SPARC: a new approach to quantifying gait smoothness in patients with Parkinson's disease.
Yoav Beck, Talia Herman, Marina Brozgol and 3 others
PMID 29914518WHAT IT FOUND
A new sensor-based measure called SPARC detected less smooth gait in patients with Parkinson's disease than standard speed or variability metrics, especially when off medication.
It correlated strongly with motor severity scores, suggesting it offers a more sensitive way to quantify gait quality from simple wearable sensors.
Key findings
01SPARC values for accelerometer and gyroscope were generally lower in patients with Parkinson's disease than in healthy controls, indicating less smooth gait.
02SPARC had a higher ability to distinguish controls from Parkinson's patients than harmonic ratios, variability measures, or gait velocity, with the largest effect size for the accelerometer-based measure.
03SPARC-Acc correlated strongly with UPDRS motor scores, suggesting it reflects overall motor control ability and disease severity.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
The control group was significantly older than the Parkinson's group, although an age-matched subgroup analysis was performed. This is a cross-sectional analysis of existing data, not a randomized trial, so it cannot establish causality. The study did not validate SPARC against a gold-standard motion capture system in this specific population, relying instead on comparisons with other inertial sensor metrics.
Declared interests
The paper is supported by non-U.S. government funding. No specific commercial conflicts of interest are declared in the provided text.
The easy way to misread this
Do not assume SPARC is a validated clinical tool for diagnosing Parkinson's or predicting falls in individual patients. The study shows it is a promising research metric that distinguishes groups, but it has not yet been proven to change patient management or predict outcomes in a clinical setting.