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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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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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Hatton AL, Chatfield MD, Gane EM, Maharaj JN, Cattagni T, Burns J, Paton J, Rome K, Kerr G. The effects of wearing textured versus smooth shoe insoles for 4-weeks in people with diabetic peripheral neuropathy: a randomised controlled trial. Disabil Rehabil 2024:1-11. [PMID: 38819206 DOI: 10.1080/09638288.2024.2360658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE To determine whether short-term wear of textured insoles alters balance, gait, foot sensation, physical activity, or patient-reported outcomes, in people with diabetic neuropathy. MATERIALS AND METHODS 53 adults with diabetic neuropathy were randomised to wear textured or smooth insoles for 4-weeks. At baseline and post-intervention, balance (foam/firm surface; eyes open/closed) and walking were assessed whilst barefoot, wearing shoes only, and two insoles (textured/smooth). The primary outcome was center of pressure (CoP) total sway velocity. Secondary outcomes included other CoP measures, spatiotemporal gait measures, foot sensation, physical activity, and patient-reported outcomes (foot health, falls efficacy). RESULTS Wearing textured insoles led to improvements in CoP measures when standing on foam with eyes open, relative to smooth insoles (p ≤ 0.04). The intervention group demonstrated a 5% reduction in total sway velocity, indicative of greater balance. The intervention group also showed a 9-point improvement in self-perceived vigour (p = 0.03). Adjustments for multiple comparisons were not applied. CONCLUSIONS This study provides weak statistical evidence in favour of textured insoles. Wearing textured insoles may alter measures of balance, suggestive of greater stability, in people with diabetic neuropathy. Plantar stimulation, through textured insoles, may have the capacity to modulate the perception of foot pain, leading to improved well-being.IMPLICATIONS FOR REHABILITATIONShort-term wear of textured insoles can lead to improvements in centre of pressure sway measures when standing on a compliant supporting surface.Wearing textured insoles may have the capacity to help relieve foot pain leading to enhanced self-perceived vitality in people with diabetic peripheral neuropathy.
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Affiliation(s)
- Anna L Hatton
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Mark D Chatfield
- Centre for Health Sciences Research, The University of Queensland, Brisbane, Australia
| | - Elise M Gane
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Jayishni N Maharaj
- School of Allied Health Sciences, Griffith University, Gold Coast, Australia
| | - Thomas Cattagni
- Laboratory Movement, Interactions, Performance EA 4334, University of Nantes, Nantes, France
| | - Joshua Burns
- Faculty of Medicine and Health & Children's Hospital at Westmead, University of Sydney School of Health Sciences, Sydney, Australia
| | - Joanne Paton
- School of Health Professions, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Keith Rome
- School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Graham Kerr
- Movement Neuroscience Group, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia
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Čepukaitytė G, Newton C, Chan D. Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence. Alzheimers Dement 2024; 20:3054-3073. [PMID: 38425234 PMCID: PMC11032572 DOI: 10.1002/alz.13716] [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: 08/14/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 03/02/2024]
Abstract
Wearable digital technologies capable of measuring everyday behaviors could improve the early detection of dementia-causing diseases. We conducted two systematic reviews following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to establish the evidence base for measuring navigation and gait, two everyday behaviors affected early in AD and non-AD disorders and not adequately measured in current practice. PubMed and Web of Science databases were searched for studies on asymptomatic and early-stage symptomatic individuals at risk of dementia, with the Newcastle-Ottawa Scale used to assess bias and evaluate methodological quality. Of 316 navigation and 2086 gait records identified, 27 and 83, respectively, were included in the final sample. We highlight several measures that may identify at-risk individuals, whose quantifiability with different devices mitigates the risk of future technological obsolescence. Beyond navigation and gait, this review also provides the framework for evaluating the evidence base for future digital measures of behaviors considered for early disease detection.
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Veeranki YR, Garcia-Retortillo S, Papadakis Z, Stamatis A, Appiah-Kubi KO, Locke E, McCarthy R, Torad AA, Kadry AM, Elwan MA, Boolani A, Posada-Quintero HF. Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1425. [PMID: 38474961 DOI: 10.3390/s24051425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/15/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
This study investigated the impact of auditory stimuli on muscular activation patterns using wearable surface electromyography (EMG) sensors. Employing four key muscles (Sternocleidomastoid Muscle (SCM), Cervical Erector Muscle (CEM), Quadricep Muscles (QMs), and Tibialis Muscle (TM)) and time domain features, we differentiated the effects of four interventions: silence, music, positive reinforcement, and negative reinforcement. The results demonstrated distinct muscle responses to the interventions, with the SCM and CEM being the most sensitive to changes and the TM being the most active and stimulus dependent. Post hoc analyses revealed significant intervention-specific activations in the CEM and TM for specific time points and intervention pairs, suggesting dynamic modulation and time-dependent integration. Multi-feature analysis identified both statistical and Hjorth features as potent discriminators, reflecting diverse adaptations in muscle recruitment, activation intensity, control, and signal dynamics. These features hold promise as potential biomarkers for monitoring muscle function in various clinical and research applications. Finally, muscle-specific Random Forest classification achieved the highest accuracy and Area Under the ROC Curve for the TM, indicating its potential for differentiating interventions with high precision. This study paves the way for personalized neuroadaptive interventions in rehabilitation, sports science, ergonomics, and healthcare by exploiting the diverse and dynamic landscape of muscle responses to auditory stimuli.
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Affiliation(s)
| | - Sergi Garcia-Retortillo
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC 27109, USA
| | - Zacharias Papadakis
- College of Health and Wellness, Barry University, Miami Shores, FL 33168, USA
| | - Andreas Stamatis
- Health and Sport Sciences, University of Louisville, Louisville, KY 40292, USA
- Sports Medicine Institute, University of Louisville Health, Louisville, KY 40208, USA
| | | | - Emily Locke
- Department of Public Health, Yale University, New Haven, CT 06520, USA
| | - Ryan McCarthy
- Department of Biology, Clarkson University, Potsdam, NY 13699, USA
- Department of Psychology, Clarkson University, Potsdam, NY 13699, USA
| | - Ahmed Ali Torad
- Department of Physical Therapy, Clarkson University, Potsdam, NY 13699, USA
- Faculty of Physical Therapy, Kafrelsheik University, Kafr El Sheik 33516, Egypt
| | - Ahmed Mahmoud Kadry
- Department of Physical Therapy, Clarkson University, Potsdam, NY 13699, USA
- Faculty of Physical Therapy, Kafrelsheik University, Kafr El Sheik 33516, Egypt
| | - Mostafa Ali Elwan
- Department of Physical Therapy, Clarkson University, Potsdam, NY 13699, USA
- Faculty of Physical Therapy, Beni-Suef University, Beni-Suef 62521, Egypt
| | - Ali Boolani
- Department of Aeronautical and Mechanical Engineering, Clarkson University, Potsdam, NY 13699, USA
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Zukowski LA, Fino PC, Levin I, Hsieh KL, Lockhart SN, Miller ME, Laurienti PJ, Kritchevsky SB, Hugenschmidt CE. Age and beta amyloid deposition impact gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface in older adults. Hum Mov Sci 2024; 93:103175. [PMID: 38198920 PMCID: PMC11195422 DOI: 10.1016/j.humov.2023.103175] [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: 08/30/2023] [Revised: 11/13/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Capturing a measure of movement quality during a complex walking task may indicate the earliest signs of detrimental changes to the brain due to beta amyloid (Aβ) deposition and be a potential differentiator of older adults at elevated and low risk of developing Alzheimer's disease. This study aimed to determine: 1) age-related differences in gait speed, stride length, and gait smoothness while transitioning from an even to an uneven walking surface, by comparing young adults (YA) and older adults (OA), and 2) if gait speed, stride length, and gait smoothness in OA while transitioning from an even to an uneven walking surface is influenced by the amount of Aβ deposition present in an OA's brain. METHODS Participants included 56 OA (>70 years of age) and 29 YA (25-35 years of age). In OA, Aβ deposition in the brain was quantified by PET imaging. All participants completed a series of cognitive assessments, a functional mobility assessment, and self-report questionnaires. Then participants performed two sets of walking trials on a custom-built walkway containing a mixture of even and uneven surface sections, including three trials with a grass uneven surface and three trials with a rocks uneven surface. Gait data were recorded using a wireless inertial measurement unit system. Stride length, gait speed, and gait smoothness (i.e., log dimensionless lumbar jerk) in the anteroposterior (AP), mediolateral (ML), and vertical (VT) directions were calculated for each stride. Outcomes were retained for five stride locations immediately surrounding the surface transition. RESULTS OA exhibited slower gait (Grass: p < 0.001; Rocks: p = 0.006), shorter strides (Grass: p < 0.001; Rocks: p = 0.008), and smoother gait (Grass AP: p < 0.001; Rocks AP: p = 0.002; Rocks ML: p = 0.02) than YA, but they also exhibited greater reductions in gait speed and stride length than YA while transitioning to the uneven grass and rocks surfaces. Within the OA group, those with greater Aβ deposition exhibited decreases in smoothness with age (Grass AP: p = 0.02; Rocks AP: p = 0.03; Grass ML: p = 0.04; Rocks ML: p = 0.03), while those with lower Aβ deposition exhibited increasing smoothness with age (Grass AP: p = 0.01; Rocks AP: p = 0.02; Grass ML: p = 0.08; Rocks ML: p = 0.07). Better functional mobility was associated with less smooth gait (Grass ML: p = 0.02; Rocks ML: p = 0.05) and with less variable gait smoothness (Grass and Rocks AP: both p = 0.04) in the OA group. CONCLUSION These results suggest that, relative to YA, OA may be adopting more cautious, compensatory gait strategies to maintain smoothness when approaching surface transitions. However, OA with greater Aβ deposition may have limited ability to adopt compensatory gait strategies to increase the smoothness of their walking as they get older because of neuropathological changes altering the sensory integration process and causing worse dynamic balance (i.e., jerkier gait). Functional mobility, in addition to age and Aβ deposition, may be an important factor of whether or not an OA chooses to employ compensatory strategies to prioritize smoothness while walking and what type of compensatory strategy an OA chooses.
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Affiliation(s)
- Lisa A Zukowski
- Department of Physical Therapy, High Point University, High Point, NC, United States of America.
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT, United States of America
| | - Ilana Levin
- Department of Physical Therapy, High Point University, High Point, NC, United States of America
| | - Katherine L Hsieh
- Department of Physical Therapy, Georgia State University, Atlanta, GA, United States of America
| | - Samuel N Lockhart
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Michael E Miller
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Stephen B Kritchevsky
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Christina E Hugenschmidt
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
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Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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Suri A, VanSwearingen J, Baillargeon EM, Crane BM, Moored KD, Carlson MC, Dunlap PM, Donahue PT, Redfern MS, Brach JS, Sejdic E, Rosso AL. Association of Gait Quality With Daily-Life Mobility: An Actigraphy and Global Positioning System Based Analysis in Older Adults. IEEE Trans Biomed Eng 2024; 71:130-138. [PMID: 37428666 PMCID: PMC10792545 DOI: 10.1109/tbme.2023.3293752] [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: 07/12/2023]
Abstract
OBJECTIVE Walking is a key component of daily-life mobility. We examined associations between laboratory-measured gait quality and daily-life mobility through Actigraphy and Global Positioning System (GPS). We also assessed the relationship between two modalities of daily-life mobility i.e., Actigraphy and GPS. METHODS In community-dwelling older adults (N = 121, age = 77±5 years, 70% female, 90% white), we obtained gait quality from a 4-m instrumented walkway (gait speed, walk-ratio, variability) and accelerometry during 6-Minute Walk (adaptability, similarity, smoothness, power, and regularity). Physical activity measures of step-count and intensity were captured from an Actigraph. Time out-of-home, vehicular time, activity-space, and circularity were quantified using GPS. Partial Spearman correlations between laboratory gait quality and daily-life mobility were calculated. Linear regression was used to model step-count as a function of gait quality. ANCOVA and Tukey analysis compared GPS measures across activity groups [high, medium, low] based on step-count. Age, BMI, and sex were used as covariates. RESULTS Greater gait speed, adaptability, smoothness, power, and lower regularity were associated with higher step-counts (0.20<|ρp| < 0.26, p < .05). Age(β = -0.37), BMI(β = -0.30), speed(β = 0.14), adaptability(β = 0.20), and power(β = 0.18), explained 41.2% variance in step-count. Gait characteristics were not related to GPS measures. Participants with high (>4800 steps) compared to low activity (steps<3100) spent more time out-of-home (23 vs 15%), more vehicular travel (66 vs 38 minutes), and larger activity-space (5.18 vs 1.88 km2), all p < .05. CONCLUSIONS Gait quality beyond speed contributes to physical activity. Physical activity and GPS-derived measures capture distinct aspects of daily-life mobility. Wearable-derived measures should be considered in gait and mobility-related interventions.
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Sjaelland NS, Gramkow MH, Hasselbalch SG, Frederiksen KS. Digital Biomarkers for the Assessment of Non-Cognitive Symptoms in Patients with Dementia with Lewy Bodies: A Systematic Review. J Alzheimers Dis 2024; 100:431-451. [PMID: 38943394 PMCID: PMC11307079 DOI: 10.3233/jad-240327] [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] [Accepted: 05/14/2024] [Indexed: 07/01/2024]
Abstract
Background Portable digital health technologies (DHTs) could help evaluate non-cognitive symptoms, but evidence to support their use in patients with dementia with Lewy bodies (DLB) is uncertain. Objective 1) To describe portable or wearable DHTs used to obtain digital biomarkers in patients with DLB, 2) to assess the digital biomarkers' ability to evaluate non-cognitive symptoms, and 3) to assess the feasibility of applying digital biomarkers in patients with DLB. Methods We systematically searched databases MEDLINE, Embase, and Web of Science from inception through February 28, 2023. Studies assessing digital biomarkers obtained by portable or wearable DHTs and related to non-cognitive symptoms were eligible if including patients with DLB. The quality of studies was assessed using a modified check list based on the NIH Quality assessment tool for Observational Cohort and Cross-sectional Studies. A narrative synthesis of data was carried out. Results We screened 4,295 records and included 20 studies. Seventeen different DHTs were identified for assessment of most non-cognitive symptoms related to DLB. No thorough validation of digital biomarkers for measurement of non-cognitive symptoms in DLB was reported. Studies did not report on aspects of feasibility in a systematic way. Conclusions Knowledge about feasibility and validity of individual digital biomarkers remains extremely limited. Study heterogeneity is a barrier for establishing a broad evidence base for application of digital biomarkers in DLB. Researchers should conform to recommended standards for systematic evaluation of digital biomarkers.
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Affiliation(s)
- Nikolai S. Sjaelland
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Mathias H. Gramkow
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Steen G. Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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10
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Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann‐Rocha F, Buckley C, Harshfield EL, Iveson MH, Madan CR, McArdle R, Newby D, Orgeta V, Tang E, Tamburin S, Thakur LS, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia-Applied models and digital health. Alzheimers Dement 2023; 19:5872-5884. [PMID: 37496259 PMCID: PMC10955778 DOI: 10.1002/alz.13391] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/19/2023] [Accepted: 05/26/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).
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Affiliation(s)
- Donald M. Lyall
- School of Health and WellbeingCollege of Medical and Veterinary Sciences, University of GlasgowGlasgowUK
| | | | | | - Jose Sousa
- Personal Health Data ScienceSANO‐Centre for Computational Personalised MedicineKrakowPoland
- Faculty of MedicineHealth and Life Science, Queen's University BelfastBelfastUK
| | - Fanny Petermann‐Rocha
- School of Health and WellbeingCollege of Medical and Veterinary Sciences, University of GlasgowGlasgowUK
- Centro de Investigación BiomédicaFacultad de Medicina, Universidad Diego PortalesSantiagoChile
| | - Christopher Buckley
- Department of SportExercise and Rehabilitation, Northumbria UniversityNewcastle upon TyneUK
| | - Eric L. Harshfield
- Stroke Research Group, Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Matthew H. Iveson
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | | | - Ríona McArdle
- Translational and Clinical Research InstituteFaculty of Medical Sciences, Newcastle UniversityNewcastle upon TyneUK
| | | | | | - Eugene Tang
- Translational and Clinical Research InstituteFaculty of Medical Sciences, Newcastle UniversityNewcastle upon TyneUK
| | - Stefano Tamburin
- Department of NeurosciencesBiomedicine and Movement Sciences, University of VeronaVeronaItaly
| | | | | | | | - David J. Llewellyn
- University of Exeter Medical SchoolExeterUK
- Alan Turing InstituteLondonUK
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MacLean MK, Rehman RZU, Kerse N, Taylor L, Rochester L, Del Din S. Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back. SENSORS (BASEL, SWITZERLAND) 2023; 23:8973. [PMID: 37960674 PMCID: PMC10647554 DOI: 10.3390/s23218973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/30/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023]
Abstract
Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.
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Affiliation(s)
- Mhairi K. MacLean
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, 7522 LW Enschede, The Netherlands
| | - Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.Z.U.R.); (L.R.)
| | - Ngaire Kerse
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (N.K.); (L.T.)
| | - Lynne Taylor
- School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand; (N.K.); (L.T.)
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.Z.U.R.); (L.R.)
