Dra. Troncoso-Pantoja, Claudia

Background: Aging causes an involution of anthropometric and health indices that can affect physical fitness. Aim: To determine the influence of anthropometric and health indices on the physical fitness of elderly women. Material and Methods: Anthropometric parameters, serum lipids, blood glucose and physical fitness evaluated using Senior Fitness Test, were assessed in 140 women aged 70 ± 5 years. The association between parameters was analyzed using Pearson's correlation coefficient and multiple regression models. Results: In the regression models serum lipids and the suprailiac skinfold were significant predictors of the up and go test (R2= 0.48). HDL cholesterol and the level of physical activity were predictors of the two minutes step test (R2= 0.31). Serum lipids, suprailiac skinfold and age were predictors of the back-scratch test (R2= 0.41). Fasting blood glucose and HDL cholesterol were predictors of the chair sit and reach test (R2= 0.24). Serum lipids and body mass index were predictors of the arm curl test (R2= 0.37). Body mass index and serum lipids were predictors of the chair stand test (R2= 0.49). Conclusions: Anthropometric variables, serum lipid levels and blood glucose were predictors of different physical fitness parameters in these women.

Background: Although cardiovascular risk factors are associated with an impaired cognitive function, the impact of diabetes on cognitive function in Chilean adults is unknown. Aim: To investigate the association of diabetes or family history of the disease with cognitive impairment in older adults. Materials and Methods: Data from the 2009-2010 Chilean National Health Survey including 1,384 participants aged ≥ 60 years were included in this study. A score below 13 points for the Mini Mental State Examination (MMSE) was considered an indication of cognitive impairment. Logistic regression analyses were performed to assess the association between MMSE, diabetes and family history of the disease. Results: Cognitive impairment increased with age (Odds ratio (OR): 1.83 [95% confidence intervals (CI): 1.53; 2.19], p < 0.01, per 5 years increment in age). This trend was greater in individuals with diabetes (OR: 2.37 [95% CI: 1.68; 3.35], p < 0.01) compared to those without the disease. A similar trend was identified among individuals with a family history of diabetes compared to those without. Conclusions: Older adults with diabetes are more susceptible to develop cognitive impairment.

The purpose of this study was to investigate healthy lifestyle behaviours across age categories in the older population in Chile. Data from 1390 older adults (≥60 years), in the 2009–2010 Chilean National Health Survey were analyzed. We derived the following age categories: 60–65, 66–70, 71–75, 76–80 and >80 years. The associations between age and compliance with healthy lifestyle behaviours (smoking, sitting time, physical activity, sleep duration and intake of salt, alcohol, fruit and vegetables) were investigated using logistic regression. The probability of meeting the guidelines for alcohol intake (OR trend: 1.35 [95% CI: 1.11; 1.64], p = 0.001) and smoking (OR trend: 1.23 [95% CI: 1.13; 1.33], p < 0.0001) increased with age, whereas spending <4 h per day sitting time or engaging in at least 150 min of physical activity per week or sleep on average between 7 and 9 h per day were less likely to be met with increasing age (OR trend: 0.77 [95% CI: 0.71; 0.83], p < 0.000; OR trend: 0.73 [95% CI: 0.67; 0.79], p < 0.0001, and OR trend: 0.89 [95% CI: 0.82; 0.96], p = 0.002, respectively). No significant trend across age categories was observed for fruit and vegetables, and salt intake. The probability of meeting at least 3 out of 7 healthy lifestyle behaviours across the age categories was also lower in older age categories compared to those aged 60 to 65 years. Overall, in older adults the probability of having the healthy lifestyle behaviours of physical activity, sitting time and sleeping behaviours was low but not for smoking or alcohol consumption. With an increasingly ageing population, these findings could inform stakeholders on which lifestyle behaviours could be targeted in the older adults and therefore which interventions should take place to promote healthy ageing.