ArticleJMIR research protocols2019
Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study.
Article in JMIR research protocols, 2019. 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 13 papers, 1 of them a synthesis that pooled it.
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- Collecting Feedback From Neurologists and Patients to Guide Development of a Parkinson Disease App (DigiPark): Qualitative, Noninterventional Study.JMIR human factors · 2024Article
- Wearable Technology Applications and Methods to Assess Clinical Outcomes in Foot and Ankle Disorders: Achievements and Perspectives.Sensors (Basel, Switzerland) · 2024Review
- The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review.Cureus · 2024Review
- Assessing the impact of body location on the accuracy of detecting daily activities with accelerometer data.iScience · 2024Article
- Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method.Micromachines · 2023Article
- Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study.JMIR formative research · 2023Article
- Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization.Biosensors · 2022Article
- A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes.Sensors (Basel, Switzerland) · 2022Article
- The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review.Sensors (Basel, Switzerland) · 2020Review
- Observational
- Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects.Parkinson's disease · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments.
objectiveThe primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns.
methodsThis paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings.
resultsData collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD.
conclusionsThis study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.
trial registrationClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12808.
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