Is what you see what you get? Standard inclinometry of set upper arm elevation angles.

Applied Ergonomics 47 (2015) 242e252

Contents lists available at ScienceDirect

Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo

Is what you see what you get? Standard inclinometry of set upper arm elevation angles €m a, b, Per Liv a, c, Jennie A. Jackson a, *, Svend Erik Mathiassen a, Jens Wahlstro Mikael Forsman a, d €vle, SE-80176 Ga €vle, Sweden Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Ga Department of Public Health & Clinical Medicine, Occupational and Environmental Medicine, Umeå University, SE-901 85 Umeå, Sweden c €vleborg, SE-801 88 Ga €vle, Sweden Centre for Research and Development, Uppsala University/County Council of Ga d Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 January 2014 Accepted 12 August 2014 Available online

Previous research suggests inclinometers (INC) underestimate upper arm elevation. This study was designed to quantify possible bias in occupationally relevant postures, and test whether INC performance could be improved using calibration. Participants were meticulously positioned in set arm flexion and abduction angles between 0 and 150 . Different subject-specific and group-level regression models comprising linear and quadratic components describing the relationship between set and INC-registered elevation were developed using subsets of data, and validated using additional data. INC measured arm elevation showed a downward bias, particularly above 60 . INC data adjusted using the regression models were superior to unadjusted data; a subject-specific, two-point calibration based on measurements at 0 and 90 gave results closest to the ‘true’ set angles. Thus, inclinometer measured arm elevation data required calibration to arrive at ‘true’ elevation angles. Calibration to a common measurement scale should be considered when comparing arm elevation data collected using different methods. © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Keywords: Measurement error Observation Working postures

1. Introduction It is generally accepted that estimates of working postures become increasingly more correct as one moves from subjective assessment to observational methods to direct measurement tools (van der Beek and Frings-Dresen, 1998; Winkel and Mathiassen, 1994). Thus, a measurement hierarchy has emerged in which data from higher echelon measurement tools are inherently considered to be ‘better’, that is, more precise (repeatable) and less biased (closer to the actual value) than data from measurement tools at lower levels. From a precision standpoint, this hierarchy is well supported: tools at lower levels are often associated with a larger methodological variability than those at higher levels. For instance, considerable variability has been reported within and between observers rating postures from the same video recordings

* Corresponding author. Tel.: þ46 723 047 995. E-mail addresses: [email protected] (J.A. Jackson), svenderik.mathiassen@ €m), per.liv@ hig.se (S.E. Mathiassen), [email protected] (J. Wahlstro lg.se (P. Liv), [email protected] (M. Forsman). http://dx.doi.org/10.1016/j.apergo.2014.08.014 0003-6870/© 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

(Mathiassen et al., 2013; Rezagholi et al., 2012), whereas direct measurement tools, for example, inclinometers, have excellent reported repeatability (Hansson et al., 2001, 2006). However, the ability of a device to produce a faithful measurement depends on the conditions and contexts in which the measurements are made, and not simply on the technical attributes of the measurement device itself (Portney and Watkins, 2009). Thus, if a device interacts with the system it is intended to measure, measurements may be biased. For example, when using an inclinometer to measure joint angles, measurement bias may be introduced if the inclinometer moves relative to the underlying skeleton. By extension, careful consideration is required regarding whether exposure estimated using different methods are directly comparable; for example, postures assessed using inclinometers and observation. If inclinometer measurements do not agree with estimates of the same joint angle obtained using standard practice observation, it must be considered how to transform measured values into a common scale e ideally, one which comes as close as possible to ‘true’ values. If disagreement is considerable, comparison between studies based on observations and inclinometry can be severely compromised, and thus the need for a bias-corrected common scale is vital.

J.A. Jackson et al. / Applied Ergonomics 47 (2015) 242e252

In 1937 (translated to English in 1972), Goldmeier demonstrated that subjects could always correctly visually identify a 90 angle when presented with simple drawings of 87, 90 , and 93 angles provided that the drawings were presented in a normal orientation. This excellent ability of humans to correctly identify normal right angles has been dubbed the ‘Goldmeier effect’ (Ferrante et al., 1995). Applying the Goldmeier effect to the human body, it would be anticipated that, if a person were asked to identify a 90 shoulder abduction angle, the selected elevation angle would look approximately like that shown on the left side of Fig. 1. Similarly, a 90 shoulder flexion angle is expected to look approximately like the angle shown on the right side of Fig. 1. This expected consistency between observers suggests inherent construct validity to observation, at least at 90 angles: arguably, this could therefore be considered a ‘true’ 90 angle. By extension, this would mean that direct technical measurement methods should also assess this angle to be 90 . Findings published by Genaidy et al. (1993) showing that observers were fairly proficient and almost unbiased (throughout the 0e180 range of shoulder flexion) at correctly judging ‘true angles’ from still frames taken from video shot perpendicularly to the worker support this notion. Over the last decade, tri-axial accelerometers have frequently been used as inclinometers to assess upper arm elevation angles with respect to the line of gravity (for example: Bernmark and Wiktorin, 2002; Delisle et al., 2006; Fethke et al., 2011; Hansson €m et al., 2010; Korshøj et al., 2014; Leijon et al., 2005; Wahlstro et al., 2010; Veiersted et al., 2008). Inclinometers are attractive direct measurement devices due to their relatively low cost (Trask et al., 2013), ease of use, and highly portable characteristics. A 2002 paper by Bernmark and Wiktorin investigated the validity of utilising tri-axial accelerometers as inclinometers (INCs) for the purpose of measure arm postures and movements. To achieve this goal, INC angle measurements were compared to angle measurements recorded from an optoelectronic (OPT) measuring

243

Fig. 2. Inclinometry readings at five fixed arm inclination angles, as identified by meticulous observation. Adapted from data published in Bernmark and Wiktorin (2002), as supplied by the authors of that paper. Line of identity shown in solid black; data from individual subjects (n ¼ 5) shown, with dashed lines illustrating quadratic regression model fits.

system at fixed arm elevation angles of 0, 45, 90, 135 and 180 that were determined by meticulous, but unassisted, observation by a single researcher (personal communication e Eva Bernmark). The paper presented a figure showing a strong correlation between the INC and OPT measured arm elevation angles (Bernmark and Wiktorin, 2002). The figure also showed that both systems underestimated the expected inclination angles at, and above, 90 arm elevation. The authors have generously shared the data behind their original figure, and we re-plotted the INC measurement data with respect to the expected angle data to highlight this additional finding (Fig. 2). Non-published data from within our research team have also suggested a similar trend of underestimated arm elevation angles across multiple brands of INC systems and INC

Fig. 1. Representative image of 90 shoulder abduction (left) and flexion (right). A true 90 angle with respect to the line of gravity is shown superimposed on each image.

244


mounting protocols. This underestimation occurs despite the excellent accuracy of tri-axial accelerometer INC systems during static testing in a rigid rack; for example, the Logger Teknologi AB system (Logger Teknologi HG, Åkarp, Sweden) has a reported ‘accuracy’ of 1.3 and a ‘reproducibility’ of 0.2 during static testing (Hansson et al., 2001), and angles measured using the Virtual Corset system (Microstrain Inc., Vermont, USA) were reported to deviate no more than 2 from correct values (Amasay et al., 2009). The purpose of the current study was therefore to evaluate the extent to which arm elevation angles measured using standard triaxial inclinometry systematically underestimate ‘true’ elevation angles, as determined from meticulous, assisted observation positioning of the arm in different postures, and whether a possible bias could be effectively adjusted for using regression models. Two different INC mounting locations on the upper arm were investigated to determine whether anatomical placement has an effect on estimation accuracy. Fig. 3. (A) INC cranial (Cr-VM) and caudal (Ca-LT) mounting locations e both systems were aligned with the long axis of the humerus at 0 upper arm elevation. (B) Subject shown at 45 upper arm elevation; orientation vectors have been drawn along both INC systems to highlight the difference in INC orientation with respect to the underlying long axis of the humerus (dashed line) at the different mounting locations.

2. Methods 2.1. Subjects A total of 19 participants (12 males, 7 females) were recruited by €vle, Sweden advertisement at three educational institutions in Ga (Table 1). Subjects were excluded if they had any ailment preventing them from comfortably moving through a full range of shoulder motion (arms at side to arms overhead) or to their maximum trunk flexion angle (trunk data not reported in the current study). The study was approved by the Regional Ethical Review Board in Uppsala, and all subjects signed an informed consent form. A subgroup of eight subjects (6 males and 2 females) returned on a second occasion, within 3 days from the first session, and repeated the test protocol (Day 2 group e Table 1). 2.2. Instrumentation One VM INC (79 39 15 mm) was mounted atop the right deltoid muscle, such that the long axes of the INC and humerus were aligned when the arm was positioned at 0 elevation (Fig. 3A). The VM INC was positioned atop as flat a portion of the lateral aspect of the muscle as was deemed possible, and with the superior edge of the INC at or below the superior aspect of the acromion process, as was done by this research group in a study of Swedish flight loaders (Trask et al., 2013, 2012). This placement is hence referred to as the ‘cranial’ mounting position (Cr-VM). During active arm elevation movements changes occur in the shape of the upper arm muscles and the skin moves with respect to the underlying muscles. Depending on the mounting location, these changes can result in varying degrees of rotation of the INC with respect to the long axis of the humerus, as shown in Fig. 3B. To investigate the possible impact of INC placement on the measurement result, a second INC mounting location was Table 1 Description of study subjects e mean [range].

Day 1 group (n ¼ 19) - Males (n ¼ 12) - Females (n ¼ 7) Day 2 group (n ¼ 8)

Age (years)

Height (m)

Weight (kg)

BMI (kg m2)

39.8 [23e59]

1.76 [1.59e1.98] 1.83 [1.70e1.98] 1.63 [1.59e1.69] 1.77 [1.60e1.98]

71.4 [55.0e97.0] 77.3 [67.0e97.0] 59.8 [55.0e70.0] 72.0 [55.0e97.0]

23.1 [18.9e29.9]

42.9 [23e59] 33.7 [27e41] 35.6 [23e57]

23.2 [18.9e29.9] 22.3 [20.6e27.7] 22.9 [18.9e29.9]

investigated. One LT INC (35 20 16 mm) was positioned with the upper edge of the INC aligned with the insertion of the deltoid muscle into the humerus and the long axes of the INC and humerus aligned. This specific LT INC alignment has been previously used in other LT INC studies (Juul-Kristensen et al., 2001; Leijon et al., 2005), although some studies first mount the LT INC on a plastic plate (55 27 mm) before affixing the plate to the arm (Hansson et al., 2006). This placement is hence referred to as the ‘caudal’ mounting position (Ca-LT) e Fig. 3. The agreement between the two INC systems was investigated prior to performing the experiment by affixing both devices to the same rigid board which was then rotated in 15 increments. The largest difference in readings between the two systems when rotated about the axis relevant to eventual recordings of upper arm elevation in situ was 1.3 , which occurred at a set angle of 180 . The mean difference between the two systems through the entire range of set angles was 0.5 . We therefore concluded that possible differences seen between the INC systems in our experiment would be attributed to mounting location (Cr vs Ca) and not to technical differences between the VM and LT systems. 2.3. Experimental protocol Prior to the experiment, subjects were asked to sit leaning to the right and holding a 2 kg dumbbell in their right hand with the right arm hanging vertically, as guided by a certified physiotherapist. INC data recorded during this reference trial were defined to represent 0 elevation (Bernmark and Wiktorin, 2002; Hansson et al., 2006; Leijon et al., 2005). Subjects then performed a series of right arm flexion (AF) and arm abduction (AA) tests. The same physiotherapist guided all subjects to each target position using set angles which were projected on the wall behind the subject as a guide. The physiotherapist attempted to meticulously match the midline of the long axis of the humerus to the projected line of the target posture, with the projected vector passing through the centre of the elbow joint (Fig. 4) e a process we have termed ‘meticulous, assisted observation’. A mirror was provided to subjects to aid in achieving and holding each set angle posture. The location of the projected arm axis system was customised for each subject so the origin of the projected axes aligned with the estimated centre of rotation of that subject's shoulder joint when standing straight with their arms at


245

Fig. 4. Experimental set up showing the projected set angles for (A) arm abduction and (B) arm flexion angle trials. The positioning technique employed is shown in figures A and B, with the estimated midline of the long axis of the humerus aligned along the projected angle vector; figure C shows erroneous positioning e here, the fingers were aligned with the projected vector in lieu of aligning the humerus.

their sides, palm towards the thigh. Once set, the projected axis system image remained in the same position throughout all tests. To help reposition the subject during subsequent trials, the estimated centre of shoulder rotation was marked using a piece of tape affixed to the subject. Subjects completed two blocks each of abduction and flexion trials e the order of the four blocks was randomly assigned. Within each block, the individual angle trials (0 , 30 , 45 , 60 , 90 , 120 , 150 ) were presented in a random order. Subjects maintained a straight arm with a downward facing palm for all flexion and abduction trials. Prior to the completion of the experiment, three additional arm flexion and three arm abduction trials were conducted (30 , 60 , 90 ) with the palm facing upwards. Subjects held each test posture for 4e5 s during which time a digital mark was made in the inclinometer data file to aid in post process identification of target angles. The day 2 subgroup repeated the above protocol with the exception of the final palm-up postures. 2.4. Data processing VM INC data were sampled at 32 Hz and LT INC data at 20 Hz, as dictated by the respective systems. Data were transferred from the data loggers to a computer for analysis. VM INC data were subsequently down-sampled to 20 Hz and all data were then processed using software developed at the Department of Occupational and Environmental Medicine, Lund University, Sweden. To account for accelerometer drift, all inclinometer data were off-set corrected at ±1 g about each of the X, Y and Z axes (Hansson et al., 2001, 2006). Specific angle events were identified in each continuous data file using the marks made during data collection. For each angle, the mean value was taken over a 2 s window about the mark. All data were visually inspected to ensure the windowed data was correctly timed. Data were partitioned with some data used to develop regression model equations and the remaining data used to evaluate the performance of these equations (see below). 2.5. Development of regression models Data from both the Cr-VM and Ca-LT INCs proved to be considerably different from the expected ‘true’ angles, thus

confirming that upper arm elevation angles were underestimated by standard inclinometry (cf. Results below). Accordingly, we continued by developing regression models of the relationship between inclinometer measurements and ‘true’ set angles, for the purpose of eventually being able to adjust INC data for bias. To this end, we developed and assessed the performance of two types of regression models: linear models specifically developed for each subject, and quadratic models developed from data at the group level. Linear equations, y ¼ b1x þ g; where y ¼ INC measurement data and x ¼ set angle, were determined for each individual subject with a forced intercept of zero (i.e. g ¼ 0) and using the slope (b1) calculated between the INC data points recorded at the vertical reference position (0 ) and at 90 set arm elevation. Three twopoint linear models were investigated using subsets of the 90 data as follows: ▪ L1 e 2-pt AA. INC data obtained at 90 arm abduction only ▪ L2 e 2-pt AF. INC data obtained at 90 arm flexion only ▪ L3 e 2-pt X. NC data at 90 elevation, calculated as the mean of 90 abduction and flexion trials These individual-level calibration models were investigated as we believed they represent a fast and feasible approach for determining a subject-specific calibration model in both field and lab inclinometry studies. The calibration positions (0 and 90 ) were selected based on (i) the particular ability of both research participant and guiding researcher in observing and correctly reproducing the two postures (according to the Goldmeier effect at 90 , as described in the introduction) and; (ii) the fact that most occupational work is done within this range e even in occupations regarded as having a high occurrence of work done with arms in elevated positions, such as house painting or hairdressing, the proportion of time spent with upper arms elevated more than 90 is most likely considerably less than 10% €m et al., 2010; Veiersted et al., (Svendsen et al., 2004a,b; Wahlstro 2008). All three models were developed using data collected during the first block of posture testing. We verified the performance of the models by using them ‘in reverse’, i.e. x ¼ y/b1, to predict set angles from Cr-VM and Ca-LT measured data collected during the second block of trials on the same day. We assessed the

246


performance of each model in this calibration process by comparing the calibrated INC data with the expected ‘true’ values (cf. Section 2.5 below). Linear regression models were derived and verified for calibration of same-day data for both day 1 (n ¼ 19) and day 2 (n ¼ 8) data sets. In addition to the three individual-level models, we investigated a group-level approach as we believed it might be attractive in studies involving a sufficient number of participants to developing a study-specific model based on information from all subjects. Alternatively, representative group-based calibration models present in the literature could be attractive for use in future studies where neither an individual-level nor a comprehensive group-level calibration procedure is feasible. Thus, four group-level quadratic models with a forced intercept through the origin (0, 0) were determined using day 1 INC arm flexion and abduction test posture data. The common format for all four models was, y ¼ b1 x þ b2 x2 þ a1 x þ a2 x2 ; where y ¼ INC measurement data, x ¼ set angle, and a1 and a2 are subjectspecific random effects terms, representing individual deviations from the group-level regression coefficients, b1 and b2, respectively. The four models were fitted to different subsets of the data as follows: ▪ Q1 e AA þ AF e all angles, no PU. Model using all day 1 data except the palm up trials ▪ Q2 e AA þ AF e no 150, no PU. Model using all day 1 data except the 150 flexion and 150 abduction trials, and the palm up trials ▪ Q3 e AA þ AF e all angles þ PU. Model using all day 1 data, including the palm up trials ▪ Q4 e AA þ AF e no 150 þ PU. Model using all day 1 data, including the palm up trials, but not including the 150 flexion and 150 abduction trials Model parameters were estimated using the restricted maximum likelihood approach. Models Q2 and Q4 excluded the most extreme arm elevation trials to determine whether these data points would substantially influence the shape of the relationship between set and measured angles. We considered this to be a possibility if the INC underestimation of the ‘true’ angles increased at greater arm elevation angles. Further, we wanted to evaluate whether the possible effect of including all the data on the model parameters would be detrimental to the ability of the model to correctly estimate mid-range elevation angles, i.e. those most often seen in occupational settings. Models Q3 and Q4 included both the palm up and palm down data. While we anticipated this would increase the within-subject variance at a given set angle and possibly result in decreased goodness of fit, it also ensured that the models included data showing the effect at the upper arm from the full range of possible forearm pronation/supination angles that might be seen in occupational settings, and might therefore be better for calibrating occupational data. All quadratic models were based on day 1 data (n ¼ 19), and verified using day 2 data (n ¼ 8). In the verification procedure, CrVM and Ca-LT measured data from the second day were calibrated using each of the four quadratic equations after re-arranging them to solve for the predicted set angle:

x¼

b1 þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b21 þ 4b2 *y 2b2

The performance of each model was assessed by comparing the calibrated INC data with the expected ‘true’ data (cf. Section 2.5 below).

