Different autonomic responses to occupational and leisure time physical activities among blue-collar workers

  • Tatiana O. Sato
  • David M. Hallman
  • Jesper Kristiansen
  • Jørgen H. Skotte
  • Andreas Holtermann
Open Access
Original Article
  • 255 Downloads

Abstract

Purpose

The differential effect of occupational and leisure time physical activity on cardiovascular health is termed the physical activity health paradox. Cardiac autonomic modulation could bring insights about the underlying mechanism behind this differential effect. The aim was to compare heart rate variability (HRV) during different activities (sitting, standing and moving) at work and leisure among blue-collar workers.

Methods

One hundred thirty-eight workers from the NOMAD cohort were included. Data from physical activity and HRV were obtained for 3–4 days using tri-axial accelerometers (Actigraph GT3X+) and a heart rate monitor (Actiheart). HRV indices were determined during sitting, standing and moving both at work and leisure. Linear mixed-models with two fixed factors (activities and domains) were applied to investigate differences in HRV indices adjusting for individual and occupational factors.

Results

The results showed significant effects of domain (p < 0.01), physical activity type (p < 0.01) and interaction between domain and activity type (p < 0.01) on HRV indices. Mean heart rate (IBI) and parasympathetic measures of HRV (RMSSD and HF) were lower for sitting (p < 0.01) and higher for moving (p < 0.01) during work compared with leisure, while no difference between domains was found for standing (p > 0.05). Sympathovagal balance (LF/HF) was higher during work for sitting and moving (p < 0.01), but showed no difference for standing (p = 0.62).

Conclusions

Differences in cardiac autonomic modulation between work and leisure were found, indicating sympathetic predominance during work and parasympathetic predominance during leisure for sitting. Autonomic responses can be part of the mechanism that explains the differential effect of occupational and leisure time physical activity on health.

Keywords

Heart rate variability Cardiovascular disease Objective measurements Occupational health 

Abbreviations

ANOVA

Analysis of variance

BMI

Body mass index

HRV

Heart rate variability

HF

High frequency spectral power

IBI

Inter beat intervals

LF

Low frequency spectral power

LF/HF

Ratio between LF and HF

LTPA

Leisure time physical activity

NOMAD

New method for objective measurements of physical activity in daily life

OPA

Occupational physical activity

PNS

Parasympathetic nervous system

RMSSD

Square root of the mean squared differences of successive IBI

SDNN

Standard deviation of IBI

SNS

Sympathetic nervous system

Introduction

It is well-established that high physical activity at leisure time decreases risk for all cause and ischemic heart disease mortality (Pedersen and Saltin 2015; Warburton and Bredin 2016). On the other hand, high physical activities at work showed an increased risk for the same mortality indicators (Holtermann et al. 2009, 2010). Thus, it seems to be an inverse relationship between occupational (OPA) and leisure time physical activity (LTPA) and cardiovascular risk (Li et al. 2013; Krause et al. 2015), termed the physical activity health paradox (Holtermann et al. 2012a). The underlying mechanisms for the physical activity health paradox remain unknown. However, it may be related to different autonomic responses during physical activity (Hallman et al. 2017).

Heart rate variability (HRV) analyses can be used as a reliable indicator of autonomic regulation in response to different daily activities, both in the occupational context as well as during leisure time (Guijt et al. 2007; McNarry and Lewis 2012; Hallman et al. 2015a).

Autonomic nervous system modulation is intrinsically related to physical activity via the sympathetic and parasympathetic nervous systems (Pomeranz et al. 1985; Bernardi et al. 1996; Chan et al. 2007; Billman 2011). Thus, comparing HRV for the same physical activity types during work and leisure time might help understanding if work has a differential effect on autonomic activity compared to leisure. However, due to the dependence of HRV indices on body posture and activity (Bernardi et al. 1996; Perini and Veicsteinas 2003; Rennie et al. 2003; Chan et al. 2007; Watanabe et al. 2007) it is important to compare the HRV indices during the same physical activity types and postures during work and leisure.

The aim of this study was to determine whether HRV measured during different activity types, such as sitting, standing and moving, differs between work and leisure in blue-collar workers. These results might bring further insight about the potential mechanisms behind the physical activity health paradox connections between OPA and LTPA and autonomic modulation and health.

Methods

Study population and exclusion criteria

This study is based on data from the cross-sectional study called “New method for Objective Measurements of physical Activity in Daily Life (NOMAD)”, conducted on blue-collar workers recruited from seven workplaces in Denmark. To be included in the study the subjects must have the possibility to participate during paid working time, to be employed for more than 20 h per week and being between 18 and 65 years. Exclusion criteria were declining to sign the informed consent, pregnancy, diabetes, cardiovascular diseases, medication prescription, and fever on the testing day. Allergy to band aid caused exclusion from the objective measurements. Population and recruitment were described in detail elsewhere (Gupta et al. 2015; Hallman et al. 2015b).

Data were obtained from 237 blue-collar workers. Subjects not filling out the questionnaire and those not wearing the objective measurements (n = 77) were excluded. Additionally, workers with less than 7 h (i.e. at least 7 day) of valid HRV recordings, for both work and leisure time were excluded (n = 22). Thus, the sample was composed by 138 blue-collar workers. The main occupational groups were manufacturing laborers (n = 38); assemblers (n = 27); mining and construction laborers (n = 24); cleaners (n = 23); personal care workers in health services (n = 12); garbage collectors (n = 9); heavy truck drivers and mobile plant operators (n = 4) and other elementary workers (n = 1).

All subjects were informed about the study prior to participation and provided an informed consent. The study was approved by the local ethics committee (Journal number H-2-2011-047) and was conducted in accordance with Helsinki declaration.

Procedure

Assessment of individual and occupational factors

A self-reported digital questionnaire was administered to the workers including age (discrete variable; in years), gender (dichotomous variable; female or male), tobacco use (dichotomous variable; yes or no), physical activity at leisure time (categorical variable; almost completely physically passive < 2 h/week, light physically active 2–4 h/week, physically active for 2–4 h/week, more strenuous physical activity > 4 h/week) and lifetime occurrence of medical diagnoses of hypertension, depression or other mental diseases (dichotomous variable; yes or no).

