Discriminative ability, responsiveness, and interpretability of smoothness index of gait in people with multiple sclerosis

Stefano Filippo Castiglia; Fulvio Dal Farra; Dante Trabassi; Andrea Turolla; Mariano Serrao; Ugo Nocentini; Paolo Brasiliano; Elena Bergamini; Marco Tramontano

doi:10.33393/aop.2025.3289

Authors

Stefano Filippo Castiglia Department of Medico – Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, Latina - Italy and Department of Brain and Behavioral Sciences, University of Pavia, Pavia - Italy https://orcid.org/0000-0001-5329-5197
Fulvio Dal Farra Department of Information Engineering, University of Brescia, Brescia - Italy https://orcid.org/0000-0001-8431-600X
Dante Trabassi Department of Medico – Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, Latina - Italy https://orcid.org/0000-0002-4739-3441
Andrea Turolla Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Bologna - Italy and Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna - Italy https://orcid.org/0000-0002-1609-8060
Mariano Serrao Department of Medico – Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, Latina - Italy and Gait Analysis LAB Policlinico Italia, Roma - Italy https://orcid.org/0000-0003-3031-680X
Ugo Nocentini Department of Clinical Sciences and Translational Medicine, University of Rome Tor Vergata, Rome - Italy https://orcid.org/0000-0001-7445-4441
Paolo Brasiliano Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome - Italy https://orcid.org/0000-0003-2545-3930
Elena Bergamini Department of Management, Information and Production Engineering, University of Bergamo, Dalmine - Italy https://orcid.org/0000-0002-9055-3981
Marco Tramontano Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Bologna - Italy and Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna - Italy https://orcid.org/0000-0001-6034-0638

DOI:

https://doi.org/10.33393/aop.2025.3289

Keywords:

IMUs, Sensor, Multiple sclerosis, Responsiveness, Smoothness, Gait

Abstract

Introduction: Gait impairments are common in People with Multiple Sclerosis (PwMS). Several studies have examined the clinometric properties of Inertial Measurement Units (IMUs), with LDLJa identified as a robust metric for gait smoothness. However, its responsiveness and interpretability have not been explored.

Methods: This cross-sectional study at IRCCS Santa Lucia Hospital enrolled 44 PwMS (age: 28-71; EDSS: 0-6) and 43 age- and gait-speed-matched healthy participants (HP). Two physiotherapists conducted assessments with five synchronized IMUs during a 10-meter walk at participants’ preferred speed. Data were collected at baseline (T0) and after 4 weeks of training (T1).

Results: Significant differences in log dimensionless jerk (LDLJa) were found between PwMS and HP in the AP (p < 0.001, d = 0.63), ML (p < 0.001, d = 1.08), and CC (p = 0.03, d = 0.68) directions. PwMS had lower LDLJaAP values (< -4.88) and LDLJaML values (< -5.40) with probabilities of 63% and 76%, respectively. ΔLDLJaML demonstrated good responsiveness to rehabilitation (AUC ~0.80), with improvements >4.02% representing the optimal MCID for clinical improvement in MiniBesTest.

Conclusion: Lower LDLJa values in the AP and ML directions characterize gait smoothness impairment in PwMS. LDLJa in the ML direction is responsive to balance-focused rehabilitation, highlighting its potential for tracking gait disorders and rehabilitation progress.

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References

Comber L, Galvin R, Coote S. Gait deficits in people with multiple sclerosis: A systematic review and meta-analysis. Gait Posture. 2017;51:25-35. PMID:27693958 DOI: https://doi.org/10.1016/j.gaitpost.2016.09.026 PMID:27693958 DOI: https://doi.org/10.1016/j.gaitpost.2016.09.026

Gunn H, Creanor S, Haas B, Marsden J, Freeman J. Risk factors for falls in multiple sclerosis: an observational study. Mult Scler. 2013;19(14):1913-1922. PMID:23633067 DOI: https://doi.org/10.1177/1352458513488233 PMID:23633067 DOI: https://doi.org/10.1177/1352458513488233

Galperin I, Mirelman A, Schmitz-Hübsch T, et al. Treadmill training with virtual reality to enhance gait and cognitive function among people with multiple sclerosis: a randomized controlled trial. J Neurol. 2023;270(3):1388-1401. PMID:36357586 DOI: https://doi.org/10.1007/s00415-022-11469-1 PMID:36357586 DOI: https://doi.org/10.1007/s00415-022-11469-1

Corrini C, Gervasoni E, Perini G, et al. Mobility and balance rehabilitation in multiple sclerosis: A systematic review and dose-response meta-analysis. Mult Scler Relat Disord. 2023;69:104424. DOI: https://doi.org/10.1016/j.msard.2022.104424 PMID:36473240 DOI: https://doi.org/10.1016/j.msard.2022.104424

