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The variability of the trunk forward bending in standing activities during work vs. leisure time
Applied Ergonomics 58 (2017) 273e280
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The variability of the trunk forward bending in standing activities during work vs. leisure time Morten Villumsen a, b, c, Pascal Madeleine a, Marie Birk Jørgensen b, Andreas Holtermann b, d, Afshin Samani a, * a Physical Activity and Human Performance e SMI, Department of Health Science and Technology, Aalborg University, Aalborg, Fredrik Bajers Vej 7, DK9220, Aalborg, Denmark b The National Research Centre for the Working Environment, Lersø Parkall e 105, DK-2100, Copenhagen Ø, Denmark c Department of Physiotherapy, University College of Northern Denmark, Aalborg, Selma Lagerløfs Vej 2, DK-9220, Aalborg, Denmark d Institute of Sports Science and Clinical Biomechanics, Physical Activity and Health in Work Life, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Campusvej 55, DK-5230, Odense M, Denmark
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 December 2015 Received in revised form 1 May 2016 Accepted 29 June 2016 Available online 19 July 2016
High level of occupational physical activity (PA), contrary to leisure time activities, is generally associated with detrimental health outcomes. We hypothesized that this contrast may be associated with a different pattern of exposure variability in PA, e.g., forward bending of the trunk. The study was conducted on 657 blue-collar workers. Two accelerometers were used to identify the body posture and forward bending of the trunk during work and leisure time. The pattern of forward bending was analyzed using exposure variation analysis (EVA). The recordings comprised of 2.6 ± 0.97 working days in average, with 19.9 ± 8.1 h work and 22.9 ± 8.9 h leisure. The standard deviation and entropy of the EVA profile indicated 11% and 6% (for about 80% of subjects) less variable pattern during work compared with the leisure time, respectively. These new findings contribute to the understanding the paradoxical outcomes of PA during work and leisure. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Exposure variation analysis Diurnal measurements Physical activity
1. Introduction Physical activity (PA) is important for prevention of lifestylerelated diseases, worksite health promotion programs and for quality of life (de Souto Barreto, 2015). Accordingly, leisure time PA is endorsed as health enhancing, and is well documented to prevent and reduce musculoskeletal disorders (MSDs), sickness absence and premature mortality (Holtermann et al. 2012, 2013; van Amelsvoort et al., 2006). Paradoxically, high levels of occupational PA are found to increase the risk for MSDs, sickness absence and premature mortality (Holtermann et al., 2012). Several factors such as work-related stress (Holte et al., 2003), cardiorespiratory response (Pollock et al., 1998), biomechanical characteristics of PA in terms of, e.g., duration, level and repetitiveness of the physical exposure (Hallman et al., 2015) have been suggested to be involved in emerging dissimilar health effects. Particularly, the PA during leisure is considered to be characterized by dynamic, varying
* Corresponding author. E-mail address: [email protected] (A. Samani). http://dx.doi.org/10.1016/j.apergo.2016.06.017 0003-6870/© 2016 Elsevier Ltd. All rights reserved.
activities permitting sufficient rest-periods and restitution (Holtermann et al., 2012). On the other hand, occupational PA is often composed of monotonous and static PA, not necessarily enabling sufficient rest-periods and restitution (Hallman et al., 2015; Mathiassen, 2006). Accordingly, the pattern of PA is generally accepted to be important for its health effects, in which high variation of PA is considered favorable with respect to health effects €n, (Madeleine et al., 2003; Madeleine, 2010; Visser and van Diee 2006). Exposure variation analysis (EVA) has been used in some studies to investigate the pattern of PA as it provides a computational framework enabling the quantification of exposure variation (Samani and Madeleine, 2014; Wells et al., 2007). The EVA summarizes the exposure into a 3-D distribution delineating the duration of particular patterns of exposure with specific levels and repetitiveness. Selective EVA indices from this 3-D distribution can be computed to describe the physical exposure variation pattern re et al., 2005; during work and leisure (Delisle et al., 2006; Larivie Straker et al., 2008). To the best of our knowledge, the EVA approach has only been used recently to quantify the duration of PA
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types (e.g., walking and sitting) among blue-collar workers without considering the presence of musculoskeletal pain (Hallman et al., 2015). Low back pain (LBP) is a major global health problem (Walker, 2000), with considerable consequences for the individual, workplaces and society (Katz, 2006; Thiese et al., 2014). High exposure to physical work demands is among the main risk factors for LBP (Ribeiro et al., 2012; Wai et al., 2010). In particular, the duration of forward bending of the trunk has been suggested to be a risk factor for LBP (Ribeiro et al., 2012; Wai et al., 2010). However, it is unknown whether forward bending duration is particularly demanding during work, or it may be a risk factor during leisure time as well. Our recent findings suggest that the intensity of LBP (LBPi) may be directly associated with duration of forward bending above 30 but they are inversely related during leisure time (Villumsen et al., 2015). However, the duration of forward bending above a certain threshold reflects a very narrow part of the exposure profile. Using EVA, we can assess the exposure profile more thoroughly compared with our previous investigations. Individual factors including age, BMI, gender, smoking habits and LBPi may all influence the pattern of PA during work and leisure (Bauman et al., 2011; Spittaels et al., 2012). Therefore, it is of importance to investigate whether these individual factors modify the pattern of forward bending of the trunk during work and leisure time. In this cross-sectional study, we hypothesized that the pattern of objectively measured forward bending would be more variable during leisure than during work among blue-collar workers. Additionally, the pattern of forward bending was expected to be modified by individual factors, such as age, gender, BMI, smoking habits and LBPi.
2. Methods 2.1. Study design This cross-sectional study (n ¼ 657) is based on data from the ‘Danish PHysical ACTivity cohort with Objective measurements’ (DPhacto), which is a cohort of blue-collar workers with the main aim of investigating the association between objectively measured physical activity and musculoskeletal pain. The sample size of the cohort was originally calculated in order to reveal a possible association between objectively measured forward bending during work and the LBPi. More detailed information can be found in the cohort study protocol (Jørgensen et al., 2013).
2.2. DPhacto In the period 2010e2013, companies widely distributed from all five regions of Denmark were invited to be a part of the DPhacto. Fifteen companies committed to allow their employees to voluntarily participate in the study during paid working hours. The data collection was conducted from December 2011 to March 2013. All blue-collar workers included in the study received verbal and written information on the form and content of the study prior to data collection. The study was approved by the local Ethics Committee (H-2-2012-011), and the Danish Data Protection Agency accepted the handling and storage of data. Written informed consent was obtained from all participants, and the study was conducted in accordance with the Declaration of Helsinki. The reporting of the study complies with the ‘Strengthening the Reporting of Observational studies in Epidemiology’ (STROBE) statement.
2.3. Participants In each included company, all workers were invited to participate in a pre-information meeting in which information of the aim and content of the study were presented. Workers interested in participating were invited to complete a session incorporating i) questionnaire and interview, and ii) anthropometric measures. At the beginning of the session, workers were instructed to fill in a structured computer-assisted questionnaire. At all times during this session, a responsible assistant was available if the participants needed support. Subsequently, participants answered a semistructured computer-assisted personal interview. Data obtained included employer information, demographic data, life style related behaviour, and health data. Subsequently, anthropometric measurements were obtained on all eligible participants. Workers were eligible for inclusion if they were aged 18e65 years. Exclusion criteria were plaster allergy, fever, pregnancy or job status as trainees, apprentices or canteen employees. As described below in detail, accelerometers were attached on the body skin surface of blue-collar workers eligible for inclusion in the study. The accelerometers were used to obtain objective diurnal measurements of PA including forward bending of the trunk. Additionally, workers were asked to fill in a diary containing information about specific time episodes during the measurement period (Fig. 1). The work and leisure time were defined based on diary information. In order to compare PA during work and leisure, the following flow criteria were applied for the accelerometer recordings. Considering work time: if the worker had at least two working days of recordings, defined as i) days with 4 h of recordings of work, or ii) 75% of average self-reported work, recordings on these days were included. Workers with only one recorded working day should meet both criteria i and ii during that specific day. The same criteria were applied for leisure time. Recording periods obtained during non-working days, bedtime and non-wear periods were excluded from the analysis for each individual. These periods were determined based on diary information where workers noted specific intervals of periods with respect to work, leisure, bedtime, non-wear, and reference measurements, whilst wearing the accelerometers. Bedtime and nonwear intervals were confirmed by visual inspection of the accelerometer data. In addition to the self-reported non-wear periods, periods of more than 60 min without any detected accelerations were regarded as non-wear periods (Skotte et al., 2014). An overview of the population flow is illustrated in Fig. 1. 2.4. Recordings and analysis of the pattern of forward bending of the trunk Two tri-axial accelerometers (ActiGraph GT3Xþ, ActiGraph LLC, Pensacola, FL, USA) with a dynamic range of ±6G and a 12 bit precision, with the x-axis pointing downwards and y- and z-axis horizontally (Skotte et al., 2014) were used to detect upper and lower body postures. One accelerometer was fixed at processus spinosus at the level of T1-T2 to measure forward bending of the trunk, and the other at the halfway mark on the vertical line between spina illiaca anterior superior and the patella in order to measure lower body positions (e.g. standing still, moving slightly), see Fig. 2. Data was sampled at 30 Hz and was initially low-pass filtered at 5 Hz with a fourth-order Butterworth filter (Korshøj et al., 2014). Workers were asked to remove the accelerometers in cases of i) pruritus (itching), ii) equipment causing sleep disturbance, iii) equipment obstructing the workers in the execution of their work tasks, and iv) experiencing inconveniences due to accelerometers. Additionally, workers were instructed to upkeep a diary whilst
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Fig. 1. Flow chart of the procedure concerning inclusion and exclusion criteria. The workers that volunteered to participate were invited to a session in which they i) answered a questionnaire, ii) the experimenter took anthropometric measures, and iii) attached two accelerometers to measure physical activity and forward bending of the trunk.
