Background/objectives

An element of obesity prevention is increasing total physical activity energy expenditure. However, this approach does not incorporate the balance of various movement behaviors—physical activity, sedentary behaviors and sleep - across domains of the day. We aimed to identify time-use profiles over work and leisure, termed ‘movement behavior profiles’ and to investigate their association with obesity.

Subjects/methods

Eight-hundred-and-seven workers completed (a) thigh accelerometry and diaries to determine their 24-h composition of behaviors (sedentary and standing, light physical activity and moderate-to-vigorous physical activity at work and leisure, and time in bed) and (b) obesity measurements. Movement behavior profiles were determined using latent profile analyses of isometric log-ratios of the 24-h composition, and labeled according to animal movement behavior traits. Linear models were applied to determine the association between profiles and obesity.

Results

Four profiles were identified, labeled as “Chimpanzees” (n = 226), “Lions” (n = 179), “Ants” (n = 244), and “Koalas” (n = 158). “Chimpanzees” work time was evenly distributed between behaviors while their leisure time was predominantly active. Compared to Chimpanzees, “Lions” were more active at work and sedentary during leisure and spent more time in bed; “Ants” were more active at work and during leisure; “Koalas” were more sedentary at work and leisure and spent similar time in bed. With “Chimpanzees” as reference, “Lions” had least favorable obesity indicators: +2.0 (95% confidence interval [CI] 0.6, 3.4) %body fat, +4.3 cm (1.4, 7.3) waist circumference and +1.0 (2.0, 0.0) Body Mass Index (BMI), followed by “Koalas” +2.0 (0.4, 3.7) %body fat, +3.1 cm (0.1, 6.0) waist circumference, and +0.8 (−0.30, 1.94) BMI. No significant differences were found between “Chimpanzees” and “Ants”.

Conclusions

Movement behavior profiles across work and leisure time-use compositions are associated with obesity. Achieving adequate balance between work and leisure movement behaviors should be further investigated as a potential obesity prevention strategy.

Introduction Obtaining a balance between exercise and recovery is an established approach in Exercise Science to improve aerobic capacity. However, it is unknown how different 24-h time-use profiles depicting a balance between physical behaviors (i.e. physical activities, sedentary behavior and sleep) across main domains of the day are associated with aerobic capacity. We aimed to identify such 'physical-behavior (PB) profiles' and to investigate their association with aerobic capacity. Methods Workers (n=807) participated in thigh-accelerometry to determine 24-h time-use composition of physical activity, sedentary and standing during work and leisure, as well as time in bed. Åstrand submaximal cycle ergometer test was used to estimate aerobic capacity. The PB profiles were determined using latent profile analysis of isometric log-ratios representing the 24-hour composition. Linear models were applied to determine the cross-sectional association between physical-behavior profiles and aerobic capacity. Results Four PB profiles were identified that were labeled based on animal traits; Chimpanzees (n=226), Lions (n=179), Ants (n=244), and Koalas (n=158). Compared to Chimpanzees (at work, in mins; sedentary=197, standing=145, physical activity=117; and at leisure in mins; physical activity=114, standing=121, time in bed= 440); Lions were more active at work, sedentary at leisure and spent more time in bed; Ants had more physical activity at work and similar physical activity and time in bed at leisure; Koalas were more sedentary at work and leisure and spent more time in bed. Compared to Chimpanzees, Koalas had lower aerobic capacity (mlO2/kg/min): -3.7 (95%CI -6.0,-1.5), followed by Lions -3.6 (-5.5,-1.7) and Ants -1.8 (-3.7,- 0.1). Conclusions Physical-behavior profiles based on 24-h time-use composition are associated with aerobic capacity. Obtaining a balance between physical behaviors at work and leisure may be a promising approach for improving aerobic capacity.

Introduction

The aim of the study was to investigate if (a) substituting total sedentary time or long sedentary bouts with standing or various types of physical activity and (b) substituting long sedentary bouts with brief sedentary bouts; is associated with obesity indicators using a cross sectional isotemporal substitution approach among blue-collar workers.

Methods

A total of 692 workers from transportation, manufacturing and cleaning sectors wore an Actigraph GT3X+ accelerometer on the thigh for 1–4 working days. The sedentary (sit and lie), standing, walking, and moderate to vigorous physical activity (MVPA) time on working days was computed using validated Acti4 software. The total sedentary time and uninterrupted sedentary time spent in brief (≤5 mins), moderate (>5 and ≤30 mins), and long (>30mins) bouts, were determined for the whole day and during work and non-work time separately. The obesity indicators, BMI (kg/m2), waist circumference (cm) and fat percentage were objectively measured. Isotemporal substitution modelling was utilized to determine the linear association with obesity indicators of replacing 30 min of total sedentary time or long sedentary bouts with standing, walking or MVPA and separately replacing 30 min of long sedentary bouts with brief sedentary bouts.

