ArticleDigital health
Development of a pragmatic multidisciplinary protocol to validate a contactless fall detection system for older adults in long-term care facilities.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Falls among residents of long-term care facilities are associated with substantial morbidity, mortality, and healthcare utilization. Despite the availability of numerous fall detection technologies, practical protocols for evaluating performance in real-world care environments remain limited. Objective: To develop a pragmatic and replicable protocol for validating contactless fall detection systems in long-term care environments. Methods: This article presents a protocol that combines structured testing by trained stunt actors, continuous real-world monitoring, and predefined procedures for event verification and classification to evaluate system performance across fall and non-fall events relevant to older adults. Protocol description: The protocol consists of three phases: pre-implementation assessment, system implementation and training, and system verification. It incorporates site assessment, sensor deployment, scripted testing, continuous monitoring, event verification, and predefined analytical procedures under real-world conditions. Conclusion: This protocol provides an approach for evaluating contactless fall detection systems in long-term care environments. Future studies are needed to apply the protocol across different sensor platforms and care settings to evaluate reproducibility, generalizability, and operational feasibility.
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