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

NCT06508892 | Recruiting

• 1 other identifier: U081219
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Mass Screening

Keywords

fall risk

Outcome Measures

Primary Outcomes (2)

• 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 Outcomes (6)

• Montreal Cognitive Assessment (MoCA)

  15 minutes

• Berg Balance Scale (BBS)

  15 minutes

• Timed Up and Go (TUG)

  5 minutes

• Five Times Sit to Stand (5XSTS)

  5 minutes

• Activities-Specific Balance Confidence (ABC) Scale

  10 minutes

Study Arms (1)

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

Interventions

risk of fall BEHAVIORAL

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.

Comparison of acceleration and 3D rotation during balance and movement

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

older adults ages 65 years and older

You may qualify if:

• years or older

You may not qualify if:

• 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.

Sponsors & Collaborators

• University of Michigan: lead
• University of Michigan-Flint: collaborator

Study Sites (1)

University of Michigan-Flint

Flint, Michigan, 48502, United States

RECRUITING

Related Publications (2)

• 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.

  PMID: 36146103 BACKGROUND

• 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.

  PMID: 30340129 BACKGROUND

Related Links

• New Approach for Fall Detection System Using Embedded Technology

Study Officials

• Jennifer Liao, PT, Ph.D.

  University of Michigan-Flint

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Nathan Miller, Ph.D.

CONTACT

810-762-3234; natmille@umich.edu

Cathy A Larson, PT, Ph.D.

CONTACT

8107623373; clarson@umich.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Physical Therapy, College of Health Sciences, The University of Michigan-Flint and Adjunct Assistant Professor of Radiology, Medical School

Study Record Dates

• First Submitted: July 12, 2024
• First Posted: July 18, 2024
• Study Start: July 29, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: August 6, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)