2.6. Evaluation of regression models To evaluate the performance of each of the seven regression models (three linear and four quadratic) in calibrating INC data, two metrics were calculated at each set angle to assess deviations from the ‘truth’. First, the mean difference ðDÞ across all subjects for the difference between the model predicted joint angle and the ‘true’ set angle was calculated. This metric assessed bias. Second, a joint bias and (im)precision metric was calculated as the root mean qffiffiffiffiffiffi 2 squared difference D utilising the same predicted vs ‘true’ angle difference. These two metrics were also used to assess the performance of unadjusted INC measurements at each set angle. To summarise the overall ability of each of the seven regression models to correctly adjust new data, the grand mean across all qffiffiffiffiffiffi 2 subjects and all set angles for D and D were calculated qffiffiffiffiffiffiffi 2 D total ; D total . For comparison, the same summary statistics were calculated for the unadjusted INC data. 3. Results Data from the Cr-VM INC were successfully collected for all subjects and days. A technical issue with the data logger for the CaLT INC resulted in loss of data for 6 of the 19 subjects on day 1. Data from one representative subject are given in Fig. 5, showing the relationships between the set angles, unadjusted INC measurements, and calibrated INC data for both arm abduction and arm flexion. Increased bias at higher elevation angles for the unadjusted INC data is clearly shown for the INCs in both mounting positions and in both planes of movement; these findings were also seen at the group level, as shown in Tables 2a and 2b. In both the flexion and abduction planes, angles greater than 90 were underestimated by at least 10 , regardless of INC mounting position; for the cranial mounted INC, this level of underestimation was reached already at angles greater than 45 . 3.1. Regression models Parameters for the linear and quadratic models are summarised in Table 3. For the linear models slope values (b1) tended to be higher (albeit typically less than 1) for the cranial INC position for the 2-pt AA calibration, and higher in the caudal INC position for the 2-pt AF calibration. Quadratic models fitted to cranial mounted INC data had linear coefficients (b1) less than one, while caudally mounted INC data had linear coefficients (b1) greater than one. A summary of all day 1 data points involved in the development of the Q1 model is shown in Fig. 7 along with the group-level Q1 equation (thick black line) and the best quadratic model fit lines for individual subjects. This figure shows the quadratic model curves were nearly linear in shape with clustering of all but one subjects' curves within the main data spread. Inclinometers at both mounting locations showed increased bias at higher elevation angles, although to differing degrees in the two movement planes. Over the full set angle range, unadjusted INC data deviated to similar extents from the set angle for cranial and caudal mounted inclinometers: Dtotal Cr ¼ 10.6; Ca ¼ 8.9; qffiffiffiffiffiffiffi 2 D total Cr ¼ 190.3; Ca ¼ 204.6 (Tables 2a and 2b, unadjusted row). The cranial mounted INCs were less biased in capturing abduction plane elevation angles than the caudally mounted INCs (Table 2a, unadjusted data row, AA columns). In contrast, the caudally mounted INCs were less biased in capturing flexion plane elevation angles (Table 2a, unadjusted data row, AF columns). Combined bias and imprecision showed a similar trend.


247

Fig. 5. Elevation angles before (un-adj INC) and after calibration using linear and quadratic models, as indicated, for upper arm abduction (left) and flexion (right) trials for one representative subject. Expected values (i.e. set angle values) shown by thick black line of identity. Model Q1 withheld for figure clarity, as described in Section 3.2.

qffiffiffiffiffiffi 2 bias ðDÞ and combination bias and precision ð D Þ parameters, as compared with said parameters from the unadjusted INC recorded data (representative subject in Fig. 5; group level results in Tables 2a and 2b).

3.2. Evaluation of calibration by inverse regression For both mounting locations, all linear and quadratic calibration efforts resulted in improved performance, as seen in the decreased

Table 2a Bias (D; mean across all subjects) and grand mean across all set angles ðDtotal Þ for unadjusted, linear model, and quadratic model data at each set angle. D

Model

Dtotal

Set angle ( )

Cranial INC (Cr-VM)

Caudal INC (Ca-LT)

Unadjusted INC L1 2-pt AA L2 2-pt AF L3 2-pt X Q1 AA þ AF e Q2 AA þ AF e Q3 AA þ AF e Q4 AA þ AF e Unadjusted INC L1 2-pt AA L2 2-pt AF L3 2-pt X Q1 AA þ AF e Q2 AA þ AF e Q3 AA þ AF e Q4 AA þ AF e

AA

AF

AA

AF

AA

AF

AA

AF

AA

AF

AA

AF

30

30

45

45

60

60

90

90

120

120

150

150

all angles, no PU no 150, no PU all angles þ PU no 150 þ PU

0.1 2.0 3.3 0.9 2.3 2.6 3.5 3.9

4.0 0.2 1.3 0.9 2.2 1.9 1.2 0.8

1.2 5.5 7.4 3.7 2.6 2.9 4.1 4.5

7.3 1.7 0.1 3.2 4.3 4.0 2.9 2.5

2.7 6.1 8.6 3.8 3.2 3.4 4.9 5.1

10.2 2.3 0.1 4.3 5.5 5.2 3.9 3.6

7.2 3.9 7.6 0.7 3.9 3.7 5.5 5.1

15.1 2.6 0.7 5.5 5.8 5.9 4.2 4.3

14.1 1.3 5.9 2.9 3.6 2.5 4.4 2.9

18.4 3.2 1.2 7.2 2.2 3.1 1.1 2.4

24.4 6.7 1.2 11.7 0.3 2.0 0.1 3.1

22.5 7.2 1.8 12.1 3.0 0.6 2.5 0.6

10.6 0.4 2.7 3.2 0.1 0.5 1.0 0.4


2.5 3.4 1.4 2.4 2.1 1.4 3.0 2.4

3.9 10.2 7.9 9.0 3.6 2.9 4.4 3.9

1.5 6.8 3.8 5.2 1.3 2.0 0.2 0.8

2.0 10.1 7.0 8.5 2.5 1.7 3.6 3.0

5.4 4.3 0.5 2.3 4.2 4.9 2.9 3.5

0.0 8.9 4.9 6.8 1.9 1.3 3.2 2.8

13.5 1.2 4.1 1.6 8.5 8.5 7.1 7.0

11.1 6.5 0.8 3.5 5.4 5.2 4.0 3.8

20.5 0.9 7.9 4.6 8.7 6.6 7.6 5.7

15.6 3.4 3.9 0.4 1.9 1.0 0.9 1.6

26.3 5.6 14.1 10.1 2.6 5.6 2.8 3.5

21.4 1.6 10.5 6.3 5.3 15.5 4.8 12.3

8.9 3.9 1.2 1.2 1.4 0.2 0.5 0.7

AA e arm abduction; AF e arm flexion; PU e palm up; all regression models described in Section 2.5.

248


qffiffiffiffiffiffi qffiffiffiffiffiffiffi Table 2b 2 2 Joint bias and precision parameter ( D e mean across all subjects) and grand mean across all set angles D total for unadjusted, linear model, and quadratic model data at each set angle. qffiffiffiffiffiffi 2 D

Model

qffiffiffiffiffiffiffi 2 D total

Set angle ( )

Cranial INC (Cr-VM)

Caudal INC (Ca-LT)

Unadjusted INC L1 2-pt AA L2 2-pt AF L3 2-pt X Q1 AA þ AF Q2 AA þ AF Q3 AA þ AF Q4 AA þ AF

e e e e

Unadjusted INC L1 2-pt AA L2 2-pt AF L3 2-pt X Q1 AA þ AF e Q2 AA þ AF e Q3 AA þ AF e Q4 AA þ AF e

