Wearable Technologies for Detecting Near-Falls: A Systematic Review With Implications for Geriatric Nursing Practice

Area of Science:

• Geriatric nursing
• Biomedical engineering
• Sensor technology

Background:

• Near-falls predict future falls and pose a risk to older adults.
• Wearable technology offers potential for accurate near-fall detection and early intervention.
• Geriatric nursing practice can benefit from advanced fall prediction tools.

Purpose of the Study:

• To systematically review wearable sensor technologies for near-fall detection.
• To explore applications in monitoring near-fall risk and prevention for older adults.

Main Methods:

• Systematic review of 18 studies.
• Searched five electronic databases (PubMed/MEDLINE, Embase, CINAHL, Web of Science, IEEE Xplore).
• Adhered to PRISMA guidelines.

Main Results:

• Inertial Measurement Units (IMUs) were most common; linear acceleration was the key biomarker.
• Lower-body sensor placement (feet, ankles, lower back) yielded best results.
• Multi-sensor systems achieved 100% sensitivity, 99% specificity, and 100% accuracy.

Conclusions:

• Wearable tech integration can improve early identification of high-risk older adults.
• Timely, personalized interventions are facilitated by wearable near-fall detection.
• Further research needed on real-world validation and user acceptability.