BACKGROUND: Observation of movement quality (MQ) is an indelible element in the process of clinical reasoning for patients with non-specific low back pain (NS-LBP). However, the observation and evaluation of MQ in common daily activities are not standardized within allied health care. This study aims to describe how Dutch allied health care professionals (AHCPs) observe and assess MQ in patients with NS-LBP and whether AHCPs feel the need to have a specific outcome measure for assessing MQ in patients with NS-LBP.METHODS: In this cross-sectional digital survey study, Dutch primary care AHCPs (n = 114) answered one open and three closed questions about MQ in NS-LBP management. Qualitative and quantitative analyses were applied.RESULTS: Qualitative analyses of the answers to the open questions revealed four main themes: 1) movement pattern features, 2) motor control features, 3) environmental influences and 4) non-verbal expressions of pain and exertion. Quantitative analyses clearly indicated that AHCPs observe MQ in the diagnostic (92%), therapeutic (91%) and evaluation phases (86%), that they do not apply any objective measurement of MQ and that 63% of the AHCPs consider it important to have a specific outcome measure to assess MQ. The AHCPs expressed added benefits and critical notes regarding clinical reasoning and quality of care.CONCLUSION: AHCPs recognize the importance of observing MQ in the assessment and management of LBP in a standardized way. However, there is no consensus amongst AHCPs how MQ should be standardized. Prior to standardization, it will be important to develop a theoretical framework to determine which observable and measurable dimensions of MQ are most valid and relevant for patients with NS-LBP to include in the assessment.
The aim of this systematic review was to provide an overview of the effectiveness of fundamental movement skill interventions in young children (2–5 years) and to identify elements that determine the effectiveness of these interventions. A systematic literature search was conducted in four electronic databases (PubMed, Academic Search Complete, Education Resources Information Centre and SPORTDiscus). First, intervention-related data (e.g., intervention length, volume, focus, and content) were extracted. Next, the methodological quality and risk of bias of the selected studies were evaluated using a 10-item checklist. Sixteen studies (13 randomised controlled trials and 3 controlled trials) met the inclusion criteria of which 9 had a high methodological quality. Fourteen studies reported statistically significant intervention effects, ranging from small negative to very strong positive effects. Four studies executed a retention test of which two showed positive effects. Elements that influence the effectiveness are: incorporating all fundamental movement skills in the intervention with a variety of activities; combining deliberate practice and deliberate play; the intervention length; the intervention volume and; providing a training programme with coaching during the intervention for the professional involved in delivering the intervention. However more studies containing retention tests are needed.
Dynamic body feedback is used in dance movement therapy (DMT), with the aim to facilitate emotional expression and a change of emotional state through movement and dance for individuals with psychosocial or psychiatric complaints. It has been demonstrated that moving in a specific way can evoke and regulate related emotions. The current study aimed to investigate the effects of executing a unique set of kinetic movement elements on an individual mover’s experience of happiness. A specific sequence consisting of movement elements that recent studies have related to the feeling of happiness was created and used in a series of conditions. To achieve a more realistic reflection of DMT practice, the study incorporated the interpersonal dimension between the dance movement therapist (DMTh) and the client, and the impact of this interbodily feedback on the emotional state of the client. This quantitative study was conducted in a within-subject design. Five male and 20 female participants (mean age = 20.72) participated in three conditions: a solo executed movement sequence, a movement sequence executed with a DMTh who attuned and mirrored the movements, and a solo executed movement sequence not associated with feelings of happiness. Participants were only informed about the movements and not the feelings that may be provoked by these movements. The effects on individuals were measured using the Positive and Negative Affect Schedule and visual analog scales. Results showed that a specific movement sequence based on movement elements associated with happiness executed with a DMTh can significantly enhance the corresponding affective state. An additional finding of this study indicated that facilitating expressed emotion through movement elements that are not associated with happiness can enhance feelings such as empowerment, pride, and determination, which are experienced as part of positive affect. The results show the impact of specific fullbody movement elements on the emotional state and the support outcome of DMT on emotion regulation.
Creating and testing the first Brand Segmentation Model in Augmented Reality using Microsoft Hololens. Sanoma together with SAMR launched an online brand segmentation tool based on large scale research, The brand model uses several brand values divided over three axes. However they cannot be displayed clearly in a 2D model. The space of BSR Quality Planner can be seen as a 3-dimensional meaningful space that is defined by the terms used to typify the brands. The third axis concerns a behaviour-based dimension: from ‘quirky behaviour’ to ‘standardadjusted behaviour’ (respectful, tolerant, solidarity). ‘Virtual/augmented reality’ does make it possible to clearly display (and experience) 3D. The Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences has created the BSR Quality Planner in Virtual Reality – as a hologram. It’s the world’s first segmentation model in AR. Breda University of Applied Sciences (professorship Digital Media Concepts) has deployed hologram technology in order to use and demonstrate the planning tool in 3D. The Microsoft HoloLens can be used to experience the model in 3D while the user still sees the actual surroundings (unlike VR, with AR the space in which the user is active remains visible). The HoloLens is wireless, so the user can easily walk around the hologram. The device is operated using finger gestures, eye movements or voice commands. On a computer screen, other people who are present can watch along with the user. Research showed the added value of the AR model.Partners:Sanoma MediaMarketResponse (SAMR)
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Ballet en moderne dans zijn een vorm van topsport. De druk op dansers is enorm. Lange en intensieve werkdagen, veel reizen en verschillende werkplekken maken het lastig om lichaam en geest goed te verzorgen. Hierdoor liggen blessures en mentale klachten op de loer. Nederlandse dansgezelschappen willen meer aandacht gaan besteden aan preventieve maatregelen om fysieke en mentale problemen bij hun dansers te voorkomen. Het ontbreekt hen echter aan kennis en kunde om dit innovatieve vraagstuk op te kunnen pakken. Het Nationale Ballet en het Scapino Ballet hebben het lectoraat Performing Arts Medicine van Codarts (Hogeschool voor de Kunsten Rotterdam) benaderd om antwoord te krijgen op de vraag hoe dansers op de hoogste podia, op gezonde wijze, hun beste performance kunnen laten zien. Gezamenlijk is deze praktijkvraag omgevormd naar drie onderzoeksdoelstellingen: 1. Opstellen van meetinstrumenten om de fysieke en mentale gezondheid van dansers te screenen en te monitoren; 2. Ontwerpen van een web-based systeem dat automatisch en real-time informatie uit de ontwikkelde meetinstrumenten kan inlezen, analyseren en interpreteren; 3. Ontwikkelen van een Fit to Perform protocol dat aanbevelingen geeft ten aanzien van het verbeteren van de fysieke en mentale gesteldheid van de danser. Het consortium bestaat uit de volgende organisaties: - Praktijkgerichte onderzoeksinstellingen: Codarts Rotterdam en Hogeschool van Amsterdam; - Universiteiten: ErasmusMC, Technische Universiteit Eindhoven en Vrije Universiteit Amsterdam; - Praktijkinstellingen: Het Nationale Ballet en het Scapino Ballet; - Overige instellingen: het Nederlands Paramedisch Instituut (NPi) en het Nationale Centrum Performing Arts (NCPA). Bij de samenstelling van het consortium is gekozen voor een goede mix tussen praktijkorganisaties, onderzoeksinstituten en onderwijsinstellingen. Daarnaast is er sprake van cross-sectorale samenwerking doordat kennis vanuit de podiumkunsten, sport, gezondheidszorg, onderwijs en technologie met elkaar verbonden wordt.