Gordt K, Gerhardy T, Najafi B, Schwenk M. Effects of Wearable Sensor-Based Balance and Gait Training on Balance, Gait, and Functional Performance in Healthy and Patient Populations: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Gerontology. 2018;64(1):74-89. DOI: https://doi.org/10.1159/000481454 PMID:29130977 DOI: https://doi.org/10.1159/000481454

Trabassi D, Castiglia SF, Bini F, et al. Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia. Sensors (Basel). 2024;24(11):3613. DOI: https://doi.org/10.3390/s24113613 DOI: https://doi.org/10.3390/s24113613

Belluscio V, Bergamini E, Tramontano M, et al. Gait Quality Assessment in Survivors from Severe Traumatic Brain Injury: An Instrumented Approach Based on Inertial Sensors. Sensors (Basel). 2019;19(23):5315. DOI: https://doi.org/10.3390/s19235315 PMID:31816843 DOI: https://doi.org/10.3390/s19235315

Tramontano M, Orejel Bustos AS, Montemurro R, et al. Dynamic Stability, Symmetry, and Smoothness of Gait in People with Neurological Health Conditions. Sensors (Basel). 2024;24(8):2451. DOI: https://doi.org/10.3390/s24082451 PMID:38676068 DOI: https://doi.org/10.3390/s24082451

Buckley C, Galna B, Rochester L, Mazzà C. Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson’s disease. Gait Posture. 2019;71:289-295. DOI: https://doi.org/10.1016/j.gaitpost.2018.06.166 PMID:30139646 DOI: https://doi.org/10.1016/j.gaitpost.2018.06.166

Castiglia SF, Trabassi D, Tatarelli A, et al. Identification of Gait Unbalance and Fallers Among Subjects with Cerebellar Ataxia by a Set of Trunk Acceleration-Derived Indices of Gait. Cerebellum. 2023;22(1):46-58. DOI: https://doi.org/10.1007/s12311-021-01361-5 PMID:35079958 DOI: https://doi.org/10.1007/s12311-021-01361-5

Castiglia SF, Tatarelli A, Trabassi D, et al. Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson's Disease. Sensors (Basel). 2021;21(10):3449. DOI: https://doi.org/10.3390/s21103449 DOI: https://doi.org/10.3390/s21103449

Tramontano M, Manzari L, Bustos ASO, et al. Instrumental assessment of dynamic postural stability in patients with unilateral vestibular hypofunction during straight, curved, and blindfolded gait. Eur Arch Otorhinolaryngol. 2024;281(1):83-94. DOI: https://doi.org/10.1007/s00405-023-08082-x PMID:37382626 DOI: https://doi.org/10.1007/s00405-023-08082-x

Spain RI, St George RJ, Salarian A, et al. Body-worn motion sensors detect balance and gait deficits in people with multiple sclerosis who have normal walking speed. Gait Posture. 2012;35(4):573-578. DOI: https://doi.org/10.1016/j.gaitpost.2011.11.026 PMID:22277368 DOI: https://doi.org/10.1016/j.gaitpost.2011.11.026

Angelini L, Carpinella I, Cattaneo D, et al. Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis. Sensors (Basel). 2019;20(1):79. DOI: https://doi.org/10.3390/s20010079 DOI: https://doi.org/10.3390/s20010079

Melendez-Calderon A, Shirota C, Balasubramanian S. Estimating Movement Smoothness From Inertial Measurement Units. Front Bioeng Biotechnol. 2021;8:558771. DOI: https://doi.org/10.3389/fbioe.2020.558771 DOI: https://doi.org/10.3389/fbioe.2020.558771

Pau M, Mandaresu S, Pilloni G, et al. Smoothness of gait detects early alterations of walking in persons with multiple sclerosis without disability. Gait Posture. 2017;58:307-309. DOI: https://doi.org/10.1016/j.gaitpost.2017.08.023 PMID:28858779 DOI: https://doi.org/10.1016/j.gaitpost.2017.08.023

Tramontano M, Belluscio V, Bergamini E, et al. Vestibular Rehabilitation Improves Gait Quality and Activities of Daily Living in People with Severe Traumatic Brain Injury: A Randomized Clinical Trial. Sensors (Basel). 2022;22(21):8553. DOI: https://doi.org/10.3390/s22218553 DOI: https://doi.org/10.3390/s22218553

Germanotta M, Iacovelli C, Aprile I. Evaluation of Gait Smoothness in Patients with Stroke Undergoing Rehabilitation: Comparison between Two Metrics. Int J Environ Res Public Health. 2022;19(20):13440. DOI: https://doi.org/10.3390/ijerph192013440 DOI: https://doi.org/10.3390/ijerph192013440

Miller HL, Templin TN, Fears NE, Sherrod GM, Patterson RM, Bugnariu NL. Movement smoothness during dynamic postural control to a static target differs between autistic and neurotypical children. Gait Posture. 2023;99:76-82. DOI: https://doi.org/10.1016/j.gaitpost.2022.10.015 PMID:36335658 DOI: https://doi.org/10.1016/j.gaitpost.2022.10.015

Dixon PC, Stirling L, Xu X, Chang CC, Dennerlein JT, Schiffman JM. Aging may negatively impact movement smoothness during stair negotiation. Hum Mov Sci. 2018;60:78-86. DOI: https://doi.org/10.1016/j.humov.2018.05.008 PMID:29843055 DOI: https://doi.org/10.1016/j.humov.2018.05.008

Suri A, Rosso AL, VanSwearingen J, et al. Mobility of Older Adults: Gait Quality Measures Are Associated With Life-Space Assessment Scores. J Gerontol A Biol Sci Med Sci. 2021;76(10):e299-e306. DOI: https://doi.org/10.1093/gerona/glab151 PMID:34038537 DOI: https://doi.org/10.1093/gerona/glab151

Garcia FDV, da Cunha MJ, Schuch CP, Schifino GP, Balbinot G, Pagnussat AS. Movement smoothness in chronic post-stroke individuals walking in an outdoor environment - A cross-sectional study using IMU sensors. PLoS One. 2021;16(4):e0250100. DOI: 10.1371/journal.pone.0250100 PMID: 33886640 DOI: https://doi.org/10.1371/journal.pone.0250100

Tramontano M, Argento O, Orejel Bustos AS, et al. Cognitive-motor dual-task training improves dynamic stability during straight and curved gait in patients with multiple sclerosis: a randomized controlled trial. Eur J Phys Rehabil Med. 2024;60(1):27-36. DOI: https://doi.org/10.23736/S1973-9087.23.08156-X PMID:37997324 DOI: https://doi.org/10.23736/S1973-9087.23.08156-X

Antonelli M, Caselli E, Gastaldi L. Comparison of Gait Smoothness Metrics in Healthy Elderly and Young People. Appl Sci (Basel). 2024;14(2):911. https://doi.org/10.3390/app14020911 DOI: https://doi.org/10.3390/app14020911

Husted JA, Cook RJ, Farewell VT, Gladman DD. Methods for assessing responsiveness: a critical review and recommendations. J Clin Epidemiol. 2000;53(5):459-468. DOI: https://doi.org/10.1016/S0895-4356(99)00206-1 PMID:10812317 DOI: https://doi.org/10.1016/S0895-4356(99)00206-1

Crosby RD, Kolotkin RL, Williams GR. Defining clinically meaningful change in health-related quality of life. J Clin Epidemiol. 2003;56(5):395-407. DOI: https://doi.org/10.1016/S0895-4356(03)00044-1 PMID:12812812 DOI: https://doi.org/10.1016/S0895-4356(03)00044-1

Baert I, Freeman J, Smedal T, et al. Responsiveness and clinically meaningful improvement, according to disability level, of five walking measures after rehabilitation in multiple sclerosis: a European multicenter study. Neurorehabil Neural Repair. 2014;28(7):621-631. DOI: https://doi.org/10.1177/1545968314521010 PMID:24503204 DOI: https://doi.org/10.1177/1545968314521010

Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444-1452. DOI: https://doi.org/10.1212/WNL.33.11.1444 PMID:6685237 DOI: https://doi.org/10.1212/WNL.33.11.1444

Franchignoni F, Horak F, Godi M, Nardone A, Giordano A. Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. J Rehabil Med. 2010;42(4):323-331. DOI: https://doi.org/10.2340/16501977-0537 PMID:20461334 DOI: https://doi.org/10.2340/16501977-0537

Godi M, Franchignoni F, Caligari M, Giordano A, Turcato AM, Nardone A. Comparison of reliability, validity, and responsiveness of the mini-BESTest and Berg Balance Scale in patients with balance disorders. Phys Ther. 2013;93(2):158-167. DOI: https://doi.org/10.2522/ptj.20120171 PMID:23023812 DOI: https://doi.org/10.2522/ptj.20120171

Yao XI, Wang X, Speicher PJ, et al. Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies. J Natl Cancer Inst. 2017;109(8):djw323. DOI: https://doi.org/10.1093/jnci/djw323 PMID:28376195 DOI: https://doi.org/10.1093/jnci/djw323

Huijben B, van Schooten KS, van Dieën JH, Pijnappels M. The effect of walking speed on quality of gait in older adults. Gait Posture. 2018;65:112-116. DOI: https://doi.org/10.1016/j.gaitpost.2018.07.004 PMID:30558916 DOI: https://doi.org/10.1016/j.gaitpost.2018.07.004

