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Ilg W, Milne S, Schmitz-Hübsch T, Alcock L, Beichert L, Bertini E, Mohamed Ibrahim N, Dawes H, Gomez CM, Hanagasi H, Kinnunen KM, Minnerop M, Németh AH, Newman J, Ng YS, Rentz C, Samanci B, Shah VV, Summa S, Vasco G, McNames J, Horak FB. Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1566-1592. [PMID: 37955812 PMCID: PMC11269489 DOI: 10.1007/s12311-023-01625-2] [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] [Accepted: 10/20/2023] [Indexed: 11/14/2023]
Abstract
With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.
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Affiliation(s)
- Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Otfried-Müller-Straße 25, 72076, Tübingen, Germany.
- Centre for Integrative Neuroscience (CIN), Tübingen, Germany.
| | - Sarah Milne
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, Melbourne University, Melbourne, VIC, Australia
- Physiotherapy Department, Monash Health, Clayton, VIC, Australia
- School of Primary and Allied Health Care, Monash University, Frankston, VIC, Australia
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, a cooperation of Max-Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
| | - Lukas Beichert
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Enrico Bertini
- Research Unit of Neuromuscular and Neurodegenerative Disorders, Bambino Gesu' Children's Research Hospital, IRCCS, Rome, Italy
| | | | - Helen Dawes
- NIHR Exeter BRC, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Hasmet Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jane Newman
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Yi Shiau Ng
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Clara Rentz
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
| | - Bedia Samanci
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
| | - Susanna Summa
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Gessica Vasco
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - James McNames
- APDM Precision Motion, Clario, Portland, OR, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
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Shoja O, Shojaei M, Hassanlouei H, Towhidkhah F, Zhang L. Quantifying Human Gait Symmetry During Blindfolded Treadmill Walking. Motor Control 2024; 28:225-240. [PMID: 38402881 DOI: 10.1123/mc.2023-0028] [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: 03/10/2023] [Revised: 12/20/2023] [Accepted: 01/05/2024] [Indexed: 02/27/2024]
Abstract
Bilateral gait symmetry is an essential requirement for normal walking since asymmetric gait patterns increase the risk of falls and injuries. While human gait control heavily relies on the contribution of sensory inputs, the role of sensory systems in producing symmetric gait has remained unclear. This study evaluated the influence of vision as a dominant sensory system on symmetric gait production. Ten healthy adults performed treadmill walking with and without vision. Twenty-two gait parameters including ground reaction forces, joint range of motion, and other spatial-temporal gait variables were evaluated to quantify gait symmetry and compared between both visual conditions. Visual block caused increased asymmetry in most parameters of ground reaction force, however mainly in the vertical direction. When vision was blocked, symmetry of the ankle and knee joint range of motion decreased, but this change did not occur in the hip joint. Stance and swing time symmetry decreased during no-vision walking while no significant difference was found for step length symmetry between the two conditions. This study provides a comprehensive analysis to reveal how the visual system influences bilateral gait symmetry and highlights the important role of vision in gait control. This approach could be applied to investigate how vision alters gait symmetry in patients with disorders to help better understand the role of vision in pathological gaits.
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Affiliation(s)
- Otella Shoja
- Faculty of Sport Sciences, Department of Motor Behavior, Alzahra University, Tehran, Iran
- Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montreal, QC, Canada
- Department of Neuroscience, Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada
| | - Masoumeh Shojaei
- Faculty of Sport Sciences, Department of Motor Behavior, Alzahra University, Tehran, Iran
| | - Hamidollah Hassanlouei
- Faculty of Sport Science and Health, Department of Motor Behavior, Shahid Beheshti University, Tehran, Iran
| | - Farzad Towhidkhah
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Lei Zhang
- Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany
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Mc Ardle R, Taylor L, Cavadino A, Rochester L, Del Din S, Kerse N. Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study. JMIR Aging 2024; 7:e53020. [PMID: 38842168 PMCID: PMC11185191 DOI: 10.2196/53020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 06/07/2024] Open
Abstract
Background Walking is important for maintaining physical and mental well-being in aged residential care (ARC). Walking behaviors are not well characterized in ARC due to inconsistencies in assessment methods and metrics as well as limited research regarding the impact of care environment, cognition, or physical function on these behaviors. It is recommended that walking behaviors in ARC are assessed using validated digital methods that can capture low volumes of walking activity. Objective This study aims to characterize and compare accelerometry-derived walking behaviors in ARC residents across different care levels, cognitive abilities, and physical capacities. Methods A total of 306 ARC residents were recruited from the Staying UpRight randomized controlled trial from 3 care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants' cognitive status was classified as mild (n=87), moderate (n=128), or severe impairment (n=61); physical function was classified as high-moderate (n=74) and low-very low (n=222) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cutoff scores, respectively. To assess walking, participants wore an accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on their lower back for 7 days. Outcomes included volume (ie, daily time spent walking, steps, and bouts), pattern (ie, mean walking bout duration and alpha), and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey honest significant difference tests for multiple comparisons were used to determine where significant differences occurred. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and 0.8: large). Results Dementia care residents showed greater volumes of walking (P<.001; Hedges g=1.0-2.0), with longer (P<.001; Hedges g=0.7-0.8), more variable (P=.008 vs hospital; P<.001 vs rest home; Hedges g=0.6-0.9) bouts compared to other care levels with a lower alpha score (vs hospital: P<.001; Hedges g=0.9, vs rest home: P=.004; Hedges g=0.8). Residents with severe cognitive impairment took longer (P<.001; Hedges g=0.5-0.6), more variable (P<.001; Hedges g=0.4-0.6) bouts, compared to those with mild and moderate cognitive impairment. Residents with low-very low physical function had lower walking volumes (total walk time and bouts per day: P<.001; steps per day: P=.005; Hedges g=0.4-0.5) and higher variability (P=.04; Hedges g=0.2) compared to those with high-moderate capacity. Conclusions ARC residents across different levels of care, cognition, and physical function demonstrate different walking behaviors. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multimorbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern, and variability of walking behaviors among ARC residents.
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Affiliation(s)
- Ríona Mc Ardle
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research Biomedical Research Centre, Newcastle University and the Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lynne Taylor
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Alana Cavadino
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research Biomedical Research Centre, Newcastle University and the Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, United Kingdom
- The Newcastle Upon Tyne Hospitals National Health Institute Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research Biomedical Research Centre, Newcastle University and the Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Ngaire Kerse
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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Hsieh KL, Beavers KM, Weaver AA, Delanie Lynch S, Shaw IB, Kline PW. Real-world data capture of daily limb loading using force-sensing insoles: Feasibility and lessons learned. J Biomech 2024; 166:112063. [PMID: 38564846 PMCID: PMC11046963 DOI: 10.1016/j.jbiomech.2024.112063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
Force-sensing insoles are wearable technology that offer an innovative way to measure loading outside of laboratory settings. Few studies, however, have utilized insoles to measure daily loading in real-world settings. This is an ancillary study of a randomized controlled trial examining the effect of weight loss alone, weight loss plus weighted vest, or weight loss plus resistance training on bone health in older adults. The purpose of this ancillary study was to determine the feasibility of using force-sensing insoles to collect daily limb loading metrics, including peak force, impulse, and loading rate. Forty-four participants completed a baseline visit of three, 2-minute walking trials while wearing force-sensing insoles. During month two of the intervention, 37 participants wore insoles for 4 days for 8 waking hours each day. At 6-month follow-up, participants completed three, two-minute walking trials and a satisfaction questionnaire. Criteria for success in feasibility was defined as: a) > 60 % recruitment rate; b) > 80 % adherence rate; c) > 75 % of usable data, and d) > 75 % participant satisfaction. A 77.3 % recruitment rate was achieved, with 44 participants enrolled. Participants wore their insoles an average of 7.4 hours per day, and insoles recorded an average of 5.5 hours per day. Peak force, impulse, and loading rate collected at baseline and follow-up were 100 % usable. During the real-world settings, 87.8 % of data was deemed usable with an average of 1200 min/participant. Lastly, average satisfaction was 80.5 %. These results suggest that force-sensing insoles appears to be feasible to capture real-world limb loading in older adults.
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Affiliation(s)
- Katherine L Hsieh
- Department of Physical Therapy, Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University.
| | | | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest University School of Medicine
| | - S Delanie Lynch
- Department of Biomedical Engineering, Wake Forest University School of Medicine
| | - Isaac B Shaw
- Department of Physical Therapy, Congdon School of Health Sciences, High Point University
| | - Paul W Kline
- Department of Physical Therapy, Congdon School of Health Sciences, High Point University; Department of Physical Therapy, Virginia Commonwealth University
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Rose MJ, Neogi T, Friscia B, Torabian KA, LaValley MP, Gheller M, Adamowicz L, Georgiev P, Viktrup L, Demanuele C, Wacnik PW, Kumar D. Reliability of Wearable Sensors for Assessing Gait and Chair Stand Function at Home in People With Knee Osteoarthritis. Arthritis Care Res (Hoboken) 2023; 75:1939-1948. [PMID: 36734316 PMCID: PMC10397366 DOI: 10.1002/acr.25096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 01/20/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To assess the reliability of wearable sensors for at-home assessment of walking and chair stand activities in people with knee osteoarthritis (OA). METHODS Baseline data from participants with knee OA (n = 20) enrolled in a clinical trial of an exercise intervention were used. Participants completed an in-person laboratory visit and a video conference-enabled at-home visit. In both visits, participants performed walking and chair stand tasks while fitted with 3 inertial sensors. During the at-home visit, participants self-donned the sensors and completed 2 sets of acquisitions separated by a 15-minute break, when they removed and redonned the sensors. Participants completed a survey on their experience with the at-home visit. During the laboratory visit, researchers placed the sensors on the participants. Spatiotemporal metrics of walking gait and chair stand duration were extracted from the sensor data. We used intraclass correlation coefficients (ICCs) and the Bland-Altman plot for statistical analyses. RESULTS For test-retest reliability during the at-home visit, all ICCs were good to excellent (0.85-0.95). For agreement between at-home and laboratory visits, ICCs were moderate to good (0.59-0.87). Systematic differences were noted between at-home and laboratory data due to faster task speed during the laboratory visits. Participants reported a favorable experience during the at-home visit. CONCLUSION Our method of estimating spatiotemporal gait measures and chair stand duration function remotely was reliable, feasible, and acceptable in people with knee OA. Wearable sensors could be used to remotely assess walking and chair stand in participant's natural environments in future studies.
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Affiliation(s)
- Michael J. Rose
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Tuhina Neogi
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Brian Friscia
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Kaveh A. Torabian
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
| | - Michael P. LaValley
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mary Gheller
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | | | - Lars Viktrup
- Neuroscience, Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Paul W. Wacnik
- Early Clinical Development, Pfizer Inc., Cambridge, MA, USA
| | - Deepak Kumar
- Department of Physical Therapy & Athletic Training, Boston University, Boston, MA, USA
- Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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Schultz MA. The Digital Evolution in Healthcare: What Does It Mean for Nursing? J Nurs Adm 2023; 53:367-369. [PMID: 37463260 DOI: 10.1097/nna.0000000000001301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
The advancement of huge data sets in healthcare has spawned unique insights into care provision. Formerly known as the "Digital Revolution," now more commonly and correctly termed an "evolution," the deployment of Big Data continues to inspire implications for how care is provided, evaluated, and experienced. This commentary reviews foremost developments that offer direction for acquisition of digital competencies by nurses.
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Affiliation(s)
- Mary Anne Schultz
- Author Affiliation: Professor Emerita, California State University, San Bernardino
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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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Bachman SL, Blankenship JM, Busa M, Serviente C, Lyden K, Clay I. Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development. J Alzheimers Dis 2023; 95:379-389. [PMID: 37545234 PMCID: PMC10578291 DOI: 10.3233/jad-230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 08/08/2023]
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.
