ArticleParkinsonism & related disorders2015
Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson's disease.
Article in Parkinsonism & related disorders, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 60 citations in OpenAlex.
- Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial.Parkinsonism & related disorders · 2023Trial
- Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial.JAMA neurology · 2021Trial
- Quantification of Hand Motor Symptoms in Parkinson's Disease: A Proof-of-Principle Study Using Inertial and Force Sensors.Annals of biomedical engineering · 2017Trial
- Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.Neuromodulation : journal of the International Neuromodulation Society · 2016Trial
- Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Are we ready for automated deep brain stimulation programming?Parkinsonism & related disorders · 2025Review
- Subthalamic stimulation evokes hyperdirect high beta interruption and cortical high gamma entrainment in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- A Wearable Dual-Mode Probe for Image-Guided Closed-Loop Ultrasound Neuromodulation.IEEE transactions on biomedical circuits and systems · 2025Article
- Assessing Parkinson's Rest Tremor from the Wrist with Accelerometry and Gyroscope Signals in Patients with Deep Brain Stimulation: An Observational Study.Journal of clinical medicine · 2025Article
- Automated Motion Sensor-Based Functional Mapping Improves Deep Brain Stimulation Programming Efficiency.Movement disorders clinical practice · 2024Article
- Digital biomarkers for precision diagnosis and monitoring in Parkinson's disease.NPJ digital medicine · 2024Review
- Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review.Neurosurgical review · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Overview on wearable sensors for the management of Parkinson's disease.NPJ Parkinson's disease · 2023Review
- Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices.Life (Basel, Switzerland) · 2022Review
- Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care.Phenomics (Cham, Switzerland) · 2022Review
- Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor.Journal of neural engineering · 2022Article
- Predictive Value of Ambulatory Objective Movement Measurement for Outcomes of Levodopa/Carbidopa Intestinal Gel Infusion.Journal of personalized medicine · 2022Article
- Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches.Frontiers in neurology · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDeep brain stimulation (DBS) is a well-established treatment for Parkinson's disease (PD). Optimization of DBS settings can be a challenge due to the number of variables that must be considered, including presence of multiple motor signs, side effects, and battery life.
methodsNine PD subjects visited the clinic for programming at approximately 1, 2, and 4 months post-surgery. During each session, various stimulation settings were assessed and subjects performed motor tasks while wearing a motion sensor to quantify tremor and bradykinesia. At the end of each session, a clinician determined final stimulation settings using standard practices. Sensor-based ratings of motor symptom severities collected during programming were then used to develop two automated programming algorithms--one to optimize symptom benefit and another to optimize battery life. Therapeutic benefit was compared between the final clinician-determined DBS settings and those calculated by the automated algorithm.
resultsSettings determined using the symptom optimization algorithm would have reduced motor symptoms by an additional 13 percentage points when compared to clinician settings, typically at the expense of increased stimulation amplitude. By adding a battery life constraint, the algorithm would have been able to decrease stimulation amplitude by an average of 50% while maintaining the level of therapeutic benefit observed using clinician settings for a subset of programming sessions.
conclusionsObjective assessment in DBS programming can identify settings that improve symptoms or obtain similar benefit as clinicians with improvement in battery life. Both options have the potential to improve post-operative patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.