Abstract
OBJECTIVE: With sensors, we are increasingly able to assess sitting behaviour during the day. However, there is no consensus among researchers on the best outcome measures for representing the accumulation of sedentary time during the day. METHODS: We analysed the pattern measures of sedentary behaviour. Articles reporting patterns measures in adults, in which behaviour data was collected with a sensor were included. We discuss the strengths and weaknesses of the pattern measures of sedentary behaviour and provide recommendations for choosing objective measures of sedentary behaviour. RESULTS: Most studies report the number of sitting bouts during the day. Others focus on the number of breaks and/or periods of physical activity. Simple measures of sedentary behaviour were most popular. More complex pattern measures, such as the Gini index or the half-life bout duration, that capture the distribution of lengths of sitting periods in a single number, were reported sparsely. The sedentary patterns that were reported in the various studies were difficult to compare, due to the differences among measurement devices, data analysis protocols and a lack of basic outcome parameters such as total wear-time and total sedentary time. CONCLUSIONS: Objective sedentary measures can be grouped into simple and complex measures of sedentary time accumulation during the day. These measures serve different goals. The answer to the question as to which measures are most suitable to report, is strongly dependent on the research question. We have shown that the reported measures were dependent on (a) the sensing method, (b) the classification method, (c) the experimental and data cleaning protocol and (d) the applied definitions of bouts and breaks. We recommend that studies should always report total wear-time, total sedentary time, number of bouts and at least one measure describing the diversity of bout lengths in the sedentary behaviour such as the half-life bout duration. Additionally, we recommend reporting the measurement conditions and data processing steps.