Occupational factors were also collected by the questionnaire, and included job seniority (discrete variable; in months), lifting and carrying during work (categorical variable; almost all the time, approximately 3/4 of the time, approximately 1/2 of the time, approximately 1/4 of the time, rarely/very little or never) and influence at work (discrete variable 0–100%). Influence at work was measured using four items from the Copenhagen Psychosocial Questionnaire (Pejtersen et al. 2010) and a higher number indicates more influence at work.

Height (cm) was measured using a scale (Seca, model 123) and weight (kg) was measured by a digital scale (Tanita modelo BC 418 MA). Body mass index (BMI) was calculated according to the formulae BMI = weight (kg)/height2 (m). Subjects also performed a submaximal fitness test on a cycle ergometer to obtain their aerobic capacity (Astrand 1960).

Assessment of the activities and HRV

Workers were asked to wear the devices, i.e. the accelerometers and the Actiheart sensors, for four continuous days, ideally a period covering two working days, and one-two days off work. The workers were instructed to not remove the equipments, including while bathing and sleeping, unless in case of itching or any kind of discomfort.

Physical activities were objectively recorded using accelerometers (ActiGraph GT3X+, Actigraph, Florida, USA), which measured the acceleration in three dimensions at 30 Hz with a range of 6G (1G = 9.81 m/s2). The accelerometers were attached to the hip (laterally and below the right iliac crest) and thigh (medial on the right thigh), mounted with the x-axis pointing downwards (up/down), and y-axis and z-axis oriented horizontally (Skotte et al. 2014). An activity diary was also provided to the worker to obtain data about the time of the following events: get up in the morning, start and finish work, bedtime and time of reference measurement.

The files were initialized for recording and downloaded using the manufacturer’s software (ActiLife, version 5.5) and afterwards the Acti4 software (The National Research Centre for the Working Environment, Copenhagen, Denmark and BAuA, Berlin, Germany) was used to detect activity types: sitting, standing still and moving (slow and fast walking, running, walking stairs, cycling). This software detected each activity with high sensitivity and specificity, allowing for precise and valid identification of activities. Details of the activities definition have been published elsewhere (Skotte et al. 2014; Stemland et al. 2015).

HRV was derived from the Actiheart system (Camntech Ltd, Cambridge, UK), which measures electrocardiography with a sensitivity of 0.250 mV. The sensor was attached below the apex of the sternum and the horizontal wire was fixed at the right side at the level of the 5th and 6th intercostal space. Respiratory rate was not controlled during data collection. Data were sampled at 128 Hz and it was processed using a band-pass filter (10–35 Hz). The power spectrum was obtained through the robust period detection method. Details of the heart rate variability data processing have been published elsewhere (Kristiansen et al. 2011; Skotte and Kristiansen 2014).

Based on the RR intervals series, HRV was analyzed from 5-min windows with less than 10% erroneous inter beat intervals (IBI), both in the time and frequency domains. Abnormal beats were automated removed before analyzing HRV. The time domain HRV indices were mean IBI (ms), RMSSD (square root of the mean squared differences of successive IBI) and SDNN (standard deviation of IBI). In the frequency domain of HRV, spectral power density was calculated in the low (LF 0.04–0.15 Hz) and high frequency (HF 0.15–0.4 Hz). Mean IBI and SDNN are measures of the mean heart rate and heart rate variability, respectively. RMSSD and HF are indicators of the parasympathetic modulation of cardiac rhythm (Malik et al., 1996; Michael et al. 2017), while LF is taken as an indicator of sympathetic modulation of cardiac rhythm although it is recognized that parasympathetic modulation also contributes to LF (Malik et al. 1996; Michael et al. 2017). The sympathovagal balance (LF/HF) was also calculated (Malik et al. 1996; Kristiansen et al. 2011; Skotte and Kristiansen 2014).

Statistical analyses

All HRV variables, except IBI had non-normal distributions according to the Kolmogorov Smirnov test (p < 0.05). Thus, non-normally distributed variables were transformed using the natural logarithm (ln) prior to further analyses.

Linear mixed-models with two fixed factors (activity type, 3-levels × domains, 2-levels) were applied to investigate differences in the HRV indices between activity types (sit, stand and move), domains (work and leisure) and their interaction. The covariance type was unstructured and the restricted maximum likelihood (REML) estimation method was chosen. Pairwise comparisons were done as a post hoc test using the estimated marginal means. Unadjusted and fully adjusted models were estimated. For the adjusted model the covariates age, sex, BMI, smoking, physical activity at leisure time, job seniority, lifting and carrying and influence at work were included as fixed effects. Subject and intercept were included as random effects. Stratified analyses on smoking (yes or no) and influence at work (high = above or equal the median value of 65, or low = below the median value of 65) were also performed. All statistical analyses were performed using SPSS software (version 17.0) and the level of significance was set at 5%.

Results

One hundred thirty-eight blue-collar workers were included in the statistical analyses and the main characteristics of the workers are presented in Table 1. The mean age of the workers was 45.2 years. Out of the 138 workers, 51.4% were females, 42.0% were smokers, 18.0% reported lifetime occurrence of hypertension and 45.2% reported to perform lifting and carrying for more than half of the work time. The mean (SD) number of measured days was 2 (0.9), with a minimum of 1 and a maximum of 4 days. The mean (SD) of valid accelerometer wear time per day was 8.6 (2.3) h for work and 8.5 (2.5) h for leisure time. The mean (SD) of valid Actiheart wear time per day was 10.7 (5.5) h for work and 16.3 (12.9) h for leisure time. Time spent sitting was higher during leisure time and time spent in standing still and moving were higher during work time.

Table 1

Individual and occupational characteristics among 138 blue-collar workers in the NOMAD cohort

 

n (%)

Mean (SD)

Minimum–maximum

Females

71 (51.4)

  

Age (years)

138

45.2 (9.8)

25–65

Body mass index (kg/m2)

138

25.8 (4.7)

17.4–40.7

Smokers

58 (42.0)

  

Life-time occurrence of medical diagnoses

   

 Hypertension

25 (18.1)

  

 Depression or other mental diseases

20 (14.5)

  

Aerobic capacitya (mlO2/min/kg)

106

32.9 (8.2)

15.7–56.5

 Low

69 (65.1)

  

 Medium

23 (21.7)

  

 High

14 (13.2)

  

Objectively measured MVPA during leisure time (h/day)

138

0.6 (0.4)

0.1–2.2

Seniority in the current occupation (months)

131

165.9 (141.3)

1–576

Lifting and carrying during work

137

  

 Almost all the time

7 (5.1)

  

 Approximately 3/4 of the time

26 (19.0)

  

 Approximately 1/2 of the time

29 (21.2)

  

 Approximately 1/4 of the time

31 (22.6)

  

 Rarely/very little

38 (27.7)

  

 Never

6 (4.4)

  

Influence at work (scale 0–100)

136

54.5 (17.4)

20–100

Time sitting (h/day)

   

 Work

138

3.1 (1.5)

0.5–6.6

 Leisure

138

5.5 (1.8)

2.0–11.9

Time standing (h/day)

   

 Work

138

2.4 (1.3)

0.3–5.2

 Leisure

138

1.5 (0.8)

0.3–4.8

Time moving (h/day)

   

 Work

138

1.7 (0.9)

0.3–3.8

 Leisure

138

0.8 (0.5)

0.2–2.8

aClassification based on age and gender according to the Danish Heart Association

NOMAD new method for objective measurements of physical activity in daily life, MVPA moderate to vigorous physical activity

The mean and standard deviation for the HRV indices obtained during work and leisure domains for each activity type are presented in Table 2 and the results from the crude and fully adjusted linear mixed models are shown in Table 3. Considering the adjusted model, the main effect of domain was only significant for LF and LF/HF (p < 0.01). That is, sympathetic-related measures of HRV and sympathovagal balance were higher during work than during leisure. The main effect of activity was significant for all HRV indices (p < 0.01), with higher estimates for sitting and standing in relation to moving, except for sympathovagal balance (LF/HF). Compared to the main effect of domain, the estimates for the activities were larger, indicating that the effect of activity is more pronounced than the effect of domain for all HRV indices. The interaction between domain and activity type was significant for IBI, RMSSD, HF and LF/HF (p < 0.01) in the adjusted model (Fig. 1). According to this model, mean heart rate (IBI) and parasympathetic measures of HRV (RMSSD and HF) were lower for sitting (p < 0.01) and higher for moving (p < 0.01) during work compared with leisure time, while no difference between domains was found for standing (p > 0.05). On the other hand, sympathovagal balance (LF/HF) was higher during work for sitting and moving (p < 0.01), but showed no difference for standing (p = 0.62).

Table 2

Heart rate variability indices during work and leisure time stratified on physical activity type among blue-collar workers in the NOMAD cohort. Data are presented as mean (SD)

Variables

Sitting

Standing

Moving

Work (n = 138)

Leisure (n = 138)

Work (n = 117)

Leisure (n = 126)

Work (n = 118)

Leisure (n = 98)

IBI (ms)

773.7 (92.8)

815.2 (103.9)

727.6 (87.0)

728.3 (103.6)

611.1 (77.5)

586.8 (84.0)

SDNN (ms)

55.9 (19.6)

54.5 (17.6)

51.5 (19.7)

50.8 (18.1)

39.0 (12.3)

39.9 (13.8)

ln SDNN

3.97 (0.34)

3.95 (0.32)

3.87 (0.38)

3.87 (0.35)

3.62 (0.29)

3.63 (0.35)

RMSSD (ms)

27.3 (13.2)

28.5 (12.9)

22.4 (10.8)

21.4 (10.5)

14.4 (5.5)

13.1 (6.5)

ln RMSSD

3.21 (0.45)

3.26 (0.43)

3.01 (0.44)

2.96 (0.46)

2.60 (0.39)

2.46 (0.47)

LF (ms2/Hz)

920.8 (715.5)

765.5 (575.6)

931.6 (883.8)

740.6 (670.8)

212.6 (157.8)

202.5 (299.2)

ln LF

6.54 (0.78)

6.37 (0.77)

6.44 (0.92)

6.24 (0.90)

5.05 (0.85)

4.57 (1.28)

HF (ms2/Hz)

246.0 (313.4)

281.2 (304.3)

155.6 (196.5)

140.1 (206.4)

38.0 (39.8)

35.9 (49.6)

ln HF

5.02 (0.99)

5.21 (0.95)

4.52 (1.01)

4.38 (1.04)

3.23 (0.92)

2.86 (1.25)

LF/HF

6.0 (3.1)

4.7 (2.7)

8.4 (4.7)

8.8 (6.4)

7.4 (3.2)

6.6 (3.3)

ln LF/HF

1.66 (0.51)

1.42 (0.51)

1.99 (0.55)

1.99 (0.60)

1.91 (0.46)

1.75 (0.55)

Table 3

Crude and adjusted linear mixed models comparing activity types (3-levels), domains (2-levels) and their interaction for heart rate variability indices among blue-collar workers in the NOMAD cohort

Variables

Crude model

Adjusted modela

Estimate

95% CI

p

Estimate

95% CI

p

IBI (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

815.2

799.6 to 830.8

 

905.8

773.3 to 1038.3

 

  Standing

728.0

712.1 to 743.9

 

816.3

683.8 to 948.8

 

  Moving

583.0

566.2 to 599.8

 

674.0

541.2 to 806.8

 

 Domain

      

  Work

22.4

7.7 to 37.1

0.10

22.1

6.5 to 37.7

0.12

 Interaction

  

< 0.01

  

< 0.01

  Sitting

− 63.9

− 83.3

 

− 63.4

− 83.9

 

  Standing

− 23.3

− 43.3

 