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Affiliation(s)
| | | | - Michael Busa
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
- Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Corinna Serviente
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
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Mc Ardle R, Hamilton C, Del Din S, Kingston A, Robinson L, Galna B, Thomas AJ, Rochester L. Associations Between Local Area Deprivation and Physical Activity Participation in People with Cognitive Impairment in the North East of England. J Alzheimers Dis 2023; 95:265-273. [PMID: 37483003 PMCID: PMC10578266 DOI: 10.3233/jad-230358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Promoting physical activity, such as habitual walking behaviors, in people with cognitive impairment may support their ability to remain independent with a good quality of life for longer. However, people with cognitive impairment participate in less physical activity compared to cognitively unimpaired older adults. The local area in which people live may significantly impact abilities to participate in physical activity. For example, people who live in more deprived areas may have less safe and walkable routes. OBJECTIVE To examine this further, this study aimed to explore associations between local area deprivation and physical activity in people with cognitive impairment and cognitively unimpaired older adults (controls). METHODS 87 participants with cognitive impairment (mild cognitive impairment or dementia) and 27 older adult controls from the North East of England were included in this analysis. Participants wore a tri-axial wearable accelerometer (AX3, Axivity) on their lower backs continuously for seven days. The primary physical activity outcome was daily step count. Individuals' neighborhoods were linked to UK government area deprivation statistics. Hierarchical Bayesian models assessed the association between local area deprivation and daily step count in people with cognitive impairment and controls. RESULTS Key findings indicated that there was no association between local area deprivation and daily step count in people with cognitive impairment, but higher deprivation was associated with lower daily steps for controls. CONCLUSION These findings suggest that cognitive impairment may be associated with lower participation in physical activity which supersedes the influence of local area deprivation observed in normal aging.
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Affiliation(s)
- Ríona Mc Ardle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Calum Hamilton
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Andrew Kingston
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Louise Robinson
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Brook Galna
- Murdoch Applied Sports Science Laboratory, School of Allied Health, Murdoch University, Perth, Western Australia, Australia;2 Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Alan J. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
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10
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Chepisheva MK. Spatial orientation, postural control and the vestibular system in healthy elderly and Alzheimer's dementia. PeerJ 2023; 11:e15040. [PMID: 37151287 PMCID: PMC10162042 DOI: 10.7717/peerj.15040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/21/2023] [Indexed: 05/09/2023] Open
Abstract
Background While extensive research has been advancing our understanding of the spatial and postural decline in healthy elderly (HE) and Alzheimer's disease (AD), much less is known about how the vestibular system contributes to the spatial and postural processing in these two populations. This is especially relevant during turning movements in the dark, such as while walking in our garden or at home at night, where the vestibular signal becomes central. As the prevention of falls and disorientation are of serious concern for the medical service, more vestibular-driven knowledge is necessary to decrease the burden for HE and AD patients with vestibular disabilities. Overview of the article The review briefly presents the current "non-vestibular based" knowledge (i.e. knowledge based on research that does not mention the "vestibular system" as a contributor or does not investigate its effects) about spatial navigation and postural control during normal healthy ageing and AD pathology. Then, it concentrates on the critical sense of the vestibular system and explores the current expertise about the aspects of spatial orientation and postural control from a vestibular system point of view. The norm is set by first looking at how healthy elderly change with age with respect to their vestibular-guided navigation and balance, followed by the AD patients and the difficulties they experience in maintaining their balance or during navigation. Conclusion Vestibular spatial and vestibular postural deficits present a considerable disadvantage and are felt not only on a physical but also on a psychological level by all those affected. Still, there is a clear need for more (central) vestibular-driven spatial and postural knowledge in healthy and pathological ageing, which can better facilitate our understanding of the aetiology of these dysfunctions. A possible change can start with the more frequent implementation of the "vestibular system examination/rehabilitation/therapy" in the clinic, which can then lead to an improvement of future prognostication and disease outcome for the patients.
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Seifallahi M, Mehraban AH, Galvin JE, Ghoraani B. Alzheimer's Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1589-1600. [PMID: 35675251 PMCID: PMC10771634 DOI: 10.1109/tnsre.2022.3181252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting cognitive and functional abilities. However, many patients presume lower cognitive or functional abilities because of aging and do not undergo clinical assessments until the symptoms become too advanced. Developing a low-cost and easy-to-use AD detection tool, which can be used in any clinical or non-clinical setting, can enable widespread AD assessments and diagnosis. This paper investigated the feasibility of developing such a tool to detect AD vs. healthy control (HC) from a simple balance and walking assessment called the Timed Up and Go (TUG) test. We collected joint position data of 47 HC and 38 AD subjects as they performed TUG in front of a Kinect V.2 camera. Our signal processing and statistical analyses provided a comprehensive analysis of balance and gait with 12 significant features for discriminating AD from HC after adjusting for age and the Geriatric Depression Scale. Using these features and a support vector machine classifier, our model classified the two groups with an average accuracy of 97.75% and an F-score of 97.67% for five-fold cross-validation and 98.68% and 98.67% for leave-one-subject out cross-validation. These results demonstrate the potential of our approach as a new quantitative complementary tool for detecting AD among older adults. Our work is novel as it presents the first application of Kinect V.2 camera and machine learning to provide a comprehensive and quantitative analysis of the TUG test to detect AD patients from HC. This study supports the feasibility of developing a low-cost and convenient AD assessment tool that can be used during routine checkups or even at home; however, future investigations could confirm its clinical diagnostic value in a larger cohort.
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12
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Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards? Eur Geriatr Med 2022; 13:817-824. [PMID: 35243600 PMCID: PMC9378320 DOI: 10.1007/s41999-022-00629-1] [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: 11/16/2021] [Accepted: 02/16/2022] [Indexed: 11/06/2022]
Abstract
Aim To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Findings Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system. Message Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts. Purpose We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system. Methods Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale. Results The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge. Conclusion Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.
