ArticleJournal of neurology2016
Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor.
Article in Journal of neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03366558 (Sensor-based Motion Characterization in Patients With Parkinson's Disease), which is not on this map. Cited by 26 papers, 8 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.
Sensor-based Motion Characterization in Patients With Parkinson's Disease
Who cites it
26 citing papers in PubMed, 8 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled 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
- Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- Wearables in the home-based assessment of abnormal movements in Parkinson's disease: a systematic review of the literature.Journal of neurology · 2022Pooled it
- Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments.Journal of neuroengineering and rehabilitation · 2020Pooled it
- Gait characteristics in people with Friedreich ataxia: daily life versus clinic measures.Frontiers in neurology · 2025Article
- Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study.Arthritis care & research · 2024Observational
- Wearable sensor-based quantitative gait analysis in Parkinson's disease patients with different motor subtypes.NPJ digital medicine · 2024Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Relation of gait measures with mild unilateral knee pain during walking using machine learning.Scientific reports · 2022Observational
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2022Article
- Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls.Journal of neuroengineering and rehabilitation · 2020Article
- Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back.Sensors (Basel, Switzerland) · 2020Article
- Comparing GPS-Based Community Mobility Measures with Self-report Assessments in Older Adults with Parkinson's Disease.The journals of gerontology. Series A, Biological sciences and medical sciences · 2020Article
- Effect of Bout Length on Gait Measures in People with and without Parkinson's Disease during Daily Life.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson's disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.
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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.