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Rekant J, Ortmeyer H, Giffuni J, Friedman B, Addison O. Physical Functioning, Physical Activity, and Variability in Gait Performance during the Six-Minute Walk Test. SENSORS (BASEL, SWITZERLAND) 2024; 24:4656. [PMID: 39066052 PMCID: PMC11280787 DOI: 10.3390/s24144656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/09/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
Instrumenting the six-minute walk test (6MWT) adds information about gait quality and insight into fall risk. Being physically active and preserving multi-directional stepping abilities are also important for fall risk reduction. This analysis investigated the relationship of gait quality during the 6MWT with physical functioning and physical activity. Twenty-one veterans (62.2 ± 6.4 years) completed the four square step test (FSST) multi-directional stepping assessment, a gait speed assessment, health questionnaires, and the accelerometer-instrumented 6MWT. An activity monitor worn at home captured free-living physical activity. Gait measures were not significantly different between minutes of the 6MWT. However, participants with greater increases in stride time (ρ = -0.594, p < 0.01) and stance time (ρ = -0.679, p < 0.01) during the 6MWT reported lower physical functioning. Neither physical activity nor sedentary time were related to 6MWT gait quality. Participants exploring a larger range in stride time variability (ρ = 0.614, p < 0.01) and stance time variability (ρ = 0.498, p < 0.05) during the 6MWT required more time to complete the FSST. Participants needing at least 15 s to complete the FSST meaningfully differed from those completing the FSST more quickly on all gait measures studied. Instrumenting the 6MWT helps detect ranges of gait performance and provides insight into functional limitations missed with uninstrumented administration. Established FSST cut points identify aging adults with poorer gait quality.
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Affiliation(s)
- Julie Rekant
- Baltimore VA Medical Center, Baltimore Department of Physical Therapy and Rehabilitation Sciences, University of Maryland, Baltimore, MD 21201, USA; (H.O.); (J.G.); (B.F.); (O.A.)
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Parsaei M, Amanollahi M, TaghaviZanjani F, Khanmohammadi S, Jameie M, Naser Moghadasi A. Effects of non-pharmacological interventions on gait and balance of persons with Multiple Sclerosis: A narrative review. Mult Scler Relat Disord 2024; 82:105415. [PMID: 38211505 DOI: 10.1016/j.msard.2023.105415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Multiple Sclerosis (MS) is among the most common reasons for disability in young adults. Mobility impairment, primarily related to gait and balance, is ranked as the preeminent concern among persons with MS (PwMS). Gait and balance dysfunction can directly affect the quality of life and activities of daily life in PwMS, hence the importance of effective treatment strategies. Previous studies have demonstrated the positive effect of various non-pharmacological rehabilitation methods, including physiotherapy and electrical stimulation, on gait and mobility in PwMS. Non-pharmacological methods can be tailored to the individual needs and abilities of each patient, allowing healthcare providers to create personalized training programs. Furthermore, these methods typically result in minimal or no side effects. PURPOSE This review provides a comprehensive overview of an array of non-pharmacological treatment approaches aimed at enhancing ambulatory performance in PwMS. METHODS We performed a narrative review of the original papers available in PubMed, investigating the effects of different nonmedical approaches on the gait and balance performance of the PwMS. Reviewed treatment approaches include "exercise, physical rehabilitation, dual-task (DT) rehabilitation, robot-assisted rehabilitation, virtual reality-assisted rehabilitation, game training, electrical stimulation devices, auditory stimulation, visual feedback, and shoe insoles". RESULTS AND CONCLUSIONS Eighty articles were meticulously reviewed. Our study highlights the positive effects of non-pharmacological interventions on patients' quality of life, reducing disability, fatigue, and muscle spasticity. While some methods, including exercise and physiotherapy, showed substantial promise, further research is needed to evaluate whether visual biofeedback and auditory stimulation are preferable over conventional approaches. Additionally, approaches such as functional electrical stimulation, non-invasive brain stimulation, and shoe insoles demonstrate substantial short-term benefits, prompting further investigation into their long-term effects. Non-pharmacological interventions can serve as a valuable complement to medication-based approaches.
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Affiliation(s)
- Mohammadamin Parsaei
- Maternal, Fetal, and Neonatal Research Center, Family Health Research Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Mobina Amanollahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Melika Jameie
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran; Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Abdorreza Naser Moghadasi
- Multiple Sclerosis Research Center, Neuroscience Institute, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran.
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Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler CR. Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective. J Med Internet Res 2023; 25:e44428. [PMID: 37498655 PMCID: PMC10415952 DOI: 10.2196/44428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/19/2022] [Accepted: 05/04/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. OBJECTIVE This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. METHODS We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. RESULTS Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. CONCLUSIONS Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. TRIAL REGISTRATION PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.
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Affiliation(s)
- Tim Woelfle
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Lucie Bourguignon
- Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
| | - Johannes Lorscheider
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Yvonne Naegelin
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
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Berg-Hansen P, Moen SM, Klyve TD, Gonzalez V, Seeberg TM, Celius EG, Austeng A, Meyer F. The instrumented single leg stance test detects early balance impairment in people with multiple sclerosis. Front Neurol 2023; 14:1227374. [PMID: 37538255 PMCID: PMC10394643 DOI: 10.3389/fneur.2023.1227374] [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: 05/23/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Balance impairment is frequent in people with multiple sclerosis (pwMS) and affects risk of falls and quality of life. By using inertial measurement units (IMUs) on the Single Leg Stance Test (SLS) we aimed to discriminate healthy controls (HC) from pwMS and detect differences in balance endurance and quality. Thirdly, we wanted to test the correlation between instrumented SLS parameters and self-reported measures of gait and balance. Fifty-five pwMS with mild (EDSS<4) and moderate disability (EDSS≥4) and 20 HC performed the SLS with 3 IMUs placed on the feet and sacrum and filled the Twelve Item Multiple Sclerosis Walking Scale (MSWS-12) questionnaire. A linear mixed model was used to compare differences in the automated balance measures. Balance duration was significantly longer in HC compared to pwMS (p < 0.001) and between the two disability groups (p < 0.001). Instrumented measures identified that trunk stability (normalized mediolateral and antero-posterior center of mass stability) had the strongest association with disability (R2 marginal 0.30, p < 0.001) and correlated well with MSWS-12 (R = 0.650, p < 0.001). PwMS tended to overestimate own balance compared to measured balance duration. The use of both self-reported and objective assessments from IMUs can secure the follow-up of balance in pwMS.
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Affiliation(s)
- Pål Berg-Hansen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | | | | | - Victor Gonzalez
- SINTEF Digital, Smart Sensor and Micro Systems, Oslo, Norway
| | | | - Elisabeth Gulowsen Celius
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Frédéric Meyer
- Department of Informatics, University of Oslo, Oslo, Norway
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Kontaxis S, Laporta E, Garcia E, Martinis M, Leocani L, Roselli L, Buron MD, Guerrero AI, Zabala A, Cummins N, Vairavan S, Hotopf M, Dobson RJB, Narayan VA, La Porta ML, Costa GD, Magyari M, Sørensen PS, Nos C, Bailon R, Comi G. Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:6017. [PMID: 37447866 DOI: 10.3390/s23136017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/29/2023] [Accepted: 06/10/2023] [Indexed: 07/15/2023]
Abstract
The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.
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Affiliation(s)
- Spyridon Kontaxis
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Estela Laporta
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Esther Garcia
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, 08193 Bellaterra, Spain
| | - Matteo Martinis
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Letizia Leocani
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Lucia Roselli
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Mathias Due Buron
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Ana Zabala
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Nicholas Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | | | - Maria Libera La Porta
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Gloria Dalla Costa
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
| | - Melinda Magyari
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Per Soelberg Sørensen
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Carlos Nos
- Multiple Sclerosis Center of Catalonia (CEMCAT), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autonoma de Barcelona, 08035 Barcelona, Spain
| | - Raquel Bailon
- Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), University of Zaragoza, 50018 Zaragoza, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28006 Barcelona, Spain
| | - Giancarlo Comi
- Department of Medicine and Surgery, University Vita-Salute and Hospital San Raffaele, 20132 Milan, Italy
- Casa di Cura del Policlinico, 20144 Milan, Italy
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Tony Hii CS, Gan KB, Zainal N, Ibrahim NM, Md. Rani SA, Shattar NA. Marker Free Gait Analysis using Pose Estimation Model. 2022 IEEE 20TH STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT (SCORED) 2022. [DOI: 10.1109/scored57082.2022.9974096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Chang Soon Tony Hii
- Electronic and Systems Engineering Universiti Kebangsaan Malaysia,Department of Electrical,Bangi,Malaysia
| | - Kok Beng Gan
- Electronic and Systems Engineering Universiti Kebangsaan Malaysia,Department of Electrical,Bangi,Malaysia
| | - Nasharuddin Zainal
- Electronic and Systems Engineering Universiti Kebangsaan Malaysia,Department of Electrical,Bangi,Malaysia
| | | | - Shahrul Azmin Md. Rani
- Universiti Kebangsaan Malaysia Medical Centre,Department of Medicine,Kuala Lumpur,Malaysia
| | - Normurniyati Abd Shattar
- Electronic and Systems Engineering Universiti Kebangsaan Malaysia,Department of Electrical,Bangi,Malaysia
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Arpan I, Shah VV, McNames J, Harker G, Carlson-Kuhta P, Spain R, El-Gohary M, Mancini M, Horak FB. Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:5940. [PMID: 36015700 PMCID: PMC9415310 DOI: 10.3390/s22165940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.
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Affiliation(s)
- Ishu Arpan
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- Advanced Imaging Research Center, Oregon Health & Science University Portland, OR 97239, USA
| | - Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
| | - James McNames
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
- Department of Electrical and Computer Engineering, Portland State University, 1825 SW Broadway, Portland, OR 97201, USA
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Rebecca Spain
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mahmoud El-Gohary
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
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