Parkinson’s Disease(PD) is a neurodegenerative condition that creates a dopamine deficient state to which levodopa has the largest therapeutic effect among pharmacological therapies. As the disease progresses, patients often develop motor complications, levodopa refractory gait and balance disorder, and worsening non-motor symptoms such as sleep disturbances, gut dysmotility, and neurobehavioral disorders. Advanced surgical therapies (e.g., deep brain stimulation or the levodopa/carbidopa intestinal gel) are effective in treating motor complications while rehabilitation therapies (e.g., physiotherapy, exercise, dance) have been shown to improve both motor--especially gait and balance--and non-motor symptoms in advanced PD.
Though neurological care can reduce morbidity and mortality among PD patients, such care is often limited as a result of logistical challenges, including shortages in neurologists and extended travel and wait times for office visits. Furthermore, the treatment approach for many advanced patients rely on infrequent, subjective efficacy measures (e.g., clinical rating scales or patient-based symptom diaries) that can make clinical optimization difficult and ultimately, lead to under utilization of advanced therapies and rehabilitation therapies. Sensor-based technology offers an opportunity to objectively measure motor and nonmotor symptoms –enabling a more tailored treatment approach to be developed for this disease.
The aim of this Research Topic is to bring together original research that uses sensor technology to advance treatment planning or further the understanding of current treatment approaches in early to late-stage Parkinson’s Disease.
Potential topics include but are not limited to the following:
1. Utilizing sensors to advance pharmacological management of motor and/or non-motor symptoms.
2. Sensor based application in advanced therapies--deep brain stimulation or levodopa intestinal gel.
3. Sensor-based rehabilitation technologies (e.g. augmented reality, virtual reality, gaming, etc.) -- both as novel treatments and instruments for treatment planning.
4. Machine learning and deep learning methods to analyze sensor data to improve utility of pharmacological, neuromodulation, or rehabilitation treatment approaches.
5. Reinforcement learning and decision-making under uncertainty and partial information methods to advance sensor-based treatment planning.
6. Methods to improve equitable use of sensor technology to mitigate disparities in treatment planning.
Dr. Kopell is a paid consultant for Medtronic, Abbott, and Clearpoint. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Parkinson’s Disease(PD) is a neurodegenerative condition that creates a dopamine deficient state to which levodopa has the largest therapeutic effect among pharmacological therapies. As the disease progresses, patients often develop motor complications, levodopa refractory gait and balance disorder, and worsening non-motor symptoms such as sleep disturbances, gut dysmotility, and neurobehavioral disorders. Advanced surgical therapies (e.g., deep brain stimulation or the levodopa/carbidopa intestinal gel) are effective in treating motor complications while rehabilitation therapies (e.g., physiotherapy, exercise, dance) have been shown to improve both motor--especially gait and balance--and non-motor symptoms in advanced PD.
Though neurological care can reduce morbidity and mortality among PD patients, such care is often limited as a result of logistical challenges, including shortages in neurologists and extended travel and wait times for office visits. Furthermore, the treatment approach for many advanced patients rely on infrequent, subjective efficacy measures (e.g., clinical rating scales or patient-based symptom diaries) that can make clinical optimization difficult and ultimately, lead to under utilization of advanced therapies and rehabilitation therapies. Sensor-based technology offers an opportunity to objectively measure motor and nonmotor symptoms –enabling a more tailored treatment approach to be developed for this disease.
The aim of this Research Topic is to bring together original research that uses sensor technology to advance treatment planning or further the understanding of current treatment approaches in early to late-stage Parkinson’s Disease.
Potential topics include but are not limited to the following:
1. Utilizing sensors to advance pharmacological management of motor and/or non-motor symptoms.
2. Sensor based application in advanced therapies--deep brain stimulation or levodopa intestinal gel.
3. Sensor-based rehabilitation technologies (e.g. augmented reality, virtual reality, gaming, etc.) -- both as novel treatments and instruments for treatment planning.
4. Machine learning and deep learning methods to analyze sensor data to improve utility of pharmacological, neuromodulation, or rehabilitation treatment approaches.
5. Reinforcement learning and decision-making under uncertainty and partial information methods to advance sensor-based treatment planning.
6. Methods to improve equitable use of sensor technology to mitigate disparities in treatment planning.
Dr. Kopell is a paid consultant for Medtronic, Abbott, and Clearpoint. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
