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

Pilot studies (ehealth) (194) Wearable Devices and Sensors (876) mHealth for Data Collection and Research (945) Formative Evaluation of Digital Health Interventions (2361) New Methods (932) Machine Learning (1436)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41178, first published .
Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study

Authors of this article:

Ahnjili Zhuparris1 Author Orcid Image ;   Ghobad Maleki1 Author Orcid Image ;   Ingrid Koopmans1 Author Orcid Image ;   Robert J Doll1 Author Orcid Image ;   Nicoline Voet2 Author Orcid Image ;   Wessel Kraaij3 Author Orcid Image ;   Adam Cohen1 Author Orcid Image ;   Emilie van Brummelen1 Author Orcid Image ;   Joris H De Maeyer4 Author Orcid Image ;   Geert Jan Groeneveld1 Author Orcid Image
Article Authors Cited by (2) Tweetations (7) Metrics

Journals

  1. Abeltino A, Riente A, Bianchetti G, Serantoni C, De Spirito M, Capezzone S, Esposito R, Maulucci G. Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutrition Reviews 2024 View