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Albrecht F, Johansson H, Ekman U, Poulakis K, Bezuidenhout L, Pereira JB, Franzén E. Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease. Sci Rep 2024; 14:9513. [PMID: 38664471 PMCID: PMC11045833 DOI: 10.1038/s41598-024-60050-5] [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] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.
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Affiliation(s)
- Franziska Albrecht
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden.
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden.
| | - Hanna Johansson
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
- Stockholm Sjukhem Foundation, Stockholm, Sweden
| | - Urban Ekman
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Medical Psychology, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Poulakis
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lucian Bezuidenhout
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
| | - Joana B Pereira
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | - Erika Franzén
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Alfred Nobels Allé 23, 141 52, Huddinge, Stockholm, Sweden
- Medical Unit Occupational Therapy & Physiotherapy, Women's Health and Allied Health Professionals Theme, Karolinska University Hospital, Stockholm, Sweden
- Stockholm Sjukhem Foundation, Stockholm, Sweden
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2
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Baudendistel ST, Franz JR, Schmitt AC, Wade FE, Pappas MC, Au KLK, Hass CJ. Visual feedback improves propulsive force generation during treadmill walking in people with Parkinson disease. J Biomech 2024; 167:112073. [PMID: 38599018 PMCID: PMC11046741 DOI: 10.1016/j.jbiomech.2024.112073] [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: 08/29/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
Persons with Parkinson's disease experience gait alterations, such as reduced step length. Gait dysfunction is a significant research priority as the current treatments targeting gait impairment are limited. This study aimed to investigate the effects of visual biofeedback on propulsive force during treadmill walking in persons with Parkinson's. Sixteen ambulatory persons with Parkinson's participated in the study. They received real-time biofeedback of anterior ground reaction force during treadmill walking at a constant speed. Peak propulsive force values were measured and normalized to body weight. Spatiotemporal parameters were also assessed, including stride length and double support percent. Persons with Parkinson's significantly increased peak propulsive force during biofeedback compared to baseline (p <.0001, Cohen's dz = 1.69). Variability in peak anterior ground reaction force decreased across repeated trials (p <.0001, dz = 1.51). While spatiotemporal parameters did not show significant changes individually, stride length and double support percent improved marginally during biofeedback trials. Persons with Parkinson's can increase propulsive force with visual biofeedback, suggesting the presence of a propulsive reserve. Though stride length did not significantly change, clinically meaningful improvements were observed. Targeting push-off force through visual biofeedback may offer a potential rehabilitation technique to enhance gait performance in Persons with Parkinson's. Future studies could explore the long-term efficacy of this intervention and investigate additional strategies to improve gait in Parkinson's disease.
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Affiliation(s)
- Sidney T Baudendistel
- Program in Physical Therapy, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA.
| | - Jason R Franz
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | - Abigail C Schmitt
- Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, AR, USA
| | - Francesca E Wade
- School of Exercise and Nutritional Sciences, San Diego State University, San Diego, CA, USA
| | - Marc C Pappas
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA
| | | | - Chris J Hass
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA; Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
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Longhurst JK, Rider JV, Cummings JL, John SE, Poston B, Landers MR. Cognitive-motor dual-task interference in Alzheimer's disease, Parkinson's disease, and prodromal neurodegeneration: A scoping review. Gait Posture 2023; 105:58-74. [PMID: 37487365 DOI: 10.1016/j.gaitpost.2023.07.277] [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] [Received: 11/22/2021] [Revised: 12/20/2022] [Accepted: 07/13/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Cognitive-motor interference (CMI) is a common deficit in Alzheimer's (AD) disease and Parkinson's disease (PD) and may have utility in identification of prodromal neurodegeneration. There is lack of consensus regarding measurement of CMI resulting from dual task paradigms. RESEARCH QUESTION How are individuals with AD, PD, and prodromal neurodegeneration impacted by CMI as measured by dual-task (DT) performance? METHODS A systematic literature search was performed in six datasets using the PRISMA guidelines. Studies were included if they had samples of participants with AD, PD, or prodromal neurodegeneration and reported at least one measure of cognitive-motor DT performance. RESULTS 4741 articles were screened and 95 included as part of this scoping review. Articles were divided into three non-mutually exclusive groups based on diagnoses, with 26 articles in AD, 56 articles in PD, and 29 articles in prodromal neurodegeneration, and results presented accordingly. SIGNIFICANCE Individuals with AD and PD are both impacted by CMI, though the impact is likely different for each disease. We found a robust body of evidence regarding the utility of measures of DT performance in the detection of subtle deficits in prodromal AD and some signals of utility in prodromal PD. There are several key methodological challenges related to DT paradigms for the measurement of CMI in neurodegeneration. Overall, DT paradigms show good potential as a clinical method to probe specific brain regions, networks, and function; however, task selection and effect measurement should be carefully considered.
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Affiliation(s)
- Jason K Longhurst
- Department of Physical Therapy and Athletic Training, Saint Louis University, 3437 Caroline St. Suite, 1011 St. Louis, MO, USA.
| | - John V Rider
- School of Occupational Therapy, Touro University Nevada, Henderson, NV, USA; Department of Physical Therapy, University of Nevada, Las Vegas, NV, USA.
| | | | - Samantha E John
- Department of Brain Health, University of Nevada, Las Vegas, NV, USA.
| | - Brach Poston
- Department of Kinesiology and Nutrition, University of Nevada, Las Vegas, NV, USA.
| | - Merrill R Landers
- Department of Physical Therapy, University of Nevada, Las Vegas, NV, USA.
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4
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Payne T, Appleby M, Buckley E, van Gelder LM, Mullish BH, Sassani M, Dunning MJ, Hernandez D, Scholz S, McNeil A, Libri V, Moll S, Marchesi JR, Taylor R, Su L, Mazzà C, Jenkins TM, Foltynie T, Bandmann O. A Double-Blind, Randomized, Placebo-Controlled Trial of Ursodeoxycholic Acid (UDCA) in Parkinson's Disease. Mov Disord 2023; 38:1493-1502. [PMID: 37246815 PMCID: PMC10527073 DOI: 10.1002/mds.29450] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 05/30/2023] Open
Abstract
BACKGROUND Rescue of mitochondrial function is a promising neuroprotective strategy for Parkinson's disease (PD). Ursodeoxycholic acid (UDCA) has shown considerable promise as a mitochondrial rescue agent across a range of preclinical in vitro and in vivo models of PD. OBJECTIVES To investigate the safety and tolerability of high-dose UDCA in PD and determine midbrain target engagement. METHODS The UP (UDCA in PD) study was a phase II, randomized, double-blind, placebo-controlled trial of UDCA (30 mg/kg daily, 2:1 randomization UDCA vs. placebo) in 30 participants with PD for 48 weeks. The primary outcome was safety and tolerability. Secondary outcomes included 31-phosphorus magnetic resonance spectroscopy (31 P-MRS) to explore target engagement of UDCA in PD midbrain and assessment of motor progression, applying both the Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS-III) and objective, motion sensor-based quantification of gait impairment. RESULTS UDCA was safe and well tolerated, and only mild transient gastrointestinal adverse events were more frequent in the UDCA treatment group. Midbrain 31 P-MRS demonstrated an increase in both Gibbs free energy and inorganic phosphate levels in the UDCA treatment group compared to placebo, reflecting improved ATP hydrolysis. Sensor-based gait analysis indicated a possible improvement of cadence (steps per minute) and other gait parameters in the UDCA group compared to placebo. In contrast, subjective assessment applying the MDS-UPDRS-III failed to detect a difference between treatment groups. CONCLUSIONS High-dose UDCA is safe and well tolerated in early PD. Larger trials are needed to further evaluate the disease-modifying effect of UDCA in PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Thomas Payne
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
| | - Matthew Appleby
- NIHR UCLH Clinical Research Facility – Leonard
Wolfson Experimental Neurology Centre, National Hospital for Neurology &
Neurosurgery, London, WC1N 3BG, United Kingdom
- Department of Clinical and Movement Neurosciences,
Institute of Neurology, University College London, London, WC1N 3BG, United
Kingdom
| | - Ellen Buckley
- Department of Mechanical Engineering and Insigneo Institute
for In Silico Medicine, The University of Sheffield, Sheffield, S1 3JD, United
Kingdom
| | - Linda M.A. van Gelder
- Department of Mechanical Engineering and Insigneo Institute
for In Silico Medicine, The University of Sheffield, Sheffield, S1 3JD, United
Kingdom
| | - Benjamin H. Mullish
- Division of Digestive Diseases, Department of Metabolism,
Digestion and Reproduction, St Mary’s Hospital Campus, Imperial College
London, London, W2 1NY, United Kingdom
| | - Matilde Sassani
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
| | - Mark J. Dunning
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
- The Bioinformatics Core, Sheffield Institute of
Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, United
Kingdom
| | - Dena Hernandez
- Molecular Genetics Section, Laboratory of Neurogenetics,
NIA, NIH, Bethesda, Maryland, MD 20814, USA
| | - Sonja Scholz
- Neurodegenerative Diseases Research Unit, Laboratory of
Neurogenetics, National Institute of Neurological Disorders and Stroke, National
Institutes of Health, Bethesda, Maryland, MD 20814, USA
- Department of Neurology, Johns Hopkins University Medical
Center, Baltimore, Maryland, MD 21287, USA
| | - Alisdair McNeil
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
| | - Vincenzo Libri
- NIHR UCLH Clinical Research Facility – Leonard
Wolfson Experimental Neurology Centre, National Hospital for Neurology &
Neurosurgery, London, WC1N 3BG, United Kingdom
| | - Sarah Moll
- NIHR Sheffield Biomedical Research Centre, Royal
Hallamshire Hospital, Sheffield, S10 2JF United Kingdom
| | - Julian R. Marchesi
- Division of Digestive Diseases, Department of Metabolism,
Digestion and Reproduction, St Mary’s Hospital Campus, Imperial College
London, London, W2 1NY, United Kingdom
| | - Rosie Taylor
- Statistical Services Unit, The University of Sheffield,
Sheffield, S3 7RH, United Kingdom
| | - Li Su
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
- Department of Psychiatry, University of Cambridge, CB2
0SP United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute
for In Silico Medicine, The University of Sheffield, Sheffield, S1 3JD, United
Kingdom
| | - Thomas M. Jenkins
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
- Royal Perth Hospital, Victoria Square, Perth, WA 6000,
Australia
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences,
Institute of Neurology, University College London, London, WC1N 3BG, United
Kingdom
| | - Oliver Bandmann
- Sheffield Institute for Translational Neuroscience,
University of Sheffield, Sheffield, S10 2HQ, United Kingdom
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5
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Wang H, Hu B, Huang J, Chen L, Yuan M, Tian X, Shi T, Zhao J, Huang W. Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics. Front Neurol 2023; 14:1172320. [PMID: 37388552 PMCID: PMC10303817 DOI: 10.3389/fneur.2023.1172320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD. Methodology A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device. Results PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%. Conclusion Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.
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Affiliation(s)
- Hui Wang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Binbin Hu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Juan Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lin Chen
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Min Yuan
- Department of Neurology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xingfu Tian
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Shi
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiahao Zhao
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Amboni M, Ricciardi C, Adamo S, Nicolai E, Volzone A, Erro R, Cuoco S, Cesarelli G, Basso L, D'Addio G, Salvatore M, Pace L, Barone P. Machine learning can predict mild cognitive impairment in Parkinson's disease. Front Neurol 2022; 13:1010147. [PMID: 36468069 PMCID: PMC9714435 DOI: 10.3389/fneur.2022.1010147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/12/2022] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI). AIM Finding significant features associated with PD-MCI through a machine learning approach. PATIENTS AND METHODS Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model. RESULTS Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2. CONCLUSIONS This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.
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Affiliation(s)
- Marianna Amboni
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
- IDC Hermitage-Capodimonte, Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples, Italy
- Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Telese Terme, Italy
| | - Sarah Adamo
- Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples, Italy
- Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Telese Terme, Italy
| | | | - Antonio Volzone
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
| | - Giuseppe Cesarelli
- Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Telese Terme, Italy
- Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, Italy
| | | | - Giovanni D'Addio
- Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Telese Terme, Italy
| | | | - Leonardo Pace
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy
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Gait alterations in Parkinson’s disease at the stage of hemiparkinsonism—A longitudinal study. PLoS One 2022; 17:e0269886. [PMID: 35862311 PMCID: PMC9302743 DOI: 10.1371/journal.pone.0269886] [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: 02/09/2022] [Accepted: 05/29/2022] [Indexed: 11/19/2022] Open
Abstract
Background
Progressive gait impairment in Parkinson’s disease (PD) leads to significant disability. Quantitative gait parameters analysis provides valuable information about fine gait alterations.
Objectives
To analyse change of gait parameters in patients with early PD at the stage of hemiparkinsonism and after 1 year of follow up, taking into account clinical asymmetry.
Methods
Consecutive early PD outpatients with strictly unilateral motor features underwent clinical and neuropsychological assessment at the study entry and after 1 year of follow up. Gait was assessed with GAITRite walkway using dual-task methodology. Spatiotemporal gait parameters (step time and length, swing time and double support time) and their coefficients of variation (CV), gait velocity and heel-to-heel base support were evaluated.
Results
We included 42 PD patients with disease duration of 1.3 years (±1.13). Progression of motor and non-motor symptoms, without significant cognitive worsening, was observed after 1 year of follow up. Significant shortening of the swing time, prolongation of the double support and increase of their CVs were observed during all task conditions similarly for most parameters on symptomatic and asymptomatic bodysides, except for CV for the swing time under the combined task.
Conclusion
Alterations of the swing time and double support time are already present even at the asymptomatic body side, and progress similarly, or even at faster pace, at this side, despite dopaminergic treatment These parameters deserve further investigation in larger, prospective studies to address their potential to serve as markers of progression in interventional disease modifying trials with early PD patients.
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8
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Russo M, Amboni M, Volzone A, Ricciardelli G, Cesarelli G, Ponsiglione AM, Barone P, Romano M, Ricciardi C. Interplay between gait and neuropsychiatric symptoms in Parkinson’s Disease. Eur J Transl Myol 2022; 32. [PMID: 35678506 PMCID: PMC9295172 DOI: 10.4081/ejtm.2022.10463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2022] [Indexed: 12/13/2022] Open
Abstract
Parkinson’s Disease (PD) is a neurodegenerative disease which involves both motor and non-motor symptoms. Non-motor mental symptoms are very common among patients with PD since the earliest stage. In this context, gait analysis allows to detect quantitative gait variables to distinguish patients affected by non-motor mental symptoms from patients without these symptoms. A cohort of 68 PD subjects (divided in two groups) was acquired through gait analysis (single and double task) and spatial temporal parameters were analysed; first with a statistical analysis and then with a machine learning (ML) approach. Single-task variables showed that 9 out of 16 spatial temporal features were statistically significant for the univariate statistical analysis (p-value< 0.05). Indeed, a statistically significant difference was found in stance phase (p-value=0.032), swing phase (p-value=0.042) and cycle length (p-value=0.03) of the dual task. The ML results confirmed the statistical analysis, in particular, the Decision Tree classifier showed the highest accuracy (80.9%) and also the highest scores in terms of specificity and precision. Our findings indicate that patients with non-motor mental symptoms display a worse gait pattern, mainly dominated by increased slowness and dynamic instability.