Results

Workers [mean (standard deviation, SD); age = 45.1 (9.9) years, BMI = 27.5 (4.9) kg/m2, %BF = 29.6 (9.5), waist circumference = 94.4 (13.0) cm] sat for 2.4 hours (~32% of the measured time, SD = 1.8 hours) across the day during work period and 5.5 hours (~62% of the measured time, SD = 1.5 hours) during non-work period. Most of the sedentary time was accrued in moderate bouts [work = 1.40 (SD = 1.09) hours] during work and in long bouts during non-work [2.7 (SD = 1.4) hours], while least in long sedentary bouts during work [work = 0.5 (SD = 0.9)] and in brief sedentary bouts [0.5 hours (SD = 0.3)] during non-work. Significant associations with all obesity indicators were found when 30 min of total sedentary time or long sedentary bouts were replaced with standing time (~1–2% lower) or MVPA (~4–9% lower) during whole day, work, and non-work periods. The exception was that a statistically significant association was not observed with any obesity indicator when replacing total sedentary time or long sedentary bouts with standing time during the work period. Significant beneficial associations were found when replacing the long sedentary bouts with brief sedentary bouts (~3–5% lower) during all domains.

Conclusion

Replacing total sedentary time and long sedentary bouts, respectively, not only with MVPA but also standing time appears to be beneficially associated with obesity indicators among blue-collar workers. Additionally, replacing long sedentary bouts with brief sedentary bouts was also beneficially associated with obesity indicators. Studies using prospective design are needed to confirm the findings.

Background. Questionnaire-based information of occupational physical activities is extensively used, but susceptible to systematic errors. Calibration modeling may reduce errors and improve precision of questionnaire-based information by transforming the selfreported data into more correct estimates of “true” exposure. We aimed (1) to determine the ability of unadjusted ratings of Saltin and Grimby’s Occupational Physical Activity (SGOPA) question to estimate objectively measured sedentary behaviour, physical activity and cardiovascular load, and (2) to develop and evaluate statistical models calibrating SGOPA ratings into expected values of objectively measured exposures.

Methods. 214 blue-collar workers responded to a questionnaire comprising the SGOPA question and questions on several individual and work-related factors. They wore two accelerometers measuring time spent in sedentary and in physical activities, and one Actiheart monitoring cardiovascular load (eventually expressed as %Heart Rate Reserve) for one to four days. Least-squares linear regression models were developed to predict each objectively measured exposure from SGOPA and additional self-reported individual and work-related predictors.

Results. SGOPA alone explained 22% (R2 adjusted=21%) of the variance between individuals in sedentary behaviour and physical activities, and 8% (R2 adjusted =7%) of the variance in high cardiorespiratory load. When adding predictors related to individual and work to the regression model, explained variance increased to 51% (R2 adjusted=46%) for both sedentary behaviour and physical activities, and to 27% (R2 adjusted=19%) for high cardiorespiratory load. Bootstrap validation suggested that explained variance would be reduced by 9-15% for the three exposures when using the model on other data sets.

Discussion. SGOPA itself shows only limited ability to predict objectively measured sedentary behaviour, physical activities and cardiovascular load at work, but the performance of a calibration model can be considerably improved by adding further self-reported predictors.

Background. Data on time spent in physical activity, sedentary behavior and sleep during a day is compositional in nature, i.e. they add up to a constant value, typically 100% time. Compositional data have fundamentally different properties from unconstrained data in real space, and require other processing and analysis procedures, referred to as compositional data analysis (CoDA). Most physical activity and sedentary behavior studies, however, still apply analytical procedures adapted to data in real space, which can lead to misleading results. The present study describes a comparison of time spent sedentary and in physical activity between age groups and sexes, and investigates the extent to which results obtained by CoDA differ from those obtained using standard analytical procedures.

Methods. Time spent sedentary, standing, and in physical activity (walking/running/stair climbing/cycling) during work and leisure was determined for 1-4 days among 677 blue-collar workers using accelerometry. Differences between sexes and age groups were tested using MANOVA, using both a standard approach and a CoDA approach based on isometric log-ratio transformed data.  

Results. When determining differences between sexes in time used for different activities at work, the effect size using standard analysis (η²=0.045, p<0.001) was 15% smaller than that obtained with CoDA (η²=0.052, p<0.001), although both approaches suggested a statistically significant difference. When determining corresponding differences between age groups, CoDA resulted in a 60% larger, and significant, effect size (η²=0.012, p=0.02) than that obtained with the standard approach (η²=0.008, p=0.07). During leisure, results with standard (age; η²=0.007, p=0.09; sex; η²=0.052, p<0.001) and CoDA (age; η²=0.007, p=0.09; sex; η²=0.051, p<0.001) analyses were similar.

Conclusion. Results and, hence, inferences concerning differences by age and sex in time spent sedentary and in physical activity at work differed between CoDA and standard analysis. We encourage researchers to use CoDA in similar studies, in order to adequately account for the compositional nature of data on physical activity and sedentary behavior

Background: Studies on the association between sitting time and low back pain (LBP) have found contrasting results. This may be due to the lack of objectively measured sitting time or because socioeconomic confounders were not considered in the analysis.

Objectives: To investigate the association between objectively measured sitting time (daily total, and occupational and leisure-time periods) and LBP among blue-collar workers.

Methods: Two-hundred-and-one blue-collar workers wore two accelerometers (GT3X+ Actigraph) for up to four consecutive working days to obtain objective measures of sitting time, estimated via Acti4 software. Workers reported their LBP intensity the past month on a scale from 0 (no pain) to 9 (worst imaginable pain) and were categorized into either low (≤5) or high (>5) LBP intensity groups. In the multivariate-adjusted binary logistic regression analysis, total sitting time, occupational and leisure-time sitting were both modeled as continuous (hours/day) and categorical variables (i.e. low, moderate and high sitting time).

Results: The multivariate logistic regression analysis showed a significant positive association between total sitting time (hours) and high LBP intensity (odds ratio; OR=1.43, 95%CI=1.15-1.77, P=0.01). Similar results were obtained for leisure-time sitting (OR=1.45, 95%CI= 1.10-1.91, P=0.01), and a similar but non-significant trend was obtained for occupational sitting time (OR=1.34, 95%CI 0.99-1.82, P=0.06). In the analysis on categorized sitting time, high sitting time was positively associated with high LBP for total (OR=3.31, 95%CI= 1.18-9.28, P=0.03), leisure (OR=5.31, 95%CI= 1.57-17.90, P=0.01), and occupational (OR=3.26, 95%CI= 0.89-11.98, P=0.08) sitting time, referencing those with low sitting time.

Conclusion: Sitting time is positively associated with LBP intensity among blue-collar workers. Future studies using a prospective design with objective measures of occupational sitting time are recommended.

Introduction

Previous studies have found substantial differences in the health effects of occupational (OPA) and leisure time physical activity (LTPA): LTPA is generally beneficial to health, while OPA may be detrimental. Why these differences occur is, however, so far not clear. Different time patterns of OPA and LTPA may be an explanation. Thus, we aimed to investigate the extent to which time patterns of OPA and LTPA differ in a population of blue-collar workers.

Methods

This study was conducted on a cross-sectional sample of 191 blue-collar workers from seven workplaces in Denmark. Physical activity and sedentary behavior were estimated using the Acti4 method on recordings from accelerometers (Actigraph GT3X+) worn on the thigh and trunk for four consecutive days. Time patterns of OPA and LTPA were retrieved separately for work and leisure using Exposure Variation Analysis (EVA), and expressed in terms of percentage time spent in uninterrupted periods of different durations (<1min, 1-5 min, 5-10 min, 10-30 min, 30-60 min and > 60 min) of sitting, standing, and walking. Differences between OPA and LTPA in selected EVA derivatives were tested using repeated measures ANOVA with adjustments for differences between work and leisure in total time for each activity.

Results

We found significant differences between work and leisure time for several EVA derivatives, with OPA showing a larger percentage time walking and standing in short periods (<5 min), and less percent time in prolonged sitting (>30 min) than LTPA. Apart from standing (p=.10), these differences remained significant even after adjusting for the difference between work and leisure in total time spent in each activity.

Conclusion

We found, among blue-collar workers, that the time patterns of OPA and LTPA were markedly different even after adjustment for total PA time. We suggest using EVA derivatives in future studies investigating potential associations of OPA and LTPA with health outcomes.

Purpose

This study aimed to identify sub-groups of workers with different trajectories of sick leave due to musculoskeletalpain over 1 year, and to investigate the extent to which the identified trajectories are associated with personal, occupational,lifestyle, and pain-related factors at baseline.

Methods

Data on 981 blue- and white-collar workers were analyzed in the DPHACTO cohort (2012–2014). The numberof days on sick leave due to pain was reported using text messages at 4-week intervals across 1 year. Latent class growthanalysis was used to distinguish sub-groups with different trajectories of sick leave. A web-based questionnaire at baselinewas used to assess personal, occupational (physical and psychosocial), lifestyle, and pain-related factors. Multinomial regressionmodels were constructed to determine associations between baseline factors and trajectories of sick leave (referencingno sick leave), with adjustment for potential confounders.

