Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Security control brought back to the user

The activity of the Ph.D. student Juri Luca De Coi involved the research field of policy languages and can be divided in three parts. The first part of the Ph.D. work investigated the state of the art in policy languages, ending up with: (i) identifying the requirements up-to-date policy languages have to fulfill; (ii) defining a policy language able to fulfill such requirements (namely, the Protune policy language); and (iii) implementing an infrastructure able to enforce policies expressed in the Protune policy language. The second part of the Ph.D. work focused on simplifying the activity of defining policies and ended up with: (i) identifying a subset of the controlled natural language ACE to express Protune policies; (ii) implementing a mapping between ACE policies and Protune policies; and (iii) adapting the ACE Editor to guide users step by step when defining ACE policies. The third part of the Ph.D. work tested the feasibility of the chosen approach by applying it to meaningful real-world problems, among which: (i) development of a security layer on top of RDF stores; and (ii) efficient policy-aware access to metadata stores. The research activity has been performed in tight collaboration with the Leibniz Universität Hannover and further European partners within the projects REWERSE, TENCompetence and OKKAM.

Predicting the transition from acute to persistent low back pain

BACKGROUND: Most people experience low back pain (LBP) at least once in their lifetime. Only a minority of them go on to develop persistent LBP. However, the socioeconomic costs of persistent LBP significantly exceed the costs of the initial acute LBP episode. AIMS: To identify factors that influence the progression of acute LBP to the persistent state at an early stage. METHODS: Prospective inception cohort study of patients attending a health practitioner for their first episode of acute LBP or recurrent LBP after a pain free period of at least 6 months. Patients were assessed at baseline addressing occupational and psychological factors as well as pain, disability, quality of life and physical activity and followed up at 3, 6, 12 weeks and 6 months. Variables were combined to the three indices 'working condition', 'depression and maladaptive cognitions' and 'pain and quality of life'. RESULTS: The index 'depression and maladaptive cognitions' was found to be a significant baseline predictor for persistent LBP up to 6 months (OR 5.1; 95% CI: 1.04-25.1). Overall predictive accuracy of the model was 81%. CONCLUSIONS: In this study of patients with acute LBP in a primary care setting psychological factors at baseline correlated with a progression to persistent LBP up to 6 months. The benefit of including factors such as 'depression and maladaptive cognition' in screening tools is that these factors can be addressed in primary and secondary prevention.

Predicting the transition from acute to persistent low back pain

BACKGROUND: Most people experience low back pain (LBP) at least once in their lifetime. Only a minority of them go on to develop persistent LBP. However, the socioeconomic costs of persistent LBP significantly exceed the costs of the initial acute LBP episode. AIMS: To identify factors that influence the progression of acute LBP to the persistent state at an early stage. METHODS: Prospective inception cohort study of patients attending a health practitioner for their first episode of acute LBP or recurrent LBP after a pain free period of at least 6 months. Patients were assessed at baseline addressing occupational and psychological factors as well as pain, disability, quality of life and physical activity and followed up at 3, 6, 12 weeks and 6 months. Variables were combined to the three indices 'working condition', 'depression and maladaptive cognitions' and 'pain and quality of life'. RESULTS: The index 'depression and maladaptive cognitions' was found to be a significant baseline predictor for persistent LBP up to 6 months (OR 5.1; 95% CI: 1.04-25.1). Overall predictive accuracy of the model was 81%. CONCLUSIONS: In this study of patients with acute LBP in a primary care setting psychological factors at baseline correlated with a progression to persistent LBP up to 6 months. The benefit of including factors such as 'depression and maladaptive cognition' in screening tools is that these factors can be addressed in primary and secondary prevention.