South Florida Hospital News
Wednesday May 22, 2019
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May 2019 - Volume 15 - Issue 11

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Automated Technology Senses How Parkinson’s Patients Respond to Medication

Parkinson’s disease (PD) is a chronic, progressive neurological disorder affecting approximately 6 million people globally and is expected to double by 2040. PD leads to disabling motor features including tremor, reduced speed, and gait/balance impairment leading to falls, as well as non-motor symptoms such as cognitive impairment and sleep and speech disorders.

One of the most prevalent complications in PD patients is medication ON and OFF motor fluctuations, which occur in 50 percent of patients diagnosed within three to five years and 80 percent diagnosed within 10 years. The onset of these motor fluctuations is a critical point in managing the disease because it requires ongoing adjustments in treatment such as changing the frequency and dosage of medication or changing parameters for deep brain stimulation.
 
Currently, PD motor fluctuations are addressed with brief clinical examinations or appropriate history-taking and patient self-reports, which rely on extensive patient education. Even then, self-reports can be unreliable and clinical examinations may not be practical, especially in rural areas. Patients often require frequent follow-up visits with their neurologist.
 
Researchers from Florida Atlantic University’s College of Engineering and Computer Science have developed an innovative way to automatically and reliably detect and monitor medication ON and OFF states in PD patients. They have combined an algorithm and sensor-based system that detects ON and OFF state patterns in PD patients using two wearable motion sensors placed on the patient’s most affected wrist and ankle.
 
For the study, published in the journal Medical Engineering and Physics, these sensors collected movement signals while patients performed seven daily living activities such as walking or getting dressed in their medication ON and OFF phases. The algorithm was trained using approximately 15 percent of the data from four activities and tested on the remaining data. Data from the two sensors provided objective measures instead of a patient diary or self-report. 
 
Results of the study reveal that the algorithm was able to detect the response to medication during the subjects’ daily routine activities with an average of 90.5 percent accuracy, 94.2 percent sensitivity, and 85.4 percent specificity.
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