Accidental Fall4279NCT03680014
Remote Monitoring and Analysis of Gait and Falls Within an Elderly Population
An observational study of Accidental Fall and Fall, run by CUSH Health Ltd..
Trial 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. Read more about this study
- Who is it for?
- Ages 65 years and olderHealthy volunteers can take part
- Conditions the study is for
- Accidental Fall, Fall, Fall Injury
- What is being tested?
- CUSH
- How many people
- 40 participants
- Where
- No sites listed yet
- Study dates
- 12 Sep 2018to 30 Sep 2019
- How long will I take part?
- 2 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- Healthy volunteers accepted
- Older adult
Study population: At risk individuals who are able to consent to take part in study.
Who may be able to join
- Aged over 65 years of age
- Able to give informed consent
- Able to mobilise independently or with mobility aid (walking stick, Zimmer frame etc.)
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet.
About this study
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.
Read the detailed description
Phase 1 - Creation The creation of a databank of measurements during stereotypical actions of daily living.
Population recruited (P1 recruits) Patients to complete a demographic questionnaire based on historical falls risk data.
Recruited patients allocated to a mobility/independence subgroup based on the information obtained from the demographic questionnaire completed at the time of recruitment.
Patients may be reclassified to different subgroups to redistribute them according to other factors including frailty, exercise tolerance, balance etc.
Patients given a structured scripted set of simple activities to carry out (such as walking, sitting and picking something off the floor etc.) to cover as many activities of daily of living as possible.
Their actions will be monitored with inertia measurement units (IMU) 1-3 per patient (Each will be off the shelf European Conformity (CE) approved products no bigger than a modern smart phone).
Data is transmitted from the IMUs wirelessly to the investigators' laptop, patients will not be attached or anchored to any secondary devices or cables. The IMUs are attached to the patients with a comfortable lightweight belt design and or non-invasive leg bands.
These scripted sessions will be repeated 5 to 10 times per subject to create an anonymous databank (D1). This will form a reference range per subgroup of "truth" data for the algorithm to search.
Patient identifiable data will be limited to the study questionnaires which will be stored securely at the corresponding study site.
Phase 2 - Validation The validation of the databank while observing previously recorded and not previously recorded subjects in an unscripted session.
Population patients included All patients from phase 1, new patients recruited (P2 recruits)
Newly recruited patients (P2) to complete a demographic questionnaire based on historical falls risk data. These patients will not be immediately allocated to a mobility/independence subgroup, but labelled as unknown (x).
All patients observed individually during daily activities in an unscripted session. Patient to wear single IMU sensor which captures mobility data (D2). The activities the subject undertakes during D2 are manually entered onto timesheet by observer for later cross referencing.
2.1 Patients (P2) who are previously unrecorded and who are unallocated to a mobility/independence subgroup have their unscripted data (D2) analysed by the motion algorithm which stratifies them to a mobility/independence subgroup based on the likeness of their data to that stored in the databank (D1). They are then allocated to a mobility/independence subgroup based on their questionnaire data and the two outcomes are compared to see if they match or differ.
- Can algorithm predict "truth"
2.2 Patients (P1) who have previously been recorded in phase 1 and who are allocated to a mobility/independence subgroup based on their questionnaire data have their unscripted data (D2) from phase 2 analysed by the motion algorithm. This then stratifies them again to a mobility/independence subgroup based on the likeness of their new data (D2) to that stored in the databank (D1). To see if their outcome remains the same or differs.
- Can algorithm confirm "truth" (See Appendix B. for diagrammatically representation of phase 1 and phase 2.) During the unscripted session (D2) any falls or patient reported loss of balance that occurs will be recorded and time stamped by the observer. These data sets will be labelled as "Falls truth" or "Balance truth" data respectively and added to the anonymous databank for cross-reference.