wearing the accelerometers and to note periods of work, leisure, sleep, non-wear and reference measurements. The Acti4 software (The National Research Centre for the Working Environment, Copenhagen, Denmark and Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany) was used to detect the body posture. In this study, we only investigated the forward bending of the trunk when the worker was either in a standing posture with subtle postural adjustments without regular walking (moving slightly) or standing still (Villumsen et al., 2016). Using the individual reference measurements (i.e. standing still in a vertically positioned stance for 15 s entailing the individual workers neutral position) obtained at the beginning of the recordings as point of reference, the duration and amplitude of forward bending, with respect to upright standing posture as the reference position in the sagittal and frontal planes was computed. EVA as described by (Mathiassen and Winkel, 1991), was performed for the time series of forward bending on the entire valid period of recording for each worker. Two separate EVA profiles were obtained to represent the pattern of exposure variation for forward bending during work and leisure time. The EVA classes categorized the level of forward bending into [<10, 10e20, 20e30, 30e40, 40e50, 50e60, 60e70, 70e80, 80e90, and >90 deg.] with “sequence duration” categories of [0e3, 3e7, 7e15, 15e31, 31 s]. A logarithmic scale was chosen for time categories to accommodate the time line of changes in exposure level and a linear scale was selected for the extent of forward bending to provide a fine resolution (Jansen et al., 2001). This resulted in matrixes in which each
element represents an accumulated elapsed time that the trunk was uninterruptedly bent to an extent determined by the EVA interval for a determined “sequence duration” category. This led to 10 levels along the level axis and 5 levels along the time axis. To summarize the EVA profile into indices which quantify the exposure variation, the centroid of the profile 10 5 plane, that is the average position of the EVA distribution along the “sequence duration” axis (EVA-Time) and the average distribution of the EVA re et al., along the level axis (EVA-Amp) (Delisle et al., 2006; Larivie 2005) and the standard deviation across EVA classes (EVA-SD) were calculated) (Delisle et al., 2006). EVA-Time provides the average position of the EVA distribution along the “sequence duration” axis, which can vary between 1 and 5 corresponding to the number of “sequence duration” categories (Delisle et al., 2006). EVA-Amp describes where the average position of the EVA distribution along the level axis, which can vary between 1 and 10 corresponding to the number of “exposure level” categories (Delisle et al., 2006). EVA-SD can vary between 0 and a maximum level, which corresponds to a case where all the samples of forward bending of the trunk stay within only one EVA class. EVA-SD would be zero if the samples of trunk bending are evenly distributed among all EVA classes. We normalized the EVA-SD to its theoretical upper bound and subtracted from 1. Thus, the EVA-SD could take values varying between 0 and 1. The EVA-SD reflects the variability of the EVA distribution. Thus, the lower the EVA-SD, the less variable the EVA pattern and vice versa. Along with previous studies pointing out the relevance of entropy measures in investigating
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measures analysis of covariance (RMANCOVA) (according to the instructions proposed by (Thomas et al., 2009)) was applied to chosen EVA indices (i.e., i) EVA-Ent, ii) EVA-SD, iii) EVA-Time, and iv) EVA-Amp). The activity context (with two levels: work and leisure) was treated as a within-subject factor. In order to measure the effect size, partial eta squared (h2) was applied. Additionally, the RMANCOVA was conducted with gender, smoking habit and LBPi as between-subject factors and age and BMI as co-variates to explore their modifying effect. Multiple regressions were applied to assess the possible association of the co-variates with significant interactions with the difference (D) between work and leisure for each of the EVA indices (i.e. EVA indices during working time subtracted from the corresponding indices during leisure time). All statistical analyses were performed using Statistical Package for the Social Sciences (IBM Corporation SPSS statistics, Version 22.0, Armonk, NY, USA). Statistical significance was considered at p < 0.05. 3. Results 3.1. Characteristics of the population
Fig. 2. Placement of two tri-axial accelerometers at the processus spinosus (T1-T2 level) to measure forward bending of the trunk and at the halfway mark on the vertical line between spina illiaca anterior superior and the patella to measure lower body positions (e.g. standing still, moving slightly).
exposure variation (Madeleine and Madsen, 2009), we also proposed a novel index by estimating Shannon entropy of the EVA profile normalized by the total recording time (EVA-Ent), indicating the complexity of the exposure variation pattern. That is, higher values of EVA-Ent indicate a more complex variability pattern. The EVA-Ent can vary between 0 and logarithm of the number of EVA classes (i.e. 5 10 ¼ 50). The EVA-Ent was normalized by its maximum achievable value, thus, in our analysis, EVA-Ent could vary between 0 and 1. All the above mentioned EVA indices were calculated separately for work and leisure time. 2.5. Individual factors Age, gender, BMI, smoking habits and LBPi were retrieved from the questionnaire, interview and anthropometric measurements. Smoking habits were obtained through the question ‘Do you smoke?’ with response possibilities: daily smoking, occasionally smoking, formerly smoked, never smoked. Smoking habits were dichotomized into smokers (daily and occasionally smoking) and non-smokers (formerly and never smoked). LBPi was self-rated and defined as of the worst intensity of LBP during the last three months on a numeric rating scale ranging from 0 (no pain) to 10 (worst imaginable pain) (Kuorinka et al., 1987). Subsequently, LBPi was following dichotomized into low (5) and high (>5) categories of LBPi (Andersen et al., 2012). 2.6. Statistical methods Descriptive data on the workers are presented as mean, standard deviation (SD), median, inter quartile range (IQR) or as frequencies. The data distribution was visually inspected and found suitable for parametric procedures. To identify a difference in the variation pattern of forward bending of the trunk during work and leisure whilst standing still or moving slightly, a repeated-
The 657 workers had a total measured recording time for work and leisure of 13,084 h and leisure 15,048 h, respectively. On average, the workers wore the accelerometers for 2.6 ± 0.97 working days with average recordings hours per worker at work of 19.9 ± 8.1 h and leisure of 22.9 ± 8.9 h. Out of this period, the average time spent standing still or moving slightly was 10.3 ± 5.7 h for work and 6.6 ± 3.4 h for leisure. For descriptive characteristics of the population, see Table 1. 3.2. Exposure variation of work and leisure The results of the RMANCOVA, adjusted for age, gender, BMI, smoking habits and LBPi disclosed significant differences for three out of four EVA indices between work and leisure (Table 2). There was a significant effect of activity context on the EVA-Ent (F(1, 656) ¼ 351, p < 0.001), with a mean of 0.73 ± 0.05 for work and 0.77 ± 0.04 for leisure. Seventy-nine percent of the workers had higher values of EVA-Ent during leisure than work (Table 1), indicating that during leisure the variability of forward bending is more complex than during work. Similarly, there was a significant effect of activity context on the EVA-SD (F(1, 656) ¼ 302, p < 0.001), with a mean of 0.75 ± 0.04 for work and 0.78 ± 0.03 for leisure. Seventyeight percent of the workers had lower values of EVA-SD during work (Table 1), indicating that the EVA profile was more static during work compared with the leisure time (Table 2). Fig. 3 illustrates this finding for a typical subject. The EVA-Time did not differ significantly between work and leisure (with 2.276 ± 0.398 vs. 2.301 ± 0.265, respectively (F(1, 656) ¼ 2.6, p ¼ 0.1). The EVAAmp was significantly higher during leisure than during work (with 2.704 ± 0.509 vs. 2.463 ± 0.585, respectively (F(1, 656) ¼ 141, p < 0.001)), with 69.7% of the workers having higher values of EVAAmp during leisure (Table 1). This implies that the amplitude of the variability pattern during leisure was significantly higher than during work (Table 2). 3.3. Exposure variation of work and leisure and modification from individual factors Table 3 shows the results of the RMANCOVA on the EVA indices for the factors, i.e., gender, smoking habits and LBPi and Table 4 shows the effect of covariates, i.e., age and BMI. The results showed that lower BMI was associated with higher EVA-Ent values, lower EVA-SD values and higher EVA-Amp values. Additionally, the
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Table 1 Descriptive characteristics of the blue-collar workers (n ¼ 657). Variables N Low back pain intensity (0e10) (Median, IQR) Low back pain intensity (n)
657 3 (1) 180 477 362 295 45.0 (±10.0) 196a 27.5 (±4.9) 657 563 371 121 7 19.9 (±8.1) 10.3 (±5.7) 22.9 (±8.9) 6.6 (±3.4) 78.5 77.6 50.4 69.7 128 469 60
High (<5) Low (5) Men Women
Gender (n) Age (M, SD) Smokers (n) BMI (M, SD) Days measured (n)
1 2 3 4 5
Working hours measured (M (h), SD) Of which standing still or moving slightly (M (h), SD) Leisure hours measured (M (h), SD) Of which standing still or moving slightly (M (h), SD) Higher values of EVA indices during leisure than work (%)
or or or or
more more more more
EVA-Ent EVA-SD EVA-Time EVA-Amp Cleaning Manufacturing Transport
Occupational sector (n)
n numbers of blue-collar workers, M mean, SD standard deviation, low back pain intensity self-rated worst intensity of low back pain during the last three months from 0 to 10 divided into high (>5) and low (5). a 196 out of 639 blue-collar workers. EVA-Ent the entropy of the EVA profile, EVA-SD the standard deviation of the EVA, EVA-Time the EVA along the time levels, EVA-Amp the EVA along the amplitude levels.
