HbA1c shows good correlation with regular post-prandial pre-exercise blood glucose measures in active individuals with type 2 diabetes mellitus

This study aimed to verify the correlation between annual average glycated hemoglobin (HbA1c) and mean capillary blood glucose (BG) with post-prandial and post-exercise in active adults with type 2 diabetes mellitus (T2D) participants of a diabetes education program with emphasis on supervised exercise

Adherence barriers and facilitators to a diabetes education program : the user’s point of view

Este estudo objetiva identificar barreiras e facilitadores da adesão a um programa para o tratamento da Diabetes Mellitus tipo 2 (DM2). O Programa Doce Desafio (PDD) é contínuo, de base anual; regular, oferecido desde 2001; multidisciplinar, com exercícios físicos supervisionados, alimentação, autocuidado, uso de medicamentos e aspectos psicossociais; multiprofissional, com profissionais da farmácia, nutrição, educação física, medicina e enfermagem; multiestratégico, com palestras, debates, jogos, oficinas, discussões, e práticas de exercício físico supervisionado; comunitário, incluindo a atenção básica; e intersetorial, entre Universidade e Serviço; e realiza sessões de 120 minutos duas vezes por semana, em três locais do Distrito Federal. A pesquisa foi qualitativa, exploratória e baseada na técnica “roda de conversa”. Todos os 132 frequentadores do programa foram convidados e 55 sujeitos aceitaram participar da pesquisa. Cada unidade de atendimento sediou um encontro mediado pelo pesquisador, utilizando roteiro semiestruturado, para estabelecer um diálogo sobre as barreiras e facilitadores da adesão ao PDD. Todas as falas foram gravadas, transcritas e submetidas à análise de conteúdo. Das falas emergiram seis facilitadores à adesão: motivação para aprender; incentivo familiar; interação afetiva; melhoria da saúde e qualidade de vida; prazer da atividade física; e orientação profissional. E, quatro barreiras: desgaste no preenchimento de formulários; complicações de saúde; dificuldades financeiras; e problemas familiares. Educação multidisciplinar, exercícios físicos orientados e acompanhamento humanizado foram diferenciais do PDD em relação a outras vivências no tratamento da DM2. Falta avançar na capacitação profissional e sensibilização dos participantes para que as avaliações clínicas se tornem menos exaustivas e mais acessíveis.This study aims to identify barriers and facilitators of adherence to a program for the treatment of type 2 diabetes mellitus (DM2). The Sweet Challenge Program (PDD) is continuous, in annual basis; regular, offered since 2001; multidisciplinary, with supervi-sed exercise, nutrition, self-care, use of medication and psychoso-cial aspects; multiprofessional, with pharmacy, nutrition, physical education, medicine and nursing professionals; multistrategic, with lectures, debates, games, workshops, discussions, and supervised exercise practices; community-based, including primary care; in-tersectoral, among University and Service; and offers 120 minutes sessions twice a week, in three places of the Federal District. The research was qualitative, exploratory and based on “conversation circle” technic. All 132 program attendees were invited and 55 accepted to participate. Each local hosted a meeting mediated by the researcher, using semi-structured script, to establish a dialogue about barriers and facilitators of adherence to PDD. All speeches were recorded, transcribed and subjected to content analysis. From speech emerged six facilitators to adherence: motivation to learn; family encouragement; affective interaction; health and quality of life improvement, physical activity enjoyment, and professional guidance. And, four barriers: form fillings; health complications, financial difficulties, and family problems. Multidisciplinary edu-cation, guided physical exercises and humanized follow-up were PDD’s differential in relation to other experiences in the treatment of DM2. There is need to progress in professional training and participants sensitization so that clinical evaluations become less exhausting and more accessible

Determination of glycemic index and glycemic load of typical northeastern preparations / Determinação de índice glicêmico e carga glicêmica de preparações típicas do Nordeste

Introduction: One of the important factors for a normal healthy diet is the glycemic index (GI) and glycemic load (CG) of foods, since diets with inadequate GI and CG seem to be directly related to the increase in the prevalence of chronic non-communicable diseases. Objective: To determine the GI and CG of three typical Northeastern preparations. Methodology: An experimental, quantitative, descriptive and analytical study was carried out. The sample size was convenience (n=10), however, considering the recommendations of the FAO / WHO Expert Consultation (1998). For the determination of GI and CG, an adaptation of the FAO / WHO Expert Consultation protocol (1998), which consisted in the standardization of a 50 g portion of the tested preparations, but considering a minimum of 25 g of glycemic carbohydrate. The volunteers were fasted for 10 hours the night before the samples and had capillary glycemia measured at the intervals of 0, 15, 30, 45, 60, 90, 120 minutes after consumption of the standard food and the proposed preparations, taken from the "Brazilian Regional Food Guide" of the Ministry of Health (2015), being tapioca with maracuja jelly, seriguela cake and macaxeira bread. Results: Tapioca preparations with passionfruit jelly, seriguela cake and macaxeira bread presented high glycemic index and glycemic load, as evidenced in the analyzes performed. Conclusion: The objective of this work was reached, since the GI and CG of the three proposed preparations were determined, contributing to the expansion of nutritional information and supporting the idea of food and nutritional education

