Remote Monitoring and Analysis of Gait and Falls Within an Elderly Population
4279
1 other identifier
observational
40
0 countries
N/A
Brief Summary
The investigators aim to do this initial pilot study as an observational prospective cohort study, evaluating elderly patients who have capacity in National Health Service (NHS) rehabilitation and community hospitals. The patients will each be recorded doing simple activities of daily living in two 2 hour sessions using a discrete wireless device. This will generate anonymous data set that can be used to train and refine our machine learning algorithm.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for all trials
Started Sep 2018
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 31, 2018
CompletedStudy Start
First participant enrolled
September 12, 2018
CompletedFirst Posted
Study publicly available on registry
September 21, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 31, 2018
CompletedStudy Completion
Last participant's last visit for all outcomes
September 30, 2019
CompletedSeptember 21, 2018
September 1, 2018
2 months
August 31, 2018
September 19, 2018
Conditions
Outcome Measures
Primary Outcomes (1)
Validation of gait analysis algorithm by use of Inertial Measurement Unit (IMU)
Validation of a motion analysis algorithm to predict falls risk in an elderly population using IMU data captured from accelerometers and gyroscopes within the IMU. The IMU will allow capture of data from wearers gait and movement.
3 months
Study Arms (6)
1
Capable of independent of daily activities and able to mobilise unaided. No previous of falls.
2
Capable of independent of daily activities and able to mobilise unaided. With a previous of atleast one fall.
3
Requires help with most daily activities, mobilises with a single walking stick. No previous falls.
4
Requires help with most daily activities, mobilises with a single walking stick. With a previous of atleast one fall.
5
Requires help with most daily activities. Mobilise with frame or roller frame. No previous falls.
6
Requires help with most daily activities. Mobilise with frame or roller frame. Previous history of at least one fall.
Interventions
Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls
Eligibility Criteria
At risk individuals who are able to consent to take part in study.
You may qualify if:
- Aged over 65 years of age
- Able to give informed consent
- Able to mobilise independently or with mobility aid (walking stick, Zimmer frame etc.)
You may not qualify if:
- Patients under the age of 65.
- Patients who are bedbound or wheelchair bound.
- Patients with cognitive impairment and are unable to give informed consent.
- Significant medical co-morbidities that make participation in the study unsafe.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- CUSH Health Ltd.lead
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Sam Fosker, BMBS
CUSH Health
- STUDY CHAIR
Kalon Hewage, MBBS BSc
CUSH Health
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 2 Days
- Sponsor Type
- INDUSTRY
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Co-Founder, Director
Study Record Dates
First Submitted
August 31, 2018
First Posted
September 21, 2018
Study Start
September 12, 2018
Primary Completion
October 31, 2018
Study Completion
September 30, 2019
Last Updated
September 21, 2018
Record last verified: 2018-09
Data Sharing
- IPD Sharing
- Will not share