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13
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Joo H, Kim H, Ryu JK, Ryu S, Lee KM, Kim SC. Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031279. [PMID: 35162308 PMCID: PMC8835219 DOI: 10.3390/ijerph19031279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 12/14/2022]
Abstract
People who exercise may benefit or be injured depending on their foot striking (FS) style. In this study, we propose an intelligent system that can recognize subtle differences in FS patterns while walking and running using measurements from a wearable smartwatch device. Although such patterns could be directly measured utilizing pressure distribution of feet while striking on the ground, we instead focused on analyzing hand movements by assuming that striking patterns consequently affect temporal movements of the whole body. The advantage of the proposed approach is that FS patterns can be estimated in a portable and less invasive manner. To this end, first, we developed a wearable system for measuring inertial movements of hands and then conducted an experiment where participants were asked to walk and run while wearing a smartwatch. Second, we trained and tested the captured multivariate time series signals in supervised learning settings. The experimental results obtained demonstrated high and robust classification performances (weighted-average F1 score > 90%) when recent deep neural network models, such as 1D-CNN and GRUs, were employed. We conclude this study with a discussion of potential future work and applications that increase benefits while walking and running properly using the proposed approach.
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Affiliation(s)
- Hyeyeoun Joo
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea; (H.J.); (K.-M.L.)
| | - Hyejoo Kim
- Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea;
| | - Jeh-Kwang Ryu
- Department of Physical Education, College of Education, Dongguk University, Seoul 04620, Korea;
| | - Semin Ryu
- Intelligent Robotics Laboratory, School of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Korea;
| | - Kyoung-Min Lee
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea; (H.J.); (K.-M.L.)
| | - Seung-Chan Kim
- Machine Learning Systems Laboratory, Department of Sports Science, Sungkyunkwan University, Suwon 16419, Korea;
- Correspondence: ; Tel.: +82-31-299-6918
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14
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Chung J, Boyle J, Pretzer-Aboff I, Knoefel J, Young HM, Wheeler DC. Using a GPS Watch to Characterize Life-Space Mobility in Dementia: A Dyadic Case Study. J Gerontol Nurs 2021; 47:15-22. [PMID: 34590977 PMCID: PMC10054372 DOI: 10.3928/00989134-20210908-03] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Persons with dementia (PWD) often experience difficulty navigating their environments and performing out-of-home activities. Life-space mobility (LSM) is an effective way of assessing functional levels and independence. We present a dyadic case study to explore the feasibility of using a global positioning system (GPS) watch to measure LSM of a Latino PWD. Methods included travel diary, LSM questionnaire, and qualitative interviews in addition to the GPS-based mobility characterization. GPS data indicated that the PWD made outdoor trips regularly and was active socially, with day-to-day variations. Caregiver and PWD interviews revealed contextual information about mobility patterns captured by other methods. The dyad had positive perceptions of the GPS watch for tracking health and activities. This study demonstrated a use for wearable location tracking technology to support accurate LSM assessment in dementia that can inform nursing practice, policy, and research to promote well-being and delay functional deterioration in PWD. [Journal of Gerontological Nursing, 47(10), 15-22.].
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15
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Muurling M, Rhodius-Meester HFM, Pärkkä J, van Gils M, Frederiksen KS, Bruun M, Hasselbalch SG, Soininen H, Herukka SK, Hallikainen M, Teunissen CE, Visser PJ, Scheltens P, van der Flier WM, Mattila J, Lötjönen J, de Boer C. Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort. J Alzheimers Dis 2021; 76:1061-1070. [PMID: 32597806 PMCID: PMC7505008 DOI: 10.3233/jad-200225] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer’s disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer’s disease pathology. Objective: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer’s disease, and 3) predict cognitive decline. Methods: Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5) in 58 CN, 58 MCI, and 26 dementia participants, while performing a walk and dual task. Ten gait features were calculated from the vertical L5 accelerations, following principal component analysis clustered in four domains, namely pace, rhythm, time variability, and length variability. Cognitive decline over time was measured using MMSE, and CSF biomarkers were available in a sub-group. Results: Linear mixed models showed that dementia patients had lower pace scores than MCI patients and CN subjects (p < 0.05). In addition, we found associations between the rhythm domain and CSF-tau, especially in the dual task. Gait was not associated with CSF Aβ42 levels and cognitive decline over time as measured with the MMSE. Conclusion: These findings suggest that gait — particularly measures related to pace and rhythm — are altered in dementia and have a direct link with measures of neurodegeneration.
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Affiliation(s)
- Marijn Muurling
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Juha Pärkkä
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.,Department of Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Merja Hallikainen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | | | - Casper de Boer
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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16
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Quantifying physical activity in aged residential care facilities: A structured review. Ageing Res Rev 2021; 67:101298. [PMID: 33592308 DOI: 10.1016/j.arr.2021.101298] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/29/2021] [Accepted: 02/09/2021] [Indexed: 01/27/2023]
Abstract
BACKGROUND Engaging aged residential care (ARC) residents with physical activity (PA) may be a useful strategy to decelerate dependence and disability. It is unclear what volume, intensity and patterns of PA ARC residents participate in. This review aims to synthesize the literature to quantify the volume, intensity and pattern of PA that ARC residents participate in across differing care levels (e.g. low, intermediate, high, mixed), and make recommendations for future research. METHODS 30 studies of 48,760 yielded were reviewed using systematic review strategies. RESULTS Questionnaires and technological tools were used to assess PA, with accelerometers employed in 70% of studies. Overall, studies reported low volumes and intensities of PA across all care levels, and suggested limited variation in patterns of PA (e.g. little day-to-day variation in total PA). There was limited inclusion of people with cognitive impairment, potentially causing representativeness bias. Findings were limited by lack of consistency in methodological approaches and PA outcomes. DISCUSSION Based on findings and limitations of current research, we recommend that total volume or low-light intensity PA are more useful interventional outcomes than higher-intensity PA. Researchers also need to consider which methodology and PA outcomes are most useful to quantify PA in ARC residents.
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17
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Kamil RJ, Bakar D, Ehrenburg M, Wei EX, Pletnikova A, Xiao G, Oh ES, Mancini M, Agrawal Y. Detection of Wandering Behaviors Using a Body-Worn Inertial Sensor in Patients With Cognitive Impairment: A Feasibility Study. Front Neurol 2021; 12:529661. [PMID: 33776875 PMCID: PMC7991404 DOI: 10.3389/fneur.2021.529661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 01/25/2021] [Indexed: 11/24/2022] Open
Abstract
Patients with Alzheimer's disease (AD) and AD related dementias (ADRD) often experience spatial disorientation that can lead to wandering behavior, characterized by aimless or purposeless movement. Wandering behavior has been associated with falls, caregiver burden, and nursing home placement. Despite the substantial clinical consequences of wandering, there is currently no standardized approach to objectively quantify wandering behavior. In this pilot feasibility study, we used a lightweight inertial sensor to examine mobility characteristics of a small group of 12 older adults with ADRD and mild cognitive impairment in their homes. Specifically, we evaluated their compliance with wearing a sensor for a minimum of 4 days. We also examined the ability of the sensor to measure turning frequency and direction changes, given that frequent turns and direction changes during walking have been observed in patients who wander. We found that all patients were able to wear the sensor yielding quantitative turn data including number of turns over time, mean turn duration, mean peak turn speed, and mean turn angle. We found that wanderers make more frequent, quicker turns compared to non-wanderers, which is consistent with pacing or lapping behavior. This study provides preliminary evidence that continuous monitoring in patients with dementia is feasible using a wearable sensor. More studies are needed to explore if objective measures of turning behaviors collected using inertial sensors can be used to identify wandering behavior.
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Affiliation(s)
- Rebecca J. Kamil
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Dara Bakar
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, United States
- Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Matthew Ehrenburg
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Eric X. Wei
- Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Alexandra Pletnikova
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Grace Xiao
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Esther S. Oh
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Martina Mancini
- Department of Neurology, Oregon Health and Science University, School of Medicine, Portland, OR, United States
| | - Yuri Agrawal
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, United States
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18
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Vizitiu C, Bîră C, Dinculescu A, Nistorescu A, Marin M. Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living. SENSORS 2021; 21:s21051837. [PMID: 33800728 PMCID: PMC7961703 DOI: 10.3390/s21051837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/10/2021] [Accepted: 03/03/2021] [Indexed: 01/13/2023]
Abstract
In this paper, we present an exhaustive description of an extensible e-Health Internet-connected embedded system, which allows the measurement of three biometric parameters: pulse rate, oxygen saturation and temperature, via several wired and wireless sensors residing to the realm of Noncommunicable Diseases (NCDs) and cognitive assessment through Choice Reaction Time (CRT) analysis. The hardware used is based on ATMEGA AVR + MySignals Hardware printed circuit board (Hardware PCB), but with multiple upgrades (including porting from ATMEGA328P to ATMEGA2560). Multiple software improvements were made (by writing high-level device drivers, text-mode and graphic-mode display driver) for increasing functionality, portability, speed, and latency. A top-level embedded application was developed and benchmarked. A custom wireless AT command firmware was developed, based on ESP8266 firmware to allow AP-mode configuration and single-command JavaScript Object Notation (JSON) data-packet pushing towards the cloud platform. All software is available in a git repository, including the measurement results. The proposed eHealth system provides with specific NCDs and cognitive views fostering the potential to exploit correlations between physiological and cognitive data and to generate predictive analysis in the field of eldercare.
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Affiliation(s)
- Cristian Vizitiu
- Space Applications for Human Health and Safety Department, Institute of Space Science, 077125 Măgurele, Romania; (C.B.); (A.N.); (M.M.)
- Correspondence: (C.V.); (A.D.)
| | - Călin Bîră
- Space Applications for Human Health and Safety Department, Institute of Space Science, 077125 Măgurele, Romania; (C.B.); (A.N.); (M.M.)
- Devices, Circuits, Electronic Architectures, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucharest, Romania
| | - Adrian Dinculescu
- Space Applications for Human Health and Safety Department, Institute of Space Science, 077125 Măgurele, Romania; (C.B.); (A.N.); (M.M.)
- Image Processing and Analysis Laboratory, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucharest, Romania
- Correspondence: (C.V.); (A.D.)
| | - Alexandru Nistorescu
- Space Applications for Human Health and Safety Department, Institute of Space Science, 077125 Măgurele, Romania; (C.B.); (A.N.); (M.M.)
- Faculty of Science Physical Education and Informatics, University of Pitesti, 110040 Pitesti, Romania
| | - Mihaela Marin
- Space Applications for Human Health and Safety Department, Institute of Space Science, 077125 Măgurele, Romania; (C.B.); (A.N.); (M.M.)
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Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration. Sci Rep 2021; 11:3950. [PMID: 33597593 PMCID: PMC7889656 DOI: 10.1038/s41598-021-82694-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 01/21/2021] [Indexed: 01/31/2023] Open
Abstract
Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans.
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20
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Varma VR, Ghosal R, Hillel I, Volfson D, Weiss J, Urbanek J, Hausdorff JM, Zipunnikov V, Watts A. Continuous gait monitoring discriminates community-dwelling mild Alzheimer's disease from cognitively normal controls. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12131. [PMID: 33598530 PMCID: PMC7864220 DOI: 10.1002/trc2.12131] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 11/25/2020] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD. METHODS Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five-fold cross-validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC. RESULTS Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD. DISCUSSION Continuous gait monitoring may be a scalable method to identify individuals at-risk for developing dementia within large, population-based studies.
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Affiliation(s)
- Vijay R. Varma
- Clinical and Translational Neuroscience SectionLaboratory of Behavioral NeuroscienceNational Institute on Aging (NIA)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Rahul Ghosal
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and MobilityTel Aviv Sourasky Medical Center, Neurological InstituteTel AvivIsrael
| | - Dmitri Volfson
- Neuroscience AnalyticsComputational Biology, TakedaCambridgeMassachusettsUSA
| | - Jordan Weiss
- Department of DemographyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Jacek Urbanek
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and MobilityTel Aviv Sourasky Medical Center, Neurological InstituteTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
- Rush Alzheimer's Disease Center and Department of Orthopaedic SurgeryRush University Medical CenterChicagoUSA
- Department of Physical Therapy, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Vadim Zipunnikov
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Amber Watts
- Department of PsychologyUniversity of KansasLawrenceKansasUSA
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21
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Mc Ardle R, Del Din S, Donaghy P, Galna B, Thomas AJ, Rochester L. The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes. SENSORS 2021; 21:s21030813. [PMID: 33530508 PMCID: PMC7865394 DOI: 10.3390/s21030813] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 01/05/2023]
Abstract
Laboratory-based gait assessments are indicative of clinical outcomes (e.g., disease identification). Real-world gait may be more sensitive to clinical outcomes, as impairments may be exaggerated in complex environments. This study aims to investigate how different environments (e.g., lab, real world) impact gait. Different walking bout lengths in the real world will be considered proxy measures of context. Data collected in different dementia disease subtypes will be analysed as disease-specific gait impairments are reported between these groups. Thirty-two people with cognitive impairment due to Alzheimer’s disease (AD), 28 due to dementia with Lewy bodies (DLB) and 25 controls were recruited. Participants wore a tri-axial accelerometer for six 10 m walks in lab settings, and continuously for seven days in the real world. Fourteen gait characteristics across five domains were measured (i.e., pace, variability, rhythm, asymmetry, postural control). In the lab, the DLB group showed greater step length variability (p = 0.008) compared to AD. Both subtypes demonstrated significant gait impairments (p < 0.01) compared to controls. In the real world, only very short walking bouts (<10 s) demonstrated different gait impairments between subtypes. The context where walking occurs impacts signatures of gait impairment in dementia subtypes. To develop real-world gait assessment as a clinical tool, algorithms and metrics must accommodate for changes in context.
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Affiliation(s)
- Ríona Mc Ardle
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
- Correspondence:
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
| | - Paul Donaghy
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
| | - Brook Galna
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
- School of Biomedical, Nutritional and Sport Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK; (S.D.D.); (P.D.); (B.G.); (A.J.T.); (L.R.)
- Newcastle Upon Tyne Hospital NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
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22
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Khoshmanesh F, Thurgood P, Pirogova E, Nahavandi S, Baratchi S. Wearable sensors: At the frontier of personalised health monitoring, smart prosthetics and assistive technologies. Biosens Bioelectron 2020; 176:112946. [PMID: 33412429 DOI: 10.1016/j.bios.2020.112946] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/24/2020] [Accepted: 12/28/2020] [Indexed: 02/07/2023]
Abstract
Wearable sensors have evolved from body-worn fitness tracking devices to multifunctional, highly integrated, compact, and versatile sensors, which can be mounted onto the desired locations of our clothes or body to continuously monitor our body signals, and better interact and communicate with our surrounding environment or equipment. Here, we discuss the latest advances in textile-based and skin-like wearable sensors with a focus on three areas, including (i) personalised health monitoring to facilitate recording physiological signals, body motions, and analysis of body fluids, (ii) smart gloves and prosthetics to realise the sensation of touch and pain, and (iii) assistive technologies to enable disabled people to operate the surrounding motorised equipment using their active organs. We also discuss areas for future research in this emerging field.
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Affiliation(s)
- Farnaz Khoshmanesh
- School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Peter Thurgood
- School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia
| | - Elena Pirogova
- School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, 3217, Australia
| | - Sara Baratchi
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia.
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IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. SENSORS 2020; 20:s20102826. [PMID: 32429331 PMCID: PMC7288187 DOI: 10.3390/s20102826] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 12/23/2022]
Abstract
The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.
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24
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Elderly Body Movement Alteration at 2nd Experience of Digital Art Installation with Cognitive and Motivation Scores. J 2020. [DOI: 10.3390/j3020012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The prevalence of advanced medical treatment has led to global population aging, resulting in increased numbers of dementia patients. One of the most intractable symptoms of dementia is apathy, or lack of interest and enthusiasm, which can accompany memory and cognitive deterioration. Development of a novel method to ameliorate apathy is desirable. In this feasibility trial, we propose a series of digital art installations as a candidate dementia intervention approach. Seven, three-minute scenes of digital images and sounds were presented to visitors either passively or in response to their reactions (motion and sound). We evaluated the potential of this application as an intervention against apathy in an elderly living home. We collected the dementia global standard Mini-Mental State Examination (MMSE) and questionnaire scores of sensory pleasure and motivation along with behavioral motion data in twenty participants. We further compared responses between the first and second experiences in the thirteen participants that were present for both days. Overall, we found a significant increase in participants’ motivation. In these subjects, head and right hand motion increased over different scenes and MMSE degrees, but was most significant during passive scenes. Despite a small number of subjects and limited evaluations, this new digital art technology holds promise as an apathy intervention in the elderly and can be improved with use of wearable motion sensors.
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25
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Ullrich M, Kuderle A, Hannink J, Din SD, Gasner H, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies. IEEE J Biomed Health Inform 2020; 24:1869-1878. [PMID: 32086225 DOI: 10.1109/jbhi.2020.2975361] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached a sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.
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26
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Conrado DJ, Duvvuri S, Geerts H, Burton J, Biesdorf C, Ahamadi M, Macha S, Hather G, Francisco Morales J, Podichetty J, Nicholas T, Stephenson D, Trame M, Romero K, Corrigan B. Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data. Clin Pharmacol Ther 2020; 107:796-805. [PMID: 31955409 DOI: 10.1002/cpt.1782] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 01/06/2020] [Indexed: 12/20/2022]
Abstract
Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.
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Affiliation(s)
| | | | - Hugo Geerts
- In Silico Biosciences, Lexington, Massachusetts, USA
| | | | | | | | | | | | - Juan Francisco Morales
- Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, Argentina
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27
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Buckley C, McArdle R, Galna B, Thomas A, Rochester L, Del Din S. Evaluation of daily walking activity and gait profiles: a novel application of a time series analysis framework. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2482-2485. [PMID: 31946401 DOI: 10.1109/embc.2019.8857250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Wearable technology allows an in-depth analysis of gait behaviour in free-living environments. This investigation aimed to use Alzheimer's disease as an example to apply the time series analysis technique of Statistical Parametric Mapping (SPM) to create daily gait profiles and test if they differed from cognitively intact controls. A framework of macro (habitual walking behaviours) and micro characteristics (spatiotemporal gait variables) characteristics were calculated on an hourly basis. SPM showed that select micro gait characteristics differed from controls at specific hours of the day. Therefore, the application of SPM may provide a more in-depth reflection of activity and gait time-dependent fluctuations than commonly used whole day values. Considering macro and micro gait hour-by- hour may have applications towards disease management, personalized care, monitoring medication and targeted interventions for people with a range of neurodegenerative diseases.
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28
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Morris R, Stuart S, McBarron G, Fino PC, Mancini M, Curtze C. Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson's disease. Physiol Meas 2019; 40:095003. [PMID: 31470423 DOI: 10.1088/1361-6579/ab4023] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Gait provides a sensitive measurement for signs of aging and neurodegenerative conditions. Measurement of gait is transitioning from the laboratory environment to the clinic with the use of inertial measurement units, providing a simple and cost-effective assessment tool. However, such assessments first need validation against reference systems. The aim of this study was to validate the APDM Mobility Lab (ML) system (version 2) against a pressure sensor walkway in younger adults (n = 18), older adults (n = 18) and people with mild Parkinson's disease (n = 21) in the laboratory. APPROACH Participants completed a two-minute walk over a pressure sensor walkway whilst wearing three sensors (strapped to the lumbar spine and both feet). Comparison of output from the systems was then performed. MAIN RESULTS Overall, we identified that ML provided good to excellent agreement (ICC > 0.75) for gait velocity, stride length, stride length SD, cadence, stride time and stride time SD. Measures of double support time, single support time and swing time had moderate to poor agreement (ICC 0.213-0.725), particularly for younger adults and PD. SIGNIFICANCE Overall, Mobility Lab provides a valid system for gait data collection for clinical and research application.
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Affiliation(s)
- Rosie Morris
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States of America
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29
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Taylor ME, Brodie MA, van Schooten KS, Delbaere K, Close JC, Payne N, Webster L, Chow J, McInerney G, Kurrle SE, Lord SR. Older People with Dementia Have Reduced Daily-Life Activity and Impaired Daily-Life Gait When Compared to Age-Sex Matched Controls. J Alzheimers Dis 2019; 71:S125-S135. [DOI: 10.3233/jad-181174] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Morag E. Taylor
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- Cognitive Decline Partnership Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Matthew A. Brodie
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Kimberley S. van Schooten
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Kim Delbaere
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Jacqueline C.T. Close
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Narelle Payne
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
| | - Lyndell Webster
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
| | - Jessica Chow
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
| | - Garth McInerney
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
| | - Susan E. Kurrle
- Cognitive Decline Partnership Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Stephen R. Lord
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, University of New South Wales, Sydney, NSW, Australia
- School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
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30
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Mc Ardle R, Del Din S, Donaghy P, Galna B, Thomas A, Rochester L. Factors That Influence Habitual Activity in Mild Cognitive Impairment and Dementia. Gerontology 2019; 66:197-208. [DOI: 10.1159/000502288] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/23/2019] [Indexed: 11/19/2022] Open
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31
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Koychev I, Galna B, Zetterberg H, Lawson J, Zamboni G, Ridha BH, Rowe JB, Thomas A, Howard R, Malhotra P, Ritchie C, Lovestone S, Rochester L. Aβ42/Aβ40 and Aβ42/Aβ38 Ratios Are Associated with Measures of Gait Variability and Activities of Daily Living in Mild Alzheimer's Disease: A Pilot Study. J Alzheimers Dis 2019; 65:1377-1383. [PMID: 30198873 PMCID: PMC6218125 DOI: 10.3233/jad-180622] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Gait disturbances are some of the earliest changes in dementia and their monitoring presents an opportunity for early diagnosis. The exact relationship between gait and well-established biomarkers of Alzheimer’s disease (AD) remains to be clarified. In this study we compared gait-related measures with cerebrospinal fluid (CSF) markers of AD pathology. We recruited seventeen participants with mild AD in a multi-site study and performed gait assessment as well as lumbar punctures to obtain CSF. CSF Aβ42/Aβ40 and Aβ42/Aβ38 correlated positively with measures of variability (step time and step length) in the clinic-based assessments. This was driven by a negative relationship between gait variability and Aβ40 and Aβ38 but not Aβ42.The amyloid ratios and gait variability measures were also associated with more severe functional impairment. We interpret these data as an indication that increasing amyloid production (i.e., increasing Aβ40 and Aβ38) is associated with diminishing cognitive-motor control of gait. These preliminary results suggest that the two amyloid ratios may be a marker of the earliest disturbances in the interplay between cognitive and motor control which characterize dementia.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, UK
| | - Brook Galna
- Institute of Neuroscience / Institute for Ageing, Newcastle University, Newcastle, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, University College London Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute, London, UK.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | | | - Giovanna Zamboni
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Italy.,Center for Neurosciences and Neurotechnology, University of Modena and Reggio Emilia, Italy
| | - Basil H Ridha
- NIHR Biomedical Research Centre, University College London, UK
| | - James B Rowe
- Neurosciences, University of Cambridge, UK and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Alan Thomas
- Institute of Neuroscience / Institute for Ageing, Newcastle University, Newcastle, UK
| | - Robert Howard
- Department of Molecular Neuroscience, University College London; Institute of Neurology, Queen Square, London, UK
| | | | | | | | - Lynn Rochester
- Institute of Neuroscience / Institute for Ageing, Newcastle University, Newcastle, UK
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32
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Godfrey A, Brodie M, van Schooten KS, Nouredanesh M, Stuart S, Robinson L. Inertial wearables as pragmatic tools in dementia. Maturitas 2019; 127:12-17. [PMID: 31351515 DOI: 10.1016/j.maturitas.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 01/02/2023]
Abstract
Dementia is a critically important issue due to its wide impact on health services as well as its personal and societal costs. Limitations exist for current dementia protocols, and there are calls to introduce modern technology that facilitates the addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data on habitual activities, but their utility in modern medicine remains low. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology; they offer a low-cost approach to quantify routine movements that are fundamental to normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to aid dementia research. We outline the history of wearables, from their humble beginnings to their current use beyond the clinic, and explore their integration into modern systems, as well as the ongoing standardisation and regulatory efforts to integrate their use in clinical trials.
