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      Mitii™ ABI: study protocol of a randomised controlled trial of a web-based multi-modal training program for children and adolescents with an Acquired Brain Injury (ABI)

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          Abstract

          Background

          Acquired brain injury (ABI) refers to multiple disabilities arising from damage to the brain acquired after birth. Children with an ABI may experience physical, cognitive, social and emotional-behavioural impairments which can impact their ability to participate in activities of daily living (ADL). Recent developments in technology have led to the emergence of internet-delivered therapy programs. “Move it to improve it” (Mitii™) is a web-based multi-modal therapy that comprises upper limb (UL) and cognitive training within the context of meaningful physical activity. The proposed study aims to compare the efficacy of Mitii™ to usual care to improve ADL motor and processing skills, gross motor capacity, UL and executive functioning in a randomised waitlist controlled trial.

          Methods/Design

          Sixty independently ambulant children (30 in each group) at least 12 months post ABI will be recruited to participate in this trial. Children will be matched in pairs at baseline and randomly allocated to receive either 20 weeks of Mitii™ training (30 min per day, six days a week, with a potential total dose of 60 h) immediately, or be waitlisted for 20 weeks. Outcomes will be assessed at baseline, immediately post-intervention and at 20 weeks post-intervention. The primary outcomes will be the Assessment of Motor and Process Skills and 30 s repetition maximum of functional strength exercises (sit-to-stand, step-ups and half kneel to stand). Measures of body structure and functions, activity, participation and quality of life will assess the efficacy of Mitii™ across all domains of the International Classification of Functioning, Disability and Health framework. A subset of children will undertake three tesla (3T) magnetic resonance imaging scans to evaluate functional neurovascular changes, structural imaging, diffusion imaging and resting state functional connectivity before and after intervention.

          Discussion

          Mitii™ provides an alternative approach to deliver intensive therapy for children with an ABI in the convenience of the home environment. If Mitii™ is found to be effective, it may offer an accessible and inexpensive intervention option to increase therapy dose.

          Trial Registration

          ANZCTR12613000403730

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12883-015-0381-6) contains supplementary material, which is available to authorized users.

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          Most cited references137

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          The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior

          Psychological Inquiry, 11(4), 227-268
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            Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM.

            Reliability, the consistency of a test or measurement, is frequently quantified in the movement sciences literature. A common metric is the intraclass correlation coefficient (ICC). In addition, the SEM, which can be calculated from the ICC, is also frequently reported in reliability studies. However, there are several versions of the ICC, and confusion exists in the movement sciences regarding which ICC to use. Further, the utility of the SEM is not fully appreciated. In this review, the basics of classic reliability theory are addressed in the context of choosing and interpreting an ICC. The primary distinction between ICC equations is argued to be one concerning the inclusion (equations 2,1 and 2,k) or exclusion (equations 3,1 and 3,k) of systematic error in the denominator of the ICC equation. Inferential tests of mean differences, which are performed in the process of deriving the necessary variance components for the calculation of ICC values, are useful to determine if systematic error is present. If so, the measurement schedule should be modified (removing trials where learning and/or fatigue effects are present) to remove systematic error, and ICC equations that only consider random error may be safely used. The use of ICC values is discussed in the context of estimating the effects of measurement error on sample size, statistical power, and correlation attenuation. Finally, calculation and application of the SEM are discussed. It is shown how the SEM and its variants can be used to construct confidence intervals for individual scores and to determine the minimal difference needed to be exhibited for one to be confident that a true change in performance of an individual has occurred.
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              A hybrid approach to the skull stripping problem in MRI.

              We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
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                Author and article information

                Contributors
                r.boyd@uq.edu.au
                e.baque@uq.edu.au
                a.piovesana@uq.edu.au
                stephanie.ross@health.qld.gov.au
                j.ziviani@uq.edu.au
                l.sakzewski1@uq.edu.au
                l.barber@uq.edu.au
                Owen.Lloyd@health.qld.gov.au
                Lynne_McKinlay@health.qld.gov.au
                koawhittingham@uq.edu.au
                asmith@uq.edu.au
                Stephen.Rose@csiro.au
                s.fiori@uq.edu.au
                r.cunnington@uq.edu.au
                r.ware@sph.uq.edu.au
                melinda.lewis@health.qld.gov.au
                t.comans@griffith.edu.au
                p.scuffham@griffith.edu.au
                Journal
                BMC Neurol
                BMC Neurol
                BMC Neurology
                BioMed Central (London )
                1471-2377
                19 August 2015
                19 August 2015
                2015
                : 15
                : 140
                Affiliations
                [ ]Queensland Cerebral Palsy and Rehabilitation Research Centre, School of Medicine, The University of Queensland, Brisbane, Queensland Australia
                [ ]Children’s Allied Health Research, Children’s Health Queensland, Brisbane, Queensland Australia
                [ ]School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland Australia
                [ ]Centre for Online Health, The University of Queensland, Brisbane, Australia
                [ ]CSIRO, ICT – Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital Centre for Clinical Research, The University of Queensland, Brisbane, Queensland Australia
                [ ]Department of Developmental Neuroscience, IRCCS Stella Maris, Pisa, Italy
                [ ]Queensland Brain Institute, The University of Queensland, Brisbane, Queensland Australia
                [ ]School of Psychology, The University of Queensland, Brisbane, Queensland Australia
                [ ]Queensland Children’s Medical Research Institute, The University of Queensland, Brisbane, Queensland Australia
                [ ]School of Population Health, The University of Queensland, Brisbane, Queensland Australia
                [ ]Griffith Health Institute and School of Medicine, Griffith University, Brisbane, Queensland Australia
                Article
                381
                10.1186/s12883-015-0381-6
                4544804
                26286324
                26788785-11c8-4a13-9d85-d921419f769c
                © Boyd et al. 2015

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 October 2014
                : 14 July 2015
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2015

                Neurology
                acquired brain injury,children,physical activity,virtual reality,motor processing,protocol,executive function,randomised controlled trial

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