Scheduled fight affect mood states of MMA athletes

[EN] Among the combat sports, mixed martial arts (MMA) has stood out over the past two decades

Mood states and self-rated health of Brazilian jiu-jitsu fighters in competition

[EN] Brazilian Jiu-Jitsu (BJJ) athletes are subjected to a large and intensive training load that may cause injuries. These injuries can be detrimental to the physical and mental health of these athletes. In this regard, the aim of this study was to compare the self-reported health and mood states of BJJ athletes. Twenty-three BJJ athletes participated in the study. A questionnaire was used for general characterization of the athletes, which included the self-rated health question, and the Brunel Mood Scale – BRUMS were used. The mood profile was similar to the Iceberg profile. Athletes with regular health had lower vigor than athletes with excellent vigor. Lower vigor in athletes who reported regular health may be related to concern about injury. Specific strength training and careful execution of the technique should be emphasized in training

Efectos y síntomas de deshidratación en atletas de jiu-jitsu brasileño

[ES] Objetivo. Medir la pérdida rápida de peso y los indicadoresurinarios relativos al estado de hidratación, así como evaluar los efectos y síntomas de deshidratación en atletas de jiu-jitsu brasileño (BJJ) en diferentes momentos. Métodos. 17 atletas masculinos de BJJ (edad: 24,4 ± 3,5 años; masa corporal (BM): 76,8 ± 14,6 kg; grasa corporal: 16,6 ± 6,4 %), fueron evaluados entre las 8 y las 9 am en tres momentos diferentes: estado basal(10 días precompetición), medición de la BM y estatura, recolección de muestras de orina y registro de alimentación de las últimas 24 horas; precompetición, medición de BM, muestras de orina y cuestionario de evaluación de pérdida rápida de peso (RWL), y postcompetición (24 h después), día posterior a la competición, con medición de la BM y muestras de orina para análisis de gravedad específica. Resultados. Hubo una tendencia a disminuir la BMpara competir (estado basal: 76,8 ± 14,6 kg, precompetición: 75,4 ± 13,4 kg, postcompetición: 77,3 ± 13,7 kg; p < 0,07), y un 88,2% de atletas utilizaron métodos que aumentan la deshidratación. La mayoría de los atletas estaban deshidratados en los tres momentos del estudio (94,1 %; USG = 1,021 ± 0,005 Usg al inicio, 88,2 %; 1,020 ± 0,007 Usgantes de la competición, 88,2 %; 1,022 ± 0,008 Usg en la postcompetición), mostrando síntomas asociados al RWL(82,4% aumento de la frecuencia cardíaca, 52,9% dolor de cabeza, 47,1% sofocos, 41,2% náuseas, 41,2% desorientación y 29,4% mareos). Conclusiones. Se observó que los atletas de BJJ tuvieron una rápida pérdida de peso precompetitiva y síntomas asociados a la deshidratación. Se recomienda que se promuevan medidas educativas para inhibir el RWL entre los atletas de BJJ, para lo cual es necesaria la participación de profesionales de la salud y de las organizaciones que regulan el deporte (federaciones y confederaciones).[EN] Objective. To measure rapid weight loss and urinary indicators of hydration status, as well as to assess the effects and symptoms of dehydration in Brazilian jiu-jitsu (BJJ) athletes at different moments. Methods. 17 male BJJ athletes (aged: 24.4 ± 3.5 years; body mass (BM): 76.8 ± 14.6 kg; body fat: 16.6 ± 6.4%), were evaluated between 8 and 9 am at three different moments: Baseline (10 days pre- competition), measurement of BM and stature, collection of urine samples, and food record of the previous 24 hours; pre-competition, measurement of BM, urine samples, and questionnaire to assess rapid weight loss (RWL), and post-competition(24h after), day after competition, with measurement of BM, and urine samples for analysis of specific gravity. Results.There was a tendency to decrease BM to compete (baseline: 76.8 ± 14.6 kg, pre-competition: 75.4 ± 13.4 kg, post-competition: 77.3 ± 13.7 kg; p < 0.07), with 88.2% of athletes using methods that increase dehydration. The majority of athletes were dehydrated at the three time points of the study (94.1%; USG = 1.021 ± 0.005 Usg at baseline, 88.2%; 1.020 ± 0.007 Usg at pre-competition, 88.2%; 1.022 ± 0.008 Usg on the post-competition day), accompanied by symptoms associated with RWL (82.4% increased heart rate, 52.9% headache, 47.1% hot flashes, 41.2% nausea, 41.2% disorientation, and 29.4% dizziness). Conclusions.Rapid pre-competitive weight loss and symptoms associated with dehydration were observed in the BJJ athletes. It is recommended that educational measures are promoted to inhibit RWL among BJJ athletes, requiring the engagement of health professionals and organizations that govern the sport (federations and confederations).[PT] Objetivo. Medir a rápida perda de peso e indicadores urinários do estado de hidratação, bem como avaliar os efeitos e sintomas de desidratação em atletas de Brazilian jiu-jitsu (BJJ) em diferentes momentos. Métodos. 17 atletas de Jiu-Jitsu do sexo masculino (idade: 24,4 ± 3,5 anos; massa corporal (MC): 76,8 ± 14,6 kg; gordura corporal: 16,6 ± 6,4%), foram avaliados entre 8 e 9 horas da manhã em três momentos diferentes: Linha de base (10 dias antes da competição), aferição de massa corporal e estatura, coleta de urina e registro alimentar das últimas 24 horas; pré-competição, mensuração de MC, amostras de urina e questionário para avaliação da perda rápida de peso (RWL), e pós-competição (24h após), dia seguinte à competição, com mensuração de MC, e amostras de urina para análise de gravidade específica. Resultados. Houve tendência de diminuição da MC para competir (basal: 76,8 ± 14,6 kg, pré-competição: 75,4 ± 13,4 kg, pós-competição: 77,3 ± 13,7 kg; p < 0,07), com 88,2% dos atletas usando métodos que aumentam a desidratação. A maioria dos atletas estava desidratada nos três momentos do estudo (94,1%; USG = 1,021 ± 0,005 Usg no início, 88,2%; 1,020 ± 0,007 Usg no pré-competição, 88,2%; 1,022 ± 0,008 Usg no pós- dia dacompetição), acompanhada de sintomas associados a RWL (82,4% aumento da frequência cardíaca, 52,9% dor de cabeça, 47,1% ondas de calor, 41,2% náusea, 41,2% desorientação e 29,4% tontura). conclusões. Perda de peso pré-competitiva rápida e sintomas associados à desidratação foram observados nos atletas de Jiu-Jitsu. Recomenda-se que medidas educativas sejam promovidas para inibir o RWL entre os atletas de Jiu-Jitsu, exigindo o engajamento dos profissionais de saúde e dos órgãos que regem o esporte (federações e confederações)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost