PURPOSE: Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCTs) to investigate moderators of exercise intervention effects on cancer-related fatigue.METHODS: We used individual patient data from 31 exercise RCTs worldwide, representing 4,366 patients, of whom 3,846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z-score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test.RESULTS: Exercise interventions had statistically significant beneficial effects on fatigue (β= -0.17 [95% confidence interval (CI) -0.22;-0.12]). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (βdifference= -0.18 [95%CI -0.28;-0.08]). Supervised interventions with a duration ≤12 weeks showed larger effects on fatigue (β= -0.29 [95% CI -0.39;-0.20]) than supervised interventions with a longer duration. CONCLUSIONS: In this individual patient data meta-analysis, we found statistically significant beneficial effects of exercise interventions on fatigue, irrespective of demographic and clinical characteristics. These findings support a role for exercise, preferably supervised exercise interventions, in clinical practice. Reasons for differential effects in duration require further exploration.
Dutch prevocational secondary schools are reforming their educational programmes to make them more competence-based. This reform has substantial implications for the roles played by teachers. Yet, little empirical research has been conducted on teachers' processes of competence development in vocational settings. This study explores teachers' individual action theories regarding the introduction of competence-based prevocational secondary education. The cognitive apprenticeship model provides a conceptual framework for addressing this issue. The research questions addressed here are: How do teachers value elements of the cognitive apprenticeship model in designing and delivering competence-based prevocational secondary education?, and, What individual action theories do teachers have regarding competence-based prevocational secondary education? The study was designed in two phases. In the qualitative phase, interviews and concept map techniques were used, while the quantitative phase employed a questionnaire. Teachers valued elements of the cognitive apprenticeship model differently, and suggested two additional features: a custommade educational approach and the professionalisation of teachers.
Middels een RAAK-impuls aanvraag wordt beoogd de vertraging van het RAAK-mkb project Praktische Predictie t.g.v. corona in te halen. In het project Praktische Predictie wordt een prototype app ontwikkeld waarmee fysiotherapeuten in een vroeg stadium het chronisch worden van lage rugpijn kunnen voorspellen. Om chronische rugpijn te voorkomen is het belangrijk om in een vroeg stadium de kans hierop in te schatten door psychosociale en mogelijk andere risicofactoren op chronische pijnklachten te herkennen en hierop te interveniëren. Fysiotherapeuten zijn met deze vraag naar het lectoraat Werkzame factoren in Fysiotherapie en Paramedisch Handelen van de Hogeschool van Arnhem en Nijmegen gegaan en dit heeft aanleiding gegeven een onderzoek op te zetten waarin een dergelijke methodiek ontwikkeld wordt. De voorgestelde methodiek betreft een Clinical Decision Support Tool waarmee een geïndividualiseerde kans op chronische rugpijn kan worden bepaald gekoppeld aan een behandeladvies conform de lage rugpijn richtlijn. Hiervoor is eerst geïnventariseerd welke methoden fysiotherapeuten reeds gebruiken en welke in de literatuur worden genoemd. Op basis hiervan is een keuze gemaakt ten aanzien van data die digitaal verzameld worden in minimaal 16 fysiotherapiepraktijken waarbij patiënten gedurende 12 weken gevolgd worden. Met de verzamelde data worden met machine learning algoritmes ontwikkeld voor het berekenen van de kans op chroniciteit. De algoritmes worden ingebouwd in de Clinical Decision Support Tool: een gebruiksvriendelijke prototype app. Bij het ontwikkelen van de tool worden eindgebruikers (fysiotherapeuten en patiënten) intensief betrokken. Op deze manier wordt gegarandeerd dat de tool aansluit bij de wensen en behoeften van de doelgroep. De tool berekent de kans op chroniciteit en geeft een behandeladvies. Daarnaast kan de tool gebruikt worden om patiënten te informeren en te betrekken bij de besluitvorming. Vanwege de coronacrisis is er een aanzienlijke vertraging in de patiënten-instroom (doel n= 300) ontstaan die we met ondersteuning van een RAAK-impuls subsidie willen inlopen.
The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
