Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts

Creation and use of a smartphone application for older adults to assess the participants' risk of fall. Phase 1: Compare the accuracy and validity of accelerometer and gyroscopic data from a smartphone and gold-standard, wearable sensors gathered during balance and gait activities. Phase 2: Develop a model that integrates wearable sensor data and individual characteristics, such as age, medical conditions, exercises, previous falls, fear of falls, along with gait and balance outcome measurements, to evaluate fall risk in older adults. Phase 3: Integrate the computational model in the design of a mobile app for wearable devices for older adults to self-administer fall risk assessments and provide individualized risk of fall information.

Study Overview

• Status: Recruiting
• Conditions: Mass Screening
• Intervention / Treatment: Behavioral: risk of fall

Detailed Description

Falls are prevalent among older adults and can cause serious problems. Falls in older adults can cause serious injuries that negatively impact their quality of life and can be life-threatening. Evaluating an individual's risk of fall is, typically, an important first step in preventing falls. Fall risk is commonly evaluated through clinical measurement scales, such as the Tinetti Performance Oriented Mobility Assessment (POMA) and Berg Balance Scale (BBS). Physical measurements using instruments, such as inertial measurement units (IMUs; accelerometers and gyroscopes) and force plates, can also be employed to evaluate an individual's fall risk. However, both clinical and instrumented measures are often only collected in clinical or research settings, thus making them less accessible to older adults and their care providers. Additionally, fall risk can only be evaluated infrequently, which can be a problem as health and environmental changes in the life of an older adult can necessitate more frequent measurement of fall risk. The research team proposes consumer-grade wearable devices (e.g. smartphones and watches) to fill the gap in current fall risk assessment. This approach has great potential as quick, simple, timely, and frequent measures of fall risk can help to reduce fall risk in older adults. The proposed research investigates older adults' gait and balance to identify potential links between wearable sensor measurements and fall risk. The types and granularity of data on physical activities that can be collected by consumer-grade wearable devices are more limited than using research-grade measurement. The investigators plan to use research-grade sensors to validate measures of gait and balance via consumer-grade wearable devices. Signal processing algorithms will be employed to extract the critical patterns from wearable device measurements that could be used for regular fall risk monitoring. A machine-learning computational model will also be developed to correlate the wearable data to clinical scales. This data will be used to design and build a mobile app for older adults to self-administer the fall risk test at home. The application design will be informed by factors such as one's physical environment, health condition, fear of falls, etc. and the goal is to develop an integrated system that offers fall risk assessment and provides alerts for older adults.

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

Contacts and Locations

• Study Contact: Name: Nathan Miller, Ph.D.; Phone Number: 810-762-3234; Email: natmille@umich.edu
• Study Contact Backup: Name: Cathy A Larson, PT, Ph.D.; Phone Number: 8107623373; Email: clarson@umich.edu

Study Locations

• United States
  • Michigan
    • Flint, Michigan, United States, 48502
      • Recruiting
      • University of Michigan-Flint
      • Contact: Nathan Miller, Ph.D.; Phone Number: 810-762-3234; Email: natmille@umich.edu
      • Contact: Cathy A Larson, Ph.D.; Phone Number: 8107623373; Email: clarson@umich.edu
      • Sub-Investigator: Linda Zhu, Ph.D.
      • Principal Investigator: Jennifer Liao, Ph.D.
      • Sub-Investigator: Charlotte Tang, Ph.D.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

older adults ages 65 years and older

Description

Inclusion Criteria:

• 65 years or older

Exclusion Criteria:

• have been diagnosed with neurological conditions such as multiple sclerosis, Parkinson's disease, traumatic brain injury, Alzheimer's disease, or have had a stroke in the last year
• have orthopedic or cardiopulmonary conditions and/or surgeries in the past year
• have physical limitations that would make it difficult or uncomfortable for individuals to perform the experimental tasks.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Comparison of acceleration and 3D rotation during balance and movement; Can consumer-grade sensors used in mobile phones provide an accurate and valid measure of balance and gait when compared to gold standard research-grade sensors? A computational model for risk of fall will be developed.	Behavioral: risk of fall; Gather information that will assist in determining risk of fall. The researchers will ask the subjects to perform several motor tests and study-related questionnaires.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
3D acceleration	Vertical, medial-lateral, and anterior-posterior acceleration	60 seconds to 6 minutes
3D rotation	Vertical, medial-lateral, and anterior-posterior rotation	60 seconds to 6 minutes

