Mass ScreeningNCT06508892
Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts
An observational study of Mass Screening, run by University of Michigan at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 65 years and olderHealthy volunteers can take part
- Conditions the study is for
- Mass Screening
- What is being tested?
- risk of fall
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 100 participants
- Where
- 1 siteUnited States
- Study dates
- 29 Jul 2024to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 65 years and older
- All sexes
- Healthy volunteers accepted
- Older adult
Study population: older adults ages 65 years and older
Who may be able to join
- 65 years or older
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Michigan-Flint
Flint, Michigan, 48502
Nathan Miller, Ph.D., 810-762-3234, natmille@umich.edu
Cathy A Larson, Ph.D., 8107623373, clarson@umich.edu
About this study
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.
Read the 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.
- Conditions
- Mass Screening
- Being tested
- risk of fall
- Keywords
- fall risk
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
risk of fallBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Behavioral: risk of fall |
What is being measured
The primary outcome is the main question the study is designed to answer.
3D acceleration
Vertical, medial-lateral, and anterior-posterior acceleration
Measured: 60 seconds to 6 minutes
3D rotation
Vertical, medial-lateral, and anterior-posterior rotation
Measured: 60 seconds to 6 minutes
Secondary outcomes (9)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 6 minutes
Falls Efficacy Scale International
FES-I is a modified measure that was introduced to improve the generalizability and the construct validity of the original. It is a measure of self efficacy which is commonly used to measure fear of falling. It gauges a person's perception of their ability to complete common activities without losing balance. It includes sixteen activities and the answer's possible lay on a scale of 1 (not at all concerned) to 4 (very concerned). Scores between 16-19 are low concern (i.e. high self efficacy), 20-27 have moderate concern, and 28-64 have high concern about falling.
Measured: 10 minutes
Updated Perceived-Control Over Falling
UP-COF was developed to uncover maladaptive fear of falling. It identifies perceived control over balance threats as the bridging attribute between fear and increased fall risk. Thus it is able to distinguish between protective and maladaptive reactions to threats. It is composed of four statements that participants reply to 0 (strongly disagree) to 5 (strongly agree). Results of 13 or below indicate low perceived control.
Measured: 5 minutes
Fear of Falling Questionnaire Revised
The FFQ-R is a multi-item scale developed from the original Fear of Falling Questionnaire (FFQ) capable of uncovering fear of falling in community dwelling adults as well as in cases post hip injury. It assesses concepts related to FoF and has low correlation with depression and negative or positive affect. It is composed of 15 items self-report questionnaire with each item reported on a Likert scale of 1 (strongly disagree) - 4 (strongly agree). Higher scores indicate a higher degree of fear of falling.
Measured: 10 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jul 2024
- Study start29 Jul 2024
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Mass ScreeningStudies of the same condition, open and under way.
- More trials run by University of MichiganThe 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
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Michigan
- Sponsor type
- University, hospital or other
- Collaborators
- University of Michigan-Flint
- Responsible party
- Principal investigator, Jennifer Liao, University of Michigan
- Investigators
- Jennifer Liao, PT, Ph.D., Principal investigator, University of Michigan-Flint
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06508892
- Sponsor's protocol ID
- U081219
- Status last verified
- Aug 2026
- First submitted
- 12 Jul 2024
- Last updated
- 14 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 36146103Hsieh 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.
PubMed 30340129
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts
- ClinicalTrials.gov ID
- NCT06508892
- Acronym / sponsor ID
- U081219
- Conditions
- Mass Screening
- Keywords
- fall risk
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- University of MichiganUniversity, hospital or other
- Collaborators
- University of Michigan-Flint
- Study start
- 29 Jul 2024 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 18 Jul 2024 (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.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Comparison of acceleration and 3D rotation during balance and movement | – | Behavioral: risk of fallCan 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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Older adult
- Study population
- older adults ages 65 years and older
Key inclusion criteria (1 listed)
- 65 years or older
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| risk of fallGather information that will assist in determining risk of fall. The researchers will ask the subjects to perform several motor tests and study-related questionnaires. | Behavioural | Comparison of acceleration and 3D rotation during balance and movement |
Primary outcome
3D acceleration
Time frame: 60 seconds to 6 minutes
Vertical, medial-lateral, and anterior-posterior acceleration
3D rotation
Time frame: 60 seconds to 6 minutes
Vertical, medial-lateral, and anterior-posterior rotation
9 secondary outcomes
Montreal Cognitive Assessment (MoCA)
Time frame: 15 minutes
Berg Balance Scale (BBS)
Time frame: 15 minutes
Timed Up and Go (TUG)
Time frame: 5 minutes
Five Times Sit to Stand (5XSTS)
Time frame: 5 minutes
Activities-Specific Balance Confidence (ABC) Scale
Time frame: 10 minutes
6 Minute Walk Test (6MWT)
Time frame: 6 minutes
Falls Efficacy Scale International
Time frame: 10 minutes
Updated Perceived-Control Over Falling
Time frame: 5 minutes
Fear of Falling Questionnaire Revised
Time frame: 10 minutes
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Aug 2026.
- Background: PubMed 36146103Chen 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.
- Background: PubMed 30340129Hsieh 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.
- New Approach for Fall Detection System Using Embedded TechnologyLink provided by the sponsor.
- Individual participant dataSharing planned: No
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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.
Jennifer Liao, PT, Ph.D. principal investigator
University of Michigan-Flint
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mass Screening, run by University of Michigan at 1 site in United States.
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 3 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: looking for participants now, as of the sponsor's last update on 14 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nathan Miller, Ph.D. (810-762-3234, natmille@umich.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
3D acceleration (time frame: 60 seconds to 6 minutes). The study lists 2 primary outcomes in all. 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 NCT06508892, which also lists 2 linked publications.
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 14 Aug 2026.