− 22.8

− 43.9

 

ln SDNN (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.95

3.88 to 4.00

 

5.15

4.77 to 5.53

 

  Standing

3.87

3.81 to 3.93

 

5.07

4.69 to 5.45

 

  Moving

3.63

3.57 to 3.70

 

4.83

4.45 to 5.22

 

 Domain

  

0.53

  

0.99

  Work

− 0.04

− 0.09 to 0.02

 

− 0.02

− 0.07 to 0.04

 

 Interaction

  

0.30

  

0.66

  Sitting at work

0.06

− 0.02 to 0.13

 

0.03

− 0.04 to 0.11

 

  Standing at work

0.03

− 0.05 to 0.10

 

0.01

− 0.07 to 0.09

 

ln RMSSD (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.26

3.18 to 3.33

 

4.40

3.84 to 4.96

 

  Standing

2.97

2.89 to 3.04

 

4.10

3.54 to 4.66

 

  Moving

2.47

2.39 to 2.55

 

3.62

3.06 to 4.19

 

 Domain

  

0.22

  

0.19

  Work

0.11

0.03 to 0.18

 

0.12

0.04 to 0.20

 

 Interaction

  

< 0.01

  

< 0.01

  Sitting at work

− 0.16

− 0.26 to − 0.06

 

− 0.18

− 0.28 to − 0.07

 

  Standing at work

− 0.09

− 0.19 to 0.01

 

− 0.10

− 0.21 to 0.01

 

ln LF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

6.37

6.22 to 6.52

 

9.88

8.93 to 10.83

 

  Standing

6.24

6.09 to 6.40

 

9.74

8.79 to 10.69

 

  Moving

4.61

4.44 to 4.78

 

8.16

7.21 to 9.12

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.39

0.23 to 0.56

 

0.39

0.22 to 0.55

 

 Interaction

  

0.06

  

0.09

  Sitting at work

− 0.22

− 0.44 to − 0.01

 

− 0.21

− 0.42 to 0.00

 

  Standing at work

− 0.23

− 0.45 to − 0.02

 

− 0.21

− 0.43 to 0.00

 

ln HF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

5.21

5.04 to 5.38

 

8.46

7.32 to 9.59

 

  Standing

4.40

4.22 to 4.57

 

7.62

6.47 to 8.75

 

  Moving

2.89

2.70 to 3.08

 

6.20

5.06 to 7.35

 

 Domain

  

0.20

  

0.22

  Work

0.32

0.13 to 0.51

 

0.30

0.11 to 0.49

 

 Interaction

  

< 0.01

  

< 0.01

  Sitting at work

− 0.51

− 0.76 to − 0.27

 

− 0.49

− 0.74 to − 0.25

 

  Standing at work

− 0.25

− 0.51 to 0.00

 

− 0.21

− 0.46 to 0.05

 

ln LF/HF

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

1.42

1.33 to 1.51

 

1.65

0.86 to 2.44

 

  Standing

1.98

1.89 to 2.07

 

2.22

1.43 to 3.01

 

  Moving

1.76

1.66 to 1.86

 

1.97

1.18 to 2.76

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.12

0.03 to 0.24

 

0.13

0.03 to 0.23

 

 Interaction

  

< 0.01

  

< 0.01

  Sitting at work

0.12

0.00 to 0.24

 

0.12

− 0.01 to 0.25

 

  Standing at work

− 0.09

− 0.21 to 0.04

 

− 0.10

− 0.24 to 0.03

 

Data are presented as estimates of fixed effects, confidence intervals (95% CI) and p values. Leisure domain was regarded as reference

NOMAD New method for Objective Measurements of physical Activity in Daily Life, RMSSD square root of the mean squared differences of successive RR intervals, SDNN standard deviation of RR intervals, LF low frequency power, HF high frequency power, LF/HF low frequency power divided by high frequency

aModel adjusted for age, sex, smoking, body mass index, leisure time physical activity, job seniority, lifting and carrying, influence at work

Fig. 1

Estimated mean values and confidence intervals for heart rate variability indices during sitting, standing and moving at work and leisure domains according to the fully adjusted model

The stratified analysis showed that for the non-smokers and for the high influence groups there were slight differences in the estimates and confidence intervals compared to the original results, but no changes in the statistical significance (results not shown). However, for smokers and low influence groups the interaction between activity and domain was no longer significant for RMSSD and HF power (Tables 4, 5). For the smokers group, the interaction between time and domain was also no longer significant for the sympathovagal balance (LF/HF).

Table 4

Crude and adjusted linear mixed models stratified by smoking status (smoke = yes) comparing activity types (3-levels), domains (2-levels) and their interaction for heart rate variability indices among blue-collar workers in the NOMAD cohort

Variables

Crude model

Adjusted modela

Estimate

95% CI

p

Estimate

95% CI

p

IBI (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

774.2

753.1 to 795.3

 

733.3

561.1 to 905.5

 

  Standing

682.5

660.6 to 704.3

 

640.3

468.0 to 812.7

 

  Moving

579.3

556.3 to 602.4

 

535.6

362.8 to 708.5

 

 Domain

  

0.68

  

0.75

  Work

17.2

− 3.5 to 37.9

 

17.0

− 4.7 to 38.8

 

 Interaction

  

< 0.01

  

< 0.01

  Sitting at work

− 43.8

− 70.9 to − 16.7

 

− 44.5

− 72.9 to − 16.1

 

  Standing at work

− 0.7

− 28.9 to 27.6

 

− 0.9

− 30.4 to 28.7

 

ln SDNN (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.81

3.73 to 3.89

 

4.61

4.02 to 5.19

 

  Standing

3.72

3.63 to 3.81

 

4.51

3.92 to 5.09

 

  Moving

3.59

3.50 to 3.69

 

4.38

3.79 to 4.97

 

 Domain

  

0.36

  

0.40

  Work

− 0.04

− 0.13 to 0.04

 

− 0.04

− 0.13 to 0.05

 

 Interaction

  

0.16

  

0.24

  Sitting at work

0.10

− 0.01 to 0.22

 

