Objective: To evaluate the effectiveness of a specialized physical therapy (SPT) program on disability in cervical dystonia (CD) compared to regular physical therapy (RPT). Design: A single-blinded randomized controlled trial. Setting: This study was performed by a physical therapist in a primary health care setting. Measurements were performed at baseline, 6 and 12 months in the botulinum toxin (BoNT) outpatient clinic of the neurology department. Participants: Patients with primary CD and stable on BoNT treatment for 1 year (N=96). Main Outcome Measures: The primary outcome was disability assessed with the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Secondary outcomes were pain, anxiety, depression, quality of life (QOL), and health related costs over 12 months. Results: A total of 72 participants (30 men, 42 women) finished the study: 40 received SPT, 32 RPT. No significant between group differences were found after 12 months of treatment (P=.326). Over these 12 months both groups improved significantly (P<.001) on the TWSTRS disability scale compared to baseline (SPT 1.7 points, RPT 1.0 points). Short Form 36 (SF-36) General Health Perceptions (P=.046) and self-perceived improvement (P=.007) showed significantly larger improvements after 12 months in favor of SPT. Total health related costs after 12 months were $1373±556 for SPT compared to $1614±917 for RPT. Conclusion: SPT revealed no significant differences compared to RPT after 12 months of treatment on the TWSTRS disability scale. Both groups showed similar improvements compared to baseline. Positive results in the SPT group were higher patient perceived effects and general health perception. Treatment costs were lower in the SPT group. With lower costs and similar effects, the SPT program seems to be the preferred program to treat CD.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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Paperbijdrage conferentie EARLI SIG 14, 11-14 september 2018, Genève Universities of Applied Sciences (UAS) that offer Professional Studies (PS) are required to educate students to become starting professionals with research competence, that enable them to deal with challenging tasks that professionals face in a dynamic knowledge society (e.g. Heggen, Karseth, & Kyvik, 2010). To assess professional and research competence, students at UAS in the Netherlands mostly develop a professional product for an external bidder as their graduation project. The professional product is an artefact that is ideally representative for students’ future professions within a specific domain, e.g. a strategic advice within the economic domain (e.g. Losse, 2016). Due to the integrative and complex character of this task, supervision is essential and we thus need to understand what expertise supervisors need and which are good pedagogic strategies. However, little is known about graduation project supervision at UAS. This literature review aims at providing knowledge about graduation project supervision and at revealing what further inquiry on graduation project supervision should aim at, by answering the following questions: 1. What expertise do supervisors need and what is known about pedagogic strategies in graduation project supervision at UAS? 2. What should further inquiry after graduation project supervision at UAS aim at?
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