ReviewFrontiers in neuroscience2017
How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.
Review in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06701318 (Ultrasound Shear Wave Elastography for Assessing Muscle Stiffness in Parkinson's Disease Patients), which is not on this map. Cited by 182 papers, 13 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.
Ultrasound Shear Wave Elastography for Assessing Muscle Stiffness in Parkinson's Disease Patients
Who cites it
182 citing papers in PubMed, 13 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
- Unsupervised Functional Capacity Testing at Home: A Systematic Review.Sports medicine - open · 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
- Evaluating the Utility of Wearable Sensors for the Early Diagnosis of Parkinson Disease: Systematic Review.Journal of medical Internet research · 2025Pooled 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
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled it
- Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Wearable Solutions for Patients with Parkinson's Disease and Neurocognitive Disorder: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review.Clinical interventions in aging · 2020Pooled it
- Gait Kinematic Parameters in Parkinson's Disease: A Systematic Review.Journal of Parkinson's disease · 2020Pooled it
- Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment.Journal of Parkinson's disease · 2020Pooled it
- Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature.Journal of neural transmission (Vienna, Austria : 1996) · 2019Pooled it
- Pooled it
- Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2020Trial
- Meaningful digital mobility outcomes for people with Parkinson's disease: Insights grounded in diverse lived experience.Journal of Parkinson's disease · 2026Article
- Next-generation epidermal patches: Bridging 3D and multidimensional printing for biomedical and personal care innovations.Bioactive materials · 2026Review
- Flexible and Self-Powered Wearable Sensors for Tremor Monitoring in Parkinson'S Disease: Recent Advances in Materials and Device Architectures.Advanced healthcare materials · 2026Review
- Multi-Task Wearable Parkinson's Disease Detection with a Pretrained Spatio-Temporal Graph Encoder and Task-Level Token Aggregation.Bioengineering (Basel, Switzerland) · 2026Article
- Parkinson's disease focused multimodal wearable vision based gait monitoring system with real time feedback to support mobility and caregiver well-being.Discover mental health · 2026Article
- Review
122 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.
resultsFinally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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.