Lowry KA, Vanswearingen JM, Perera S, Studenski SA, Brach JS. Walking smoothness is associated with self-reported function after accounting for gait speed. J Gerontol A Biol Sci Med Sci. 2013;68(10):1286-1290. DOI: https://doi.org/10.1093/gerona/glt034 PMID:23689828 DOI: https://doi.org/10.1093/gerona/glt034

Trojaniello D, Cereatti A, Pelosin E, et al. Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait. J Neuroeng Rehabil. 2014;11:152. DOI: https://doi.org/10.1186/1743-0003-11-152 DOI: https://doi.org/10.1186/1743-0003-11-152

Bergamini E, Ligorio G, Summa A, Vannozzi G, Cappozzo A, Sabatini AM. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sensors (Basel). 2014;14(10):18625-18649. DOI: https://doi.org/10.3390/s141018625 DOI: https://doi.org/10.3390/s141018625

Jiang D, Huang J, Zhang Y. The cross-validated AUC for MCP-logistic regression with high-dimensional data. Stat Methods Med Res. 2013;22(5):505-518. DOI: https://doi.org/10.1177/0962280211428385 PMID:22127580 DOI: https://doi.org/10.1177/0962280211428385

Eertink JJ, Heymans MW, Zwezerijnen GJC, Zijlstra JM, de Vet HCW, Boellaard R. External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients. EJNMMI Res. 2022;12(1):58. DOI: https://doi.org/10.1186/s13550-022-00931-w DOI: https://doi.org/10.1186/s13550-022-00931-w

Szeghalmy S, Fazekas A. A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors (Basel). 2023;23(4):2333.DOI: https://doi.org/10.3390/s23042333 DOI: https://doi.org/10.3390/s23042333

Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection; Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI’95); Montreal, QC, Canada. 20–25 August 1995; pp. 1137-1145. https://dl.acm.org/doi/10.5555/1643031.1643047

Forman G, Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. SIGKDD Explor. 2010;12(1):49-57. DOI: https://doi.org/10.1145/1882471.1882479 DOI: https://doi.org/10.1145/1882471.1882479

Morrison S, Rynders CA, Sosnoff JJ. Deficits in medio-lateral balance control and the implications for falls in individuals with multiple sclerosis. Gait Posture. 2016;49:148-154. DOI: https://doi.org/10.1016/j.gaitpost.2016.06.036 PMID:27423077 DOI: https://doi.org/10.1016/j.gaitpost.2016.06.036

Comber L, Sosnoff JJ, Galvin R, Coote S. Postural control deficits in people with Multiple Sclerosis: A systematic review and meta-analysis. Gait Posture. 2018;61:445-452. DOI: https://doi.org/10.1016/j.gaitpost.2018.02.018 PMID:29486362 DOI: https://doi.org/10.1016/j.gaitpost.2018.02.018

Lencioni T, Anastasi D, Carpinella I, et al. Strategies for maintaining dynamic balance in persons with neurological disorders during overground walking. Proc Inst Mech Eng H. 2021;235(9):1079-1087. DOI: https://doi.org/10.1177/09544119211023624 PMID:34112028 DOI: https://doi.org/10.1177/09544119211023624

Gulde P, Hermsdörfer J, Rieckmann P. Speed but Not Smoothness of Gait Reacts to Rehabilitation in Multiple Sclerosis. Mult Scler Int. 2021;2021:5589562. Published 2021 Jun 3. DOI: https://doi.org/10.1155/2021/558956245. Paltamaa J, Sarasoja T, Leskinen E, Wikström J, Mälkiä E. Measures of physical functioning predict self-reported performance in self-care, mobility, and domestic life in ambulatory persons with multiple sclerosis. Arch Phys Med Rehabil. 2007;88(12):1649-1657. DOI: https://doi.org/10.1016/j.apmr.2007.07.032 PMID:18047881 DOI: https://doi.org/10.1016/j.apmr.2007.07.032

Kieseier, B. C., & Pozzilli, C. Assessing walking disability in multiple sclerosis. Multiple sclerosis 2012 (Houndmills, Basingstoke, England), 18(7), 914-924. DOI: https://doi.org/10.1177/1352458512444498), DOI: https://doi.org/10.1177/1352458512444498

Dujmovic I, Radovanovic S, Martinovic V, et al. Gait pattern in patients with different multiple sclerosis phenotypes. Mult Scler Relat Disord. 2017;13:13-20. DOI: https://doi.org/10.1016/j.msard.2017.01.012 PMID:28427694 DOI: https://doi.org/10.1016/j.msard.2017.01.012