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Montreal Cognitive Assessment (MoCA)	Detect cognitive involvement, Scores greater than 26 indicates normal cognition; scores less than 26 indicate cognitive impairment. Therefore, lower scores are a worse outcome. 18-25: Mild cognitive impairment; 10-17: Moderate cognitive impairment; Less than 10: Severe cognitive impairment.	15 minutes
Berg Balance Scale (BBS)	Assess static and dynamic balance and risk of fall. 14 item scale. Lower scores indicate poorer balance and higher scores indicate better balance. Score of less than 45 out of 56 indicates that the individual may be at a greater risk of fall. Out of 56, 41-56 Independent, 21-40 walking with assistance, 0-20 wheelchair bound.	15 minutes
Timed Up and Go (TUG)	Assess mobility, balance, walking ability and risk of fall. Less than or equal to 10 seconds: normal; 11-30 seconds: good mobility, can go outside alone, mobile without a gait assistive device; greater than 30 seconds: problems, cannot go outside alone, requires a gait assistive device.	5 minutes
Five Times Sit to Stand (5XSTS)	Assess functional lower extremity strength. The time it takes to complete the 5XSTS task is recorded. For community-dwelling older adults, the cut-off score is greater than or equal to 15 seconds which indicates risk of fall. Greater than or equal to 12 seconds identifies the need for further assessment for falls. Therefore, the greater the number of seconds, the greater the risk of fall.	5 minutes
Activities-Specific Balance Confidence (ABC) Scale	Self-report measure of perceived balance confidence. 16 items are rated on a 0% to 100% whole number rating scale. Scores of zero represent no confidence; scores of 100 indicate complete confidence. Total the ratings (possible range = 0-1600) and divide by 16 (number of items) to get the patient's ABC score or overall percent of balance confidence. For older adults, scores less than 67% indicate risk for falling and accurately classify people who fall 84% of the time. Greater than 80% indicates a high level of physical functioning, 50-80% indicates moderate level of physical functioning, and less than 50% indicates low level of physical functioning.	10 minutes
6 Minute Walk Test (6MWT)	Assess distance walked over a duration of 6 minutes, submaximal test for endurance. For community-dwelling older adults: 60-69 years old: 572 (male) and 538 (female) meters; 70-79 years old: 527 (male) and 471 (female) meters, and 80-90 years old: 417 (male) and 392 (female) meters. Therefore, the less distance walked in 6 minutes indicates that the individual has less submaximal aerobic and functional walking capacity.	6 minutes

Collaborators and Investigators

• Sponsor: University of Michigan
• Collaborators: University of Michigan-Flint
• Investigators: Principal Investigator: Jennifer Liao, PT, Ph.D., University of Michigan-Flint

Publications and helpful links

General Publications

• Chen M, Wang H, Yu L, Yeung EHK, Luo J, Tsui KL, Zhao Y. A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults. Sensors (Basel). 2022 Sep 7;22(18):6752. doi: 10.3390/s22186752.
• Hsieh KL, Roach KL, Wajda DA, Sosnoff JJ. Smartphone technology can measure postural stability and discriminate fall risk in older adults. Gait Posture. 2019 Jan;67:160-165. doi: 10.1016/j.gaitpost.2018.10.005. Epub 2018 Oct 9.

Helpful Links

• New Approach for Fall Detection System Using Embedded Technology

Study record dates

Study Major Dates

• Study Start (Actual): July 29, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: July 12, 2024
• First Submitted That Met QC Criteria: July 12, 2024
• First Posted (Actual): July 18, 2024

Study Record Updates

• Last Update Posted (Actual): August 6, 2025
• Last Update Submitted That Met QC Criteria: August 4, 2025
• Last Verified: August 1, 2025

More Information

Terms related to this study

• Keywords: fall risk
• Other Study ID Numbers: U081219

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
• product manufactured in and exported from the U.S.: No