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Background: In general, the prevalence of work-related musculoskeletal disorders (WMSD) in dentistry is high, and dental assistants (DA) are even more affected than dentists (D). Furthermore, differentiations between the fields of dental specialization (e.g., general dentistry, endodontology, oral and maxillofacial surgery, or orthodontics) are rare. Therefore, this study aims to investigate the ergonomic risk of the aforementioned four fields of dental specialization for D and DA on the one hand, and to compare the ergonomic risk of D and DA within each individual field of dental specialization. Methods: In total, 60 dentists (33 male/27 female) and 60 dental assistants (11 male/49 female) volunteered in this study. The sample was composed of 15 dentists and 15 dental assistants from each of the dental field, in order to represent the fields of dental specialization. In a laboratory setting, all tasks were recorded using an inertial motion capture system. The kinematic data were applied to an automated version of the Rapid Upper Limb Assessment (RULA). Results: The results revealed significantly reduced ergonomic risks in endodontology and orthodontics compared to oral and maxillofacial surgery and general dentistry in DAs, while orthodontics showed a significantly reduced ergonomic risk compared to general dentistry in Ds. Further differences between the fields of dental specialization were found in the right wrist, right lower arm, and left lower arm in DAs and in the neck, right wrist, right lower arm, and left wrist in Ds. The differences between Ds and DAs within a specialist discipline were rather small. Discussion: Independent of whether one works as a D or DA, the percentage of time spent working in higher risk scores is reduced in endodontologists, and especially in orthodontics, compared to general dentists or oral and maxillofacial surgeons. In order to counteract the development of WMSD, early intervention should be made. Consequently, ergonomic training or strength training is recommended.
Triathletes often experience incoordination at the start of a transition run (TR); this is possibly reflected by altered joint kinematics. In this study, the first 20 steps of a run after a warm-up run (WR) and TR (following a 90 min cycling session) of 16 elite, male, long-distance triathletes (31.3 ± 5.4 years old) were compared. Measurements were executed on the competition course of the Ironman Frankfurt in Germany. Pacing and slipstream were provided by a cyclist in front of the runner. Kinematic data of the trunk and leg joints, step length, and step rate were obtained using the MVN Link inertial motion capture system by Xsens. Statistical parametric mapping was used to compare the active leg (AL) and passive leg (PL) phases of the WR and TR. In the TR, more spinal extension (~0.5–1°; p = 0.001) and rotation (~0.2–0.5°; p = 0.001–0.004), increases in hip flexion (~3°; ~65% AL−~55% PL; p = 0.001–0.004), internal hip rotation (~2.5°; AL + ~0–30% PL; p = 0.001–0.024), more knee adduction (~1°; ~80–95% AL; p = 0.001), and complex altered knee flexion patterns (~2–4°; AL + PL; p = 0.001–0.01) occurred. Complex kinematic differences between a WR and a TR were detected. This contributes to a better understanding of the incoordination in transition running.
Background: The Rapid Upper Limb Assessment (RULA) is used for the risk assessment of workplace-related activities. Thus far, the paper and pen method (RULA-PP) has been predominantly used for this purpose. In the present study, this method was compared with an RULA evaluation based on kinematic data using inertial measurement units (RULA-IMU). The aim of this study was, on the one hand, to work out the differences between these two measurement methods and, on the other, to make recommendations for the future use of the respective method on the basis of the available findings. Methods: For this purpose, 130 (dentists + dental assistants, paired as teams) subjects from the dental profession were photographed in an initial situation of dental treatment and simultaneously recorded with the IMU system (Xsens). In order to compare both methods statistically, the median value of the difference of both methods, the weighted Cohen’s Kappa, and the agreement chart (mosaic plot) were applied. Results: In Arm and Wrist Analysis—area A—here were differences in risk scores; here, the median difference was 1, and the agreement in the weighted Cohen’s kappa test also remained between 0.07 and 0.16 (no agreement to poor agreement). In area B—Neck, Trunk, and Leg Analysis—the median difference was 0, with at least one poor agreement in the Cohen’s Kappa test of 0.23–0.39. The final score has a median of 0 and a Cohen’s Kappa value of 0.21–0.28. In the mosaic plot, it can be seen that RULA-IMU had a higher discriminatory power overall and more often reached a value of 7 than RULA-PP. Conclusion: The results indicate a systematic difference between the methods. Thus, in the RULA risk assessment, RULA-IMU is mostly one assessment point above RULA-PP. Therefore, future study results of RULA by RULA-IMU can be compared with literature results obtained by RULA-PP to further improve the risk assessment of musculoskeletal diseases.
Background: Vacuum cleaning, which is associated with musculoskeletal complaints, is frequently carried out in private households and by professional cleaners. The aim of this pilot study was to quantify the movements during habitual vacuuming and to characterize the movement profile with regard to its variability. Methods: The data were collected from 31 subjects (21 f/10 m) using a 3D motion analysis system (XSens). Eight vacuum cleaners were used to vacuum polyvinyl chloride (PVC) and carpet floors. In 15 joints of the right upper extremity, the trunk and the lower extremities, Principal Component Analysis was used to determine the predominantly varying joints during vacuuming. Results: The movements of the trunk and the lower extremities were relatively constant and, therefore, had less influence. The shoulder, elbow and wrist joints were identified as joints that can be decisive for the movement profile and that can be influenced. These joints were represented in the course of the vacuuming cycle by the mean movement with its standard deviation. Conclusion: In summary, the generalization of a movement profile is possible for the trunk and the lower extremities due to the relative homogeneity. In future it will be necessary to identify factors influencing variability in order to draw conclusions about movement ergonomics.