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9
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Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis. SENSORS 2022; 22:s22103700. [PMID: 35632109 PMCID: PMC9148133 DOI: 10.3390/s22103700] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
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10
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Troisi Lopez E, Minino R, Sorrentino P, Manzo V, Tafuri D, Sorrentino G, Liparoti M. Sensitivity to gait improvement after levodopa intake in Parkinson's disease: A comparison study among synthetic kinematic indices. PLoS One 2022; 17:e0268392. [PMID: 35551300 PMCID: PMC9098031 DOI: 10.1371/journal.pone.0268392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 04/28/2022] [Indexed: 02/07/2023] Open
Abstract
The synthetic indices are widely used to describe balance and stability during gait. Some of these are employed to describe the gait features in Parkinson's disease (PD). However, the results are sometimes inconsistent, and the same indices are rarely used to compare the individuals affected by PD before and after levodopa intake (OFF and ON condition, respectively). Our aim was to investigate which synthetic measure among Harmonic Ratio, Jerk Ratio, Golden Ratio and Trunk Displacement Index is representative of gait stability and harmony, and which of these are more sensitive to the variations between OFF and ON condition. We found that all indices, except the Jerk Ratio, significantly improve after levodopa. Only the improvement of the Trunk Displacement Index showed a direct correlation with the motor improvement measured through the clinical scale UPDRS-III (Unified Parkinson's Disease Rating Scale-part III). In conclusion, we suggest that the synthetic indices can be useful to detect motor changes induced by, but not all of them clearly correlate with the clinical changes achieved with the levodopa administration. In our analysis, only the Trunk Displacement Index was able to show a clear relationship with the PD clinical motor improvement.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Pierpaolo Sorrentino
- Institut de Neuroscience des Systemès, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
| | - Valentino Manzo
- Alzheimer Unit and Movement Disorders Clinic, Department of Neurology, Cardarelli Hospital, Naples, Italy
| | - Domenico Tafuri
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli (NA), Italy
- Institute for Diagnosis and Care, Hermitage Capodimonte, Naples, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples “Parthenope”, Naples, Italy
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11
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Amboni M, Ricciardi C, Cuoco S, Donisi L, Volzone A, Ricciardelli G, Pellecchia MT, Santangelo G, Cesarelli M, Barone P. Mild Cognitive Impairment Subtypes Are Associated With Peculiar Gait Patterns in Parkinson's Disease. Front Aging Neurosci 2022; 14:781480. [PMID: 35299943 PMCID: PMC8923162 DOI: 10.3389/fnagi.2022.781480] [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: 09/22/2021] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Background Mild cognitive impairment (MCI) is frequent in Parkinson's disease (PD) and represents a risk factor for the development of dementia associated with PD (PDD). Since PDD has been associated with disability, caregiver burden, and an increase in health-related costs, early detection of MCI associated with PD (PD-MCI) and its biomarkers is crucial. Objective Given that gait is considered a surrogate marker for cognitive decline in PD, the aim of this study was to compare gait patterns in PD-MCI subtypes in order to verify the existence of an association between specific gait features and particular MCI subtypes. Methods A total of 67 patients with PD were consecutively enrolled and assessed by an extensive clinical and cognitive examination. Based on the neuropsychological examination, patients were diagnosed as patients with MCI (PD-MCI) and without MCI (no-PD-MCI) and categorized in MCI subtypes. All patients were evaluated using a motion capture system of a BTS Bioengineering equipped with six IR digital cameras. Gait of the patients was assessed in the ON-state under three different tasks (a single task and two dual tasks). Statistical analysis included the t-test, the Kruskal-Wallis test with post hoc analysis, and the exploratory correlation analysis. Results Gait pattern was poorer in PD-MCI vs. no-PD-MCI in all tasks. Among PD-MCI subtypes, multiple-domain PD-MCI and amnestic PD-MCI were coupled with worse gait patterns, notably in the dual task. Conclusion Both the magnitude of cognitive impairment and the presence of memory dysfunction are associated with increased measures of dynamic unbalance, especially in dual-task conditions, likely mirroring the progressive involvement of posterior cortical networks.
