Advertisement

Recording context matters: Differences in gait parameters collected by the OneStep smartphone application

      Highlights

      • The effect of recording context on smartphone-based gait analysis was investigated.
      • Spontaneous walks were slower with shorter strides versus when consciously initiated.
      • Users' ambulation patterns may differ based on the context of remote data collection.

      Abstract

      Background

      Detailed understanding of impairments that underlie walking dysfunction through objective measures is essential to diagnosis, evaluation and care planning. Despite significant developments in motion tracking technologies, there is a dearth of research about the influence of remote monitoring context on performance. The objective of this study was to determine whether gait parameters collected by the OneStep smartphone application differ based on the recording condition.

      Methods

      Retrospective repeated measures univariate analysis was performed on data extracted based on detected activity, either spontaneous (background recording) or consciously initiated (in app) walks, of 25 patients enrolled in a physical therapy program.

      Findings

      Across 7227 walking bouts, significant differences between the two paradigms in velocity (g = 0.48), double support (g = 0.37), stride length (g = 0.37) and step length of the affected side (g = 0.32) were revealed. Overall, the passively recorded walks presented a less clinically favorable spatiotemporal pattern for each of these variables.

      Interpretation

      The recording context of walks that were used for analysis appears to significantly affect the biomechanical output of the OneStep application. It is unclear whether the disparity found would impact functional recovery of individuals undergoing rehabilitation due to neurological or musculoskeletal disorder. Clinicians may consider this information when incorporating remotely-acquired quantitative gait analysis and interpreting care outcomes as part of therapeutic practice. Future work can further investigate the behavioral and environmental factors contributing to how movement occurs in specific clinical populations when monitored via mobile health systems.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Biomechanics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Abou L.
        • Peters J.
        • Wong E.
        • Akers R.
        • Dossou M.S.
        • Sosnoff J.J.
        • et al.
        Gait and balance assessments using smartphone applications in Parkinson’s disease: a systematic review.
        J. Med. Syst. 2021 Aug 15; 45: 87
        • Abou L.
        • Wong E.
        • Peters J.
        • Dossou M.S.
        • Sosnoff J.J.
        • Rice L.A.
        Smartphone applications to assess gait and postural control in people with multiple sclerosis: a systematic review.
        Mult Scler Relat Disord. 2021 Jun; 51102943
        • Ajami S.
        • Teimouri F.
        Features and application of wearable biosensors in medical care.
        J. Res. Med. Sci. 2015; 20: 1208
        • Atrsaei A.
        • Corrà M.F.
        • Dadashi F.
        • Vila-Chã N.
        • Maia L.
        • Mariani B.
        • et al.
        Gait speed in clinical and daily living assessments in Parkinson’s disease patients: performance versus capacity.
        NPJ Park Dis. 2021 Dec; 7: 24
        • Baker R.
        Gait analysis methods in rehabilitation.
        J. Neuro Eng. Rehabil. 2006; 3: 4
        • Balaban B.
        • Tok F.
        Gait disturbances in patients with stroke.
        PM&R. 2014 Jul; 6: 635-642
        • Bock O.
        • Beurskens R.
        Changes of locomotion in old age depend on task setting.
        Gait Posture. 2010 Oct; 32: 645-649
        • Boyer K.A.
        • Johnson R.T.
        • Banks J.J.
        • Jewell C.
        • Hafer J.F.
        Systematic review and meta-analysis of gait mechanics in young and older adults.
        Exp. Gerontol. 2017 Sep; 95: 63-70
        • Brodie M.A.D.
        • Coppens M.J.M.
        • Lord S.R.
        • Lovell N.H.
        • Gschwind Y.J.
        • Redmond S.J.
        • et al.
        Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different.
        Med. Biol. Eng. Comput. 2016 Apr; 54: 663-674
        • Carcreff L.
        • Gerber C.N.
        • Paraschiv-Ionescu A.
        • De Coulon G.
