Clinical trial NCT03628768: Risk Factors for Falls and Neurocognitive Disorders CLSA
Official title: Risk Factors for Falls and Neurocognitive Disorders in the Older Canadian Population: A Population-based Cross-sectional Study
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Neurocognitive Disorders
Geriatrics
Lead sponsor: Jewish General Hospital
Sponsor class: OTHER
First posted: 2018-08-14
Enrollment: 12000
Sex: ALL
Minimum age (years): 65
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals eligible for this study will be the participants of the CLSA

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.

Eligibility criteria: Inclusion Criteria:

* Age ≥ 65 and
* Participants of the Comprehensive CLSA (i.e., individuals who participated in a 90-minute in-home face-to-face interview, and a visit to one of 11 Data Collection Sites across Canada where they took part in a range of physical assessments).

Exclusion Criteria:

* A fall resulting from acute medical events and/or external force,
* Moderate to severe dementia (estimated from performance on cognitive tests and scores of the Activity of Daily Living (ADL) and Instrumental Activity Daily Living (IADL) scales. Abnormal scores will be defined as 2 Standard Deviations (SDs) or more below the normal score of all cognitive tests associated with scores of ADL (/6) \<3 and a score of IADL (/8) \<4)

Primary outcomes:
measure: Fall-related injury
timeFrame: around 12 months
Secondary outcomes:
measure: Medication
timeFrame: around 12 months
Other outcomes:
Locations:
city: Montréal
country: Canada
facility: Jewish General Hospital
state: Quebec
Study officials:
affiliation: Jewish General Hospital
name: Olivier Beauchet, MD
role: PRINCIPAL_INVESTIGATOR
References: