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A Mixed Methods Exploration of Temporospatial Fall Alert Patterns in Australian Aged Care Settings
Nida Afzal1, Amy D Nguyen2, Annie Y S Lau1
1Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.
Applied Clinical Informatics
|November 7, 2025
Summary
Fall alert patterns differ across aged care settings. Residential aged care facilities (RACFs) show nighttime falls, while retirement villages (RVs) and home dwelling communities (HDCs) have varied patterns, needing tailored prevention strategies.
Area of Science:
- Gerontology
- Health Informatics
- Public Health
Background:
- Falls in older adults are a significant global health issue, particularly in Australia.
- Effective fall prevention strategies require understanding fall patterns in diverse living environments.
- Ambient fall detection sensors offer a novel approach to monitoring fall events.
Purpose of the Study:
- To investigate temporospatial fall alert patterns using ambient sensors in three Australian aged care settings: residential aged care facilities (RACFs), retirement villages (RVs), and home dwelling communities (HDCs).
- To analyze fall risks through a mixed-methods approach, combining quantitative sensor data with qualitative insights from older adults and caregivers.
- To inform the development of tailored fall prevention strategies based on identified patterns and risks.
Main Methods:
- Utilized ambient fall detection sensors across 31 households in RACFs, RVs, and HDCs.
- Conducted quantitative temporospatial analysis of fall alerts by time of day and location.
- Gathered qualitative data through semistructured interviews with 14 older adults and 9 caregivers to understand fall risks.
Main Results:
- Distinct fall alert patterns were observed: RACFs had frequent nighttime bedroom alerts (linked to physical/cognitive decline); RVs showed even distribution (influenced by mobility, social activities, pets); HDCs had lowest rates with nighttime bedroom alerts (reflecting resident status, family support).
- Qualitative data confirmed factors like cognitive/physical impairments (RACFs), mobility/social/pet influences (RVs), and shared living (HDCs) affecting fall risks and alert patterns.
- Sensor accuracy was noted as a concern in RVs due to pets and daily activities.
Conclusions:
- Fall alert patterns and associated risks vary significantly across RACFs, RVs, and HDCs, necessitating customized prevention approaches.
- RACF strategies should prioritize nighttime safety, improved monitoring, bed alarms, and medication reviews.
- RVs require mobility programs and sensor improvements, while HDCs need adaptable technology for shared living environments.
