We focussed on five postures that are described as risk factors for the development of knee osteoarthritis, according to the definition of the respective occupational disease listed in the German schedule of occupational diseases (No. 2112) (BMGS 2005). These included unsupported kneeling (one or both knees on the ground without supporting the trunk with the upper extremities), supported kneeling (one or both knees on the ground with additional support of the upper extremities), sitting on heels (both knees on the ground and contact between heels and backside), squatting (no knee on the ground), and crawling (moving on all four extremities) (Fig. 1). For identification of the particular postures, knee flexion was defined as the angle between the imaginary axis of the thigh and the front side of the lower leg; standing with straight legs was defined as neutral position. Kneeling or squatting with thigh-calf-contact (Caruntu et al. 2003) was defined as deepest flexion with a knee angle of 155° (maximum flexion, Zelle et al. 2009).
Posture capturing was performed using the ambulant measuring system CUELA (German abbreviation for “computer-assisted recording and long-term analysis of musculoskeletal loads”). The system has been used for several years in various studies to assess physical stress in numerous occupations and settings (e.g. Ellegast et al. 2009; Freitag et al. 2007, 2012; Glitsch et al. 2007). The system consists of gyroscopes, inclinometers, and potentiometers that are integrated in a belt system to be fixed on a person’s clothing (Fig. 1, b, c, and d). This system allows for time-continuous recording of body angles and the calculation of postures and movements of the trunk (thoracic spine, lumbar spine) and lower limb (hip and knee joints) with a sample rate of 50 Hz. A rechargeable battery pack runs the system allowing the subject to do his work independently and in a usual manner. All sensor data are directly logged on the system itself and saved on a memory card for subsequent IT-analyses. Every measurement is accompanied by video-recording, allowing a parallel view on the measured exposure and the real working situation after synchronisation of sensor and video data within the appropriate analysis software (Fig. 2, top left and right). The video data are only used for verification purposes and do not contribute to the posture analysis.
The software features an automated recognition for various body postures and movements and allows for the analysis of occurrence, frequency, duration and dynamics of the defined postures (unsupported kneeling, supported kneeling, sitting on heels, squatting, and crawling), and measured variables (e.g. knee flexion, Fig. 2, bottom).
All measurements were performed by experienced technical services of the Statutory Accident Insurance companies, applying a total of ten measuring systems used in parallel at various locations in Germany.
Task modules or typical shifts
For all examined occupations, a board of technical experts of the German Statutory Accident Insurance defined typical tasks in which knee-straining postures were assumed to occur frequently and which were usually carried out for a whole work shift, for example tilers’ work can be separated into floor tiling, wall tiling, et cetera. These single tasks and their concomitant activities such as preparation and clearance work, breaks, and driving time were combined as task modules or typical shifts. It was planned to measure at least three work shifts performed by different workers per task module to capture inter-individual variations. In reality, working conditions limited this protocol to a total of 81 task modules, and 30 modules (=37.0 %) were measured less than three times (15 modules (=18.5 %) were measured just once; another 15 modules (=18.5 %) were measured just twice).
As one of the aims of the study was to assess daily exposure of a task module without measuring the entire work shift, it was necessary to obtain the full information about all single tasks occurring during a shift and to prioritise tasks to be measured based on the criteria of them containing knee-straining postures. For this purpose, in preparation for the measuring day, information regarding the tasks was collected from the participating enterprises and a measuring plan was developed. Finally, this plan was completed by the subjects themselves reporting all tasks, concomitant activities, and breaks of the day using a sort of diary. For example, when investigating floor layers’ task module laying carpet, we were measuring the single tasks application of glue and laying carpet in the morning, and he reported all tasks and breaks happening in the afternoon (Table 1). By combining the information from the diary with the actually measured data that could be copied to cover all respective task periods, a reconstruction of the work shift was developed (Table 1, last column).
As a result, the reconstructed work shift could consist of four different time periods: single tasks accompanied by original measurements, single tasks with time-related copies of measurement data, non relevant parts (i.e. concomitant activities), and breaks. The median duration of the original measurements per work shift was 2.2 h (0.5–7.7 h), and 530 h in total were used for analysis.
The accuracy of the CUELA system and the sensors used in the system has been validated in earlier studies with a multiple-camera motion analysis system (Ellegast 1998; Schiefer et al. 2011). In addition, the automatic identification of the five knee-straining postures by the analysis software (Fig. 2) was validated by comparing the duration of the single knee-straining activities as derived from the automatic analysis of the measurement data with the video-taped time intervals of knee-straining postures in the first measuring sample of every single occupation (n = 16) by one observer (DMD).
To validate the specific method of shift reconstruction performed in this study, a validation study was initiated comparing the “reconstructed” exposure with the results of “total shift measurements”. The test consisted of 14 work shifts (eight service technicians, four ramp agents, and two nursery nurses). In each case, posture capturing with CUELA for an entire work shift of seven to 8 h in total was performed.
As a result, we could indicate the time proportions per day spent in the five different knee-straining postures (“measured shift”). Additionally, for every single work shift, a schedule was filled out containing the time periods of all single tasks that have been performed during the shift (similar to Table 1). From these schedules, two or three typical task periods of about 30–50 % of the whole working time were selected and defined as being representative for the whole work shift.
After the measurement, the measuring data of these time periods (“snippets”) were extracted by one of the authors (TG) from the whole measuring data and used as sample files to reconstruct a new working shift by copying and transferring them according to the schedule filled out before (“reconstructed shift”). Thus, we were able to compare the knee-straining postures of the “measured shift” with the “reconstructed shift” by descriptive and nonparametric statistics.
The validation study was conducted with 14 subjects with a mean age of 35.0 years (SD = 12.5) in three different occupations (eight male service technicians, four male ramp agents, and two female nursery nurses).
The main study involved a total of 16 different occupations known as professions at risk of developing knee osteoarthritis or other knee pathologies (Coggon et al. 2000; Vingard et al. 1991; Kivimäki et al. 1992; Jensen et al. 2000a; Wickström et al. 1983). From the respective industry sectors, 110 employers were contacted by the German Statutory Accident Insurance and all agreed to participate in the study with 213 male employees from these enterprises volunteering to participate in the measurements. Their mean age was 35.5 years (SD = 11.3), and all subjects were skilled craftsmen. As 17 subjects participated in more than one measurement, a total of 242 work shifts were analysed (Table 2).
The validity of the automatic posture identification in the pretest was confirmed using linear regression and t test for paired samples. For the comparison of the measured and reconstructed work shifts in the validation study, the Wilcoxon signed-rank test (paired samples) and Spearman’s rank correlation coefficient were used. The time spent in knee-straining postures in different task modules is depicted by descriptive statistics (arithmetic means, standard deviations, and box-plots showing percentiles 5, 25, 50, 75, and 95).