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Werner C, Hezel N, Dongus F, Spielmann J, Mayer J, Becker C, Bauer JM. Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors. Sci Rep 2023; 13:5350. [PMID: 37005465 PMCID: PMC10067003 DOI: 10.1038/s41598-023-32550-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/29/2023] [Indexed: 04/04/2023] Open
Abstract
This study assessed the concurrent validity and test-retest-reliability of the Apple Health app on iPhone for measuring gait parameters in different age groups. Twenty-seven children, 28 adults and 28 seniors equipped with an iPhone completed a 6-min walk test (6MWT). Gait speed (GS), step length (SL), and double support time (DST) were extracted from the gait recordings of the Health app. Gait parameters were simultaneously collected with an inertial sensors system (APDM Mobility Lab) to assess concurrent validity. Test-retest reliability was assessed via a second iPhone-instrumented 6MWT 1 week later. Agreement of the Health App with the APDM Mobility Lab was good for GS in all age groups and for SL in adults/seniors, but poor to moderate for DST in all age groups and for SL in children. Consistency between repeated measurements was good to excellent for all gait parameters in adults/seniors, and moderate to good for GS and DST but poor for SL in children. The Health app on iPhone is reliable and valid for measuring GS and SL in adults and seniors. Careful interpretation is required when using the Health app in children and when measuring DST in general, as both have shown limited validity and/or reliability.
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Affiliation(s)
- Christian Werner
- Geriatric Center, Agaplesion Bethanien Hospital Heidelberg, Heidelberg University Hospital, 69126, Heidelberg, Germany.
| | - Natalie Hezel
- Geriatric Center, Agaplesion Bethanien Hospital Heidelberg, Heidelberg University Hospital, 69126, Heidelberg, Germany
| | - Fabienne Dongus
- Institute of Sports and Sports Science, Heidelberg University, 69120, Heidelberg, Germany
| | | | - Jan Mayer
- TSG ResearchLab, 74939, Zuzenhausen, Germany
| | - Clemens Becker
- Unit of Digital Geriatric Medicine, Heidelberg University Hospital, 69115, Heidelberg, Germany
| | - Jürgen M Bauer
- Geriatric Center, Agaplesion Bethanien Hospital Heidelberg, Heidelberg University Hospital, 69126, Heidelberg, Germany
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Rinderknecht MD, Zanon M, Boonstra TA, Angelini L, Stanev D, Chan GG, Bunn L, Dondelinger F, Hosking R, Freeman J, Hobart J, Marsden J, Craveiro L. An observational study to assess validity and reliability of smartphone sensor-based gait and balance assessments in multiple sclerosis: Floodlight GaitLab protocol. Digit Health 2023; 9:20552076231205284. [PMID: 37868156 PMCID: PMC10588425 DOI: 10.1177/20552076231205284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
Background Gait and balance impairments are often present in people with multiple sclerosis (PwMS) and have a significant impact on quality of life and independence. Gold-standard quantitative tools for assessing gait and balance such as motion capture systems and force plates usually require complex technical setups. Wearable sensors, including those integrated into smartphones, offer a more frequent, convenient, and minimally burdensome assessment of functional disability in a home environment. We developed a novel smartphone sensor-based application (Floodlight) that is being used in multiple research and clinical contexts, but a complete validation of this technology is still lacking. Methods This protocol describes an observational study designed to evaluate the analytical and clinical validity of Floodlight gait and balance tests. Approximately 100 PwMS and 35 healthy controls will perform multiple gait and balance tasks in both laboratory-based and real-world environments in order to explore the following properties: (a) concurrent validity of the Floodlight gait and balance tests against gold-standard assessments; (b) reliability of Floodlight digital measures derived under different controlled gait and balance conditions, and different on-body sensor locations; (c) ecological validity of the tests; and (d) construct validity compared with clinician- and patient-reported assessments. Conclusions The Floodlight GaitLab study (ISRCTN15993728) represents a critical step in the technical validation of Floodlight technology to measure gait and balance in PwMS, and will also allow the development of new test designs and algorithms.
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Affiliation(s)
| | | | | | | | | | | | - Lisa Bunn
- Faculty of Health, University of Plymouth, Plymouth, UK
| | | | | | - Jenny Freeman
- Faculty of Health, University of Plymouth, Plymouth, UK
| | - Jeremy Hobart
- Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
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Hamilton RI, Williams J, Holt C. Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:1005000. [PMID: 36451804 PMCID: PMC9701737 DOI: 10.3389/fresc.2022.1005000] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/05/2022] [Indexed: 01/14/2024]
Abstract
The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.
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Affiliation(s)
- Rebecca I. Hamilton
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Jenny Williams
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
| | | | - Cathy Holt
- Musculoskeletal Biomechanics Research Facility, School of Engineering, Cardiff University, Cardiff, United Kingdom
- Osteoarthritis Technology NetworkPlus (OATech+), EPSRC UK-Wide Research Network+, United Kingdom
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Homan K, Yamamoto K, Kadoya K, Ishida N, Iwasaki N. Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis. BMC Sports Sci Med Rehabil 2022; 14:71. [PMID: 35430808 PMCID: PMC9013462 DOI: 10.1186/s13102-022-00461-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/11/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). METHODS Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. RESULTS Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. CONCLUSION The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.
