Fibromyalgia (FM) is associated with a number of comorbidities, including chronic widespread pain, fatigue and non-restorative sleep. Evidence has shown that FM is closely associated with overweight and obesity. The objective of the present study was to investigate the relationship between obesity and sleepiness in women with FM. A total of 100 adult female patients with a prior medical diagnosis of FM participated in the study. Body mass, height and waist circumference were measured, and body mass index (BMI) was calculated. The diet quality was evaluated by the Healthy Eating Index. Subjective analyses of daytime sleepiness [Epworth Sleepiness Scale (ESS)] and sleep quality (Pittsburgh Sleep Quality) were performed. An obesity rate of 41 % was found in all women (56.1 % were sleepy and 43.9 % were not, p = 0.04). Obese women showed a greater level of sleepiness when compared with non-obese (10.2 and 7.0, respectively, p = 0.004). Sleepy women showed a greater weight gain after the diagnosis of FM when compared with non-sleepy women (11.7 and 6.4 kg, respectively, p = 0.04). A positive and significant correlation between BMI and sleepiness (r = 0.35, p = 0.02) was also found. In multivariate logistic regression, moderate or severe sleepiness (ESS >12) was associated with obesity (odds ratio 3.44, 95 % CI 1.31-9.01, p = 0.04). These results demonstrate an important association between sleepiness and FM, suggesting that the occurrence of obesity may be involved with sleepiness in these patients.

Background Machine learning investigates how computers can automatically learn. The present study aimed to predict dietary patterns and compare algorithm performance in making predictions of dietary patterns. Methods We analysed the data of public employees (n = 12,667) participating in the Brazilian Longitudinal Study of Adult Health (ELSA‐Brasil). The K‐means clustering algorithm and six other classifiers (support vector machines, naïve Bayes, K‐nearest neighbours, decision tree, random forest and xgboost) were used to predict the dietary patterns. Results K‐means clustering identified two dietary patterns. Cluster 1, labelled the Western pattern, was characterised by a higher energy intake and consumption of refined cereals, beans and other legumes, tubers, pasta, processed and red meats, high‐fat milk and dairy products, and sugary beverages; Cluster 2, labelled the Prudent pattern, was characterised by higher intakes of fruit, vegetables, whole cereals, white meats, and milk and reduced‐fat milk derivatives. The most important predictors were age, sex, per capita income, education level and physical activity. The accuracy of the models varied from moderate to good (69%–72%). Conclusions The performance of the algorithms in dietary pattern prediction was similar, and the models presented may provide support in screener tasks and guide health professionals in the analysis of dietary data.

Objetivos: avaliar a prevalência da insegurança alimentar e nutricional e fatores associados de estudantes, moradores do Conjunto Residencial da Universidade de São Paulo - CRUSP. Métodos: este estudo transversal avaliou por meio de questionário virtual 84 estudantes no período de junho a julho de 2020. Utilizou-se a versão curta (7 questões) da Escala Brasileira de Insegurança Alimentar para definição da variável desfecho: Insegurança Alimentar (IA). Variáveis sociodemográficas e econômicas, e questões relacionadas à ingestão alimentar foram utilizadas para descrever a amostra e associação com a IA, avaliados por meio do teste de qui-quadrado. Resultados: a prevalência de IA foi de 84,5%, associada a renda insuficiente (p<0,001), número menor a 3 refeições por dia (p:0,002) e relato de não preparo de refeições em casa (p:0,048). Aqueles estudantes que não conseguiam utilizar a cozinha comunitária também tiveram maior proporção de IA (56,3%). Conclusão: além da renda, a ausência de equipamentos básicos que impedem o preparo de refeições tem contribuído para alta prevalência de IA entre moradores do CRUSP, durante a pandemia do COVID-19. A omissão no cuidado a essa população coloca em risco a saúde e qualidade de vida destes estudantes.

Resumo O objetivo deste artigo é identificar fatores associados à hipertensão arterial sistêmica (HAS) não diagnosticada entre adultos mais velhos no Brasil. Foram avaliados 5.416 participantes hipertensos do Estudo Longitudinal da Saúde dos Idosos Brasileiros (ELSI-Brasil). HAS não diagnosticada foi definida como a presença de pressão arterial (PA) ≥140/90 mmHg sem diagnóstico prévio. Regressão logística foi utilizada para verificar fatores associados à HAS não diagnosticada. No estudo, 19,8% dos hipertensos avaliados não relataram diagnóstico prévio de HAS. Ter entre 60 e 69 anos (OR: 0,68, IC95% 0,55-0,85) e 70 e 79 (OR: 0,67, IC95% 0,51-0,89), cor preta (OR: 0,67, IC95% 0,49-0,91), ser obeso (OR: 0,51, IC95% 0,40-0,65), ter uma doença crônica (OR: 0,54, IC95% 0,44-0,66) ou mais (OR: 0,32, IC95% 0,25-0,42) e consultas no último ano (OR: 0,47, IC95% 0,38-0,58) foram fatores associados a menores chances de HAS não diagnosticada, enquanto sexo masculino (OR: 1,27, IC95% 1,05-1,54), baixo peso (OR: 1,33, IC95% 1,00-1,78) e consumo de álcool (OR: 1,36, IC95% 1,09-1,68) elevaram as chances para apresentar a doença não diagnosticada. As características identificadas nesse estudo devem ser observadas em serviços de saúde, ampliando o diagnóstico precoce e prevenindo a progressão da PA e suas futuras consequências.

To examine changes in body mass index (BMI) among older Brazilian adults and associated factors. Longitudinal, population-based study, conducted in São Paulo, Brazil. Adults aged 60 years or over (n = 1,796) from the first wave of data collection from the Health, Well-Being, and Aging Study (SABE Project) conducted from 2000 to 2010. Repeated mixed-effects linear regression was used to analyze longitudinal changes in BMI and to examine whether sociodemographic characteristics, health conditions, and social behaviors were associated with these changes. Mean BMI decreased after 70 years. Men had lower BMI than women (β = -1.86, 95%CI: -2.35; -1.37). Older adults who consumed alcohol (β = 0.30, 95%CI: 0.06; 0.54), had more than one chronic disease (β = 0.19, 95%CI: 0.26; 0.72) and who did not perform physical activity (β = 0.56, 95%CI: 0.38; 0.74) had higher BMI. Subjects who smoked (β = -0.40, 95%CI: -0.76; -0.04) and who reported having eaten less food in recent months (β = -0.48, 95%CI: -0.71; -0.24) had lower BMI. In older Brazilians, several sociodemographic characteristics, health conditions, and behaviors predict BMI. Increasing prevalence of chronic diseases and growing sedentary behaviors in Brazil may have detrimental effects on BMI at older ages.