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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AbstractObjective: Many older individuals receive rehabilitation in an out-of-hospital setting (OOHS) after acute hospitalization; however, its effect onmobility and unplanned hospital readmission is unclear. Therefore, a systematic review and meta-analysis were conducted on this topic.Data Sources: Medline OVID, Embase OVID, and CINAHL were searched from their inception until February 22, 2018.Study Selection: OOHS (ie, skilled nursing facilities, outpatient clinics, or community-based at home) randomized trials studying the effect ofmultidisciplinary rehabilitation were selected, including those assessing exercise in older patients (mean age 65y) after discharge from hospitalafter an acute illness.Data Extraction: Two reviewers independently selected the studies, performed independent data extraction, and assessed the risk of bias.Outcomes were pooled using fixed- or random-effect models as appropriate. The main outcomes were mobility at and unplanned hospitalreadmission within 3 months of discharge.Data Synthesis: A total of 15 studies (1255 patients) were included in the systematic review and 12 were included in the meta-analysis (7assessing mobility using the 6-minute walk distance [6MWD] test and 7 assessing unplanned hospital readmission). Based on the 6MWD, patientsreceiving rehabilitation walked an average of 23 m more than controls (95% confidence interval [CI]Z: 1.34 to 48.32; I2: 51%). Rehabilitationdid not lower the 3-month risk of unplanned hospital readmission (risk ratio: 0.93; 95% CI: 0.73-1.19; I2: 34%). The risk of bias was present,mainly due to the nonblinded outcome assessment in 3 studies, and 7 studies scored this unclearly.Conclusion: OOHS-based multidisciplinary rehabilitation leads to improved mobility in older patients 3 months after they are discharged fromhospital following an acute illness and is not associated with a lower risk of unplanned hospital readmission within 3 months of discharge.However, the wide 95% CIs indicate that the evidence is not robust.
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