Anat Mirelman1,2, Mor Ben Or Frank1, Michal Melamed3, Lena Granovsky3, Alice Nieuwboer4, Lynn Rochester5, Silvia Del Din5, Laura Avanzino6,7, Elisa Pelosin6,7, Bastiaan R Bloem8, Ugo Della Croce9, Andrea Cereatti9,10, Paolo Bonato11, Richard Camicioli12, Theresa Ellis13, Jamie L Hamilton14, Chris J Hass15, Quincy J Almeida16, Maidan Inbal1,2, Avner Thaler1,2, Julia Shirvan17, Jesse M Cedarbaum18,19, Nir Giladi1,2, Jeffrey M Hausdorff1,2,20,21. 1. Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel. 2. Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. 3. Teva Pharmaceutical Industries Ltd., Netanya, Israel. 4. Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium. 5. Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK. 6. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy. 7. IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy. 8. Department of Neurology, Radboud University Medical Center; Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands. 9. Department of Biomedical Sciences, University of Sassari, Sassari, Italy. 10. Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. 11. Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA. 12. Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. 13. Department of Physical Therapy & Athletic Training, Boston University, Boston, Massachusetts, USA. 14. Michael J. Fox Foundation for Parkinson's Research, New York, New York, USA. 15. College of Health & Human Performance, Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA. 16. Movement Disorders Research & Rehabilitation Centre, Wilfrid Laurier University, Waterloo, Canada. 17. Biogen, Cambridge, Massachusetts, USA. 18. Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA. 19. Yale University School of Medicine, New Haven, Connecticut, USA. 20. Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel. 21. Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA.
Abstract
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.
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.
Authors: Deepak K Ravi; Christian R Baumann; Elena Bernasconi; Michelle Gwerder; Niklas K Ignasiak; Mechtild Uhl; Lennart Stieglitz; William R Taylor; Navrag B Singh Journal: Neurorehabil Neural Repair Date: 2021-09-22 Impact factor: 3.919
Authors: Manu Airaksinen; Anastasia Gallen; Anna Kivi; Pavithra Vijayakrishnan; Taru Häyrinen; Elina Ilén; Okko Räsänen; Leena M Haataja; Sampsa Vanhatalo Journal: Commun Med (Lond) Date: 2022-06-15