Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?

Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.

To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.

People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).

The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).

Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.