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. Issue 11 (30th November 2020)

• Record Type: Journal Article
• Authors: Morgan, Catherine; Craddock, Ian; Tonkin, Emma L; Kinnunen, Kirsi M; McNaney, Roisin; Whitehouse, Sam; Mirmehdi, Majid; Heidarivincheh, Farnoosh; McConville, Ryan; Carey, Julia; Horne, Alison; Rolinski, Michal; Rochester, Lynn; Maetzler, Walter; Matthews, Helen; Watson, Oliver; Eardley, Rachel; Whone, Alan L
• Is Part Of: BMJ open. Volume 10:Issue 11(2020)
• Publication Date: 2020-11-30
• Subjects: parkinson's disease -- information technology -- qualitative research -- statistics & research methods; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-041303
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: The 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.