Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson's Disease

Stefano Filippo Castiglia, Antonella Tatarelli, Dante Trabassi, Roberto De Icco, Valentina Grillo, Alberto Ranavolo, Tiwana Varrecchia, Fabrizio Magnifica, Davide Di Lenola, Gianluca Coppola, Donatella Ferrari, Alessandro Denaro, Cristina Tassorelli, Mariano Serrao, Stefano Filippo Castiglia, Antonella Tatarelli, Dante Trabassi, Roberto De Icco, Valentina Grillo, Alberto Ranavolo, Tiwana Varrecchia, Fabrizio Magnifica, Davide Di Lenola, Gianluca Coppola, Donatella Ferrari, Alessandro Denaro, Cristina Tassorelli, Mariano Serrao

Abstract

The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson's disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQAdet in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability.

Keywords: Parkinson’s disease; accelerometry; falls; gait disorders; harmonic ratio; neurologic; postural balance; recurrence quantification analysis.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Graphical representation of the accelerations-derived gait indexes of a representative healthy subject: (a) amplitudes of the filtered acceleration signals in the antero-posterior (AP), medio-lateral (ML), and vertical (V) direction as a function of the time range data; (b) Harmonic Ratio values for each of the 20 considered strides; (c) 2D-reconstructed state space of the acceleration and its time-delayed copies (time delay of 12 data samples). The distance (d) of two neighboring trajectories at a one-time sample, which is needed to calculate the Lyapunov exponent, is highlighted; (d) representation of the jerks during the whole gait cycle; (e) recurrence matrix. Based on the percent of the recurrent points in the diagonal line structure parallel to the main diagonal (i.e., the blue circled points), the RQAdet was calculated.
Figure 2
Figure 2
(a) Graphical representation of the Harmonic Ratios in the antero-posterior, medio-lateral, and vertical directions of a representative age-and-speed-matched healthy subject (blue) and a subject with PD at Hoehn and Yahr stage = 3 (red); (b) recurrence matrices in the antero-posterior direction of the same representative subjects.
Figure 3
Figure 3
ROC curves for the HRs in identifying pwPD vs. HSmatched, pwPD at HY = 3 from milder HY and recurrent fallers. The red line represents the HRAP, the blue line the HRML, and the green line the HRV.
Figure 4
Figure 4
ROC curves of the CV (a) and RQAdetAP (b) in discriminating pwPD from HSmatched.

References

    1. Morris M.E., Iansek R., Matyas T.A., Summers J.J. The pathogenesis of gait hypokinesia in parkinson’s disease. Brain. 1994;117:1169–1181. doi: 10.1093/brain/117.5.1169.
    1. Amboni M., Iuppariello L., Iavarone A., Fasano A., Palladino R., Rucco R., Picillo M., Lista I., Varriale P., Vitale C., et al. Step length predicts executive dysfunction in Parkinson’s disease: A 3-year prospective study. J. Neurol. 2018;265:2211–2220. doi: 10.1007/s00415-018-8973-x.
    1. De Boer A.G.E.M., Wijker W., Speelman J.D., De Haes J.C.J.M. Quality of life in patients with Parkinson’s disease: Development of a questionnaire. J. Neurol. Neurosurg. Psychiatry. 1996;61:70–74. doi: 10.1136/jnnp.61.1.70.
    1. Martínez-Martín P. An introduction to the concept of “quality of life in Parkinson’s disease”. J. Neurol. 1998;245(Suppl. S1) doi: 10.1007/PL00007733.
    1. McCrone P., Allcock L.M., Burn D.J. Predicting the cost of Parkinson’s disease. Mov. Disord. 2007;22:804–812. doi: 10.1002/mds.21360.
    1. Martinez-Martin P., Macaulay D., Jalundhwala Y.J., Mu F., Ohashi E., Marshall T., Sail K. The long-term direct and indirect economic burden among Parkinson’s disease caregivers in the United States. Mov. Disord. 2019;34:236–245. doi: 10.1002/mds.27579.
