Dataset Information

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.

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.

SUBMITTER: Morgan C

PROVIDER: S-EPMC7705501 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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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.

Morgan Catherine C Craddock Ian I Tonkin Emma L EL Kinnunen Kirsi M KM McNaney Roisin R Whitehouse Sam S Mirmehdi Majid M Heidarivincheh Farnoosh F McConville Ryan R Carey Julia J Horne Alison A Rolinski Michal M Rochester Lynn L Maetzler Walter W Matthews Helen H Watson Oliver O Eardley Rachel R Whone Alan L AL

BMJ open 20201130 11

<h4>Introduction</h4>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, relativel ...[more]

PMID: 33257491

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Project description:Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Project description:BACKGROUND:Clinical phenotypic heterogeneity represents a major barrier to trials in motor neuron disease (MND) and objective surrogate outcome measures are required, especially for slowly progressive patients. We assessed responsiveness of clinical, electrophysiological and radiological muscle-based assessments to detect MND-related progression. MATERIALS AND METHODS:A prospective, longitudinal cohort study of 29 MND patients and 22 healthy controls was performed. Clinical measures, electrophysiological motor unit number index/size (MUNIX/MUSIX) and relative T2- and diffusion-weighted whole-body muscle magnetic resonance (MR) were assessed three times over 12 months. Multi-variable regression models assessed between-group differences, clinico-electrophysiological associations, and longitudinal changes. Standardized response means (SRMs) assessed sensitivity to change over 12 months. RESULTS:MND patients exhibited 18% higher whole-body mean muscle relative T2-signal than controls (95% CI 7-29%, p?<?0.01), maximal in leg muscles (left tibialis anterior 71% (95% CI 33-122%, p?<?0.01). Clinical and electrophysiological associations were evident. By 12 months, 16 patients had died or could not continue. In the remainder, relative T2-signal increased over 12 months by 14-29% in right tibialis anterior, right quadriceps, bilateral hamstrings and gastrocnemius/soleus (p?<?0.01), independent of onset-site, and paralleled progressive weakness and electrophysiological loss of motor units. Highest clinical, electrophysiological and radiological SRMs were found for revised ALS-functional rating scale scores (1.22), tibialis anterior MUNIX (1.59), and relative T2-weighted leg muscle MR (right hamstrings: 0.98), respectively. Diffusion MR detected minimal changes. CONCLUSION:MUNIX and relative T2-weighted MR represent objective surrogate markers of progressive denervation in MND. Radiological changes were maximal in leg muscles, irrespective of clinical onset-site.

Dataset Information

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.

Publications

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.

Similar Datasets

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