Table 2 Differences between work and leisure for the EVA indices. Repeated measures ANCOVA (RMANCOVA) for tests of differences between work and leisure forward bending variability in selected EVA indices. EVA indices
Work
Leisure
df
F
p
h2
Entropy of the EVA profile Standard deviation of the EVA EVA along the time levels EVA along the amplitude levels
0.73 (0.05) 0.75 (0.04) 2.3 (0.4) 2.5 (0.6)
0.77 (0.05) 0.78 (0.03) 2.3 (0.3) 2.7 (0.5)
1,656 1,656 1,656 1,656
351.3 302.0 2.6 141.3
<0.001 <0.001 0.1 <0.001
0.3 0.3 0.004 0.1
EVA-Time values were higher among men than women. The interaction analysis from the RMANCOVA revealed a significant interaction effect of age and activity context on EVA-Ent (F(1, 614) ¼ 3.887, p ¼ 0.049). Additionally, gender and activity context
also unveiled an interactive effect on EVA-Time (F(1, 614) ¼ 38.695, p ¼ 0.000) and EVA-Amp (F(1, 614) ¼ 18.522, p ¼ 0.000). Consequently, multiple regressions were applied to assess the possible association of the covariates and factors which showed significant
Fig. 3. EVA profile for a typical subject during a) work time b) leisure time. A more variable pattern of activity can be seen during leisure time. For this typical EVA, entropy of the EVA profile (EVA-Ent) was 0.55 and 0.80 for work and leisure time, respectively. The standard deviation of EVA profile (EVA-SD) was 0.65 and 0.80 for work and leisure time respectively.
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Table 3 Variability patterns of forward bending during work and leisure for the EVA indices. Repeated measures ANCOVA shows p-values for the main effects and interactions of gender, smoking habits and LBPi as well as mean and standard deviation of EVA indices. Main effect df Gender The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels Smoking habits The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels Low back pain intensity The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels
Men 0.75 (0.06) 0.77 (0.04) 2.3 (0.3) 2.5 (0.6) Smoker 0.76 (0.05) 0.77 (0.03) 2.3 (0.3) 2.6 (0.5) Low 0.75 (0.06) 0.77 (0.04) 2.3 (0.3) 2.6 (0.6)
Women 0.75 (0.05) 0.77 (0.03) 2.2 (0.3) 2.6 (0.5) Non-smoker 0.75 (0.06) 0.77 (0.03) 2.3 (0.3) 2.6 (0.6) High 0.75 (0.05) 0.77 (0.04) 2.3 (0.3) 2.6 (0.5)
Interaction F
p
F
p
1, 1, 1, 1,
614 614 614 614
1.786 0.886 34.249 3.327
0.182 0.347 0.000 0.069
1.146 1.774 38.695 18.522
0.285 0.183 0.000 0.000
1, 1, 1, 1,
614 614 614 614
0.790 0.636 0.042 0.974
0.375 0.425 0.838 0.324
0.941 0.794 3.478 0.269
0.332 0.373 0.063 0.604
1, 1, 1, 1,
614 614 614 614
1.647 1.372 0.461 0.046
0.204 0.242 0.498 0.830
0.084 0.022 0.203 0.869
0.773 0.883 0.653 0.352
D Men 0.04 (0.06) 0.02 (0.04) 0.07 (0.3) 0.3 (0.5) Non-smoker 0.04 (0.05) 0.02 (0.03) 0.08 (0.4) 0.2 (0.5) Low 0.04 (0.06) 0.03 (0.04) 0.02 (0.4) 0.2 (0.5)
Women 0.05 (0.05) 0.03 (0.03) 0.1 (0.3) 0.1 (0.5) Smoker 0.04 (0.06) 0.03 (0.04) 0.0 (0.4) 0.2 (0.5) High 0.04 (0.06) 0.03 (0.04) 0.03 (0.4) 0.2 (0.5)
The repeated measures ANCOVA was constructed with gender as a between subject factor and PA context (work vs. leisure) as within subject factor. D represents the difference between leisure and work. Partial ɳ2 is an index of effect size. Bold fonts indicate a statistically significant effect.
Table 4 Variability patterns of forward bending during work and leisure for the EVA indices. Repeated measures ANCOVA shows p-values for the main effects covariates (i.e., age, BMI) and their interactions.
h2
Main effect df
Age The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels BMI The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels
Interaction
Work
F
p
F
p
B
h2
Leisure B
h2
<0.001 <0.001 0.004 0.002
1, 1, 1, 1,
614 614 614 614
0.178 0.002 2.283 1.208
0.673 0.965 0.131 0.272
3.887 1.942 1.185 0.185
0.049 0.164 0.277 0.667
<0.0001 (0.0e0.0) <0.0001 (0.0e0.0) 0.002 (0.006,0.001) 0.003 (0.007,0.002)
0.003 0.001 0.004 0.002
<0.0001 (0.0e0.001) <0.0001 (0.0e0.0) 0.001 (0.003,0.001) 0.002 (0.006,0.002)
0.002 0.002 0.001 0.001
0.01 0.007 0.004 0.02
1, 1, 1, 1,
614 614 614 614
8.916 4.537 2.265 15.759
0.003 0.034 0.133 0.00
1.278 0.610 0.068 2.458
0.259 0.435 0.795 0.117
0.001 (0.002,0.0) 0.001 (0.001,0.0) 0.004 (0.002,0.01) 0.01 (0.02,0.003)
0.004 0.002 0.002 0.01
0.001 (0.002,0.001) 0.001 (0.001,0.0) 0.003 (0.001,0.007) 0.02 (0.03,0.01)
0.02 0.001 0.003 0.03
B represents the coefficient of the covariates in the repeated measure ANCOVA model. Partial h2 is an index of effect size. Bold fonts indicate a statistically significance effect.
