[en] This study evaluates mobility in patients with multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and Parkinson's disease (PD) by integrating clinical assessments, instrumented gait analysis (IGA) in the hospital, and 1 week of physical activity monitoring (PAM) at home, using wearable sensors. Clinical scores provide a broad measure of disease severity but lack precision in quantifying gait impairments. IGA offers objective gait metrics under standardized conditions, identifying deficits in stride dynamics and postural control. However, these controlled assessments do not reflect real-world mobility. PAM addresses this gap by continuously tracking movement patterns and physical activity during daily-life, offering insights into how patients walk beyond clinical settings. The combination of IGA and PAM provides a more comprehensive understanding of mobility limitations, particularly in MSA and PSP, where gait and balance impairments differ from PD. This dual approach enhances patient assessment, supports personalized disease management, and improves clinical decision-making. Trial registration: ClinicalTrials.gov, NCT04608604, date of registration: 19/10/2020, first patient enrollment: 01/02/2021.
Disciplines :
Neurology
Author, co-author :
Sidoroff, Victoria; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Moradi, Hamid; Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
Prigent, Gaëlle; Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Jagusch, Frank; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Teckenburg, Isabelle; Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
Asalian, Marzieh; Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
Hergenroeder-Lenzner, Nina; Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
GIRAITIS, Marijus ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine
Schoenfeldt-Reichmann, Eva Tabea; Neurology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland ; Université de Lausanne, Lausanne, Switzerland
Ndayisaba, Jean-Pierre; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Goebel, Georg; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Seppi, Klaus; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Ionescu, Anisoara; Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Krismer, Florian; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
Winkler, Juergen; Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany ; Center for Rare Diseases Erlangen (ZSEER), University Hospital Erlangen, Erlangen, Germany
Eskofier, Bjoern M; Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany ; Institute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
KLUCKEN, Jochen ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine ; Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg
Aminian, Kamiar; Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Wenning, Gregor; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
SAPIENZA, Stefano ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine
Gassner, Heiko; Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany. heiko.gassner@uk-erlangen.de ; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany. heiko.gassner@uk-erlangen.de
Raccagni, Cecilia; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. cecilia.raccagni@sabes.it ; Department of Neurology, Provincial Hospital of Bolzano, Bolzano, Italy. cecilia.raccagni@sabes.it ; Paracelsus Private Medical University, Salzburg, Austria. cecilia.raccagni@sabes.it
Austrian Science Fund Deutsche Forschungsgemeinschaft Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung Fonds National de la Recherche Luxembourg Fonds National de la Recherche Luxembourg Fonds National de la Recherche Luxembourg
Köllensperger M et al. Red flags for multiple system atrophy Mov. Disord. 2008 23 1093 1099 18442131
Raccagni C et al. Gait and postural disorders in Parkinsonism: a clinical approach J. Neurol. 2019 267 3169 3176 31119450 7578144
Litvan I et al. Natural history of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome) and clinical predictors of survival: a clinicopathological study J. Neurol. Neurosurg. Psychiatry 1996 60 615 620 1:STN:280:DyaK283ksFCrtw%3D%3D 8648326 1073943
Ebersbach G et al. Clinical syndromes: Parkinsonian gait Mov. Disord. 2013 28 1552 1559 24132843
Espay AJ 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 30901495 6520192
Barth J et al. Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data Sensors 2015 15 6419 6440 25789489 4435165
Klucken J et al. Unbiased and mobile gait analysis detects motor impairment in Parkinson’s disease PLoS ONE 2013 8 e56956 1:CAS:528:DC%2BC3sXjsF2jsrk%3D 23431395 3576377
Schlachetzki JCM et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease PLoS ONE 2017 12 e0183989 29020012 5636070
Indelicato E et al. Instrumented gait analysis defines the walking signature of CACNA1A disorders J. Neurol. 2022 269 2941 2947 1:CAS:528:DC%2BB38Xjtlemur0%3D 34755206
Marini K et al. Associations of gait disorders and recurrent falls in older people: a prospective population-based study Gerontology 2022 68 1139 1144 34963115
Del Din S et al. Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length J. Neuroeng. Rehabil. 2016 13 46 27175731 4866360
Salarian A et al. Ambulatory monitoring of physical activities in patients with Parkinson’s disease IEEE Trans. Biomed. Eng. 2007 54 2296 2299 18075046
Atrsaei A et al. Gait speed in clinical and daily living assessments in Parkinson’s disease patients: performance versus capacity NPJ Parkinsons Dis. 2021 7 24 1:CAS:528:DC%2BB3MXht1GisLzE 33674597 7935857
Gassner H et al. The diagnostic scope of sensor-based gait analysis in atypical parkinsonism: further observations Front. Neurol. 2019 10 5 30723450 6349719
