Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity
Introduction
Sedentary behaviour is associated with a range of poor health outcomes, typically high levels of body fat/obesity, blood glucose levels and type 2 diabetes and cardiovascular problems [1]. Western lifestyle is becoming increasingly sedentary, at home, work and during leisure time [2]. This has driven global efforts to quantify physical. Consequently sedentary behaviour has generally been inferred from studies of physical activity where sedentary behaviour has been considered as the bottom end of a physical activity continuum. However, there is now mounting evidence, [1], [3], [4], [5], [6], [7] that sedentary behaviour per se rather than just low level of physical activity, is an independent risk factor for chronic disease and poor health outcomes. This evidence has shown that there is a need to study and quantify sedentary behaviour.
Early studies of sedentary behaviour relied on self-reported methods, often using television viewing time as a proxy marker for sedentary time [8], [9], [10], [11], [12], [13]. These subjective methods have the obvious caveats, with any self-report methods, that they tend to under report sedentary behaviour [14]. However, using these methods, associations between subjectively recorded total sedentary time and obesity [8], abnormal glucose metabolism [10] and the metabolic syndrome [11] have been reported, and it has been suggested that there is a need for more precise objective measures of sedentary behaviour [10].
Several studies [15], [16], [17], [18], [19] have used objective measures of energy expenditures, recorded by accelerometry. These studies have reported relationship between total sedentary time and abnormal glucose metabolism [16], metabolic risks [17] and obesity markers [18].
These findings do not provide insight into the drivers for adopting a sedentary lifestyle which are also poorly understood [20]. In order to investigate these, global measures of total sedentary time is not sufficient. Dietz [4] suggested that the study of “sedentarism” as a behaviour, rather than accounts of energy spent in sedentary pursuit might offer richer insight and assessment of factors that contribute to obesity and other diseases. Recent studies [5], [6] highlight the fact that the pattern of inactivity has important physiological impact on muscles, cardiovascular health and metabolism.
Devices have been developed which enable long term recording of accelerometer signals. This technology offers the possibility to explore the temporal patterns sedentary behaviour. A recent study by Healy et al. [21] found a relationship between the number of breaks in sedentary periods and metabolic markers. This study illustrated the potential importance for studying patterns of sedentary periods.
The aim of this study was to develop a novel generic method for analysing and quantifying patterns of sedentary behaviour based on an objective monitoring technique and to test this to explore how sedentary behaviour is modified by occupation and disease.
Section snippets
Definition
A major difficulty in monitoring sedentary behaviour is finding a practical and accepted definition of sedentary activity [22]. A review by Bennett et al. [23] revealed that most studies of “sedentarism” define sedentary behaviour as a low level of physical activity. Owen et al. [20] proposed that sedentary behaviour is identified by an energy expenditure threshold. Using an energy threshold leads to large uncertainty about the sedentary data recorded, first of all because estimating energy
Design
This was a cross-sectional study of sedentary behaviour of four different groups. This study was approved by the ethic committee of the School of Health and Social care of Glasgow Caledonian University.
The demographics for these groups are presented in Table 1. The first group were healthy postal workers (Ha) whose occupation is by nature active involving mail delivery on foot. The second group were healthy office based postal workers (Hs), whose daily work activity was sedentary [35]. The
Global sedentary time
Overall subjects spent an average of around 75% of their time in sedentary behaviour. Individual values ranged from 41% to 92%. Comparison between these groups based on an ANOVA analysis reveals that there was no significant difference (p = 0.167) in the total daily sedentary time between groups.
Distribution of the length of sedentary bouts
The sedentary bout histogram represented on a loglog scale (Fig. 2) seems to follow a straight line. This means that the sedentary bouts, with respect to their length x, appear to be distributed as a
Discussion
Subjects spent on average around 75% of their daily time in sedentary behaviour irrespectively of their health status and occupation. This value is larger than that reported elsewhere in the literature [2], [16], [17], [18], [21]. The difference arises from the fact that sleep was included as sedentary time in this study.
The total sedentary time was not a sensitive outcome measure or diagnostic since it was not possible to differentiate between those a priori active and sedentary groups.
This
Conclusion
This paper develops a novel generic technique for analysis objectively measured sedentary behaviour that minimise a priori assumption. This technique enabled the quantification of the pattern of accumulation of sedentary time using statistically robust parameters. This study found evidence that sedentary bouts are power law distributed and that this pattern is modified by occupation and disease with total sedentary time being insensitive to these differences. This study suggests that the
Conflict of interest
Professor Malcolm Granat is a coinventor of the activPAL physical activity monitor and a director of PAL Technologies Ltd.
Acknowledgements
The authors wish to thank Dr William Tigbe, Dr Lorna Paul, Danny Rafferty and Dr Cormac Ryan.
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