interactions (i.e., age and gender). Age only had a tendency for being associated with DEVA-Ent (work vs. leisure) (R2 ¼ 0.01, p ¼ 0.05). There was a tendency for increased DEVA-Ent, as participants’ age increased. The multiple regressions revealed significant associations between gender and the difference (work vs. leisure) in the DEVA-Time (R2 ¼ 0.08, p < 0.001) and DEVA-Amp (R2 ¼ 0.03, p < 0.001). Women exhibited an increase in EVA-Time from work to leisure (2.138e2.284, respectively), contrary to men, where a decrease was observed (2.389e2.314, respectively). From work to leisure, men showed a larger increase in EVA-Amp than women (2.393e2.715 and 2.549e2.691, respectively). 4. Discussion This study is the first to reveal, in line with the hypothesis that the pattern of forward bending of the trunk among blue-collar workers is more variable during leisure compared to during work. Furthermore, the pattern of forward bending was observed to be significantly modified by gender, and only marginally by age. 4.1. Patterns of forward bending during work and leisure In this study, we recorded daily activity by means of accelerometers over 2.6 ± 0.97 working days on average, with 19.9 ± 8.1 h work and 22.9 ± 8.9 h leisure among blue-collar workers. Out of this period, 10.3 ± 5.7 h and 6.6 ± 3.4 h were associated with
standing or moving slightly during work and leisure, respectively. The duration of recording was therefore above the recommendations on the strategies for job sampling based on the type of task and jobs (e.g. The Washington State SHARP approach (Bao et al., 2006)). The use of such small, wireless wearable sensors like accelerometers enables recording of daily activity in situ during entire working days in a rather un-obstructive way (Zheng et al., 2014). The pattern of exposures to physical work demands within individuals has received increasing attention in the last few decades, and many studies have pointed out its functional relevance for improved performance, and prevention of MSDs and fatigue (Madeleine, 2010). As such, a high motor variability is considered a key part of a healthy movement pattern (Madeleine et al., 2003; Mathiassen, 2006). In accordance with our hypothesis, during leisure time, the EVA-Ent and EVA-SD were observed to be higher compared to during work. Even though EVA-Ent and EVA-SD were only about 10% larger during leisure time than work, this difference was found in about 80% of the participants (Table 1). Additionally, the effect size suggests a medium to large effect for the activity context (Richardson, 2011). The size of the difference in movement pattern at work and leisure for generating different health effects is unknown. Therefore, it is hard to make any direct interpretation of the clinical relevance of the effect size difference found. However, we believe that the found difference between work and leisure may be practically significant for its health effects, and may explain a piece in the physical activity health paradox puzzle. Additionally,
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static levels of PA are recognized as a risk factor for MSDs (Holtermann et al., 2012). These results can therefore be interpreted as forward bending of the trunk is performed in a manner, which is probably less harmful during leisure time compared to work. Interestingly, leisure time PA has positive health effects while high levels of PA during work has been associated with an increase in sickness absence, MSDs and premature mortality underlining the health paradox (Holtermann et al., 2012). However, longitudinal studies are needed to investigate if such different variation patterns of forward bending of the trunk during leisure and work are differently associated with health outcomes, like LBP. EVA-Amp was also higher during leisure compared to work. This indicates that workers actually bend forward to a higher level during leisure time compared with work. This higher level of forward bending is probably not a favorable trait, as high duration of forward bending has been reported to increase the likelihood of LBP incidence (Wai et al., 2010). However, we have recently found no positive association between duration of forward bending of the trunk 30 and LBPi in a cross-sectional study (Villumsen et al., 2016). Anyhow, our previous findings cannot support or refute the role of forward bending in development of LBP because of the cross-sectional design. 4.2. Effects of individual factors Individual factors such as age, gender, BMI, smoking habits and LBPi have been frequently reported to affect PA (Balogh et al., 2004; Madeleine, 2010). The patterns of forward bending during leisure and work were associated with BMI and modified by gender and only marginally by age. In contrast, smoking habits and LBPi did not play a role. The absence of LBPi effect on the pattern of forward bending during work and leisure seems to be in contrast with the body of literature suggesting that the pain level is a modifier of motor variability (e.g. (Madeleine, 2010)). This may be explained by job crafting or healthy worker effect (Villumsen et al., 2015). As the BMI increases, EVA-Amp, EVA-Ent and EVA-SD decrease. Thus, BMI seems to be associated with less varying pattern of PA and therefore less health promoting (Christensen et al., 2011). Gender was also found to modify the pattern of forward bending of the trunk during work and leisure time, which is in accordance with a recent study observing differences in other PA types (e.g. walking, sitting, or standing) during work and leisure time (Hallman et al., 2015). Women were found to experience a larger difference in uninterrupted periods of forward bending during leisure and work compared to men. On the contrary, with respect to the level of forward bending, men showed higher DEVA-Amp than women, meaning that the difference in the level of forward bending between work and leisure was more marked among men than women. Interestingly, recent studies have reported lower motor variability among women compared with men, and considered it to be related to the higher prevalence of MSDs among ^ te , 2012). These findings confirm the complex interplay women (Co between PA exposure, in particular forward bending, during work and leisure and modification of individual factors. 4.3. Methodological considerations The use of objective long-term field measures of forward bending during work and leisure with accelerometers is a considerable strength of the present study. The Acti4 software used to determine the body posture, activity type and duration have shown high sensitivity and specificity during free-living settings as well as in controlled conditions (Stemland et al., 2015), and during simulated working tasks such as lifting tasks and assembly work, the Acti4 software has provided valid estimates of forward bending,
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with only 5 degrees of error margin (Korshøj et al., 2014). The level categories of the EVA plane are defined with 10 intervals, insuring the interval being at least twice the size of the error margin. The measurement method would mainly be sensitive to the upper back inclination and not very much to the changes caused by the lower back position. Thus, the contribution of slouch position in upper trunk inclination would be poorly portrayed in our analysis. Similarly, our measurement method was not sensitive to hip flexion even though extensive hip flexion or slouch back would be unlikely to occur during standing and moving slightly because otherwise the Acti4 software would categorize the posture different from standing and moving slightly. Thus, the diurnal measurements of forward bending during standing or moving slightly can be considered as a relatively valid measure. The EVA indices used in this study characterize the EVA profile in term of the center point of the EVA (i.e. EVA-time and EVA-Amp) and the spread and complexity of the distribution (EVA-Ent); however, these indices reduce the available information in the EVA profile. These indices provide relevant information to compare the exposure variation during work and leisure time as they have been re et al., 2005; used in several studies (Delisle et al., 2006; Larivie Samani et al., 2009). However, theses indices do not differentiate between two EVA profiles at the level of a single EVA. To the authors’ knowledge, this is the first study targeting the pattern of forward bending among blue-collar workers using diurnal long-term recordings at both work and leisure. We were merely focused on the pattern of forward bending during work and leisure time and other factors such as such as work-related stress (Holte et al., 2003), cardiorespiratory response (Pollock et al., 1998) which could potentially be different between work and leisure. However, one should bear in mind that the trunk forward bending has received quite a lot of attention in the research field, but so far it has mainly been investigated through self-reported measures (Wai et al., 2010). Further studies investigating the potential mechanisms behind the health paradox in relation to e.g. work-related stress and cardiorespiratory responses are warranted. 5. Conclusion In this study, the pattern of forward bending of the trunk during work and leisure among blue-collar workers was found to differ significantly, indicating a higher variability during leisure compared to work. The novel results of this study, using long-term objective recording of forward bending, indicates a more variable pattern of movement trajectories during leisure time compared with wok. Thus, the pattern of forward bending during leisure time may be associated with less adverse health outcomes compared with work. Interestingly, this finding is in line with the previously reported health paradox of PA during work and leisure which needs to be investigated further for example through prospective studies. Conflict of interest No conflicts of interest are declared by authors. Acknowledgements The study is partly supported by a grant from the Danish government (satspulje). References Andersen, L.L., Clausen, T., Burr, H., Holtermann, A., 2012. Threshold of musculoskeletal pain intensity for increased risk of long-term sickness absence among female healthcare workers in eldercare. PLoS One 7, e41287.
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it, and why would we like to know? Appl. Ergon. 37, 419e427. Mathiassen, S.E., Winkel, J., 1991. Quantifying variation in physical load using exposure-vs-time data. Ergonomics 34, 1455e1468. Pollock, M.L., Gaesser, G.A., Butcher, J.D., et al., 1998. ACSM position stand: the recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med. Sci. Sports Exerc. 30, 975e991. Ribeiro, D.C., Aldabe, D., Abbott, J.H., Sole, G., Milosavljevic, S., 2012. Dose-response relationship between work-related cumulative postural exposure and low back pain: a systematic review. Ann. Occup. Hyg. 56, 684e696. http://dx.doi.org/ 10.1093/annhyg/mes003. Richardson, J.T., 2011. Eta squared and partial eta squared as measures of effect size in educational research. Educ. Res. Rev. 6, 135e147. Samani, A., Holtermann, A., Søgaard, K., Madeleine, P., 2009. Active pauses induces more variable electromyographic pattern of the trapezius muscle activity during computer work. J. Electromyogr. Kinesiol. 19, e430e437. http://dx.doi.org/ 10.1016/j.jelekin.2008.11.011. Samani, A., Madeleine, P., 2014. A comparison of cluster-based exposure variation and exposure variation analysis to detect muscular adaptation in the shoulder joint to subsequent sessions of eccentric exercise during computer work. J. Electromyogr. Kinesiol. 24, 192e199. http://dx.doi.org/10.1016/ j.jelekin.2013.12.001. Skotte, J., Korshoj, M., Kristiansen, J., Hanisch, C., Holtermann, A., 2014. Detection of physical activity types using triaxial accelerometers. J. Phys. Act. Health 11, 76e84. Spittaels, H., Van Cauwenberghe, E., Verbestel, V., et al., 2012. Objectively measured sedentary time and physical activity time across the lifespan: a cross-sectional study in four age groups. Int. J. Behav. Nutr. Phys. Act. 9 http://dx.doi.org/ 10.1186/1479-5868-9-149, 149e5868-9-149. Stemland, I., Ingebrigtsen, J., Christiansen, C.S., et al., 2015. Validity of the Acti4 method for detection of physical activity types in free-living settings: comparison with video analysis. Ergonomics 58, 953e965. Straker, L.M., Coleman, J., Skoss, R., Maslen, B.A., Burgess-Limerick, R., Pollock, C.M., 2008. A comparison of posture and muscle activity during tablet computer, desktop computer and paper use by young children. Ergonomics 51, 540e555. Thiese, M.S., Hegmann, K.T., Wood, E.M., et al., 2014. Prevalence of low back pain by anatomic location and intensity in an occupational population. BMC Musculoskelet. Disord. 15, 1. Thomas, M.S.C., Annaz, D., Ansari, D., Scerif, G., Jarrold, C., Karmiloff-Smith, A., 2009. Using developmental trajectories to understand developmental disorders. J. Speech, Lang. Hear. Res. 52, 336. van Amelsvoort, L.G., Spigt, M.G., Swaen, G.M., Kant, I., 2006. Leisure time physical activity and sickness absenteeism; a prospective study. Occup. Med. (Lond.) 56, 210e212 doi:56/3/210 [pii]. Villumsen, M., Holtermann, A., Samani, A., Madeleine, P., Jørgensen, M.B., 2016. Social support modifies association between forward bending of the trunk and low-back pain: cross-sectional field study of blue-collar workers. Scand. J. Work Environ. Health 42. Villumsen, M., Samani, A., Jørgensen, M.B., Gupta, N., Madeleine, P., Holtermann, A., 2015. Are forward bending of the trunk and low back pain associated in bluecollar workers? A cross-sectional field study based on objective measures in Denmark. Ergonomics 58, 246e258. €n, J.H., 2006. Pathophysiology of upper extremity muscle disorVisser, B., van Diee ders. J. Electromyogr. Kinesiol. 16, 1e16. Wai, E.K., Roffey, D.M., Bishop, P., Kwon, B.K., Dagenais, S., 2010. Causal assessment of occupational bending or twisting and low back pain: results of a systematic review. Spine J. 10, 76e88. Walker, B.F., 2000. The prevalence of low back pain: a systematic review of the literature from 1966 to 1998. J. Spinal Disord. Tech. 13, 205e217. Wells, R., Mathiassen, S.E., Medbo, L., Winkel, J., 2007. Timeda key issue for musculoskeletal health and manufacturing. Appl. Ergon. 38, 733e744. Zheng, Y., Ding, X., Poon, C.C.Y., et al., 2014. Unobtrusive sensing and wearable devices for health informatics. Biomedical Engineering. IEEE Trans. 61, 1538e1554.
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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo
The variability of the trunk forward bending in standing activities during work vs. leisure time Morten Villumsen a, b, c, Pascal Madeleine a, Marie Birk Jørgensen b, Andreas Holtermann b, d, Afshin Samani a, * a Physical Activity and Human Performance e SMI, Department of Health Science and Technology, Aalborg University, Aalborg, Fredrik Bajers Vej 7, DK9220, Aalborg, Denmark b The National Research Centre for the Working Environment, Lersø Parkall e 105, DK-2100, Copenhagen Ø, Denmark c Department of Physiotherapy, University College of Northern Denmark, Aalborg, Selma Lagerløfs Vej 2, DK-9220, Aalborg, Denmark d Institute of Sports Science and Clinical Biomechanics, Physical Activity and Health in Work Life, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Campusvej 55, DK-5230, Odense M, Denmark
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 December 2015 Received in revised form 1 May 2016 Accepted 29 June 2016 Available online 19 July 2016
High level of occupational physical activity (PA), contrary to leisure time activities, is generally associated with detrimental health outcomes. We hypothesized that this contrast may be associated with a different pattern of exposure variability in PA, e.g., forward bending of the trunk. The study was conducted on 657 blue-collar workers. Two accelerometers were used to identify the body posture and forward bending of the trunk during work and leisure time. The pattern of forward bending was analyzed using exposure variation analysis (EVA). The recordings comprised of 2.6 ± 0.97 working days in average, with 19.9 ± 8.1 h work and 22.9 ± 8.9 h leisure. The standard deviation and entropy of the EVA profile indicated 11% and 6% (for about 80% of subjects) less variable pattern during work compared with the leisure time, respectively. These new findings contribute to the understanding the paradoxical outcomes of PA during work and leisure. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Exposure variation analysis Diurnal measurements Physical activity
1. Introduction Physical activity (PA) is important for prevention of lifestylerelated diseases, worksite health promotion programs and for quality of life (de Souto Barreto, 2015). Accordingly, leisure time PA is endorsed as health enhancing, and is well documented to prevent and reduce musculoskeletal disorders (MSDs), sickness absence and premature mortality (Holtermann et al. 2012, 2013; van Amelsvoort et al., 2006). Paradoxically, high levels of occupational PA are found to increase the risk for MSDs, sickness absence and premature mortality (Holtermann et al., 2012). Several factors such as work-related stress (Holte et al., 2003), cardiorespiratory response (Pollock et al., 1998), biomechanical characteristics of PA in terms of, e.g., duration, level and repetitiveness of the physical exposure (Hallman et al., 2015) have been suggested to be involved in emerging dissimilar health effects. Particularly, the PA during leisure is considered to be characterized by dynamic, varying
* Corresponding author. E-mail address: [email protected] (A. Samani). http://dx.doi.org/10.1016/j.apergo.2016.06.017 0003-6870/© 2016 Elsevier Ltd. All rights reserved.
activities permitting sufficient rest-periods and restitution (Holtermann et al., 2012). On the other hand, occupational PA is often composed of monotonous and static PA, not necessarily enabling sufficient rest-periods and restitution (Hallman et al., 2015; Mathiassen, 2006). Accordingly, the pattern of PA is generally accepted to be important for its health effects, in which high variation of PA is considered favorable with respect to health effects €n, (Madeleine et al., 2003; Madeleine, 2010; Visser and van Diee 2006). Exposure variation analysis (EVA) has been used in some studies to investigate the pattern of PA as it provides a computational framework enabling the quantification of exposure variation (Samani and Madeleine, 2014; Wells et al., 2007). The EVA summarizes the exposure into a 3-D distribution delineating the duration of particular patterns of exposure with specific levels and repetitiveness. Selective EVA indices from this 3-D distribution can be computed to describe the physical exposure variation pattern re et al., 2005; during work and leisure (Delisle et al., 2006; Larivie Straker et al., 2008). To the best of our knowledge, the EVA approach has only been used recently to quantify the duration of PA
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types (e.g., walking and sitting) among blue-collar workers without considering the presence of musculoskeletal pain (Hallman et al., 2015). Low back pain (LBP) is a major global health problem (Walker, 2000), with considerable consequences for the individual, workplaces and society (Katz, 2006; Thiese et al., 2014). High exposure to physical work demands is among the main risk factors for LBP (Ribeiro et al., 2012; Wai et al., 2010). In particular, the duration of forward bending of the trunk has been suggested to be a risk factor for LBP (Ribeiro et al., 2012; Wai et al., 2010). However, it is unknown whether forward bending duration is particularly demanding during work, or it may be a risk factor during leisure time as well. Our recent findings suggest that the intensity of LBP (LBPi) may be directly associated with duration of forward bending above 30 but they are inversely related during leisure time (Villumsen et al., 2015). However, the duration of forward bending above a certain threshold reflects a very narrow part of the exposure profile. Using EVA, we can assess the exposure profile more thoroughly compared with our previous investigations. Individual factors including age, BMI, gender, smoking habits and LBPi may all influence the pattern of PA during work and leisure (Bauman et al., 2011; Spittaels et al., 2012). Therefore, it is of importance to investigate whether these individual factors modify the pattern of forward bending of the trunk during work and leisure time. In this cross-sectional study, we hypothesized that the pattern of objectively measured forward bending would be more variable during leisure than during work among blue-collar workers. Additionally, the pattern of forward bending was expected to be modified by individual factors, such as age, gender, BMI, smoking habits and LBPi.
2. Methods 2.1. Study design This cross-sectional study (n ¼ 657) is based on data from the ‘Danish PHysical ACTivity cohort with Objective measurements’ (DPhacto), which is a cohort of blue-collar workers with the main aim of investigating the association between objectively measured physical activity and musculoskeletal pain. The sample size of the cohort was originally calculated in order to reveal a possible association between objectively measured forward bending during work and the LBPi. More detailed information can be found in the cohort study protocol (Jørgensen et al., 2013).
2.2. DPhacto In the period 2010e2013, companies widely distributed from all five regions of Denmark were invited to be a part of the DPhacto. Fifteen companies committed to allow their employees to voluntarily participate in the study during paid working hours. The data collection was conducted from December 2011 to March 2013. All blue-collar workers included in the study received verbal and written information on the form and content of the study prior to data collection. The study was approved by the local Ethics Committee (H-2-2012-011), and the Danish Data Protection Agency accepted the handling and storage of data. Written informed consent was obtained from all participants, and the study was conducted in accordance with the Declaration of Helsinki. The reporting of the study complies with the ‘Strengthening the Reporting of Observational studies in Epidemiology’ (STROBE) statement.
