Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT03628768 |
| Other study ID # |
2019-1453 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
July 23, 2019 |
| Est. completion date |
February 2025 |
Study information
| Verified date |
February 2024 |
| Source |
Jewish General Hospital |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
The study evaluates the association between the neurocognitive decline and falls.
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.
