Literature DB >> 22843355

Can falls be predicted with gait analytical and posturographic measurement systems? A prospective follow-up study in a nursing home population.

René Schwesig1, David Fischer, Andreas Lauenroth, Stephan Becker, Siegfried Leuchte.   

Abstract

OBJECTIVE: To validate previously proposed findings and to develop an objective, feasible and efficient bifactorial (risk factors: gait impairment and balance disorders) fall risk assessment.
DESIGN: Prospective follow-up study Setting: Nursing homes (Halle/Saale, Germany).
SUBJECTS: One hundred and forty-six nursing home residents (aged 62-101 years) were recruited.
METHODS: Gait data were collected using a mobile inertial sensor-based system (RehaWatch). Postural regulation data were measured with the Interactive Balance System. Falls were recorded in standardized protocols over a follow-up period of 12 months. MAIN MEASURES: Gait parameters (e.g. spatial-temporal parameters), posturographic parameters (e.g. postural subsystems), number of falls.
RESULTS: Seventeen (12%) of the participants had more than two falls per year. The predictive validity of the previously selected posturographic parameters was inadequate (sensitivity: 47%). The new measurement tool defined 67 participants showing an increased risk of falls. In reality, only 8 participants actually fell more than twice during the follow-up period (positive predictive value (PPV): 12%). The negative predictive value (NPV) was 88%. The posturographic frequency range F2-4 (peripheral-vestibular system), stride time and standard deviation of landing phase were the most powerful parameters for fall prediction. Gait and postural variability were larger in the high-risk group (e.g. gait speed; confidence interval (CI)(high): 0.57-0.79 vs. CI(low): 0.72-0.81 m/s).
CONCLUSION: RehaWatch and the Interactive Balance System are able to measure two of the most important fall risk factors, but their current predictive ability is not satisfactory yet. The correlation with physiological mechanisms is only shown by the Interactive Balance System.

Entities:  

Mesh:

Year:  2012        PMID: 22843355     DOI: 10.1177/0269215512452880

Source DB:  PubMed          Journal:  Clin Rehabil        ISSN: 0269-2155            Impact factor:   3.477


  11 in total

Review 1.  Objective falls-risk prediction using wearable technologies amongst patients with and without neurogenic gait alterations: a narrative review of clinical feasibility.

Authors:  Callum M W Betteridge; Pragadesh Natarajan; R Dineth Fonseka; Daniel Ho; Ralph Mobbs; Wen Jie Choy
Journal:  Mhealth       Date:  2021-10-20

2.  Immediate Effect of Restricted Knee Extension on Ground Reaction Force and Trunk Acceleration during Walking.

Authors:  Hiroshi Osaka; Daisuke Fujita; Kenichi Kobara; Tadanobu Suehiro
Journal:  Rehabil Res Pract       Date:  2021-07-08

3.  Gait asymmetry, ankle spasticity, and depression as independent predictors of falls in ambulatory stroke patients.

Authors:  Ta-Sen Wei; Peng-Ta Liu; Liang-Wey Chang; Sen-Yung Liu
Journal:  PLoS One       Date:  2017-05-23       Impact factor: 3.240

4.  Elderly fall risk prediction using static posturography.

Authors:  Jennifer Howcroft; Edward D Lemaire; Jonathan Kofman; William E McIlroy
Journal:  PLoS One       Date:  2017-02-21       Impact factor: 3.240

5.  Home-based balance training using Wii Fit™: a pilot randomised controlled trial with mobile older stroke survivors.

Authors:  André Golla; Tobias Müller; Kai Wohlfarth; Patrick Jahn; Kerstin Mattukat; Wilfried Mau
Journal:  Pilot Feasibility Stud       Date:  2018-08-25

6.  Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults.

Authors:  Tal Shany; Kejia Wang; Ying Liu; Nigel H Lovell; Stephen J Redmond
Journal:  Healthc Technol Lett       Date:  2015-08-03

Review 7.  Review of fall risk assessment in geriatric populations using inertial sensors.

Authors:  Jennifer Howcroft; Jonathan Kofman; Edward D Lemaire
Journal:  J Neuroeng Rehabil       Date:  2013-08-08       Impact factor: 4.262

8.  Identification of functional parameters for the classification of older female fallers and prediction of 'first-time' fallers.

Authors:  N König; W R Taylor; G Armbrecht; R Dietzel; N B Singh
Journal:  J R Soc Interface       Date:  2014-08-06       Impact factor: 4.118

Review 9.  Novel sensing technology in fall risk assessment in older adults: a systematic review.

Authors:  Ruopeng Sun; Jacob J Sosnoff
Journal:  BMC Geriatr       Date:  2018-01-16       Impact factor: 3.921

10.  Posturographic measures did not improve the predictive power to identify recurrent falls in community-dwelling elderly fallers.

Authors:  Kelem de Negreiros Cabral; Guilherme Carlos Brech; Angelica Castilho Alonso; Aline Thomaz Soares; Davi Camara Opaleye; Julia Maria D'Andrea Greve; Wilson Jacob-Filho
Journal:  Clinics (Sao Paulo)       Date:  2020-04-03       Impact factor: 2.365

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