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Welbourn M, Sheriff P, Tuttle PG, Adamowicz L, Psaltos D, Kelekar A, Selig J, Messere A, Mei W, Caouette D, Ghafoor S, Santamaria M, Zhang H, Demanuele C, Karahanoglu FI, Cai X. In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer. Sci Rep 2024; 14:20128. [PMID: 39209869 PMCID: PMC11362325 DOI: 10.1038/s41598-024-67675-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/15/2024] [Indexed: 09/04/2024] Open
Abstract
Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants' self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xuemei Cai
- Pfizer, Inc, Cambridge, MA, USA.
- Tufts Medical Center, Boston, MA, USA.
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Capato TTC, Chen J, Miranda JDA, Chien HF. Assisted technology in Parkinson's disease gait: what's up? ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-10. [PMID: 38395424 PMCID: PMC10890908 DOI: 10.1055/s-0043-1777782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/21/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD. OBJECTIVE To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim. METHODS In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria. RESULTS We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics. CONCLUSION This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.
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Affiliation(s)
- Tamine T. C. Capato
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Nijmegen, The Netherlands.
| | - Janini Chen
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
| | - Johnny de Araújo Miranda
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| | - Hsin Fen Chien
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
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3
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Kluge F, Brand YE, Micó-Amigo ME, Bertuletti S, D'Ascanio I, Gazit E, Bonci T, Kirk C, Küderle A, Palmerini L, Paraschiv-Ionescu A, Salis F, Soltani A, Ullrich M, Alcock L, Aminian K, Becker C, Brown P, Buekers J, Carsin AE, Caruso M, Caulfield B, Cereatti A, Chiari L, Echevarria C, Eskofier B, Evers J, Garcia-Aymerich J, Hache T, Hansen C, Hausdorff JM, Hiden H, Hume E, Keogh A, Koch S, Maetzler W, Megaritis D, Niessen M, Perlman O, Schwickert L, Scott K, Sharrack B, Singleton D, Vereijken B, Vogiatzis I, Yarnall A, Rochester L, Mazzà C, Del Din S, Mueller A. Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study. JMIR Form Res 2024; 8:e50035. [PMID: 38691395 PMCID: PMC11097052 DOI: 10.2196/50035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. OBJECTIVE The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. METHODS Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. RESULTS The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. CONCLUSIONS Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. TRIAL REGISTRATION ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2021-050785.
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Affiliation(s)
- Felix Kluge
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Yonatan E Brand
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Stefano Bertuletti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Francesca Salis
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
- Unit Digitale Geriatrie, Universitätsklinikum Heidelberg, Heidelberg, Germany
| | - Philip Brown
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Joren Buekers
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Carlos Echevarria
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Tilo Hache
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
- Department of Orthopaedic Surgery, Rush Medical College, Chicago, IL, United States
| | - Hugo Hiden
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom
| | | | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Lars Schwickert
- Robert Bosch Gesellschaft für Medizinische Forschung, Stuttgart, Germany
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience, The University of Sheffield, Sheffield, United Kingdom
- Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Arne Mueller
- Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland
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Lin W, Karahanoglu FI, Psaltos D, Adamowicz L, Santamaria M, Cai X, Demanuele C, Di J. Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers? SENSORS (BASEL, SWITZERLAND) 2023; 23:8542. [PMID: 37896635 PMCID: PMC10611403 DOI: 10.3390/s23208542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023]
Abstract
Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.
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Affiliation(s)
- Wenyi Lin
- Pfizer Inc., Cambridge, MA 02139, USA (C.D.); (J.D.)
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Gong NJ, Clifford GD, Esper CD, Factor SA, McKay JL, Kwon H. Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics. SENSORS (BASEL, SWITZERLAND) 2023; 23:8330. [PMID: 37837160 PMCID: PMC10575216 DOI: 10.3390/s23198330] [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: 08/11/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.
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Affiliation(s)
- N. Jabin Gong
- School of Computer Science, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (C.D.E.); (S.A.F.)
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (C.D.E.); (S.A.F.)
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
| | - Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
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Abdul Jabbar K, Mc Ardle R, Lord S, Kerse N, Del Din S, Teh R. Physical Activity in Community-Dwelling Older Adults: Which Real-World Accelerometry Measures Are Robust? A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7615. [PMID: 37688071 PMCID: PMC10490754 DOI: 10.3390/s23177615] [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: 07/18/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
Measurement of real-world physical activity (PA) data using accelerometry in older adults is informative and clinically relevant, but not without challenges. This review appraises the reliability and validity of accelerometry-based PA measures of older adults collected in real-world conditions. Eight electronic databases were systematically searched, with 13 manuscripts included. Intraclass correlation coefficient (ICC) for inter-rater reliability were: walking duration (0.94 to 0.95), lying duration (0.98 to 0.99), sitting duration (0.78 to 0.99) and standing duration (0.98 to 0.99). ICCs for relative reliability ranged from 0.24 to 0.82 for step counts and 0.48 to 0.86 for active calories. Absolute reliability ranged from 5864 to 10,832 steps and for active calories from 289 to 597 kcal. ICCs for responsiveness for step count were 0.02 to 0.41, and for active calories 0.07 to 0.93. Criterion validity for step count ranged from 0.83 to 0.98. Percentage of agreement for walking ranged from 63.6% to 94.5%; for lying 35.6% to 100%, sitting 79.2% to 100%, and standing 38.6% to 96.1%. Construct validity between step count and criteria for moderate-to-vigorous PA was rs = 0.68 and 0.72. Inter-rater reliability and criterion validity for walking, lying, sitting and standing duration are established. Criterion validity of step count is also established. Clinicians and researchers may use these measures with a limited degree of confidence. Further work is required to establish these properties and to extend the repertoire of PA measures beyond "volume" counts to include more nuanced outcomes such as intensity of movement and duration of postural transitions.
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Affiliation(s)
- Khalid Abdul Jabbar
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
| | - Ríona Mc Ardle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.M.A.); (S.D.D.)
| | - Sue Lord
- School of Clinical Sciences, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Ngaire Kerse
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.M.A.); (S.D.D.)
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Ruth Teh
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (K.A.J.); (R.T.)
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Buchman AS, Wang T, Oveisgharan S, Zammit AR, Yu L, Li P, Hu K, Hausdorff JM, Lim ASP, Bennett DA. Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project. SENSORS (BASEL, SWITZERLAND) 2023; 23:4152. [PMID: 37112493 PMCID: PMC10142139 DOI: 10.3390/s23084152] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 06/19/2023]
Abstract
This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.
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Affiliation(s)
- Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Tianhao Wang
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Shahram Oveisgharan
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Andrea R. Zammit
- Rush Alzheimer’s Disease Center, Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Lei Yu
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Peng Li
- Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Kun Hu
- Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey M. Hausdorff
- Rush Alzheimer’s Disease Center, Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrew S. P. Lim
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
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Li P, van Wezel R, He F, Zhao Y, Wang Y. The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review. Front Neuroinform 2023; 17:1135300. [PMID: 37124068 PMCID: PMC10130445 DOI: 10.3389/fninf.2023.1135300] [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: 12/31/2022] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.
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Affiliation(s)
- Peng Li
- Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- *Correspondence: Peng Li,
| | - Richard van Wezel
- Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, United Kingdom
| | - Yifan Zhao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, United Kingdom
| | - Ying Wang
- Biomedical Signals and Systems (BSS) Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, Netherlands
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