Ignite Creation Date:
2025-12-24 @ 5:26 PM
Ignite Modification Date:
2026-02-21 @ 9:25 PM
Study NCT ID:
NCT03628768
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-09-19
First Post:
2018-08-09
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Risk Factors for Falls and Neurocognitive Disorders CLSA
Sponsor:
Jewish General Hospital
Organization:
Study Overview
Official Title:
Risk Factors for Falls and Neurocognitive Disorders in the Older Canadian Population: A Population-based Cross-sectional Study
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The study evaluates the association between the neurocognitive decline and falls.
Detailed Description:
Falls in older adults are a major Canadian public health concern because: 1) They have a high prevalence and incidence (e.g., up to 30% each year in Canada, regardless the cognitive status of fallers), 2) They negatively impact an individual's health condition (e.g., hip fracture) and quality of life (e.g., social withdraw), and 3) They impose a high financial burden on the Canadian health care system (e.g., $2 billion per year). Major neurocognitive disorders are strongly associated with falls and their adverse outcomes. There is a greater risk for falls and fall-related injuries in cognitively impaired individuals, more than doubled compared to cognitively healthy individuals (CHI). The nature of the interactions between neurocognitive disorders and the other risk factors for falls and fall-related injuries are still a matter of debate. For instance, the presence of specific patterns (i.e., types and combinations) of risk factors for falls and fall-related injuries associated with neurocognitive disorders at their onset (i.e., mild cognitive impairment \[MCI\] and mild dementia) compared to CHI is questioned. Recently, the investigators howed that the identification of risk factors for falls is influenced by the method of data analysis used. The investigators demonstrated that emerging modeling techniques such as artificial neural networks (ANNs) improve the performance criteria of fall prediction compared to classical linear models. Other methods such as Factor Mixture Models (FMMs) may also be helpful in identification of patterns of risk factors for falls and fall-related injuries associated with neurocognitive disorders. Using baseline data from the Canadian Longitudinal Study on Aging (CLSA), the investigator will examine the patterns (i.e., types and combinations) of risk factors for falls and fall-related injuries associated with neurocognitive disorders at their onset by 1) Examining the epidemiology of falls and fall-related injuries, and 2) Modeling and comparing the associations of risk for falls and fall-related injuries between cognitively healthy and impaired (i.e., MCI and mild dementia) older adults participating in the CLSA.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: