A novel smartphone application is reliable for repeat administration and comparable to the Tekscan Strideway for spatiotemporal gait

Marie Kelly, Peter Jones, Ryan Wuebbles, Vipul Lugade, Daniel Cipriani, Nicholas G Murray, Marie Kelly, Peter Jones, Ryan Wuebbles, Vipul Lugade, Daniel Cipriani, Nicholas G Murray

Abstract

Smartphone applications are increasingly being used to measure gait due to their portability and cost-effectiveness. Important reliability metrics are not available for most of these devices. The purpose of this article was to evaluate the test-retest reliability and concurrent validity of spatiotemporal gait using the novel Gait Analyzer smartphone application compared to the Tekscan Strideway. Healthy participants (n=23) completed 12 trials of 10-meter walking, at two separate time points, using Gait Analyzer and while walking across the Tekscan Strideway. The results suggest excellent test-retest reliability for the Gait Analyzer and good test-retest reliability for the Tekscan Strideway for both velocity and cadence. At both time points, these devices were moderately to strongly correlated to one another for both velocity and cadence. These data suggest that the Gait Analyzer and Tekscan Strideway are reliable over time and can comparably calculate velocity and cadence.

Conflict of interest statement

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1:
Figure 1:
Walking diagram for data collection, participants walked 10-meters in total for 12 consecutive trials using the Gait Analyzer application and directly through the Tekscan Strideway. Once the person reached the stop area, they waited 10 seconds, then turned around and walked back to the start position. During the analysis the first step from the start position and last two steps were removed to reduce gait initiation and termination. The same steps were removed consistently regardless of walking direction (right to left or left to right).
Figure 2:
Figure 2:
Bland Altman plots displaying the mean difference from time 1 to time 2 for A) Gait Analyzer and B) Tekscan Strideway gait velocity.
Figure 3:
Figure 3:
Bland Altman plots displaying the mean difference from time 1 to time 2 for A) Gait Analyzer and B) Tekscan Strideway gait cadence.

References

    1. Vella MA, Li H, Reilly PM, Raza SS. Unlocked yet untapped: The ubiquitous smartphone and utilization of emergency medical identification technology in the care of the injured patient. Surg Open Sci. 2020;2(3):122–126. Published 2020 Apr 12. doi:10.1016/j.sopen.2020.03.001
    1. Wiechmann W, Kwan D, Bokarius A, Toohey SL. There's an App for That? Highlighting the Difficulty in Finding Clinically Relevant Smartphone Applications. West J Emerg Med. 2016;17(2):191–194. doi:10.5811/westjem.2015.12.28781
    1. Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst. 2021. Aug;45(9) 87. doi:10.1007/s10916-021-01760-5. PMID: 34392429; PMCID: PMC8364438.
    1. Omberg L, Chaibub Neto E, Perumal TM, et al. Remote smartphone monitoring of Parkinson's disease and individual response to therapy. Nature Biotechnology. 2021. Aug. DOI: 10.1038/s41587-021-00974-9. PMID: 34373643.
    1. Fréchette Guillaume R., Lizzeri Alessandro, and Salz Tobias. 2019. "Frictions in a Competitive, Regulated Market: Evidence from Taxis." American Economic Review, 109 (8): 2954–92.DOI: 10.1257/aer.20161720
    1. Howell DR, Seehusen CN, Wingerson MJ, Wilson JC, Lynall RC, Lugade V. Reliability and Minimal Detectable Change for a Smartphone-Based Motor-Cognitive Assessment: Implications for Concussion Management. J Appl Biomech. 2021. Jul 13; 1–8. PMID: 34257159.
    1. Marziyeh Ghoreshi Beyrami S, Ghaderyan P. A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement. 2020;156:107579.doi:10.1016/j.measurement.2020.107579
    1. Ghaderyan P, Fathi G. Inter-limb time-varying singular value: A new gait feature for parkinson’s disease detection and stage classification. Measurement. 2021;177:109249. doi:10.1016/j.measurement.2021.109249
    1. Lugade V, Kuntapun J, Prupetkaew P, Boripuntakul S, Verner E, Silsupadol P. Three-Day Remote Monitoring of Gait Among Young and Older Adults Using Participants' Personal Smartphones [published online ahead of print, 2021 Aug 4]. J Aging Phys Act. 2021;1–8. doi:10.1123/japa.2020-0353
    1. Prupetkaew P, Lugade V, Kamnardsiri T, Silsupadol P. Cognitive and visual demands, but not gross motor demand, of concurrent smartphone use affect laboratory and free-living gait among young and older adults. Gait Posture. 2019;68:30–36. doi:10.1016/j.gaitpost.2018.11.003
    1. Benson LC, Clermont CA, Bošnjak E, Ferber R. The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review. Gait Posture. 2018;63:124–138. doi:10.1016/j.gaitpost.2018.04.047
    1. Eades MT, Tsanas A, Juraschek SP, Kramer DB, Gervino E, Mukamal KJ. Smartphone-recorded physical activity for estimating cardiorespiratory fitness. Sci Rep. 2021;11(1):14851. Published 2021 Jul 21. doi:10.1038/s41598-021-94164-x
    1. Kanko RM, Laende EK, Strutzenberger G, et al. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech. 2021;122:110414. doi:10.1016/j.jbiomech.2021.110414
    1. Roggio F, Ravalli S, Maugeri G, et al. Technological advancements in the analysis of human motion and posture management through digital devices. World J Orthop. 2021;12(7):467–484. Published 2021 Jul 18. doi:10.5312/wjo.v12.i7.467
    1. Zheng N, Barrentine SW. Biomechanics and motion analysis applied to sports. Phys Med Rehabil Clin N Am. 2000;11(2):309–322.
    1. Murray NG, Moran R, Islas A, et al. Sport-related concussion adopt a more conservative approach to straight path walking and turning during tandem gait. Journal of Clinical and Translational Research. 2021. doi:10.18053/jctres.07.202104.002
    1. Hegde N, Zhang T, Uswatte G, et al. The Pediatric SmartShoe: Wearable Sensor System for Ambulatory Monitoring of Physical Activity and Gait. IEEE Trans Neural Syst Rehabil Eng. 2018;26(2):477–486. doi:10.1109/TNSRE.2017.2786269
    1. Donisi L, Pagano G, Cesarelli G, Coccia A, Amitrano F, D'Addio G. Benchmarking between two wearable inertial systems for gait analysis based on a different sensor placement using several statistical approaches. Measurement. 2021;173:108642. doi:10.1016/j.measurement.2020.108642
    1. Liu T, Inoue Y, Shibata K. Development of a wearable sensor system for quantitative gait analysis. Measurement. 2009;42(7):978–988. doi:10.1016/j.measurement.2009.02.002
    1. Palermo E, Rossi S, Marini F, Patanè F, Cappa P. Experimental evaluation of accuracy and repeatability of a novel body-to-sensor calibration procedure for inertial sensor-based gait analysis. Measurement. 2014;52:145–155. doi:10.1016/j.measurement.2014.03.004
    1. Zhang Z, Xie L. Adaptive body movement system for wearable IOT instruments based on matrix vector parameter estimation. Measurement. 2021;169:108350. doi:10.1016/j.measurement.2020.108350
    1. Wong JS, Jasani H, Poon V, Inness EL, McIlroy WE, Mansfield A. Inter- and intra-rater reliability of the GAITRite system among individuals with sub-acute stroke. Gait Posture. 2014;40(1):259–261. doi:10.1016/j.gaitpost.2014.02.007
    1. O'Dea S. Number of smartphone users from 2016 to 2021. Statista. . Published June 1, 2021.
    1. Silsupadol P, Teja K, Lugade V. Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. Gait Posture. 2017;58:516–522. doi:10.1016/j.gaitpost.2017.09.030
    1. Lugade V, Fortune E, Morrow M, Kaufman K (2014). Validity of using tri-axial accelerometers to measure human movement-part I: posture and movement detection. Med. Eng. Phys 36, 169–176. 10.1016/j.medengphy.2013.06.005
    1. Kelly M, Taylor MR, Pavilionis P, et al. Analysis of mobile smartphone application for the assessment Of Facioscapulohumeral muscular Dystrophy (fshd) gait. Medicine & Science in Sports & Exercise. 2021;53(8S):137–137. doi:10.1249/01.mss.0000760728.64290.b2
    1. Silsupadol P, Prupetkaew P, Kamnardsiri T, Lugade V. Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments. IEEE J Biomed Health Inform. 2020;24(4):1188–1195. doi:10.1109/JBHI.2019.2930091
    1. Silsupadol P, Teja K, Lugade V. Reliability and validity of a smartphone-based assessment of gait parameters across walking speed and smartphone locations: Body, bag, belt, hand, and pocket. Gait Posture. 2017;58:516–522. doi:10.1016/j.gaitpost.2017.09.030
    1. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research [published correction appears in J Chiropr Med. 2017 Dec;16(4):346]. J Chiropr Med. 2016;15(2):155–163. doi:10.1016/j.jcm.2016.02.012
    1. Schober P, Boer C, Schwarte LA. Correlation Coefficients: Appropriate Use and Interpretation. Anesth Analg. 2018;126(5):1763–1768. doi:10.1213/ANE.0000000000002864
    1. Oberg T, Karsznia A, Oberg K. Basic gait parameters: reference data for normal subjects, 10-79 years of age. J Rehabil Res Dev. 1993;30(2):210–223.
    1. Gélat T, Pellec AL, Brenière Y. Evidence for a common process in gait initiation and stepping on to a new level to reach gait velocity. Exp Brain Res. 2006;170(3):336–344. doi:10.1007/s00221-005-0214-8
    1. Van Deventer KA, Seehusen CN, Walker GA, Wilson JC, Howell DR. The diagnostic and prognostic utility of the dual-task tandem gait test for pediatric concussion. J Sport Health Sci. 2021;10(2):131–137. doi:10.1016/j.jshs.2020.08.005
    1. Godfrey A, Del Din S, Barry G, Mathers JC, Rochester L. Instrumenting gait with an accelerometer: a system and algorithm examination. Med Eng Phys. 2015;37(4):400–407. doi:10.1016/j.medengphy.2015.02.003
    1. Tudor-Locke C, Ducharme SW, Aguiar EJ, et al. Walking cadence (steps/min) and intensity in 41 to 60-year-old adults: the CADENCE-adults study. Int J Behav Nutr Phys Act. 2020;17(1):137. Published 2020 Nov 10. doi:10.1186/s12966-020-01045-z
    1. Silsupadol P, Prupetkaew P, Kamnardsiri T, Lugade V. Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments. IEEE J Biomed Health Inform. 2020;24(4):1188–1195. doi:10.1109/JBHI.2019.2930091

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera