Frailty assessment based on trunk kinematic parameters during walking

Alicia Martínez-Ramírez, Ion Martinikorena, Marisol Gómez, Pablo Lecumberri, Nora Millor, Leocadio Rodríguez-Mañas, Francisco José García García, Mikel Izquierdo, Alicia Martínez-Ramírez, Ion Martinikorena, Marisol Gómez, Pablo Lecumberri, Nora Millor, Leocadio Rodríguez-Mañas, Francisco José García García, Mikel Izquierdo

Abstract

Background: Physical frailty has become the center of attention of basic, clinical and demographic research due to its incidence level and gravity of adverse outcomes with age. Frailty syndrome is estimated to affect 20 % of the population older than 75 years. Thus, one of the greatest current challenges in this field is to identify parameters that can discriminate between vulnerable and robust subjects. Gait analysis has been widely used to predict frailty. The aim of the present study was to investigate whether a collection of parameters extracted from the trunk acceleration signals could provide additional accurate information about frailty syndrome.

Methods: A total of 718 subjects from an elderly population (319 males, 399 females; age: 75.4 ± 6.1 years, mass: 71.8 ± 12.4 kg, height: 158 ± 6 cm) volunteered to participate in this study. The subjects completed a 3-m walk test at their own gait velocity. Kinematic data were acquired from a tri-axial inertial orientation tracker.

Findings: The spatio-temporal and frequency parameters measured in this study with an inertial sensor are related to gait disorders and showed significant differences among groups (frail, pre-frail and robust). A selection of those parameters improves frailty classification obtained to gait velocity, compared to classification model based on gait velocity solely.

Interpretation: Gait parameters simultaneously used with gait velocity are able to provide useful information for a more accurate frailty classification. Moreover, this technique could improve the early detection of pre-frail status, allowing clinicians to perform measurements outside of a laboratory environment with the potential to prescribe a treatment for reversing their physical decline.

Figures

Fig. 1
Fig. 1
Mean antero-posterior, medio-lateral and vertical accelerations over multiple steps for one subject of each group (frail, pre-frail and robust)
Fig. 2
Fig. 2
95 % Confidence Intervals (CI) for the difference of means between three groups are shown for all parameters measured in VT direction
Fig. 3
Fig. 3
Decision Tree model to identify selected gait parameters
Fig. 4
Fig. 4
ROC curves of the two classificatory performance

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