0.10

− 0.02 to 0.22

 

  Standing at work

0.09

− 0.03 to 0.21

 

0.09

− 0.04 to 0.21

 

ln RMSSD (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.11

2.99 to 3.22

 

3.96

3.06 to 4.86

 

  Standing

2.78

2.66 to 2.90

 

3.63

2.73 to 4.54

 

  Moving

2.47

2.35 to 2.60

 

3.34

2.43 to 4.25

 

 Domain

  

0.11

  

0.19

  Work

0.09

− 0.03 to 0.22

 

0.08

− 0.05 to 0.21

 

 Interaction

  

0.25

  

0.30

  Sitting at work

− 0.12

− 0.27 to 0.04

 

− 0.11

− 0.27 to 0.06

 

  Standing at work

− 0.01

− 0.17 to 0.16

 

0.01

− 0.17 to 0.18

 

ln LF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

6.09

5.86 to 6.32

 

8.64

7.13 to 10.16

 

  Standing

5.80

5.57 to 6.04

 

8.35

6.84 to 9.87

 

  Moving

4.54

4.29 to 4.79

 

7.09

5.56 to 8.61

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.31

0.07 to 0.55

 

0.29

0.04 to 0.55

 

 Interaction

  

0.69

  

0.68

  Sitting at work

− 0.08

− 0.39 to 0.24

 

− 0.07

− 0.41 to 0.26

 

  Standing at work

0.06

− 0.27 to 0.39

 

0.07

− 0.28 to 0.41

 

ln HF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

4.86

4.60 to 5.12

 

6.93

5.22 to 8.64

 

  Standing

3.95

3.68 to 4.22

 

6.02

4.31 to 7.73

 

  Moving

2.84

2.55 to 3.13

 

4.95

3.22 to 6.67

 

 Domain

  

0.10

  

0.18

  Work

0.25

− 0.04 to 0.55

 

0.22

− 0.09 to 0.53

 

 Interaction

  

0.06

  

0.08

  Sitting at work

− 0.38

− 0.76 to 0.01

 

− 0.36

− 0.77 to 0.05

 

  Standing at work

0.01

− 0.39 to 0.41

 

0.04

− 0.38 to 0.47

 

ln LF/HF

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

1.50

1.37 to 1.63

 

2.08

0.99 to 3.18

 

  Standing

2.00

1.86 to 2.14

 

2.58

1.49 to 3.67

 

  Moving

1.74

1.60 to 1.89

 

2.29

1.20 to 3.39

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.09

− 0.06 to 0.23

 

0.10

− 0.05 to 0.25

 

 Interaction

  

0.12

  

0.13

  Sitting at work

0.13

− 0.06 to 0.32

 

0.12

− 0.08 to 0.31

 

  Standing at work

− 0.06

− 0.25 to 0.14

 

− 0.07

− 0.28 to 0.13

 

Data are presented as estimates of fixed effects, confidence intervals (95% CI) and p values. Leisure domain was regarded as reference

NOMAD New method for Objective Measurements of physical Activity in Daily Life, RMSSD square root of the mean squared differences of successive RR intervals, SDNN standard deviation of RR intervals, LF low frequency power, HF high frequency power, LF/HF low frequency power divided by high frequency

aModel adjusted for age, sex, body mass index, leisure time physical activity, job seniority, lifting and carrying, influence at work

Table 5

Crude and adjusted linear mixed models stratified by influence at work (influence = low) comparing activity types (3-levels), domains (2-levels) and their interaction for heart rate variability indices among blue-collar workers in the NOMAD cohort

Variables

Crude model

Adjusted modela

Estimate

95% CI

p

Estimate

95% CI

p

IBI (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

821.3

792.7 to 849.9

 

1009.6

811.0 to 1208.2

 

  Standing

744.9

715.8 to 773.9

 

930.5

731.6 to 1129.3

 

  Moving

591.1

560.6 to 621.6

 

774.1

579.4 to 973.3

 

 Domain

  

< 0.01

  

0.01

  Work

7.1

− 18.5 to 32.7

 

8.0

− 18.9 to 34.9

 

 Interaction

  

< 0.01

  

< 0.01

  Sitting at work

− 52.6

− 85.8

 

− 55.2

− 89.8 to − 20.5

 

  Standing at work

− 22.4

− 56.4

 

− 23.0

− 58.4 to 12.5

 

ln SDNN (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.90

3.80 to 4.00

 

5.41

4.84 to 5.98

 

  Standing

3.85

3.75 to 3.95

 

5.36

4.79 to 5.93

 

  Moving

3.66

3.55 to 3.77

 

5.17

4.60 to 5.75

 

 Domain

  

0.16

  

0.15

  Work

− 0.12

− 0.22 to − 0.03

 

− 0.13

− 0.23 to − 0.03

 

 Interaction

  

0.02

  

0.04

  Sitting at work

0.17

0.05 to 0.29

 

0.17

0.04 to 0.30

 

  Standing at work

0.09

− 0.03 to 0.22

 

0.10

− 0.04 to 0.23

 

ln RMSSD (ms)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

3.20

3.08 to 3.32

 

4.63

3.86 to 5.41

 

  Standing

2.97

2.85 to 3.09

 

4.40

3.63 to 5.18

 

  Moving

2.51

2.38 to 2.64

 

3.95

3.18 to 4.73

 

 Domain

  

0.79

  

0.72

  Work

0.02

− 0.11 to 0.14

 

0.01

− 0.12 to 0.14

 

 Interaction

  

0.82

  

0.88

  Sitting at work

− 0.05

− 0.21 to 0.11

 

− 0.04

− 0.22 to 0.13

 

  Standing at work

− 0.03

− 0.20 to 0.13

 

− 0.02

− 0.20 to 0.15

 

ln LF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

6.24

5.97 to 6.51

 

10.07

8.62 to 11.51

 

  Standing

6.16

5.88 to 6.43

 

9.97

8.52 to 11.42

 

  Moving

4.52

4.23 to 4.81

 

8.35

6.90 to 9.81

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.32

0.05 to 0.59

 

