• JMIR mHealth and uHealth · Apr 2020

    Multicenter Study

    Design, Development, and Testing of an App for Dual-Task Assessment and Training Regarding Cognitive-Motor Interference (CMI-APP) in People With Multiple Sclerosis: Multicenter Pilot Study.

    • Andrea Tacchino, Renee Veldkamp, Karin Coninx, Jens Brulmans, Steven Palmaers, Päivi Hämäläinen, Mieke D'hooge, Ellen Vanzeir, Alon Kalron, Giampaolo Brichetto, Peter Feys, and Ilse Baert.
    • Scientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.
    • JMIR Mhealth Uhealth. 2020 Apr 16; 8 (4): e15344.

    BackgroundDual tasking constitutes a large portion of most activities of daily living; in real-life situations, people need to not only maintain balance and mobility skills, but also perform other cognitive or motor tasks at the same time. Interest toward dual-task training (DTT) is increasing as traditional interventions may not prepare patients to adequately face the challenges of most activities of daily living. These usually involve simultaneous cognitive and motor tasks, and they often show a decline in performance. Cognitive-motor interference (CMI) has been investigated in different neurological populations, but limited evidence is present for people with multiple sclerosis (MS). The use of computerized tools is mandatory to allow the application of more standardized assessment and rehabilitation intervention protocols and easier implementation of multicenter and multilanguage studies.ObjectiveTo describe the design and development of CMI-APP, an adaptive and interactive technology tablet-based app, and to present the preliminary results of a multicenter pilot study involving people with MS performed in several European centers for evaluating the feasibility of and adherence to a rehabilitation program based on CMI-APP.MethodsCMI-APP includes user-friendly interfaces for personal data input and management, assessment of CMI, and DTT. A dedicated team developed CMI-APP for Android tablets above API level 14 (version 4.0), using C# as the programming language and Unity and Visual Studio as development tools. Three cognitive assessment tests for working memory, information processing speed, and sustained attention and four motor assessment tests for walking at different difficulty levels were implemented. Dual cognitive-motor tasks were performed by combining single cognitive and motor tasks. CMI-APP implements exercises for DTT involving the following 12 cognitive functions: sustained attention, text comprehension, verbal fluency, auditory discrimination, visual discrimination, working memory, information processing speed, auditory memory, visual memory, verbal analog reasoning, visual analog reasoning, and visual spatial planning, which can be performed during walking or stepping on the spot. Fifteen people with MS (mean age 52.6, SD 8.6 years; mean disease duration 9.4, SD 8.4 years; mean Expanded Disability Status Scale score 3.6, SD 1.1) underwent DTT (20 sessions). Adherence to the rehabilitation program was evaluated according to the percentage of performed sessions, perceived exertion during the training (Borg 15-point Ratings of Perceived Exertion [RPE] Scale), and subjective experience of the training (Intrinsic Motivation Inventory [IMI]).ResultsThe adherence rate was 91%. DTT was perceived as "somewhat difficult" (mean RPE Scale score 12.6, SD 1.9). IMI revealed that participants enjoyed the training and felt that it was valuable and, to some extent, important, without feelings of pressure. They felt competent, although they did not always feel they could choose the exercises, probably because the therapist chose the exercises and many exercises had few difficulty levels.ConclusionsCMI-APP is safe, highly usable, motivating, and well accepted for DTT by people with MS. The findings are fundamental for the preparation of future large-sample studies examining CMI and the effectiveness of DTT interventions with CMI-APP in people with MS.©Andrea Tacchino, Renee Veldkamp, Karin Coninx, Jens Brulmans, Steven Palmaers, Päivi Hämäläinen, Mieke D'hooge, Ellen Vanzeir, Alon Kalron, Giampaolo Brichetto, Peter Feys, Ilse Baert. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 19.04.2020.

      Pubmed     Free full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…

What will the 'Medical Journal of You' look like?

Start your free 21 day trial now.

We guarantee your privacy. Your email address will not be shared.