    1. Mirelman A., Bonato P., Camicioli R., Ellis T.D., Giladi N., Hamilton J.L., Hass C.J., Hausdorff J.M., Pelosin E., Almeida Q.J. Gait impairments in Parkinson’s disease. Lancet Neurol. 2019;18:697–708. doi: 10.1016/S1474-4422(19)30044-4.
    1. Espay A.J., Bonato P., Nahab F.B., Maetzler W., Dean J.M., Klucken J., Eskofier B.M., Merola A., Horak F., Lang A.E., et al. Technology in Parkinson’s disease: Challenges and opportunities. Mov. Disord. 2016;31:1272–1282. doi: 10.1002/mds.26642.
    1. Espay A.J., Hausdorff J.M., Sánchez-Ferro Á., Klucken J., Merola A., Bonato P., Paul S.S., Horak F.B., Vizcarra J.A., Mestre T.A., et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies. Mov. Disord. 2019;34:657–663. doi: 10.1002/mds.27671.
    1. Winser S.J., Kannan P., Bello U.M., Whitney S.L. Measures of balance and falls risk prediction in people with Parkinson’s disease: A systematic review of psychometric properties. Clin. Rehabil. 2019;33:1949–1962. doi: 10.1177/0269215519877498.
    1. Jacobs J.V., Horak F.B., Tran V.K., Nutt J.G. Multiple balance tests improve the assessment of postural stability in subjects with Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry. 2006;77:322–326. doi: 10.1136/jnnp.2005.068742.
    1. Bloem B.R., Marinus J., Almeida Q., Dibble L., Nieuwboer A., Post B., Ruzicka E., Goetz C., Stebbins G., Martinez-Martin P., et al. Measurement instruments to assess posture, gait, and balance in Parkinson’s disease: Critique and recommendations. Mov. Disord. 2016;31:1342–1355. doi: 10.1002/mds.26572.
    1. Sangarapillai K., Norman B.M., Almeida Q.J. Rehabilitation of falls in parkinson’s disease: Self-perception vs. objective measures of fall risk. Brain Sci. 2021;11:320. doi: 10.3390/brainsci11030320.
    1. Dingwell J.B., Cusumano J.P. Nonlinear time series analysis of normal and pathological human walking. Chaos. 2000;10:848–863. doi: 10.1063/1.1324008.
    1. England S.A., Granata K.P. The influence of gait speed on local dynamic stability of walking. Gait Posture. 2007;25:172–178. doi: 10.1016/j.gaitpost.2006.03.003.
    1. Hamacher D., Singh N.B., Van Dieën J.H., Heller M.O., Taylor W.R. Kinematic measures for assessing gait stability in elderly individuals: A systematic review. J. R. Soc. Interface. 2011;8:1682–1698. doi: 10.1098/rsif.2011.0416.
    1. Bruijn S.M., Meijer O.G., Beek P.J., Van Dieen J.H. Assessing the stability of human locomotion: A review of current measures. J. R. Soc. Interface. 2013;10:20120999. doi: 10.1098/rsif.2012.0999.
    1. Siragy T., Nantel J. Quantifying Dynamic Balance in Young, Elderly and Parkinson’s Individuals: A Systematic Review. Front. Aging Neurosci. 2018;10:387. doi: 10.3389/fnagi.2018.00387.
    1. Frenkel-Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J.M. Effect of gait speed on gait rhythmicity in Parkinson’s disease: Variability of stride time and swing time respond differently. J. Neuroeng. Rehabil. 2005;2 doi: 10.1186/1743-0003-2-23.
    1. Baltadjieva R., Giladi N., Gruendlinger L., Peretz C., Hausdorff J.M. Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson’s disease. Eur. J. Neurosci. 2006;24:1815–1820. doi: 10.1111/j.1460-9568.2006.05033.x.
    1. Auriel E., Hausdorff J.M., Herman T., Simon E.S., Giladi N. Effects of methylphenidate on cognitive function and gait in patients with Parkinson’s disease: A pilot study. Clin. Neuropharmacol. 2006;29:15–17. doi: 10.1097/00002826-200601000-00005.