- Conditions
- Accidental Fall, Fall, Fall Injury, Hip Fractures
- Being tested
- CUSH
- Medical subject headings
- Hip Fractures
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CUSHDiagnostic test
Machine learning assisted remote monitoring/ telehealth platform to predict and prevent falls
| Study group | Role | What they receive |
|---|---|---|
| 1 | Capable of independent of daily activities and able to mobilise unaided. No previous of falls. Receives: Diagnostic Test: CUSH | |
| 2 | Capable of independent of daily activities and able to mobilise unaided. With a previous of atleast one fall. Receives: Diagnostic Test: CUSH | |
| 3 | Requires help with most daily activities, mobilises with a single walking stick. No previous falls. Receives: Diagnostic Test: CUSH | |
| 4 | Requires help with most daily activities, mobilises with a single walking stick. With a previous of atleast one fall. Receives: Diagnostic Test: CUSH | |
| 5 | Requires help with most daily activities. Mobilise with frame or roller frame. No previous falls. Receives: Diagnostic Test: CUSH | |
| 6 | Requires help with most daily activities. Mobilise with frame or roller frame. Previous history of at least one fall. Receives: Diagnostic Test: CUSH |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Sep 2018
- Study start12 Sep 2018 (estimated)
- Primary completion31 Oct 2018 (estimated)
- Study completion30 Sep 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Accidental FallStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
- Target duration
- 2 Days
Sponsor and investigators
- Lead sponsor
- CUSH Health Ltd.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Principal investigator, Kalon Hewage, CUSH Health Ltd.
- Investigators
- Sam Fosker, BMBS, Study director, CUSH HealthKalon Hewage, MBBS BSc, Study chair, CUSH Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03680014
- Sponsor's protocol ID
- CUSH DHTC 4279
- Acronym
- 4279
- Status last verified
- Sep 2018
- First submitted
- 31 Aug 2018
- Last updated
- 21 Sep 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Remote Monitoring and Analysis of Gait and Falls Within an Elderly Population
- ClinicalTrials.gov ID
- NCT03680014
- Acronym / sponsor ID
- 4279 / CUSH DHTC 4279
- Conditions
- Accidental Fall, Fall, Fall Injury, Hip Fractures
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- CUSH Health Ltd.Industry (pharma, biotech, devices)
- Study start
- 12 Sep 2018 (estimated)
- Primary completion
- 31 Oct 2018 (estimated)
- Study completion
- 30 Sep 2019 (estimated)
- First posted
- 21 Sep 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Phase 1 - Creation The creation of a databank of measurements during stereotypical actions of daily living.
Population recruited (P1 recruits) Patients to complete a demographic questionnaire based on historical falls risk data.
Recruited patients allocated to a mobility/independence subgroup based on the information obtained from the demographic questionnaire completed at the time of recruitment.
Patients may be reclassified to different subgroups to redistribute them according to other factors including frailty, exercise tolerance, balance etc.
Patients given a structured scripted set of simple activities to carry out (such as walking, sitting and picking something off the floor etc.) to cover as many activities of daily of living as possible.
Their actions will be monitored with inertia measurement units (IMU) 1-3 per patient (Each will be off the shelf European Conformity (CE) approved products no bigger than a modern smart phone).
Data is transmitted from the IMUs wirelessly to the investigators' laptop, patients will not be attached or anchored to any secondary devices or cables. The IMUs are attached to the patients with a comfortable lightweight belt design and or non-invasive leg bands.
These scripted sessions will be repeated 5 to 10 times per subject to create an anonymous databank (D1). This will form a reference range per subgroup of "truth" data for the algorithm to search.
Patient identifiable data will be limited to the study questionnaires which will be stored securely at the corresponding study site.
Phase 2 - Validation The validation of the databank while observing previously recorded and not previously recorded subjects in an unscripted session.
Population patients included All patients from phase 1, new patients recruited (P2 recruits)
Newly recruited patients (P2) to complete a demographic questionnaire based on historical falls risk data. These patients will not be immediately allocated to a mobility/independence subgroup, but labelled as unknown (x).
All patients observed individually during daily activities in an unscripted session. Patient to wear single IMU sensor which captures mobility data (D2). The activities the subject undertakes during D2 are manually entered onto timesheet by observer for later cross referencing.
