Abstract
Introduction Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefit on symptoms including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. Here, we investigate the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single wrist-accelerometer.
Methods As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist-accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the whole one-hour recordings, and medication-state classification analyses were performed on one-minute segments of data. The influence of individual versus group model training, data window length, and total amount of training patients included in group model training on classification was analyzed.
Results Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, +/− respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 seconds, and with additional training patient datasets.
Conclusion Medication induced fluctuations in bradykinesia can be classified using wrist worn accelerometery at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has important clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵Financial disclosures, conflict of interest None of the authors have a conflict of interest. JH received funding from the Dutch health care research organization ZonMW (Translational Research 2017 - 2024 grant nr. 446001063). JH, PT and YT received funding from the Stichting Weijerhorst. CH received a VENI-funding from the Dutch Research Council (WMO). SL and research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number K23NS120037. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.