만성 뇌졸중 환자에서 스마트폰을 이용한 보행변수 평가의 신뢰도와 타당도 = Reliability and Validity of a Smartphone-based Assessment of Gait Parameters in Patients with Chronic Stroke

다국어 초록 (Multilingual Abstract)

PURPOSE: Most gait assessment tools are expensive and require controlled laboratory environments. Tri-axial accelerometers have been used in gait analysis as an alternative to laboratory assessments. Many smartphones have added an accelerometer, making it possible to assess spatio-temporal gait parameters. This study was conducted to confirm the reliability and validity of a smartphone-based accelerometer at quantifying spatio-temporal gait parameters of stroke patients when attached to the body.
METHODS: We measured gait parameters using a smartphone accelerometer and gait parameters through the GAITRite analysis system and the reliability and validity of the smartphone-based accelerometer for quantifying spatio-temporal gait parameters for stroke patients were then evaluated. Thirty stroke patients were asked to walk at self-selected comfortable speeds over a 10 m walkway, during which time gait velocity, cadence and step length were computed from smartphone-based accelerometers and validated with a GAITRite analysis system.
RESULTS: Smartphone data was found to have excellent reliability (ICC2,1≥.98) for measuring the tested parameters, with a high correlation being observed between smartphone-based gait parameters and GAITRite analysis system-based gait parameters (r = .99, .97, .41 for gait velocity, cadence, step length, respectively).
CONCLUSION: The results suggest that specific opportunities exist for smartphone-based gait assessment as an alternative to conventional gait assessment. Moreover, smartphone-based gait assessment can provide objective information about changes in the spatio-temporal gait parameters of stroke subjects.

참고문헌 (Reference)

1 안보라, "정상인의 보행시 보행분석을 위한 스마트폰 어플리케이션의 타당성 연구" 대한치료과학회 8 (8): 59-66, 2016

2 Fortune E, "Validity of using tri-axial accelerometers to measure human movement- part II: Step counts at a wide range of gait velocities" 36 (36): 659-669, 2014

3 Webster KE, "Validity of the GAITRite walkway system for the measurement of averaged and individual step parameters of gait" 22 (22): 317-321, 2005

4 Furrer M, "Validation of a smartphone-based measurement tool for the quantification of level walking" 42 (42): 289-294, 2015

5 Jung T, "The influence of applying additional weight to the affected leg on gait patterns during aquatic treadmill walking in people poststroke" 91 (91): 129-136, 2010

6 Hoseinabadi MR, "The effects of physical therapy on exaggerated muscle tonicity, balance and quality of life on hemiparetic patients due to stroke" 63 (63): 735-738, 2013

7 Obuchi SP, "Test-retest reliability of daily life gait speed as measured by smartphone global positioning system" 61 : 282-286, 2018

8 Hsu CY, "Test-retest reliability of an automated infrared-assisted trunk accelerometer-based gait analysis system" 16 (16): E1156-, 2016

9 Manor B, "Smartphone app-based assessment of gait during normal and dual-task walking: demonstration of validity and reliability" 6 (6): e36-, 2018

10 Hartmann A, "Reproducibility of spatio-temporal gait parameters under different conditions in older adults using a trunk tri-axial accelerometer system" 30 (30): 351-355, 2009

11 Silsupadol P, "Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket" 58 : 516-522, 2017

12 Balasubramanian CK, "Relationships between step length asymmetry and walking performance in subjects with chronic heparesis" 88 (88): 43-49, 2007

13 Januario F, "Rehabilitation of postural stability in ataxic/hemiplegic patients after stroke" 32 (32): 1775-1779, 2010

14 Jorgensen HS, "Recovery of walking function in stroke patients: the copenhagen stroke study" 76 (76): 27-32, 1995

15 Peurala SH, "Postural instability in patients with chronic stroke" 25 (25): 101-108, 2007

16 Yamada M, "Objective assessment of abnormal gait in patients with rheumatoid arthritis using a smartphone" 32 (32): 3869-3874, 2012

17 Busis N, "Mobile phones to improve the practice of neurology" 28 (28): 395-410, 2010

18 Rueterbories J, "Methods for gait event detection and analysis in ambulatory systems" 32 (32): 545-552, 2010

19 Lord S, "Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach" 68 (68): 820-827, 2013

20 Antos SA, "Hand, belt, pocket or bag: Practical activity tracking with mobile phones" 231 : 22-30, 2014

21 Eng JJ, "Functional walk tests in individuals with stroke: relation to perceived exertion and myocardial exertion" 33 (33): 756-761, 2002

22 Gunaydin R, "Determinants of quality of life (QoL) in elderly stroke patients: a short-term follow-up study" 53 (53): 19-23, 2011

23 Meichtry A, "Criterion validity of 3D trunk accelerations to assess external work and power in able-bodied gait" 25 (25): 25-32, 2007

24 Hartmann A, "Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults" 29 (29): 444-448, 2009

25 Bilney B, "Concurrent related validity of the GAITRite walkway system for quantification of the spatial and temporal parameters of gait" 17 (17): 68-74, 2003

26 Moore SA, "Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study" 14 (14): 130-, 2017

27 Zijlstra W, "Assessment of spatio-temporal gait parameters from trunk accelerations during human walking" 18 (18): 1-10, 2003

28 Bowden MG, "Anterior-posterior ground reaction forces as a measure of paretic leg contribution in hemiplegic walking" 37 (37): 872-876, 2006

29 Ellis RJ, "A validated smartphone-based assessment of gait and gait variability in parkinson’s disease" 10 (10): e0141694-, 2015

30 Moe-Nilssen R., "A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument" 13 (13): 320-327, 1998

31 Ayu MA, "A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition" 41 : 224-229, 2012