ReviewFrontiers in computational neuroscience2018
Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling.
Review in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Wearable Solutions for Patients with Parkinson's Disease and Neurocognitive Disorder: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- User Experiences of Limb-Worn Wearable Devices for Monitoring Parkinson Disease Motor Function and Blood Pressure: Usability Study.JMIR formative research · 2025Article
- Advancing Parkinson's disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics.Frontiers in physiology · 2025Article
- Kirigami-Inspired Stretchable Piezoelectret Sensor for Analysis and Assessment of Parkinson's Tremor.Advanced healthcare materials · 2025Article
- Testing machine learning algorithms to evaluate fluctuating and cognitive profiles in Parkinson's disease by motion sensors and EEG data.Computational and structural biotechnology journal · 2025Article
- Evaluation of the Parkinson's Remote Interactive Monitoring System in a Clinical Setting: Usability Study.JMIR human factors · 2024Article
- Machine Learning Models for Parkinson Disease: Systematic Review.JMIR medical informatics · 2024Review
- The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration.JMIR formative research · 2022Article
- Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation.Frontiers in psychology · 2022Article
- Classification of Parkinson's disease with freezing of gait based on 360° turning analysis using 36 kinematic features.Journal of neuroengineering and rehabilitation · 2021Article
- Improving Medication Regimen Recommendation for Parkinson's Disease Using Sensor Technology.Sensors (Basel, Switzerland) · 2021Article
- Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III.Biomedical engineering online · 2021Article
- Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease.Frontiers in neurology · 2021Article
- Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review.Brain sciences · 2020Review
- Age-Related Parkinsonian Signs in Microdeletion 22q11.2.Movement disorders : official journal of the Movement Disorder Society · 2020Article
- Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments.JMIR mHealth and uHealth · 2020Observational
- Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?IEEE open journal of engineering in medicine and biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.
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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.