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Citation
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Martins AC, Moreira J, Silva C, Silva J, Tonelo C, Baltazar D, Rocha C, Pereira T, Sousa I. JMIR Res. Protoc. 2018; 7(8): e10304. |
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Abstract
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BACKGROUND: Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors.
OBJECTIVE: The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling.
METHODS: This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior's universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test "modified," and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise.
RESULTS: The enrollment began in June 2016 and we anticipate study completion by the end of 2018.
CONCLUSIONS: The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. REGISTERED REPORT IDENTIFIER: RR1-10.2196/10304.
©Anabela Correia Martins, Juliana Moreira, Catarina Silva, Joana Silva, Cláudia Tonelo, Daniela Baltazar, Clara Rocha, Telmo Pereira, Inês Sousa. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 02.08.2018.
Language: en |