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Affiliation(s)
- Kentaro Homan
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Keizo Yamamoto
- School of Lifelong Sport, Hokusho University, 23 Bunkyodai, Ebetsu, 069-8511, Japan
| | - Ken Kadoya
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Naoki Ishida
- Department of Orthopedic Surgery, Hokuto Medical Corporation Hokuto Hospital, Kisen 7-5 Inada-cho, Obihiro, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8638, Japan
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Jansen CP, Gordt-Oesterwind K, Schwenk M. Wearable Motion Sensors in Older Adults: On the Cutting Edge of Health and Mobility Research. SENSORS 2022; 22:s22030973. [PMID: 35161719 PMCID: PMC8837998 DOI: 10.3390/s22030973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/21/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Carl-Philipp Jansen
- Department of Clinical Gerontology and Geriatric Rehabilitation, Robert Bosch Hospital, 70376 Stuttgart, Germany
- Institute of Sports and Sports Sciences, Heidelberg University, 69120 Heidelberg, Germany;
- Correspondence:
| | | | - Michael Schwenk
- Network Aging Research (NAR), Heidelberg University, 69115 Heidelberg, Germany;
- Human Performance Research Centre, Department of Sport Science, University of Konstanz, 78457 Konstanz, Germany
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Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data. Brain Sci 2021; 11:brainsci11111507. [PMID: 34827506 PMCID: PMC8615930 DOI: 10.3390/brainsci11111507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/08/2021] [Accepted: 11/11/2021] [Indexed: 11/22/2022] Open
Abstract
One of the core problems for people with multiple sclerosis (pwMS) is the impairment of their ability to walk, which can be severely restrictive in everyday life. Therefore, monitoring of ambulatory function is of great importance to be able to effectively counteract disease progression. An extensive gait analysis, such as the Dresden protocol for multidimensional walking assessment, covers several facets of walking impairment including a 2-min walk test, in which the distance taken by the patient in two minutes is measured by an odometer. Using this approach, it is questionable how precise the measuring methods are at recording the distance traveled. In this project, we investigate whether the current measurement can be replaced by a digital measurement method based on accelerometers (six Opal sensors from the Mobility Lab system) that are attached to the patient’s body. We developed two algorithms using these data and compared the validity of these approaches using the results from 2-min walk tests from 562 pwMS that were collected with a gold-standard odometer. In 48.4% of pwMS, we detected an average relative measurement error of less than 5%, while results from 25.8% of the pwMS showed a relative measurement error of up to 10%. The algorithm had difficulties correctly calculating the walking distances in another 25.8% of pwMS; these results showed a measurement error of more than 20%. A main reason for this moderate performance was the variety of pathologically altered gait patterns in pwMS that may complicate the step detection. Overall, both algorithms achieved favorable levels of agreement (r = 0.884 and r = 0.980) with the odometer. Finally, we present suggestions for improvement of the measurement system to be implemented in the future.
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Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System. SENSORS 2021; 21:s21227497. [PMID: 34833576 PMCID: PMC8622042 DOI: 10.3390/s21227497] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/13/2021] [Accepted: 11/09/2021] [Indexed: 12/01/2022]
Abstract
Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent validity of a smartphone placed in a front-facing pocket to perform gait analysis. Sixty community-dwelling healthy adults equipped with a smartphone and an application for gait analysis completed a 2-min walk on a marked path. Concurrent validity was assessed against an APDM mobility lab (APDM Inc.; Portland, OR, USA). Bland–Altman plots and intraclass correlation coefficients (agreement and consistency) for gait speed, cadence, and step length indicate good to excellent agreement (ICC2,1 > 0.8). For right leg stance and swing % of gait cycle and double support % of gait cycle, results were moderate (0.52 < ICC2,1 < 0.62). For left leg stance and swing % of gait cycle left results show poor agreement (ICC2,1 < 0.5). Consistency of results was good to excellent for all tested parameters (ICC3,1 > 0.8). Thus we have a valid and reliable instrument for measuring healthy adults’ spatio-temporal gait parameters in a controlled walking environment.
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Trentzsch K, Schumann P, Śliwiński G, Bartscht P, Haase R, Schriefer D, Zink A, Heinke A, Jochim T, Malberg H, Ziemssen T. Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis. Brain Sci 2021; 11:brainsci11081049. [PMID: 34439668 PMCID: PMC8391565 DOI: 10.3390/brainsci11081049] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 11/24/2022] Open
Abstract
In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.
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Affiliation(s)
- Katrin Trentzsch
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
| | - Paula Schumann
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany; (P.S.); (G.Ś.); (A.H.); (T.J.); (H.M.)
| | - Grzegorz Śliwiński
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany; (P.S.); (G.Ś.); (A.H.); (T.J.); (H.M.)
| | - Paul Bartscht
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
| | - Rocco Haase
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
| | - Dirk Schriefer
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
| | - Andreas Zink
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
| | - Andreas Heinke
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany; (P.S.); (G.Ś.); (A.H.); (T.J.); (H.M.)
| | - Thurid Jochim
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany; (P.S.); (G.Ś.); (A.H.); (T.J.); (H.M.)
| | - Hagen Malberg
- Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany; (P.S.); (G.Ś.); (A.H.); (T.J.); (H.M.)
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany; (K.T.); (P.B.); (R.H.); (D.S.); (A.Z.)
- Correspondence: ; Tel.: +49-351-458-4465; Fax: +49-351-458-5717
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