Literature DB >> 27670925

Use of Mobile Device Accelerometry to Enhance Evaluation of Postural Instability in Parkinson Disease.

Sarah J Ozinga1, Susan M Linder2, Jay L Alberts3.   

Abstract

OBJECTIVE: To determine the accuracy of inertial measurement unit data from a mobile device using the mobile device relative to posturography to quantify postural stability in individuals with Parkinson disease (PD).
DESIGN: Criterion standard.
SETTING: Motor control laboratory at a clinic. PARTICIPANTS: A sample (N=28) of individuals with mild to moderate PD (n=14) and age-matched community-dwelling individuals without PD (n=14) completed the study.
INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Center of mass (COM) acceleration measures were compared between the mobile device and the NeuroCom force platform to determine the accuracy of mobile device measurements during performance of the Sensory Organization Test (SOT). Analyses examined test-retest reliability of both systems and sensitivity of (1) the equilibrium score from the SOT and (2) COM acceleration measures from the force platform and mobile device to quantify postural stability across populations.
RESULTS: Metrics of COM acceleration from inertial measurement unit data and the NeuroCom force platform were significantly correlated across balance conditions and groups (Pearson r range, .35 to .97). The SOT equilibrium scores failed to discriminate individuals with and without PD. However, the multiplanar measures of COM acceleration from the mobile device exhibited good to excellent reliability across SOT conditions and were able to discriminate individuals with and without PD in conditions with the greatest balance demands.
CONCLUSIONS: Metrics employing medial-lateral movement produce a more sensitive outcome than the equilibrium score in identifying postural instability associated with PD. Overall, the output from the mobile device provides an accurate and reliable method of rapidly quantifying balance in individuals with PD. The portable and affordable nature of a mobile device with the application makes it ideally suited to use biomechanical data to aid in clinical decision making.
Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Accelerometry; Feedback, sensory; Mobile applications; Parkinson disease; Postural balance; Rehabilitation

Mesh:

Year:  2016        PMID: 27670925      PMCID: PMC5364077          DOI: 10.1016/j.apmr.2016.08.479

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  58 in total

1.  Kinematic and kinetic validity of the inverted pendulum model in quiet standing.

Authors:  William H Gage; David A Winter; James S Frank; Allan L Adkin
Journal:  Gait Posture       Date:  2004-04       Impact factor: 2.840

2.  A model of the standing man for the description of his dynamic behaviour.

Authors:  J B Geursen; D Altena; C H Massen; M Verduin
Journal:  Agressologie       Date:  1976

3.  Is lower leg proprioception essential for triggering human automatic postural responses?

Authors:  B R Bloem; J H Allum; M G Carpenter; F Honegger
Journal:  Exp Brain Res       Date:  2000-02       Impact factor: 1.972

4.  Undisturbed upright stance control in the elderly: Part 1. Age-related changes in undisturbed upright stance control.

Authors:  L Berger; M Chuzel; G Buisson; P Rougier
Journal:  J Mot Behav       Date:  2005-09       Impact factor: 1.328

5.  Trunk accelerometry reveals postural instability in untreated Parkinson's disease.

Authors:  Martina Mancini; Fay B Horak; Cris Zampieri; Patricia Carlson-Kuhta; John G Nutt; Lorenzo Chiari
Journal:  Parkinsonism Relat Disord       Date:  2011-06-08       Impact factor: 4.891

6.  Effect of vision and stance width on human body motion when standing: implications for afferent control of lateral sway.

Authors:  B L Day; M J Steiger; P D Thompson; C D Marsden
Journal:  J Physiol       Date:  1993-09       Impact factor: 5.182

7.  Parkinsonism: onset, progression and mortality.

Authors:  M M Hoehn; M D Yahr
Journal:  Neurology       Date:  1967-05       Impact factor: 9.910

8.  Influence of dopaminergic medication on automatic postural responses and balance impairment in Parkinson's disease.

Authors:  B R Bloem; D J Beckley; J G van Dijk; A H Zwinderman; M P Remler; R A Roos
Journal:  Mov Disord       Date:  1996-09       Impact factor: 10.338

9.  A comparison of accelerometry and center of pressure measures during computerized dynamic posturography: a measure of balance.