AA

AF

AA

AF

AA

AF

AA

AF

AA

AF

AA

AF

30

30

45

45

60

60

90

90

120

120

150

150


3.3 3.0 4.9 3.7 4.3 4.5 5.1 5.4

5.5 3.8 4.2 3.8 4.7 4.6 4.4 4.4

3.3 5.1 8.9 6.8 4.3 4.5 5.4 5.7

8.3 4.9 3.9 4.1 6.1 5.9 5.3 5.1

3.4 4.4 9.4 6.6 4.0 4.2 5.4 5.7

10.6 5.4 3.0 3.7 6.4 6.2 5.2 4.9

7.6 2.2 8.7 4.6 5.0 4.8 6.3 5.9

15.5 6.7 2.9 3.8 7.1 7.1 5.8 5.8

15.0 4.3 8.4 3.8 7.7 7.0 7.9 6.9

18.8 8.5 4.2 4.7 5.1 5.4 4.6 4.9

25.1 13.0 7.8 8.8 8.1 7.9 7.6 7.7

23.1 14.5 8.2 10.2 8.3 7.3 7.7 6.8

13.8 7.3 6.7 5.8 6.1 5.9 6.0 5.8


3.2 5.0 4.5 4.6 2.9 2.4 3.6 3.1

6.8 9.9 8.3 9.0 6.8 6.4 7.4 7.1

2.4 10.5 8.5 9.3 2.4 2.9 2.1 2.3

6.0 10.3 7.6 8.8 6.6 6.4 7.2 7.0

5.9 6.3 6.1 5.8 4.9 5.5 3.9 4.3

3.7 9.6 5.8 7.5 4.7 4.5 5.4 5.2

13.7 3.9 5.4 3.4 9.1 9.2 7.8 7.8

13.2 7.7 2.2 4.2 10.4 10.6 9.7 9.9

21.4 2.8 8.8 5.0 12.1 11.4 11.2 10.6

16.1 8.3 3.0 3.8 6.3 6.8 5.9 6.6

27.4 7.6 13.6 9.7 12.2 15.6 11.7 13.6

21.8 12.7 11.3 10.4 9.0 18.2 8.4 14.8

14.3 8.4 7.8 7.2 7.9 9.5 7.6 8.6


Bias for the group level models is shown in Fig. 6 for each set angle and show that the increase in bias (increased underestimation) at larger elevation angles present in the unadjusted data is diminished using both linear and quadratic calibration models. At the group level, the best subject-specific 2-pt linear model (2pt X) showed better overall performance at both INC mounting qffiffiffiffiffiffiffi 2 positions ( D total Cr ¼ 5.8; Ca ¼ 7.2) than the best quadratic qffiffiffiffiffiffiffi 2 model ( D total Cr ¼ 5.8; Ca ¼ 7.6) (Table 2b). These results also

Also for both mounting locations, the 2-pt model based on the 90 test posture made in the same plane as the subsequent posture trials showed the best performance when calibrating INC data from trails in that same plane (e.g. Fig. 5a and c, 2pt-AA linear calibration of AA planar trials). Predictions were less correct for data collected in the plane perpendicular to that in which the 90 calibration posture was made (ex. Fig. 5b and d, 2-pt-AA linear calibration of AF planar trials). The 2-pt X regression model produced curves that were close in performance for both planes to the plane specific models. The calibrated values across the four quadratic models were all similar, as shown in Tables 2a and 2b, and Fig. 5aed by the three nearly overlying lines (model 1 was barely distinguishable from model 3, so was omitted from the figure for clarity); none of the quadratic models was consistently better or worse than the 2-pt X model across the whole angle range.

indicate that the cranial position INC data could be more effectively adjusted than the caudal position data. Following calibration, both performance parameters (Dtotal and qffiffiffiffiffiffiffi 2 D total ) were markedly improved for INC data from both mounting locations (Tables 2a and 2b, unadjusted row compared to

Table 3 Regression parameters for the linear and quadratic models of the association between set angles and inclinometer readings. (a) Shows the average regression model coefficients across all subjects (day 1), b1, and the standard deviation between subjects for regression coefficients, SD (b1), (cranial, n ¼ 19; caudal, n ¼ 13) for each of the linear models (L1, L2, L3). (b) Shows the (group-level) linear (b1) and quadratic (b2) model coefficients, the standard deviations of the model terms describing individual effects, SD (a1) and SD (a2), and the residual standard deviation (residual SD) for each of the four quadratic models (Q1, Q2, Q3, Q4). a. Linear models

Cranial

Caudal

Cranial

b1 L1 L2 L3

2-pt AA 2-pt AF 2-pt X

b. Quadratic models

0.91 0.85 0.88

Cranial

Caudal

Q1 Q2 Q3 Q4

AA AA AA AA

þ þ þ þ

AF AF AF AF

e e e e

no PU no 150, no PU all angles þ PU no 150 þ PU

0.96 0.95 0.92 0.90

0.87 0.92 0.90

Cranial

b1

Caudal

8.17E04 6.55E04 5.41E04 3.13E04

0.06 0.04 0.04

Cranial

b2 1.06 1.10 1.03 1.06

Caudal

SD (a1) 1.50E03 1.92E03 1.28E03 1.63E03

Caudal SD (b1)

0.07 0.02 0.06 0.02


0.09 0.08 0.07 0.06

Cranial

0.07 0.05 0.06

Caudal SD (a2)

4.25E04 3.24E04 3.22E04 3.32E04

5.65E04 4.87E04 5.53E04 3.42E04

Cranial

Caudal

Residual SD 3.78 4.19 4.60 4.97

3.92 3.79 4.52 4.54


249

Fig. 6. : Bias (mean across all subjects) at each set angle before (un-adj) and after calibration using linear and quadratic models, as indicated, for upper arm abduction (left) and flexion (right) trials. Model Q1 withheld for figure clarity, as described in Section 3.2.

all other rows). Further, neither mounting location performed consistently better in either of the specific movement planes (AA or AF). For all regression models, cranially mounted INC data were more successfully adjusted than caudally mounted INC data qffiffiffiffiffiffiffi 2 (Table 2b, D total column). 4. Discussion Our data demonstrate that arm elevation angles recorded using standard inclinometry systematically underestimate what we argue to be a ‘true’ assessment of arm elevation angle, i.e. by meticulous, assisted observation, particularly at elevation angles greater than 60 . By extension, our results question the notion that different measurement tools assess upper arm postures according to a common scale, and further, whether un-calibrated inclinometers provide the most accurate data possible. Our results indicate that if inclinometers are used for measuring upper arm elevation, data need to be calibrated to approach ‘true’ angle values. The ability to correctly quantify exposures is paramount for the determination of exposureeresponse relationships in epidemiological studies. Erroneous exposure estimates may result in biased risk estimates and salient relationships may be disregarded. As a case in point, inclinometers have been shown to have excellent accuracy and reproducibility (Hansson et al., 2001, 2006), however, they cannot be rigidly fixed to the underlying skeletal system on humans. This may result in measurement bias since the relative position of an inclinometer to the underlying bone will change throughout the range of motion, and will do so to different extents at different arm elevation angles. The concept of error due to skin

