Validation of a smartphone-based measurement tool for the quantification of level walking

被引:34
|
作者
Furrer, Martina [1 ]
Bichsel, Lukas [2 ]
Niederer, Michael [3 ]
Baur, Heiner [1 ]
Schmid, Stefan [1 ]
机构
[1] Bern Univ Appl Sci, Hlth Div, Discipline Physiotherapy, CH-3008 Bern, Switzerland
[2] Bern Univ Appl Sci, Dept Engn & Informat Technol, Inst Rehabil & Performance Technol, Burgdorf, Switzerland
[3] Zurich Univ Appl Sci, Sch Hlth Profess, Inst Physiotherapy, Zurich, Switzerland
关键词
Validity; Reliability; Gait analysis; Trunk accelerometry; TEST-RETEST RELIABILITY; GAIT ANALYSIS; VALIDITY; MYELOMENINGOCELE; PARAMETERS; MOVEMENT; CHILDREN; MOTION; WORK; COST;
D O I
10.1016/j.gaitpost.2015.06.003
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Introduction: It is important to assess and quantify gait in order to determine the severity of impairments during gait and to evaluate therapeutic interventions. However, laboratory gait assessment is expensive and time consuming and there is a lack of an easily applicable tool for the quantification of gait in clinical practice. The aim of this study was to validate a smartphone-based measurement tool for the quantification of level walking. Methods: Vertical center of mass displacement and step duration of 22 healthy young adults were assessed by a smartphone application and a motion capture system. Intra-session reliability was evaluated by repeated-measures ANOVA, intraclass correlation coefficient (ICC), and standard error of measurement. In order to evaluate the concurrent validity of the smartphone application, smartphone- and motion capture-derived values were compared by Pearson correlation coefficient and Bland-Altman limits of agreement. Results: Six out of eight variables derived by the smartphone application showed an excellent reliability (ICC >= 0.75) and all variables correlated significantly with measurements of the motion capture system with moderate to strong correlations ranging from 0.61 to 0.92. Conclusion: The results showed a great potential of the smartphone application to be a user-friendly and valid tool for the assessment of gait in clinical practice. Further research needs to investigate whether the smartphone application is able to detect differences in gait patterns following therapeutic or orthopedic interventions and whether it is valid for the quantification of gait in people with movement disorders. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:289 / 294
页数:6
相关论文
共 50 条
  • [1] Smartphone-Based Photoplethysmogram Measurement
    Kurylyak, Yuriy
    Lamonaca, Francesco
    Grimaldi, Domenico
    [J]. DIGITAL IMAGE AND SIGNAL PROCESSING FOR MEASUREMENT SYSTEMS, 2012, : 135 - 164
  • [2] Smartphone-based sound level measurement apps: Evaluation of directional response
    Celestina, Metod
    Kardous, Chucri A.
    Trost, Andrej
    [J]. APPLIED ACOUSTICS, 2021, 171
  • [3] Solar Survey: Development and validation of a smartphone-based solar site assessment tool
    Ranalli, Joseph A.
    [J]. SOLAR ENERGY, 2015, 122 : 1199 - 1213
  • [4] A Smartphone-Based Shopping Mall Walking Program and Daily Walking Steps
    Matsuoka, Yoko
    Yoshida, Hiroaki
    Hanazato, Masamichi
    [J]. JAMA NETWORK OPEN, 2024, 7 (01) : E2353957
  • [5] Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms
    Sean Bae
    Silviu Borac
    Yunus Emre
    Jonathan Wang
    Jiang Wu
    Mehr Kashyap
    Si-Hyuck Kang
    Liwen Chen
    Melissa Moran
    Julie Cannon
    Eric S. Teasley
    Allen Chai
    Yun Liu
    Neal Wadhwa
    Michael Krainin
    Michael Rubinstein
    Alejandra Maciel
    Michael V. McConnell
    Shwetak Patel
    Greg S. Corrado
    James A. Taylor
    Jiening Zhan
    Ming Jack Po
    [J]. Communications Medicine, 2
  • [6] Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms
    Bae, Sean
    Borac, Silviu
    Emre, Yunus
    Wang, Jonathan
    Wu, Jiang
    Kashyap, Mehr
    Kang, Si-Hyuck
    Chen, Liwen
    Moran, Melissa
    Cannon, Julie
    Teasley, Eric S.
    Chai, Allen
    Yun, Liu
    Wadhwa, Neal
    Krainin, Michael
    Rubinstein, Michael
    Maciel, Alejandra
    McConnell, Michael V.
    Patel, Shwetak
    Corrado, Greg S.
    Taylor, James A.
    Zhan, Jiening
    Po, Ming Jack
    [J]. COMMUNICATIONS MEDICINE, 2022, 2 (01):
  • [7] Smartphone-based Walking Speed Estimation for Stroke Mitigation
    Cox, Jeffrey
    Cao, Yu
    Chen, Guanling
    He, Jianbiao
    Xiao, Degui
    [J]. 2014 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2014, : 328 - 332
  • [8] Smartphone-based Tool Measuring soil pH
    Arjumand, Tahera
    [J]. CURRENT SCIENCE, 2021, 120 (05): : 754 - 754
  • [9] Efficient smartphone-based measurement of phosphorus in water
    Ai, Haiping
    Zhang, Kai
    Zhang, Huichun
    [J]. WATER RESEARCH X, 2024, 22
  • [10] Evaluation of Smartphone-based Sound Level Meters
    Cheng, Trinity
    [J]. 2020 9TH IEEE INTEGRATED STEM EDUCATION CONFERENCE (ISEC 2020), 2020,