Fall Prevention or Fall Detection? What Smart Care Technology Can Really Deliver
Updated: Oct 20
Smart care technologies can detect falls, identify changes in mobility and support earlier intervention. But can they actually prevent falls, and how should aged care providers evaluate their effectiveness?
Falls are a significant safety risk for older people, potentially resulting in injury, hospitalisation, reduced confidence and loss of independence. Wearable devices, environmental sensors and artificial intelligence increasingly offer ways to detect incidents and identify changes associated with fall risk.
However, detecting a fall, predicting an increased likelihood of falling and preventing a fall are fundamentally different capabilities. Understanding these distinctions is essential for aged care providers evaluating smart care technology investments.
Fall Detection Is Not Fall Prevention
Most established fall-related technologies focus on detecting incidents or situations requiring assistance. Wearable devices may recognise falls, while bed sensors and environmental monitoring systems can identify potentially unsafe movements or unusual inactivity.
Other technologies analyse changes in mobility and behaviour to identify increasing risk. These capabilities can support earlier intervention, but detecting an incident or estimating future risk does not, by itself, prevent a fall.
Four distinct capabilities should be considered:
Fall detection: Identifies an event that has occurred or is occurring.
Risk identification: Recognises circumstances associated with an increased likelihood of falling.
Fall prediction: Estimates future fall risk using information about an individual's condition, behaviour or movement patterns.
Fall prevention: Involves interventions intended to reduce the likelihood of a fall or its consequences.
A technology may support several of these functions. However, the presence of predictive analytics does not establish that it prevents falls. Suppliers may demonstrate high detection accuracy while providing limited evidence of improved clinical outcomes.
Understanding What Causes Falls
Falls rarely result from a single factor. Previous falls, impaired balance, muscle weakness, medication effects, cognitive changes, vision problems and environmental hazards can all contribute to risk. These factors often interact.
An older person experiencing declining mobility may remain relatively safe in familiar surroundings. However, an acute illness, medication change or unfamiliar obstacle may substantially increase their immediate risk.
Research by Robinovitch and colleagues examined video recordings of falls among older people in long-term care. Incorrect weight shifting accounted for approximately 41% of recorded falls, demonstrating that many incidents arise during ordinary activities such as walking, standing and transferring.
These findings reinforce an important principle: effective fall prevention requires more than detecting emergencies or removing environmental hazards. It must also address changes in mobility, balance, strength and everyday functional ability.
Technology can contribute valuable information, but it should complement individualised clinical assessment and established falls-prevention interventions.
Can Changes in Gait Predict Falls?
One promising application of sensor technology involves monitoring changes in how people move.
Gait speed, stride length, walking frequency and overall activity patterns can provide insights into functional ability. Someone who begins walking more slowly, taking shorter steps or moving less frequently may be experiencing changes that warrant further assessment.
Research using sensors in independent living environments has explored whether these changes can help identify increased fall risk. However, changes in movement are not necessarily specific to falls. They may reflect pain, illness, medication effects, fatigue or changes in daily routines.
This creates a distinction between identifying a meaningful change and accurately predicting a future event.
A monitoring system may recognise that an individual's mobility has deteriorated. That information could prompt a review of their care needs, medication, physical environment or mobility support. The value lies in enabling an appropriate intervention, rather than claiming certainty about when a fall will occur.
For providers, the important question is not simply whether a technology can identify patterns, but whether those patterns are sufficiently reliable and clinically meaningful to inform care decisions.
How Reliable Are Fall Detection Technologies?
Fall detection technologies use different approaches, each with strengths and limitations.
Wearable devices commonly rely on accelerometers and other motion sensors to identify movement patterns associated with falls. Environmental technologies may use radar, cameras, pressure sensors or other monitoring methods to detect unusual movement or inactivity.
Accuracy depends on the technology, its configuration and the environment in which it operates.
A system that performs well under controlled testing conditions may behave differently in everyday care. Real-world environments introduce variations in movement, positioning, lighting, furniture, connectivity and individual behaviour.