movement for technical motion measurement tools is hardly novel (Della Croce et al., 2005). What is, perhaps, more novel and challenging in the present study is the idea that a lower echelon measurement method, such as meticulous, assisted observation, might prove more ‘valid’ in the sense that output data show better agreement with a generally accepted notion of how a posture should look. We developed a procedure for calibrating upper-arm mounted INC data in situ, that is, an ‘on-body calibration’. Such a calibration was performed in addition to an off-set bias correction which was done to account for accelerometer drift. The developed calibration procedure differs from a purely technical INC calibration that could be performed by placing the INC on a rigid rack and setting the rack at different angles (Hansson et al., 2001) as the suggested procedure accounts for the effect of a subject's anatomy on the interaction between the INC and the underlying bone which the INC is purported to measure. We selected pure flexion and abduction angles (or as close to ‘pure’ angles as possible) for use in the development and verification of the calibration equations to ensure a conservative approach. These planes were considered the outer edges of the cubic space in which arm elevation will, for the most part, occur in occupational settings. Similarly, to capture the total range of possible upper arm internal/external rotation due to forearm pronation/supination, both palm up and palm down data were collected. We therefore believe that the performance of the regression models when adjusting subsequently collected INC data occurring anywhere within a normal range of movements will match or exceed the performance for the ‘extreme’ INC data presented in this study. Recent data comparing simultaneous assessments of upper arm elevation angles among hairdressers by INC and unassisted

250


observation of video recordings reported the proportion of time spent with arms above 60 , above 90 , and below 15 was 12.8, 4.1, and 29.3%, respectively, for the OBS approach, compared with 10.2, 1.9, and 23.7%, respectively, for the INC approach (Rezagholi et al., 2012). These results agree with our laboratory study findings in showing less time spent at ‘extreme’ angles when elevation was measured by inclinometry, and it is therefore possible that the field OBS results might also be more correct than the inclinometer data. The difference between INC and OBS even for ‘small’ angles was not expected, however, and is perhaps an indication that the OBS method employed in the Rezagholi study is also biased from the ‘true’ elevation measurement scale we have suggested (i.e. meticulous, assisted observation). A bias between field observation and meticulous, assisted observation data may arise from observers not having access to a projected image or any other guides to aid in correctly identifying angles (although this has been provided in some video-based observation studies (Bao et al., 2009; Trask et al., 2013)), or from having to rate postures which are not presented in a plane perpendicular to the observer (estimates of shoulder flexion during a work task made from video taken simultaneously at 0, 30, 45 and 90 to the joint showed only 58% agreement (Sutherland et al., 2007)). Further, in field studies involving continuous, event-based observation of dynamic movements (for example; Fransson-Hall et al., 1995; Hooftman et al., 2009; Punnett et al., 1991), an additional degree of difficulty and possible source of measurement bias is introduced. Whether the perception of ‘correct’ angles change for dynamic movements compared to still images is not known. Field- or video-based observation will also introduce additional variance in the eventual angle data due to within- and betweenobserver variability (Rezagholi et al., 2012). Observer variability will negatively affect the precision of posture observations, while inclinometry does not suffer from this methodological uncertainty. In our study, observer variability was virtually eliminated by using the guide angles projected on the wall, and much can be done to minimise this effect, even in field studies utilising work sampling. In video-based observation, assisted posture matching (Bao et al., 2009; Trask et al., 2013) may not only decrease the risk of observation bias, as suggested above, but also reduce observer variability. Whether assisted observation procedures represent an attractive alternative for correct posture OBS assessment in the field is an exciting issue for continued research. Previous studies collecting upper arm posture data with inclinometers have often reduced data to categorical variables, such as the proportion of time spent working with arms elevated above 60 (Hansson et al., 2006; Leijon et al., 2005; Rezagholi et al., 2012; € m et al., 2010; van den Heuvel Svendsen et al., 2004a; Wahlstro et al., 2006; Veiersted et al., 2008) or above 90 (Rezagholi et al., €m et al., 2010; 2012; Svendsen et al., 2004a,b, 2005; Wahlstro Veiersted et al., 2008). The use of such bins is supported from an injury prevention perspective by studies showing an increased susceptibility to shoulder injury for subjects with extended exposure to arm elevation angles greater than 60 (van den Heuvel et al., 2006) or 90 (Punnett et al., 2000; Svendsen et al., 2004a,b). Our study suggests that studies utilising inclinometers for measuring arm elevation may systematically underestimate the relative occurrence of postures larger than 60 or 90 . This, in turn, may lead to an exaggeration of the risk associated with working in ‘extreme’ postures, since the exposure levels associated with an increased risk will be lower due to the systematic underestimation by the inclinometer measurement. This, in turn, may lead to an exaggeration of the risk associated with working in ‘extreme’ postures, since disorders will appear to occur at lower absolute levels of exposure than they actually do. However, if risks are assessed in relative terms, as when estimating the odds ratio

associated with increased occurrence of upper arm elevation (Svendsen et al., 2004a), exposure underestimation will be less critical, even if it will still imply biased results due to the fact that bias in exposure increases with increasing exposure levels. These aforementioned posture categories are also common to studies employing observational (OBS) assessment methods to quantifying arm elevation postures (Punnett et al., 2000). Thus, data from both OBS and INC studies could, in theory, be used in concert to examine exposureeresponse relationships, for instance in meta-analyses. Our results suggest that it may be necessary to first ensure that data from these two methods are reported on comparable scales. While, in the present study, we have, by definition, chosen the scale associated with meticulously assisted observation to be ‘true’, it is possible that OBS data obtained using more relaxed procedures that are feasible in the field may also be biased, as noted above. Therefore, it might be necessary to calibrate both INC and OBS data to the proposed ‘true’ scale, or, at very least, adjust results to the scale used by one of the measurement methods to allow for fair comparison of numerical differences between results. Data from 19 subjects were collected for this study. The subjects spanned a range of ages, heights, weights, BMIs and body types, however all were Caucasian, were reasonably fit and toned, and 17 of the 19 had a ‘normal’ BMI (18.5e24.9 kg m2). The bottom curve in Fig. 7 demonstrates a markedly different shape for a single subject in of the relationship between INC data and set angles. This subject, subjectively, had the least toned upper arms and was one of only two individuals who would be classified as ‘overweight’ based on BMI (25.0e29.9 kg m2). It is therefore possible that the reported findings are most applicable to individuals of similar body types to those studied. Our findings lead us to a general recommendation of using an on-body calibration based on the subject-specific linear regression model based on both the 90 abduction and 90 flexion reference postures (the 2-pt X model). The overall statistical performance of 2-pt X calibration was superior, at the group level, compared to group-based quadratic models and other subject-specific linear models. There are occasions, however, where other calibration methods may prove better suited. For example, if a majority of