2.1 Patients (P2) who are previously unrecorded and who are unallocated to a mobility/independence subgroup have their unscripted data (D2) analysed by the motion algorithm which stratifies them to a mobility/independence subgroup based on the likeness of their data to that stored in the databank (D1). They are then allocated to a mobility/independence subgroup based on their questionnaire data and the two outcomes are compared to see if they match or differ.
- Can algorithm predict "truth"
2.2 Patients (P1) who have previously been recorded in phase 1 and who are allocated to a mobility/independence subgroup based on their questionnaire data have their unscripted data (D2) from phase 2 analysed by the motion algorithm. This then stratifies them again to a mobility/independence subgroup based on the likeness of their new data (D2) to that stored in the databank (D1). To see if their outcome remains the same or differs.
- Can algorithm confirm "truth" (See Appendix B. for diagrammatically representation of phase 1 and phase 2.) During the unscripted session (D2) any falls or patient reported loss of balance that occurs will be recorded and time stamped by the observer. These data sets will be labelled as "Falls truth" or "Balance truth" data respectively and added to the anonymous databank for cross-reference.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 2 Days
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| 1 | – | Diagnostic Test: CUSHCapable of independent of daily activities and able to mobilise unaided. No previous of falls. |
| 2 | – | Diagnostic Test: CUSHCapable of independent of daily activities and able to mobilise unaided. With a previous of atleast one fall. |
| 3 | – | Diagnostic Test: CUSHRequires help with most daily activities, mobilises with a single walking stick. No previous falls. |
| 4 | – | Diagnostic Test: CUSHRequires help with most daily activities, mobilises with a single walking stick. With a previous of atleast one fall. |
| 5 | – | Diagnostic Test: CUSHRequires help with most daily activities. Mobilise with frame or roller frame. No previous falls. |
| 6 | – | Diagnostic Test: CUSHRequires help with most daily activities. Mobilise with frame or roller frame. Previous history of at least one fall. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
- Study population
- At risk individuals who are able to consent to take part in study.
Key inclusion criteria (3 listed)
- Aged over 65 years of age
- Able to give informed consent
- Able to mobilise independently or with mobility aid (walking stick, Zimmer frame etc.)
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CUSHMachine learning assisted remote monitoring/ telehealth platform to predict and prevent falls | Diagnostic test | 1, 2, 3, 4, 5, 6 |
Primary outcome
Validation of gait analysis algorithm by use of Inertial Measurement Unit (IMU)
Time frame: 3 months
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2018.
- Individual participant dataSharing planned: No
Related trials for Accidental Fall
Not yet recruiting
Comparative Effects of Exercises on Reactive Balance and Fall Risk in Community-Dwelling Older Adults
COMMUNITY-DWELLING OLDER ADULTS
Not yet recruiting
Pilates vs. Core Stabilization for Balance and Mobility in Elderly Type 2 Diabetics
Mellitus Balance Deficits Postural Balance Mobility Limitation, Type 2 Diabetes Mellitus
Not yet recruiting
Multicomponent Exercise and Pain Science Education for Fall Prevention in Older Adults With Chronic Musculoskeletal Pain (MovePainFall)
Chronic Pain, Musculoskeletal Pain
RecruitingObservational study
Association Between Intake of Psychotropic Medications and Risk of Falls in Older Adults
Falls (Accidents) in Old Age, Psychotropic Drugs
Not yet recruiting
Implementing Action Falls for Domiciliary Care
Social Care, Falls (Accidents) in Old Age
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sam Fosker, BMBS study director
CUSH Health
Kalon Hewage, MBBS BSc study chair
CUSH Health
Questions about this trial
What is this clinical trial studying?
An observational study of Accidental Fall and Fall, run by CUSH Health Ltd..
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Sep 2018. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Kalon Hewage, MBBS BSc (+447776328379, kalon@cushhealth.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Validation of gait analysis algorithm by use of Inertial Measurement Unit (IMU) (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Sep 2018.