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Bode M, Kalbe E, Liepelt-Scarfone I. Cognition and Activity of Daily Living Function in people with Parkinson's disease. J Neural Transm (Vienna) 2024:10.1007/s00702-024-02796-w. [PMID: 38976044 DOI: 10.1007/s00702-024-02796-w] [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: 04/12/2024] [Accepted: 06/08/2024] [Indexed: 07/09/2024]
Abstract
The ability to perform activities of daily living (ADL) function is a multifaceted construct that reflects functionality in different daily life situations. The loss of ADL function due to cognitive impairment is the core feature for the diagnosis of Parkinson's disease dementia (PDD). In contrast to Alzheimer's disease, ADL impairment in PD can be compromised by various factors, including motor and non-motor aspects. This narrative review summarizes the current state of knowledge on the association of cognition and ADL function in people with PD and introduces the concept of "cognitive ADL" impairment for those problems in everyday life that are associated with cognitive deterioration as their primary cause. Assessment of cognitive ADL impairment is challenging because self-ratings, informant-ratings, and performance-based assessments seldomly differentiate between "cognitive" and "motor" aspects of ADL. ADL function in PD is related to multiple cognitive domains, with attention, executive function, and memory being particularly relevant. Cognitive ADL impairment is characterized by behavioral anomalies such as trial-and-error behavior or task step omissions, and is associated with lower engagement in everyday behaviors, as suggested by physical activity levels and prolonged sedentary behavior. First evidence shows that physical and multi-domain interventions may improve ADL function, in general, but the evidence is confounded by motor aspects. Large multicenter randomized controlled trials with cognitive ADL function as primary outcome are needed to investigate which pharmacological and non-pharmacological interventions can effectively prevent or delay deterioration of cognitive ADL function, and ultimately the progression and conversion to PDD.
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Affiliation(s)
- Merle Bode
- Hertie Institute for Clinical Brain Research, Department of Neurodegenerative Diseases, Eberhard Karls University Tübingen, Hoppe-Seyler Str. 3, 72076, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Elke Kalbe
- Medical Psychology | Neuropsychology and Gender Studies & Center for Neuropsychological Diagnostics and Intervention (CeNDI), University Hospital Cologne, Cologne, Germany
- Medical Faculty, University of Cologne, Cologne, Germany
| | - Inga Liepelt-Scarfone
- Hertie Institute for Clinical Brain Research, Department of Neurodegenerative Diseases, Eberhard Karls University Tübingen, Hoppe-Seyler Str. 3, 72076, Tübingen, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
- IB-Hochschule, Stuttgart, Germany.
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Kocuvan P, Hrastič A, Kareska A, Gams M. Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:8294. [PMID: 37837123 PMCID: PMC10575458 DOI: 10.3390/s23198294] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone.
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Affiliation(s)
- Primož Kocuvan
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | | | - Andrea Kareska
- Faculty of Veterinary Medicine, 1000 Ljubljana, Slovenia;
| | - Matjaž Gams
- Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
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Shah VV, Jagodinsky A, McNames J, Carlson-Kuhta P, Nutt JG, El-Gohary M, Sowalsky K, Harker G, Mancini M, Horak FB. Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease. Front Neurol 2023; 14:1096401. [PMID: 36937534 PMCID: PMC10015637 DOI: 10.3389/fneur.2023.1096401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/02/2023] [Indexed: 03/05/2023] Open
Abstract
Objectives To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. Methods We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls. Results Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). Conclusions These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Adam Jagodinsky
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - James McNames
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, United States
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - John G. Nutt
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Mahmoud El-Gohary
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Kristen Sowalsky
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
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Shah VV, McNames J, Carlson‐Kuhta P, Nutt JG, El‐Gohary M, Sowalsky K, Mancini M, Horak FB. Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease. Mov Disord Clin Pract 2023; 10:223-230. [PMID: 36825056 PMCID: PMC9941945 DOI: 10.1002/mdc3.13601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/04/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022] Open
Abstract
Background It is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non-fallers in people with Parkinson's disease (PD). Objectives To investigate which specific digital gait and turning measures, obtained with body-worn sensors, best discriminated fallers from non-fallers with PD in the clinic and during daily life. Methods We recruited 34 subjects with PD (17 fallers and 17 non-fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3-minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3-minute walk and from daily life. Results In clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non-fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non-fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient-reported outcomes. Conclusions Digital measures of turning, but not gait, were different in fallers compared to non-fallers with PD, in the laboratory when off medication and during a daily life.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
| | - James McNames
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
- Department of Electrical and Computer EngineeringPortland State UniversityPortlandOregonUSA
| | | | - John G. Nutt
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
| | | | | | - Martina Mancini
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
| | - Fay B. Horak
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
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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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Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
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Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
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The Forward and Lateral Tilt Angle of the Neck and Trunk Measured by Three-Dimensional Gait and Motion Analysis as a Candidate for a Severity Index in Patients with Parkinson’s Disease. Neurol Int 2022; 14:727-737. [PMID: 36135996 PMCID: PMC9504699 DOI: 10.3390/neurolint14030061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/28/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Objective: To evaluate the usefulness of a three-dimensional motion-analysis system (AKIRA®) as a quantitative measure of motor symptoms in patients with Parkinson’s disease (PD). (2) Method: This study included 48 patients with PD. We measured their motion during 2 m of walking using AKIRA®, we calculated the tilt angles of the neck and trunk, ankle height, and gait speed, then we compared these parameters with the MDS-UPDRS and the Hoehn and Yahr scale. Furthermore, we measured these AKIRA indicators before and after 1 year of observation. (3) Results: The forward tilt angle of the neck showed a strong correlation with the scores on parts II, III, and the total MDS-UPDRS, and the tilt angle of the trunk showed a moderate correlation with those measures. The lateral tilt angle of the trunk showed a moderate correlation with a freezing of the gait and a postural instability. Regarding changes over the course of 1 year (n = 34), the total scores on part III of the MDS-UPDRS and the forward tilt angle of the neck improved, while the lateral tilt angle of the trunk worsened. (4) Conclusion: Taken together, the forward and lateral tilt angles of the neck and trunk as measured by AKIRA® can be a candidate for quantitative severity index in patients with PD.