Translation and validation of the caffeine expectancy questionnaire in Brazil (CaffEQ-BR)

Caffeine is the world’s most commonly used stimulant of the central nervous system. Caffeine is present in coffee and other beverages such as tea, soft drinks, and cocoa-based foods. The caffeine expectancy questionnaire was developed to investigate the effects of caffeine expectations and thus contribute to knowledge about its usage and subjective effects (response expectancies). This study aimed to evaluate caffeine expectation psychometrically in a sample of the Brazilian population. The original version of the “Caffeine Expectancy Questionnaire (CaffEQ)” was translated and validated into Brazilian-Portuguese and adapted to Brazilian culture to be used in the Brazilian adult (19–59 y) population. After the translation and back-translation processes of the original CaffEQ questionnaire, the content and semantic validation were performed by a group of experts. The Brazilian-Portuguese version of the questionnaire consists of 47 items, in seven factors, which assess subjective perceptions about the effects of caffeine. Interobserver reproducibility and internal consistency of the questionnaire were tested with a convenience sample (n = 50) of Brazilian adult consumers of caffeine sources, who completed the Brazilian CaffEQ (CaffEQ-BR) on two occasions separated by 24 h. All of the 47 questions were adequate regarding reliability, clarity, and comprehension. Psychometric properties could be replicated consistently. Appropriate internal consistency and validation were confirmed by Cronbach’s alpha (α) 0.948, and an intraclass correlation coefficient of 0.976 was observed. The CaffEQ-BR was applied using a web-based platform to a convenience sample of Brazilian adults from all 27 Brazilian states (n = 4202 participants), along with measures of sociodemographic and caffeine consumption data. Factor validity was verified by confirmatory factor analysis. The seven factors presented a good fit for Root Mean Square Error of Approximation—RMSEA = 0.0332 (95% CI: 0.0290–0.0375). By confirming the validity and reliability of CaffEQ-BR, a useful tool is now available to assess caffeine expectations in the Brazilian adult population

Can the Brazilian caffeine expectancy questionnaires differentiate the CYP1A2 and ADORA2A gene polymorphisms? : an exploratory study with Brazilian athletes

This study investigated the ability of the Brazilian Caffeine Expectancy Questionnaire (CaffEQ-BR), full and brief versions, to differentiate genetic profiles regarding the polymorphisms of the CYP1A2 (rs 762551) and ADORA2A (rs 5751876) genes in a cohort of Brazilian athletes. Onehundred and fifty participants were genotyped for CYP1A2 and ADORA2A. After the recruitment and selection phase, 71 (90% male and 10% female, regular caffeine consumers) completed the CaffEQ-BR questionnaires and a self-report online questionnaire concerning sociodemographic data, general health status, and frequency of caffeine consumption. The order of completion of the CaffEQBR questionnaires was counterbalanced. The concordance between the full and brief versions of the CaffEQ-BR was analyzed using the intraclass correlation coefficient (ICC). To determine the discriminatory capacity of the questionnaires for genotype, the receiver operating characteristic (ROC) curve was applied for sensitivity and specificity (significance level of 5%). Mean caffeine intake was 244 161 mg day1. The frequency of AA genotypes for CYP1A2 was 47.9% (n = 34) and 52.1% (n = 37) for C-allele carriers (AC and CC). The frequencies of TT genotypes for ADORA2A were 22.7% (n = 15) and 77.3% (n = 51) for C-allele carriers (TC and CC). All CaffEQ-BR factors, for the full and brief versions, were ICCs > 0.75, except for factor 6 (anxiety/negative effects; ICC = 0.60), and presented ROC curve values from 0.464 to 0.624 and 0.443 to 0.575 for CYP1A2 and ADORA2A. Overall, the CaffEQ-BR (full and brief versions) did not show discriminatory capacity for CYP1A2 and ADORA2A gene polymorphisms. In conclusion, the CaffEQ-BR was not able to differentiate genotypes for the CYP1A2 or ADORA2A genes in this group of Brazilian athletes

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