Patients with coronary artery disease (CAD) are more sedentary compared with the general population, but contemporary cardiac rehabilitation (CR) programmes do not specifically target sedentary behaviour (SB). We developed a 12-week, hybrid (centre-based+home-based) Sedentary behaviour IntervenTion as a personaLisEd Secondary prevention Strategy (SIT LESS). The SIT LESS programme is tailored to the needs of patients with CAD, using evidence-based behavioural change methods and an activity tracker connected to an online dashboard to enable self-monitoring and remote coaching. Following the intervention mapping principles, we first identified determinants of SB from literature to adapt theory-based methods and practical applications to target SB and then evaluated the intervention in advisory board meetings with patients and nurse specialists. This resulted in four core components of SIT LESS: (1) patient education, (2) goal setting, (3) motivational interviewing with coping planning, and (4) (tele)monitoring using a pocket-worn activity tracker connected to a smartphone application and providing vibrotactile feedback after prolonged sedentary bouts. We hypothesise that adding SIT LESS to contemporary CR will reduce SB in patients with CAD to a greater extent compared with usual care. Therefore, 212 patients with CAD will be recruited from two Dutch hospitals and randomised to CR (control) or CR+SIT LESS (intervention). Patients will be assessed prior to, immediately after and 3 months after CR. The primary comparison relates to the pre-CR versus post-CR difference in SB (objectively assessed in min/day) between the control and intervention groups. Secondary outcomes include between-group differences in SB characteristics (eg, number of sedentary bouts); change in SB 3 months after CR; changes in light-intensity and moderate-to-vigorous-intensity physical activity; quality of life; and patients’ competencies for self-management. Outcomes of the SIT LESS randomised clinical trial will provide novel insight into the effectiveness of a structured, hybrid and personalised behaviour change intervention to attenuate SB in patients with CAD participating in CR.
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This study focuses on revealing and developing personal constructs regarding problem behaviour in classrooms. Twenty-nine teachers (initial and in-service students) took part in the project. The main idea is that teachers opinions about their pupils and themselves influence the way they act in their classrooms. Their thoughts and ideas about students - their personal constructs - are generally unconscious. To clarify and to develop teachers constructs, we used Kellys repertory grid technique and Garmans reflective approach. Both methods give a powerful impulse to the development of thinking and acting of teachers. They can use the experiences as an integral part of their own action research. & I am one of the teachers who took part in the constructs research.A personal set of fifteen constructs on twenty-eight pupils was collected. These constructs showed me what kinds of constructs I have (mainly social-emotional and cognitive ones) and made me reflect. They also made clear to me that I think less positively on problem children. Participation in this research includes coaching, theoretical orientation and continuous reflection, making me conscious of what (problem) behaviour I like or dislike and what I should change to get a professional, holistic view. Then problem behaviour will be more easily tolerated by me and I can teach my colleagues about my new insights in intercommunicative sessions and by personal counselling.
As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.
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