SynthesisSensors (Basel, Switzerland)2021
Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges.
Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05221697 (Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People), which is not on this map. Cited by 14 papers, 3 of them syntheses that pooled it.
What it found
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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.
Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People
Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Effects of Digital Health Interventions on Motor Symptoms, Nonmotor Symptoms, and Quality of Life in Patients With Parkinson Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled 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
- Comparing the efficacy and safety of safinamide with rasagiline in China Parkinson's disease patients with a matching adjusted indirect comparison.Scientific reports · 2025Trial
- Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.Journal of medical Internet research · 2026Article
- Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson's Disease Using a Single Ankle-Positioned Smartwatch.Sensors (Basel, Switzerland) · 2024Article
- The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions.Current opinion in clinical nutrition and metabolic care · 2023Review
- A smartphone-based tapping task as a marker of medication response in Parkinson's disease: a proof of concept study.Journal of neural transmission (Vienna, Austria : 1996) · 2023Article
- Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests.Frontiers in neurology · 2023Review
- Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care.Phenomics (Cham, Switzerland) · 2022Review
- Sensor Verification and Analytical Validation of Algorithms to Measure Gait and Balance and Pronation/Supination in Healthy Volunteers.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- Motor Fluctuations Development Is Associated with Non-Motor Symptoms Burden Progression in Parkinson's Disease Patients: A 2-Year Follow-Up Study.Diagnostics (Basel, Switzerland) · 2022Article
- A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONFrontiers in neurology · 2022Review
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
Monitoring of motor symptom fluctuations in Parkinson's disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation's occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56-96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.
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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.