        • Aminian K.
        • Newman C.J.
        • et al.
        Walking speed of children and adolescents with cerebral palsy: laboratory versus daily life.
        Front. Bioeng. Biotechnol. 2020 Jul 14; 8: 812
        • Casartelli N.C.
        • Item-Glatthorn J.F.
        • Bizzini M.
        • Leunig M.
        • Maffiuletti N.A.
        Differences in gait characteristics between total hip, knee, and ankle arthroplasty patients: a six-month postoperative comparison.
        BMC Musculoskelet. Disord. 2013 Dec; 14: 176
        • Del Din S.
        • Godfrey A.
        • Mazzà C.
        • Lord S.
        • Rochester L.
        Free-living monitoring of Parkinson’s disease: lessons from the field: wearable Technology for Parkinson’S disease.
        Mov. Disord. 2016 Sep; 31: 1293-1313
        • Díaz S.
        • Stephenson J.B.
        • Labrador M.A.
        Use of wearable sensor technology in gait, balance, and range of motion analysis.
        Appl. Sci. 2019 Dec 27; 10: 234
        • Ebara T.
        • Azuma R.
        • Shoji N.
        • Matsukawa T.
        • Yamada Y.
        • Akiyama T.
        • et al.
        Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels.
        J. Occup. Health. 2017 Nov 25; 59: 506-512
        • Friesen K.B.
        • Zhang Z.
        • Monaghan P.G.
        • Oliver G.D.
        • Roper J.A.
        All eyes on you: how researcher presence changes the way you walk.
        Sci. Rep. 2020 Dec; 10: 17159
        • Fritz S.
        • Lusardi M.
        White paper: “walking speed: the sixth vital sign.”.
        J. Geriatr. Phys. Ther. 2001. 2009; 32: 46-49
        • Fukuchi C.A.
        • Fukuchi R.K.
        • Duarte M.
        Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis.
        Syst. Rev. 2019 Dec; 8: 153
        • Furrer M.
        • Bichsel L.
        • Niederer M.
        • Baur H.
        • Schmid S.
        Validation of a smartphone-based measurement tool for the quantification of level walking.
        Gait Posture. 2015 Sep; 42: 289-294
        • Glass T.A.
        Conjugating the “tenses” of function: discordance among hypothetical, experimental, and enacted function in older adults.
        The Gerontologist. 1998 Feb 1; 38: 101-112
        • Hutchinson L.A.
        • Brown M.J.
        • Deluzio K.J.
        • De Asha A.R.
        Self-selected walking speed increases when individuals are aware of being recorded.
        Gait Posture. 2019 Feb; 68: 78-80
        • Iluz T.
        • Weiss A.
        • Gazit E.
        • Tankus A.
        • Brozgol M.
        • Dorfman M.
        • et al.
        Can a body-fixed sensor reduce Heisenberg’s uncertainty when it comes to the evaluation of mobility? Effects of aging and fall risk on transitions in daily living.
        J. Gerontol. A Biol. Sci. Med. Sci. 2016 Nov; 71: 1459-1465
        • Kuntapun J.
        • Silsupadol P.
        • Kamnardsiri T.
        • Lugade V.
        Smartphone monitoring of gait and balance during irregular surface walking and obstacle crossing.
        Front. Sports Act. Liv. 2020 Nov 27; 2560577
        • LeMoyne R.
        • Mastroianni T.
        Wearable and wireless gait analysis platforms.
        in: Wireless MEMS Networks and Applications [Internet]. Elsevier, 2017: 129-152 ([cited 2021 Aug 27]. Available from:)
        • Liu X.
        • Zhao C.
        • Zheng B.
        • Guo Q.
        • Duan X.
        • Wulamu A.
        • et al.
        Wearable devices for gait analysis in intelligent healthcare.
        Front. Comput. Sci. 2021 May 13; 3661676
        • Malasinghe L.P.
        • Ramzan N.
        • Dahal K.
        Remote patient monitoring: a comprehensive study.
        J. Ambient. Intell. Humaniz. Comput. 2019 Jan; 10: 57-76
        • Middleton A.
        • Fritz S.L.
        • Lusardi M.
        Walking speed: the functional vital sign.
        J. Aging Phys. Act. 2015 Apr; 23: 314-322
        • Muro-de-la-Herran A.
        • Garcia-Zapirain B.
        • Mendez-Zorrilla A.
        Gait analysis methods: an overview of wearable and non-wearable systems.
        Highlight. Clin. Appl. Sens. 2014 Feb 19; 14: 3362-3394
        • Noah B.
        • Keller M.S.
        • Mosadeghi S.
        • Stein L.
        • Johl S.
        • Delshad S.
        • et al.
        Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials.
        NPJ Digit. Med. 2018 Dec; 1: 20172