Authors:  S L Whitney; J L Roche; G F Marchetti; C-C Lin; D P Steed; G R Furman; M C Musolino; M S Redfern
Journal:  Gait Posture       Date:  2011-02-17       Impact factor: 2.840

10.  Setting the minimal metrically detectable change on disability rating scales.

Authors:  R Hébert; D J Spiegelhalter; C Brayne
Journal:  Arch Phys Med Rehabil       Date:  1997-12       Impact factor: 3.966

View more
  11 in total

1.  Digital Phenotyping in Clinical Neurology.

Authors:  Anoopum S Gupta
Journal:  Semin Neurol       Date:  2022-01-11       Impact factor: 3.212

Review 2.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Front Neurosci       Date:  2017-10-06       Impact factor: 4.677

3.  Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

Authors:  Mandy Miller Koop; Sarah J Ozinga; Anson B Rosenfeldt; Jay L Alberts
Journal:  IBRO Rep       Date:  2018-06-21

4.  A low-cost quantitative continuous measurement of movements in the extremities of people with Parkinson's disease.

Authors:  Gregory Neal McKay; Timothy P Harrigan; James Robert Brašić
Journal:  MethodsX       Date:  2019-01-04

5.  Measures of balance and falls risk prediction in people with Parkinson's disease: a systematic review of psychometric properties.

Authors:  Stanley J Winser; Priya Kannan; Umar Muhhamad Bello; Susan L Whitney
Journal:  Clin Rehabil       Date:  2019-10-01       Impact factor: 3.477

6.  Integrating Technology Into Clinical Practice for the Assessment of Balance and Mobility: Perspectives of Exercise Professionals Practicing in Retirement and Long-term Care.

Authors:  Karen Van Ooteghem; Avril Mansfield; Elizabeth L Inness; Jaimie Killingbeck; Kathryn M Sibley
Journal:  Arch Rehabil Res Clin Transl       Date:  2020-01-16

7.  Use of a Smartphone to Gather Parkinson's Disease Neurological Vital Signs during the COVID-19 Pandemic.

Authors:  Jay L Alberts; Mandy Miller Koop; Marisa P McGinley; Amanda L Penko; Hubert H Fernandez; Steven Shook; Robert A Bermel; André Machado; Anson B Rosenfeldt
Journal:  Parkinsons Dis       Date:  2021-04-08

8.  Effectiveness of a Long-Term, Home-Based Aerobic Exercise Intervention on Slowing the Progression of Parkinson Disease: Design of the Cyclical Lower Extremity Exercise for Parkinson Disease II (CYCLE-II) Study.

Authors:  Jay L Alberts; Anson B Rosenfeldt; Cielita Lopez-Lennon; Erin Suttman; A Elizabeth Jansen; Peter B Imrey; Leland E Dibble
Journal:  Phys Ther       Date:  2021-11-01

Review 9.  Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders.

Authors:  Alessandro Zampogna; Ilaria Mileti; Eduardo Palermo; Claudia Celletti; Marco Paoloni; Alessandro Manoni; Ivan Mazzetta; Gloria Dalla Costa; Carlos Pérez-López; Filippo Camerota; Letizia Leocani; Joan Cabestany; Fernanda Irrera; Antonio Suppa
Journal:  Sensors (Basel)       Date:  2020-06-07       Impact factor: 3.576

Review 10.  Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review.

Authors:  Marco Ghislieri; Laura Gastaldi; Stefano Pastorelli; Shigeru Tadano; Valentina Agostini
Journal:  Sensors (Basel)       Date:  2019-09-20       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.