Our Technology
Our cutting-edge technology for monitoring real-world mobility, designed to offer a comprehensive assessment of gait that reflects an individual's lived experience.
Our cutting-edge technology for mobility monitoring, designed to offer a comprehensive, real-world assessment patient movement patterns.
We use a single sensor worn on the lower back that can be worn up for up to 7 days. Because the data is captured in everyday life, it is context rich and reflective of the patient's lived experience.
Single Sensor
We employ a set of complex algorithms to identify walking bouts within the raw data. We measure various parameters such as gait speed, steps, turns and waking bout duration.
Algorithms
Digital Mobility Outcomes
We then apply restrictions (e.g. walking bouts > 10 seconds) and aggregate the data to produce Digital Mobility Outcomes (DMOs), such as mean gait speed for walking bouts > 10 seconds.
We can also capture many types of data from other sensors/instruments, as well as eCOAs and patient-reported data, providing a comprehensive data capture and reporting solution.
Comprehensive Data & Reports
Our team played leading roles in the Mobilise-D project which set new standards in the capture, processing and interpretation of real-world mobility data. Prof. Lynn Rochester was the overall academic lead on the project, while Prof. Brian Caufield led the data management workstream. Dr. Alison Keogh led the Patient and Public Involvement program, which saw extensive patient involvement in the study design as well as the publication of a paper on the patient walking experience co-authored 2 of the Mobilise-D patient group. Dr. Hugo Hiden oversaw the data collection systems and David Singleton managed the operation of the project. This world class team of mobility experts and the extensive experienced gained in delivering Mobilise-D ensures that Enoda is uniquely placed to put the Mobilise-D technology and methods into practice.
Our team played leading roles in the Mobilise-D project which set new standards in the capture, processing and interpretation of real-world mobility data. Prof. Lynn Rochester was the overall academic lead on the project, while Prof. Brian Caufield led the data management workstream. Dr. Alison Keogh led the Patient and Public Involvement program, which saw extensive patient involvement in the study design as well as the publication of a paper on the patient walking experience co-authored 2 of the Mobilise-D patient group. Dr. Hugo Hiden oversaw the data collection systems and David Singleton managed the operation of the project.
This world class team of mobility experts and the extensive experienced gained in delivering Mobilise-D ensures that Enoda is uniquely placed to put the Mobilise-D technology and methods into practice.
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Unrivalled Expertise
Whitepaper Series
Enoda has created a white paper series to outline the benefits of real-world mobility data and describe how it can be gathered and applied.
We provide a comprehensive overview of the Mobilise-D method, and how it can be used to produce the highest quality real-world mobility data.
An End-to-End Solution
We provide an end-to-end 'plug-in' solution that includes all the systems required to capture, store, manage and analyse the highest quality mobility data. We can do this using the unique experience and knowledge our team has gained from the Mobilise-D project.
FOR CLINICAL TRIALS & RESEARCH
Trial Design & Support
Select Sensor
Recommending and deploying the right sensor for your trial.
Select Endpoints
Selecting appropriate mobility endpoints for the research question.
Select Forms
Selection/design of appropriate eCOA and eCRF forms.
Provide Training
Training & support on all devices/forms for clinicians & patients.
It has been known for some time that mobility measures such as gait speed are excellent predictors of physical and mental health in older adults, it is also the case in midlife. The decline in the speed and smoothness with which people walk can be an early indicator of neurodegenerative conditions such as Parkinson’s disease.
Mobility data can therefore perform an important role in the ongoing care of patients across a range of conditions. Deterioration in mobility can be a sign of underlying issues, and can flag the need for immediate clinical attention.
Mobility Data & Remote Patient Monitoring
Want to discuss the role that mobility data can play in your trial or remote patient monitoring program?
Demographic Data
We have captured a host of demographic data, including date of birth, weight, height, living-employment-marital status, ethnicity, etc.
