title: Reliability and criterion-related validity with a smartphone used in timed-up-and-go test.
abstract: BACKGROUND
  The capacity to diagnosys, quantify and evaluate movement beyond the general confines of a clinical environment under effectiveness conditions may alleviate rampant strain on limited, expensive and highly specialized medical resources. An iPhone 4® mounted a three dimensional accelerometer subsystem with highly robust software applications. The present study aimed to evaluate the reliability and concurrent criterion-related validity of the accelerations with an iPhone 4® in an Extended Timed Get Up and Go test. Extended Timed Get Up and Go is a clinical test with that the patient get up from the chair and walking ten meters, turn and coming back to the chair.
  
  
  METHODS
  A repeated measure, cross-sectional, analytical study. Test-retest reliability of the kinematic measurements of the iPhone 4® compared with a standard validated laboratory device. We calculated the Coefficient of Multiple Correlation between the two sensors acceleration signal of each subject, in each sub-stage, in each of the three Extended Timed Get Up and Go test trials. To investigate statistical agreement between the two sensors we used the Bland-Altman method.
  
  
  RESULTS
  With respect to the analysis of the correlation data in the present work, the Coefficient of Multiple Correlation of the five subjects in their triplicated trials were as follows: in sub-phase Sit to Stand the ranged between r = 0.991 to 0.842; in Gait Go, r = 0.967 to 0.852; in Turn, 0.979 to 0.798; in Gait Come, 0.964 to 0.887; and in Turn to Stand to Sit, 0.992 to 0.877. All the correlations between the sensors were significant (p < 0.001). The Bland-Altman plots obtained showed a solid tendency to stay at close to zero, especially on the y and x-axes, during the five phases of the Extended Timed Get Up and Go test.
  
  
  CONCLUSIONS
  The inertial sensor mounted in the iPhone 4® is sufficiently reliable and accurate to evaluate and identify the kinematic patterns in an Extended Timed Get and Go test. While analysis and interpretation of 3D kinematics data continue to be dauntingly complex, the iPhone 4® makes the task of acquiring the data relatively inexpensive and easy to use.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Galán-Mercant
authors.forename: Francisco Javier
authors.initials: FJ
authors.lastname: Barón-López
authors.forename: María T
authors.initials: MT
authors.lastname: Labajos-Manzanares
authors.affiliation: Departamento de Psiquiatría y Fisioterapia, Facultad de Ciencias de la Salud, Universidad de Málaga, Andalucia Tech, Cátedra de Fisioterapia y Discapacidad, Instituto de Biomedicina de Málaga (IBIMA), Grupo de Clinimetria (AE-14), Av/ Arquitecto Peñalosa s/n (Teatinos Campus Expansion), 29009 Málaga, Spain. acuesta.var@gmail.com.
authors.forename: Antonio I
authors.initials: AI
authors.lastname: Cuesta-Vargas
mesh_terms: D000054:Acceleration; D000368:Aged; D000886:Anthropometry; D001696:Biomechanical Phenomena; D040421:Cell Phone; D003430:Cross-Sectional Studies; D004867:Equipment Design; D005080:Exercise Test / Q000379:methods*; D005684:Gait / Q000502:physiology*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods; D009068:Movement; D004856:Postural Balance; D015203:Reproducibility of Results; D016138:Walking