Raccagni C et al. Sensor-based gait analysis in atypical parkinsonian disorders Brain Behav. 2018 8 e00977 29733529 5991583
Sidoroff V et al. Characterization of gait variability in multiple system atrophy and Parkinson’s disease J. Neurol. 2021 268 1770 1779 33382439
De Vos M et al. Discriminating progressive supranuclear palsy from Parkinson’s disease using wearable technology and machine learning Gait Posture 2020 77 257 263 32078894
Hatanaka N et al. Comparative gait analysis in progressive supranuclear palsy and Parkinson’s disease Eur. Neurol. 2016 75 282 289 27288001
Buekers J et al. Reliability of real-world walking activity and gait assessment in people with COPD. How many hours and days are needed? Eur. Respir. J. 2024 64 PA791
Ginis P et al. Validation of commercial activity trackers in everyday life of people with Parkinson’s disease Sensors 2023 23 4156 37112496 10144957
Amboni M et al. Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages Sci. Rep. 2021 11 1:CAS:528:DC%2BB3MXhtVartb7J 33927317 8084977 9297
Na BS et al. Comparison of gait parameters between drug-naive patients diagnosed with multiple system atrophy with predominant parkinsonism and Parkinson’s disease Parkinsonism Relat. Disord. 2019 60 87 91 30266299
Wielinski CL et al. Falls and injuries resulting from falls among patients with Parkinson’s disease and other parkinsonian syndromes Mov. Disord. 2005 20 410 415 15580552
O’Sullivan SS et al. Clinical outcomes of progressive supranuclear palsy and multiple system atrophy Brain 2008 131 1362 1372 18385183
Raccagni C et al. The footprint of orthostatic hypotension in parkinsonian syndromes Parkinsonism Relat. Disord. 2020 77 107 109 32712561
Amano S et al. Discriminating features of gait performance in progressive supranuclear palsy Parkinsonism Relat. Disord. 2015 21 888 893 26032992
Hausdorff JM Gait variability: methods, modeling and meaning J. Neuroeng. Rehabil. 2005 2 19 16033650 1185560
Girnis JL et al. Natural walking intensity in persons with Parkinson disease J. Neurol. Phys. Ther. 2023 47 146 154 37016469 10330027
von der Recke F et al. Reduced range of gait speed: a Parkinson’s disease-specific symptom? J. Parkinsons Dis. 2023 13 197 202 36872788 10041422
Williams DR Litvan I Parkinsonian syndromes Continuum 2013 19 1189 1212 24092286 4234134
Gilman S et al. Second consensus statement on the diagnosis of multiple system atrophy Neurology 2008 71 670 676 1:STN:280:DC%2BD1crktlWmsA%3D%3D 18725592 2676993
Höglinger GU et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria Mov. Disord. 2017 32 853 864 28467028 5516529
Postuma RB et al. MDS clinical diagnostic criteria for Parkinson’s disease Mov. Disord. 2015 30 1591 1601 26474316
Raccagni C et al. Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP) BMJ Open 2024 14 e081317 38692728 11086482
Del Din S et al. Analysis of free-living gait in older adults with and without Parkinson’s disease and with and without a history of falls: identifying generic and disease-specific characteristics J. Gerontol. A Biol. Sci. Med. Sci. 2019 74 500 506 29300849
Nasreddine ZS et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment J. Am. Geriatr. Soc. 2005 53 695 699 15817019
Dubois B et al. The FAB: a Frontal Assessment Battery at bedside Neurology 2000 55 1621 1626 1:STN:280:DC%2BD3M%2FnvFSrsA%3D%3D 11113214
Freeman R et al. Consensus statement on the definition of orthostatic hypotension, neurally mediated syncope and the postural tachycardia syndrome Clin. Auton. Res. 2011 21 69 72 21431947
Goetz CG 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 19025984
Berg KO et al. Measuring balance in the elderly: validation of an instrument Can. J. Public Health 1992 83 S7 S11 1468055
Barry E et al. Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta-analysis BMC Geriatr. 2014 14 14 24484314 3924230
Podsiadlo D Richardson S The timed “Up & Go”: a test of basic functional mobility for frail elderly persons J. Am. Geriatr. Soc. 1991 39 142 148 1:STN:280:DyaK3M7itlKgsA%3D%3D 1991946
Jenkinson C Fitzpatrick R Cross-cultural evaluation of the short form 8-item Parkinson’s Disease Questionnaire (PDQ-8): results from America, Canada, Japan, Italy and Spain Parkinsonism Relat. Disord. 2007 13 22 28 16931104
Craig CL et al. International physical activity questionnaire: 12-country reliability and validity Med. Sci. Sports Exerc. 2003 35 1381 1395 12900694
Kuderle A et al. Gaitmap—an open ecosystem for IMU-based human gait analysis and algorithm benchmarking IEEE Open J. Eng. Med. Biol. 2024 5 163 172 38487091
Zhao, H. a. W. et al. IMU-based gait analysis for rehabilitation assessment of patients with gait disorders. In 2017 4th International Conference on Systems and Informatics (ICSAI) 622–626 https://doi.org/10.1109/ICSAI.2017.8248364 (2017).
Motl RW et al. Reliability of scores from physical activity monitors in adults with multiple sclerosis Adapt Phys. Act. Q 2007 24 245 253
Rehman RZU et al. Investigating the impact of environment and data aggregation by walking bout duration on Parkinson’s Disease Classification Using Machine Learning Front. Aging Neurosci. 2022 14 808518 35391750 8981298
Prigent G et al. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings Med. Biol. Eng. Comput. 2023 61 2341 2352 37069465 10412496
Lord S Galna B Rochester L Moving forward on gait measurement: toward a more refined approach Mov. Disord. 2013 28 1534 1543 24132841
Agiovlasitis S Motl RW Step-rate thresholds for physical activity intensity in persons with multiple sclerosis Adapt Phys. Act. Q 2014 31 4 18
Paraschiv-Ionescu A et al. Barcoding human physical activity to assess chronic pain conditions PLoS ONE 2012 7 e32239 1:CAS:528:DC%2BC38XjsFyiurY%3D 22384191 3285674
