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Faccioli S, Cavalagli A, Falocci N, Mangano G, Sanfilippo I, Sassi S. Gait analysis patterns and rehabilitative interventions to improve gait in persons with hereditary spastic paraplegia: a systematic review and meta-analysis. Front Neurol 2023; 14:1256392. [PMID: 37799279 PMCID: PMC10548139 DOI: 10.3389/fneur.2023.1256392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023] Open
Abstract
Background Hereditary spastic paraplegias (HSPs) are a group of inheritance diseases resulting in gait abnormalities, which may be detected using instrumented gait analysis. The aim of this systematic review was 2-fold: to identify specific gait analysis patterns and interventions improving gait in HSP subjects. Methods A systematic review was conducted in PubMed, Cochrane Library, REHABDATA, and PEDro databases, in accordance with reporting guidelines of PRISMA statement and Cochrane's recommendation. The review protocol was recorded on the PROSPERO register. Patients with pure and complicated HSP of any age were included. All types of studies were included. Risk of bias, quality assessment, and meta-analysis were performed. Results Forty-two studies were included: 19 were related to gait analysis patterns, and 24 were intervention studies. The latter ones were limited to adults. HSP gait patterns were similar to cerebral palsy in younger subjects and stroke in adults. Knee hyperextension, reduced range of motion at knee, ankle, and hip, reduced foot lift, and increased rapid trunk and arm movements were reported. Botulinum injections reduced spasticity but uncovered weakness and improved gait velocity at follow-up. Weak evidence supported intrathecal baclofen, active intensive physical therapy (i.e., robot-assisted gait training, functional exercises, and hydrotherapy), and functional electrical stimulation. Some improvements but adverse events were reported after transcranial magnetic stimulation, transcutaneous spinal direct current stimulation, and spinal cord stimulation implant. Conclusion Knee hyperextension, non-sagittal pelvic movements, and reduced ROM at the knee, ankle, and hip represent the most peculiar patterns in HSP, compared to diplegic cerebral palsy and stroke. Botulinum improved comfortable gait velocity after 2 months. Nonetheless, interventions reducing spasticity might result in ineffective functional outcomes unveiling weakness. Intensive active physical therapy and FES might improve gait velocity in the very short term.
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Affiliation(s)
- Silvia Faccioli
- Children Rehabilitation Unit, Azienda Unità Sanitaria Locale IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Clinical and Experimental Medicine, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Angela Cavalagli
- Children Rehabilitation Unit, IRCCS Fondazione Don Carlo Gnocchi, Milano, Italy
| | - Nicola Falocci
- Office of Policy Evaluation and Statistical Studies, Umbria Legislative Assembly, Perugia, Italy
| | - Giulia Mangano
- Department of Physical Medicine and Rehabilitation, Azienda Sanitaria Provinciale 3 (ASP 3), Acireale Hospital, Catania, Italy
| | | | - Silvia Sassi
- Children Rehabilitation Unit, Azienda Unità Sanitaria Locale IRCCS di Reggio Emilia, Reggio Emilia, Italy
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Siow SF, Yeow D, Rudaks LI, Jia F, Wali G, Sue CM, Kumar KR. Outcome Measures and Biomarkers for Clinical Trials in Hereditary Spastic Paraplegia: A Scoping Review. Genes (Basel) 2023; 14:1756. [PMID: 37761896 PMCID: PMC10530989 DOI: 10.3390/genes14091756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Hereditary spastic paraplegia (HSP) is characterized by progressive lower limb spasticity. There is no disease-modifying treatment currently available. Therefore, standardized, validated outcome measures to facilitate clinical trials are urgently needed. We performed a scoping review of outcome measures and biomarkers for HSP to provide recommendations for future studies and identify areas for further research. We searched Embase, Medline, Scopus, Web of Science, and the Central Cochrane database. Seventy studies met the inclusion criteria, and eighty-three outcome measures were identified. The Spastic Paraplegia Rating Scale (SPRS) was the most widely used (27 studies), followed by the modified Ashworth Scale (18 studies) and magnetic resonance imaging (17 studies). Patient-reported outcome measures (PROMs) were infrequently used to assess treatment outcomes (28% of interventional studies). Diffusion tensor imaging, gait analysis and neurofilament light chain levels were the most promising biomarkers in terms of being able to differentiate patients from controls and correlate with clinical disease severity. Overall, we found variability and inconsistencies in use of outcome measures with a paucity of longitudinal data. We highlight the need for (1) a standardized set of core outcome measures, (2) validation of existing biomarkers, and (3) inclusion of PROMs in HSP clinical trials.
