1.4.2.1. Accelerometry
As outlined in section 1.2, accelerometer-derived data when measuring sedentary time is referred to as stationary time, based on the recent Sedentary Behaviour Research Network consensus paper (16). The use of objective measures, specifically accelerometry, has increased rapidly in recent years. For example, in the review exploring the relationship between SB and health outcomes in children and adolescents by Carson et al. (42), 35 studies that used accelerometry were identified from 2010- 2015, whereas in the preceding review (35), not a single study was identified from searchers before 2010 that used an objective measurement of sedentary time. There are several accelerometer devices widely available, however, the ActiGraph is the most commonly adopted (39). The ActiGraph can be worn in several locations on the body but the hip has traditionally been the most common position adopted, although recently there has been a shift towards wrist-worn devices (51). Accelerometers measure the frequency and amplitude of acceleration of the body section in which they are attached to in up to three axes, sampling at typically 30-100Hz, which is then converted to movement counts (38). These devices detect stationary time based on a lack of movement under a specific counts-per-minute-threshold (52), which has commonly been established at <100cpm, in the vertical axis, in children (53). The threshold is based on criterion measures of energy expenditure determined by indirect calorimetry and subsequent regression or receiver operating curve analysis (23,54). These devices are able to measure the total volume of stationary time by accumulating all segments of time recording movement below the sedentary threshold. Furthermore, breaks in stationary time (and therefore stationary bout length) can be detected when the sedentary threshold is briefly exceeded and stationary time is then resumed. This outcome, as well as time spent in different bout lengths, cannot be provided by self/proxy-report measures (38). In addition, due to data being time stamped, time spent stationary during specific periods of the day can be extracted (38). However, hip-worn accelerometers cannot accurately distinguish between sitting and standing postures and therefore sitting time is not accurately determined (23). The distinction between sitting and standing is important because standing, whether active or passive, is associated with higher energy expenditure and may therefore influence different
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physiological responses and effects on health outcomes than sitting (55–57). Furthermore, there are a wide range of settings and data management options to navigate with accelerometer data such as non-wear time criteria, minimum wear time, valid number of days of data, sampling rate, epoch length, cut points, operationalisation of sleep and the use of axis data (typically uniaxial or triaxial). The parameters of many of these factors can vary widely between studies which is a major issue because many of these data management components have the potential to affect the outcome variable of interest (58). For example, associations between stationary time and metabolic risk factors have been found to be moderated by the choice of cut points, with stronger associations found in a higher stationary threshold (59). With sitting time often misclassified with standing and the heterogeneous settings and data processing methods implemented across studies, the SB literature is somewhat littered with potentially inaccurate and invalid evidence.
1.4.2.2. Posture monitors
Posture monitors such as the activPAL have more recently been adopted in SB research due to their ability to distinguish between sitting and standing more accurately (93). These devices are typically worn on the anterior aspect of the thigh between the hip and knee. Instead of using movement accelerations to determine sedentary time, the angle and position of the thigh mounted inclinometer are detected, classifying body position as either sitting/lying, standing or stepping (52). Consequently, sitting and standing time is directly measured and recorded. The activPAL is also an accelerometer, allowing for the detection of non-wear time from periods where no movement is detected. Like the ActiGraph, the device can provide data on total sedentary time, breaks in sedentary time (i.e. brief change in posture followed by a return to a lying, reclining or sitting posture) and bouts of sedentary time. Furthermore, information on sit-to-stand and stand-to-sit transitions, cadence, steps/day and estimates of energy expenditure are produced (38), and all data can be extracted during specific times of a 24-h period. More consistent device settings would appear to be utilised within the literature (i.e. sampling rate of 10Hz, epoch length of 15s) compared to the ActiGraph, which provides better comparison between studies. Also, these monitors can be waterproofed using a nitrile sleeve and medical adhesive dressing, which enables 24-h wear time and potentially
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improves wear time compliance compared to the ActiGraph (often removed when the participant is in water and during sleep at night). The activPAL has demonstrated an almost perfect correlation with direct observation for time spent sitting/lying, standing and walking in simulated free living activities in primary school children as well as strong correlations for sit-to-stand and stand-to-sit transitions (r = 0.99) and step counts (0.88- 1.00) (60). The device has also demonstrated good accuracy and precision for assessing time spent sitting (Rho (mean difference) = 0.86 (-5.6%)) and standing (Rho = 0.78) during free living within the school classroom in 9-12 year olds (52). However, misclassification between sitting and standing postures can occur in irregular sitting styles (i.e. sitting on the edge of a seat) where the thigh position is more towards a vertical plane instead of a horizontal position (52). Furthermore, like with all objective measures, posture monitors cannot provide information on the context or modality of SB, and consequently the use of both self/proxy-report and direct measurement tools is recommended whenever possible (29).