The Nutritional Status of Long-Term Institutionalized Older Adults Is Associated with Functional Status, Physical Performance and Activity, and Frailty

[EN] Among older adults living in long-term nursing homes (LTNHs), maintaining an adequate functional status and independence is a challenge. Whilst a poor nutritional status is a potential risk factor for a decreased function in this population, its role is not fully understood. Here, using a transversal multicenter study of 105 older adults living in 13 LTNHs, we analyzed the associations between nutritional status, as measured by the Mini Nutritional Assessment (MNA), and the parameters of functional status, physical performance, physical activity, and frailty as well as comorbidity and body composition. The MNA scores were positively correlated with the Barthel Index, handgrip strength, Short Physical Performance Battery (SPPB) scores, absolute muscle power, and Assessment of Physical Activity in Frail Older People (APAFOP) scores and were negatively correlated with dynamic balance and frailty. In a multiple linear regression model controlling for gender and age, the APAFOP score (β = 0.386), BMI (β = 0.301), and Barthel Index (β = 0.220) explained 31% of the variance in the MNA score. Given the observed close relationship between the MNA score and functional status, physical performance and activity, and frailty, interventions should jointly target improvements in both the nutritional status and functional status of LTNH residents. Strategies designed and implemented by interdisciplinary professional teams may be the most successful in improving these parameters to lead to better health and quality of lifeSIThis research was funded by Diputación Foral de Gipuzkoa (ADINBERRI DG18/25) and the Professional Association of Nurses of Gipuzkoa (COEGI Nursing Research Grants 2019

The Nutritional Status of Long-Term Institutionalized Older Adults Is Associated with Functional Status, Physical Performance and Activity, and Frailty

Among older adults living in long-term nursing homes (LTNHs), maintaining an adequate functional status and independence is a challenge. Whilst a poor nutritional status is a potential risk factor for a decreased function in this population, its role is not fully understood. Here, using a transversal multicenter study of 105 older adults living in 13 LTNHs, we analyzed the associations between nutritional status, as measured by the Mini Nutritional Assessment (MNA), and the parameters of functional status, physical performance, physical activity, and frailty as well as comorbidity and body composition. The MNA scores were positively correlated with the Barthel Index, handgrip strength, Short Physical Performance Battery (SPPB) scores, absolute muscle power, and Assessment of Physical Activity in Frail Older People (APAFOP) scores and were negatively correlated with dynamic balance and frailty. In a multiple linear regression model controlling for gender and age, the APAFOP score (β = 0.386), BMI (β = 0.301), and Barthel Index (β = 0.220) explained 31% of the variance in the MNA score. Given the observed close relationship between the MNA score and functional status, physical performance and activity, and frailty, interventions should jointly target improvements in both the nutritional status and functional status of LTNH residents. Strategies designed and implemented by interdisciplinary professional teams may be the most successful in improving these parameters to lead to better health and quality of life

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts