@article{ref1,
title="A self-reported clinical tool predicts falls in people with Parkinson's disease",
journal="Movement disorders clinical practice",
year="2021",
author="Almeida, Lorena Rosa S. and Piemonte, Maria Elisa Pimentel and Cavalcanti, Helen M. and Canning, Colleen G. and Paul, Serene S.",
volume="8",
number="3",
pages="427-434",
abstract="BACKGROUND: A 3-step clinical prediction tool including falling in the previous year, freezing of gait in the past month and self-selected gait speed <1.1 m/s has shown high accuracy in predicting falls in people with Parkinson's disease (PD). The accuracy of this tool when including only self-report measures is yet to be determined. <br><br>OBJECTIVES: To validate the 3-step prediction tool using only self-report measures (3-step self-reported prediction tool), and to externally validate the 3-step clinical prediction tool. <br><br>METHODS: The clinical tool was used with 137 individuals with PD. Participants also answered a question about self-reported gait speed, enabling scoring of the self-reported tool, and were followed-up for 6 months. An intraclass correlation coefficient (ICC(2,1)) was calculated to evaluate test-retest reliability of the 3-step self-reported prediction tool. Multivariate logistic regression models were used to evaluate the performance of both tools and their discriminative ability was determined using the area under the curve (AUC). <br><br>RESULTS: Forty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC(2,1) of 0.991 (95% CI 0.971-0.997; P < 0.001) and AUC = 0.68; 95% CI 0.59-0.77, while the 3-step clinical tool had an AUC = 0.69; 95% CI 0.60-0.78. <br><br>CONCLUSIONS: The 3-step self-reported prediction tool showed excellent test-retest reliability and was validated with acceptable accuracy in predicting falls in the next 6 months. The 3-step clinical prediction tool was externally validated with similar accuracy. The 3-step self-reported prediction tool may be useful to identify people with PD at risk of falls in e/tele-health settings.<p /> <p>Language: en</p>",
language="en",
issn="2330-1619",
doi="10.1002/mdc3.13170",
url="http://dx.doi.org/10.1002/mdc3.13170"
}