    1. Plotnik M., Giladi N., Hausdorff J.M. A new measure for quantifying the bilateral coordination of human gait: Effects of aging and Parkinson’s disease. Exp. Brain Res. 2007;181:561–570. doi: 10.1007/s00221-007-0955-7.
    1. Herman T., Giladi N., Gruendlinger L., Hausdorff J.M. Six Weeks of Intensive Treadmill Training Improves Gait and Quality of Life in Patients with Parkinson’s Disease: A Pilot Study. Arch. Phys. Med. Rehabil. 2007;88:1154–1158. doi: 10.1016/j.apmr.2007.05.015.
    1. Cole M.H., Silburn P.A., Wood J.M., Worringham C.J., Kerr G.K. Falls in Parkinson’s disease: Kinematic evidence for impaired head and trunk control. Mov. Disord. 2010;25:2369–2378. doi: 10.1002/mds.23292.
    1. Latt M.D., Menz H.B., Fung V.S., Lord S.R. Acceleration patterns of the head and pelvis during gait in older people with Parkinson’s disease: A comparison of fallers and nonfallers. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2009;64:700–706. doi: 10.1093/gerona/glp009.
    1. Lowry K.A., Smiley-Oyen A.L., Carrel A.J., Kerr J.P. Walking stability using harmonic ratios in Parkinson’s disease. Mov. Disord. 2009;24:261–267. doi: 10.1002/mds.22352.
    1. Thumm P.C., Maidan I., Brozgol M., Shustak S., Gazit E., Shema Shiratzki S., Bernad-Elazari H., Beck Y., Giladi N., Hausdorff J.M., et al. Treadmill walking reduces pre-frontal activation in patients with Parkinson’s disease. Gait Posture. 2018;62:384–387. doi: 10.1016/j.gaitpost.2018.03.041.
    1. Miller Koop M., Ozinga S.J., Rosenfeldt A.B., Alberts J.L. Quantifying turning behavior and gait in Parkinson’s disease using mobile technology. IBRO Rep. 2018;5:10–16. doi: 10.1016/j.ibror.2018.06.002.
    1. Palmerini L., Mellone S., Avanzolini G., Valzania F., Chiari L. Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans. Neural Syst. Rehabil. Eng. 2013;21:664–673. doi: 10.1109/TNSRE.2012.2236577.
    1. Sylos Labini F., Meli A., Ivanenko Y.P., Tufarelli D. Recurrence quantification analysis of gait in normal and hypovestibular subjects. Gait Posture. 2012;35:48–55. doi: 10.1016/j.gaitpost.2011.08.004.
    1. Ramdani S., Tallon G., Bernard P.L., Blain H. Recurrence quantification analysis of human postural fluctuations in older fallers and non-fallers. Ann. Biomed. Eng. 2013;41:1713–1725. doi: 10.1007/s10439-013-0790-x.
    1. Cole M.H., Sweeney M., Conway Z.J., Blackmore T., Silburn P.A. Imposed Faster and Slower Walking Speeds Influence Gait Stability Differently in Parkinson Fallers. Arch. Phys. Med. Rehabil. 2017;98:639–648. doi: 10.1016/j.apmr.2016.11.008.
    1. 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;8:153. doi: 10.1186/s13643-019-1063-z.
    1. Zampogna A., Mileti I., Palermo E., Celletti C., Paoloni M., Manoni A., Mazzetta I., Costa G.D., Pérez-López C., Camerota F., et al. Fifteen years of wireless sensors for balance assessment in neurological disorders. Sensors. 2020;20:3247. doi: 10.3390/s20113247.
    1. Brognara L., Palumbo P., Grimm B., Palmerini L. Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review. Diseases. 2019;7:18. doi: 10.3390/diseases7010018.
    1. Schlachetzki J.C.M., Barth J., Marxreiter F., Gossler J., Kohl Z., Reinfelder S., Gassner H., Aminian K., Eskofier B.M., Winkler J., et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS ONE. 2017;12:e0183989. doi: 10.1371/journal.pone.0183989.
    1. Rovini E., Maremmani C., Cavallo F. How wearable sensors can support parkinson’s disease diagnosis and treatment: A systematic review. Front. Neurosci. 2017;11:555. doi: 10.3389/fnins.2017.00555.
    1. Maetzler W., Domingos J., Srulijes K., Ferreira J.J., Bloem B.R. Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov. Disord. 2013;28:1628–1637. doi: 10.1002/mds.25628.
    1. Ramdhani R.A., Khojandi A., Shylo O., Kopell B.H. Optimizing clinical assessments in Parkinson’s disease through the use of wearable sensors and data driven modeling. Front. Comput. Neurosci. 2018;12:72. doi: 10.3389/fncom.2018.00072.
    1. Kuo A.D., Donelan J.M. Dynamic principles of gait and their clinical implications. Phys. Ther. 2010;90:157–174. doi: 10.2522/ptj.20090125.
    1. Horak F.B., Mancini M. Objective biomarkers of balance and gait for Parkinson’s disease using body-worn sensors. Mov. Disord. 2013;28:1544–1551. doi: 10.1002/mds.25684.
    1. Czech M., Demanuele C., Erb M.K., Ramos V., Zhang H., Ho B., Patel S. The impact of reducing the number of wearable devices on measuring gait in parkinson disease: Noninterventional exploratory study. JMIR Rehabil. Assist. Technol. 2020;7 doi: 10.2196/17986.
    1. Hughes A.J., Daniel S.E., Kilford L., Lees A.J. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: A clinico-pathological study of 100 cases. J. Neurol. Neurosurg. Psychiatry. 1992;55:181–184. doi: 10.1136/jnnp.55.3.181.
    1. Hoehn M.M., Yahr M.D. Parkinsonism: Onset, progression, and mortality. Neurology. 1967;17:427–442. doi: 10.1212/WNL.17.5.427.
    1. Kroneberg D., Elshehabi M., Meyer A.C., Otte K., Doss S., Paul F., Nussbaum S., Berg D., Kühn A.A., Maetzler W., et al. Less is more—Estimation of the number of strides required to assess gait variability in spatially confined settings. Front. Aging Neurosci. 2019;11:435. doi: 10.3389/fnagi.2018.00435.
    1. Riva F., Bisi M.C., Stagni R. Gait variability and stability measures: Minimum number of strides and within-session reliability. Comput. Biol. Med. 2014;50:9–13. doi: 10.1016/j.compbiomed.2014.04.001.
    1. Pasciuto I., Bergamini E., Iosa M., Vannozzi G., Cappozzo A. Overcoming the limitations of the Harmonic Ratio for the reliable assessment of gait symmetry. J. Biomech. 2017;53:84–89. doi: 10.1016/j.jbiomech.2017.01.005.
    1. Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6.
    1. Perneczky R., Wagenpfeil S., Komossa K., Grimmer T., Diehl J., Kurz A. Mapping scores onto stages: Mini-mental state examination and clinical dementia rating. Am. J. Geriatr. Psychiatry. 2006;14:139–144. doi: 10.1097/01.JGP.0000192478.82189.a8.
    1. Beck A.T., Ward C.H., Mendelson M., Mock J., Erbaugh J. An Inventory for Measuring Depression. Arch. Gen. Psychiatry. 1961;4:561–571. doi: 10.1001/archpsyc.1961.01710120031004.
    1. Goodarzi Z., Mrklas K.J., Roberts D.J., Jette N., Pringsheim T., Holroyd-Leduc J. Detecting depression in Parkinson disease: A systematic review and meta-analysis. Neurology. 2016;87:426–437. doi: 10.1212/WNL.0000000000002898.
    1. Altman R., Alarcón G., Appelrouth D., Bloch D., Borenstein D., Brandt K., Brown C., Cooke T.D., Daniel W., Feldman D., et al. The American College of Rheumatology criteria for the classification and reporting of osteoarthritis of the hip. Arthritis Rheum. 1991;34:505–514. doi: 10.1002/art.1780340502.
    1. Fitzgerald G.K., Hinman R.S., Zeni J., Risberg M.A., Snyder-Mackler L., Bennell K.L. OARSI Clinical Trials Recommendations: Design and conduct of clinical trials of rehabilitation interventions for osteoarthritis. Osteoarthr. Cartil. 2015;23:803–814. doi: 10.1016/j.joca.2015.03.013.
    1. Damen J., Van Rijn R.M., Emans P.J., Hilberdink W.K.H.A., Wesseling J., Oei E.H.G., Bierma-Zeinstra S.M.A. Prevalence and development of hip and knee osteoarthritis according to American College of Rheumatology criteria in the CHECK cohort. Breast Cancer Res. 2019;21 doi: 10.1186/s13075-018-1785-7.
    1. Serrao M., Chini G., Caramanico G., Bartolo M., Castiglia S.F., Ranavolo A., Conte C., Venditto T., Coppola G., Di Lorenzo C., et al. Prediction of responsiveness of gait variables to rehabilitation training in Parkinson’s disease. Front. Neurol. 2019;10:826. doi: 10.3389/fneur.2019.00826.
    1. Rinaldi M., Ranavolo A., Conforto S., Martino G., Draicchio F., Conte C., Varrecchia T., Bini F., Casali C., Pierelli F., et al. Increased lower limb muscle coactivation reduces gait performance and increases metabolic cost in patients with hereditary spastic paraparesis. Clin. Biomech. 2017;48:63–72. doi: 10.1016/j.clinbiomech.2017.07.013.
    1. Mari S., Serrao M., Casali C., Conte C., Martino G., Ranavolo A., Coppola G., Draicchio F., Padua L., Sandrini G., et al. Lower limb antagonist muscle co-activation and its relationship with gait parameters in cerebellar ataxia. Cerebellum. 2014;13:226–236. doi: 10.1007/s12311-013-0533-4.
    1. Cofré L.E., Lythgo N., Morgan D., Galea M.P. Aging modifies joint power and work when gait speeds are matched. Gait Posture. 2011;33:484–489. doi: 10.1016/j.gaitpost.2010.12.030.
    1. Peterson D.S., Mancini M., Fino P.C., Horak F., Smulders K. Speeding Up Gait in Parkinson’s Disease. J. Parkinsons Dis. 2020;10:245–253. doi: 10.3233/JPD-191682.
    1. Smidt G.L. Methods of studying gait. Phys. Ther. 1974;54:13–17. doi: 10.1093/ptj/54.1.13.
    1. Iosa M., Picerno P., Paolucci S., Morone G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices. 2016;13:641–659. doi: 10.1080/17434440.2016.1198694.
    1. Webber C.L., Zbilut J.P. Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 1994;76:965–973. doi: 10.1152/jappl.1994.76.2.965.
    1. Kennel M.B., Abarbanel H.D.I. False neighbors and false strands: A reliable minimum embedding dimension algorithm. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 2002;66 doi: 10.1103/PhysRevE.66.026209.
    1. Wallot S., Mønster D. Calculation of Average Mutual Information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time series in matlab. Front. Psychol. 2018;9:1679. doi: 10.3389/fpsyg.2018.01679.
    1. Toebes M.J.P., Hoozemans M.J.M., Furrer R., Dekker J., Van Dieën J.H. Local dynamic stability and variability of gait are associated with fall history in elderly subjects. Gait Posture. 2012;36:527–531. doi: 10.1016/j.gaitpost.2012.05.016.
    1. Fraser A.M., Swinney H.L. Independent coordinates for strange attractors from mutual information. Phys. Rev. A. 1986;33:1134–1140. doi: 10.1103/PhysRevA.33.1134.
    1. Zijlstra W. Assessment of spatio-temporal parameters during unconstrained walking. Eur. J. Appl. Physiol. 2004;92:39–44. doi: 10.1007/s00421-004-1041-5.
    1. Serrao M., Pierelli F., Ranavolo A., Draicchio F., Conte C., Don R., Di Fabio R., Lerose M., Padua L., Sandrini G., et al. Gait pattern in inherited cerebellar ataxias. Cerebellum. 2012;11:194–211. doi: 10.1007/s12311-011-0296-8.
    1. Van Schooten K.S., Rispens S.M., Elders P.J.M., van Dieën J.H., Pijnappels M. Toward ambulatory balance assessment: Estimating variability and stability from short bouts of gait. Gait Posture. 2014;39:695–699. doi: 10.1016/j.gaitpost.2013.09.020.
    1. Chini G., Ranavolo A., Draicchio F., Casali C., Conte C., Martino G., Leonardi L., Padua L., Coppola G., Pierelli F., et al. Local Stability of the Trunk in Patients with Degenerative Cerebellar Ataxia During Walking. Cerebellum. 2017;16:26–33. doi: 10.1007/s12311-016-0760-6.
    1. Goetz C.G., Tilley B.C., Shaftman S.R., Stebbins G.T., Fahn S., Martinez-Martin P., Poewe W., Sampaio C., Stern M.B., Dodel R., et al. Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Mov. Disord. 2008;23:2129–2170. doi: 10.1002/mds.22340.
    1. Yao X.I., Wang X., Speicher P.J., Hwang E.S., Cheng P., Harpole D.H., Berry M.F., Schrag D., Pang H.H. Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies. J. Natl. Cancer Inst. 2017;109:djw323. doi: 10.1093/jnci/djw323.
    1. Nemanich S.T., Duncan R.P., Dibble L.E., Cavanaugh J.T., Ellis T.D., Ford M.P., Foreman K.B., Earhart G.M. Predictors of gait speeds and the relationship of gait speeds to falls in men and women with parkinson disease. Parkinsons Dis. 2013;2013 doi: 10.1155/2013/141720.
    1. Noh B., Youm C., Lee M., Cheon S.M. Gait characteristics in individuals with Parkinson’s disease during 1-minute treadmill walking. PeerJ. 2020;2020 doi: 10.7717/peerj.9463.
    1. Lindemann U. Spatiotemporal gait analysis of older persons in clinical practice and research: Which parameters are relevant? Z. Gerontol. Geriatr. 2020;53:171–178. doi: 10.1007/s00391-019-01520-8.
    1. Huijben B., van Schooten K.S., van Dieën J.H., Pijnappels M. The effect of walking speed on quality of gait in older adults. Gait Posture. 2018;65:112–116. doi: 10.1016/j.gaitpost.2018.07.004.
    1. Craig J.J., Bruetsch A.P., Huisinga J.M. Coordination of trunk and foot acceleration during gait is affected by walking velocity and fall history in elderly adults. Aging Clin. Exp. Res. 2019;31:943–950. doi: 10.1007/s40520-018-1036-4.
    1. Lee S., Lee D.K. What is the proper way to apply the multiple comparison test? Korean J. Anesthesiol. 2018;71:353–360. doi: 10.4097/kja.d.18.00242.
    1. Paul S.S., Allen N.E., Sherrington C., Heller G., Fung V.S.C., Close J.C.T., Lord S.R., Canning C.G. Risk factors for frequent falls in people with Parkinson’s disease. J. Parkinsons Dis. 2014;4:699–703. doi: 10.3233/JPD-140438.
    1. Allen N.E., Schwarzel A.K., Canning C.G. Recurrent falls in parkinson’s disease: A systematic review. Parkinsons Dis. 2013;2013:906274. doi: 10.1155/2013/906274.
    1. Thomas A.A., Rogers J.M., Amick M.M., Friedman J.H. Falls and the falls efficacy scale in Parkinson’s disease. J. Neurol. 2010;257:1124–1128. doi: 10.1007/s00415-010-5475-x.
    1. Carter J.V., Pan J., Rai S.N., Galandiuk S. ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery. 2016;159:1638–1645. doi: 10.1016/j.surg.2015.12.029.
    1. Kallner A. Bayes’ theorem, the roc diagram and reference values: Definition and use in clinical diagnosis. Biochem. Med. 2018;28 doi: 10.11613/BM.2018.010101.
    1. Fasano A., Canning C.G., Hausdorff J.M., Lord S., Rochester L. Falls in Parkinson’s disease: A complex and evolving picture. Mov. Disord. 2017;32:1524–1536. doi: 10.1002/mds.27195.
    1. Eusebi P. Diagnostic accuracy measures. Cerebrovasc. Dis. 2013;36:267–272. doi: 10.1159/000353863.
    1. Afsar O., Tirnakli U., Marwan N. Recurrence Quantification Analysis at work: Quasi-periodicity based interpretation of gait force profiles for patients with Parkinson disease. Sci. Rep. 2018;8 doi: 10.1038/s41598-018-27369-2.
    1. Afşar Ö. Recurrence Quantification Analysis on Gait Reaction Forces of Elderly Adults for Determination of Pathological States. Celal Bayar Univ. Fen Bilim. Derg. 2018;14:309–314. doi: 10.18466/cbayarfbe.428648.
    1. Varrecchia T., Castiglia S.F., Ranavolo A., Conte C., Tatarelli A., Coppola G., Di Lorenzo C., Draicchio F., Pierelli F., Serrao M. An artificial neural network approach to detect presence and severity of Parkinson’s disease via gait parameters. PLoS ONE. 2021;16:e0244396. doi: 10.1371/journal.pone.0244396.
    1. Creaby M.W., Cole M.H. Gait characteristics and falls in Parkinson’s disease: A systematic review and meta-analysis. Park. Relat. Disord. 2018;57:1–8. doi: 10.1016/j.parkreldis.2018.07.008.
    1. Pham T.D. Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson’s disease subjects using fuzzy recurrence and scalable recurrence network features. J. Neurosci. Methods. 2018;307:194–202. doi: 10.1016/j.jneumeth.2018.05.019.
    1. Djurić-Jovičić M., Belić M., Stanković I., Radovanović S., Kostić V.S. Selection of gait parameters for differential diagnostics of patients with de novo Parkinson’s disease. Neurol. Res. 2017;39:853–861. doi: 10.1080/01616412.2017.1348690.
    1. Kwon K.Y., Lee H.M., Kang S.H., Pyo S.J., Kim H.J., Koh S.B. Recuperation of slow walking in de novo Parkinson’s disease is more closely associated with increased cadence, rather than with expanded stride length. Gait Posture. 2017;58:1–6. doi: 10.1016/j.gaitpost.2017.06.266.
    1. Mancini M., Carlson-Kuhta P., Zampieri C., Nutt J.G., Chiari L., Horak F.B. Postural sway as a marker of progression in Parkinson’s disease: A pilot longitudinal study. Gait Posture. 2012;36:471–476. doi: 10.1016/j.gaitpost.2012.04.010.
    1. Bovonsunthonchai S., Vachalathiti R., Pisarnpong A., Khobhun F., Hiengkaew V. Spatiotemporal Gait Parameters for Patients with Parkinson’s Disease Compared with Normal Individuals. Physiother. Res. Int. 2014;19:158–165. doi: 10.1002/pri.1579.
    1. Mak M.K.Y. Reduced step length, not step length variability is central to gait hypokinesia in people with Parkinson’s disease. Clin. Neurol. Neurosurg. 2013;115:587–590. doi: 10.1016/j.clineuro.2012.07.014.
    1. Warabi T., Furuyama H., Kato M. Gait bradykinesia: Difficulty in switching posture/gait measured by the anatomical y-axis vector of the sole in Parkinson’s disease. Exp. Brain Res. 2020;238:139–151. doi: 10.1007/s00221-019-05704-x.
    1. Zampier V.C., Vitório R., Beretta V.S., Jaimes D.A.R., Orcioli-Silva D., Santos P.C.R., Gobbi L.T.B. Gait bradykinesia and hypometria decrease as arm swing frequency and amplitude increase. Neurosci. Lett. 2018;687:248–252. doi: 10.1016/j.neulet.2018.09.051.
    1. Fino P.C., Mancini M., Curtze C., Nutt J.G., Horak F.B. Gait stability has phase-dependent dual-task costs in Parkinson’s disease. Front. Neurol. 2018;9:373. doi: 10.3389/fneur.2018.00373.

Source: PubMed

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