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Shokouhi N, Khodakarami H, Fernando C, Osborn S, Horne M. Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring. Front Aging Neurosci 2022; 14:904895. [PMID: 35783129 PMCID: PMC9244695 DOI: 10.3389/fnagi.2022.904895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives There are concerns regarding the accuracy of step count in Parkinson’s disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relatively low errors in step count. Materials and Methods A total of 21 normal walkers (10 without PD) and 27 abnormal walkers were videoed while wearing a sensor [Parkinson’s KinetiGraph (PKG)]. Median step count error rates were observed to be <3% in normal walkers but ≥3% in abnormal walkers. The simultaneous accelerometry data and data from a 6-day PKG were examined and revealed that the 5th percentile of the spectral entropy distribution, among 10-s walking epochs (obtained separately), predicted whether subjects had low error rate on step count with reference to the manual step count from the video recording. Subjects with low error rates had lower Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) scores and UPDRS III Q10–14 scores than the high error rate counterparts who also had high freezing of gait scores (i.e., freezing of gait questionnaire). Results Periods when walking occurred were identified in a 6-day PKG from 190 non-PD subjects aged over 60, and 155 people with PD were examined and the 5th percentile of the spectral entropy distribution, among 10-s walking epochs, was extracted. A total of 84% of controls and 72% of people with PD had low predicted error rates. People with PD with low bradykinesia scores (measured by the PKG) had step counts similar to controls, whereas those with high bradykinesia scores had step counts similar to those with high error rates. On subsequent PKGs, step counts increased when bradykinesia was reduced by treatment and decreased when bradykinesia increased. Among both control and people with PD, low error rates were associated with those who spent considerable time making walks of more than 1-min duration. Conclusion Using a measure of the loss of rhythmicity in walking appears to be a useful method for detecting the likelihood of error in step count. Bradykinesia in subjects with low predicted error in their step count is related to overall step count but when the predicted error is high, the step count should be assessed with caution.
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Affiliation(s)
| | | | - Chathurini Fernando
- Parkinson’s Laboratory, Florey Institute of Neurosciences and Mental Health, Parkville, VIC, Australia
- Department of Clinical Neurosciences, St Vincent’s Hospital, Fitzroy, VIC, Australia
| | - Sarah Osborn
- Parkinson’s Laboratory, Florey Institute of Neurosciences and Mental Health, Parkville, VIC, Australia
- Department of Clinical Neurosciences, St Vincent’s Hospital, Fitzroy, VIC, Australia
| | - Malcolm Horne
- Parkinson’s Laboratory, Florey Institute of Neurosciences and Mental Health, Parkville, VIC, Australia
- Department of Clinical Neurosciences, St Vincent’s Hospital, Fitzroy, VIC, Australia
- *Correspondence: Malcolm Horne,
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Burq M, Rainaldi E, Ho KC, Chen C, Bloem BR, Evers LJW, Helmich RC, Myers L, Marks WJ, Kapur R. Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function. NPJ Digit Med 2022; 5:65. [PMID: 35606508 PMCID: PMC9126938 DOI: 10.1038/s41746-022-00607-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/28/2022] [Indexed: 12/30/2022] Open
Abstract
Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
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Affiliation(s)
- Maximilien Burq
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Erin Rainaldi
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - King Chung Ho
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Chen Chen
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Bastiaan R. Bloem
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Luc J. W. Evers
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands ,grid.5590.90000000122931605Radboud University, Institute for Computing and Information Sciences, Nijmegen, the Netherlands
| | - Rick C. Helmich
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Lance Myers
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | | | - Ritu Kapur
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts. SENSORS 2022; 22:s22103859. [PMID: 35632266 PMCID: PMC9143761 DOI: 10.3390/s22103859] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 12/17/2022]
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
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