Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket.

The assessment of spatiotemporal gait parameters is a useful clinical indicator of health status. Unfortunately, most assessment tools require controlled laboratory environments which can be expensive and time consuming. As smartphones with embedded sensors are becoming ubiquitous, this technology can provide a cost-effective, easily deployable method for assessing gait. Therefore, the purpose of this study was to assess the reliability and validity of a smartphone-based accelerometer in quantifying spatiotemporal gait parameters when attached to the body or in a bag, belt, hand, and pocket. Thirty-four healthy adults were asked to walk at self-selected comfortable, slow, and fast speeds over a 10-m walkway while carrying a smartphone. Step length, step time, gait velocity, and cadence were computed from smartphone-based accelerometers and validated with GAITRite. Across all walking speeds, smartphone data had excellent reliability (ICC2,1≥0.90) for the body and belt locations, with bag, hand, and pocket locations having good to excellent reliability (ICC2,1≥0.69). Correlations between the smartphone-based and GAITRite-based systems were very high for the body (r=0.89, 0.98, 0.96, and 0.87 for step length, step time, gait velocity, and cadence, respectively). Similarly, Bland-Altman analysis demonstrated that the bias approached zero, particularly in the body, bag, and belt conditions under comfortable and fast speeds. Thus, smartphone-based assessments of gait are most valid when placed on the body, in a bag, or on a belt. The use of a smartphone to assess gait can provide relevant data to clinicians without encumbering the user and allow for data collection in the free-living environment.

[1]  Susan L Murphy,et al.  Review of physical activity measurement using accelerometers in older adults: considerations for research design and conduct. , 2009, Preventive medicine.

[2]  G. ÓLaighin,et al.  Direct measurement of human movement by accelerometry. , 2008, Medical engineering & physics.

[3]  Konrad P. Kording,et al.  Journal of Neuroscience Methods , 2013 .

[4]  J. Weir Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. , 2005, Journal of strength and conditioning research.

[5]  Wiebren Zijlstra,et al.  Accelerometry based assessment of gait parameters in children. , 2006, Gait & posture.

[6]  Kazuya Okamoto,et al.  Reliability and validity of gait analysis by android-based smartphone. , 2012, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[7]  D. Cicchetti Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[8]  A. Godfreya,et al.  Instrumenting gait with an accelerometer: A system and algorithm examination , 2017 .

[9]  Valentina Agostini,et al.  Does texting while walking really affect gait in young adults? , 2015, Journal of NeuroEngineering and Rehabilitation.

[10]  M. Hirvensalo,et al.  Mobility Difficulties and Physical Activity as Predictors of Mortality and Loss of Independence in the Community‐Living Older Population , 2000, Journal of the American Geriatrics Society.

[11]  Babak Ziaie,et al.  A wearable smartphone-enabled camera-based system for gait assessment. , 2015, Gait & posture.

[12]  Fumiko Ichikawa,et al.  A Cross Culture Study on Phone Carrying and Physical Personalization , 2007, HCI.

[13]  K. Webster,et al.  Validity of the GAITRite walkway system for the measurement of averaged and individual step parameters of gait. , 2005, Gait & posture.

[14]  Steven Morrison,et al.  Reliability of segmental accelerations measured using a new wireless gait analysis system. , 2006, Journal of biomechanics.

[15]  Susan E. Hardy,et al.  Improvement in Usual Gait Speed Predicts Better Survival in Older Adults , 2007, Journal of the American Geriatrics Society.

[16]  Lynn Rochester,et al.  Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. , 2013, The journals of gerontology. Series A, Biological sciences and medical sciences.

[17]  E. D. de Bruin,et al.  Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. , 2009, Gait & posture.

[18]  Jeffrey M. Hausdorff Gait variability: methods, modeling and meaning , 2005, Journal of NeuroEngineering and Rehabilitation.

[19]  Emma Fortune,et al.  Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection. , 2014, Medical engineering & physics.

[20]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[21]  Martina Furrer,et al.  Validation of a smartphone-based measurement tool for the quantification of level walking. , 2015, Gait & posture.

[22]  O. Chan,et al.  Hand , 2005, BMJ : British Medical Journal.

[23]  K. Kaufman,et al.  Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. , 2014, Medical engineering & physics.

[24]  Greta C Bernatz,et al.  How humans walk: bout duration, steps per bout, and rest duration. , 2008, Journal of rehabilitation research and development.

[25]  A Leardini,et al.  Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis , 2012, Comput. Methods Programs Biomed..

[26]  J Hausdroff Gait variability : methods, modeling and meaning , 2005 .

[27]  M. Mukaka,et al.  Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.

[28]  M. Morris,et al.  Concurrent related validity of the GAITRite walkway system for quantification of the spatial and temporal parameters of gait. , 2003, Gait & posture.

[29]  Xiaonan Xue,et al.  Quantitative gait dysfunction and risk of cognitive decline and dementia , 2007, Journal of Neurology, Neurosurgery & Psychiatry.

[30]  A. Hof,et al.  Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. , 2003, Gait & posture.

[31]  Angelo M. Sabatini,et al.  Accelerometry-based recognition of the placement sites of a wearable sensor , 2015, Pervasive Mob. Comput..

[32]  H. Stähelin Guidelines , 1994, Communicating Science.