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
- National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (R.Z.U.R.); (L.R.)
- National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK
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Waldon KT, Stout A, Manning K, Gray L, Wilson DG, Kang GE. Dual-Task Interference Effects on Lower-Extremity Muscle Activities during Gait Initiation and Steady-State Gait among Healthy Young Individuals, Measured Using Wireless Electromyography Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:8842. [PMID: 37960541 PMCID: PMC10647760 DOI: 10.3390/s23218842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023]
Abstract
To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual tasks on lower-limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. The participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and coactivation in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that, during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle coactivation between the swing and stance limbs compared with the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared with the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, the knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.
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Affiliation(s)
- Ke’Vaughn Tarrel Waldon
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA (A.S.)
| | - Angeloh Stout
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA (A.S.)
| | - Kaitlin Manning
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA (A.S.)
| | - Leslie Gray
- Department of Prosthetics-Orthotics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - David George Wilson
- Department of Prosthetics-Orthotics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Gu Eon Kang
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA (A.S.)
- Department of Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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13
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Nichols CJ, Mabrouk SA, Ozmen GC, Gazi AH, Inan OT. Validating Adhesive-Free Bioimpedance of the Leg in Mid-Activity and Uncontrolled Settings. IEEE Trans Biomed Eng 2023; 70:2679-2689. [PMID: 37027282 DOI: 10.1109/tbme.2023.3262206] [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: 03/29/2023]
Abstract
OBJECTIVE Musculoskeletal health monitoring is limited in everyday settings where patient symptoms can substantially change - delaying treatment and worsening patient outcomes. Wearable technologies aim to quantify musculoskeletal health outside clinical settings but sensor constraints limit usability. Wearable localized multi-frequency bioimpedance assessment (MFBIA) shows promise for tracking musculoskeletal health but relies on gel electrodes, hindering extended at-home use. Here, we address this need for usable technologies for at-home musculoskeletal health assessment by designing a wearable adhesive-free MFBIA system using textile electrodes in extended uncontrolled mid-activity settings. METHODS An adhesive-free multimodal wearable leg MFBIA system was developed in-lab under realistic conditions (5 participants, 45 measurements). Mid-activity textile and gel electrode MFBIA was compared across multiple compound movements (10 participants). Accuracy in tracking long-term changes in leg MFBIA was assessed by correlating gel and textile MFBIA simultaneously recorded in uncontrolled settings (10 participants, 80+ measurement hours). RESULTS Mid-activity MFBIA measurements with textile electrodes agreed highly with (ground truth) gel electrode measurements (average [Formula: see text], featuring <1-Ohm differences (0.618 ± 0.340 Ω) across all movements. Longitudinal MFBIA changes were successfully measured in extended at-home settings (repeated measures r = 0.84). Participant responses found the system to be comfortable and intuitive (8.3/10), and all participants were able to don and operate the system independently. CONCLUSION This work demonstrates wearable textile electrodes can be a viable substitute for gel electrodes when monitoring leg MFBIA in dynamic, uncontrolled settings. SIGNIFICANCE Adhesive-free MFBIA can improve healthcare by enabling robust wearable musculoskeletal health monitoring in at-home and everyday settings.
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Cai F, Patharkar A, Wu T, Lure FYM, Chen H, Chen VC. STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning. IEEE SENSORS JOURNAL 2023; 23:10998-11006. [PMID: 37547101 PMCID: PMC10399976 DOI: 10.1109/jsen.2023.3263071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a "digital-twin" of STRIDE, consisting of a human walking simulation model and a micro-Doppler radar simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, shoulder, etc.). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.
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Affiliation(s)
- Fulin Cai
- School of Computing and Augmented Intelligence and ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA
| | - Abhidnya Patharkar
- School of Computing and Augmented Intelligence and ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA
| | - Teresa Wu
- School of Computing and Augmented Intelligence and ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85287, USA
| | | | - Harry Chen
- MS Technologies Corp, Rockville, MD 20580, USA
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15
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Fall history in older adults impacts acceleration profiles after a near collision with a moving pedestrian hazard. Aging Clin Exp Res 2023; 35:621-631. [PMID: 36705894 DOI: 10.1007/s40520-023-02345-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/11/2023] [Indexed: 01/28/2023]
Abstract
BACKGROUND Environmental hazards (e.g., pedestrian traffic) cause falls and testing environment impacts gait in older adults. However, most fall risk evaluations do not assess real-world moving hazard avoidance. AIMS This study examined the effect of fall history in older adults on acceleration profiles before, during, and after a near collision with a moving hazard, in laboratory and real-world settings. METHODS Older adults with (n = 14) and without a fall history (n = 15) performed a collision avoidance walking task with a sudden moving hazard in real-world and laboratory settings. Gait acceleration and video data of participants' first-person views were recorded. Four mixed effects multilevel models analyzed the magnitude and variability of mean and peak anteroposterior and mediolateral acceleration while walking before, during, and after the moving hazard in both environments. RESULTS In the real-world environment, older adults without a fall history increased their mean anteroposterior acceleration after the moving hazard (p = 0.046), but those with a fall history did not (p > 0.05). Older adults without a fall history exhibited more intersubject variability than those with a fall history in mean (p < 0.001) and peak anteroposterior (p = 0.015) acceleration across environments and epochs. Older adults without a fall history exhibited a slower peak mediolateral reaction during the moving hazard (p = 0.014) than those with a fall history. CONCLUSIONS These results suggest that compared to older adults with a fall history, older adults without a fall history are more adaptable and able to respond last-minute to unexpected hazards. Older adults with a fall history exhibited more homogenous responses.
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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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Kirk C, Zia Ur Rehman R, Galna B, Alcock L, Ranciati S, Palmerini L, Garcia-Aymerich J, Hansen C, Schaeffer E, Berg D, Maetzler W, Rochester L, Del Din S, Yarnall AJ. Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease? JOURNAL OF PARKINSON'S DISEASE 2023; 13:999-1009. [PMID: 37545259 PMCID: PMC10578274 DOI: 10.3233/jpd-230044] [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: 07/03/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD). OBJECTIVE Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally. METHODS 88 PD and 111 OA participants from ICICLE-GAIT (UK) were included. RWS was evaluated using an accelerometer at four time points. RWS was aggregated within walking bout (WB) duration thresholds. Between-group-comparisons in RWS between PD and OAs were conducted cross-sectionally, and longitudinally with mixed effects models (MEMs). Cross-sectional association between RWS and MDS-UPDRS III was explored using linear regression, and longitudinal association explored with MEMs. RESULTS RWS was significantly lower in PD (1.04 m/s) in comparison to OAs (1.10 m/s) cross-sectionally. RWS significantly decreased over time for both cohorts and decline was more rapid in PD by 0.02 m/s per year. Significant negative relationship between RWS and the MDS-UPDRS III only existed at a specific WB threshold (30 to 60 s, β= - 3.94 points, p = 0.047). MDS-UPDRS III increased significantly by 1.84 points per year, which was not related to change in RWS. CONCLUSION Digital mobility assessment of gait may add unique information to quantify disease progression remotely, but further validation in research and clinical settings is needed.
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Affiliation(s)
- Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Janssen Research & Development, High Wycombe, UK
| | - Brook Galna
- School of Allied Health (Exercise Science) / Health Futures Institute, Murdoch University, Perth, Australia
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
| | - Saverio Ranciati
- Department of Statistical Science “Paolo Fortunati”, University of Bologna, Bologna, Italy
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering, “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- University Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiologica y Salud Publica (CIBERESP), Barcelona, Spain
| | - Clint Hansen
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Eva Schaeffer
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Daniela Berg
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
- German Centre of Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Walter Maetzler
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundations Trust, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundations Trust, Newcastle upon Tyne, UK
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Baroudi L, Yan X, Newman MW, Barton K, Cain SM, Shorter KA. Investigating walking speed variability of young adults in the real world. Gait Posture 2022; 98:69-77. [PMID: 36057208 DOI: 10.1016/j.gaitpost.2022.08.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/10/2022] [Accepted: 08/15/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Walking speed strongly correlates with health outcomes, making accurate assessment essential for clinical evaluations. However, assessments tend to be conducted over short distances, often in a laboratory or clinical setting, and may not capture natural walking behavior. To address this gap, the following questions are investigated in this work: Is walking speed significantly influenced by the continuity and duration of a walking bout? Can preferred walking speed be inferred by grouping walking bouts using duration and continuity? METHODS We collected two weeks of continuous data from fifteen healthy young adults using a thigh-worn accelerometer and a heart rate monitor. Walking strides were identified and grouped into walking periods. We quantified the duration and the continuity of each walking period. Continuity is used to parameterize changes in stepping rate related to pauses during a bout of walking. Finally, we analyzed the influence of duration and continuity on estimates of stride speed, and examined how the distribution of walking speed varies depending on different walking modes (defined by duration and continuity). RESULTS We found that continuity and duration can be used to explain some of the variability in real-world walking speed (p<0.001). Speeds estimated from long continuous walks with many strides (42% of all recorded strides) had the lowest standard deviation. Walking speed during these bouts was 1.41ms-1 (SD = 0.26ms-1). SIGNIFICANCE Walking behavior in the real world is largely variable. Features of real-world walks, like duration and continuity, can be used to explain some of the variability observed in walking speed. As such, we recommend using long continuous walks to confidently isolate the preferred walking behavior of an individual.
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Affiliation(s)
- Loubna Baroudi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
| | - Xinghui Yan
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Mark W Newman
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Kira Barton
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
| | - K Alex Shorter
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
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Brand YE, Schwartz D, Gazit E, Buchman AS, Gilad-Bachrach R, Hausdorff JM. Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187094. [PMID: 36146441 PMCID: PMC9502704 DOI: 10.3390/s22187094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 05/14/2023]
Abstract
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
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Affiliation(s)
- Yonatan E. Brand
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dafna Schwartz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ran Gilad-Bachrach
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Edmond J. Safra Center for Bioinformatics, Tel-Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL 60612, USA
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Correspondence:
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20
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Natarajan P, Fonseka RD, Sy LW, Maharaj MM, Mobbs RJ. Analysing Gait Patterns in Degenerative Lumbar Spine Disease Using Inertial Wearable Sensors: An Observational Study. World Neurosurg 2022; 163:e501-e515. [PMID: 35398575 DOI: 10.1016/j.wneu.2022.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Using a chest-based inertial wearable sensor, we examined the quantitative gait patterns associated with lumbar disc herniation (LDH), lumbar spinal stenosis (LSS), and chronic mechanical low back pain (CMLBP). 'Pathological gait signatures' were reported as statistically significant group difference (%) from the 'normative' gait values of an age-matched control population. METHODS A sample of patients presenting to the Prince of Wales Private Hospital (Sydney, Australia) with primary diagnoses of LDH, LSS, or CMLBP were recruited. Spatial, temporal, asymmetry, and variability metrics were compared with age-matched (±2 years) control participants recruited from the community. Participants were fitted at the sternal angle with an inertial measurement unit, MetaMotionC, and walked unobserved (at a self-selected pace) for 120 m along an obstacle-free, carpeted hospital corridor. RESULTS LDH, CMLBP, and LSS groups had unique pathological signatures of gait impairment. The LDH group (n = 33) had marked asymmetry in terms of step length, step time, stance, and single-support asymmetry. The LDH group also involved gait variability with increased step length variation. However, distinguishing the CMLBP group (n = 33) was gait variability in terms increased single-support time variation. The gait of participants with LSS (n = 22) was both asymmetric and variable in step length. CONCLUSIONS Wearable sensor-based accelerometry was found to be capable of detecting the gait abnormalities present in patients with LDH, LSS, and CMLBP, when compared to age-matched controls. Objective and quantitative patterns of gait deterioration uniquely varied between these subtypes of lumbar spine disease. With further testing and validation, gait signatures may aid clinical identification of gait-altering pathologies.
