Overweight in Older Adults: A Follow-Up of Fifteen Years of the SABE Survey.

Despite extensive research on overweight and obesity, there are few studies that present longitudinal statistical analyses among non-institutionalized older adults, particularly in low- and middle-income countries. This study aimed to assess the prevalence and factors associated with excess weight in older adults from the same cohort over a period of fifteen years. A total of 264 subjects aged (≥60 years) from the SABE survey (Health, Wellbeing and Aging) in the years 2000, 2006, 2010, and 2015 in the city of São Paulo, Brazil, were evaluated. Overweight was assessed by a BMI of ≥28 kg/m2. Multinomial logistic regression models adjusted for sociodemographic and health data were used to assess factors associated with excess weight. After normal weight, overweight was the most prevalent nutritional status in all evaluated periods: 34.02% in 2000 (95%CI: 28.29-40.26); 34.86% in 2006 (95%CI: 28.77-41.49%); 41.38% in 2010 (95%CI: 35.25-47.79); 33.75% in 2015 (95%CI: 28.02-40.01). Being male was negatively associated with being overweight in all years (OR: 0.34 in 2000; OR: 0.36 in 2006; OR: 0.27 in 2010; and OR: 0.43 in 2015). A greater number of chronic diseases and worse functionality were the main factors associated with overweight, regardless of gender, age, marital status, education, physical activity, and alcohol or tobacco consumption. Older adults with overweight and obesity, a greater number of chronic diseases, and difficulties in carrying out daily tasks required a greater commitment to healthcare. Health services must be prepared to accommodate this rapidly growing population in low- and middle-income countries.

Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms.

The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (p < 0.05). The random forest performed well (r = 0.98, R2 = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.

Factors Contributing to Traffic Accidents in Hospitalized Patients in Terms of Severity and Functionality.

Trauma-related injuries in traffic-accident victims can be quite serious. Evaluating the factors contributing to traffic accidents is critical for the effective design of programs aimed at reducing traffic accidents. Therefore, this study identified which factors related to traffic accidents are associated with injury severity in hospitalized victims. Factors related to traffic accidents, injury severity, disability and data collected from blood toxicology were evaluated, along with associated severity and disability indices with data collected from toxicology on victims of traffic accidents at the largest tertiary hospital in Latin America. One hundred and twenty-eight victims of traffic accidents were included, of whom the majority were young adult men, motorcyclists, and pedestrians. The most frequent injuries were traumatic brain injury and lower-limb fractures. Alcohol use, hit-and-run victims, and longer hospital stays were shown to lead to greater injury severity. Women, elderly individuals, and pedestrians tend to suffer greater disability post-injury. Therefore, traffic accidents occur more frequently among young male adults, motorcyclists, and those who are hit by a vehicle, with trauma to the head and lower limbs being the most common injury. Injury severity is greater in pedestrians, elderly individuals and inebriated individuals. Disability was higher in older individuals, in women, and in pedestrians.

Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods.

Introduction: The aim of the present study was to use cluster analysis and ensemble methods to evaluate the association between quality of life, socio-demographic factors to predict nutritional risk in community-dwelling Brazilians aged 80 and over. Methods: This cross-sectional study included 104 individuals, both sexes, from different community locations. Firstly, the participants answered the sociodemographic questionnaire, and were sampled for anthropometric data. Subsequently, the Mini-Mental State Examination (MMSE) was applied, and Mini Nutritional Assessment Questionnaire (MAN) was used to evaluate their nutritional status. Finally, quality of life (QoL) was assessed by a brief version of World Health Organizations' Quality of Life (WHOQOL-BREF) questionnaire and its older adults' version (WHOQOL-OLD). Results: The K-means algorithm was used to identify clusters of individuals regarding quality-of-life characteristics. In addition, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms were used to predict nutritional risk. Four major clusters were derived. Although there was a higher proportion of individuals aged 80 and over with nutritional risk in cluster 2 and a lower proportion in cluster 3, there was no statistically significant association. Cluster 1 showed the highest scores for psychological, social, and environmental domains, while cluster 4 exhibited the worst scores for the social and environmental domains of WHOQOL-BREF and for autonomy, past, present, and future activities, and intimacy of WHOQOL-OLD. Conclusion: Handgrip, household income, and MMSE were the most important predictors of nutritional. On the other hand, sex, self-reported health, and number of teeth showed the lowest levels of influence in the construction of models to evaluate nutritional risk. Taken together, there was no association between clusters based on quality-of-life domains and nutritional risk, however, predictive models can be used as a complementary tool to evaluate nutritional risk in individuals aged 80 and over.

Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study.

This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008-2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35-74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms-user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88-91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms' performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice.

Clustering analysis and machine learning algorithms in the prediction of dietary patterns: Cross-sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

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.

Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis.

