Published February 11, 2020 | Version v1
Journal article Open

Long-term unsupervised mobility assessment in movement disorders

  • 1. Department of Neurology, Kiel University, Kiel, Germany. Faculty of Engineering, Kiel University, Kiel, Germany
  • 2. Center for the Study of Movement, Cognition, and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. Department of Physical Therapy, 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
  • 3. Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  • 4. University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, Netherlands
  • 5. Center for the Study of Movement, Cognition, and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
  • 6. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
  • 7. Department of Neurology, Kiel University, Kiel, Germany
  • 8. Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
  • 9. Center for Bioinformatics, Saarland University, Saarbrücken, Germany. Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, USA
  • 10. Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy. Parkinson's Disease Rehabilitation Centre, Fondazione Europea Ricerca Biomedica, St Isidoro Hospital, Bergamo, Italy
  • 11. Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK. Newcastle upon Tyne Hospitals, National Health Service Foundation Trust, Newcastle upon Tyne, UK
  • 12. Faculty of Engineering, Kiel University, Kiel, Germany

Description

Summary: Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them.

This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

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Funding

MOBILISE-D – Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement 820820
European Commission