Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity

Abstract

The purpose of this study was to develop and test a generic technique to robustly quantify the pattern of sedentary behaviour from objective records.

The technique was applied to four groups of subjects: a healthy group with an active occupation (N = 54), a healthy group with a sedentary occupation (N = 53), a group of subjects with chronic low back pain (N = 5) and a group of subjects with chronic fatigue syndrome (N = 14).

This study presents the first evidence that bouts of sedentary activity are power law distributed.

Results showed that there was no significant difference in total sedentary time between the groups, however, the patterns of accumulation of sedentary time were significantly different for the groups. Sedentary groups accumulated their total sedentary time from a small number of longer sedentary bouts. Active groups tended to break their sedentary time into a greater number of shorter bouts. This suggests that the power law exponent α and the GINI index G, used to describe the pattern of accumulation of sedentary time, could be used to evaluate and quantify sedentary behaviour.

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.