Gait variability is sensitive to detect Parkinson’s disease patients at high fall risk

Abstract

Background

Gait disturbance is an important risk factor for falls in Parkinson’s disease (PD). Using wearable sensors, we can obtain the spatiotemporal parameters of gait and calculate the gait variability. This prospective study aims to objectively evaluate the gait characteristics of PD fallers, and further explore the relationship between spatiotemporal parameters of gait, gait variability and falls in PD patients followed for six months.

Methods

Fifty-one PD patients were enrolled in this study. A seven-meter timed up and go test was performed. Gait characteristics were determined by a gait analysis system. Patients were followed monthly by telephone until the occurrence of falls or till the end of six months. The patients were categorized into fallers and non-fallers based on whether fell during the follow-up period. Gait parameters were compared between two groups, and binary logistic regression was used to establish the falls prediction model. In the receiver-operating characteristic curve, area under the curve (AUC) was utilized to evaluate the prediction accuracy of each indicator.

Results

All subjects completed the follow-up, and 14 (27.5%) patients reported falls. PD fallers had greater gait variability. The range of motion of the trunk in sagittal plane variability was an independent risk factor for falls and achieved moderate prediction accuracy (AUC = 0.751), and the logistic regression model achieved a good accuracy of falls prediction (AUC = 0.838).

Conclusions

Increased gait variability is a significant feature of PD fallers and is more sensitive to detect PD patients at high risk of falls than spatiotemporal parameters.

Keywords:

• Parkinson’s disease
• wearable sensors
• gait variability
• fall
• prediction

Acknowledgments

Paper previously presented, in part, at International Congress of Parkinson’s Disease and Movement Disorders.

Disclosure statement

The authors report no conflict of interest.

Author contributions

LM and PC designed the study. LM, TMM and QJ collected the data. LM and CH did statistical analysis. LM and JK wrote the first draft. PC and JK reviewed and critiqued the manuscript.

Additional information

Funding

This work was supported by the The National Key R&D Program of China under grant number 2018YFC1312001 and 2017YFC0840105, Beijing Municipal Administration of Hospitals’ Mission Plan under grant number SML20150803 and Beijing Municipal Science & Technology Commission under grant number Z171100000117013.