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Affiliation(s)
- A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
| | - M Brodie
- Falls Balance & Injury Research Centre, Neuroscience Research Australia, NSW, Australia; Graduate School of Biomedical Engineering, University of New South Wales, NSW, Australia
| | - K S van Schooten
- Neuroscience Research Australia, University of New South Wales, Sydney, Australia; School of Public Health and Community Medicine, University of New South Wales, NSW, Australia
| | - M Nouredanesh
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
| | - S Stuart
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - L Robinson
- Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
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33
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Koychev I, Lawson J, Chessell T, Mackay C, Gunn R, Sahakian B, Rowe JB, Thomas AJ, Rochester L, Chan D, Tom B, Malhotra P, Ballard C, Chessell I, Ritchie CW, Raymont V, Leroi I, Lengyel I, Murray M, Thomas DL, Gallacher J, Lovestone S. Deep and Frequent Phenotyping study protocol: an observational study in prodromal Alzheimer's disease. BMJ Open 2019; 9:e024498. [PMID: 30904851 PMCID: PMC6475176 DOI: 10.1136/bmjopen-2018-024498] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION Recent failures of potential novel therapeutics for Alzheimer's disease (AD) have prompted a drive towards clinical studies in prodromal or preclinical states. However, carrying out clinical trials in early disease stages is extremely challenging-a key reason being the unfeasibility of using classical outcome measures of dementia trials (eg, conversion to dementia) and the lack of validated surrogate measures so early in the disease process. The Deep and Frequent Phenotyping (DFP) study aims to resolve this issue by identifying a set of markers acting as indicators of disease progression in the prodromal phase of disease that could be used as indicative outcome measures in proof-of-concept trials. METHODS AND ANALYSIS The DFP study is a repeated measures observational study where participants will be recruited through existing parent cohorts, research interested lists/databases, advertisements and memory clinics. Repeated measures of both established (cognition, positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) markers of pathology, structural MRI markers of neurodegeneration) and experimental modalities (functional MRI, magnetoencephalography and/or electroencephalography, gait measurement, ophthalmological and continuous smartphone-based cognitive and other assessments together with experimental CSF, blood, tear and saliva biomarkers) will be performed. We will be recruiting male and female participants aged >60 years with prodromal AD, defined as absence of dementia but with evidence of cognitive impairment together with AD pathology as assessed using PET imaging or CSF biomarkers. Control participants without evidence of AD pathology will be included at a 1:4 ratio. ETHICS AND DISSEMINATION The study gained favourable ethical opinion from the South Central-Oxford B NHS Research Ethics Committee (REC reference 17/SC/0315; approved on 18 August 2017; amendment 13 February 2018). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.
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Affiliation(s)
- Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Tharani Chessell
- IMED Neuroscience, AstraZeneca UK Ltd, Cambridge, Cambridgeshire, UK
| | - Clare Mackay
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Roger Gunn
- Invicro, London, UK
- Department of Medicine, Imperial College London, London, UK
| | - Barbara Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Alan J Thomas
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Dennis Chan
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, Cambridge, Cambridgeshire, UK
| | - Brian Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Paresh Malhotra
- Department of Neurology, Imperial College London Faculty of Medicine, London, UK
| | | | - Iain Chessell
- IMED Neuroscience, AstraZeneca UK Ltd, Cambridge, Cambridgeshire, UK
| | - Craig W Ritchie
- Department of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Vanessa Raymont
- Department of Psychiatry, University of Oxford, Oxford, UK
- Department of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Iracema Leroi
- Manchester Academic Health Sciences Centre, Institute of Brain, Behaviour, and Mental Health, Manchester, UK
| | | | | | - David L Thomas
- Leonard Wolfson Experimental Neurology Centre, University College London Institute of Neurology, London, London, UK
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
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34
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Buckley C, Alcock L, McArdle R, Rehman RZU, Del Din S, Mazzà C, Yarnall AJ, Rochester L. The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control. Brain Sci 2019; 9:E34. [PMID: 30736374 PMCID: PMC6406749 DOI: 10.3390/brainsci9020034] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 01/31/2019] [Indexed: 12/22/2022] Open
Abstract
Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.
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Affiliation(s)
- Christopher Buckley
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
| | - Lisa Alcock
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
| | - Ríona McArdle
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
| | - Rana Zia Ur Rehman
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
| | - Silvia Del Din
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
| | - Claudia Mazzà
- Department of Mechanical Engineering, Sheffield University, Sheffield S1 3JD, UK.
| | - Alison J Yarnall
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK.
| | - Lynn Rochester
- Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK.
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