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Affiliation(s)
- Marianna Amboni
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
- Istituto di Diagnosi e Cura (IDC) Hermitage-Capodimonte, Naples, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
- Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Leandro Donisi
- Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Antonio Volzone
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Gianluca Ricciardelli
- Department of Medicine, Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi D’Aragona, Salerno, Italy
| | - Maria Teresa Pellecchia
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Gabriella Santangelo
- Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy
| | - Mario Cesarelli
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
- Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
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12
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Tao P, Shao X, Zhuang J, Wang Z, Dong Y, Shen X, Guo Y, Shu X, Wang H, Xu Y, Li Z, Adams R, Han J. Translation, Cultural Adaptation, and Reliability and Validity Testing of a Chinese Version of the Freezing of Gait Questionnaire (FOGQ-CH). Front Neurol 2021; 12:760398. [PMID: 34887830 PMCID: PMC8649621 DOI: 10.3389/fneur.2021.760398] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/22/2021] [Indexed: 01/26/2023] Open
Abstract
Freezing of gait is a disabling symptom with a complex episodic nature that is frequently experienced by people with Parkinson's disease (PD). Although China has the largest population with PD in the world, no Chinese version of the freezing of gait questionnaire (FOGQ), the instrument that has been most widely used to assess FOG, has yet been developed. This study aimed to translate and adapt the original version of FOGQ to create a Chinese version, the FOGQ-CH, then assess its reliability, calculate the Minimal Detectable Change (MDC) and investigate its validity. The forward-backwards translation model was adopted, and cultural adaptation included expert review and pretesting. For the reliability study, 31 Chinese native speaking patients with PD were assessed two times in a 7–10 days interval. Internal consistency and test-retest reliability of the FOGQ-CH were measured by Cronbach's alpha (Cα) and the Intraclass Correlation Coefficient (ICC). For the validity study, 34 native speakers of Chinese with PD were included. To explore the convergent validity, relationships between the FOGQ-CH and the Unified Parkinson's Disease Rating Scale Part II (UPDRS II) and Part III (UPDRS III), Timed Up and Go Test (TUGT), Timed Up and Go Test in cognitive task (TUGT-Cog), walking speed (10 MWT speed), and step length (10 MWT step length) in a 10-m Walk Test were tested. To explore predictive validity, the number of falls followed up for 6 months were assessed. The area under the ROC curve (AUC) was employed to test the capacity of FOGQ-CH to discriminate those with falls. From the reliability study, Cα = 0.823, ICC = 0.786. The MDC0.90 = 4.538. From the validity study, the FOGQ-CH showed moderate correlations with UPDRS II (rho = 0.560, p = 0.001), UPDRS III (rho = 0.451, p = 0.007), TUGT (rho = 0.556, p = 0.007), TUGT-Cog (rho = 0.557, p = 0.001), 10MWT-speed (rho = −0.478, p = 0.004), 10MWT-step length (rho = −0.419, p = 0.014), and the number of falls followed up for 6 months (rho = 0.356, p = 0.045). The AUC = 0.777 (p = 0.036) for predicting whether the participants will have multiple falls (two or more) in the following 6 months. The FOGQ-CH showed good reliability and validity for assessing Chinese native speaking patients with PD. In addition, the FOGQ-CH showed good efficacy for predicting multiple falls in the following 6 months.
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Affiliation(s)
- Ping Tao
- School of Kinesiology, Shanghai University of Sport, Shanghai, China.,School of Medicine, Jinhua Polytechnic, Jinhua, China
| | - Xuerong Shao
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Jie Zhuang
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Zhen Wang
- School of Martial Arts, Shanghai University of Sport, Shanghai, China
| | - Yuchen Dong
- School of Medicine, Jinhua Polytechnic, Jinhua, China
| | - Xia Shen
- School of Medicine, Tongji University, Shanghai, China.,Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Shanghai, China
| | - Yunjie Guo
- Department of Rehabilitation Medicine, Shenzhen Samii International Medical Center (The Fourth People's Hospital of Shenzhen), Shenzhen, China
| | - Xiaoyi Shu
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Hong Wang
- College of Rehabilitation Science, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yuanhong Xu
- Rehabilitation Department, Affiliated Taihe Hospital of Hubei University of Medicine, Shiyan, China
| | - Zhenlan Li
- School of Kinesiology, Shanghai University of Sport, Shanghai, China.,Department of Rehabilitation Sciences, Ningbo College of Health Sciences, Ningbo, China
| | - Roger Adams
- Research Institute for Sports and Exercise, University of Canberra, Canberra, ACT, Australia
| | - Jia Han
- School of Kinesiology, Shanghai University of Sport, Shanghai, China.,Research Institute for Sports and Exercise, University of Canberra, Canberra, ACT, Australia.,Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VIC, Australia
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13
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Polhemus A, Delgado-Ortiz L, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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14
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Rucco R, Lardone A, Liparoti M, Troisi Lopez E, De Micco R, Tessitore A, Granata C, Mandolesi L, Sorrentino G, Sorrentino P. Brain networks and cognitive impairment in Parkinson's disease. Brain Connect 2021; 12:465-475. [PMID: 34269602 DOI: 10.1089/brain.2020.0985] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Aim The aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson's disease (PD). Introduction PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods Starting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. Conclusion Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.
