It is unknown how movement patterns that are learned carry over to the field. The objective was to deter- mine whether training during a jump-landing task would transfer to lower extremity kinematics and kinetics during sidestep cutting.Methods Forty healthy athletes were assigned to the ver- bal internal focus (IF, n = 10), verbal external focus (EF, n = 10), video (VI, n = 10) or control (CTRL, n = 10) group. A jump-landing task was performed as baseline followed by training blocks (TR1 and TR2) and a post-test. Group-spe- cific instructions were given in TR1 and TR2. In addition, participants in the IF, EF and VI groups were free to ask for feedback after every jump during TR1 and TR2. Retention was tested after 1 week. Transfer of learned skill was deter- mined by having participants perform a 45° unanticipated sidestep cutting task. 3D hip, knee and ankle kinematics and kinetics were the main outcome measures.Results During sidestep cutting, the VI group showed greater hip flexion ROM compared to the EF and IF groups (p < 0.001). The EF (p < 0.036) and VI (p < 0.004) groups had greater knee flexion ROM compared to the IF group. Conclusions Improved jump-landing technique car- ried over to sidestep cutting when stimulating an external attentional focus combined with self-controlled feedback. Transfer to more sport-specific skills may demonstrate potential to reduce injuries on the field. Clinicians and practitioners are encouraged to apply instructions that stimulate an external focus of attention, of which visual instructions seem to be very powerful.
Little is known about the effects of different instructional approaches on learner affect in oral interaction in the foreign language classroom. In a randomized experiment with Dutch pre-vocational learners (N = 147), we evaluated the effects of 3 newly developed instructional programs for English as a foreign language (EFL). These programs differed in instructional focus (form-focused vs. interaction strategies- oriented) and type of task (pre-scripted language tasks vs. information gap tasks). Multilevel analyses revealed that learners’ enjoyment of EFL oral interaction was not affected by instruction, that willingness to communicate (WTC) decreased over time, and that self-confidence was positively affected by combining information gap tasks with interactional strategies instruction. In addition, regression analyses revealed that development in learners’ WTC and enjoyment did not have predictive value for achievement in EFL oral interaction, but that development in self-confidence did explain achievement in EFL oral interaction in trained interactional contexts.
Background: Persons with profound intellectual and multiple disabilities (PIMD) are vulnerable when it comes to experiencing pain. Reliable assessment of pain-related behaviour in these persons is difficult. Aim To determine how pain items can be reliably scored in adults with PIMD.Methods: We developed an instruction protocol for the assessment of pain-related behaviour in four phases. We used videos of 57 adults with PIMD during potentially painful situations. The items were assessed for inter-rater reliability (Cohen's kappa or percentage of agreement).Results: The developed instruction protocol appeared to be adequate. Twelve items had satisfactory inter-rater reliability (n = 9: .30–1.00; n = 3: 85%–100%).Discussion: Calibrating and adjustments to the instructions and item set appeared to be crucial to reliably score 12 items in adults with PIMD. Further research should focus on creating an assessment instrument based on these reliably scored items.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