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Affiliation(s)
- Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia; NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia; NeuroSpine Surgery Research Group, Sydney, Australia; Wearables and Gait Assessment Research Group, Sydney, Australia.
| | - R Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia; NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia; NeuroSpine Surgery Research Group, Sydney, Australia; Wearables and Gait Assessment Research Group, Sydney, Australia
| | - Luke Wincent Sy
- School of Mathematics, University of New South Wales, Sydney, Australia
| | - Monish Movin Maharaj
- Faculty of Medicine, University of New South Wales, Sydney, Australia; NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia; NeuroSpine Surgery Research Group, Sydney, Australia; Wearables and Gait Assessment Research Group, Sydney, Australia
| | - Ralph Jasper Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia; NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia; NeuroSpine Surgery Research Group, Sydney, Australia; Wearables and Gait Assessment Research Group, Sydney, Australia
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21
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Küderle A, Roth N, Zlatanovic J, Zrenner M, Eskofier B, Kluge F. The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking. PLoS One 2022; 17:e0269567. [PMID: 35679231 PMCID: PMC9182246 DOI: 10.1371/journal.pone.0269567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/24/2022] [Indexed: 11/19/2022] Open
Abstract
Gait analysis using foot-worn inertial measurement units has proven to be a reliable tool to diagnose and monitor many neurological and musculoskeletal indications. However, only few studies have investigated the robustness of such systems to changes in the sensor attachment and no consensus for suitable sensor positions exists in the research community. Specifically for unsupervised real-world measurements, understanding how the reliability of the monitoring system changes when the sensor is attached differently is from high importance. In these scenarios, placement variations are expected because of user error or personal preferences. In this manuscript, we present the largest study to date comparing different sensor positions and attachments. We recorded 9000 strides with motion-capture reference from 14 healthy participants with six synchronized sensors attached at each foot. Spatial gait parameters were calculated using a double-integration method and compared to the reference system. The results indicate that relevant differences in the accuracy of the stride length exists between the sensor positions. While the average error over multiple strides is comparable, single stride errors and variability parameters differ greatly. We further present a physics model and an analysis of the raw sensor data to understand the origin of the observed differences. This analysis indicates that a variety of attachment parameters can influence the systems’ performance. While this is only the starting point to understand and mitigate these types of errors, we conclude that sensor systems and algorithms must be reevaluated when the sensor position or attachment changes.
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Affiliation(s)
- Arne Küderle
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Nils Roth
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jovana Zlatanovic
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Markus Zrenner
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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22
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McGuirk TE, Perry ES, Sihanath WB, Riazati S, Patten C. Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings. Front Hum Neurosci 2022; 16:867485. [PMID: 35754772 PMCID: PMC9224754 DOI: 10.3389/fnhum.2022.867485] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/09/2022] [Indexed: 12/30/2022] Open
Abstract
Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9-87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC2,1) and consistency (ICC3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length, and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.
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Affiliation(s)
- Theresa E. McGuirk
- Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States
- UC Davis Healthy Aging in a Digital World Initiative, a UC Davis “Big Idea”, Sacramento, CA, United States
- Center for Neuroengineering and Medicine, University of California, Davis, Davis, CA, United States
- Veterans Affairs Northern California Health Care System, Martinez, CA, United States
| | - Elliott S. Perry
- Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States
- UC Davis Healthy Aging in a Digital World Initiative, a UC Davis “Big Idea”, Sacramento, CA, United States
- Veterans Affairs Northern California Health Care System, Martinez, CA, United States
| | - Wandasun B. Sihanath
- Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States
- UC Davis Healthy Aging in a Digital World Initiative, a UC Davis “Big Idea”, Sacramento, CA, United States
- Center for Neuroengineering and Medicine, University of California, Davis, Davis, CA, United States
| | - Sherveen Riazati
- Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States
- UC Davis Healthy Aging in a Digital World Initiative, a UC Davis “Big Idea”, Sacramento, CA, United States
- Center for Neuroengineering and Medicine, University of California, Davis, Davis, CA, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States
- UC Davis Healthy Aging in a Digital World Initiative, a UC Davis “Big Idea”, Sacramento, CA, United States
- Center for Neuroengineering and Medicine, University of California, Davis, Davis, CA, United States
- Veterans Affairs Northern California Health Care System, Martinez, CA, United States
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23
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Teipel SJ, Amaefule CO, Lüdtke S, Görß D, Faraza S, Bruhn S, Kirste T. Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment. Front Psychol 2022; 13:882446. [PMID: 35548510 PMCID: PMC9083357 DOI: 10.3389/fpsyg.2022.882446] [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: 02/23/2022] [Accepted: 03/22/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To determine whether gait and accelerometric features can predict disorientation events in young and older adults. Methods Cognitively healthy younger (18–40 years, n = 25) and older (60–85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and Hidden Markov models (HMM). Results Bayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors. Conclusion Disorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.
