Falls are a key public health concern, resulting in disability and increased mortality risk. An extensive body of literature has examined risk factors for falls; however, results vary across different studies and populations. We aimed to synthesize systematic reviews of fall risk factors in community-dwell...
Falls are a leading cause of injury in older adults, and accurate tools to predict fall risk are essential. This study evaluates the predictive accuracy of the 3-m Backward Walk Test (3 MBWT) for fall risk, comparing it with the Short Physical Performance Battery (SPPB), 10-m walk test (10 MWT), and fall-r...
Multimorbidity is linked with an increased risk of falls in older adults. The study objective is to determine the relationship of multimorbidity and muscle strength in falls among older Mexican Americans without a history of falls at baseline.
Few studies have explored specific trajectories or patterns of falls over time in older adults, and the role of sex and self-reported risk factors for these trajectories were overlooked. This study aimed to identify sex-specific fall trajectories over 3 years and the self-reported risk factors associated w...
Outdoor falls can negatively impact the health and functional abilities of community-dwelling older adults. Although there are existing evidence-based programs for falls prevention, none specifically target outdoor falls. To fill this gap in research and practice, the Stroll Safe program was developed. Pri...
The study aimed to develop a machine learning (ML) model to predict early postdischarge falls in older adults using data that are easy to collect in acute care hospitals. This may reduce the burden imposed by complex measures on patients and health care staff.
To assess the effectiveness of smart home technologies (SHTs) in preventing and detecting falls among older adults in community and residential care settings.
To explore a comprehensive overview of digital technologies used for fall detection in older adults, categorizing the types, functions, and usability of these systems.
Ambulatory measurements of trunk accelerations can provide valuable insight into the amount and quality of daily life activities. Such information has been used to create models to identify individuals at high risk of falls. However, external validation of such prediction models is lacking, yet crucial for...