4.5 [0.4] ln[ms]; F 3,52 = 2.148; P > .05; ηp2=.112 [.01–.25], medium effect), but HR during sleep was significantly higher after training days (range of HR: 56 [4] to 63 [7] beats/min vs 54 [4] to 57 [6] beats/min; F 2,32 = 15.956; P < .001; ηp2=.484 [.20–.63], large effect). Conclusion: Overall, the results indicate that exercise training conducted at night may disturb sleep and affect HR, whereas limited effects can be expected in HR variability assessed during sleep in high-level female soccer players.