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Affiliation(s)
- Stefan J Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/Greifswald, Rostock, Germany.,Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Chimezie O Amaefule
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/Greifswald, Rostock, Germany
| | - Stefan Lüdtke
- Mobile Multimedia Information Systems, Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany.,Institute for Enterprise Systems, University of Mannheim, Mannheim, Germany
| | - Doreen Görß
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Sofia Faraza
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Sven Bruhn
- Institute for Sports Science, University of Rostock, Rostock, Germany
| | - Thomas Kirste
- Mobile Multimedia Information Systems, Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
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24
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Henríquez F, Cabello V, Baez S, de Souza LC, Lillo P, Martínez-Pernía D, Olavarría L, Torralva T, Slachevsky A. Multidimensional Clinical Assessment in Frontotemporal Dementia and Its Spectrum in Latin America and the Caribbean: A Narrative Review and a Glance at Future Challenges. Front Neurol 2022; 12:768591. [PMID: 35250791 PMCID: PMC8890568 DOI: 10.3389/fneur.2021.768591] [Citation(s) in RCA: 2] [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: 08/31/2021] [Accepted: 12/13/2021] [Indexed: 12/14/2022] Open
Abstract
Frontotemporal dementia (FTD) is the third most common form of dementia across all age groups and is a leading cause of early-onset dementia. The Frontotemporal dementia (FTD) includes a spectrum of diseases that are classified according to their clinical presentation and patterns of neurodegeneration. There are two main types of FTD: behavioral FTD variant (bvFTD), characterized by a deterioration in social function, behavior, and personality; and primary progressive aphasias (PPA), characterized by a deficit in language skills. There are other types of FTD-related disorders that present motor impairment and/or parkinsonism, including FTD with motor neuron disease (FTD-MND), progressive supranuclear palsy (PSP), and corticobasal syndrome (CBS). The FTD and its associated disorders present great clinical heterogeneity. The diagnosis of FTD is based on the identification through clinical assessments of a specific clinical phenotype of impairments in different domains, complemented by an evaluation through instruments, i.e., tests and questionnaires, validated for the population under study, thus, achieving timely detection and treatment. While the prevalence of dementia in Latin America and the Caribbean (LAC) is increasing rapidly, there is still a lack of standardized instruments and consensus for FTD diagnosis. In this context, it is important to review the published tests and questionnaires adapted and/or validated in LAC for the assessment of cognition, behavior, functionality, and gait in FTD and its spectrum. Therefore, our paper has three main goals. First, to present a narrative review of the main tests and questionnaires published in LAC for the assessment of FTD and its spectrum in six dimensions: (i) Cognitive screening; (ii) Neuropsychological assessment divided by cognitive domain; (iii) Gait assessment; (iv) Behavioral and neuropsychiatric symptoms; (v) Functional assessment; and (vi) Global Rating Scale. Second, to propose a multidimensional clinical assessment of FTD in LAC identifying the main gaps. Lastly, it is proposed to create a LAC consortium that will discuss strategies to address the current challenges in the field.
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Affiliation(s)
- Fernando Henríquez
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Memory and Neuropsychiatric Clinic (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department – Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
- Laboratory for Cognitive and Evolutionary Neuroscience (LaNCE), Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Victoria Cabello
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department – Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Sandra Baez
- Universidad de los Andes, Departamento de Psicología, Bogotá, Colombia
| | - Leonardo Cruz de Souza
- Programa de Pós-Graduação em Neurociências da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
- Departamento de Clínica Médica, Faculdade de Medicina da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Patricia Lillo
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Departamento de Neurología Sur, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Unidad de Neurología, Hospital San José, Santiago, Chile
| | - David Martínez-Pernía
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Memory and Neuropsychiatric Clinic (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Loreto Olavarría
- Memory and Neuropsychiatric Clinic (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department – Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Teresa Torralva
- Institute of Cognitive and Translational Neuroscience (INCYT), Instituto de Neurología Cognitiva Foundation, Favaloro University, Buenos Aires, Argentina
| | - Andrea Slachevsky
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Memory and Neuropsychiatric Clinic (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department – Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana-Universidad del Desarrollo, Santiago, Chile
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25
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Faisal MAA, Chowdhury MEH, Khandakar A, Hossain MS, Alhatou M, Mahmud S, Ara I, Sheikh SI, Ahmed MU. An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning. Comput Biol Med 2022; 142:105184. [PMID: 35016098 DOI: 10.1016/j.compbiomed.2021.105184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/16/2021] [Accepted: 12/26/2021] [Indexed: 11/03/2022]
Abstract
Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses.
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Affiliation(s)
- Md Ahasan Atick Faisal
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Md Shafayet Hossain
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital and Department of Neurology, Alkhor Hospital, Doha, 3050, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Iffat Ara
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Shah Imran Sheikh
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Mosabber Uddin Ahmed
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
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26
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Peraza LR, Kinnunen KM, McNaney R, Craddock IJ, Whone AL, Morgan C, Joules R, Wolz R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2021; 21:8286. [PMID: 34960379 PMCID: PMC8707484 DOI: 10.3390/s21248286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/06/2021] [Accepted: 12/08/2021] [Indexed: 12/29/2022]
Abstract
The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events-bout segmentation, initial contact (IC), and final contact (FC)-from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson's disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56-64.66 and 40.19-72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06-48.42, 40.19-72.70 and 36.06-60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.
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Affiliation(s)
- Luis R. Peraza
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | | | - Roisin McNaney
- Department of Human Centred Computing, Monash University, Clayton, VIC 3800, Australia;
| | - Ian J. Craddock
- Electrical and Electronic Engineering, School of Computer Science, University of Bristol, Bristol BS8 1QU, UK;
| | - Alan L. Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Richard Joules
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | - Robin Wolz
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
- Department of Computing, Imperial College London, London SW7 2AZ, UK
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27
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Weizman Y, Tirosh O, Beh J, Fuss FK, Pedell S. Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312735. [PMID: 34886459 PMCID: PMC8656771 DOI: 10.3390/ijerph182312735] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 11/28/2022]
Abstract
The ability of people living with dementia to walk independently is a key contributor to their overall well-being and autonomy. For this reason, understanding the relationship between dementia and gait is significant. With rapidly emerging developments in technology, wearable devices offer a portable and affordable alternative for healthcare experts to objectively estimate kinematic parameters with great accuracy. This systematic review aims to provide an updated overview and explore the opportunities in the current research on wearable sensors for gait analysis in adults over 60 living with dementia. A systematic search was conducted in the following scientific databases: PubMed, Cochrane Library, and IEEE Xplore. The targeted search identified 1992 articles that were potentially eligible for inclusion, but, following title, abstract, and full-text review, only 6 articles were deemed to meet the inclusion criteria. Most studies performed adequately on measures of reporting, in and out of a laboratory environment, and found that sensor-derived data are successful in their respective objectives and goals. Nevertheless, we believe that additional studies utilizing standardized protocols should be conducted in the future to explore the impact and usefulness of wearable devices in gait-related characteristics such as fall prognosis and early diagnosis in people living with dementia.
