Sedentary Time (vs. Sedentary Behavior)
Sedentary behavior has been defined as “any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture” (Tremblay et al. 2017). Common sedentary behaviors include watching television, reading a book, working at a computer, and driving motorized transport.
Sedentary time refers to the sum of all sedentary behaviors that are undertaken throughout the course of a day. For example, time spent traveling to work by car, sitting working at an office desk, and watching television during leisure time all represent different sedentary behaviors but accumulate to contribute toward “total sedentary time.” However, within the field of sedentary behavior research, the terms sedentary time and sedentary behavior are often used interchangeably, and incorrectly.
Measurement of Sedentary Time
Methods employed to assess sedentary time include both self-report measures and objective devices. Until recently, self-report measures were more frequently used in large-scale studies or epidemiological research due to their ease of application and relatively low-cost. Examples include diaries and questionnaires, such as the Bouchard Physical Activity Record and the International Physical Activity Questionnaire, respectively. However, the validity of the data collected using these measures is somewhat limited, due to social desirability bias and errors in participant recall. Moreover, most self-report measures largely focus on assessing specific sedentary behaviors (e.g., screen-based behaviors such as television viewing) rather than overall time spent sedentary.
Objective devices, such as accelerometers and posture sensors, are being more readily employed for the measurement of sedentary time due to their superior validity and reliability relative to self-report measures. Accelerometers (e.g., the ActiGraph) assess sedentary time on the basis of accelerations recorded over prespecified time periods (epochs). These accelerations are subsequently converted to accelerometer “counts,” which are interpreted against validated “count thresholds” or “cut points,” to determine the frequency and duration of sedentary time. Numerous cut points have been developed for this purpose, for example, Troiano et al. (2008) defines adults’ sedentary time as ≤99 counts per minute (cpm), and Freedson et al. (2005) have proposed cut points of ≤149 cpm to classify sedentary time in children. Such cut points have been derived by establishing the upper limit of accelerometer counts recorded while undertaking activities requiring ≤1.5 METs (measured with indirect calorimetry). Consequently, accelerometers enable measurement of sedentary time on the basis of energy expenditure, but they typically do not afford the ability to determine the posture at which the behavior was undertaken. This may lead to misclassification of low energy, non-sitting behaviors (e.g., standing) as contributors toward sedentary time. Similarly, posture sensors (e.g., the activPAL) are not able to quantify the energy cost of sitting/lying behaviors they measure and therefore may misclassify seated active behaviors (e.g., cycling) as sedentary time. Bearing these limitations in mind, devices that have the ability to combine both accelerometry and posture classification should provide the most comprehensive and accurate assessment of sedentary time.
Sedentary Time and Health
The problem of sedentariness is receiving increased attention due to the high prevalence of this behavior among youth, adults, and older adults, coupled with growing evidence for the role of sedentary time in the development of poor health (Biswas et al. 2015; Hoare et al. 2016). For example, epidemiological research indicates that adolescents accumulate 6 h/day of accelerometer-assessed sedentary time, and sedentary time estimates increase with age (Collings et al. 2014). For adults, the National Health and Nutrition Examination Survey (Healy et al. 2011) indicated that sedentary time represents around 50–60% of waking hours when measured with accelerometry. Still, older adults represent the most sedentary age group, with sedentary time estimates of almost 10 h per day (Harvey et al. 2015).
The adverse health consequences of high sedentary time include increased risk of developing cardiovascular disease, type 2 diabetes, metabolic syndrome, and compromised mental health (Biswas et al. 2015; Hoare et al. 2016). Furthermore, a review of recent epidemiological studies indicated a probable causal positive association between sedentary time and all-cause mortality (Biddle et al. 2016). Importantly, the deleterious health consequences of sedentary time are observed to be independent of participation in moderate-vigorous physical activity (i.e., activity ≥3 METs).
Still, despite growing evidence for negative health consequences of sedentary time, studies are yet to determine exactly “how much” sedentary time is bad for us. As a result, current guidelines can only recommend reducing overall sedentary time. Research examining the dose-response association between sedentary time and poor health is therefore necessary to refine sedentary time guidelines.
References and Further Reading
- Biswas, A., Oh, P. I., Faulkner, G. E., Bajaj, R. R., Silver, M. A., Mitchell, M. S., et al. (2015). Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta-analysis. Annals of Internal Medicine, 162(2), 123–132.CrossRefPubMedGoogle Scholar
- Collings, P. J., Wijndaele, K., Corder, K., Westgate, K., Ridgway, C. L., Dunn, V., et al. (2014). Levels and patterns of objectively-measured physical activity volume and intensity distribution in UK adolescents: The ROOTS study. International Journal of Behavioral Nutrition and Physical Activity, 11(23), 1–12.PubMedGoogle Scholar
- Tremblay, M. S., Aubert, S., Barnes, J. D., Saunders, T. J., Carson, V., Latimer-Cheung, A. E., et al. (2017). Sedentary behaviour research network (SBRN) – Terminology consensus project process and outcome. International Journal of Behavioral Nutrition and Physical Activity, 14(75), 1–17.Google Scholar