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Affiliation(s)
- Sue-Faye Siow
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Department of Clinical Genetics, Royal North Shore Hospital, St Leonards 2065, Australia
| | - Dennis Yeow
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Neuroscience Research Australia, University of New South Wales, Randwick 2031, Australia
- Rare Disease Program, Garvan Institute of Medical Research, Darlinghurst 2010, Australia
- Translational Neurogenomics Group, Molecular Medicine Laboratory and Department of Neurology, Concord Hospital, Concord 2139, Australia
- Neurodegenerative Service, Prince of Wales Hospital, Randwick 2031, Australia
| | - Laura I. Rudaks
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Department of Clinical Genetics, Royal North Shore Hospital, St Leonards 2065, Australia
- Rare Disease Program, Garvan Institute of Medical Research, Darlinghurst 2010, Australia
- Translational Neurogenomics Group, Molecular Medicine Laboratory and Department of Neurology, Concord Hospital, Concord 2139, Australia
| | - Fangzhi Jia
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
| | - Gautam Wali
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Neuroscience Research Australia, University of New South Wales, Randwick 2031, Australia
| | - Carolyn M. Sue
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Neuroscience Research Australia, University of New South Wales, Randwick 2031, Australia
- Neurodegenerative Service, Prince of Wales Hospital, Randwick 2031, Australia
- School of Clinical Medicine, UNSW Medicine & Health, University of New South Wales, Kensington 2052, Australia
| | - Kishore R. Kumar
- Sydney Medical School, University of Sydney, Camperdown 2050, Australia (C.M.S.)
- Rare Disease Program, Garvan Institute of Medical Research, Darlinghurst 2010, Australia
- Translational Neurogenomics Group, Molecular Medicine Laboratory and Department of Neurology, Concord Hospital, Concord 2139, Australia
- School of Clinical Medicine, UNSW Medicine & Health, University of New South Wales, Kensington 2052, Australia
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Regensburger M, Spatz IT, Ollenschläger M, Martindale CF, Lindeburg P, Kohl Z, Eskofier B, Klucken J, Schüle R, Klebe S, Winkler J, Gaßner H. Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-sectional Multicenter Clinical Study. Neurology 2022; 99:e1079-e1089. [PMID: 35667840 PMCID: PMC9519248 DOI: 10.1212/wnl.0000000000200819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurologic examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient-dependent and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of this study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross-sectional cohort of patients with HSP and a longitudinal fast progressing subcohort. METHODS Using wearable sensors attached to the shoes, patients with HSP and controls performed a 4 × 10 m walking test during regular visits in 3 outpatient centers. Patients were also rated according to the SPRS, and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was used to extract stride parameters and respective coefficients of variation. RESULTS Mobile gait analysis was performed in a total of 112 ambulatory patients with HSP and 112 age-matched and sex-matched controls. Although swing time was unchanged compared with controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. Although stride parameters did not correlate with age, weight, or height of the patients, there were significant associations of absolute stride parameters with single SPRS items reflecting impaired mobility (|r| > 0.50), with patients' quality of life (|r| > 0.44), and notably with disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, the absolute values of mobile gait parameters had significantly worsened compared with baseline. DISCUSSION The presented wearable sensor system provides parameters of stride characteristics which seem clinically valid to reflect gait impairment in HSP. Owing to the feasibility regarding time, space, and costs, this study forms the basis for larger scale longitudinal and interventional studies in HSP.
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Affiliation(s)
- Martin Regensburger
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
| | - Imke Tabea Spatz
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Malte Ollenschläger
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Christine F Martindale
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Philipp Lindeburg
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Zacharias Kohl
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Björn Eskofier
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Jochen Klucken
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Rebecca Schüle
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Stephan Klebe
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Jürgen Winkler
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Heiko Gaßner
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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van de Venis L, Weerdesteyn V, Konijnenburg A, van de Warrenburg BPC, Geurts ACH, Nonnekes J. Increased trunk movements in people with hereditary spastic paraplegia: do these involve balance correcting strategies? J Neurol 2022; 269:4264-4269. [PMID: 35307753 PMCID: PMC9293820 DOI: 10.1007/s00415-022-11054-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 12/04/2022]
Abstract
Objective Hereditary spastic paraplegia (HSP) is characterized by a bilaterally spastic gait pattern. During gait, increased trunk movements are often observed. People with HSP likely generate trunk movements to improve foot clearance and step length, but there may be additional explanations. Here, we investigate whether there is an association between reduced balance performance and increased trunk movements, as an increase in trunk movements may partly reflect balance correcting strategies. Methods We analyzed an historic cohort of 86 people with HSP who underwent gait analysis and balance examination. Two researchers reviewed gait analyses videos and classified the observed trunk movement as (1) normal, (2) moderately increased, or (3) markedly increased, and categorized participants as ‘toe walkers’ (yes/no). Balance performance and spatiotemporal gait parameters were collected from the medical files. Parameters were compared between people with normal vs. moderately increased trunk movements, moderately vs. markedly increased trunk movements, and normal vs. markedly increased trunk movements. Results Patients with moderately increased trunk movements during gait scored lower on the Berg Balance Scale (p = 0.002) and/or the Mini Balance Evaluation Test (p = 0.043) than patients with normal trunk movements. Likewise, patients with markedly increased trunk movements performed worse on the BBS (p = 0.037) and/or the Mini-BESTest (p = 0.004) than patients with moderately increased trunk movements. Patients with markedly increased trunk movements were more often toe walkers than patients with moderately increased (68% vs. 6%; p < 0.001). Conclusions We found an association between increased trunk movements and reduced balance capacity. This may have several—not mutually exclusive—explanations. One of these explanations is that trunk movements, at least partly, reflect balance correcting strategies. With the disease progression, ankle strategies and foot placement strategies become impaired and insufficient to restore balance after intrinsic perturbations. Hip strategies are then potentially recruited to maintain balance, resulting in increased trunk movements.
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Affiliation(s)
- Lotte van de Venis
- Department of Rehabilitation, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Vivian Weerdesteyn
- Department of Rehabilitation, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Aletta Konijnenburg
- Department of Rehabilitation, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Bart P C van de Warrenburg
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alexander C H Geurts
- Department of Rehabilitation, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.,Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, The Netherlands
| | - Jorik Nonnekes
- Department of Rehabilitation, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.,Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, The Netherlands
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Okawara H, Tashiro S, Sawada T, Sugai K, Matsubayashi K, Kawakami M, Nori S, Tsuji O, Nagoshi N, Matsumoto M, Nakamura M. Neurorehabilitation using a voluntary driven exoskeletal robot improves trunk function in patients with chronic spinal cord injury: a single-arm study. Neural Regen Res 2022; 17:427-432. [PMID: 34269219 PMCID: PMC8463976 DOI: 10.4103/1673-5374.317983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Body weight-supported treadmill training with the voluntary driven exoskeleton (VDE-BWSTT) has been shown to improve the gait function of patients with chronic spinal cord injury. However, little is known whether VDE-BWSTT can effectively improve the trunk function of patients with chronic spinal cord injury. In this open-label, single-arm study, nine patients with chronic spinal cord injury at the cervical or thoracic level (six males and three females, aged 37.8 ± 15.6 years, and time since injury 51.1 ± 31.8 months) who underwent outpatient VDE-BWSTT training program at Keio University Hospital, Japan from September 2017 to March 2019 were included. All patients underwent twenty 60-minute gait training sessions using VDE. Trunk muscular strength, i.e., the maximum force against which patient could maintain a sitting posture without any support, was evaluated in four directions: anterior, posterior, and lateral (right and left) after 10 and 20 training sessions. After intervention, lateral muscular strength significantly improved. In addition, a significant positive correlation was detected between the change in lateral trunk muscular strength after 20 training sessions relative to baseline and gait speed. The change in trunk muscular strength after 20 training sessions relative to baseline was greatly correlated with patient age. This suggests that older adult patients with chronic spinal cord injury achieved a greater improvement in trunk muscle strength following VDE-BWSTT. All these findings suggest that VDE-BWSTT can improve the trunk function of patients with chronic spinal cord injury and the effect might be greater in older adult patients. The study was approved by the Keio University of Medicine Ethics Committee (IRB No. 20150355-3) on September 26, 2017.
