ArticleGait & posture2022
Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors.
Article in Gait & posture, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.Sensors (Basel, Switzerland) · 2026Review
- A systematic review of real-world gait-related digital mobility outcomes in Parkinson's disease.NPJ digital medicine · 2025Article
- Assessing the Generalizability of Foundation Models for the Recognition of Motor Examinations in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Gait Instability and Compensatory Mechanisms in Parkinson's Disease Patients With Camptocormia: An Exploratory Study.Journal of movement disorders · 2025Article
- Quantitative analysis of gait parameters in Parkinson's disease and the clinical significance.Frontiers in neurology · 2025Article
- Objective assessment of gait and posture symptoms in Parkinson's disease using wearable sensors and machine learning.Frontiers in aging neuroscience · 2025Article
- Circular walking is useful for assessing the risk of falls in early progressive supranuclear palsy.Journal of neurology · 2024Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- A multimodal dataset of real world mobility activities in Parkinson's disease.Scientific data · 2023Article
- Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough?Sensors (Basel, Switzerland) · 2023Article
- Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor.Sensors (Basel, Switzerland) · 2022Article
- Parkinson's disease and Covid-19: The effect and use of telemedicine.International review of neurobiology · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundTelemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk. RESEARCH QUESTION: Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores?
methodsThirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy.
resultsThe number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway. SIGNIFICANCE: The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.
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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.