This paper is in the following e-collection/theme issue:

Digital Health Reviews (1174) New Methods (901) Wearable Devices and Sensors (811) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) mHealth for Data Collection and Research (907) Mobile Health (mhealth) (2748)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44428, first published .
Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

Authors of this article:

Tim Woelfle1, 2 Author Orcid Image ;   Lucie Bourguignon3 Author Orcid Image ;   Johannes Lorscheider1, 2 Author Orcid Image ;   Ludwig Kappos1, 2 Author Orcid Image ;   Yvonne Naegelin1, 2 Author Orcid Image ;   Catherine Ruth Jutzeler3 Author Orcid Image
Article Authors Cited by (12) Tweetations (17) Metrics

Journals

  1. Rego S, Henriques A, Serra S, Costa T, Rodrigues A, Nunes F. Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract. JMIR Research Protocols 2023;12:e47119 View
  2. de Queiroz R, Alves J, Sasaki J. Digital Biomarkers in the Assessment of Mobility in Individuals with Multiple Sclerosis. Sclerosis 2023;1(3):134 View
  3. Woelfle T, Pless S, Reyes Ó, Wiencierz A, Kappos L, Granziera C, Lorscheider J. Smartwatch-derived sleep and heart rate measures complement step counts in explaining established metrics of MS severity. Multiple Sclerosis and Related Disorders 2023;80:105104 View
  4. Bannwart M. Präzisionsrehabilitation im Neuroreha-Alltag. neuroreha 2023;15(04):194 View
  5. Masanneck L, Pawlitzki M, Meuth S. Digitale Medizin in der neurologischen Forschung – zwischen Hype und Evidenz. Der Nervenarzt 2024;95(3):230 View
  6. González-Castro A, Leirós-Rodríguez R, Prada-García C, Benítez-Andrades J. The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review. Journal of Medical Internet Research 2024;26:e54934 View
  7. Podda J, Pedullà L, Brichetto G, Tacchino A. Evaluating Cognitive-Motor Interference in Multiple Sclerosis: A Technology-Based Approach. Bioengineering 2024;11(3):277 View
  8. Tryfonos C, Chrysafi M, Papadopoulou S, Vadikolias K, Spanoudaki M, Mentzelou M, Fotiou D, Pavlidou E, Gkouvas G, Vorvolakos T, Michailidis A, Bisbinas A, Alexatou O, Giaginis C. Association of Mediterranean diet adherence with disease progression, quality of life and physical activity, sociodemographic and anthropometric parameters, and serum biomarkers in community-dwelling older adults with multiple sclerosis: a cross-sectional study. Aging Clinical and Experimental Research 2024;36(1) View
  9. Macias Alonso A, Hirt J, Woelfle T, Janiaud P, Hemkens L. Definitions of digital biomarkers: a systematic mapping of the biomedical literature. BMJ Health & Care Informatics 2024;31(1):e100914 View
  10. Wyatt J, Guo C. From Research to Application of Wearable-Derived Digital Health Measures—A Perspective From ActiGraph. Journal for the Measurement of Physical Behaviour 2024;7(1) View
  11. Grijalvo M, Ordieres-Meré J, Villalba-Díez J, Aladro-Benito Y, Martín-Ávila G, Simon-Hurtado A, Vivaracho-Pascual C. Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective. Heliyon 2024;10(9):e30001 View

Books/Policy Documents

  1. Vullers B, Smid J, Pashalis G, Goutzounis D, Lourakis E, Neves N. Digital Sustainability: Inclusion and Transformation. View