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Affiliation(s)
- Hiroki Okawara
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Syoichi Tashiro
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Tomonori Sawada
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Keiko Sugai
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Kohei Matsubayashi
- Department of Orthopedic Surgery, National Hospital Organization, Murayama Medical Center, Tokyo, Japan
| | - Michiyuki Kawakami
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Satoshi Nori
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Osahiko Tsuji
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Narihito Nagoshi
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Morio Matsumoto
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Masaya Nakamura
- Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
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Bickley C, Mitchell K, Scott A, Bury M, Oyelami M. Familiarity with Hereditary Spastic Paraplegia (HSP) and Differentiation of Upper Body Gait Characteristics between Children with HSP and Spastic Diplegic Cerebral Palsy. Phys Occup Ther Pediatr 2021; 41:99-113. [PMID: 32942942 DOI: 10.1080/01942638.2020.1819934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AIMS The aim of this two-part, prospective study was to determine therapist familiarity with HSP and examine diagnostic accuracy between individuals with HSP and those with Spastic Diplegic Cerebral Palsy (SD-CP). METHODS Part-one surveyed physical therapists (PT) and physical therapist assistants (PTA) throughout Texas to determine familiarity with HSP and its gait deviations. Part-two examined accuracy in differential diagnosis of HSP versus SD-CP using gait analysis and the effects of an educational module on upper body gait deviations observed in individuals with HSP. RESULTS Both PTs and PTAs indicated a high degree (≥73.2%) of unfamiliarity with HSP. While a majority of respondents (≥88.7%) indicated use of observational gait analysis in clinical practice, ≥92.5% indicated never receiving instruction on HSP or its associated gait deviations. Whole group analysis revealed diagnostic accuracy increased 21.7% post educational module. In addition, individual case diagnostic accuracy yielded significant improvement in 14 out of 20 cases. CONCLUSIONS Physical and occupational therapists are in a unique position to assist with the identification and distinction of HSP from CP. This study demonstrated that brief instruction on common upper body gait deviations seen in individuals with HSP may improve a clinician's ability to distinguish SD-CP from HSP via gait analysis.
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Affiliation(s)
- Christina Bickley
- School of Physical Therapy, Texas Woman's University, Houston, Texas, USA
| | - Katy Mitchell
- School of Physical Therapy, Texas Woman's University, Houston, Texas, USA
| | - Allison Scott
- Shriners Hospitals for Children, Houston, Texas, USA
| | | | - Mayowa Oyelami
- Neurological Residency Program, Harris Health System, Houston, Texas, USA
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Martindale CF, Roth N, Gasner H, Jensen D, Kohl Z, Eskofier BM. Mobile Gait Analysis using Personalised Hidden Markov Models for Hereditary Spastic Paraplegia Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:5430-5433. [PMID: 30441565 DOI: 10.1109/embc.2018.8513508] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Gait analysis provides a quantitative method to assess disease progression or intervention effect on gait disorders. While mobile gait analysis enables continuous monitoring in free living conditions, state of the art gait analysis for diseases such as hereditary spastic paraplegia (HSP) is currently limited to motion capture systems which are large and expensive. The challenge with HSP is its heterogeneous nature and rarity, leading to a wide range of ages, severity and gait patterns as well as small patient numbers. We propose a sensor-based mobile solution, based on a personalised hierarchical hidden Markov Model (hHMM) to extract spatio-temporal gait parameters. This personalised hHMM achieves a mean absolute error of 0.04 s ± 0.03 s for stride time estimation with respect to a GAITRite® reference system. We use the successful extraction of initial ground contact to explore the limits of the double integration method for such heterogeneous diseases. While our personalised model compensates for the heterogeneity of the disease, it would require a new model per patient. We observed that the general model was sufficient for some of the less severely affected patients.
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Martindale CF, Roth N, Gasner H, List J, Regensburger M, Eskofier BM, Kohl Z. Technical Validation of an Automated Mobile Gait Analysis System for Hereditary Spastic Paraplegia Patients. IEEE J Biomed Health Inform 2019; 24:1490-1499. [PMID: 31449035 DOI: 10.1109/jbhi.2019.2937574] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Hereditary spastic paraplegias (HSP) represents a group of orphan neurodegenerative diseases with gait disturbance as the predominant clinical feature. Due to its rarity, research within this field is still limited. Aside from clinical analysis using established scales, gait analysis has been employed to enhance the understanding of the mechanisms behind the disease. However, state of the art gait analysis systems are often large, immobile and expensive. To overcome these limitations, this paper presents the first clinically relevant mobile gait analysis system for HSP patients. We propose an unsupervised model based on local cyclicity estimation and hierarchical hidden Markov models (LCE-hHMM). The system provides stride time, swing time, stance time, swing duration and cadence. These parameters are validated against a GAITRite system and manual sensor data labelling using a total of 24 patients within 2 separate studies. The proposed system achieves a stride time error of -0.00 ± 0.09 s (correlation coefficient, r = 1.00) and a swing duration error of -0.67 ± 3.27 % (correlation coefficient, r = 0.93) with respect to the GAITRite system. We show that these parameters are also correlated to the clinical spastic paraplegia rating scale (SPRS) in a similar manner to other state of the art gait analysis systems, as well as to supervised and general versions of the proposed model. Finally, we show a proof of concept for this system to be used to analyse alterations in the gait of individual patients. Thus, with further clinical studies, due to its automated approach and mobility, this system could be used to determine treatment effects in future clinical trials.
