Developing a Falls Prediction Tool Using Both Accelerometer and Video Gait Analysis Data in Older Adults (APDM system)

Our group, consisting of academic clinicians and research engineers, seeks to create a database of stability measures (accelerometers, gyroscopes and altitude sensor data) in older adults monitored longitudinally. This Stability Measures (SM) database will allow us to use new machine learning methods to develop and then validate algorithms that predict future falls, allowing for better targeting of vulnerable patients.

Study Overview

• Status: Not yet recruiting
• Conditions: Fall Injury Prevention
• Intervention / Treatment: Other: A 6-minute walk test

Detailed Description

Recent advances in machine learning have disrupted the standard approach to assessing medical prognosis. Our group, consisting of academic clinicians and research engineers, seeks to create a database of stability measures ( accelerometers, gyroscopes and altitude sensors data) in older adults monitores longitudinally. This Stability Measures (SM) database will allow us to use new machine learning methods to develop and validate algorithms that predict future falls, allowing to better targetting of vulnerable individuals.

Although there have been numerous attempts to quantify fall risk in older adults using bedside scales 7-12, no previous group has attempted to use a combination of both accelerometer and video measures to assess gait stability. Since these measures will be captured in both frequently falling and infrequently falling patients, we will have SM data for various windows of time (1, 2, 3 and 4-weeks) prior to at least 100 fall events, a dataset that has never been captured before.

HYPOTHESES:

1. A combination of accelerometer, gyroscope, and video data can be used to predict falls longitudinally, first by the use of a training dataset followed by verification on a validation data set.
2. All the above sensor-based inputs can be combined as a simple, automated predcition tool to predict fall risk in older adults Current Methods of Falls Risk Assessment: Current methods of predicting falls in physician offices rely heavily on simple bedside tests12-14. Although useful, all of these measures have quite low sensitivity and specificity, with an Area Under the Curve (AUC) of approximately 0.707-12.

In fact, a recent meta-analysis "could not identify any tool which had an optimal balance between sensitivity and specificity, or which was clearly better than a simple clinical judgment of risk of falling"

METHODS:

a) Subjects: i) High Risk Subjects (n=50): All subjects will be recruited from falls and geriatrics clinics at Vancouver General Hospital. These clinics see about 2500 patients per year and are currently used for research recruitment. Each clinic patient has gait speed measured, which will allow to recruit both high and low risk fallers. This test will allow us to recruit 50 subjects at marked risk for falls, providing us with prospectively gathered dataset of greater than 100 events, five times higher than any other sensor study.

ii) Low Risk Subjects (n=50): In addition, we will use newspaper advertisements to recruit and then screen low risk subjects. All subjects will have a gait speed > 0.8 m/s and have had no falls in the last year.

All study patients with come to the laboratory (Gerontology and Diabetes Research Laboratory, VGH Research Pavilion) for a one hour session. Each subject will perform a 6-minute walk test during which gait assessment will be obtained from the APDM system (Portland, OR). In addition there will be four video cameras (on the front, back and sides) that will measure raw video data for our gait analysis. The camera does not record any facial data (in fact, 'deepfake' software in the system deletes all facial details) and the patient's movements are converted to a 'stick figure' prior to being saved in the system. In addition, a Xethru X4M03 kit was will be used to collect ultra-sideband radar data (UWB). The UWB radar operates in 5.9-10.3 GHz, providing high spatial resolution. The radar is placed 1.5 m above the floor level. To collect heel-toe strike timing data, the subject will ambulate on the GAITRite system (CIR Systems Inc, Franklin, NJ), with a 90 × 700-cm × 3.2-mm walkway.

• Study Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Boris Feldman; Phone Number: 6048755115; Email: boris.feldman@vch.ca

Study Locations

• Canada
  • British Columbia
    • Vancouver, British Columbia, Canada, V5Z 1M9
      • Vancouver Coastal Health Research Institute, VGH Research Pavilion Room 186

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years and older (Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

We're planning to recruit 100 subjects from the Geriatric Medicine clinic at Vancouver General Hospital

Description

Inclusion Criteria:

• All subjects must be aged 65 years and older • All subjects must have had at least one fall in the last year and have been referred to the falls clinic (High Risk Subjects) OR have had no falls on the last year and have responded to our newspaper advertisement (Low Risk Subjects)

Exclusion Criteria:

• Subjects age less than 65 years old

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
High Risk Subjects (n=50); All subjects will be recruited from falls and geriatrics clinics at Vancouver General Hospital. These clinics see about 2500 patients per year and are currently used for research recruitment. Each clinic patient has gait speed measured, which will allow to recruit both high and low risk fallers. This test will allow us to recruit 50 subjects at marked risk for falls, providing us with prospectively gathered dataset of greater than 100 events, five times higher than any other sensor study.	Other: A 6-minute walk test; Each subject will perform a 6-minute walk test during which gait assessment will be obtained from the APDM system (Portland, OR). In addition there will be four video cameras (on the front, back and sides) that will measure raw video data for our gait analysis. The camera does not record any facial data (in fact, 'deepfake' software in the system deletes all facial details) and the patient's movements are converted to a 'stick figure' prior to being saved in the system.; Other Names: Video recording
Low Risk Subjects (n=50); We will use newspaper advertisements to recruit and then screen low risk subjects. All subjects will have a gait speed > 0.8 m/s and have had no falls in the last year.	Other: A 6-minute walk test; Each subject will perform a 6-minute walk test during which gait assessment will be obtained from the APDM system (Portland, OR). In addition there will be four video cameras (on the front, back and sides) that will measure raw video data for our gait analysis. The camera does not record any facial data (in fact, 'deepfake' software in the system deletes all facial details) and the patient's movements are converted to a 'stick figure' prior to being saved in the system.; Other Names: Video recording

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Classification of accuracy of the algorithm on the validation dataset	Consisting of True positive (TP), False positive (FP) rate, Precision, Recall (similar to sensitivity), F-measure (conveys the balance between precision and recall) and the Receiver Operation Curve area	60 min

Collaborators and Investigators

• Sponsor: University of British Columbia
• Investigators: Principal Investigator: Kenneth Madden, MD, UBC

Study record dates

Study Major Dates

• Study Start (Estimated): March 1, 2024
• Primary Completion (Estimated): April 1, 2025
• Study Completion (Estimated): April 1, 2026

Study Registration Dates

• First Submitted: April 16, 2020
• First Submitted That Met QC Criteria: April 16, 2020
• First Posted (Actual): April 21, 2020

Study Record Updates

• Last Update Posted (Actual): November 29, 2023
• Last Update Submitted That Met QC Criteria: November 27, 2023
• Last Verified: November 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: H19-03094

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No