Goal-directed upper limb movement patterns and hand grip forces in multiple sclerosis. Issue 3 (July 2022)

• Record Type: Journal Article
• Authors: Kanzler, Christoph M; Sylvester, Ramona; Gassert, Roger; Kool, Jan; Lambercy, Olivier; Gonzenbach, Roman
• Is Part Of: Multiple sclerosis journal, experimental, translational and clinical. Volume 8:Issue 3(2022)
• Publication Date: 2022-07
• Subjects: digital health metrics -- upper limb -- motor control -- multiple sclerosis -- movement pattern -- grip forces; Multiple sclerosis -- Periodicals; 616.834
• DOI: 10.1177/20552173221116272
• Languages: English
• ISSNs: 2055-2173

Abstract

Background: Upper limb disability in persons with Multiple Sclerosis (pwMS) leads to increased dependence on caregivers. To better understand upper limb disability, observer-based or time-based clinical assessments have been applied. However, these only poorly capture the behavioural aspects underlying goal-directed task performance. Objective: We aimed to document alterations in goal-directed upper limb movement patterns and hand grip forces in a cohort of pwMS (n = 123) with mild to moderate upper limb impairments. Methods: We relied on the Virtual Peg Insertion Test (VPIT), a technology-aided assessment with a goal-directed pick-and-place task providing a set of validated digital health metrics. Results: All metrics indicated significant differences to an able-bodied reference sample (p < 0.001), with smoothness, speed, and grip force control during object manipulation being most affected in pwMS. Such abnormalities negatively influenced the time to complete the goal-directed task (p < 0.001, R 2 = 0.77), thereby showing their functional relevance. Lastly, abnormalities in movement patterns and grip force control were consistently found even in pwMS with clinically normal gross dexterity and grip strength. Conclusion: This work provides a systematic documentation on goal-directed upper limb movement patterns and hand grip forces in pwMS, ultimately paving the way for an early detection of MS sign using digital health metrics.