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Affiliation(s)
- Rosaria Rucco
- University of Naples - Parthenope, 18993, Department of Motor Sciences and Wellness, Napoli, Campania, Italy.,Eduardo Caianiello Institute for Applied Science and Intelligent Systems National Research Council, 96973, Pozzuoli, Campania, Italy;
| | - Anna Lardone
- University of Rome La Sapienza Department of Developmental and Social Psychology, 247818, Roma, Lazio, Italy;
| | - Marianna Liparoti
- University of Naples - Parthenope, 18993, Department of Motor Sciences and Wellness, Napoli, Campania, Italy;
| | - Emahnuel Troisi Lopez
- University of Naples - Parthenope, 18993, Department of Motor Sciences and Wellness, Napoli, Campania, Italy;
| | - Rosa De Micco
- University of Campania Luigi Vanvitelli Department of Advanced Medical and Surgical Sciences, 217742, Napoli, Campania, Italy;
| | - Alessandro Tessitore
- University of Campania Luigi Vanvitelli Department of Advanced Medical and Surgical Sciences, 217742, Napoli, Campania, Italy;
| | - Carmine Granata
- Eduardo Caianiello Institute for Applied Science and Intelligent Systems National Research Council, 96973, Pozzuoli, Campania, Italy;
| | - Laura Mandolesi
- University of Naples Federico II, 9307, Department of Humanistic Studies, Napoli, Campania, Italy;
| | - Giuseppe Sorrentino
- University of Naples - Parthenope, 18993, Department of Motor and Wellness Sciences, Via Medina 40, 3, Napoli, Italy, 80133.,Institute of Diagnosis and Treatment Hermitage Capodimont, Naples, Campania, Italy.,National Research Council Research Area Naples 3 - Pozzuoli, 462880, Institute of Applied Sciences and Intelligent Systems , Pozzuoli, Campania, Italy;
| | - Pierpaolo Sorrentino
- Eduardo Caianiello Institute for Applied Science and Intelligent Systems National Research Council, 96973, Pozzuoli, Campania, Italy.,Aix-Marseille Universite, 128791, Institut de Neurosciences des Systèmes, Marseille, France;
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15
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Picillo M, Ricciardi C, Tepedino MF, Abate F, Cuoco S, Carotenuto I, Erro R, Ricciardelli G, Russo M, Cesarelli M, Barone P, Amboni M. Gait Analysis in Progressive Supranuclear Palsy Phenotypes. Front Neurol 2021; 12:674495. [PMID: 34177779 PMCID: PMC8224759 DOI: 10.3389/fneur.2021.674495] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/29/2021] [Indexed: 11/13/2022] Open
Abstract
The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD.
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Affiliation(s)
- Marina Picillo
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Maria Francesca Tepedino
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Filomena Abate
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Immacolata Carotenuto
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Gianluca Ricciardelli
- Department of Medicine, Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi D'Aragona, Salerno, Italy
| | - Michela Russo
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Mario Cesarelli
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Marianna Amboni
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy.,Istituto di Diagnosi e Cura (IDC) Hermitage-Capodimonte, Naples, Italy
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16
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Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson's Disease. SENSORS 2021; 21:s21103449. [PMID: 34063468 PMCID: PMC8156709 DOI: 10.3390/s21103449] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/08/2021] [Accepted: 05/13/2021] [Indexed: 12/24/2022]
Abstract
The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQAdet in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability.
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17
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Amboni M, Ricciardi C, Picillo M, De Santis C, Ricciardelli G, Abate F, Tepedino MF, D'Addio G, Cesarelli G, Volpe G, Calabrese MC, Cesarelli M, Barone P. Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages. Sci Rep 2021; 11:9297. [PMID: 33927317 PMCID: PMC8084977 DOI: 10.1038/s41598-021-88877-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/16/2021] [Indexed: 12/22/2022] Open
Abstract
Progressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait evaluation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.
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Affiliation(s)
- Marianna Amboni
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy. .,IDC Hermitage-Capodimonte, Naples, Italy.
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Marina Picillo
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Chiara De Santis
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Gianluca Ricciardelli
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Filomena Abate
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Maria Francesca Tepedino
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | | | - Giuseppe Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Naples, Italy
| | - Giampiero Volpe
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Maria Consiglia Calabrese
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Mario Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Paolo Barone
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
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18
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Troisi Lopez E, Minino R, Sorrentino P, Rucco R, Carotenuto A, Agosti V, Tafuri D, Manzo V, Liparoti M, Sorrentino G. A synthetic kinematic index of trunk displacement conveying the overall motor condition in Parkinson's disease. Sci Rep 2021; 11:2736. [PMID: 33531608 PMCID: PMC7854606 DOI: 10.1038/s41598-021-82348-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 01/15/2021] [Indexed: 12/21/2022] Open
Abstract
Parkinson's disease (PD) is characterized by motor impairment, affecting quality of life and increasing fall risk, due to ineffective postural control. To this day, the diagnosis remains based on clinical approach. Similarly, motor evaluation is based on heterogeneous, operator-dependent observational criteria. A synthetic, replicable index to quantify motor impairment is still lacking. Hence, we have designed a new measure of postural stability which assesses the trunk displacement in relation to the center of mass, that we named trunk displacement index (TDI). Twenty-three PD patients and twenty-three healthy controls underwent motor examination through a stereophotogrammetric system. A correlation analysis was performed to assess the relationship of TDI with gait parameters and clinical motor scale (UPDRS-III). The TDI sensitivity was estimated, comparing pre- and post- L-DOPA subclinical dose intake. The TDI showed significant correlations with many gait parameters and with the UPDRS-III. Furthermore, the TDI resulted capable in discriminating between off and on state in PD, whereas gait parameters failed two show any difference between those two conditions. Our results suggest that the TDI may be considered a highly sensitive biomechanical index, reflecting the overall motor condition in PD, and provided of clinical relevance due to the correlation with the clinical evaluation.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Roberta Minino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Pierpaolo Sorrentino
- Institut de Neurosciences Des Systemès, Aix-Marseille University, Marseille, France
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy
| | - Rosaria Rucco
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy
| | - Anna Carotenuto
- Alzheimer Unit and Movement Disorders Clinic, Department of Neurology, Cardarelli Hospital, Naples, Italy
| | - Valeria Agosti
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
| | - Domenico Tafuri
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
| | - Valentino Manzo
- Alzheimer Unit and Movement Disorders Clinic, Department of Neurology, Cardarelli Hospital, Naples, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy.