OBJECTIVE: This study aimed to analyze the physical and pulmonary capacities of hospitalized patients with severe coronavirus disease and its correlation with the time of hospitalization and complications involved. METHODS: A total of 54 patients, aged ≥18 years of both sexes, were evaluated 2-4 months after hospital discharge in São Paulo, Brazil. The physical characteristics analyzed were muscle strength, balance, flexibility, and pulmonary function. The K-means cluster algorithm was used to identify patients with similar physical and pulmonary capacities, related to the time of hospitalization. RESULTS: Two clusters were derived using the K-means algorithm. Patients allocated in cluster 1 had fewer days of hospitalization, intensive care, and intubation than those in cluster 2, which reflected a better physical performance, strength, balance, and pulmonary condition, even 2-4 months after discharge. Days of hospitalization were inversely related to muscle strength, physical performance, and lung function: hand grip D (r=-0.28, p=0.04), Short Physical Performance Battery score (r=-0.28, p=0.03), and forced vital capacity (r=-0.29, p=0.03). CONCLUSION: Patients with a longer hospitalization time and complications progressed with greater loss of physical and pulmonary capacities.

Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis

OBJECTIVE: This study aimed to analyze the physical and pulmonary capacities of hospitalized patients with severe coronavirus disease and its correlation with the time of hospitalization and complications involved. METHODS: A total of 54 patients, aged ≥18 years of both sexes, were evaluated 2-4 months after hospital discharge in São Paulo, Brazil. The physical characteristics analyzed were muscle strength, balance, flexibility, and pulmonary function. The K-means cluster algorithm was used to identify patients with similar physical and pulmonary capacities, related to the time of hospitalization. RESULTS: Two clusters were derived using the K-means algorithm. Patients allocated in cluster 1 had fewer days of hospitalization, intensive care, and intubation than those in cluster 2, which reflected a better physical performance, strength, balance, and pulmonary condition, even 2-4 months after discharge. Days of hospitalization were inversely related to muscle strength, physical performance, and lung function: hand grip D (r=−0.28, p=0.04), Short Physical Performance Battery score (r=−0.28, p=0.03), and forced vital capacity (r=−0.29, p=0.03). CONCLUSION: Patients with a longer hospitalization time and complications progressed with greater loss of physical and pulmonary capacities.

Aplicação de algoritmos de machine learning na avaliação do consumo alimentar: resultados da linha de base do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil) / Application of machine learning algorithms in the assessment of food consumption: baseline results from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

Introdução: A avaliação do consumo alimentar permite gerar conhecimento sobre a alimentação de indivíduos e populações, além de identificar os determinantes e tendências no consumo. Com ela é possível planejar ações, orientar serviços e implementar políticas públicas de saúde adequadas as necessidades da população. Com o apoio da tecnologia é possível automatizar algumas etapas do processo de análise de dados, com redução do tempo e recursos necessários, especialmente em grandes grupos. Entretanto, em países como o Brasil, ainda são escassas as aplicações de algoritmos de machine learning na avaliação da dieta. Objetivo: Aplicar algoritmos de machine learning na avaliação do consumo alimentar de servidores públicos em um grande estudo brasileiro. Métodos: Este estudo analisou transversalmente os dados da linha de base do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil). A partir destes dados, para explorar e classificar padrões alimentares, foi utilizado o algoritmo de cluster - K-Means. Na sequência, quatro algoritmos preditivos - Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), K-Nearest Neighbours (Knn) - foram aplicados incluindo variáveis demográficas, socioeconômicas e clínicas para predizer padrões alimentares. Adicionalmente, Sistemas de Recomendações foram construídos com algoritmos de Filtragem Colaborativa Baseada em Usuário e Itens (UBCF / IBCF) para o aconselhamento personalizado de dieta. As análises foram realizadas com a utilização do ambiente R. Resultados: Dois padrões alimentares foram derivados na amostra. O primeiro padrão, rotulado como "Padrão Ocidental", no qual os participantes apresentaram ingestões médias superiores para cereais refinados, feijões, carnes vermelhas e processadas, leite e produtos lácteos com alto teor de gorduras e bebidas adoçadas, quando comparados aqueles incluídos no outro padrão. O segundo padrão, rotulado como "Padrão Prudente", os participantes apresentaram consumo superior de frutas, vegetais, cereais integrais, aves, peixes, leite e produtos lácteos com redução de gorduras. Para a construção dos Sistemas de Recomendações foi fixado o limite de cinco itens, por participante, para evitar recomendações extensas e inespecíficas sobre a dieta (precisão entre 90% [IBCF] e 91% [UBCF]). Conclusão: Através da aplicação de algoritmos de machine learning foi possível realizar a análise de dados sobre o consumo, predizer padrões e personalizar recomendações sobre a dieta. Com o apoio das técnicas utilizadas, é possível subsidiar profissionais na gestão e no planejamento de ações de educação alimentar e nutricional personalizadas.

Introduction: The evaluation of food consumption allows generating knowledge about the diet of individuals and populations, in addition to identifying the determinants and trends in consumption. With it is possible to plan actions, guide services and implement public health policies appropriate to the needs of the population. With the support of technology, it is possible to automate some stages of the data analysis process, reducing the time and resources needed, especially in large groups. However, in countries like Brazil, the applications of machine learning algorithms in diet assessment are still scarce. Objective: Apply machine learning algorithms in the evaluation of food consumption by public servants in a large Brazilian study. Methods: This study cross-sectionally analyzed the baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). From these data, to explore and classify dietary patterns, the cluster algorithm K-Means was used. Next, four predictive algorithms - Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), K-Nearest Neighbors (Knn) - were applied including demographic, socioeconomic and clinical variables to predict dietary patterns. Additionally, Recommendation Systems were built with User- and Items-Based Collaborative Filtering algorithms (UBCF / IBCF) for personalized diet advice. The analyzes were performed using the environment R. Results: Two dietary patterns were derived in the sample. The first pattern, labeled as "Western Pattern", in which the participants had higher average intakes for refined cereals, beans, red and processed meats, milk and dairy products with a high fat content and sweetened drinks, when compared to those included in the other pattern. The second pattern, labeled "Prudent Pattern", participants showed a higher consumption of fruits, vegetables, whole grains, poultry, fish, milk and dairy products with reduced fats. For the construction of the Recommender Systems, a limit of five items was set, per participant, to avoid extensive and unspecific recommendations on the diet (accuracy between 90% [IBCF] and 91% [UBCF]). Conclusion: Through the application of machine learning algorithms, it was possible to perform data analysis on consumption, predict patterns and personalize diet recommendations. With the support of the techniques used, it is possible to subsidize professionals in the management and planning of personalized food and nutrition education actions.

Aposentadoria, alimentação e fatores de risco à saúde no Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil) / Retirement, food and health risk factors in the Longitudinal Study of Adult Health (ELSA-Brazil)

A sociedade brasileira tem passado nas últimas décadas por um intenso processo de envelhecimento. Entretanto, o número de estudos sobre a influência da aposentadoria na alimentação e outros possíveis fatores de risco ainda é baixo. Esta dissertação foi dividida em duas partes. Na primeira, foi analisado o papel da aposentadoria na alimentação, e na segunda, a sua associação com o tabagismo, a prática de atividade física e o consumo excessivo de álcool. A amostra foi composta por 6.529 servidores públicos de 50 a 69 anos de idade, sendo 2.854 homens e 3.675 mulheres, provenientes do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil), um estudo de coorte realizado em seis centros de ensino superior no Brasil. O Índice de Qualidade da Dieta-Revisado (IQD-R) foi utilizado para a avaliação do consumo a partir do Questionário de Frequência Alimentar aplicado na primeira onda de avaliações, que ocorreu entre agosto de 2008 e dezembro de 2010. Nas duas análises, foram utilizados modelos de regressão logística com efeitos fixos por centro de investigação e ajuste por variáveis sociodemográficas e de saúde. Os resultados mostraram que a aposentadoria esteve associada com uma dieta de melhor qualidade apenas entre os homens (OR 1,70; IC95 por cento : 1,04-2,76). Foi encontrada também uma associação positiva para homens com cônjuge também aposentado (OR 2,24; IC95 por cento : 1,01-4,95). Quanto aos demais fatores analisados, entre os homens foi encontrada uma associação da aposentadoria com maior prática de atividade física (OR 1,73; IC95 por cento : 1,08-2,78) e neutra com tabagismo e consumo de álcool. Entre as mulheres, foi encontrada associação da aposentadoria com maior prática de atividade física apenas quando o cônjuge não estava aposentado (OR 2,35; IC95 por cento : 1,20-4,64). Este estudo apresenta análises e resultados novos sobre a relação entre aposentadoria e fatores de risco como alimentação e atividade física, essenciais para a preservação da saúde e da qualidade de vida durante o processo de envelhecimento

Brazil has undergone an intense aging process in recent decades. However, the number of studies on the influence of retirement on individual diet and other possible risk factors is still low. This dissertation was divided in two parts. In the first, we analyzed the role of retirement in diet, and in the second, its association with smoking, physical activity and excessive alcohol consumption. The sample consisted of 6,529 public servants from 50 to 69 years old, of which 2,854 were men and 3,675 women. Data was obtained from the Longitudinal Study of Adult Health (ELSA-Brazil), a cohort study with civil servants from six Brazilian higher education centers. The Diet-Revised Quality Index (IQD-R) was used to evaluate the intake from the Food Frequency Questionnaire applied in the first wave, which occurred between 2008 and 2010. We used logistic regression with fixed effects per research center, adjusted for sociodemographic and health variables. Retirement was associated with a better quality diet only among men (OR 1,70; CI95 per cent : 1,04-2,76). There was also a positive association for men with a retired spouse (OR 2,24; CI95 per cent : 1,01-4,95). Regarding the other factors analyzed, for men we found an association of retirement with greater physical activity practice (OR 1,73; CI95 per cent : 1,08-2,78) and neutral with smoking and alcohol consumption. Among women, we found an association of retirement with greater physical activity when spouse was not retired (OR 2,35; CI95 per cent : 1,20-4,64). The study presents new results on the relationship between retirement and risk factors such as diet and physical activity, which are essential for the preservation of health and quality of life during the aging process