Related Experiment Video
Updated: May 20, 2026
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
Can falls be predicted with gait analytical and posturographic measurement systems? A prospective follow-up study in
René Schwesig1, David Fischer, Andreas Lauenroth
1Department of Sport-Science, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany. rene.schwesig@sport.uni-halle.de
Objective:
To validate previously proposed findings and to develop an objective, feasible and efficient bifactorial (risk factors: gait impairment and balance disorders) fall risk assessment.
Design:
Prospective follow-up study Setting: Nursing homes (Halle/Saale, Germany).
Subjects:
One hundred and forty-six nursing home residents (aged 62-101 years) were recruited.
Methods:
Gait data were collected using a mobile inertial sensor-based system (RehaWatch). Postural regulation data were measured with the Interactive Balance System. Falls were recorded in standardized protocols over a follow-up period of 12 months.
Main Measures:
Gait parameters (e.g. spatial-temporal parameters), posturographic parameters (e.g. postural subsystems), number of falls.
Results:
Seventeen (12%) of the participants had more than two falls per year. The predictive validity of the previously selected posturographic parameters was inadequate (sensitivity: 47%). The new measurement tool defined 67 participants showing an increased risk of falls. In reality, only 8 participants actually fell more than twice during the follow-up period (positive predictive value (PPV): 12%). The negative predictive value (NPV) was 88%. The posturographic frequency range F2-4 (peripheral-vestibular system), stride time and standard deviation of landing phase were the most powerful parameters for fall prediction. Gait and postural variability were larger in the high-risk group (e.g. gait speed; confidence interval (CI)(high): 0.57-0.79 vs. CI(low): 0.72-0.81 m/s).
Conclusion:
RehaWatch and the Interactive Balance System are able to measure two of the most important fall risk factors, but their current predictive ability is not satisfactory yet. The correlation with physiological mechanisms is only shown by the Interactive Balance System.
