ArticleBMJ open2020
Protocol for PD SENSORS: Parkinson's Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson's disease.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- On the Necessity of Multidisciplinarity in the Development of at-Home Health Monitoring Platforms for Older Adults: Systematic Review.JMIR human factors · 2025Pooled it
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry.Scientific data · 2026Article
- A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2024Article
- A multimodal dataset of real world mobility activities in Parkinson's disease.Scientific data · 2023Article
- Validation of Gait Measurements on Short-Distance Walkways Using Azure Kinect DK in Patients Receiving Chronic Hemodialysis.Journal of personalized medicine · 2023Article
- Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset.Parkinsonism & related disorders · 2022Article
- Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.Journal of medical Internet research · 2022Article
- An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities.Sensors (Basel, Switzerland) · 2021Article
- Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson's Disease.Neuropsychiatric disease and treatment · 2021Review
- Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity.Digital biomarkersArticle
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 1 country.
Funding
Abstract
introductionThe impact of disease-modifying agents on disease progression in Parkinson's disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson's disease. METHODS AND ANALYSIS: This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson's and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson's disease and control, and between Parkinson's disease symptoms 'on' and 'off' medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews. ETHICS AND DISSEMINATION: Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate.
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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.