Fig. 7. Individual subject best-fit quadratic equations (model Q1) shown in different colours; group level best-fit model shown in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)


movements occur in a narrow plane (i.e. consistently close to abduction or flexion), the use of a single 90 angle in that same plane is supported as a basis for calibration. In the absence of subject-specific calibration trial data at 0 and 90 flexion and abduction, the group level quadratic models presented in this paper could be utilised in other INC studies employing similar mounting techniques. For studies utilising a cranially mounted INC, we recommend calibration according to our fourth model (Table 3, row Q4 e b1 and b2) which results in the following reduced quadratic model:

x¼

0:90 þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0:81 0:00125*y 0:00063

For caudally mounted INC data, we recommend calibration according to our third model (Table 3, row Q3 e b1 and b2) which results in the following reduced quadratic model:

x¼

1:03 þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1:06 0:00512*y 0:00256

where x is the calibrated value for each INC data point, y. As a final note, the recommendation to commence calibrating INC data in studies of upper arm elevation provides a challenge for comparison of future INC data to previously published work using inclinometers. Publishing both unadjusted and adjusted (calibrated) data could overcome the issue, however, may lead to difficulties in interpreting results and/or overwhelming amounts of data. It is possible that some previously published INC data has undergone an on-body calibration/adjustment process, but that the process has not been explicitly stated in the methods; this also serves to complicate comparisons. Further still, the issue of not yet knowing whether other methods or procedures for assessing arm elevation (for example, 3D Motion Capture) would also require calibration to arrive at data on a common measurement scale remains. What is clear, however, is that it is paramount to consider whether output data from different posture measurement systems are comparable, and that more attention is required in how data from different systems are treated and compared.

5. Conclusion A systematic underestimation of upper arm elevation measured using standard inclinometry was found, particularly at larger elevation angles. The bias could be adjusted for by applying regression calibration models; the most effective model found was a subject-specific, two-point linear approach that utilised the slope from the origin to the mean of inclinometer recorded values at 90 arm abduction and 90 arm flexion. This model outperformed quadratic models using group-level data as input to the regression. An inclinometer mounted atop the flat portion of the lateral aspect of the deltoid muscle showed to perform better after calibration than an inclinometer placed just below the insertion of the deltoid. Our paper shows that different measurement tools for upper arm postures may not produce results according to a common scale. Inclinometer measurements obtained using standard procedures are commonly believed to generate data very close to ‘true’ angles; the fact that they were shown to be biased relative to what we argue are ‘true’ angles (i.e. angles determined by meticulous, assisted observation) challenges the reader to consider how a ‘best’ method or ‘true’ value should be defined, and specifically, whether unadjusted inclinometer data deserve to be regarded as a gold standard in field assessments of upper arm elevation. We suggest the on-body calibration of INC data is required to minimise bias in INC data compared to ‘true’ angle data.

251

References Amasay, T., Zodrow, K., Kincl, L., Hess, J., Karduna, A., 2009. Validation of tri-axial accelerometer for the calculation of elevation angles. Int. J. Ind. Ergon. 39 (5), 783e789. http://dx.doi.org/10.1016/j.ergon.2009.03.005. Bao, S., Howard, N., Spielholz, P., Silverstein, B., Polissar, N., 2009. Interrater reliability of posture observations. Hum. Factors 51 (3), 292e309. Bernmark, E., Wiktorin, C., 2002. A triaxial accelerometer for measuring arm movements. Appl. Ergon. 33 (6), 541e547. http://dx.doi.org/10.1016/s00036870(02)00072-8. Delisle, A., Lariviere, C., Plamondon, A., Imbeau, D., 2006. Comparison of three computer office workstations offering forearm support: impact on upper limb posture and muscle activation. Ergonomics 49 (2), 139e160. http://dx.doi.org/ 10.1080/10610270500450739. Della Croce, U., Leardini, A., Chiari, L., Cappozzo, A., 2005. Human movement analysis using stereophotogrammetry e part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. Gait Posture 21 (2), 226e237. http://dx.doi.org/10.1016/j.gaitpost.2004.05.003. Ferrante, D., Gerbino, W., Rock, I., 1995. Retinal vs. environmental orientation in the perception of the right angle. Acta Psychol. 88 (1), 25e32. http://dx.doi.org/10. 1016/0001-6918(93)E0057-9. Fethke, N.B., Gant, L.C., Gerr, F., 2011. Comparison of biomechanical loading during use of conventional stud welding equipment and an alternate system. Appl. Ergon. 42 (5), 725e734. http://dx.doi.org/10.1016/j.apergo.2010.11.007. Fransson-Hall, C., Gloria, R., Kilbom, A., Winkel, J., Karlqvist, L., Wiktorin, C., Stockholm Music 1 Study, G, 1995. A portable ergonomic observation method (PEO) for computerized on-line recording of postures and manual handling. Appl. Ergon. 26 (2), 93e100. Genaidy, A.M., Simmons, R.J., Guo, L., Hidalgo, J.A., 1993. Can visual perception be used to estimate body part angles? Ergonomics 36 (4), 323e329. http:// dx.doi.org/10.1080/00140139308967889. € bei gesehenen Figuren. Psychol. Forsch. 21 (1), Goldmeier, E., 1937. Über Ahnlichkeit 146e208. http://dx.doi.org/10.1007/BF02441206. Goldmeier, E., 1972. Similarity in visually perceived forms. Psychol. Issues 8 (1), 135. Hansson, G.A., Asterland, P., Holmer, N.G., Skerfving, S., 2001. Validity and reliability of triaxial accelerometers for inclinometry in posture analysis. Med. Biol. Eng. Comput. 39 (4), 405e413. Hansson, G.A., Arvidsson, I., Ohlsson, K., Nordander, C., Mathiassen, S.E., Skerfving, S., Balogh, I., 2006. Precision of measurements of physical workload during standardised manual handling. Part II: inclinometry of head, upper back, neck and upper arms. J. Electromyogr. Kinesiol. 16 (2), 125e136. http:// dx.doi.org/10.1016/j.jelekin.2005.06.009. Hansson, G.-Å., Balogh, I., Ohlsson, K., Granqvist, L., Nordander, C., Arvidsson, I., €mberg, U., Skerfving, S., 2010. Physical Åkesson, I., Unge, J., Rittner, R., Stro workload in various types of work: Part II. Neck, shoulder and upper arm. Int. J. Ind. Ergon. 40 (3), 267e281. http://dx.doi.org/10.1016/j.ergon.2009.11.002. Hooftman, W.E., van der Beek, A.J., van de Wal, B.G., Knol, D.L., Bongers, P.M., Burdof, A., van Mechelen, W., 2009. Equal task, equal exposure? Are men and women with the same tasks equally exposed to awkward working postures? Ergonomics 52 (9), 1079e1086. http://dx.doi.org/10.1080/00140130902915921. Juul-Kristensen, B., Hansson, G.Å., Fallentin, N., Andersen, J.H., Ekdahl, C., 2001. Assessment of work postures and movements using a video-based observation method and direct technical measurements. Appl. Ergon. 32 (5), 517e524. http://dx.doi.org/10.1016/S0003-6870(01)00017-5. Korshøj, M., Skotte, J.H., Christiansen, C.S., Mortensen, P., Kristiansen, J., Hanisch, C., Ingebrigtsen, J., Holtermann, A., 2014. Validity of the Acti4 software using ActiGraph GT3X þ accelerometer for recording of arm and upper body inclination in simulated work tasks. Ergonomics, 1e7. http://dx.doi.org/10.1080/ 00140139.2013.869358. Leijon, O., Bernmark, E., Karlqvist, L., H€ arenstam, A., 2005. Awkward work postures: association with occupational gender segregation. Am. J. Ind. Med. 47 (5), 381e393. http://dx.doi.org/10.1002/ajim.20166. € m, J., 2013. Cost-efficient measurement strategies Mathiassen, S.E., Liv, P., Wahlstro for posture observations based on video recordings. Appl. Ergon. 44 (4), 609e617. http://dx.doi.org/10.1016/j.apergo.2012.12.003. Portney, L.G., Watkins, M.P., 2009. Foundations of Clinical Research: Applications to Practice, third ed. Pearson/Prentice Hall. Punnett, L., Fine, L.J., Keyserling, W.M., Herrin, G.D., Chaffin, D.B., 1991. Back disorders and nonneutral trunk postures of automobile assembly workers. Scand. J. Work Environ. Health 17 (5), 337e346 doi:1700 [pii]. Punnett, L., Fine, L.J., Keyserling, W.M., Herrin, G.D., Chaffin, D.B., 2000. Shoulder disorders and postural stress in automobile assembly work. Scand. J. Work Environ. Health 26 (4), 283e291. Rezagholi, M., Mathiassen, S.E., Liv, P., 2012. Cost efficiency comparison of four video-based techniques for assessing upper arm postures. Ergonomics 55 (3), 350e360. http://dx.doi.org/10.1080/00140139.2011.642007. Sutherland, C.A., Albert, W.J., Wrigley, A.T., Callaghan, J.P., 2007. The effect of camera viewing angle on posture assessment repeatability and cumulative spinal loading. Ergonomics 50 (6), 877e889. http://dx.doi.org/10.1080/ 00140130701237675. Svendsen, S.W., Bonde, J.P., Mathiassen, S.E., Stengaard-Pedersen, K., Frich, L.H., 2004a. Work related shoulder disorders: quantitative exposureeresponse relations with reference to arm posture. Occup. Environ. Med. 61 (10), 844e853. http://dx.doi.org/10.1136/oem.2003.010637.

