ReviewNPJ Parkinson's disease2023
Overview on wearable sensors for the management of Parkinson's disease.
Review in NPJ Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 5 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
80 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Effects of Wearable Devices on Parkinson Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials Within the International Classification of Functioning, Disability, and Health Framework.Journal of medical Internet research · 2026Pooled it
- Determining Falls Risk in People with Parkinson's Disease Using Wearable Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Digital Outcomes as Biomarkers of Disease Progression in Early Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2025Pooled it
- Pooled it
- Clinical trials in late-stage Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Validity of a Wearable Digital Insole for Assessing Gait ON and OFF in Parkinson's Disease.Annals of clinical and translational neurology · 2026Article
- Bidirectional Mechanisms Linking Circadian Rhythm Disruption and Parkinson's Disease: Chronobiomarkers and Therapeutic Implications.International journal of molecular sciences · 2026Review
- A Self-Administered, Digitized Approach to Quantifying the Cardinal Motor Symptoms in Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.Journal of Parkinson's disease · 2026Review
- From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Human-AI collaboration for dysphagia rehabilitation from effectiveness to implementation complexity: a systematic review.NPJ digital medicine · 2026Article
- Freezing of Gait in Parkinson's Disease: A Heterogeneous Phenomenon Across Assessment Tools.The European journal of neuroscience · 2026Article
- Opicapone as Add-On Therapy to Continuous Subcutaneous Foslevodopa/Foscarbidopa Infusion: Clinical Improvement and Wearable Sensor-Based Gait Analysis.Brain sciences · 2026Article
- Validation of remote multimodal AI screening for Parkinson disease across diverse settings.Communications medicine · 2026Article
- Weekly Variability of an Objective Bradykinesia Score in Parkinson's Disease-An Observational Longitudinal Study.Journal of clinical medicine · 2026Article
- Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry.Scientific data · 2026Article
- Association of wearable sensor-based gait analysis with phenoconversion trajectories in idiopathic REM sleep behavior disorder.NPJ Parkinson's disease · 2026Article
- AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.Sensors (Basel, Switzerland) · 2026Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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.