NDLI: Assessing hand motor function in chronic immune-mediated neuropathies: a proof-of-concept study using a data glove

Content Provider	Springer Nature : BioMed Central
Author	Gilliam, Elisa Achenbach, Pascal Suemmermann, Gernot J. Wessely, Manuel N. Rossmanith, Peter Dohrn, Maike F. Schulz, Jörg B. Waschbisch, Anne Brunkhorst, Robert
Abstract	Background Chronic immune-mediated neuropathies are clinically heterogeneous and require regular, objective, and multidimensional monitoring to individualize treatment. However, established outcome measures are insufficient regarding measurement quality criteria (e.g., reliability, objectivity) or functional relevance. Wearables such as data gloves might be helpful, allowing repeated quantification of complex everyday life-relevant motor function of the hand. Methods 25 patients with chronic inflammatory demyelinating polyneuropathy or multifocal motor neuropathy were followed-up at five time points during maintenance therapy with intravenous immunoglobulin. 14 of them showed clinically relevant hand motor impairment. We examined the patients’ hand function using a data glove which quantifies the active range of motion (ROM) of the hand based on three different movement patterns. In addition, clinical outcome parameters (grip strength measurement, MRC Sum Score, INCAT disability score), nerve conduction studies (NCS), and high-resolution ultrasound (HRUS) were performed, and patient-reported outcome measures (PROMs) like the Rasch-built Overall Disability Scale (R-ODS) were assessed. We calculated correlation coefficients, performed Receiver Operating Characteristic analysis, as well as correlation analyses for the glove data and clinical outcome parameters. Longitudinal analyses were based on a Linear Mixed Model, and we assessed construct validity of the data glove by analyzing correlations between the glove measurements and well-established clinical parameters. Results We found good to excellent test–retest reliability for the ROM in all glove movement patterns (Intraclass correlation coefficients = 0.83–0.94), underlining the ability to capture clinical stability. Moreover, the glove demonstrated adequate, sensitivity and specificity in detecting hand motor impairment (area under the curve (AUC): 0.714–0.780), and it performed better than NCS and HRUS (AUC: 0.552/0.701). The AUC values for the metrically scaled parameters include: Vigorimeter (AUC: 0.929) and R-ODS (AUC: 0.698). Additionally, the data glove proved to be a valid tool, as we demonstrated moderate to strong, significant correlations between the glove and established clinical parameters (especially Vigorimeter), as well as PROMs (especially R-ODS). Conclusions This data glove allowed for a non-invasive assessment of the hand motor function and yielded investigator-independent results that reliably reflected individual functional deficits with relevance to everyday life. Future studies should explore the ability to predict clinically meaningful responses to immunomodulatory treatment and to support and monitor rehabilitation progress, with potential applications in other neurological diseases as well. Trial registration at the German Clinical Trials Register, Deutsches Register Klinischer Studien (DRKS: 00027345), retrospectively registered on 23rd March 2022: https://drks.de/search/de/trial/DRKS00027345
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-024-01518-3.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-024-01518-3
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2024-12-20
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Data glove Wearables Chronic immune-mediated neuropathies CIDP MMN Outcome measures Nerve conduction studies High-resolution ultrasound Intravenous immunoglobulin Rehabilitation
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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