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.

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.

Obesity and sleepiness in women with fibromyalgia.

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.