        • Pannucci C.J.
        • Wilkins E.G.
        Identifying and avoiding bias in research.
        Plast. Reconstr. Surg. 2010 Aug; 126: 619-625
        • Pepa L.
        • Verdini F.
        • Spalazzi L.
        Gait parameter and event estimation using smartphones.
        Gait Posture. 2017 Sep; 57: 217-223
        • Picerno P.
        • Iosa M.
        • D’Souza C.
        • Benedetti M.G.
        • Paolucci S.
        • Morone G.
        Wearable inertial sensors for human movement analysis: a five-year update.
        Exp. Rev. Med. Devic. 2021 Dec 3; 18: 79-94
        • Pioli M.R.
        • Ritter A.M.V.
        • de Faria A.P.
        • Modolo R.
        White coat syndrome and its variations: differences and clinical impact.
        Integr. Blood Press Contr. 2018 Nov; 11: 73-79
        • Renggli D.
        • Graf C.
        • Tachatos N.
        • Singh N.
        • Meboldt M.
        • Taylor W.R.
        • et al.
        Wearable inertial measurement units for assessing gait in real-world environments.
        Front. Physiol. 2020 Feb 20; 11: 90
        • Rens N.
        • Gandhi N.
        • Mak J.
        • Paul J.
        • Bent D.
        • Liu S.
        • et al.
        Activity data from wearables as an indicator of functional capacity in patients with cardiovascular disease. Lazzeri C, editor.
        PLoS One. 2021 Mar 24; 16e0247834
        • Rudisch J.
        • Jöllenbeck T.
        • Vogt L.
        • Cordes T.
        • Klotzbier T.J.
        • Vogel O.
        • et al.
        Agreement and consistency of five different clinical gait analysis systems in the assessment of spatiotemporal gait parameters.
        Gait Posture. 2021 Mar; 85: 55-64
        • Shahar R.T.
        • Agmon M.
        Gait analysis using accelerometry data from a single smartphone: agreement and consistency between a smartphone application and gold-standard gait analysis system.
        Sensors. 2021 Nov 11; 21: 7497
        • 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 Oct; 58: 516-522
        • Stellmann J.P.
        • Neuhaus A.
        • Götze N.
        • Briken S.
        • Lederer C.
        • Schimpl M.
        • et al.
        Ecological validity of walking capacity tests in multiple sclerosis. Reindl M, editor.
        PLoS One. 2015 Apr 16; 10e0123822
        • Takayanagi N.
        • Sudo M.
        • Yamashiro Y.
        • Lee S.
        • Kobayashi Y.
        • Niki Y.
        • et al.
        Relationship between daily and in-laboratory gait speed among healthy community-dwelling older adults.
        Sci. Rep. 2019 Dec; 9: 3496
        • Tao W.
        • Liu T.
        • Zheng R.
        • Feng H.
        Gait analysis using wearable sensors.
        Sensors. 2012 Feb 16; 12: 2255-2283
        • Toosizadeh N.
        • Mohler J.
        • Lei H.
        • Parvaneh S.
        • Sherman S.
        • Najafi B.
        Motor performance assessment in Parkinson’s Disease: association between objective in-clinic, objective in-home, and subjective/semi-objective measures. Maetzler W, editor.
        PLoS One. 2015 Apr 24; 10e0124763
        • Van Ancum J.M.
        • van Schooten K.S.
        • Jonkman N.H.
        • Huijben B.
        • van Lummel R.C.
        • Meskers C.G.M.
        • et al.
        Gait speed assessed by a 4-m walk test is not representative of daily-life gait speed in community-dwelling adults.
        Maturitas. 2019 Mar; 121: 28-34
        • Vegesna A.
        • Tran M.
        • Angelaccio M.
        • Arcona S.
        Remote patient monitoring via non-invasive digital technologies: a systematic review.
        Telemed E-Health. 2017 Jan; 23: 3-17
        • Warmerdam E.
        • Hausdorff J.M.
        • Atrsaei A.
        • Zhou Y.
        • Mirelman A.
        • Aminian K.
        • et al.
        Long-term unsupervised mobility assessment in movement disorders.
        Lancet Neurol. 2020 May; 19: 462-470
        • Weiss A.
        • Mirelman A.
        • Buchman A.S.
        • Bennett D.A.
        • Hausdorff J.M.
        Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go. Kado D, editor.
        PLoS One. 2013 Jul 29; 8e68885
        • Wuehr M.
        • Huppert A.
        • Schenkel F.
        • Decker J.
        • Jahn K.
        • Schniepp R.
        Independent domains of daily mobility in patients with neurological gait disorders.
        J. Neurol. 2020 Dec; 267: 292-300