| | - Giuseppe Sorrentino
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy
- Institute for Diagnosis and Care, Hermitage Capodimonte, Naples, Italy
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19
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Morris R, Mancin M. Lab-on-a-chip: wearables as a one stop shop for free-living assessments. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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20
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Abel B, Bongartz M, Eckert T, Ullrich P, Beurskens R, Mellone S, Bauer JM, Lamb SE, Hauer K. Will We Do If We Can? Habitual Qualitative and Quantitative Physical Activity in Multi-Morbid, Older Persons with Cognitive Impairment. SENSORS 2020; 20:s20247208. [PMID: 33339293 PMCID: PMC7766414 DOI: 10.3390/s20247208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/04/2020] [Accepted: 12/14/2020] [Indexed: 11/16/2022]
Abstract
This study aimed to identify determinants of quantitative dimensions of physical activity (PA; duration, frequency, and intensity) in community-dwelling, multi-morbid, older persons with cognitive impairment (CI). In addition, qualitative and quantitative aspects of habitual PA have been described. Quantitative PA and qualitative gait characteristics while walking straight and while walking turns were documented by a validated, sensor-based activity monitor. Univariate and multiple linear regression analyses were performed to delineate associations of quantitative PA dimensions with qualitative characteristics of gait performance and further potential influencing factors (motor capacity measures, demographic, and health-related parameters). In 94 multi-morbid, older adults (82.3 ± 5.9 years) with CI (Mini-Mental State Examination score: 23.3 ± 2.4), analyses of quantitative and qualitative PA documented highly inactive behavior (89.6% inactivity) and a high incidence of gait deficits, respectively. The multiple regression models (adjusted R2 = 0.395–0.679, all p < 0.001) identified specific qualitative gait characteristics as independent determinants for all quantitative PA dimensions, whereas motor capacity was an independent determinant only for the PA dimension duration. Demographic and health-related parameters were not identified as independent determinants. High associations between innovative, qualitative, and established, quantitative PA performances may suggest gait quality as a potential target to increase quantity of PA in multi-morbid, older persons.
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Affiliation(s)
- Bastian Abel
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Center for Geriatric Medicine, Heidelberg University, 69126 Heidelberg, Germany
| | - Martin Bongartz
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Network Aging Research (NAR), Heidelberg University, 69115 Heidelberg, Germany
| | - Tobias Eckert
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Department for Social and Health Sciences in Sport, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Phoebe Ullrich
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
| | - Rainer Beurskens
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Department of Health and Social Affairs, FHM Bielefeld, University of Applied Sciences, 33602 Bielefeld, Germany
| | - Sabato Mellone
- Department of Electrical, Electronic, and Information Engineering, University of Bologna, 40136 Bologna, Italy;
| | - Jürgen M. Bauer
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Center for Geriatric Medicine, Heidelberg University, 69126 Heidelberg, Germany
| | - Sallie E. Lamb
- Institute of Health Research, University of Exeter, South Cloisters, St. Luke’s Campus, Exeter EX1 2LU, UK;
| | - Klaus Hauer
- Department of Geriatric Research, AGAPLESION Bethanien Hospital Heidelberg, Geriatric Center at the University of Heidelberg, 69126 Heidelberg, Germany; (B.A.); (M.B.); (T.E.); (P.U.); (R.B.); (J.M.B.)
- Correspondence: ; Tel.: +49-6221-319-1532
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21
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Ehgoetz Martens KA, Matar E, Shine JM, Phillips JR, Georgiades MJ, Grunstein RR, Halliday GM, Lewis SJ. The Neural Signature of Impaired
Dual‐Tasking
in Idiopathic Rapid Eye Movement Sleep Behavior Disorder Patients. Mov Disord 2020; 35:1596-1606. [DOI: 10.1002/mds.28114] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/29/2020] [Accepted: 05/01/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Kaylena A. Ehgoetz Martens
- ForeFront Research Team, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Department of Kinesiology University of Waterloo Ontario Canada
| | - Elie Matar
- ForeFront Research Team, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
| | - James M. Shine
- ForeFront Research Team, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
| | - Joseph R. Phillips
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- School of Social Sciences and Psychology Western Sydney University Sydney New South Wales Australia
| | - Matthew J. Georgiades
- ForeFront Research Team, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Department of Kinesiology University of Waterloo Ontario Canada
| | - Ron R. Grunstein
- Sleep and Circadian Group (CIRUS) Woolcock Institute of Medical Research, Glebe, New South Wales, Australia; and University of Sydney and Royal Prince Alfred Hospital Sydney New South Wales Australia
- Department of Kinesiology University of Waterloo Ontario Canada
| | - Glenda M. Halliday
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
| | - Simon J.G. Lewis
- ForeFront Research Team, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
- Parkinson's Disease Research Clinic, Brain and Mind Centre University of Sydney Camperdown New South Wales Australia
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22
