Published on in Vol 6, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31775, first published .
Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study

Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study

Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study

Journals

  1. Zhuparris A, Maleki G, Koopmans I, Doll R, Voet N, Kraaij W, Cohen A, van Brummelen E, De Maeyer J, Groeneveld G. Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study. JMIR Formative Research 2023;7:e41178 View
  2. ZhuParris A, de Goede A, Yocarini I, Kraaij W, Groeneveld G, Doll R. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. Sensors 2023;23(11):5243 View
  3. Zhuparris A, Maleki G, van Londen L, Koopmans I, Aalten V, Yocarini I, Exadaktylos V, van Hemert A, Cohen A, Gal P, Doll R, Groeneveld G, Jacobs G, Kraaij W. A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity. Scientific Reports 2023;13(1) View
  4. Poleur M, Markati T, Servais L. The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review. Orphanet Journal of Rare Diseases 2023;18(1) View
  5. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View