In this large sample of Australian adults, we found that compared to those with normal glucose metabolism, working adults with undiagnosed dysglycemia spent the same amount of time sitting at work, more time sitting during transport (3%) and watching TV (9%), and less time sitting using computer for leisure (2%) and during other activities (9%). In non-working adults, those with undiagnosed dysglycemia spent more time sitting while watching TV (9%), and during other activities (5%) and less time sitting during transport (26%) and using a computer for leisure (26%) compared to those with normal glucose metabolism.
To date, no studies have described the differences in time spent in interrelated contexts of sedentary behavior, between those with and without undiagnosed dysglycemia, in both working and non-working adults. Since previous experimental evidence suggests that the impacts of prolonged sitting on postprandial glycemia are greater in those who have a degree of underlying dysglycemia (28, 29), an improved understanding of the relative contribution to overall sitting of each respective sedentary behavior is clinically relevant. Irrespective of whether working or not, the observation that those with dysglycemia spent a greater proportion of time sitting watching TV, although not statistically significant, provides further support for the recent calls within clinical practice recommendations to target the reduction of specific sedentary behaviors for the prevention of T2D (11).
Extensive evidence suggests increased TV viewing time is associated with poorer cardiometabolic health (6, 30–33) and increased risk of CVD events and mortality (34–36). In comparison to other contexts of sedentary behavior, TV viewing can often occur concurrently with other detrimental health behaviors such as; poorer dietary patterns (37, 38); sitting for longer, uninterrupted periods of time; and often occurs in the evening following a large meal (39). Indeed, a recent study reported that overweight/obese adults had a lower postprandial glycemic response when sitting was interrupted during advertisement breaks on TV after an evening meal (40). Some contexts of sitting, such as TV viewing, may be more strongly linked with adverse outcomes due to the nature of the behavior itself - more habitual, less variable and easily recalled – meaning they may be more accurately measured than others (41). Time spent watching TV is also associated with lower socioeconomic status and other factors which are known to strongly influence health outcomes (30). Regardless, our results indicate that reducing and interrupting sitting while watching TV is an important target area to improve the cardiometabolic health of those with dysglycemia.
The contribution from occupational sitting to total sitting time was similar in both groups. This is not unexpected, since sitting while at work, in most cases, is less discretionary than sitting in other contexts (due to the constraints of the workplace and set working hours). Workplaces have been identified as environments which are high risk for excessive prolonged sitting. Occupational sitting contributes a high proportion of total sitting time (41), and intervention evidence supports reducing and interrupting occupational sitting time to improve markers of cardiometabolic health (42, 43). Thus, potential future initiatives within occupational health and safety regulations may contribute to improved health outcomes through reductions to overall sitting time, particularly in working adults. The current data does not allow insight into the discretionary nature of occupational sitting (i.e. whether work had to be performed seated) and as such is a limitation.
Those with undiagnosed dysglycemia spent less time sitting while using a computer for leisure than those with normal glucose metabolism (2% in workers, 29% in non-workers). A potential strategy for reducing time spent in what may be deemed, more “metabolically harmful” sedentary behaviors, for example TV viewing (6, 32, 34, 37, 44–46), aside from promoting movement, could be the reallocation of time from non-active to active sedentary behaviors. A recent study found non-active sedentary behaviors (e.g. sitting on a chair or reclining) were positively correlated with BMI, body fat % and insulin, whereas active sitting (e.g. sitting while typing on a computer) was negatively associated with the same outcomes (45). Additionally, evidence has emerged on the potential impacts of mentally-active compared to passive sedentary behaviors on mental health outcomes. Isotemporal substitution analyses reported reduced odds of depressive symptoms and clinician-diagnosed major depressive disorders when 30 minutes of passive sedentary behaviors (watching TV, listening to music) were substituted with 30 minutes of mentally-active sedentary behaviors (office work, sitting in a meeting, knitting) (47). While care should be taken so as not to promote accumulation of large volumes of sitting time in any context, it is possible that the more mentally-active behavior of leisure-time computer use provides an appropriate alternative to passive sedentary behaviors such as TV viewing. Future studies which combine objective measures of contextualized sitting with longitudinal study designs are needed to further explore the temporality of these findings. Additionally, the current data were collected between 2011–2012; the changing landscape of TV viewing will need to be considered with the rise of web-based streaming services.
Sitting accumulated in ‘other’ activities contributed greatly to overall sitting time and differed between glycemic groups. In working adults, those with dysglycemia spent 9% less time sitting during other activities than those with normal glucose metabolism. Conversely, in non-working adults, those with dysglycemia spent 5% more time sitting in other activities than those without. The survey question which collected this data was phrased; “…this could include sitting for reading or hobbies, socializing with friends or family including time on the telephone eating meals; or listening to music.” Presumably, a combination of both active and non-active sedentary behaviors are captured within this question, which makes interpretation difficult. Future studies could direct attention to expanding the self-reporting of these various behaviors to better understand their unique contributions to overall sitting time. Categorizing sedentary behaviors into non-active and active sitting (46) is one method of addressing this issue that future studies could employ.
Limitations include the cross-sectional nature of the data; one week of self-reported, recalled data gives limited detail around time of day sitting was accumulated or bout duration, and may not be representative of broader sitting patterns. The large contribution to overall sitting time from ‘other’ sitting behaviors increases the risk for biases due to residual confounding, meaning that important contexts of sitting are not captured. The level of detail obtained from the data does not provide insight on the underlying biological mechanisms which may contribute to the different sedentary behavior profiles observed. In addition, the sample was not population representative and may be prone to selection bias. Specifically, the analysis included a sample healthier than the general population, within the third wave of a cohort which was subject to some loss to follow up (48). Since the AusDiab study is limited to middle-aged/older adults, the findings are not generalizable to younger population age groups.
There are several strengths to our study, including the large and diverse sample of Australian adults incorporating measures of both fasting and post-challenge plasma glucose levels to identify those with previously undiagnosed dysglycemia. Furthermore, the context-specific sitting measurement was a novel aspect; unique to this wave of the AusDiab study. We also employed a compositional data analysis approach, which respects the inherently relative nature of time-use data by expressing the information as a set of log ratios. Most prior analyses of time-use data use statistical methods which focus on the absolute information in the data.