The contribution of office work to sedentary behaviour associated risk

Background: Sedentary time has been found to be independently associated with poor health and mortality. Further, a greater proportion of the workforce is now employed in low activity occupations such as office work. To date, there is no research that specifically examines the contribution of sedentary work to overall sedentary exposure and thus risk. The purpose of the study was to determine the total exposure and exposure pattern for sedentary time, light activity and moderate/vigorous physical activity (MVPA) of office workers during work and non-work time.Methods: 50 office workers from Perth, Australia wore an Actical (Phillips, Respironics) accelerometer during waking hours for 7 days (in 2008–2009). Participants recorded wear time, waking hours, work hours and daily activities in an activity diary. Time in activity levels (as percentage of wear time) during work and non-work time were analysed using paired t-tests and Pearson’s correlations.Results: Sedentary time accounted for 81.8% of work hours (light activity 15.3% and MVPA 2.9%), which was significantly greater than sedentary time during non-work time (68.9% p 30 minutes) and significantly less brief duration (0–10 minutes) light intensity activity during work hours compared to non-work time (p < 0.001). Further, office workers had fewer breaks in sedentary time during work hours compared to non-work time (p < 0.001).Conclusions: Office work is characterised by sustained sedentary time and contributes significantly to overall sedentary exposure of office workers

Navigating through the r packages for movement

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community

Dynamic sitting: Measurement and associations with metabolic health

Dynamic sitting, such as fidgeting and desk work, might be associated with health, but remains difficult to identify out of accelerometry data. We examined, in a laboratory study, whether dynamic sitting can be identified out of triaxial activity counts. Among 18 participants (56% men, 27.3 ± 6.5 years), up to 236 counts per minute were recorded in the anteroposterior and mediolateral axes during dynamic sitting using a hip-worn accelerometer. Subsequently, we examined in 621 participants (38% men, 80.0 ± 4.7 years) from the AGES-Reykjavik Study whether dynamic sitting was associated with cardio-metabolic health. Compared to participants who recorded the fewest dynamic sitting minutes (Q1), those with more dynamic sitting minutes had a lower BMI (Q2 = −1.39 (95%CI = −2.33;–0.46); Q3 = −1.87 (−2.82;–0.92); Q4 = −3.38 (−4.32;–2.45)), a smaller waist circumference (Q2 = −2.95 (−5.44;–0.46); Q3 = −3.47 (−6.01;–0.93); Q4 = −8.21 (−10.72;–5.71)), and a lower odds for the metabolic syndrome (Q2 = 0.74 [0.45;1.20] Q3 = 0.58 [0.36;0.95]; Q4 = 0.36 [0.22;0.59]). Our findings suggest that dynamic sitting might be identified using accelerometry and that this behaviour was associated with health. This might be important given the large amounts of time people spend sitting. Future studies with a focus on validation, causation and physiological pathways are needed to further examine the possible relevance of dynamic sitting

Employment, work hours and weight gain among middle-aged women.

Objective: To investigate the influence of employment and work hours on weight gain and weight loss among middle-aged women. Design: Quantile regression techniques were used to estimate the influence of employment and hours worked on percentage weight change over 2 years across the entire distribution of weight change in a cohort of middle-aged women. A range of controls was included in the models to isolate the effect of work status. Subjects: A total of 9276 women aged 45–50 years at baseline who were present in both the 1996 and 1998 surveys of the Australian Longitudinal Study of Women’s Health. The women were a representative sample of the Australian population. Results: Being out of the labour force or unemployed was associated with lower weight gain and higher weight loss than being employed. The association was stronger at low to moderate levels of weight gain. Among employed women, working regular (35–40), long (41–48) or very long (49+) hours was associated with increasingly higher levels of weight gain compared with working part-time hours. The association was stronger for women with greater weight gain overall. The association between unemployment and weight change became insignificant when health status was controlled for. Conclusions: Employment was associated with more weight gain and less weight loss. Among the employed, working longer hours was associated with more weight gain, especially at the higher levels of weight gain where the health consequences are more serious. These findings suggest that as women work longer hours they are more likely to make lifestyle choices that are associated with weight gain

Socioeconomic differences in physical activity in the middle-aged working population

Hintergrund: Regelmäßige Bewegung hat in jedem Alter einen positiven Einfluss auf die Gesundheit. Im vorliegenden Beitrag wurde untersucht, inwieweit körperliche Aktivität sowie regelmäßiges Sporttreiben als spezifischere Form körperlicher Betätigung mit sozioökonomischen Merkmalen von Erwerbstätigen im mittleren Lebensalter zusammenhängen. Methoden: Die Analysen basieren auf Daten von 21.699 Erwerbstätigen im Alter zwischen 30 und 64 Jahren, die an den deutschlandweiten Querschnittsstudien „Gesundheit in Deutschland aktuell“ (GEDA) 2009 und 2010 des Robert Koch-Instituts teilgenommen haben. Neben einem mehrdimensionalen Index des sozioökonomischen Status (SES) wurden auch die statusbildenden Einzeldimensionen Bildung, Beruf und Einkommen betrachtet, um sozioökonomische Unterschiede in der körperlichen Aktivität und im regelmäßigen Sporttreiben zu analysieren. Ergebnisse: Während die Prävalenz von körperlicher Aktivität allgemein mit sinkendem SES anstieg, nahm der Anteil regelmäßig Sporttreibender mit sinkendem SES ab. Diese Zusammenhänge blieben jeweils nach Kontrolle für das Lebensalter bei Männern und Frauen bestehen. Bei wechselseitiger Kontrolle der SES-Einzeldimensionen war die körperliche Aktivität mit geringerer Bildung und niedrigerem Berufsstatus assoziiert. Regelmäßiges Sporttreiben ging hingegen mit besserer Bildung, höherem Berufsstatus und höherem Einkommen einher. Diskussion: Die Ergebnisse weisen auf deutliche sozioökonomische Unterschiede in der körperlichen Aktivität und sportlichen Betätigung von Erwerbstätigen im mittleren Lebensalter hin. Dabei kommt Bildung, Beruf und Einkommen eine jeweils eigenständige Bedeutung für das Bewegungsverhalten zu. Bei der Ermittlung von Zielgruppen für bewegungsfördernde Maßnahmen sollten diese Unterschiede berücksichtigt werden