Accessibility settings

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

Formative Evaluation of Digital Health Interventions (4903) Mobile Health (mhealth) (5009) Clinical Informatics (2126) Research Instruments, Questionnaires, and Tools (1157) mHealth for Data Collection and Research (1664) Wearable Devices and Sensors (1515)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46521, first published .
Pinching test on a smartphone screen, showing a strawberry icon.

Assessment of Upper Extremity Function in Multiple Sclerosis: Feasibility of a Digital Pinching Test

Assessment of Upper Extremity Function in Multiple Sclerosis: Feasibility of a Digital Pinching Test

Authors of this article:

Jennifer S Graves1 Author Orcid Image ;   Marcin Elantkowski2 Author Orcid Image ;   Yan-Ping Zhang2 Author Orcid Image ;   Frank Dondelinger2 Author Orcid Image ;   Florian Lipsmeier2 Author Orcid Image ;   Corrado Bernasconi2 Author Orcid Image ;   Xavier Montalban3 Author Orcid Image ;   Luciana Midaglia3, 4 Author Orcid Image ;   Michael Lindemann2 Author Orcid Image
Article Authors Cited by (10) Tweetations (6) Metrics

Journals

  1. Galati A, Kriara L, Lindemann M, Lehner R, Jones J. User Experience of a Large-Scale Smartphone-Based Observational Study in Multiple Sclerosis: Global, Open-Access, Digital-Only Study. JMIR Human Factors 2024;11:e57033 View
  2. Scaramozza M, Ruet A, Chiesa P, Ahamada L, Bartholomé E, Carment L, Charre-Morin J, Cosne G, Diouf L, Guo C, Juraver A, Kanzler C, Karatsidis A, Mazzà C, Penalver-Andres J, Ruiz M, Saubusse A, Simoneau G, Scotland A, Sun Z, Tang M, van Beek J, Zajac L, Belachew S, Brochet B, Campbell N. Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study. JMIR Formative Research 2024;8:e60673 View
  3. Jiang X, McGinley M, Johnston J, Alberts J, Bermel R, Ontaneda D, Naismith R, Hyde R, Levitt N, van Beek J, Sun Z, Campbell N, Barro C. A digital version of the nine-hole peg test: Speed may be a more reliable measure of upper-limb disability than completion time in patients with multiple sclerosis. Multiple Sclerosis Journal 2025;31(1):81 View
  4. Walter E, Traunfellner M, Meyer F, Enzinger C, Guger M, Bsteh C, Altmann P, Hegen H, Goger C, Mikl V. Cost-effectiveness of the Floodlight® MS app in Austria. Unlocking the mystery of costs and outcomes of a digital health application for patients with multiple sclerosis. DIGITAL HEALTH 2025;11 View
  5. Kriara L, Dondelinger F, Capezzuto L, Bernasconi C, Lipsmeier F, Galati A, Lindemann M. Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study. Journal of Medical Internet Research 2025;27:e63090 View
  6. Xia Z, Chikersal P, Venkatesh S, Walker E, Dey A, Goel M. Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation. Journal of Medical Internet Research 2025;27:e70871 View
  7. Filippatou A, Mowry E. Sensors in Multiple Sclerosis. Current Neurology and Neuroscience Reports 2025;25(1) View
  8. Bove R, Capezzuto L, West I, Dryden S, Ghafur S, Halligan J, Hubeaux S, Kazlauskaite A. Simulation of Clinical Visits as a Novel Approach to Evaluate Digital Health in Multiple Sclerosis: Simulation Study. JMIR Medical Informatics 2025;13:e67845 View
  9. Islam S, Baldasso B, Cattaneo D, Jiang X, Ploughman M. Machine Learning and AI Applied to fNIRS Data Reveals Novel Brain Activity Biomarkers in Stable Subclinical Multiple Sclerosis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2026;34:2883 View
  10. Grosserová B, Novotná K, Holm McCormick W, Hvid L, Janatová M, Dalgas U. Current evidence and knowledge gaps on remote monitoring in people with multiple sclerosis – a systematic scoping review. Disability and Rehabilitation: Assistive Technology 2026:1 View