2.3. Participants In each included company, all workers were invited to participate in a pre-information meeting in which information of the aim and content of the study were presented. Workers interested in participating were invited to complete a session incorporating i) questionnaire and interview, and ii) anthropometric measures. At the beginning of the session, workers were instructed to fill in a structured computer-assisted questionnaire. At all times during this session, a responsible assistant was available if the participants needed support. Subsequently, participants answered a semistructured computer-assisted personal interview. Data obtained included employer information, demographic data, life style related behaviour, and health data. Subsequently, anthropometric measurements were obtained on all eligible participants. Workers were eligible for inclusion if they were aged 18e65 years. Exclusion criteria were plaster allergy, fever, pregnancy or job status as trainees, apprentices or canteen employees. As described below in detail, accelerometers were attached on the body skin surface of blue-collar workers eligible for inclusion in the study. The accelerometers were used to obtain objective diurnal measurements of PA including forward bending of the trunk. Additionally, workers were asked to fill in a diary containing information about specific time episodes during the measurement period (Fig. 1). The work and leisure time were defined based on diary information. In order to compare PA during work and leisure, the following flow criteria were applied for the accelerometer recordings. Considering work time: if the worker had at least two working days of recordings, defined as i) days with 4 h of recordings of work, or ii) 75% of average self-reported work, recordings on these days were included. Workers with only one recorded working day should meet both criteria i and ii during that specific day. The same criteria were applied for leisure time. Recording periods obtained during non-working days, bedtime and non-wear periods were excluded from the analysis for each individual. These periods were determined based on diary information where workers noted specific intervals of periods with respect to work, leisure, bedtime, non-wear, and reference measurements, whilst wearing the accelerometers. Bedtime and nonwear intervals were confirmed by visual inspection of the accelerometer data. In addition to the self-reported non-wear periods, periods of more than 60 min without any detected accelerations were regarded as non-wear periods (Skotte et al., 2014). An overview of the population flow is illustrated in Fig. 1. 2.4. Recordings and analysis of the pattern of forward bending of the trunk Two tri-axial accelerometers (ActiGraph GT3Xþ, ActiGraph LLC, Pensacola, FL, USA) with a dynamic range of ±6G and a 12 bit precision, with the x-axis pointing downwards and y- and z-axis horizontally (Skotte et al., 2014) were used to detect upper and lower body postures. One accelerometer was fixed at processus spinosus at the level of T1-T2 to measure forward bending of the trunk, and the other at the halfway mark on the vertical line between spina illiaca anterior superior and the patella in order to measure lower body positions (e.g. standing still, moving slightly), see Fig. 2. Data was sampled at 30 Hz and was initially low-pass filtered at 5 Hz with a fourth-order Butterworth filter (Korshøj et al., 2014). Workers were asked to remove the accelerometers in cases of i) pruritus (itching), ii) equipment causing sleep disturbance, iii) equipment obstructing the workers in the execution of their work tasks, and iv) experiencing inconveniences due to accelerometers. Additionally, workers were instructed to upkeep a diary whilst
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Fig. 1. Flow chart of the procedure concerning inclusion and exclusion criteria. The workers that volunteered to participate were invited to a session in which they i) answered a questionnaire, ii) the experimenter took anthropometric measures, and iii) attached two accelerometers to measure physical activity and forward bending of the trunk.
wearing the accelerometers and to note periods of work, leisure, sleep, non-wear and reference measurements. The Acti4 software (The National Research Centre for the Working Environment, Copenhagen, Denmark and Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany) was used to detect the body posture. In this study, we only investigated the forward bending of the trunk when the worker was either in a standing posture with subtle postural adjustments without regular walking (moving slightly) or standing still (Villumsen et al., 2016). Using the individual reference measurements (i.e. standing still in a vertically positioned stance for 15 s entailing the individual workers neutral position) obtained at the beginning of the recordings as point of reference, the duration and amplitude of forward bending, with respect to upright standing posture as the reference position in the sagittal and frontal planes was computed. EVA as described by (Mathiassen and Winkel, 1991), was performed for the time series of forward bending on the entire valid period of recording for each worker. Two separate EVA profiles were obtained to represent the pattern of exposure variation for forward bending during work and leisure time. The EVA classes categorized the level of forward bending into [<10, 10e20, 20e30, 30e40, 40e50, 50e60, 60e70, 70e80, 80e90, and >90 deg.] with “sequence duration” categories of [0e3, 3e7, 7e15, 15e31, 31 s]. A logarithmic scale was chosen for time categories to accommodate the time line of changes in exposure level and a linear scale was selected for the extent of forward bending to provide a fine resolution (Jansen et al., 2001). This resulted in matrixes in which each
element represents an accumulated elapsed time that the trunk was uninterruptedly bent to an extent determined by the EVA interval for a determined “sequence duration” category. This led to 10 levels along the level axis and 5 levels along the time axis. To summarize the EVA profile into indices which quantify the exposure variation, the centroid of the profile 10 5 plane, that is the average position of the EVA distribution along the “sequence duration” axis (EVA-Time) and the average distribution of the EVA re et al., along the level axis (EVA-Amp) (Delisle et al., 2006; Larivie 2005) and the standard deviation across EVA classes (EVA-SD) were calculated) (Delisle et al., 2006). EVA-Time provides the average position of the EVA distribution along the “sequence duration” axis, which can vary between 1 and 5 corresponding to the number of “sequence duration” categories (Delisle et al., 2006). EVA-Amp describes where the average position of the EVA distribution along the level axis, which can vary between 1 and 10 corresponding to the number of “exposure level” categories (Delisle et al., 2006). EVA-SD can vary between 0 and a maximum level, which corresponds to a case where all the samples of forward bending of the trunk stay within only one EVA class. EVA-SD would be zero if the samples of trunk bending are evenly distributed among all EVA classes. We normalized the EVA-SD to its theoretical upper bound and subtracted from 1. Thus, the EVA-SD could take values varying between 0 and 1. The EVA-SD reflects the variability of the EVA distribution. Thus, the lower the EVA-SD, the less variable the EVA pattern and vice versa. Along with previous studies pointing out the relevance of entropy measures in investigating
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measures analysis of covariance (RMANCOVA) (according to the instructions proposed by (Thomas et al., 2009)) was applied to chosen EVA indices (i.e., i) EVA-Ent, ii) EVA-SD, iii) EVA-Time, and iv) EVA-Amp). The activity context (with two levels: work and leisure) was treated as a within-subject factor. In order to measure the effect size, partial eta squared (h2) was applied. Additionally, the RMANCOVA was conducted with gender, smoking habit and LBPi as between-subject factors and age and BMI as co-variates to explore their modifying effect. Multiple regressions were applied to assess the possible association of the co-variates with significant interactions with the difference (D) between work and leisure for each of the EVA indices (i.e. EVA indices during working time subtracted from the corresponding indices during leisure time). All statistical analyses were performed using Statistical Package for the Social Sciences (IBM Corporation SPSS statistics, Version 22.0, Armonk, NY, USA). Statistical significance was considered at p < 0.05. 3. Results 3.1. Characteristics of the population
Fig. 2. Placement of two tri-axial accelerometers at the processus spinosus (T1-T2 level) to measure forward bending of the trunk and at the halfway mark on the vertical line between spina illiaca anterior superior and the patella to measure lower body positions (e.g. standing still, moving slightly).
exposure variation (Madeleine and Madsen, 2009), we also proposed a novel index by estimating Shannon entropy of the EVA profile normalized by the total recording time (EVA-Ent), indicating the complexity of the exposure variation pattern. That is, higher values of EVA-Ent indicate a more complex variability pattern. The EVA-Ent can vary between 0 and logarithm of the number of EVA classes (i.e. 5 10 ¼ 50). The EVA-Ent was normalized by its maximum achievable value, thus, in our analysis, EVA-Ent could vary between 0 and 1. All the above mentioned EVA indices were calculated separately for work and leisure time. 2.5. Individual factors Age, gender, BMI, smoking habits and LBPi were retrieved from the questionnaire, interview and anthropometric measurements. Smoking habits were obtained through the question ‘Do you smoke?’ with response possibilities: daily smoking, occasionally smoking, formerly smoked, never smoked. Smoking habits were dichotomized into smokers (daily and occasionally smoking) and non-smokers (formerly and never smoked). LBPi was self-rated and defined as of the worst intensity of LBP during the last three months on a numeric rating scale ranging from 0 (no pain) to 10 (worst imaginable pain) (Kuorinka et al., 1987). Subsequently, LBPi was following dichotomized into low (5) and high (>5) categories of LBPi (Andersen et al., 2012). 2.6. Statistical methods Descriptive data on the workers are presented as mean, standard deviation (SD), median, inter quartile range (IQR) or as frequencies. The data distribution was visually inspected and found suitable for parametric procedures. To identify a difference in the variation pattern of forward bending of the trunk during work and leisure whilst standing still or moving slightly, a repeated-
The 657 workers had a total measured recording time for work and leisure of 13,084 h and leisure 15,048 h, respectively. On average, the workers wore the accelerometers for 2.6 ± 0.97 working days with average recordings hours per worker at work of 19.9 ± 8.1 h and leisure of 22.9 ± 8.9 h. Out of this period, the average time spent standing still or moving slightly was 10.3 ± 5.7 h for work and 6.6 ± 3.4 h for leisure. For descriptive characteristics of the population, see Table 1. 3.2. Exposure variation of work and leisure The results of the RMANCOVA, adjusted for age, gender, BMI, smoking habits and LBPi disclosed significant differences for three out of four EVA indices between work and leisure (Table 2). There was a significant effect of activity context on the EVA-Ent (F(1, 656) ¼ 351, p < 0.001), with a mean of 0.73 ± 0.05 for work and 0.77 ± 0.04 for leisure. Seventy-nine percent of the workers had higher values of EVA-Ent during leisure than work (Table 1), indicating that during leisure the variability of forward bending is more complex than during work. Similarly, there was a significant effect of activity context on the EVA-SD (F(1, 656) ¼ 302, p < 0.001), with a mean of 0.75 ± 0.04 for work and 0.78 ± 0.03 for leisure. Seventyeight percent of the workers had lower values of EVA-SD during work (Table 1), indicating that the EVA profile was more static during work compared with the leisure time (Table 2). Fig. 3 illustrates this finding for a typical subject. The EVA-Time did not differ significantly between work and leisure (with 2.276 ± 0.398 vs. 2.301 ± 0.265, respectively (F(1, 656) ¼ 2.6, p ¼ 0.1). The EVAAmp was significantly higher during leisure than during work (with 2.704 ± 0.509 vs. 2.463 ± 0.585, respectively (F(1, 656) ¼ 141, p < 0.001)), with 69.7% of the workers having higher values of EVAAmp during leisure (Table 1). This implies that the amplitude of the variability pattern during leisure was significantly higher than during work (Table 2). 3.3. Exposure variation of work and leisure and modification from individual factors Table 3 shows the results of the RMANCOVA on the EVA indices for the factors, i.e., gender, smoking habits and LBPi and Table 4 shows the effect of covariates, i.e., age and BMI. The results showed that lower BMI was associated with higher EVA-Ent values, lower EVA-SD values and higher EVA-Amp values. Additionally, the
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Table 1 Descriptive characteristics of the blue-collar workers (n ¼ 657). Variables N Low back pain intensity (0e10) (Median, IQR) Low back pain intensity (n)
657 3 (1) 180 477 362 295 45.0 (±10.0) 196a 27.5 (±4.9) 657 563 371 121 7 19.9 (±8.1) 10.3 (±5.7) 22.9 (±8.9) 6.6 (±3.4) 78.5 77.6 50.4 69.7 128 469 60
High (<5) Low (5) Men Women
Gender (n) Age (M, SD) Smokers (n) BMI (M, SD) Days measured (n)
1 2 3 4 5
Working hours measured (M (h), SD) Of which standing still or moving slightly (M (h), SD) Leisure hours measured (M (h), SD) Of which standing still or moving slightly (M (h), SD) Higher values of EVA indices during leisure than work (%)
or or or or
more more more more
EVA-Ent EVA-SD EVA-Time EVA-Amp Cleaning Manufacturing Transport
Occupational sector (n)
n numbers of blue-collar workers, M mean, SD standard deviation, low back pain intensity self-rated worst intensity of low back pain during the last three months from 0 to 10 divided into high (>5) and low (5). a 196 out of 639 blue-collar workers. EVA-Ent the entropy of the EVA profile, EVA-SD the standard deviation of the EVA, EVA-Time the EVA along the time levels, EVA-Amp the EVA along the amplitude levels.
Table 2 Differences between work and leisure for the EVA indices. Repeated measures ANCOVA (RMANCOVA) for tests of differences between work and leisure forward bending variability in selected EVA indices. EVA indices
Work
Leisure
df
F
p
h2
Entropy of the EVA profile Standard deviation of the EVA EVA along the time levels EVA along the amplitude levels
0.73 (0.05) 0.75 (0.04) 2.3 (0.4) 2.5 (0.6)
0.77 (0.05) 0.78 (0.03) 2.3 (0.3) 2.7 (0.5)
1,656 1,656 1,656 1,656
351.3 302.0 2.6 141.3
<0.001 <0.001 0.1 <0.001
0.3 0.3 0.004 0.1
EVA-Time values were higher among men than women. The interaction analysis from the RMANCOVA revealed a significant interaction effect of age and activity context on EVA-Ent (F(1, 614) ¼ 3.887, p ¼ 0.049). Additionally, gender and activity context
also unveiled an interactive effect on EVA-Time (F(1, 614) ¼ 38.695, p ¼ 0.000) and EVA-Amp (F(1, 614) ¼ 18.522, p ¼ 0.000). Consequently, multiple regressions were applied to assess the possible association of the covariates and factors which showed significant
Fig. 3. EVA profile for a typical subject during a) work time b) leisure time. A more variable pattern of activity can be seen during leisure time. For this typical EVA, entropy of the EVA profile (EVA-Ent) was 0.55 and 0.80 for work and leisure time, respectively. The standard deviation of EVA profile (EVA-SD) was 0.65 and 0.80 for work and leisure time respectively.
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Table 3 Variability patterns of forward bending during work and leisure for the EVA indices. Repeated measures ANCOVA shows p-values for the main effects and interactions of gender, smoking habits and LBPi as well as mean and standard deviation of EVA indices. Main effect df Gender The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels Smoking habits The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels Low back pain intensity The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels
Men 0.75 (0.06) 0.77 (0.04) 2.3 (0.3) 2.5 (0.6) Smoker 0.76 (0.05) 0.77 (0.03) 2.3 (0.3) 2.6 (0.5) Low 0.75 (0.06) 0.77 (0.04) 2.3 (0.3) 2.6 (0.6)
Women 0.75 (0.05) 0.77 (0.03) 2.2 (0.3) 2.6 (0.5) Non-smoker 0.75 (0.06) 0.77 (0.03) 2.3 (0.3) 2.6 (0.6) High 0.75 (0.05) 0.77 (0.04) 2.3 (0.3) 2.6 (0.5)
Interaction F
p
F
p
1, 1, 1, 1,
614 614 614 614
1.786 0.886 34.249 3.327
0.182 0.347 0.000 0.069
1.146 1.774 38.695 18.522
0.285 0.183 0.000 0.000
1, 1, 1, 1,
614 614 614 614
0.790 0.636 0.042 0.974
0.375 0.425 0.838 0.324
0.941 0.794 3.478 0.269
0.332 0.373 0.063 0.604
1, 1, 1, 1,
614 614 614 614
1.647 1.372 0.461 0.046
0.204 0.242 0.498 0.830
0.084 0.022 0.203 0.869
0.773 0.883 0.653 0.352
D Men 0.04 (0.06) 0.02 (0.04) 0.07 (0.3) 0.3 (0.5) Non-smoker 0.04 (0.05) 0.02 (0.03) 0.08 (0.4) 0.2 (0.5) Low 0.04 (0.06) 0.03 (0.04) 0.02 (0.4) 0.2 (0.5)
Women 0.05 (0.05) 0.03 (0.03) 0.1 (0.3) 0.1 (0.5) Smoker 0.04 (0.06) 0.03 (0.04) 0.0 (0.4) 0.2 (0.5) High 0.04 (0.06) 0.03 (0.04) 0.03 (0.4) 0.2 (0.5)
The repeated measures ANCOVA was constructed with gender as a between subject factor and PA context (work vs. leisure) as within subject factor. D represents the difference between leisure and work. Partial ɳ2 is an index of effect size. Bold fonts indicate a statistically significant effect.
