title: Quantifying individual components of the timed up and go using the kinect in people living with stroke.
abstract: Background. The Microsoft Kinect presents a simple, inexpensive, and portable method of examining the independent components of the Timed Up and Go (TUG) without any intrusion on the patient. Objective. This study examined the reliability of these measures, and whether they improved prediction of performance on common clinical tests. Methods. Thirty individuals with stroke completed 4 clinical assessments, including the TUG, 10-m walk test (10MWT), Step Test, and Functional Reach test on 2 testing occasions. The TUG was assessed using the Kinect to determine 7 different functional components. Test-retest reliability was assessed using intraclass correlation coefficient (ICC), redundancy using Spearman's correlation, and score prediction on the clinical tests using multiple regression. Results. All Kinect-TUG variables possessed excellent reliability (ICC(2,k) > 0.90) except trunk flexion angle (ICC = 0.73). Trunk flexion angle and first step length were nonredundant with total TUG time. When predicting 10MWT and Step Test scores, adding step length into regression models comprising age and total TUG time improved model performance by 7% (P <.01) and 6% (P =.03), respectively. Specifically, an interquartile range increase in first step length (0.19 m) was associated with a 0.15 m/s faster gait speed and 1.8 more repetitions on the Step Test. These effect sizes were comparable to our minimal detectable change scores of 0.17 m/s for gait speed and 1.71 repetitions for the Step Test. Conclusions. Using the Kinect to independently assess the multiple components of the TUG may provide reliable and clinically useful information. This could enable efficient and information-rich large-scale assessments of physical deficits following stroke.
authors.affiliation: Australian Catholic University, Melbourne, Victoria, Australia.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Vernon
authors.affiliation: The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Kade
authors.initials: K
authors.lastname: Paterson
authors.affiliation: The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Kelly
authors.initials: K
authors.lastname: Bower
authors.affiliation: The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Jennifer
authors.initials: J
authors.lastname: McGinley
authors.affiliation: The University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Kimberly
authors.initials: K
authors.lastname: Miller
authors.affiliation: Singapore General Hospital, Singapore.
authors.forename: Yong-Hao
authors.initials: YH
authors.lastname: Pua
authors.affiliation: Australian Catholic University, Melbourne, Victoria, Australia ross.clark@acu.edu.au.
authors.forename: Ross A
authors.initials: RA
authors.lastname: Clark
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D004185:Disability Evaluation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D015203:Reproducibility of Results; D012984:Software; D018709:Statistics, Nonparametric; D020521:Stroke / Q000503:physiopathology*; D000071939:Stroke Rehabilitation; D013808:Therapeutic Community; D016896:Treatment Outcome
keywords: balance; gait; gaming; kinematic; stroke