Quantitative falls risk assessment in elderly people: results from a clinical study with distance based timed up-and-go test recordings. (9th December 2020)

• Record Type: Journal Article
• Authors: Ziegl, Andreas; Hayn, Dieter; Kastner, Peter; Löffler, Kerstin; Weidinger, Lisa; Brix, Bianca; Goswami, Nandu; Schreier, Günter
• Is Part Of: Physiological measurement. Volume 41:Number 11(2020)
• Publication Date: 2020-12-09
• Subjects: fall prevention -- timed up-and-go test -- frailty -- fall risk assessment; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/abc352
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective : A third of people over 65 years experiences at least one fall a year. The Timed Up-and-Go (TUG) test is commonly used to assess gait and balance and to evaluate an individual's risk of falling. Approach : We conducted a clinical study with 46 older participants for evaluating the fall risk assessment capabilities of an ultra-sound based TUG test device. The fall protocols over a period of one year were used to classify participants as fallers and non-fallers. For frailty evaluation, state-of-the-art questionnaires were used. Fall recordings were compared to six TUG test measurements that were recorded in fallers and non-fallers. Main results : TUG test data were available for 39 participants (36 f, age 84.2 ± 8.2, BMI 26.0 ± 5.1). Twenty-three participants did fall at least once within the fall screening period. We fitted two different regression and probability models into a region of interest of the distance over time curve as derived from the TUG device. We found that the coefficient of determination for Gaussian bell-shaped curves ( p < 0.05, AUC = 0.71) and linear regression lines ( p < 0.02, AUC = 0.74) significantly separated fallers from non-fallers. Subtasks of the TUG test like the sit-up time showed near significance ( p < 0.07, AUC = 0.67). Significance : We found that specific features calculated from the TUG distance over time curve were significantly different between fallers and non-fallers in our study population. Automatic recording and analysis of TUG measurements could, therefore, reduce time of measurements and improve precision as compared to other methods currently being used in the assessments of fall risk.