Literature DB >> 28797962

Quantitative trunk sway and prediction of incident falls in older adults.

Jeannette R Mahoney1, Mooyeon Oh-Park2, Emmeline Ayers3, Joe Verghese4.   

Abstract

Poor balance and balance impairments are major predictors of falls. The purpose of the current study was to determine the clinical validity of baseline quantitative static trunk sway measurements in predicting incident falls in a cohort of 287 community-dwelling non-demented older Americans (mean age 76.14±6.82years; 54% female). Trunk sway was measured using the SwayStar™ device, and quantified as angular displacement in degrees in anterior-posterior (pitch) and medio-lateral (roll) planes. Over a one-year follow-up period, 66 elders (23%) reported incident falls. Anterior-posterior angular displacement was a strong predictor of incident falls in older adults in Cox proportional hazards models (hazard ratio adjusted for age, gender, education, RBANS total score, medical comorbidities, geriatric depression scale score, sensory impairments, gait speed, and history of fall in the past 1year ((aHR)=1.59; p=0.033) whereas, angular displacement in the medio-lateral plane was not predictive of falls (aHR=1.35; p=0.276). Our results reveal the significance of quantitative trunk sway, specifically anterior-posterior angular displacement, in predicting incident falls in older adults.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aging; Falls; Older adults; Static balance; Trunk sway

Mesh:

Year:  2017        PMID: 28797962      PMCID: PMC5645228          DOI: 10.1016/j.gaitpost.2017.07.115

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  27 in total

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Journal:  Gait Posture       Date:  2018-03-28       Impact factor: 2.840

3.  Anteroposterior Stability: A Determinant of Gait Dysfunction and Falls in Spinocerebellar Ataxia.

Authors:  V S Ganapathy; Tittu T James; Mariamma Philip; Nitish Kamble; Amitabh Bhattacharya; Pradnya Dhargave; Pramod Kumar Pal
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4.  Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor.

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