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Clinical sensor-based fall risk assessment at an orthopedic clinic: A case study of the staff’s views on utility and effectiveness
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5179-7158
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-4368-4751
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 4, article id 1904Article in journal (Refereed) Published
Abstract [en]

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Place, publisher, year, edition, pages
2023. Vol. 23, no 4, article id 1904
Keywords [en]
falls, healthcare, hospital, prevention, fall risk, assessment, inertial sensors, wearable sensors, technology adoption
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-61809DOI: 10.3390/s23041904ISI: 000941750500001PubMedID: 36850500Scopus ID: 2-s2.0-85148970681OAI: oai:DiVA.org:mdh-61809DiVA, id: diva2:1735343
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-04-12Bibliographically approved

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Ehn, MariaKristoffersson, Annica

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