Providers should examine two important performance measures:
Sensitivity indicates how effectively a system identifies actual falls. Missed events can create significant safety concerns.
Specificity indicates how effectively a system avoids incorrectly identifying ordinary activities as falls. Excessive false alarms can increase workload and undermine confidence.
Neither measure should be considered in isolation. A highly sensitive system may generate unacceptable numbers of false alerts, while a system designed to minimise false alarms may miss genuine incidents.
Providers should seek evidence from realistic operating environments and understand how performance varies across different populations and care settings.
From Detection to Effective Intervention
Technology only contributes to better outcomes when the information it generates leads to appropriate action.
Consider a monitoring system that identifies a sustained decline in a person's daily activity. The information may be technically accurate, but it delivers limited value if nobody is responsible for reviewing it or determining whether intervention is required.
Similarly, detecting a fall does not guarantee timely assistance. The complete response depends on reliable communication, monitoring arrangements, escalation procedures and the availability of appropriate responders.
For aged care providers, implementation therefore requires more than selecting devices. It involves integrating monitoring information into existing care processes and establishing clear accountability.
Key questions include who receives alerts, how they are prioritised, when clinical review is required and how actions are documented.
These considerations are particularly important when introducing AI-enabled monitoring systems. Automated analysis may help identify patterns, but clinical decisions and interventions require appropriate human oversight.
For a broader discussion of monitoring approaches, see What Is Remote Monitoring?
Evaluating Technology Against Care Outcomes
When assessing fall-related technologies, providers should distinguish between technical performance and demonstrated care outcomes.
A supplier may report that its system detects a high percentage of falls. This is useful information, but it does not necessarily establish that the solution reduces injuries, hospitalisations or overall fall rates.
Likewise, identifying increased fall risk does not demonstrate that the resulting intervention is effective.
A sound evaluation should consider whether the technology addresses a clearly defined care need, whether its performance is supported by appropriate evidence and whether the organisation can respond effectively to the information generated.
Providers should also assess usability, privacy, integration requirements, supplier support and the ongoing cost of operating the solution.
Most importantly, evaluation should include measures of actual care and operational outcomes, not simply the number of devices deployed or alerts generated.
Making Better Digital Investment Decisions
Fall-related technologies can contribute to safer and more responsive care, particularly when they identify incidents promptly or reveal changes that might otherwise go unnoticed.
However, providers should be cautious about claims that technology can independently predict or prevent falls.
The greatest value often comes from combining monitoring capabilities with clinical assessment, established prevention strategies and clearly defined response processes.
For aged care organisations, this reinforces a broader digital investment principle: technology should be selected according to the care outcomes it can realistically support, rather than the sophistication of its features.
How eHomeCare Can Help
eHomeCare provides independent technology advisory services to aged care providers evaluating assistive technologies, remote monitoring and other digital care solutions.
We help organisations assess technology suitability, supplier claims, integration requirements and the operational capabilities needed to achieve sustainable care outcomes.
Explore our Technology Advisory services to make informed, evidence-based digital investment decisions.
Hubert van Dalen is the founder of eHomeCare, providing independent digital strategy, governance, technology and transformation advisory services to Australian aged care and disability providers.
REFERENCES
Australian Commission on Safety and Quality in Health Care. Preventing Falls and Harm from Falls in Older People: Best Practice Guidelines for Australian Residential Aged Care Services. 2025.
Australian Commission on Safety and Quality in Health Care. Preventing Falls and Harm from Falls in Older People: Best Practice Guidelines for Community Care in Australia. 2025.
Robinovitch SN et al. Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study. The Lancet. 2013.
Phillips LJ et al. Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls. Western Journal of Nursing Research. 2017.
Schwickert L et al. Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Care. Gerontology. 2013.
Shao L et al. Incidence and Risk Factors of Falls in Nursing Homes: A Systematic Review and Meta-analysis. Journal of the American Medical Directors Association. 2023.
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