0.30

0.02 to 0.58

 

 Interaction

  

0.57

  

0.63

  Sitting at work

− 0.06

− 0.41 to 0.28

 

− 0.05

− 0.41 to 0.31

 

  Standing at work

− 0.19

− 0.54 to 0.17

 

− 0.17

− 0.54 to 0.20

 

ln HF (ms2/Hz)

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

5.01

4.74 to 5.28

 

8.71

7.18 to 10.24

 

  Standing

4.32

4.05 to 4.60

 

8.01

6.48 to 9.55

 

  Moving

2.29

2.63 to 3.22

 

6.63

5.09 to 8.17

 

 Domain

      

  Work

0.15

− 0.14 to 0.44

0.75

0.13

− 0.17 to 0.44

0.85

 Interaction

  

0.36

  

0.42

  Sitting at work

− 0.27

− 0.64 to 0.11

 

− 0.25

− 0.65 to 0.14

 

  Standing at work

− 0.11

− 0.49 to 0.27

 

− 0.09

− 0.50 to 0.31

 

ln LF/HF

      

 Activity

  

< 0.01

  

< 0.01

  Sitting

1.47

1.32 to 1.62

 

1.54

0.41 to 2.67

 

  Standing

1.97

1.81 to 2.12

 

2.02

0.90 to 3.15

 

  Moving

1.65

1.49 to 1.82

 

1.71

0.58 to 2.84

 

 Domain

  

< 0.01

  

< 0.01

  Work

0.17

0.02 to 0.32

 

0.17

0.01 to 0.32

 

 Interaction

  

0.04

  

0.04

  Sitting at work

0.08

− 0.11 to 0.28

 

0.08

− 0.12 to 0.28

 

  Standing at work

− 0.15

− 0.35 to 0.05

 

− 0.16

− 0.36 to 0.05

 

Data are presented as estimates of fixed effects, confidence intervals (95% CI) and p values. Leisure domain was regarded as reference

NOMAD New method for Objective Measurements of physical Activity in Daily Life, RMSSD square root of the mean squared differences of successive RR intervals, SDNN standard deviation of RR intervals, LF low frequency power, HF high frequency power, LF/HF low frequency power divided by high frequency

aModel adjusted for age, sex, smoke, body mass index, leisure time physical activity, job seniority, lifting and carrying

Discussion

This study assessed heart rate variability (HRV) during sitting, standing and moving at work and leisure time in the NOMAD cohort. The results showed significant effects of domain and activity type on HRV indices. Generally, sympathetic modulation was higher at work than during leisure. Moving activity showed the lowest HRV indices, followed by standing still and sitting. The interaction between domain and activity type was also significant. That is, mean heart rate and parasympathetic modulation was lower for sitting and higher for moving during work, while no difference between work and leisure was found for standing. Sympathovagal balance was higher during work for sitting and moving, but showed no difference for standing.

Similarly to other studies, our findings indicate a significant effect of the activity types on HRV. Other studies have also shown the effect of body posture and physical activity on autonomic modulation (Pomeranz et al. 1985; Bernardi et al. 1996; Perini and Veicsteinas 2003; Rennie et al. 2003; Chan et al. 2007; Watanabe et al. 2007; Valentini and Parati 2009; Silva et al. 2015). Based on the above mentioned studies, it was expected that the highest HRV indices would be found while sitting, as a result of the vagal predominance during rest. On the other hand, we also expected a lower HRV during standing and moving, which can be attributed to vagal withdrawal and sympathetic predominance (Malliani et al. 1991; Michael et al. 2017).

Our findings also showed that the sympathetic modulation (LF) and sympathovagal balance (LF/HF) were higher during work, although LF can be influenced by both sympathetic and parasympathetic activity. These findings indicate that work has a differential effect on autonomic activity compared to leisure. Other studies have shown that increased sympathetic modulation is related to increased cardiovascular risk and mortality (Tsuji et al. 1994, 1996). Thus, increased sympathetic and reduced vagal activity at work can be part of the mechanism explaining why OPA has a negative effect on cardiovascular health.

It is already known that the relationship between physical activity and health depends on whether the activity occurs at work or leisure (Li et al. 2013; Holtermann et al. 2012a). Specifically, moderate and high levels of LTPA are associated with favorable health outcomes, while OPA shows no clear or even inverse relationship (Holtermann et al. 2012b, 2013; Allesøe et al. 2014; Saidj et al. 2014). Hallman et al. (2017) evaluated HRV during sleep and found beneficial effects of LTPA only when OPA was low. Thus, the autonomic cardiac modulation seems to be one possible physiological response behind the physical activity health paradox. However, the underlying mechanisms for a different autonomic regulation during the same physical activity type and body posture during work and leisure are unknown. One potential factor explaining this effect can be that the autonomy and mental load during performance of specific tasks differ between work and leisure, as shown in laboratory studies that simulated increased mental load (Hjortskov et al. 2004; Chandola et al. 2010). Thus, a stratified analysis was performed to verify if differences between work and leisure depend on influence at work. The findings suggest that influence at work modified the relationship between physical activity and the parasympathetic modulation of the heart. The interaction between activity and domain with regard to parasympathetic cardiac modulation (HF and RMSSD) was reduced and became statistically non-significant for the low influence group, but remained statistically significant for the high influence group. Since the high influence group presumably has the lowest stress levels, these results do not suggest that work stress can explain the moderating effect of domain on activity. However, as no data about mental stress was available we could not infer whether stress factors are responsible for the differences in parasympathetic activity between work and leisure during the same physical activity/posture.

For the smokers group, the interaction between activity and domain with regard to parasympathetic cardiac modulation (HF and RMSSD) was reduced and became statistically non-significant, but remained statistically significant for the non-smokers group. Thus, these results also suggest that smoking modified the relation between physical activity and the parasympathetic modulation of the heart.

Strengths and limitations

The main limitations are lacking information about specific work tasks performed, and respiration rate which both could influence HRV. In addition, our findings on blue-collar workers may not be representative of the general working population, e.g. white-collar workers. Future studies could also include more recording days to allow some familiarization of the subjects with the devices and to remove potential bias, e.g. increased physical activity due to the use of the accelerometers. Information about diet, circadian clock, occupational activity and mental stress could also bring more insights about this issue. This is the first study, using objective measurements of physical activity and HRV for multiple days in a large and homogeneous socioeconomic sample, showing HRV differences between work and leisure during physical activities. This finding may contribute to the understanding of the health paradox of occupational and leisure-time physical activity.

Conclusions

Differences in cardiac autonomic modulation between work and leisure domains were found, indicating a sympathetic predominance during work and parasympathetic predominance during leisure for sitting. Autonomic responses can be part of the mechanism that explains the differential effect of occupational and leisure time physical activity on health. Smoking and low influence at work modifies the relation between physical activity and the HRV.

Notes

Acknowledgements

This study was conducted with the financial support from Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany; National Research Centre for the Working Environment (NRCWE), Copenhagen, Denmark and São Paulo Research Foundation (FAPESP), São Paulo, Brazil (Grant#2015/18310-1).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

References

  1. Allesøe K, Holtermann A, Aadahl M, Thomsen JF, Hundrup YA, Søgaard K (2014) High occupational physical activity and risk of ischaemic heart disease in women: the interplay with physical activity during leisure time. Eur J Prev Cardiol 22(12):1601–1608.  https://doi.org/10.1177/2047487314554866 CrossRefGoogle Scholar
  2. Astrand I (1960) Aerobic work capacity in men and women with special reference to age. Acta Physiol Scand Suppl 49(169):1–92Google Scholar
  3. Bernardi L, Valle F, Coco M, Calciati A, Sleight P (1996) Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovasc Res 32(2):234–237CrossRefGoogle Scholar
  4. Billman GE (2011) Heart rate variability—a historical perspective. Front Physiol 29:2:86.  https://doi.org/10.3389/fphys.2011.00086 Google Scholar
  5. Chan H-L, Lin M-A, Chao P-K, Lin C-H (2007) Correlates of the shift in heart rate variability with postures and walking by time–frequency analysis. Comp Meth Program Biomed 86(2):124–130.  https://doi.org/10.1016/j.cmpb.2007.02.003 CrossRefGoogle Scholar
  6. Chandola T, Heraclides A, Kumari M (2010) Psychophysiological biomarkers of workplace stressors. Neurosci Biobehav Rev 35(1):51–57.  https://doi.org/10.1016/j.neubiorev.2009.11.005 CrossRefGoogle Scholar
  7. Guijt AM, Sluiter JK, Frings-Dresen MH (2007) Test-retest reliability of heart rate variability and respiration rate at rest and during light physical activity in normal subjects. Arch Med Res 38(1):113–120.  https://doi.org/10.1016/j.arcmed.2006.07.009 CrossRefGoogle Scholar
  8. Gupta N, Christiansen CS, Hallman DM, Korshøj M, Carneiro IG, Holtermann A (2015) Is objectively measured sitting time associated with low back pain? a cross-sectional investigation in the NOMAD study. PLoS One.  https://doi.org/10.1371/journal.pone.0121159 Google Scholar
  9. Hallman DM, Srinivasan D, Mathiassen SE (2015a) Short- and long-term reliability of heart rate variability indices during repetitive low-force work. Eur J Appl Physiol 115(4):803–812.  https://doi.org/10.1007/s00421-014-3066-8 CrossRefGoogle Scholar
  10. Hallman DM, Sato T, Kristiansen J, Gupta N, Skotte J, Holtermann A (2015b) Prolonged sitting is associated with attenuated heart rate variability during sleep in blue-collar workers. Int J Environ Res Public Health 12:14811–14827.  https://doi.org/10.3390/ijerph121114811 CrossRefGoogle Scholar
  11. Hallman DM, Jørgensen MB, Holtermann A (2017) On the health paradox of occupational and leisure-time physical activity using objective measurements: effects on autonomic imbalance. PLoS One 12(5):e0177042.  https://doi.org/10.1371/journal.pone.0177042 CrossRefGoogle Scholar
  12. Hjortskov N, Rissén D, Blangsted AK, Fallentin N, Lundberg U, Søgaard K (2004) The effect of mental stress on heart rate variability and blood pressure during computer work. Eur J Appl Physiol 92(1–2):84–89.  https://doi.org/10.1007/s00421-004-1055-z Google Scholar
  13. Holtermann A, Mortensen OS, Burr H, Søgaard K, Gyntelberg F, Suadicani P (2009) The interplay between physical activity at work and during leisure time–risk of ischemic heart disease and all-cause mortality in middle-aged Caucasian men. Scand J Work Environ Health 35(6):466–474.  https://doi.org/10.5271/sjweh.1357 CrossRefGoogle Scholar
  14. Holtermann A, Mortensen OS, Burr H, Søgaard K, Gyntelberg F, Suadicani P (2010) Physical demands at work, physical fitness, and 30-year ischaemic heart disease and all-cause mortality in the Copenhagen Male Study. Scand J Work Environ Health 36(5):357–365.  https://doi.org/10.5271/sjweh.2913 CrossRefGoogle Scholar
  15. Holtermann A, Hansen JV, Burr H, Søgaard K, Sjøgaard G (2012a) The health paradox of occupational and leisure-time physical activity. Br J Sports Med 46(4):291–295.  https://doi.org/10.1136/bjsm.2010.079582 CrossRefGoogle Scholar
  16. Holtermann A, Burr H, Hansen J, Krause N, Søgaard K, Mortensen O (2012b) Occupational physical activity and mortality among Danish workers. Int Arch Occup Environ Health 85(3):305–310.  https://doi.org/10.1007/s00420-011-0668-x CrossRefGoogle Scholar
