Sensor-Based Gait Analysis Can Enhance Individualized Evaluation of Parkinson’s Patients, Study Suggests

Sensor-Based Gait Analysis Can Enhance Individualized Evaluation of Parkinson’s Patients, Study Suggests
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sensor-based gait assessment

Use of sensor-based methods to evaluate gait can improve individual assessments of Parkinson’s disease patients who are undergoing dopaminergic treatment, researchers suggest.

The study with that finding, “Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease,” was published in the Journal of Neurology.

Gait impairment as a consequence of Parkinson’s disease progression can drastically reduce patients’ quality of life. However, available strategies to evaluate gait alterations for individual patient care are still limited.

More recently, the development of mobile sensor-based gait analysis methods has enabled the objective assessment of gait deficits in Parkinson’s patients. Still, the applicability and effectiveness as an individualized evaluation approach has not been established.

A team led by researchers at FAU Erlangen-Nürnberg in Germany compared gait outcomes measured with standard and sensor-based methods in Parkinson’s patients undergoing dopamine replacement therapy.

The study enrolled 13 patients who had mean disease duration of about 15 years and were receiving a mean levodopa equivalent daily dose of 1,077 mg.

All participants started treatment with Apokyn (apomorphine) according to standard protocol, by injecting a defined dose subcutaneously (under the skin) every 15 minutes until achieving the best motor response. Apokyn is an injectable agent usually used to restore body movement control between doses of levodopa, during “off” periods — periods when medication wears off and symptoms reappear.

To track gait movement, researchers used sensors (3D-acceerometers and 3D-gyroscopes) attached to the shoes that could detect small changes in movement orientation and speed. These sensors measure parameters such as rotation and dynamic acceleration resulting from motion, shock or vibration, and can measure tremor in these patients.

After treatment with Apokyn, patients showed a significant improvement in gait movement, as shown by increase in certain gait parameters, including stride speed and length, maximum toe clearance, gait velocity, swing time, heel strike angle, and toe-off angle.

To better evaluate the potential of sensor-based gait analysis to perform individualized evaluations, researchers compared the data obtain between Apokyn administrations within in each patient.

This strategy allowed them to confirm that sensor-based results could effectively measure small gait differences resulting from Apokyn dosages. It could discriminate significant improvements in stride speed, length, and time, and maximum toe clearance between two sequential administrations, as well as detect when no additional improvements were achieved with higher doses.

To validate these findings, researchers compared the sensor-based data with motor scores collected with the standard measure Unified Parkinson Disease Rating Scale (UPDRS) (a 50-question assessment of both motor and non-motor symptoms associated with Parkinson’s).

Improvement in gait parameters (obtained using sensor-based gait analysis) between Apokyn injections reflected improvement in patients’ overall motor performance as measured by the UPDRS, in particular in items related to postural stability and gait.

“[S]ensor-based gait analysis provides objective target outcome measure of gait performance, reflecting apomorphine-induced improvement of motor performance in [Parkinson’s disease],” researchers wrote.

“We show that using instrumented gait analysis to measure individual changes in gait parameters … may be a powerful assessment strategy for routine clinical care in individual [Parkinson’s disease] patients,” they concluded.

However, the authors caution that additional studies in larger groups of patients are still warranted to further validate the applicability and implementation of sensor-based gait analysis as an objective and individualized diagnostic tool for real-life healthcare.

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