Age ProblemNCT05308563
Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography
An observational study of Age Problem and Fall, run by National Taiwan University Hospital.
Trial summary
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. Read more about this study
- Who is it for?
- Ages 60 years and olderHealthy volunteers can take part
- Conditions the study is for
- Age Problem, Fall
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- Apr 2022to Dec 2023
- 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 60 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Community-living aged group
Who may be able to join
- can walk in the household without device independently
Who cannot join
- with terminal disease
- with cognitive impairment to follow verbal instruction
- with neurological conditions that are associated with leg weakness
- with significant visual impairment that interferes with daily living and walking
Trial locations and contacts
No sites are listed yet.
About this study
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests and the tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls n the past one year and verified in a 6-month follow up. The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools.
Read the detailed description
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests (Berg Balance scale, Timed-up-and-go, 30s-sit-to-stand, four-stage balance tests) and a tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls in the past one year and verified in a 6-month follow up.
The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools. The investigators will evaluate the correlation of these posturographic features and data obtained by other methods. Risk factors of previous falls and future falls will also analyzed.
- Conditions
- Age Problem, Fall
- Keywords
- fall risk, posturography, machine learning, fall efficacy, deep learning
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of fall events
self-reported fall events according to a followup questionnaire and defined as the sudden, involuntary transfer of body to the ground and at a lower level than the previous one
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Apr 2022
- Study startApr 2022 (estimated)
- Primary completionJun 2023 (estimated)
- Study completionDec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Age ProblemStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- 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
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Taiwan University Hospital, Yun-Lin Branch, National Yunlin University of Science and Technology
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05308563
- Sponsor's protocol ID
- 202112114RINA
- Status last verified
- Mar 2022
- First submitted
- 24 Mar 2022
- Last updated
- 4 Apr 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Liang HW, Chi SY, Chen BY, Hwang YH. Reliability and Validity of a Virtual Reality-Based System for Evaluating Postural Stability. IEEE Trans Neural Syst Rehabil Eng. 2021;29:85-91. doi: 10.1109/TNSRE.2020.3034876. Epub 2021 Feb 25.
PubMed 33125332
Study at a glance
From the registry record, last updated by the sponsor on 4 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography
- ClinicalTrials.gov ID
- NCT05308563
- Acronym / sponsor ID
- 202112114RINA
- Conditions
- Age Problem, Fall
- Keywords
- fall risk, posturography, machine learning, fall efficacy, deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- National Taiwan University Hospital, Yun-Lin Branch, National Yunlin University of Science and Technology
- Study start
- Apr 2022 (estimated)
- Primary completion
- Jun 2023 (estimated)
- Study completion
- Dec 2023 (estimated)
- First posted
- 4 Apr 2022 (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 purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests and the tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls n the past one year and verified in a 6-month follow up. The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools.
Detailed description
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests (Berg Balance scale, Timed-up-and-go, 30s-sit-to-stand, four-stage balance tests) and a tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls in the past one year and verified in a 6-month follow up.
The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools. The investigators will evaluate the correlation of these posturographic features and data obtained by other methods. Risk factors of previous falls and future falls will also analyzed.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Community-living aged group
Key inclusion criteria (1 listed)
- can walk in the household without device independently
Key exclusion criteria (4 listed)
- with terminal disease
- with cognitive impairment to follow verbal instruction
- with neurological conditions that are associated with leg weakness
- with significant visual impairment that interferes with daily living and walking
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Number of fall events
Time frame: 6 months
self-reported fall events according to a followup questionnaire and defined as the sudden, involuntary transfer of body to the ground and at a lower level than the previous one
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Apr 2022.
- Background: PubMed 33125332Liang HW, Chi SY, Chen BY, Hwang YH. Reliability and Validity of a Virtual Reality-Based System for Evaluating Postural Stability. IEEE Trans Neural Syst Rehabil Eng. 2021;29:85-91. doi: 10.1109/TNSRE.2020.3034876. Epub 2021 Feb 25.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Age Problem and Fall, run by National Taiwan University Hospital.
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 1 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 4 Apr 2022. 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 Huey-Wen Liang (+886-02-23123456 ext. 66697, lianghw@ntu.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of fall events (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05308563, which also lists 1 linked publication.
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 4 Apr 2022.