Implementation of the SELFY-MPI in five European countries: a multicenter international feasibility study

Main Article Content

Sabrina Zora
Katerin Leslie Quispe Guerrero
Nicola Veronese
Alberto Ferri
An L.D. Boone
Marta Pisano Gonzalez
Yves-Marie Pers
Hein Raat
Graham Baker
Alberto Cella
Alberto Pilotto *
(*) Corresponding Author:
Alberto Pilotto | alberto.pilotto@galliera.it

Abstract

It is essential for welfare systems to predict the health and care needs of people with chronic diseases. The Multidimensional Prognostic Index (MPI) proved excellent accuracy in predicting negative health outcomes. Recently, a selfadministered version of MPI (SELFY-MPI) was developed and validated in community- dwelling subjects showing an excellent agreement between the two instruments regardless of age. This is a feasibility study concerns the implementation of SELFYMPI in five European countries. The SELFY-MPI includes the self-administration of Barthel Index, Instrumental Activities of daily Living (IADL), Test Your Memory (TYM) Test, Mini Nutritional Assessment-Short Form (MNA-SF), comorbidity, number of medications, and the Gijon’s Socio-Familial Evaluation Scale (SFES). A descriptive analysis was performed on the data collected. 300 subjects (mean age 62 years, range 19-88 years; male/female ratio 0.81) completed the SELFY-MPI. The mean value of the SELFY-MPI was 0.131 (range: 0.0- 0.563) showing a significant correlation with age (Pearson coefficient=0.373, P<0.001). The mean value of the SELFYMPI filling time was 15 minutes (range: 5- 45 minutes) showing a significant correlation between age and filling time (Pearson coefficient=0.547, P<0.001). The SELFYMPI is an excellent self-administered tool for comprehensive self-assessment screening of community-dwelling people at risk of physical and cognitive frailty and/or socioeconomic vulnerability.

Downloads

Download data is not yet available.

PUBLICATION METRICS

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.


Article Details