BACKGROUND: Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The objective of this prospective study was to investigate clinical and instrumented tests of balance and gait in both OFF and ON medication states and to verify their utility in the prediction of future falls in PD patients. METHODS: Forty-five patients with idiopathic PD were examined in defined OFF and ON medication states within one examination day including PD-specific clinical tests, instrumented Timed Up and Go test (iTUG) and computerized dynamic posturography. The same gait and balance tests were performed in 22 control subjects of comparable age and sex. Participants were then followed-up for 6 months using monthly fall diaries and phone calls. RESULTS: During the follow-up period, 27/45 PD patients and 4/22 control subjects fell one or more times. Previous falls, fear of falling, more severe motor impairment in the OFF state, higher PD stage, more pronounced depressive symptoms, higher daily levodopa dose and stride time variability in the OFF state were significant risk factors for future falls in PD patients. Increased stride time variability in the OFF state in combination with faster walking cadence appears to be the most significant predictor of future falls, superior to clinical predictors. CONCLUSION: Incorporating instrumented gait measures into the baseline assessment battery as well as accounting for both OFF and ON medication states might improve future fall prediction in PD patients. However, instrumented testing in the OFF state is not routinely performed in clinical practice and has not been used in the development of fall prevention programs in PD. New assessment methods for daylong monitoring of gait, balance and falls are thus required to more effectively address the risk of falling in PD patients.

Department of Control Engineering Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic

Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic

Department of Health Sciences University of York York United Kingdom

Department of Kinanthropology Charles University Prague Prague Czech Republic

Department of Neurology and Centre of Clinical Neuroscience 1st Faculty of Medicine and General University Hospital Prague Charles University Prague Prague Czech Republic

Institute of Neuroradiology University Medicine Goettingen Goettingen Germany

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