Clinical Outcomes
Our platform has been used to capture a wide range of clinical outcome data, such as:
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Falls & Injuries
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Medications
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Mortality
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Blood pressure
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Pain
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Hospital Admission
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Timed Up & Go
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Fried Frailty Index
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6 Minute Walking Test
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BioImpedance Analysis
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EuroQol 5 Dimension 5 Level
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Short Physical Performance Battery
As well as providing real world mobility data from sensors, we can capture data from other forms of mobility assessment such as the Nursing Home Life Space Diameter and the University of Alabama Life-Space Assessment.
Mobility Performance
Condition Specific Tests
We have also captured data/measures used to track and diagnose numerous conditions such as Parkinson's, MS, COPD, Congestive Heart Failure and Proximal Femoral Fracture. Specific examples include:
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Multiple Sclerosis Functional Composite
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Multiple Sclerosis Walking Scale
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COPD Assessment Test
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Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
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New Freezing of Gait Questionnaire
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American Society of Anaesthesiologists Assessment
Neuropsychological Measures
These include:
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Falls Efficacy Scale International
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Social isolation and loneliness (UCLA)
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Patient Health Questionnaire
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Standardized Mini-Mental State Examination
We have captured a host of demographic data, including date of birth, weight, height, living-employment-marital status, ethnicity, etc.
Demographic Data
Demographic Data
We have captured a host of demographic data, including date of birth, weight, height, living-employment-marital status, ethnicity, etc.
We have captured a host of demographic data, including date of birth, weight, height, living-employment-marital status, ethnicity, etc.
Our Electronic Data Capture system captures far more than sensor data. It has been used to capture a wide range of data types such as
Other Forms of Data
Contact Us
Want to find out more about how real-world mobility data can help assess and manage conditions impacting mobility?
Clinical Areas
Algorithms technically validated among 19 patients with proximal femur fracture (PFF) with ongoing clinical validation among 566 patients.
Scoping review on the use of the DMOs in evaluating mobility recovery after hip fracture.
Multiple other measures available including:
American Society of Anesthesiologists assessment
Barthel Index (BI)
Nottingham Extended Activities of Daily Living scale's Test
Clinical Dementia Rating scale
Algorithms technically validated among 17 patients with COPD with ongoing clinical validation among 600 patients.
Numerous publications on how the algorithms and DMOs can play a role in the assessment and clinical care of patients with COPD.
Multiple other measures available including:
Spirometry (FEV1/FVC)
Exacerbations
COPD Assessment Test mMRC
C-PPAC
Isometric quads muscle force
Oxygen saturation at rest
Algorithms technically validated among 20 Parkinson’s patients with ongoing clinical validation among 600 patients.
Numerous publications on how the algorithms and DMOs can play a role in the assessment and clinical care of patients with Parkinson’s.
As well as real-world mobility data, we can provide multiple other measures used in assessing Parkinson’s such as:
MDS-UPDRS
Mini-BESTest
New FOG Questionnaire
MoCA
Falls diaries
Falls reports
Algorithms technically validated among 20 MS patients. Ongoing clinical validation among 600 MS patients.
Numerous publications on how the algorithms and DMOs can play a role in the assessment and clinical care of patients with MS.
We can also provide other measures such as:
Mod. Fatigue Impact Scale
MS Functional Composite
Exp. Disability Status Scale
PDDS
MSWS-12
Symbol Digit Modalities Test
Low-Contrast Letter Acuity
Falls diariesAlgorithms technically validated among 20 Congestive Heart Failure patients.
Data Collection & Processing
Sensor Logistics
Devices delivered to location of choice (test centre or home).
Data Upload
Devices sent back to site /Enoda and data uploaded to our EDC.
Data Processing
Sensor data run through cleaning & processing algorithms.
Generate DMOs
Aggregate data to create Digital Mobility Outcomes.
Collect eCOA/eCRF
eCOA and eCRF data collected & cross validated with DMOs.
Reporting & Analysis
Patient Monitoring
Project management reports (patient retention, task completion, etc.)
DMO Reports
Reports generated analysing the DMOs and other data.
DMO Interpretation
Advice on the analysis and interpretation of the DMOs.