SynthesisPloS one2021
Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.
Synthesis in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 7 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
53 citing papers in PubMed, 7 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
- Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Activity and Behavioral Recognition Using Sensing Technology in Persons with Parkinson's Disease or Dementia: An Umbrella Review of the Literature.Sensors (Basel, Switzerland) · 2025Pooled 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
- The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.Orphanet journal of rare diseases · 2023Pooled it
- Measurement instruments to assess basic functional mobility in Parkinson's Disease: A systematic review of clinimetric properties and feasibility for use in clinical practice.Japanese journal of comprehensive rehabilitation science · 2023Pooled 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
- A Stand-Alone Wearable Measurement System for Continuous Hand Tremor Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Body placement of inertial measurement units differentially affects physical activity assessment accuracy in drug-naïve Parkinson's disease.Scientific reports · 2026Article
- Association between wearable-derived dyskinesia time burden and longitudinal change in disability in people with Parkinson's disease: a real-world treat-to-target cohort study.Scientific reports · 2026Article
- Quantifying Slowness in Parkinson Disease Using a Serious Game: Cross-Sectional Study.JMIR serious games · 2026Article
- Automated video analysis for early detection of bradykinesia in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Wearable inertial device for monitoring Parkinson's disease symptoms: a pilot study in a controlled environment.Scientific reports · 2025Article
- L-Dopa Comparably Improves Gait and Limb Movements in Parkinson's Disease: A Wearable Sensor Analysis.Biomedicines · 2025Article
- Innovative Sensor-Based Approaches for Assessing Neurodegenerative Diseases: A Brief State-of-the-Art Review.Sensors (Basel, Switzerland) · 2025Review
- Multidisciplinary inpatient care for Parkinson's disease: a single-centre cohort study on improvements in gait, overall motor function, and quality of life.Neurological research and practice · 2025Article
- Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson's Disease Diagnosis and Monitoring.Biomedicines · 2025Review
- Assessing Eligibility for Apomorphine Therapy in Parkinson's Disease: The Telemonitoring Potential.Movement disorders clinical practice · 2025Article
- New Parameters Based on Ground Reaction Forces for Monitoring Rehabilitation Following Tibial Fractures and Assessment of Heavily Altered Gait.Sensors (Basel, Switzerland) · 2025Observational
- A 15-year bibliometric analysis of body mass index moderation in the management of Parkinson's disease.Frontiers in aging neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient's condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients' status and the disease's symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson's, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms' assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.
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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.