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Background: Dentists (Ds) and dental assistants (DAs) have a high lifetime prevalence of musculoskeletal disorders (MSDs). In this context, it is assumed that they have an increased intake of substances such as pain medication. Currently, there exist no data on the use of medication among Ds and DAs with MSDs in Germany. Methods: The online questionnaire (i.e., the Nordic Questionnaire) analysed the medical therapies used by 389 Ds (240 f/149 m) and 406 DAs (401 f/5 m) to treat their MSDs. Results: Ds (28.3–11.5%) and DAs (29.4–10.3%) with MSDs took medication depending on the affected body region. A trend between the Ds and DAs in the intake of drug therapy and the frequency was found for the neck region (Ds: 21.1%, DAs: 28.7%). A single medication was taken most frequently (Ds: 60.0–33.3%, DAs: 71.4–27.3%). The frequency of use varied greatly for both occupational groups depending on the region affected. Conclusion: Ds and DAs perceived the need for medical therapies because of their MSDs. Painkillers such as ibuprofen and systemic diclofenac were the medications most frequently taken by both occupational groups. The intake of pain killers, most notably for the neck, should prevent sick leave.
Traditional ergonomic risk assessment tools such as the Rapid Upper Limb Assessment (RULA) are often not sensitive enough to evaluate well-optimized work routines. An implementation of kinematic data captured by inertial sensors is applied to compare two work routines in dentistry. The surgical dental treatment was performed in two different conditions, which were recorded by means of inertial sensors (Xsens MVN Link). For this purpose, 15 (12 males/3 females) oral and maxillofacial surgeons took part in the study. Data were post processed with costume written MATLAB® routines, including a full implementation of RULA (slightly adjusted to dentistry). For an in-depth comparison, five newly introduced levels of complexity of the RULA analysis were applied, i.e., from lowest complexity to highest: (1) RULA score, (2) relative RULA score distribution, (3) RULA steps score, (4) relative RULA steps score occurrence, and (5) relative angle distribution. With increasing complexity, the number of variables times (the number of resolvable units per variable) increased. In our example, only significant differences between the treatment concepts were observed at levels that are more complex: the relative RULA step score occurrence and the relative angle distribution (level 4 + 5). With the presented approach, an objective and detailed ergonomic analysis is possible. The data-driven approach adds significant additional context to the RULA score evaluation. The presented method captures data, evaluates the full task cycle, and allows different levels of analysis. These points are a clear benefit to a standard, manual assessment of one main body position during a working task.