The validity and reliability of school-based fundamental movement skills screening to identify children with motor difficulties

Aim Assess whether school-based teacher-led screening is effective at identifying children with motor difficulties. Methods Teachers tested 217 children aged between 5 and 11 years old, after a one hour training session, using a freely available tool (FUNMOVES). Four classes (n = 91) were scored by both researchers and teachers to evaluate inter-rater reliability. Researchers assessed 22 children using the Movement Assessment Battery for Children (MABC-2; considered to be the ‘gold standard’ in Europe for use as part of the diagnostic process for Developmental Coordination Disorder) to assess concurrent and predictive validity. Results Inter-rater reliability for all individual activities within FUNMOVES ranged from 0.85–0.97 (unweighted Kappa; with 95%CI ranging from 0.77–1). For total score this was lower (κ = 0.76, 95%CI = 0.68–0.84), however when incorporating linear weighting, this improved (κ = 0.94, 95%CI = 0.89–0.99). When evaluating FUNMOVES total score against the MABC-2 total score, the specificity (1, 95%CI = 0.63–1) and positive predictive value (1; 95%CI = 0.68–1) of FUNMOVES were high, whereas sensitivity (0.57, 95%CI = 0.29–0.82) and negative predictive values (0.57, 95%CI = 0.42–0.71) were moderate. Evaluating only MABC-2 subscales which are directly related to fundamental movement skills (Aiming & Catching, and Balance) improved these values to 0.89 (95%CI = 0.52–1) and 0.93 (95%CI = 0.67–0.99) respectively. Interpretation Teacher-led screening of fundamental movement skills (via FUNMOVES) is an effective method of identifying children with motor difficulties. Such universal screening in schools has the potential to identify movement difficulties and enable earlier intervention than the current norm

Developing and validating a school-based screening tool of Fundamental Movement Skills (FUNMOVES) using Rasch analysis

YesA large proportion of children are not able to perform age-appropriate fundamental movement skills (FMS). Thus, it is important to assess FMS so that children needing additional support can be identified in a timely fashion. There is great potential for universal screening of FMS in schools, but research has established that current assessment tools are not fit for purpose. To develop and validate the psychometric properties of a FMS assessment tool designed specifically to meet the demands of universal screening in schools. A working group consisting of academics from developmental psychology, public health and behavioural epidemiology developed an assessment tool (FUNMOVES) based on theory and prior evidence. Over three studies, 814 children aged 4 to 11 years were assessed in school using FUNMOVES. Rasch analysis was used to evaluate structural validity and modifications were then made to FUNMOVES activities after each study based on Rasch results and implementation fidelity. The initial Rasch analysis found numerous psychometric problems including multidimensionality, disordered thresholds, local dependency, and misfitting items. Study 2 showed a unidimensional measure, with acceptable internal consistency and no local dependency, but that did not fit the Rasch model. Performance on a jumping task was misfitting, and there were issues with disordered thresholds (for jumping, hopping and balance tasks). Study 3 revealed a unidimensional assessment tool with good fit to the Rasch model, and no further issues, once jumping and hopping scoring were modified. The finalised version of FUNMOVES (after three iterations) meets standards for accurate measurement, is free and able to assess a whole class in under an hour using resources available in schools. Thus FUNMOVES has the potential to allow schools to efficiently screen FMS to ensure that targeted support can be provided and disability barriers removed.The work of the lead author (L.H. Eddy) was supported by an ESRC White Rose Doctoral Training Partnership Pathway Award (ES/P000745/ 1). M. Mon-Williams was supported by a Fellowship from the Alan Turing Institute. The work was conducted within infrastructure provided by the Centre for Applied Education Research (funded by the Department for Education through the Bradford Opportunity Area) and ActEarly: a City Collaboratory approach to early promotion of good health and wellbeing funded by the Medical Research Council (grant reference MR/S037527/). L.J.B. Hill, M. Mon-Williams, N. Preston and D. D. Bingham’s involvement was supported by the National Institute for Health Research Yorkshire and Humber ARC (reference: NIHR20016)

An introduction to deep learning on biological sequence data: Examples and solutions

Motivation: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Results: Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. Availability and implementation: All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. Supplementary information: Supplementary data are available at Bioinformatics online.Fil: Jurtz, Vanessa Isabell. Technical University of Denmark; DinamarcaFil: Johansen, Alexander Rosenberg. Technical University of Denmark; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Almagro Armenteros, Jose Juan. Technical University of Denmark; DinamarcaFil: Nielsen, Henrik. Technical University of Denmark; DinamarcaFil: Sønderby, Casper Kaae. Universidad de Copenhagen; DinamarcaFil: Winther, Ole. Universidad de Copenhagen; DinamarcaFil: Sønderby, Søren Kaae. Universidad de Copenhagen; Dinamarc