Work related musculoskeletal disorders amongst therapists in physically demanding roles: Qualitative analysis of risk factors and strategies for prevention

Background Physiotherapy and occupational therapy are two professions at high risk of work related musculoskeletal disorders (WRMD). This investigation aimed to identify risk factors for WRMD as perceived by the health professionals working in these roles (Aim 1), as well as current and future strategies they perceive will allow them to continue to work in physically demanding clinical roles (Aim 2). Methods A two phase exploratory investigation was undertaken. The first phase included a survey administered via a web based platform with qualitative open response items. The second phase involved four focus group sessions which explored topics obtained from the survey. Thematic analysis of qualitative data from the survey and focus groups was undertaken. Results Overall 112 (34.3%) of invited health professionals completed the survey; 66 (58.9%) were physiotherapists and 46 (41.1%) were occupational therapists. Twenty-four health professionals participated in one of four focus groups. The risk factors most frequently perceived by health professionals included: work postures and movements, lifting or carrying, patient related factors and repetitive tasks. The six primary themes for strategies to allow therapists to continue to work in physically demanding clinical roles included: organisational strategies, workload or work allocation, work practices, work environment and equipment, physical condition and capacity, and education and training. Conclusions Risk factors as well as current and potential strategies for reducing WRMD amongst these health professionals working in clinically demanding roles have been identified and discussed. Further investigation regarding the relative effectiveness of these strategies is warranted.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.