Svend Erik Mathiassen
Effects of Time in Sitting and Standing on Pleasantness, Acceptability, Fatigue, and Pain When Using a Sit–Stand Desk: An Experiment on Overweight and Normal-Weight Subjects
Dechristian França Barbieri, Luiz Augusto Brusaca, Svend Erik Mathiassen, and Ana Beatriz Oliveira
Background: Sit–stand desks have been suggested as an initiative to increase posture variation among office workers. However, there is limited evidence of what would be preferable combinations of time sitting and standing. The aim of this study was to determine and compare perceived pleasantness, acceptability, pain, and fatigue for 5 time patterns of sitting and standing at a sit–stand desk. Methods: Thirty postgraduate students were equally divided into a normal-weight (mean body mass index 22.8 kg/m2) and an overweight/obese (mean body mass index 28.1 kg/m2) group. They performed 3 hours of computer work at a sit–stand desk on 5 different days, each day with a different time pattern (A: 60-min sit/0-min stand; B: 50/10; C: 40/20; D: 30/30; E: 20/40). Pleasantness, acceptability, pain, and fatigue ratings were obtained at the beginning and at the end of the 3-hour period. Results: High ratings of pleasantness were observed for time patterns B, C, and D in both groups. All participants rated acceptability to be good for time patterns A to D. A minor increase in perceived fatigue and pain was observed in time pattern E. Conclusion: For new sit–stand desk users, regardless of body mass index, 10 to 30 minutes of standing per hour appears to be an amenable time pattern.
Capturing the Pattern of Physical Activity and Sedentary Behavior: Exposure Variation Analysis of Accelerometer Data
Leon Straker, Amity Campbell, Svend Erik Mathiassen, Rebecca Anne Abbott, Sharon Parry, and Paul Davey
Capturing the complex time pattern of physical activity (PA) and sedentary behavior (SB) using accelerometry remains a challenge. Research from occupational health suggests exposure variation analysis (EVA) could provide a meaningful tool. This paper (1) explains the application of EVA to accelerometer data, (2) demonstrates how EVA thresholds and derivatives could be chosen and used to examine adherence to PA and SB guidelines, and (3) explores the validity of EVA outputs.
EVA outputs are compared with accelerometer data from 4 individuals (Study 1a and1b) and 3 occupational groups (Study 2): seated workstation office workers (n = 8), standing workstation office workers (n = 8), and teachers (n = 8).
Line graphs and related EVA graphs highlight the use of EVA derivatives for examining compliance with guidelines. EVA derivatives of occupational groups confirm no difference in bouts of activity but clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity.
EVA offers a unique and comprehensive generic method that is able, for the first time, to capture the time pattern (both frequency and intensity) of PA and SB, which can be tailored for both occupational and public health research.
Sedentary and Physical Activity Behavior in “Blue-Collar” Workers: A Systematic Review of Accelerometer Studies
Nicholas D. Gilson, Caitlin Hall, Andreas Holtermann, Allard J. van der Beek, Maaike A. Huysmans, Svend Erik Mathiassen, and Leon Straker
Background: This systematic review assessed evidence on the accelerometer-measured sedentary and physical activity (PA) behavior of nonoffice workers in “blue-collar” industries. Methods: The databases CINAHL, Embase, MEDLINE, PubMed, and Scopus were searched up to April 6, 2018. Eligibility criteria were accelerometer-measured sedentary, sitting, and/or PA behaviors in “blue-collar” workers (≥10 participants; agricultural, construction, cleaning, manufacturing, mining, postal, or transport industries). Data on participants’ characteristics, study protocols, and measured behaviors during work and/or nonwork time were extracted. Methodologic quality was assessed using a 12-item checklist. Results: Twenty studies (representing 11 data sets), all from developed world economies, met inclusion criteria. The mean quality score for selected studies was 9.5 (SD 0.8) out of a maximum of 12. Data were analyzed using a range of analytical techniques (eg, accelerometer counts or pattern recognition algorithms). “Blue-collar” workers were more sedentary and less active during nonwork compared with work time (eg, sitting 5.7 vs 3.2 h/d; moderate to vigorous PA 0.5 vs 0.7 h/d). Drivers were the most sedentary (work time 5.1 h/d; nonwork time 8.2 h/d). Conclusions: High levels of sedentary time and insufficient PA to offset risk are health issues for “blue-collar” workers. To better inform interventions, research groups need to adopt common measurement and reporting methodologies.