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252 results on '"Maidan, Inbal"'

4. Validity of the Short Weekly Calendar Planning Activity in patients with Parkinson disease and nonmanifesting LRRK2 and GBA carriers

Author

Schejter‐Margalit, Tamara, primary, Binyamin, Noam Ben, additional, Thaler, Avner, additional, Maidan, Inbal, additional, Cedarbaum, Jesse M., additional, Orr‐Urtreger, Avi, additional, Gana Weisz, Mali, additional, Goldstein, Orly, additional, Giladi, Nir, additional, Mirelman, Anat, additional, and Kizony, Rachel, additional

Published
2024
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33. Gait

Author

Mirelman, Anat, primary, Shema, Shirley, additional, Maidan, Inbal, additional, and Hausdorff, Jeffery M., additional

Published
2018
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37. Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning

Author

Jesse M. Cedarbaum, Theresa Ellis, Paolo Bonato, Lynn Rochester, Elisa Pelosin, Lena Granovsky, Nir Giladi, Michal Melamed, Quincy J. Almeida, Laura Avanzino, Chris J. Hass, Andrea Cereatti, Jamie L. Hamilton, Alice Nieuwboer, Avner Thaler, Mor Ben Or Frank, Bastiaan R. Bloem, Jeffrey M. Hausdorff, Maidan Inbal, Richard Camicioli, Anat Mirelman, Julia C Shirvan, Silvia Del Din, and Ugo Della Croce

Subjects
0301 basic medicine, medicine.medical_specialty, Parkinson's disease, accelerometer, gait, machine learning, wearables, Cross-Sectional Studies, Gait, Humans, Machine Learning, Postural Balance, Time and Motion Studies, Walking, Parkinson Disease, STRIDE, Timed Up and Go test, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, medicine, business.industry, Cognition, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], medicine.disease, Trunk, Preferred walking speed, 030104 developmental biology, Neurology, Feature (computer vision), Neurology (clinical), business, human activities, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 238527.pdf (Publisher’s version ) (Closed access) BACKGROUND: It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE: To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS: Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS: High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS: Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Published
2021

45. Clinical experience using a 5-week treadmill training program with virtual reality to enhance gait in an ambulatory physical therapy service

Author

Shema, Shirley Roth, Brozgol, Marina, Dorfman, Moran, Maidan, Inbal, Sharaby-Yeshayahu, Lior, Malik-Kozuch, Hila, Yannai, Orly Wachsler, Hausdorff, Jeffrey M., and Mirelman, Anat

Subjects
Therapeutics, Physiological -- Methods -- Equipment and supplies, Physical therapy -- Methods -- Equipment and supplies, Treadmills (Exercise equipment) -- Usage, Gait -- Physiological aspects, Health
Abstract

Background. Current literature views safe gait as a complex task, relying on motor and cognitive resources. The use of virtual reality (VR) in gait training offers a multifactorial approach, showing positive effects on mobility, balance, and fall risk in elderly people and individuals with neurological disorders. This form of training has been described as a viable research tool; however, it has not been applied routinely in clinical practice. Recently, VR was used to develop an adjunct training method for use by physical therapists in an ambulatory clinical setting. Objective. The aim of this article is to describe the initial clinical experience of applying a 5-week VR clinical service to improve gait and mobility in people with a history of falls, poor mobility, or postural instability. Design. A retrospective data analysis was conducted. Methods. The clinical records of the first 60 patients who completed the VR gait training program were examined. Training was provided 3 times per week for 5 weeks, with each session lasting approximately 1 hour and consisting of walking on a treadmill while negotiating virtual obstacles. Main outcome measures were compared across time and included the Timed 'Up & Go' Test (TUG), the Two-Minute Walk Test (2MWT), and the Four Square Step Test (FSST). Results. After 5 weeks of training, time to complete the TUG decreased by 10.3%, the distance walked during the 2MWT increased by 9.5%, and performance on the FSST improved by 13%. Limitations. Limitations of the study include the use of a retrospective analysis with no control group and the lack of objective cognitive assessment. Conclusions. Treadmill training with VR appears to be an effective and practical tool that can be applied in an outpatient physical therapy clinic. This training apparently leads to improvements in gait, mobility, and postural control. It, perhaps, also may augment cognitive and functional aspects., Gait disturbances are prevalent in aging and contribute to an increased risk of falls, reduced mobility, and poor quality of life. (1) The result is a major health problem. Impaired [...]

Published
2014
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46. CONSORT_2010_Checklist_NNR_Droby_et_al – Supplemental material for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease

Author

Amgad Droby, Maidan, Inbal, Jacob, Yael, Giladi, Nir, Hausdorff, Jeffrey M., and Mirelman, Anat

Subjects
FOS: Clinical medicine, 110308 Geriatrics and Gerontology, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, CONSORT_2010_Checklist_NNR_Droby_et_al for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease by Amgad Droby, Inbal Maidan, Yael Jacob, Nir Giladi, Jeffrey M. Hausdorff and Anat Mirelman in Neurorehabilitation and Neural Repair

Published
2020
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47. Flow_Diagram_Droby_et_al_NNR – Supplemental material for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease

Author

Amgad Droby, Maidan, Inbal, Jacob, Yael, Giladi, Nir, Hausdorff, Jeffrey M., and Mirelman, Anat

Subjects
FOS: Clinical medicine, 110308 Geriatrics and Gerontology, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Flow_Diagram_Droby_et_al_NNR for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease by Amgad Droby, Inbal Maidan, Yael Jacob, Nir Giladi, Jeffrey M. Hausdorff and Anat Mirelman in Neurorehabilitation and Neural Repair

Published
2020
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48. Suppl_Table_1 – Supplemental material for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease

Author

Amgad Droby, Maidan, Inbal, Jacob, Yael, Giladi, Nir, Hausdorff, Jeffrey M., and Mirelman, Anat

Subjects
FOS: Clinical medicine, 110308 Geriatrics and Gerontology, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Suppl_Table_1 for Distinct Effects of Motor Training on Resting-State Functional Networks of the Brain in Parkinson’s Disease by Amgad Droby, Inbal Maidan, Yael Jacob, Nir Giladi, Jeffrey M. Hausdorff and Anat Mirelman in Neurorehabilitation and Neural Repair

Published
2020
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