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McCahill J, Stebbins J, Lewis A, Prescott R, Harlaar J, Theologis T. Validation of the foot profile score. Gait Posture 2019; 71:120-125. [PMID: 31054493 DOI: 10.1016/j.gaitpost.2019.03.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 03/18/2019] [Accepted: 03/30/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND There are numerous static measures of foot posture but there is no published score of dynamic foot motion. Three-dimensional gait analysis can include a multi-segment foot model like the Oxford Foot Model (OFM) to comprehensively quantify foot kinematic deviations across the gait cycle but it lacks an overall score, like the Gait Profile score (GPS), used to summarize the quality of lower extremity motion. RESEARCH QUESTION This paper introduces the Foot Profile Score (FPS), a single number, analogous to the GPS but based on kinematic data of the OFM. The aim of this study is to validate the FPS by studying its properties and design, and analyse it against a clinical assessment of foot deformity. METHODS Concurrent validity was established for the FPS analysing the relationship with Clinical Foot Deformity Score (CFDS) in 60 subjects with a condition affecting the lower limbs globally Content validity was established for the six Foot Variable Scores (FVS) that make up the FPS using a multiple regression of the CFDS on the 6 FVS in the 60 subjects. Predictive validity was established analysing the relationship of the FPS and GPS comparing 60 global involvement subjects with 60 subjects with isolated foot deformity. RESULTS Pearson correlation between the FPS and CFDS was significant at 0.62 (p < 0.001). Each element of FVS contributes positively to predicting the CFDS with R2 = 0.456 (p < 0.001). FPS contributed independently to the prediction of CFDS (t = 3.9, p < 0.001). The correlation between the GPS and FPS in the global involvement group was significant at r = 0.64 (p < 0.001), while there was no correlation found with r = 0.08 (p = 0.54) in the foot deformity group. SIGNIFICANCE The FPS is the first validated score of dynamic foot motion.
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Affiliation(s)
- Jennifer McCahill
- Oxford Gait Laboratory, Nuffield Orthopaedic Centre, Oxford, UK; Amsterdam University Medical Centers, Amsterdam Movement Science, Dept Rehabilitation Medicine, the Netherlands.
| | - Julie Stebbins
- Oxford Gait Laboratory, Nuffield Orthopaedic Centre, Oxford, UK
| | - Andrew Lewis
- Oxford Gait Laboratory, Nuffield Orthopaedic Centre, Oxford, UK
| | - Robin Prescott
- Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jaap Harlaar
- Amsterdam University Medical Centers, Amsterdam Movement Science, Dept Rehabilitation Medicine, the Netherlands; Delft University of Technology, Dept of Biomechanical Engineering, the Netherlands
| | - Tim Theologis
- Oxford Gait Laboratory, Nuffield Orthopaedic Centre, Oxford, UK
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Postural stability during gait for adults with hereditary spastic paraparesis. J Biomech 2019; 88:12-17. [PMID: 30862381 DOI: 10.1016/j.jbiomech.2019.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 01/31/2019] [Accepted: 03/01/2019] [Indexed: 11/22/2022]
Abstract
Individuals with hereditary spastic paraparesis (HSP) are often impaired in their ability to control posture as a result of the neurological and musculoskeletal implications of their condition. This research aimed to assess postural stability during gait in a group of adults with HSP. Ten individuals with HSP and 10 healthy controls underwent computerized gait analysis while walking barefoot along a 10-m track. Two biomechanics methods were used to assess stability: the center of pressure and center of mass separation (COP-COM) method, and the extrapolated center of mass (XCOM) method. Spatiotemporal and kinematic variables were also investigated. The XCOM method identified deficits in mediolateral stability for the HSP group at both heel strike and mid-stance. The group with HSP also had slower walking velocity, lower cadence, more time spent in double stance, larger step widths, and greater lateral trunk flexion than the control group. These results suggest that individuals with HSP adjust characteristics of their gait to minimize the instability arising from their impairments but have residual deficits in mediolateral stability. This may result in an increased risk of falls, particularly in the sideways direction.
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Ounpuu S. The importance of understanding gait features in hereditary spastic paraplegia: accomplishments and next steps. Dev Med Child Neurol 2016; 58:790-1. [PMID: 26946256 DOI: 10.1111/dmcn.13099] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sylvia Ounpuu
- Center for Motion Analysis, Division of Orthopedics, Connecticut Children's Medical Center, Farmington, CT, USA
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