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Affiliation(s)
- Yehuda Weizman
- Department of Health and Medical Science, School of Health Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
- Correspondence: ; Tel.: +61-3921-45320
| | - Oren Tirosh
- Department of Health and Medical Science, School of Health Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
| | - Jeanie Beh
- Centre for Design Innovation, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (J.B.); (S.P.)
| | - Franz Konstantin Fuss
- Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany;
| | - Sonja Pedell
- Centre for Design Innovation, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (J.B.); (S.P.)
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28
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Roth N, Wieland GP, Kuderle A, Ullrich M, Gladow T, Marxreiter F, Klucken J, Eskofier BM, Kluge F. Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1932-1935. [PMID: 34891665 DOI: 10.1109/embc46164.2021.9630378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Driven by the advancements of wearable sensors and signal processing algorithms, studies on continuous real-world monitoring are of major interest in the field of clinical gait and motion analysis. While real-world studies enable a more detailed and realistic insight into various mobility parameters such as walking speed, confounding and environmental factors might skew those digital mobility outcomes (DMOs), making the interpretation of results challenging. To consider confounding factors, context information needs to be included in the analysis. In this work, we present a context-aware mobile gait analysis system that can distinguish between gait recorded at home and not at home based on Bluetooth proximity information. The system was evaluated on 9 healthy subjects and 6 Parkinsons disease (PD) patients. The classification of the at home/not at home context reached an average F1-score of 98.2 ± 3.2 %. A context-aware analysis of gait parameters revealed different walking bout length distributions between the two environmental conditions. Furthermore, a reduction of gait speed within the at home context compared to walking not at home of 8.9 ± 9.4 % and 8.7 ±5.9 % on average for healthy and PD subjects was found, respectively. Our results indicate the influence of the recording environment on DMOs and, therefore, emphasize the importance of context in the analysis of continuous motion data. Hence, the presented work contributes to a better understanding of confounding factors for future real-world studies.
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Keren K, Busse M, Fritz NE, Muratori LM, Gazit E, Hillel I, Scheinowitz M, Gurevich T, Inbar N, Omer N, Hausdorff JM, Quinn L. Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease. Front Neurol 2021; 12:719442. [PMID: 34777196 PMCID: PMC8579964 DOI: 10.3389/fneur.2021.719442] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.
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Affiliation(s)
- Karin Keren
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | - Nora E. Fritz
- Departments of Health Care Sciences and Neurology, Wayne State University, Detroit, MI, United States
| | - Lisa M. Muratori
- Department of Physical Therapy, School of Health Technology and Management, Stony Brook University, Stony Brook, NY, United States
- George Huntington's Institute, Muenster, Germany
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Micky Scheinowitz
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
| | - Tanya Gurevich
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Noit Inbar
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Nurit Omer
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Lori Quinn
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States
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30
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Suri A, Rosso AL, VanSwearingen J, Coffman LM, Redfern MS, Brach JS, Sejdić E. Mobility of Older Adults: Gait Quality Measures are associated with Life-Space Assessment Scores. J Gerontol A Biol Sci Med Sci 2021; 76:e299-e306. [PMID: 34038537 DOI: 10.1093/gerona/glab151] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The relation of gait quality to real-life mobility among older adults is poorly understood. This study examined the association between gait quality, consisting of step variability, smoothness, regularity, symmetry and gait speed with the Life-Space Assessment (LSA). METHODS In community-dwelling older adults (N=232, age 77.5±6.6, 65% females), gait quality was derived from: a) an instrumented walkway: gait speed, variability and walk-ratio; and b) accelerometer: signal variability, smoothness, regularity, symmetry, and time-frequency spatiotemporal variables during 6-minute walk. In addition to collecting LSA scores, cognitive functioning, walking-confidence, and falls were recorded. Spearman correlations (speed as covariate) and Random Forest Regression were used to assess associations between gait quality and LSA, and Gaussian-mixture modeling (GMM) was used to cluster participants. RESULTS Spearman correlations of ρp=0.11 (signal amplitude variability ML), ρp=0.15, ρp=-0.13 (symmetry AP-V, ML-AP), ρp=0.16 (power V) and ρ=0.26 (speed), all p<0.05 and marginally related, ρp=-0.12 (regularity V), ρp=0.11 (smoothness AP) and ρp=-0.11 (step-time variability), p<0.1 were obtained. The cross-validated Random Forest model indicated good fit LSA prediction error of 17.77; gait and cognition were greater contributors than age and gender. GMM indicated two clusters. Group-1(N=189) had better gait quality than Group-2(N=43): greater smoothness AP (2.94±0.75 vs 2.30±0.71); greater similarity AP-V (0.58±0.13 vs 0.40±0.19); lower regularity V (0.83±0.08 vs 0.87±0.10); greater power V (1.86±0.18 vs 0.97±1.84); greater speed (1.09±0.16 vs 1.00±0.16 m/s); lower step time CoV (3.70±1.09 vs 5.09±2.37) and better LSA (76±18 vs 67±18), padjusted<0.004. CONCLUSIONS Gait quality measures taken in the clinic are associated with real-life mobility in the community.
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Affiliation(s)
- Anisha Suri
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh
| | - Jessie VanSwearingen
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Leslie M Coffman
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
| | - Jennifer S Brach
- Department of Physical Therapy, School of Rehabilitation Sciences, University of Pittsburgh, Pittsburgh
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh
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