Writers: Antonio R. Jiménez Ruiz and Luisa Ruiz-Ruiz

Medical and technological advances continue to improve the span and quality of life of the general population. According to the World Health Organization, the proportion of the world’s population over 60 years will double from 11% to 22% in 2050. Global aging is a triumph, but also a challenge for healthcare systems, because of physical decline and an intensification of chronic diseases associated with the elderly.

Frailty is one of the most common diseases related to age, it presents difficulties in walking and higher fall risk. Our main objective is to detect frailty in its preliminary stages (pre-frailty) to prevent its progression, to reverse it and to improve the quality of life of older people. Traditionally, frailty has been diagnosed using the Fried criteria or other frailty scales or functional tests performed in the doctor’s office, such as gait speed test, the Timed Up and Go test (TUG) or the Short Physical Performance Battery (SPPB). Currently, technological advances are providing more accurate gait analysis methods to detect frailty and fall risk in elderly people.

Gait analysis is one of the most used methods to detect frailty and fall risk in elderly people. It consists of identifying the distinct phases of the gait cycle and extracting their characteristic parameters. As mentioned above, frailty is directly related to changes in gait, namely slower gait speed. As frailty increases, mobility capacity decreases and gait changes become more evident. Through gait analysis and quantification of the gait parameters, differences can be found between robust, prefrail, and frail patients. These differences allow discriminating between patient groups, to diagnose frailty, and to provide early medical attention.

In recent months, we have conducted a study to determine which gait parameters are the most important to detect frailty and fall risks (https://www.mdpi.com/1318920). The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant.

Identifying these parameters is the first step in developing new tools for early identification of frailty and monitoring its evolution. We are currently working on the development of new sensors and algorithms to detect these parameters.

The first tests have already been carried out in the Lleida Living Lab SetUp, where the installation of a system for real-time location and gait analysis was studied. Older adults’ patients were required to walk and to do some home activities. Soon, new tests will be carried out on a larger number of patients.
For more information, please contact Antonio R. Jiménez Ruiz (antonio.jimenez@csic.es) or visit our website