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Li H, Derrode S, Pieczynski W. An adaptive and on-line IMU-based locomotion activity classification method using a triplet Markov model. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.06.081] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Ricciardi C, Amboni M, De Santis C, Improta G, Volpe G, Iuppariello L, Ricciardelli G, D'Addio G, Vitale C, Barone P, Cesarelli M. Using gait analysis' parameters to classify Parkinsonism: A data mining approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 180:105033. [PMID: 31445485 DOI: 10.1016/j.cmpb.2019.105033] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/08/2019] [Accepted: 08/11/2019] [Indexed: 06/10/2023]
Abstract
INTRODUCTION Parkinson's disease (PD) is the second most common neurodegenerative disorder in the world, while Progressive Supranuclear Palsy (PSP) is an atypical Parkinsonism resembling PD, especially in early stage. Assumed that gait dysfunctions represent a major motor symptom for both pathologies, gait analysis can provide clinicians with subclinical information reflecting subtle differences between these diseases. In this scenario, data mining can be exploited in order to differentiate PD patients at different stages of the disease course and PSP using all the variables acquired through gait analysis. METHODS A cohort of 46 subjects (divided into three groups) affected by PD patients at different stages and PSP patients was acquired through gait analysis and spatial and temporal parameters were analysed. Synthetic Minority Over-sampling Technique was used to balance our imbalanced dataset and cross-validation was applied to provide different training and testing sets. Then, Random Forests and Gradient Boosted Trees were implemented. RESULTS Accuracy, error, precision, recall, specificity and sensitivity were computed for each group and for both algorithms, including 16 features. Random Forests obtained the highest accuracy (86.4%) but also specificity and sensitivity were particularly high, overcoming the 90% for PSP group. CONCLUSION The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns. This application would be helpful for clinicians to distinguish PSP from PD at early stage, when the differential diagnosis is particularly challenging.
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Affiliation(s)
- Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Via S. Pansini, 5, Naples 80131, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy
| | - Marianna Amboni
- Center for Neurodegenerative Diseases, Department of Medicine and Surgery, University of Salerno, Via San Leonardo, Salerno 84131, Italy; Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy
| | - Chiara De Santis
- Center for Neurodegenerative Diseases, Department of Medicine and Surgery, University of Salerno, Via San Leonardo, Salerno 84131, Italy
| | - Giovanni Improta
- Department of Public Health, University Hospital of Naples 'Federico II', Via S. Pansini, 5, Naples 80131, Italy
| | - Giampiero Volpe
- Azienda Ospedaliera Universitaria OO.RR. San Giovanni di Dio Ruggi d'Aragona - Scuola Medica Salernitana, Via San Leonardo, Salerno 84131, Italy
| | - Luigi Iuppariello
- Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Via Claudio, 21, Naples, Italy; Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Gianluca Ricciardelli
- Azienda Ospedaliera Universitaria OO.RR. San Giovanni di Dio Ruggi d'Aragona - Scuola Medica Salernitana, Via San Leonardo, Salerno 84131, Italy
| | - Giovanni D'Addio
- Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy
| | - Carmine Vitale
- Department of Motor Sciences and Wellness, University of Naples Parthenope, Via Ammiraglio Ferdinando Acton, 38, Naples 80133, Italy
| | - Paolo Barone
- Center for Neurodegenerative Diseases, Department of Medicine and Surgery, University of Salerno, Via San Leonardo, Salerno 84131, Italy
| | - Mario Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Via bagni vecchi, 1, Telese Terme (BN), Italy; Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Via Claudio, 21, Naples, Italy.
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24
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Mirelman A, Bonato P, Camicioli R, Ellis TD, Giladi N, Hamilton JL, Hass CJ, Hausdorff JM, Pelosin E, Almeida QJ. Gait impairments in Parkinson's disease. Lancet Neurol 2019; 18:697-708. [PMID: 30975519 DOI: 10.1016/s1474-4422(19)30044-4] [Citation(s) in RCA: 304] [Impact Index Per Article: 60.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 01/16/2019] [Accepted: 01/23/2019] [Indexed: 12/19/2022]
Abstract
Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | | | - Terry D Ellis
- Department of Physical Therapy and Athletic Training, Boston University, Boston, MA, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jamie L Hamilton
- Michael J Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Chris J Hass
- College of Health and Human Performance, Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Elisa Pelosin
- Department of Neuroscience (DINOGMI), University of Genova, Genova, Italy; IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Quincy J Almeida
- Movement Disorders Research and Rehabilitation Centre, Wilfrid Laurier University, Waterloo, ON, Canada
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25
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Ribeiro NF, André J, Costa L, Santos CP. Development of a Strategy to Predict and Detect Falls Using Wearable Sensors. J Med Syst 2019; 43:134. [PMID: 30949770 DOI: 10.1007/s10916-019-1252-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Accepted: 03/18/2019] [Indexed: 11/27/2022]
Abstract
Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering.
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Affiliation(s)
- Nuno Ferrete Ribeiro
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058, Guimarães, Portugal.
| | - João André
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058, Guimarães, Portugal
| | - Lino Costa
- Production and Systems Department, University of Minho, 4800-058, Guimarães, Portugal
| | - Cristina P Santos
- Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4800-058, Guimarães, Portugal
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