ArticleFrontiers in neurology2019
A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders.
Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03638479 (Utilizing Smart Devices to Identify New Phenotypical Characteristics in Movement Disorders), which is not on this map. Cited by 18 papers, 1 of them a synthesis 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.
Utilizing Smart Devices to Identify New Phenotypical Characteristics in Movement Disorders
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures.JAMA neurology · 2020Trial
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Essential Tremor and Digital Biomarkers: A Scoping Review Using the TRACE Framework to Map Readiness for Clinical Trials and Routine Practice.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Telemedicine for Personalized Nutritional Intervention of Rare Diseases: A Narrative Review on Approaches, Impact, and Future Perspectives.Nutrients · 2025Review
- Psychometric assessment of the Persian translated version of the "medical artificial intlligence readiness scale for medical students".PloS one · 2025Article
- Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison.JMIR formative research · 2024Article
- Machine Learning in the Parkinson's disease smartwatch (PADS) dataset.NPJ Parkinson's disease · 2024Article
- Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study.Scientific reports · 2023Article
- Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe.Sensors (Basel, Switzerland) · 2022Article
- Artificial intelligence as an emerging technology in the current care of neurological disorders.Journal of neurology · 2021Review
- Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders.Sensors (Basel, Switzerland) · 2021Article
- Development and Validation of a New Wearable Mobile Device for the Automated Detection of Resting Tremor in Parkinson's Disease and Essential Tremor.Diagnostics (Basel, Switzerland) · 2021Article
- Wearable Devices for Assessment of Tremor.Frontiers in neurology · 2021Review
- Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial.JMIR mHealth and uHealth · 2020Article
- Predicting motor, cognitive & functional impairment in Parkinson's.Annals of clinical and translational neurology · 2019Article
- Editorial: AI-powered advances in diagnosis and management of movement disorders.Frontiers in neurologyArticle
- Applications of deep learning methods in digital biomarker research using noninvasive sensing data.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease and Essential Tremor are two of the most common movement disorders and are still associated with high rates of misdiagnosis. Collected data by technology-based objective measures (TOMs) has the potential to provide new promising and highly accurate movement data for a better understanding of phenotypical characteristics and diagnostic support. A technology-based system called Smart Device System (SDS) is going to be implemented for multi-modal high-resolution acceleration measurement of patients with PD or ET within a clinical setting. The 2-year prospective observational study is conducted to identify new phenotypical biomarkers and train an Artificial Intelligence System. The SDS is going to be integrated and tested within a 20-min assessment including smartphone-based questionnaires, two smartwatches at both wrists and tablet-based Archimedean spirals drawing for deeper tremor-analyses. The electronic questionnaires will cover data on medication, family history and non-motor symptoms. In this paper, we describe the steps for this novel technology-utilizing examination, the principal steps for data analyses and the targeted performances of the system. Future work considers integration with Deep Brain Stimulation, dissemination into further sites and patient's home setting as well as integration with further data sources as neuroimaging and biobanks. Study Registration ID on ClinicalTrials.gov: NCT03638479.
Indexed as
Identifiers
What OpenQuestion holds
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.