Development and Applications of Daily-use Fall Risk Assessment Device to Prevent Elderly People From Falling

Fall Clinical Trial

— ITF-Balance  

Official title:

Development and Applications of Daily-use Fall Risk Assessment Device to Prevent Elderly People From Falling

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05693480
• Other study ID #: ITB/FBL/4018/21/P
• Status: Recruiting
• Phase: N/A
• Start date: July 1, 2022
• Est. completion date: October 30, 2024

Study information

• Verified date: February 2023
• Source: The University of Hong Kong
• Contact: Vivian W. Q. Lou
• Phone: 2831-5334/3917-4835
• Email: wlou[@]hku.hk
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This research attempts to develop an artificial intelligence (AI) enabled device for measuring the dynamic balance ability of older people with a sensor using an optical principle called Frustrated Total Internal Reflection. The AI-based algorithm embedded in the device performs the data analysis for balance ability assessment and falling risk prediction. As a critical part of the research, a large-scale user study is needed to test the validity of the device regarding the dynamic balance ability assessment and the accuracy of the falling risk prediction provided by the device. Also, we plan to study the factors influencing user engagement in this device through the questionnaire-based survey and interview.

Description:

Falling is a significant threat to the health and independent living quality of the elderly. The loss of balance leads to falls. Balance is a very complex neuromuscular reflex function that may be affected by the general condition of the body and one or more impairments in the sensory, nervous, cardiovascular, and musculoskeletal systems. Currently, no test or tool can directly measure balancing capability in the clinical setting. Most clinicians use proxy-based methods to estimate the likelihood of falling, such as the Timed Up and Go Test which has low diagnostic accuracy. Other commonly used observational fall assessment tools (e.g., Berg Balance Scale and Performance-Oriented Mobility Assessment) require long administration time, suffering ceiling effects and subjective judgment. The computerized dynamic posturography devices are used in specialized fall assessment and rehabilitation centers. However, these devices are not routinely used in clinical or home settings due to high user costs and special training requirements.

To overcome the abovementioned limitations of extant approaches for measuring dynamic balance ability and predicting the risk of falling. This research attempts to develop an artificial intelligence (AI) enabled device for measuring the dynamic balance ability of older people with a sensor using an optical principle called Frustrated Total Internal Reflection (FTIR). The AI-based algorithm embedded in the device performs the data analysis for balance ability assessment and falling risk prediction.

As a critical part of the research, a large-scale user study is needed to test the validity of the device regarding the dynamic balance ability assessment compared with the common clinical assessment tools used by doctors and other healthcare professionals. In addition, this large-scale user study will also investigate the accuracy of the falling risk prediction provided by the device. Also, the investigators plan to study the factors influencing user engagement in this device through the questionnaire-based survey.

The targeted direct user group of this device is people aged 60 years old or above who live in Hong Kong. The whole study will last six months, and the outcome will be assessed pre-and post-implementation for comparison and validation. The major testing criteria include the device specifications for validity and reliability, user experiences and adoption, prediction model accuracy.

First Round: The participant will be invited to take three tests: A device test, a time up and go test, and a Berg balance test. The device test involves five simple tasks that require participants to complete while using the balance sensor to assess the balance ability. In addition, participants will be required to complete physical data measurements to collect the weight, height, blood pressure, muscle mass, body mass index (BMI), and other physical data. Besides, participants will be required to complete questionnaires.

Second Round (after 90 days): Participants will be invited to participate in the device test:

The device test involves five simple tasks that request participants to finish when using the device to assess participants' balance ability. Besides, after the tests, participants are invited to fill out a questionnaire.

Third Round (after 180 days): Participants will be invited to participate in the device test:

The device test involves five simple tasks that request participants to finish when using the device to assess the balance ability. Besides, after the tests, participants are invited to fill out a questionnaire.

Investigators will conduct three types of analysis to suit the project needs. First, the investigators will use Descriptive Statistics to summarize demographic information, test results of baseline test and device test, as well as the participant responses to the surveys. Also, the investigators will transform any skewed variables and correct their skewness by means of log transformation before inferential analysis. To assess normality, the investigators will perform the Shapiro-Wilk test of normality as well as the normal Q-Q Plot. Second, the investigators will apply Bivariate Correlation Analysis (Pearson, Two-tailed) to verify the validity of the balance ability assessment of the device. Third, the investigators will apply the Discriminant Analysis (i.e., sensitivity and specificity analysis) to verify the precision of the falling risk prediction provided by the device.

The principal investigator will be responsible for the safekeeping of the personal data of participants during and after the experiment. These data will be used for academic and clinical research only. Moreover, the data with the identifiable information will be kept for up to 5 years after the first publication. Anonymous data will be kept for up to 10 years after the first publication arising from this project. All information obtained will be used for research purposes only. All the data will be stored in an encrypted workstation and on password-protected online cloud storage.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1300
• Est. completion date: October 30, 2024
• Est. primary completion date: June 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 60 Years to 80 Years

Eligibility

Inclusion Criteria for reference group participants:

• Age = 60

• Ability to communicate with experiment operators (in Chinese or English)

• Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participate

Exclusion Criteria for reference group participants:

• Do not fall in the past twelve months

Inclusion Criteria for intervention group participants:

• Age = 60

• Ability to communicate with experiment operators (in Chinese or English)

• Ability to provide informed consent to participate after being given information about the experiment and other information that the participant must know to participate

Exclusion Criteria for intervention group participants:

• Inability to give written informed consent (e.g., illiterate or with cognitive impairment)

• Inability to stand still without any assistance for 30 seconds

Related Conditions & MeSH terms

• Fall

Intervention

Device:
• Balance sensor
Participants will engage in the device test, clinical assessment tests, and related user experience surveys to uncover problems in the effectiveness, efficiency, and acceptance of the device prototype. The whole test will last six months, and the outcome will be assessed pre- and post-implementation for comparison and validation. The major testing criteria include the validity and reliability of the device, user experiences, and the accuracy of the device's fall prediction.

Locations

Country	Name	City	State
Hong Kong	Name: The University of Hong Kong	Hong Kong

Sponsors (3)

Lead Sponsor	Collaborator
The University of Hong Kong	Aberdeen Kai-fong Welfare Association Social Service, Hong Kong Young Women's Christian Association

Country where clinical trial is conducted

Hong Kong, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Precision assessment of falling risk prediction	A primary outcome is the precision assessment of falling risk prediction. The specific measurement variables for this primary outcome are the classification results of high/low falling risk based on the scoring results (a composite result, namely Falling Probability (FP)) of the device test and participants' actual falling incident indicators, including the number of falls, the severity of falling incidents after the device test, and medical history of the participants (say, the falls history within past six months). The FP ranges from zero to one, in which the higher score indicates a higher possibility of fall. The analysis metric for this primary outcome is sensitivity and specificity performance analysis of the device assessment.	Six Months
Secondary	Validity of the balance ability measurement made by the device	The secondary outcome is the validity of the balance ability measurement made by the device. The specific measurement variables for this secondary outcome are the balance ability score measured by the baseline tests and the balance ability score measured by the device test. The analysis metric for this secondary outcome is the correlation coefficient and the significant level of the correlation between the balance ability score of the device test and the balance ability score of baseline tests.	Six Months
Secondary	User Experience	The survey data about user experience and adoption would also be a secondary measurement. We will conduct structural equation modeling to identify an effective model explaining user adoption. This model could guide us in improving our device development.	Six Months