ArticleJMIR rehabilitation and assistive technologies2018
A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use.
Article in JMIR rehabilitation and assistive technologies, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 7 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 7 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Recent advances (2022-2024) in eye-tracking for Parkinson's disease: a promising tool for diagnosing and monitoring symptoms.Frontiers in aging neuroscience · 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
- Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges.Sensors (Basel, Switzerland) · 2021Pooled it
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled 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
- Cost-utility analysis of a coadjutant telemedicine intervention for fall prevention in Parkinson's disease.European journal of neurology · 2025Trial
- Effect of continuous subcutaneous foslevodopa/foscarbidopa infusion on gait in advanced Parkinson's disease: a real-world wearable-based study.Journal of neurology · 2026Article
- Parkinson's disease medication adjustments based on wearable device information compared to other methods: randomized clinical trial.NPJ Parkinson's disease · 2025Article
- Dual stream transformer for medication state classification in Parkinson's disease patients using facial videos.NPJ digital medicine · 2025Article
- Comparison of clinical measures of motor function with a Holter monitor in Parkinson's disease.Clinical parkinsonism & related disorders · 2025Article
- Improving Parkinson's disease management through wearable technology: A cost-benefit perspective.PloS one · 2025Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Identification of motor progression in Parkinson's disease using wearable sensors and machine learning.NPJ Parkinson's disease · 2023Article
- A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson's disease.Frontiers in neurology · 2023Article
- Improving naturalistic neuroscience with patient engagement strategies.Frontiers in human neuroscience · 2023Article
- Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease.PloS one · 2023Observational
- Longitudinal Monitoring of Progressive Supranuclear Palsy using Body-Worn Movement Sensors.Movement disorders : official journal of the Movement Disorder Society · 2022Article
- The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration.JMIR formative research · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods).
objectiveThe goal of this study was to analyze the accuracy of this algorithm under real conditions of use.
methodsThis validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard.
resultsThe algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%.
conclusionsThe kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.
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