Intensity-dependent clinical effects of an individualized technology-supported task-oriented upper limb training program in Multiple Sclerosis: A pilot randomized controlled trial

Abstract

Number of figures: 5 Supplementary material: 2 tables Number of references: 37 Declaration of Interest Ilse Lamers has received teaching honoraria from Sanofi Genzyme Europe. Peter Feys is steering committee member of Neurocompass, participated to advisory board meetings of BIOGEN IDEC, and received teaching honoraria for EXCEMED and PARADIGMS. The devices used in this study were given in loan by the Tyromotion and Symbio Therapy. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Research data Raw data of the pre and post testing can be shared (coded) on request and after approval of Medical Ethics and privacy committee of the involved parties. The personal data collected in this study remains confidential and would not be shared.

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

“…The analysis presented in this work builds on data from different studies that included assessments with the VPIT 35,[73][74][75] . Age-matched reference data was based on 120 neurologically intact subjects.…”

Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning

“…Interestingly, knowledge of the intervention type (i.e., task-oriented therapy at high or moderate intensity, or occupational therapy) only led to slight improvements in the prediction of ARAT and BBT scores and solely for the k-nearest neighbor models. While stronger intensity-dependent effects were found in the clinical analysis of this trial [6], its rather minor impact in the analysis presented here might be explained by the reduced number of datasets being available for this work, with only two of them belonging to the occupational therapy group.…”

“…One of the active ingredients to ensure successful neurorehabilitation is a careful adaptation of the therapy regimen to the characteristics and deficits of an individual (i.e., personalized therapy) [5,8,6]. For this purpose, predicting whether a patient is susceptible to positively respond to a specific neurorehabilitation intervention is of primary interest to researchers and clinicians, as it can help to set more realistic therapy goals, optimize therapy time, and reduce costs related to unsuccessful interventions [9,10,11,12].…”

“…This strongly affects the ability to perform daily life activities, leads to increased dependence on caregivers, and ultimately a reduced quality of life [3]. Inter-disciplinary neurorehabilitation approaches combining, for example, physiotherapy and occupational therapy have shown promise to reduce upper limb disability [4,5,6]. This is reflected by a reduction in sensorimotor impairments and an increase in the spectrum of executable activities, as defined by the International Classification of Functioning, Disability, and Health (ICF) [7].…”

“…The objective of this work was to explore the feasibility of predicting upper limb rehabilitation outcomes in individual pwMS by combining clinical data, digital health metrics, and machine learning ( Figure 1). For this purpose, clinical data including routinely collected information (e.g., age and chronicity) and conventional assessments were recorded pre-and post-intervention in 11 pwMS that participated in a clinical study on task-oriented upper limb rehabilitation [6]. In addition, digital health metrics describing upper limb movement and grip force patterns were recorded using the Virtual Peg Insertion Test (VPIT), a previously validated technology-aided assessment of upper limb sensorimotor impairments in neurological subjects relying on a haptic end-effector and a virtual goal-directed object manipulation task [24,29,28].…”