Journal article Open Access

Long-term unsupervised mobility assessment in movement disorders

Warmerdam, Elke; Hausdorff, Jeffrey M.; Atrsaei, Arash; Zhou, Yuhan; Mirelman, Anat; Aminian, Kamiar; Espay, Alberto J.; Hansen, Clint; Evers, Luc J. W.; Keller, Andreas; Lamoth, Claudine; Pilotto, Andrea; Rochester, Lynn; Schmidt, Gerhard; Bloem, Bastiaan R.; Maetzler, Walter


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  <dc:creator>Warmerdam, Elke</dc:creator>
  <dc:creator>Hausdorff, Jeffrey M.</dc:creator>
  <dc:creator>Atrsaei, Arash</dc:creator>
  <dc:creator>Zhou, Yuhan</dc:creator>
  <dc:creator>Mirelman, Anat</dc:creator>
  <dc:creator>Aminian, Kamiar</dc:creator>
  <dc:creator>Espay, Alberto J.</dc:creator>
  <dc:creator>Hansen, Clint</dc:creator>
  <dc:creator>Evers, Luc J. W.</dc:creator>
  <dc:creator>Keller, Andreas</dc:creator>
  <dc:creator>Lamoth, Claudine</dc:creator>
  <dc:creator>Pilotto, Andrea</dc:creator>
  <dc:creator>Rochester, Lynn</dc:creator>
  <dc:creator>Schmidt, Gerhard</dc:creator>
  <dc:creator>Bloem, Bastiaan R.</dc:creator>
  <dc:creator>Maetzler, Walter</dc:creator>
  <dc:date>2020-02-11</dc:date>
  <dc: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.</dc:description>
  <dc:identifier>https://zenodo.org/record/5179930</dc:identifier>
  <dc:identifier>10.1016/S1474-4422(19)30397-7</dc:identifier>
  <dc:identifier>oai:zenodo.org:5179930</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/820820/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/mobilise-d</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>The Lancet Neurology 19(5) 462-470</dc:source>
  <dc:title>Long-term unsupervised mobility assessment in movement disorders</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
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