Table 4 Variability patterns of forward bending during work and leisure for the EVA indices. Repeated measures ANCOVA shows p-values for the main effects covariates (i.e., age, BMI) and their interactions.
h2
Main effect df
Age The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels BMI The entropy of the EVA profile The standard deviation of the EVA The EVA along the time levels The EVA along the amplitude levels
Interaction
Work
F
p
F
p
B
h2
Leisure B
h2
<0.001 <0.001 0.004 0.002
1, 1, 1, 1,
614 614 614 614
0.178 0.002 2.283 1.208
0.673 0.965 0.131 0.272
3.887 1.942 1.185 0.185
0.049 0.164 0.277 0.667
<0.0001 (0.0e0.0) <0.0001 (0.0e0.0) 0.002 (0.006,0.001) 0.003 (0.007,0.002)
0.003 0.001 0.004 0.002
<0.0001 (0.0e0.001) <0.0001 (0.0e0.0) 0.001 (0.003,0.001) 0.002 (0.006,0.002)
0.002 0.002 0.001 0.001
0.01 0.007 0.004 0.02
1, 1, 1, 1,
614 614 614 614
8.916 4.537 2.265 15.759
0.003 0.034 0.133 0.00
1.278 0.610 0.068 2.458
0.259 0.435 0.795 0.117
0.001 (0.002,0.0) 0.001 (0.001,0.0) 0.004 (0.002,0.01) 0.01 (0.02,0.003)
0.004 0.002 0.002 0.01
0.001 (0.002,0.001) 0.001 (0.001,0.0) 0.003 (0.001,0.007) 0.02 (0.03,0.01)
0.02 0.001 0.003 0.03
B represents the coefficient of the covariates in the repeated measure ANCOVA model. Partial h2 is an index of effect size. Bold fonts indicate a statistically significance effect.
interactions (i.e., age and gender). Age only had a tendency for being associated with DEVA-Ent (work vs. leisure) (R2 ¼ 0.01, p ¼ 0.05). There was a tendency for increased DEVA-Ent, as participants’ age increased. The multiple regressions revealed significant associations between gender and the difference (work vs. leisure) in the DEVA-Time (R2 ¼ 0.08, p < 0.001) and DEVA-Amp (R2 ¼ 0.03, p < 0.001). Women exhibited an increase in EVA-Time from work to leisure (2.138e2.284, respectively), contrary to men, where a decrease was observed (2.389e2.314, respectively). From work to leisure, men showed a larger increase in EVA-Amp than women (2.393e2.715 and 2.549e2.691, respectively). 4. Discussion This study is the first to reveal, in line with the hypothesis that the pattern of forward bending of the trunk among blue-collar workers is more variable during leisure compared to during work. Furthermore, the pattern of forward bending was observed to be significantly modified by gender, and only marginally by age. 4.1. Patterns of forward bending during work and leisure In this study, we recorded daily activity by means of accelerometers over 2.6 ± 0.97 working days on average, with 19.9 ± 8.1 h work and 22.9 ± 8.9 h leisure among blue-collar workers. Out of this period, 10.3 ± 5.7 h and 6.6 ± 3.4 h were associated with
standing or moving slightly during work and leisure, respectively. The duration of recording was therefore above the recommendations on the strategies for job sampling based on the type of task and jobs (e.g. The Washington State SHARP approach (Bao et al., 2006)). The use of such small, wireless wearable sensors like accelerometers enables recording of daily activity in situ during entire working days in a rather un-obstructive way (Zheng et al., 2014). The pattern of exposures to physical work demands within individuals has received increasing attention in the last few decades, and many studies have pointed out its functional relevance for improved performance, and prevention of MSDs and fatigue (Madeleine, 2010). As such, a high motor variability is considered a key part of a healthy movement pattern (Madeleine et al., 2003; Mathiassen, 2006). In accordance with our hypothesis, during leisure time, the EVA-Ent and EVA-SD were observed to be higher compared to during work. Even though EVA-Ent and EVA-SD were only about 10% larger during leisure time than work, this difference was found in about 80% of the participants (Table 1). Additionally, the effect size suggests a medium to large effect for the activity context (Richardson, 2011). The size of the difference in movement pattern at work and leisure for generating different health effects is unknown. Therefore, it is hard to make any direct interpretation of the clinical relevance of the effect size difference found. However, we believe that the found difference between work and leisure may be practically significant for its health effects, and may explain a piece in the physical activity health paradox puzzle. Additionally,
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static levels of PA are recognized as a risk factor for MSDs (Holtermann et al., 2012). These results can therefore be interpreted as forward bending of the trunk is performed in a manner, which is probably less harmful during leisure time compared to work. Interestingly, leisure time PA has positive health effects while high levels of PA during work has been associated with an increase in sickness absence, MSDs and premature mortality underlining the health paradox (Holtermann et al., 2012). However, longitudinal studies are needed to investigate if such different variation patterns of forward bending of the trunk during leisure and work are differently associated with health outcomes, like LBP. EVA-Amp was also higher during leisure compared to work. This indicates that workers actually bend forward to a higher level during leisure time compared with work. This higher level of forward bending is probably not a favorable trait, as high duration of forward bending has been reported to increase the likelihood of LBP incidence (Wai et al., 2010). However, we have recently found no positive association between duration of forward bending of the trunk 30 and LBPi in a cross-sectional study (Villumsen et al., 2016). Anyhow, our previous findings cannot support or refute the role of forward bending in development of LBP because of the cross-sectional design. 4.2. Effects of individual factors Individual factors such as age, gender, BMI, smoking habits and LBPi have been frequently reported to affect PA (Balogh et al., 2004; Madeleine, 2010). The patterns of forward bending during leisure and work were associated with BMI and modified by gender and only marginally by age. In contrast, smoking habits and LBPi did not play a role. The absence of LBPi effect on the pattern of forward bending during work and leisure seems to be in contrast with the body of literature suggesting that the pain level is a modifier of motor variability (e.g. (Madeleine, 2010)). This may be explained by job crafting or healthy worker effect (Villumsen et al., 2015). As the BMI increases, EVA-Amp, EVA-Ent and EVA-SD decrease. Thus, BMI seems to be associated with less varying pattern of PA and therefore less health promoting (Christensen et al., 2011). Gender was also found to modify the pattern of forward bending of the trunk during work and leisure time, which is in accordance with a recent study observing differences in other PA types (e.g. walking, sitting, or standing) during work and leisure time (Hallman et al., 2015). Women were found to experience a larger difference in uninterrupted periods of forward bending during leisure and work compared to men. On the contrary, with respect to the level of forward bending, men showed higher DEVA-Amp than women, meaning that the difference in the level of forward bending between work and leisure was more marked among men than women. Interestingly, recent studies have reported lower motor variability among women compared with men, and considered it to be related to the higher prevalence of MSDs among ^ te , 2012). These findings confirm the complex interplay women (Co between PA exposure, in particular forward bending, during work and leisure and modification of individual factors. 4.3. Methodological considerations The use of objective long-term field measures of forward bending during work and leisure with accelerometers is a considerable strength of the present study. The Acti4 software used to determine the body posture, activity type and duration have shown high sensitivity and specificity during free-living settings as well as in controlled conditions (Stemland et al., 2015), and during simulated working tasks such as lifting tasks and assembly work, the Acti4 software has provided valid estimates of forward bending,
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with only 5 degrees of error margin (Korshøj et al., 2014). The level categories of the EVA plane are defined with 10 intervals, insuring the interval being at least twice the size of the error margin. The measurement method would mainly be sensitive to the upper back inclination and not very much to the changes caused by the lower back position. Thus, the contribution of slouch position in upper trunk inclination would be poorly portrayed in our analysis. Similarly, our measurement method was not sensitive to hip flexion even though extensive hip flexion or slouch back would be unlikely to occur during standing and moving slightly because otherwise the Acti4 software would categorize the posture different from standing and moving slightly. Thus, the diurnal measurements of forward bending during standing or moving slightly can be considered as a relatively valid measure. The EVA indices used in this study characterize the EVA profile in term of the center point of the EVA (i.e. EVA-time and EVA-Amp) and the spread and complexity of the distribution (EVA-Ent); however, these indices reduce the available information in the EVA profile. These indices provide relevant information to compare the exposure variation during work and leisure time as they have been re et al., 2005; used in several studies (Delisle et al., 2006; Larivie Samani et al., 2009). However, theses indices do not differentiate between two EVA profiles at the level of a single EVA. To the authors’ knowledge, this is the first study targeting the pattern of forward bending among blue-collar workers using diurnal long-term recordings at both work and leisure. We were merely focused on the pattern of forward bending during work and leisure time and other factors such as such as work-related stress (Holte et al., 2003), cardiorespiratory response (Pollock et al., 1998) which could potentially be different between work and leisure. However, one should bear in mind that the trunk forward bending has received quite a lot of attention in the research field, but so far it has mainly been investigated through self-reported measures (Wai et al., 2010). Further studies investigating the potential mechanisms behind the health paradox in relation to e.g. work-related stress and cardiorespiratory responses are warranted. 5. Conclusion In this study, the pattern of forward bending of the trunk during work and leisure among blue-collar workers was found to differ significantly, indicating a higher variability during leisure compared to work. The novel results of this study, using long-term objective recording of forward bending, indicates a more variable pattern of movement trajectories during leisure time compared with wok. Thus, the pattern of forward bending during leisure time may be associated with less adverse health outcomes compared with work. Interestingly, this finding is in line with the previously reported health paradox of PA during work and leisure which needs to be investigated further for example through prospective studies. Conflict of interest No conflicts of interest are declared by authors. Acknowledgements The study is partly supported by a grant from the Danish government (satspulje). References Andersen, L.L., Clausen, T., Burr, H., Holtermann, A., 2012. Threshold of musculoskeletal pain intensity for increased risk of long-term sickness absence among female healthcare workers in eldercare. PLoS One 7, e41287.
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