252


Svendsen, S.W., Gelineck, J., Mathiassen, S.E., Bonde, J.P., Frich, L.H., StengaardPedersen, K., Egund, N., 2004b. Work above shoulder level and degenerative alterations of the rotator cuff tendons: a magnetic resonance imaging study. Arthritis Rheum. 50 (10), 3314e3322. http://dx.doi.org/10.1002/art.20495. Svendsen, S.W., Mathiassen, S.E., Bonde, J.P., 2005. Task based exposure assessment in ergonomic epidemiology: a study of upper arm elevation in the jobs of machinists, car mechanics, and house painters. Occup. Environ. Med. 62 (1), 18e27. http://dx.doi.org/10.1136/oem.2004.015966. Trask, C., Mathiassen, S.E., Wahlstrom, J., Heiden, M., Rezagholi, M., 2012. Data collection costs in industrial environments for three occupational posture exposure assessment methods. BMC Med. Res. Methodol. 12, 89. http:// dx.doi.org/10.1186/1471-2288-12-89. Trask, C., Mathiassen, S.E., Jackson, J., Wahlstrom, J., 2013. Data processing costs for three posture assessment methods. BMC Med. Res. Methodol. 13 (1), 124. http:// dx.doi.org/10.1186/1471-2288-13-124. van den Heuvel, S.G., van der Beek, A.J., Blatter, B.M., Bongers, P.M., 2006. Do workrelated physical factors predict neck and upper limb symptoms in office

workers? Int. Arch. Occup. Environ. Health 79 (7), 585e592. http://dx.doi.org/ 10.1007/s00420-006-0093-8. van der Beek, A.J., Frings-Dresen, M.H., 1998. Assessment of mechanical exposure in ergonomic epidemiology. Occup. Environ. Med. 55 (5), 291e299. Veiersted, K.B., Gould, K.S., Osteras, N., Hansson, G.A., 2008. Effect of an intervention addressing working technique on the biomechanical load of the neck and shoulders among hairdressers. Appl. Ergon. 39 (2), 183e190. http://dx.doi.org/ 10.1016/j.apergo.2007.05.007. € m, J., Mathiassen, S.E., Liv, P., Hedlund, P., Ahlgren, C., Forsman, M., 2010. Wahlstro Upper arm postures and movements in female hairdressers across four full working days. Ann. Occup. Hyg. 54 (5), 584e594. http://dx.doi.org/10.1093/ annhyg/meq028. Winkel, J., Mathiassen, S.E., 1994. Assessment of physical work load in epidemiologic studies: concepts, issues and operational considerations. Ergonomics 37, 979e988. http://dx.doi.org/10.1080/00140139408963711.

Letter to the editor. Re: Is what you see what you get? Standard inclinometry of set upper arm elevation angles.

Television and aging: is what you see what you get?

Intrastent thrombus – what you see is what you get?

Intermittent entrapment of a prosthetic mitral valve disc: what you see is not what you get.

What you see is what you get: motor resonance in peripheral vision.

What you see is not always what you get in atrophic macular disease.

What you see is not what you get in the PDF document format.

What you see is what you (don't) get: a comment on Funke's (2014) opinion paper.

Metabolomics: what you see is what you extract.

It is amazing what you can see if you look.

Digging deeper into the assessment of upper arm elevation angles using standard inclinometry.

Reply to "Isolating Viable Ancient Bacteria: What You Put In Is What You Get Out".

Acute epididymitis: what you think is not always what you get.

Isolating Viable Ancient Bacteria: What You Put In Is What You Get Out.

In healthcare, you get what you pay for.

What you learn is more than what you see: what can sequencing effects tell us about inductive category learning?

I see what you are saying.

Changing What You See by Changing What You Know: The Role of Attention.

You don't always get what you want, and you don't always want what you get: An examination of control-desire for control congruence in transactional relationships.

Reply to "Letter to the editor: When what you see may not be what you get: prudent considerations of anesthetics for murine echocardiography".

Letter to the editor: When what you see might not be what you get: prudent considerations of anesthetics for murine echocardiography.

Love What You Do and Do What You Love.

Sensory systems: Do you hear what I see?

"You Can't Always Get What You Want" - Linearity as the Golden Ratio of Toxicology.