Background: Collar-related pressure ulcers (CRPU) are a problem in trauma patients with a suspicion of cervical cord injury patients. Indentation marks (IM), skin temperature (Tsk) and comfort could play a role in the development of CRPU. Two comparable cervical collars are the Stifneck® and Philadelphia®. However, the differences between them remain unclear. Aim: To determine and compare occurrence and severity of IM, Tsk and comfort of the Stifneck® and Philadelphia® in immobilized healthy adults. Methods: This single-blinded randomized controlled trial compared two groups of immobilized participants in supine position for 20 min. Results: All participants (n = 60) generated IM in at least one location in the observed area. Total occurrence was higher in the Stifneck®-group (n = 95 versus n = 69; p = .002). Tsk increased significantly with 1.0 °C in the Stifneck®-group and 1.3 °C in the Philadelphia®-group (p = .024). Comfort was rated 3 on a scale of 5 (p = .506). Conclusion: The occurrence of IM in both groups was high. In comparison to the Stifneck®, fewer and less severe IM were observed from the Philadelphia®. The Tsk increased significantly with both collars; however, no clinical difference in increase of Tsk between them was found. The results emphasize the need for a better design of cervical collars regarding CRPU.
LINK
This textbook is intended for a basic course in problem solving and program design needed by scientists and engineers using the TI-92. The TI-92 is an extremely powerful problem solving tool that can help you manage complicated problems quickly. We assume no prior knowledge of computers or programming, and for most of its material, high school algebra is sufficient mathematica background. It is advised that you have basic skills in using the TI-92. After the course you will become familiar with many of the programming commands and functions of the TI-92. The connection between good problem solving skills and an effective program design method, is used and applied consistently to most examples and problems in the text. We also introduce many of the programming commands and functions of the TI-92 needed to solve these problems. Each chapter ends with a number of practica problems that require analysis of programs as well as short programming exercises.
Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
