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Machine Learning to Build a Fall Risk Prediction Model for Community-Dwelling Older Adults

Development and Validation of a Machine Learning–Based Fall Risk Prediction Model for Community-Dwelling Older Adults

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500104155
Enrollment
Unknown
Registered
2025-06-12
Start date
2025-06-15
Completion date
Unknown
Last updated
2025-06-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Elderly falls

Interventions

Faller group:None
Non-faller group:None

Sponsors

Beijing Sport University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to No maximum

Inclusion criteria

Inclusion criteria: Inclusion Criteria: (1) Age >= 65 years; (2) No diseases affecting balance function, such as Parkinson's disease, post-stroke sequelae, severe arthritis, or neurodegenerative diseases; (3) No severe cardiovascular or respiratory system diseases; (4) Possess basic cognitive function, without severe cognitive impairment, such as Alzheimer's disease; (5) Able to walk independently or with simple assistive devices (e.g., canes); (6) The participant or their legal representative signs an informed consent form, agreeing to participate in the study and follow the relevant testing procedures.

Exclusion criteria

Exclusion criteria: Exclusion Criteria: (1) Having severe cardiovascular disease, respiratory disease, neurodegenerative disease, visual impairment, or other serious conditions that may affect balance or mobility or pose a risk to the subject's safety; (2) Being diagnosed with moderate or severe cognitive impairment, unable to understand or perform test tasks; (3) Having undergone major surgery or severe trauma within the past six months, which may affect balance and mobility; (4) Currently taking medications that affect balance or cognitive function, such as certain types of psychotropic drugs or sedatives; (5) Having experienced a fall due to non-iatrogenic causes (e.g., car accidents, external violence, acute illnesses such as stroke or heart attack) within the past year; (6) Currently not participating in any pharmacological research; (7) Declaring unwillingness to participate in the study

Design outcomes

Primary

MeasureTime frame
Gait variability parameters: Step length Step width Gait speed Cadence (steps per minute) Gait cycle duration Percentage of stance phase Percentage of swing phase Percentage of double-support phase;

Secondary

MeasureTime frame
Gait Asymmetry Index ;

Countries

China

Contacts

Public ContactHaizhou Hu

Beijing Sport University

18811713698@163.com+86 188 1171 3698

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026