Results

Four distinct sub-groups were identified, with trajectories of sick leave due to pain ranging from no sick leave(prevalence 76%; average 0.5 days/year) to some days and increasing sick leave due to pain over 1 year (2%; 89 days/year).The increasing trajectory of sick leave was associated with higher perceived physical exertion, more time in manual work,less social community and influence at work, less leisure-time physical activity, smoking, and more severe symptoms (e.g.,multisite pain, low back pain intensity, and pain interference).

Conclusions

We identified four distinct trajectories of sick leave due to musculoskeletal pain. The sub-group with increasingsick leave due to pain was associated with several modifiable physical and psychosocial factors at work and outside work,which may have implications for prevention.

Background

Our aim was to examine the extent to which temporal patterns of sitting during occupational work and during leisure-time, assessed using accelerometry, are associated with intense neck–shoulder pain (NSP) in blue-collar workers.

Methods

The population consisted of 659 Danish blue-collar workers. Accelerometers were attached to the thigh, hip, trunk and upper dominant arm to measure sitting time and physical activity across four consecutive days. Temporal sitting patterns were expressed separately for work and leisure by the proportion of total time spent sitting in brief bursts (0–5 min), moderate (>5–20 min) and prolonged (>20 min) periods. The peak NSP intensity during the previous 3 months was assessed using a numerical rating scale (range 0–10) and dichotomized into a lower (≤4) and higher (>4) NSP score. Logistic regression analyses with multiple adjustments for individual and occupational factors were performed to determine the association between brief, moderate and prolonged sitting periods, and NSP intensity.

Results

Time in brief bursts of occupational sitting was negatively associated with NSP intensity (adjusted OR 0.68, 95 % CI 0.48–0.98), while time in moderate periods of occupational sitting showed a positive association with NSP (adjusted OR 1.32, 95 % CI 1.04–1.69). Time in prolonged periods of occupational sitting was not associated with NSP (adjusted OR 0.78, 95 % CI 0.78–1.09). We found no significant association between brief, moderate or prolonged sitting periods during leisure, and NSP.

Conclusion

Our findings indicate that the association between occupational sitting time and intense NSP among blue-collar workers is sensitive to the temporal pattern of sitting.

Excessive sitting and standing are proposed risk factors for cardiovascular diseases (CVDs), possibly due to autonomic imbalance. This study examines the association of objectively measured sitting and standing with nocturnal autonomic cardiac modulation. The cross-sectional study examined 490 blue-collar workers in three Danish occupational sectors. Sitting and standing during work and leisure were assessed during 1–5 days using accelerometers. Heart rate (HR) and heart rate variability (HRV) were obtained during nocturnal sleep as markers of resting autonomic modulation. The associations of sitting and standing still (h/day) with HR and HRV were assessed with linear regression models, adjusted for age, gender, body mass index, smoking, and physical activity. More sitting time during leisure was associated with elevated HR (p = 0.02), and showed a trend towards reduced HRV. More standing time at work was associated with lower HR (p = 0.02), and with increased parasympathetic indices of HRV (root mean squared successive differences of R-R intervals p = 0.05; high-frequency power p = 0.07). These findings, while cross-sectional and restricted to blue-collar workers, suggest that sitting at leisure is detrimental to autonomic cardiac modulation, but standing at work is beneficial. However, the small effect size is likely insufficient to mitigate the previously shown detrimental effects of prolonged standing on CVD.

Background

Imbalance in the autonomic nervous system due to a disrupted circadian rhythm may be a cause of shift work-related cardiovascular diseases.

Objective

We aimed to determine the association between shift work and cardiac autonomic activity in blue-collar workers.

Methods

The study included 665 blue-collar workers aged 18–68 years in different occupations from two Danish cohort studies. Time and frequency domain parameters of heart rate variability (HRV) were measured during sleep using the Actiheart monitor, and used as markers of cardiac autonomic function. Multiple linear regression analyses were used to investigate differences in HRV between day and shift workers.

Results

Shift workers had no significantly different HRV parameters than day workers, except for a lower VLF (B: 0.21; 95% CI: −0.36–0.05). The lower VLF was only present among non-night shift workers (p < 0.05) and not among night shift workers (p > 0.05). Results differed significantly by gender (p for interaction < 0.10): among men, shift work was negatively associated with RMSSD (B: −7.83; 95% CI: −14.28–1.38), SDNN (B: −7.0; 95% CI: −12.27–1.78), VLF (B: −0.27; 95% CI: −0.46–0.09) and Total Power (B: −0.61; 95% CI: −1.20–0.03), while among women, shift work was only associated with the LF/HF ratio (B: −0.29; 95% CI: −0.54–0.03).

Conclusion

Shift work was particularly associated with lower HRV during sleep among men. This indicates that shift work causes imbalance in the autonomic nervous system among men, which might increase their risk of cardiovascular diseases.