  17. Holtermann A, Marott JL, Gyntelberg F, Søgaard K, Suadicani P, Mortensen OS, Prescott E, Schnohr P (2013) Does the benefit on survival from leisure time physical activity depend on physical activity at work? A prospective cohort study. PLoS One.  https://doi.org/10.1371/journal.pone.0054548 Google Scholar
  18. Krause N, Brand RJ, Arah OA, Kauhanen J (2015) Occupational physical activity and 20-year incidence of acute myocardial infarction: results from the Kuopio Ischemic Heart Disease Risk Factor Study. Scand J Work Environ Health 41(2):124–139.  https://doi.org/10.5271/sjweh.3476 CrossRefGoogle Scholar
  19. Kristiansen J, Korshøj M, Skotte JH, Jespersen T, Søgaard K, Mortensen OS, Holtermann A (2011) Comparison of two systems for long-term heart rate variability monitoring in free-living conditions—a pilot study. Biomed Eng.  https://doi.org/10.1186/1475-925X-10-27 Google Scholar
  20. Li J, Loerbroks A, Angerer P (2013) Physical activity and risk of cardiovascular disease: what does the new epidemiological evidence show? Curr Opin Cardiol 28(5):575–583.  https://doi.org/10.1097/HCO.0b013e328364289c CrossRefGoogle Scholar
  21. Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ, Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17(3):354–381CrossRefGoogle Scholar
  22. Malliani A, Pagani M, Lombardi F, Cerutti S (1991) Cardiovascular neural regulation explored in the frequency domain. Circulation 84(2):482–492.  https://doi.org/10.1161/01.CIR.84.2.482 CrossRefGoogle Scholar
  23. McNarry MA, Lewis MJ (2012) Heart rate variability reproducibility during exercise. Physiol Meas 33(7):1123–1133.  https://doi.org/10.1088/0967-3334/33/7/1123 CrossRefGoogle Scholar
  24. Michael S, Graham KS, Davis GM, Oam (2017) Cardiac autonomic responses during exercise and post-exercise recovery using heart rate variability and systolic time intervals - a review. Front Physiol 29:8:301.  https://doi.org/10.3389/fphys.2017.00301 CrossRefGoogle Scholar
  25. Pedersen BK, Saltin B (2015) Exercise as medicine – evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports 25(suppl 3):1–72.  https://doi.org/10.1111/sms.12581 CrossRefGoogle Scholar
  26. Pejtersen JH, Kristensen TS, Borg V, Bjorner JB (2010) The second version of the Copenhagen psychosocial questionnaire. Scand J Public Health 38(3 suppl):8–24.  https://doi.org/10.1177/1403494809349858 CrossRefGoogle Scholar
  27. Perini R, Veicsteinas A (2003) Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions. Eur J Appl Physiol 90(3–4):317–325.  https://doi.org/10.1007/s00421-003-0953-9 CrossRefGoogle Scholar
  28. Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon D, Kilborn KM, Barger AC, Shannon DC, Cohen RJ et al (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 248(1 Pt 2):H151–H153Google Scholar
  29. Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot M (2003) Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. Am J Epidemiol 158(2):135–143.  https://doi.org/10.1093/aje/kwg120 CrossRefGoogle Scholar
  30. Saidj M, Jørgensen T, Jacobsen RK, Linneberg A, Aadahl M (2014) Differential cross-sectional associations of work-and leisure-time sitting, with cardiorespiratory and muscular fitness among working adults. Scand J Work Environ Health 40(5):531–538.  https://doi.org/10.5271/sjweh.3443 CrossRefGoogle Scholar
  31. Silva VP, Oliveira NA, Silveira H, Mello RG, Deslandes AC (2015) Heart rate variability indexes as a marker of chronic adaptation in athletes: a systematic review. Ann Noninvasive Electrocardiol 20(2):108–118.  https://doi.org/10.1111/anec.12237 CrossRefGoogle Scholar
  32. Skotte JH, Kristiansen J (2014) Heart rate variability analysis using robust period detection. Biomed Eng Online doi.  https://doi.org/10.1186/1475-925X-13-138 Google Scholar
  33. Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A (2014) Detection of physical activity types using triaxial accelerometers. J Phys Act Health 11(1):76–84.  https://doi.org/10.1123/jpah.2011-0347 CrossRefGoogle Scholar
  34. Stemland I, Ingebrigtsen J, Christiansen CS, Jensen BR, Hanisch C, Skotte J, Holtermann A (2015) Validity of the Acti4 method for detection of physical activity types in free-living settings: comparison with video analysis. Ergonomics 15:1–13Google Scholar
  35. Tsuji H, Venditti FJ Jr, Manders ES, Evans JC, Larson MG, Feldman CL, Levy D (1994) Reduced heart rate variability and mortality risk in an elderly cohort. Framingham Heart Study Circ 90(2):878–883CrossRefGoogle Scholar
  36. Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D (1996) Impact of reduced heart rate variability on risk for cardiac events. Framingham Heart Study Circ 94(11):2850–2855.  https://doi.org/10.1161/01.CIR.94.11.2850 CrossRefGoogle Scholar
  37. Valentini M, Parati G (2009) Variables influencing heart rate. Prog Cardiovasc Dis 52(1):11–19.  https://doi.org/10.1016/j.pcad.2009.05.004 CrossRefGoogle Scholar
  38. Warburton DE, Bredin SS (2016) Reflections on physical activity and health: what should we recommend? Can J Cardiol 32(4):495–504.  https://doi.org/10.1016/j.cjca.2016.01.024 CrossRefGoogle Scholar
  39. Watanabe N, Reece J, Polus BI (2007) Effects of body position on autonomic regulation of cardiovascular function in young, healthy adults. Chiropr Osteopat 15:19.  https://doi.org/10.1186/1746-1340-15-19 CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Physical Therapy DepartmentFederal University of São Carlos (UFSCar)São CarlosBrazil
  2. 2.Department of Occupational and Public Health Sciences, Centre for Musculoskeletal ResearchUniversity of GävleGävleSweden
  3. 3.National Research Centre for the Working Environment (NRCWE)Copenhagen ØDenmark
  4. 4.Institute of Sports Science and Clinical BiomechanicsUniversity of Southern DenmarkOdenseDenmark

Personalised recommendations