Prediction of early falls using adherence and balance assessments in a convalescent rehabilitation ward

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  • Teranishi Toshio
    Faculty of Rehabilitation, Fujita Health University, School of Health Sciences
  • Suzuki Megumi
    Faculty of Rehabilitation, Fujita Health University, School of Health Sciences
  • Yamada Masayuki
    Faculty of Rehabilitation, Fujita Health University, School of Health Sciences
  • Maeda Akiko
    Faculty of Rehabilitation, Fujita Health University, School of Health Sciences
  • Yokota Motomi
    Faculty of Rehabilitation, Fujita Health University, School of Health Sciences
  • Itoh Naoki
    National Center for Geriatrics and Gerontology, Department of Rehabilitation Medicine
  • Tanimoto Masanori
    National Center for Geriatrics and Gerontology, Department of Rehabilitation Medicine
  • Osawa Aiko
    National Center for Geriatrics and Gerontology, Department of Rehabilitation Medicine
  • Kondo Izumi
    National Center for Geriatrics and Gerontology, Department of Rehabilitation Medicine

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<p>Objectives: To predict falls by adding an adherence assessment to a static balance ability assessment, and to evaluate fall prediction accuracy.</p><p>Methods: This study included 416 patients who were admitted to a 45-bed convalescent rehabilitation ward over a 2-year period. The patients were assessed at the time of admission using the Standing Test for Imbalance and Disequilibrium (SIDE) and three additional, newly developed adherence items. Patients were divided into two groups: a group that experienced falls (fall group) and a group that did not experience falls (non-fall group) within 14 days of admission. The sensitivity and specificity of the assessment items for predicting falls were calculated.</p><p>Results: Sensitivity was 0.86 and specificity was 0.42 when the cutoff was between SIDE levels 0–2a and 2b–4. Combining balance assessment using the SIDE with the memory and instruction adherence items improved fall prediction accuracy such that the sensitivity was 0.75 and the specificity was 0.64.</p><p>Conclusions: Our analysis suggested that adherence assessment can improve fall risk prediction accuracy.</p>

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